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Ad Hoc Networks xxx (2014) xxx–xxx

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Ad Hoc Networks

journal homepage: www.elsevier .com/locate /adhoc

Designing delay constrained hybrid ad hoc networkinfrastructure for post-disaster communication

http://dx.doi.org/10.1016/j.adhoc.2014.08.0091570-8705/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author.E-mail addresses: [email protected] (S. Saha), subrata.nandi@gmail.

com (S. Nandi), [email protected] (P.S. Paul), [email protected](V.K. Shah), [email protected] (A. Roy), [email protected] (S.K. Das).

Please cite this article in press as: S. Saha et al., Designing delay constrained hybrid ad hoc network infrastructure for post-disastemunication, Ad Hoc Netw. (2014), http://dx.doi.org/10.1016/j.adhoc.2014.08.009

Sujoy Saha a,⇑, Subrata Nandi a, Partha Sarathi Paul a, Vijay K. Shah a, Akash Roy a, Sajal K. Das b

a Department of Computer Science & Engineering, National Institute of Technology, Durgapur 713 209, Indiab Department of Computer Science, Missouri University of Science and Technology, Rolla, MO 65409, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 8 March 2014Received in revised form 30 July 2014Accepted 16 August 2014Available online xxxx

Keywords:Delay Tolerant NetworkDisaster managementData MuleAd hocHybrid networkPerformance Analysis

Following a disaster-strike, rapid and reliable communication between relief/rescue work-ers in the affected regions, and the control stations located at a distance, is essential. This isto facilitate seamless information exchange about the status of victims, requirement ofrelief personnel/commodities, supply chain of goods and services, and so on, thus renderingrelief operations more timely and effective. However, the availability of Internet in a post-disaster scenario is ruled out more often than not; wireless communication and mobilephones may not be usable either except in only selected areas. Besides, geographicalobstructions such as broken bridges or closed roads add to the worries of personnel tryingto develop a temporary network infrastructure for effective communication. Furthermore,availability of resources – both technological and financial – may prove to be a bottleneckin case of disasters in under-developed regions. Under such circumstances, a post-disastercommunication network need to be developed to meet the following: (i) close to 100% infor-mation packet delivery, (ii) minimum latency for information exchange, and (iii) compli-ance to the resource constraints. In this paper, we propose a latency-aware four-tierplanned hybrid architecture to tackle the aforementioned challenges and focus on extensivemodeling and analysis of the planned network architecture. For detailed evaluation with thehelp of case study scenarios, we make use of the ONE Simulator, customized to suit ourrequirements. Additionally, we measure the improvement in network resources utilizationand performance achieved with modeling of the architecture over that without modeling.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction regions like the Sundarbans national forest or the Himala-

Internet-based distributed communication systemshave been very popular in providing day-to-day servicesin our life, but history has shown that disasters like cycloneAila in India, hurricane Katrina in USA, and the earthquakeand Tsunami in Japan (leading to nuclear disaster) canseverely impair all forms of communication, thus jeopar-dizing lives and assets. Besides, many of the disaster-prone

yas in India do not have quality communication infrastruc-ture even in normal conditions. In such scenarios, due tointermittent connectivity, mobile phones working in theDelay Tolerant Network (DTN) [1,2] mode can be the onlyoption for information exchange; however, this mode ofcommunication suffers from unpredictable latency (packetdelivery time) and poor packet delivery probability whendeployed in a large area. Our initial studies suggest thatin such cases hybrid architectures [3] are suitable fordeveloping an ad hoc communication backbone forefficient information management, required to carry outrescue and relief operations in the wake of disasters.Such an architecture must be easy-to-deploy, cost-effective

r com-

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2 S. Saha et al. / Ad Hoc Networks xxx (2014) xxx–xxx

(must meet budgetary constraints and ensure maximumutilization of the costly devices like long range WiFi com-munication devices), reliable (ensures a desired level of per-formance), robust and scalable (able to cover a largeaffected area).

1.1. Proposed hybrid architecture

In an attempt to build a network system imbibing theaforementioned features, this paper proposes an ad hocfour-tier hybrid architecture dedicated for collectingrescue and relief information in a sparse, post-disastermanagement network. The architecture assumes that theaffected area (AA) consists of many temporary shelterpoints (SPs) and a centralized control station which co-ordinates rescue/relief operations called Master ControlStation (MCS). Rescue personnel/victims within each SPcarry smart phones or tablets as DTN nodes [Tier-1] toexchange information in the form of video clips, images,voice clips, and short text messages among themselvesand deliver packets periodically to the nearest InformationDrop Box (IDB) [Tier-2] belonging to each SP. Laptops orany other customized hardware equipped with a storageunit and Bluetooth and WiFi interface act as IDBs.

As the density of DTN nodes is sparse, and IDBs are farapart, we propose that, vehicles (e.g., boats, ambulances)used by rescue/relief teams or Unmanned Ariel Vehicles(UAV)1 equipped with WiFi interface will act as Data Mules(DMs) as a part of mechanical backhauls2 [Tier-3] to carryinformation from IDBs to MCS and vice versa, within a stipu-lated time L. If AA has a large diameter, even deploying a ded-icated DM for each SP may not meet latency constraints andmay not also be a cost-effective option. Hence we proposegrouping of IDBs. At the center of each such group will beplaced, one (non-line of sight (NLOS)/near line of sight(LOS)) Long range WiFi3 communication (LWC) device [Tier-4], accumulating data from a non-overlapping set of IDBs.4

Fig. 1(a) shows the schematic view of the proposedfour-tier hybrid architecture. The tier-wise interactionamong the various network components has been summa-rized in Fig. 1(b). DTN to DTN at Tier-1: contact intervaland duration is random and depends on DTN mobility pat-tern. DTN to IDB/GC and vice versa at Tier-2: contact atperiodic specified intervals in a random order for somespecified duration. DM to IDB/GC and vice versa at Tier 3:contact at periodic specified intervals in a specified order(based on DM’s trajectory) for some specified duration.GC to IDB/GC and vice versa at Tier 4: contact at specifiedintervals in a specified order (one neighbor at a time) forsome specified duration.

1 http://irevolution.net/.2 Ferrying data via mechanical means, e.g. vehicles [4,5].3 Supports data communication at an effective rate of 4–10 Mbps. within

an effective range of 10–12 km. in contrast to normal WiFi transmissionwith rate 10–54 Mbps within a range of 50–100 Mts.

4 Further, in situations where SPs at small physical distances apartbecome inaccessible to DMs due to physical obstructions, wireless meshtechnology [6,7] can be a good choice. Moreover, few of the DMs equippedwith satellite phone connected to Very Small Aperture Terminal (VSAT)may be used for transmitting emergency messages from Tier-3 directly tothe MCS and vice versa.

Please cite this article in press as: S. Saha et al., Designing delay constramunication, Ad Hoc Netw. (2014), http://dx.doi.org/10.1016/j.adhoc.20

In the following subsections, we highlight the issues,challenges and motivations for design of such a hybridarchitecture in wake of large-scale disasters. The contribu-tion of this paper also has been clearly mentioned.

1.2. Design issues

Timely reception of accurate updates at MCS from spar-sely populated yet huge affected area AA is crucial forproper planning and execution of relief and rescue work.Hence, the average latency (delivery time) of data packetdelivery is an important design parameter for planning ofan ad hoc infrastructure in post-disaster communication.Moreover, maximum utilization of network resources isalso desired such that cost constraints are also met. DMshave noticeable advantage over LWCs in terms of costand reach since LWCs incur high deployment costs andtheir LOS range is often reduced by physical barriers. How-ever, DMs consume a lot of time in packet exchange, whichbeats the purpose of minimum latency. Therefore, themain design challenge is to strike a proper balancebetween usage of both DMs and LWCs so that the systemis cost-effective, easy-to-deploy, and still meets latencyconstraints.

Let us analyse the post-disaster situation in-depth. Weassume the information and resources available to usimmediately after an area has been disaster-affected willtypically consist of the following: (i) a map of the disas-ter-affected area, with details of topographical changesbrought about by the disaster; (ii) a pool (quantity of com-ponents in each tier) of network resources available whichcan be used to build a post-disaster communicationnetwork; (iii) the network load, which will typically be afunction of the density of the population participating indata exchange, and the rate at which information isdissipated by them; and (iv) target latency (L) or theamount of time that can be allowed to lapse betweenpacket generation at source and reception at destination.

Given the information, the fundamental questions for anetwork designer are: (1) what is the optimal quantity ofeach type of network resource to be used so that almost100% packet delivery can be ensured, keeping the designparameters in mind and (2) is it worth taking a plannedapproach in deploying an ad hoc infrastructure for post-disas-ter communication? If yes, what are the issues, benefits, andshortcomings of modeling and analysis of such system?

1.3. Design challenges

It is important to note that even if L is allowed toassume a value of a few hours, finding a suitable solutionpertaining to the answer of the above questions isnon-trivial and quite challenging. This is because of the fol-lowing reasons: (1) AA may span over several hundredkilometers and existing communication infrastructure(WLL,5 GSM,6 PSTN7) gets disrupted completely; smallclusters of DTN network consisting of smart phones (density

5 Wireless Local Loop.6 Global System for Mobile Communications (cellular technology).7 Public Switched Telephone Network.

ined hybrid ad hoc network infrastructure for post-disaster com-14.08.009

http://irevolution.net/


Fig. 1. Proposed 4-Tier hybrid architecture for post-disaster communication.

S. Saha et al. / Ad Hoc Networks xxx (2014) xxx–xxx 3

4–10 per km2) carried by rescue/relief workers and victims areformed. Such networks can produce delivery probability of nomore than 15% with average latency no less than 12–15 h evenwhen using epidemic routing [1,2]; (2) alternative technologyavailable in the market is either expensive or takes huge timeto deploy (e.g., Satellite phone, WiMax, LWC); (3) disaster


management authorities are often equipped with finite, inade-quate pool of resources as compared to the requirement, espe-cially in developing countries, and (4) the post-disasterscenario is dynamic, hence modeling each and everyparameter with a satisfactory level of precision and detailedanalysis of the system in absence of simulators is difficult.




Being aware of the issues and challenges, next, webriefly review the field practices as well the state of theart to draw the motivation of our work.

1.4. Motivation

We observe from the reports [8], that in the 2011 Tsu-nami, although Japan used nearly 1300 satellite phones,1770 emergency-use portable communication facilities,100 portable power generators, 22 mobile base stationtrucks, under-sea robots, and UAVs (for damage assess-ment) it took more than a week to get control over thesituation. However, due to financial constraints, such ahuge, sophisticated resource pool can hardly be madeavailable to tackle large scale disasters [9] in subtropicaland tropical regions of developing countries. Secondly,although several hybrid solutions and systems exist[10,11] to enable communication during disasters, to thebest of our knowledge, none of these focus on having aproper deployment plan to ensure optimal resource utili-zation under constraints that are critical to the perfor-mance of a post-disaster communication infrastructure.As rightly pointed out by [12], we too strongly believedisaster management solutions especially for thedeveloping countries need to reviewed from a differentperspective altogether and cost-effective architecturesneed to be developed with scope for proper planning andpreparedness.

1.5. Contributions

The contribution of this paper is not in proposinganother hybrid architecture, but in providing a novel for-mulation of the latency-aware design problem, developingsuitable heuristic algorithms to enable planned deploy-ment of resources ensuring maximal utilization of theresource pool and analyzing in details the proposed archi-tecture to justify its usefulness using the customized ONE[13]8 simulator on a case study taking a region of disaster-prone Sundarbans from Eastern-India, as an example.

Some of the interesting insights drawn from theexperimental results are stated next. (a) Especially at lowerlatency values, even with 2 times higher cost, anunplanned deployment can deliver only (80–90)% packets(refer Figs. 7 and 10) which shows that the plannedapproach supercedes an unplanned approach. (b) Thecost-benefit analysis shows that the dependence of perfor-mance (delivery probability) over deployment cost is non-linear (refer Fig. 19 inset). Compared to optimaldeployment at initial stage, a reduction of LWC devices(costliest component) by a small amount can significantly(�25%) reduce the deployment cost, with a small (�5%)decrease in delivery probability, however a cost reductionbeyond a point will severely impair the performance. Sothe design plan at the point of sharp decline may be the

8 The ONE [13] is specifically designed only to simulate DTN protocols.We have added several modules to the ONE to enable it towards simulatinghybrid architectures. These modules include different network components(e.g., LWC, DM, IDB, VSAT), hybrid routing strategies, mobility and trafficmodels for emulating post-disaster scenario (for details refer Section 7.2).


best (cost-effective) option. (c) The planned deploymentis robust in terms of performance as delivery probabilityfalls only to 70% even at twice higher the load consideredfor the design. (d) As Tiers 1–3 adopts store and forwardstrategy, the choice of packet generation model (uniform/Poisson distributed inter-creation) and packet prioritieshas little impact on performance. (d) The mobility of theDTN significantly impact the overall performance. DTNnodes may move randomly but should have a bias towardsvisiting the nearest IDB at periodic intervals to ensurepacket delivery within latency constraints.

The rest of the paper is organized as follows. Section 2,presents a comprehensive extensive survey on the relatedworks. In Sections 3 and 4, we highlight the tier-wise sys-tem models and the algorithms for optimal deploymentplan. Section 5, analyzes the system performance. Section6 considers a case study selecting a region of disaster-prone Sundarbans. In Section 7, we describe the simulationparameters and present experimental results. Section 8concludes the paper with directions for future research.

2. Related work

In this section we review the different systems thathave been designed to provide communication to chal-lenged (rural/remote, underdeveloped) scenarios in gen-eral, with specific focus on understanding theirlimitations in addressing issues/challenges related todisaster management. The field practices used for commu-nication in real disasters are also briefly reviewed. Thesesystems generally use one of the technologies like wirelessmesh, Long range WiFi, WiMax, Low power GSM base sta-tion, VSAT, or mechanical backhaul, or use a combinationthereof as a hybrid infrastructure.

Systems based on mesh technologies include the ServalProject [6] (BatPhone-provides mesh mobile telephony plat-form), Hybrid Wireless Mesh Network (HWMN) for emer-gency situations [11]. AirJaldi [14] and JaldiMAC [7] useslong distance outdoor WiFi mesh to provide point-to-multi-point connectivity to provide broadband Internet in sparselypopulated areas that suffer from large set-up delays.Although mesh technology is a good option for general chal-lenged scenarios, for a large scale disaster, the number ofmesh devices increase drastically and ensuring line of sightfeatures during their deployment is not always feasible.

The Daknet project [4,5] uses buses and cars as mechan-ical backhaul to ferry data to provide rural Internet butincurs high latency. Systems Village Base Station (VBTS)[15] uses low power GSM base station for rural telephony.Projects LifeNet [16] and Twimight [17] provide connectiv-ity under transient conditions using only handheld devicesusing DTN protocols. Use of vehicle mounted communica-tion devices as mechanical backhaul can be effective dur-ing disasters with little additional operating cost byadding communication and storage devices to the ambu-lances and relief vehicles. Application of UAVs [18] canovercome hurdles where physical connectivity is restricteddue to landslides, for example.

VSAT terminals are reliable, but require considerableup-front capital expenditure. They are expensive in terms




of cost-per-bit. Outdoor long range WiFi or WiMax canachieve significant coverage, however, it requires consider-able planning for large scale deployment and, Line-of-sightis necessary for WiFi/WiMax communication. Moreover,WiMax have licensing problems in some countries.

Contrary to the above approaches, Braunstein et al. [10]deployed a hybrid network architecture, called ExtremeNetworking System (ENS), for the support of a medicalemergency response that consisted of three hierarchies; aWiFi network, a wireless mesh network, and multiplebackhaul networks (wired/wireless/cellular/satellite).

Except ENS [10] and RESCUE [11], most of the systemsmentioned above aims at enhancing rural infrastructuretelephony, Internet, and so on. Although wireless hybridnetworking solutions have the potential to combine theadvantages of different technologies to provide low-cost,scalable and reliable architectures [12,10], however suchsolutions never attempt to optimize the utilizations ofthe network resources.

To the best of our knowledge, none of the existing systems/solutions consider rapid deployment and latency constraintsthat are critical to the performance of a post-disaster commu-nication infrastructure, especially in the context of developingregions, which is the key focus of this paper.

3. Deployment plan for Tiers-1, 2 and 3

In this subsection, first, we model the system and statethe underlying assumptions that are used to compute theplan to deploy the devices. We then specify the inputparameters and identify the underlying challenges in

Table 1Notations used for modeling the Disaster area, Tier1, Tier2 and Tier3 components

Notation Meaning

GðS;PÞ Affected area (AA) modeled as a graph GDTN Delay tolerant protocol enabled handheld deviIDB Information drop boxes located in each shelteDM Data Mules, which are vehicles equipped withMCS Master control station where the entire rescueN Number of shelter points in GS ¼ fsig; 1 6 i 6 N Set of shelter points, each si equipped with ans Shelter point where Master Control Station isP ¼ fpijg; 1 6 i; j 6 N Set of path segments in affected area. pij is the

is directly connectedn Number of DTN nodes registered per IDBFDTN Time interval between two consecutive visitsðlBT ; rBT Þ Bluetooth interface with range lBT mts. and daðlSWiFi; rSWiFiÞ Small range WiFi interface with range lSWiFi anRe Relief/Rescue Request packets generated fromRs Relief/Rescue Reply packets generated from ML Worst case packet delivery delayge/gs Number of Re/Rs packets generated per minutep Size of each data packetM ¼ fmig; 1 6 i 6 jMj Set of jMj DMs used to cover the IDBs in the av Speed km/h of a DMIðmiÞ ¼ fxig; xi 2 S Trajectory, the ordered list of IDBs visited by tTtrv lðmiÞ Total time taken by mule mi only to travelTserv ðmiÞ Service time, time taken by mi to exchange paTworsðmiÞ Worst case time mi takes to reach MCS after uTwaitðmiÞ Time that a mi waits in MCS to get its turn toTrndðmiÞ Round trip time of the mule mi . TrndðmiÞ ¼ Ttrvl

to an IDB or MCSCserv ðmiÞ Number of IDBs served by mi excluding MCS


obtaining a plan to ensure optimal utilization of thedevices. Finally, we present the problem formulation.

3.1. System model

The disaster affected area is modeled as a graph GðS;PÞ,where the vertex set S ¼ fsi : 1 6 i 6 Ng corresponds tolocation of all of the N shelter points in A. Each shelter pointsi 2 S is expected to locally coordinate/control the relief/rescue operation of a portion of affected area A exclusiveto others. The edge set P ¼ fpijg, where pij denotes thephysical distance (length of the path/road segment inkm) through which si and sj is directly connected. Hencepij > 0 if there exists a direct path between si and sj and0, otherwise 81 6 i; j 6 N and i – j. All the notations usedin Section 3 are summarized in Table 1.

We assume an information drop box (IDB) located ateach si which has got buffer space to temporarily storepackets. An IDB is equipped with two types of short rangewireless communication interfaces: (1) Type 1 – withrange lBT and data rate rBT bytes/s. (e.g. Bluetooth interface)and Type 2 – with range lSWiFi > lBT and data rate rSWiFi >

rBT bytes/s. (e.g. short range WiFi interface). Moreover,there are n DTN nodes allocated to each shelter point forcollection of information from the area covered by therespective shelter point, i.e. there are total (n� jSj) DTNsin the system (entire area). Each DTN node is carried by arescue/relief volunteer who moves randomly in its desig-nated area, generates (collects) information packets andvisits the corresponding shelter point (say, si) periodicallywith an average inter-arrival time of FDTN min. A DTN isassumed to exchange data with its registered IDB si using

.

ces enabled with Bluetooth interfacer point having buffer and enabled with Bluetooth and WiFi interfacebuffer and WiFi communication interface/relief is coordinated from

Information Drop Box (IDB)located

physical path length (km) through which si and sj

of DTN to its IDBta rate rBT bytes/sd data rate rSWiFi bytes/sDTN nodes towards MCSCS towards DTN nodes

ffected area

he mule mi in its trip

ckets with one IDB in IðmiÞploading data from an IDBs it visitexchange data in presence of other mulesðmiÞ þ Tserv ðmiÞ þ TwaitðmiÞ. Interval between two successive visits of mi




Type 1 interface, i.e. at a rate rBT bytes/s to drop its gener-ated packets and collect packets accumulated in si.

One specific shelter point denoted as s is designated asthe Master Control Station (MCS) from where the entire res-cue-relief operations are controlled and coordinated. In thetraffic model we assume that generated packets are of twotypes, (a) Relief/Rescue Request (Re) – source a DTN node,destination MCS and (b) Relief/Rescue Reply (Rs) – sourceMCS and destination a DTN node. System-wide Re packetsare generated at a rate ge packets/min such that any singleDTN creates a new Re packet at a average rate of ( ge

n�jSj)which are destined for MCS. The MCS creates Rs packetsat a rate gs packets/min destined for DTN nodes, wherethe probability to select an arbitrary DTN is ( gs

n�jSj).IDBs located at shelter points are far off from each other

and do not lie within each others communication range.Hence, DMs are deployed to bring/send information (pack-ets) from IDBs to the MCS and vice versa. Let, the set ofDMs required be M ¼ fmi : 1 6 i 6 jMjg. A DM is assumedto exchange data with an IDB say, si that it visits, using Type2 interface, i.e. at a rate rSWiFi bytes/s to drop(collect) packetsit received from the MCS(accumulated in si). Each DM movesat an average speed of v km/s and has a defined trajectory(circuit) which starts from MCS, i.e., s, passes through a sub-set of IDBs and returns to the MCS. The trajectory of the ithDM mi is represented by an ordered list of points denotedas IðmiÞ ¼ fx0; x1; x2; . . . xi; xiþ1; . . . xj; . . . xk; . . . xtg wherexi 2 S;8i; 0 6 i 6 m. It may be noted that x0 ¼ xt ¼ s. More-over, in between x0 and xm any IDB including the one atMCS may be repeated more than once. The trip time takenby DM mi to cover the trajectory TrndðmiÞ which is the sumof travel time (TtrvlðmiÞ) (time to move across the shelterpoints) and service time (TservðmiÞ)(time to wait in the IDBsfor data transfer).

3.2. Problem formulation

The key design parameter for our deployment plan isthe Latency L. L is defined as the worst case packet deliverydelay. It is the maximum time taken by the network todeliver any packet from source to its destination, i.e., fromDTN to MCS for request packets ðReÞ and from MCS to DTNfor reply packets ðRsÞ. A survey of the area A provides thelocation of temporary shelters for victims. An assessmentof damage reveals the information of transport path seg-ments functional even after the damage. Hence, we assumethat for a disaster affected area GðS;PÞ is known. The firstproblem is formulated as following:

Problem 1. Given GðS;PÞ; s;n; FDTN; ge; gs;v and L (a) is itpossible to design a trajectory plan for DMs such that all(100%) Re and Rs packets will be delivered within worstcase latency of L minutes? (b) If possible, what is theminimum number of DMs jMjmin required to achieve it andwhat are the trajectories?

The answer to 1(a) is trivial. Let, sf be the shelter whichis farthest from the MCS in terms of shortest path distancein G and say, ðx0; x1; . . . ; xsÞ be the ordered set of IDBs alongthe path such that x0 ¼ s and xs ¼ sf . Then there exists afeasible DM trajectory under latency constraint L, only if


L > 2ðP

pijÞ; such that xi 2 S;80 6 i 6 s and j ¼ iþ 1. Itimplies that a solution must exist if a single DM can visitsf along the shortest path without waiting in the interme-diate IDBs to serve it and return to MCS within time L. In ageneral case, for each of the ðjMjP 1Þ DMs, the trajectoryneed not have restriction in the number of times a DMvisits an IDB, MCS or a pathway in a round trip. HenceProblem 1(b) which is a scheduling problem is of NP-hardnature. The problem is almost similar to MDVRP [19]problem. To find answer to 1(b), we design a heuristic algo-rithm presented in Section 3.3.

3.3. Algorithm: DM count and trajectory computation

The heuristic algorithm is based on some observations. InIðmiÞ an IDB may be visited multiple times by mi. A particularIDB may be visited by multiple DMs but is sufficient enoughto be served only by one DM. Round trip time of DM mi is,

TrndðmiÞ ¼ TtrvlðmiÞ þ TservðmiÞ þ TwaitðmiÞ ð1Þ

TtrvlðmiÞ is the sum of travel times of DM through all IDBsin IðmiÞ, i.e, TtrvlðmiÞ ¼

Ppij;8si; sj 2 IðmiÞ and j ¼ iþ 1.

TrndðmiÞ is same for all IDBs the mi visits. Hence servicetime for each of CservðmiÞ IDBs is given by the equation:

TservðmiÞ ¼TrndðmiÞ � g � p

rSWiFi

¼ ðTtrvlðmiÞ þ TservðmiÞ þ TwaitðmiÞÞ � g � prSWiFi

ð2Þ

To minimize delivery delay in a particular trip a DMshould download packets only during its first visit andupload packets only during its last visit to a serving IDB.The TwaitðmiÞ is the time a DM waits in the MCS to get itsturn to download its packets in presence of other DMs.

The total delay suffered by a packet in Tier-2 consists ofthe time to wait before the DM arrives to upload, secondly,service time of the IDB, thirdly, time to stay in DM afterbeing uploaded before it reaches MCS and fourthly, timeto wait in MCS before being downloaded in presence ofother DMs. The maximum packet delay in Tier-2 can beestimated by considering the worst case situations for allfour factors. In worst case, a packet originates (droppedby DTN in an IDB) just after the DM leaves the IDB, henceit needs to wait for TrndðmiÞ before the DM arrives foruploading. The second factor TservðmiÞ is same for all pack-ets in a IDB served by a DM. The third factor is maximumfor a packet originated in the first IDB from which theDM starts uploading in its way back to MCS, sayTworsðmiÞ. The fourth factor at most can be the total servicetime for all DMs in the MCS assuming a DM is queuedbehind all others. Hence to meet delay constraint the max-imum packet delay in Tier-2 must not exceed (L� FDTN):

ðTrndðmiÞ þ TservðmiÞ þ TworsðmiÞþX8j

ðCservðmjÞ � TservðmjÞÞÞ < ðL � FDTNÞ ð3Þ

It may be noted that estimation of service time of the ithDM, TservðmiÞ is a function of TservðmjÞ8mj 2M satisfying Eqs.(2) and (3). Hence if the trajectories of all DMs are known,their service times TservðmiÞ can have a solution if the set ofð2� jMjÞ linear inequalities does have a feasible solution.




We develop a greedy heuristic algorithm (Algorithm1) to generate a feasible DM trail satisfying Eqs. (2)and (3). Consider the partial trail for DM mi to beIðmiÞ ¼ fx0; x1; x2; . . . ; xkg where xk is the last visitedIDB. The next IDB xkþ1 is chosen to the be closest unvis-ited IDB (if possible) from the neighbors of the currentIDB xk only if it is possible to return from xkþ1 to MCSin the reverse path via xk such that there exists a feasiblesolution for service times which satisfy the set of Eqs. (2)and (3). Here, to consider feasibility, we assume the newIðmiÞ ¼ fx0; x1; x2; . . . ; xk; xkþ1; xk; . . . ; x2; x1; x0g obtainedafter the inclusion of xkþ1 will not violate the constraintsmentioned earlier. If no such unvisited IDB is found thena visited neighbor IDB which is closest to xk satisfyingthe feasibility criterion is chosen with a hope that thisextension may lead us to some unvisited IDB. If no suchIDB is found, then IðmiÞ cannot be extended. In such acase the DM trajectory is accepted if it contains at leastone newly visited IDB in the list IðmiÞ which is thentruncated at the last occurrence of s as all trails muststart and end at MCS. If all nodes are visited, the algo-rithm terminates successfully and service time of theDMs in M are calculated by solving the set of Eqns. in2 and 3. If new trails cannot be generated and still someIDBs remain unvisited, algorithm terminates unsuccess-fully, implying Tier-4 devices are essential to meet thedelay constraint. The algorithm is detailed in Algorithm1 through 4.

Algorithm 1. DM count and trajectory computation


Algorithm description:Given a partial trail, the step 2.2 in Algorithm 1 looks for

the next IDB to consume, if possible. The topmost priorityis given to so far unvisited ones, if any (step 2.2.2). OurAlgorithm 2 elaborates how to find a so far unvisitedneighbor of the current IDB by taking care of all the feasi-bility constraints. If such a neighbor is found, repeat fur-ther extension of the trail. Else we try a visited neighborhoping that this extension may lead us to some unvisitedone-hop neighbor of the current node (steps 2.2.3 and2.2.4 of Algorithm 1). Algorithm 3 elaborates the case ofneighboring node visited in some previous trail and Algo-rithm 4 elaborates the case of neighboring node visitedin current trail. A global stack is used to store the trailinformation like the node ids and return-time to MCS fromthe corresponding node. In case of choosing a node fromthe current trail, the roundtrip times for some nodes inthe trail needs to be recalculated. Steps 3.2–3.5 in Algo-rithm 4 does this recalculation with the help of a queue.The queue is needed to store the nodes from relevant por-tion of the trail, which requires recalculation and hencepopped from the stack, so that they can be pushed backonto the stack in upside-down fashion after the recompu-tation of roundtrip times. Since we are allowing visitedneighbors as possible next node, there is a potential riskof ending up in an infinite loop. The conditional statementin step 2.3 in Algorithm 1 takes necessary protectionagainst that. Finally we report successful return whenunion of nodes from all the trails is equal to the set of allIDBs (step 3 in Algorithm 1). Otherwise grouping of IDBsand use of Tier-4 devices are required.




Algorithm 2. FindNearestNeighborUnvisited.

Algorithm 3. FindNearestNeighborVisitedInPrevious-Trails.

Algorithm 4. FindNearestNeighborVisitedInCurrentTrail.

4. Deployment plan for Tier-4

Now, if (L 6 2ðP

pijÞ), latency constraint is met by usingLWCs equipped with long range non-line of sight(NLOS) ornear-line-of sight (nLOS) WiFi communication devices(LWiFi). The set of shelter points S are partitioned intonon-overlapping sub-groups and each sub-group isassigned an LWiFi tower to handle its inbound/outboundtraffic load.

4.1. System model

Let the set of sub-groups be denoted asG ¼ fGi : 2 6 i 6 jGjg. A set of shelter points in the ithgroup be Gi ¼ fxijg where the jth point in the ith group

Please cite this article in press as: S. Saha et al., Designing delay constrained hybrid ad hoc network infrastructure for post-disaster communication, Ad Hoc Netw. (2014), http://dx.doi.org/10.1016/j.adhoc.2014.08.009

xij 2 S and 1 6 j 6 jGij. Consider G1 contains MCS. Thegrouping should be non-overlapping such thatðGi \ GjÞ ¼£;8i – j and ðG1 [ G2 [ G3 [ . . . [ GjGjÞ ¼ S. Eachsub-group Gi is equipped with an LWC, deployed in a suit-able shelter point within it, called group center(GC). Let,the set of group centers be GC ¼ fgi : 2 6 i 6 jGjg wheregi 2 Gi. The set of LWCs be denoted asT ¼ fti : 2 6 i 6 jGjg. LWC ti located at gi can communicatewith its IDB via wired interface and also with other LWCsin T, that are located within a range lLWiFi km, at a data raterSWiFi bytes/s via its wireless interface. Let NeighðtiÞ denotethe neighbors of LWC ti. Here, ðlLWiFi � lSWiFi > lBTÞ andðrSWiFi > rSWiFi > rBTÞ. It is trivial to note that the MCS mustbe equipped with one LWC (Master Control LWC), saydenoted as t to be located at g1. Hence, if Tier-4 is required,

-



then at least two LWCs are required, i.e. 2 6 jTj 6 N. All thenotations used in Section 4 are summarized in Table 2.

Let the set of DMs used for collecting/disseminating infor-mation from/to IDBs in Gi to the group center gi isMðGiÞ ¼ fmijg;2 6 i 6 jGj. Here mij is the jth DM in the ithgroup. The trajectory of the DM mij is represented by anordered list of points denoted by IðmijÞ ¼ fxijg wherexij 2 Gi. It may be noted that xi0 ¼ xit ¼ bgi . The trip time takenby DM mij to cover the trajectory TrndðmijÞwhich is the sumof travel time (TtrvlðmijÞ) (time to move across the shelterpoints) and service time (TservðmijÞ) (time to wait in the IDBsfor data transfer). Hence, TrndðmijÞ ¼ TtrvlðmijÞ þ TservðmijÞ.

4.2. Problem formulation

The computational problems are, minimize the numberof subgroups/LWCs first, then determine the best locationof the LWCs within its sub-group of IDBs/shelter pointssuch that each LWC will have at least another LWC withinits communication range lLWiFi as well as the number ofDMs within the sub-group is also minimized, and finally,compute the optimal trajectory for each DM.

Problem 2. Given GðS;PÞ; s;n; FDTN ; ge; gs;v , if (L 6 2ðP

pijÞ)(a) minimize jGj, (b) determine GC such that NeighðtiÞ – £, 8iLWCs and jMij is minimized, 8i groups (c) for each DM mij

compute IðmijÞ such that TrndðmijÞ 6 L.Our Problem 2 of group formation reduce to clustering

problem which can be proved to be NP complete for 3 ormore clusters [20]. Now since most of the times numberof groups we obtain in our group formation problem morethan 2 in general so our problem is also NP complete.

4.3. Algorithm: LWC count and placement

If the affected area is large then the shelter points aredivided into a number of groups within each of which asuitable shelter point location is selected as group center.We place long range WiFi devices (LWC) at each groupcenter. Algorithm 5 will efficiently find the possible groupsand also find a suitable location to place LWCs.

Algorithm 5. Group Formation :: Get Groups(MCS,G(V,E))

4.4. Algorithm: calculating maximum latency for Tier-4

Previous section takes care of the delivery of Re packetsfrom any group to respective GC for all groups. Now, it is


the Tier-4 devices that has to ensure the delivery of Re

packets to MCS. Similarly, this layer also takes care of thedelivery of Rs packets from MCS destined for a particularDTN node to the corresponding GC. Thus, this layer model-ing deals with latency incurred only due to LWC intercon-nections. Since LWC devices, being Layer-2 networkdevices, do not allow loops in the network, we need toreduce the graph network, where LWCs behave as verticesand connection active between adjacent LWC devicesbehave as Edges, into tree like network based on a suitableminimum spanning tree algorithm. For now, we considerminimum hopcount from MCS as a criterion to decide routefrom LWC to MCS. After a topology has been defined for Tier4 by Algorithm 5, we need to model it in terms of givenLatency value fulfilling the below mentioned constraints.

Algorithm 6. Calculating maximum latency for Tier-4.



Table 2Notations used for modeling Tier4 components.

Notation Meaning

LWC Tower/balloon mounted Long range WiFi Communication deviceðlLWiFi; rLWiFiÞ Long range WiFi interface with range lLWiFi and data rate rLWiFi bytes/sG ¼ fGig; 2 6 i 6 jGj Set of sub-sets (sub-groups) of IDBsGi ¼ fxijg; 2 6 i 6 jGj Sub-set (sub-group) of IDBs

GC ¼ f bgig; bgi 2 Gi;2 6 i 6 jGj Set of group centersW ¼ fLWCig;2 6 i 6 jGj Set of LWCs, LWCi to be deployed in bgi

MðGiÞ ¼ fmijg;2 6 i 6 jGj Set of DMs used in Gi

IðmijÞ ¼ fxijg; xij 2 Gi Trajectory, the ordered list of IDBs from Gi visited by mij in its tripTtrv lðmijÞ; Tserv ðmijÞ; TworsðmijÞ, TwaitðmijÞ; TrndðmijÞ Total travel time, Service time, Worst case delay, Waiting time and Round trip time of DM mij

Cserv ðmijÞ Number of IDBs served by mij excluding MCS

Fig. 2. Tier-4 latency calculation.


Constraints

(i) At any timestamp, if two LWCs, say LWCx and LWCy,have their connections active (transmits data), thenneither LWCx nor LWCy can have connection activewith any other LWC, say LWCz, at the same timeuntil and unless connection becomes inactive.

(ii) However, any two LWCs, say LWCa and LWCb) otherthan LWCx and LWCy, can have connection active atthe same time provided that LWCa and LWCb arecompletely non-overlapping with LWCx and LWCy.

Given Tier-4 as tree-like network having MCS as rootand satisfying above mentioned constraints, our objectiveis to deliver Rs packets to GC and Re packets to MCS withminimum delay. Since both types of relief packets usethe same network, they affect each other’s delay. Thus,we use an optimal heuristic strategy that minimizeslatency for both types of packets. Our Algorithm 6 findsthe worst-case delay that a packet can encounter at Tier-4.

The above mentioned algorithm (Algorithm 6) usesminimum maximal matching [21] to find pattern for paral-lel ‘active connections’ in worst case for the LWC networks.

Algorithm description:Initially in step 1, the Re packets are in leaf LWCs and

Rs packets are in MCS. From step 2 to step 3, we use min-imum maximal matching algorithm to get the minimumnumber of parallel active connections available at anypoint of time which is used to calculate worst caselatency at Tier-4. In step 3.4 we update the active connec-tion time for the loads calculated in the step 3.3 to bedelivered completely. Now we get the minT, i.e. minimumconnection up time for the LWCs that are connected. Instep 3.6 we mark and update if data packets from leafnodes are departed to MCS or vice versa, we update theactive connection information by updating the colors ofpaths. In step 3.7 we check whether the terminationcondition is satisfied or not. If satisfied, we report themaximum latency obtained, else update the time infor-mation and continue the process.

4.4.1. IllustrationAccording to the algorithm, we are using minimum

maximal matching to get the worst case scenario. Nowlet the given topology be Fig. 2(a). At time-stamp 100, min-imum maximal matching yields 1–2 and 4–5 as paired up


connected LWCs. Let Re packets of load 120 MB arrived atLWC 5 and 60 MB at LWC 3 at time stamp 100. The transferrate for the transmission of data is 8 Mbps. Thus, 120 Mbwould take 2 min to send and will be completed on time-stamp 102 through active connection 4–5. Similarly, incase of 1–2, Re packets created at LWC 2 will be forwardedto LWC 1(or MCS). Hence MCS will also send the Rs packetto the LWC 2. Now within this gap of 2 min the pair 1–2will break out (or disconnected) and hence will form somenew connections, i.e. 1–6 and 2–3; and the process willcontinue as illustrated in Fig. 2.

5. Performance Analysis

A packet generated at a DTN node suffers delay at thefollowing three stages:

(a) Tier 1–Tier 2 where the packet moves from DTN toIDB.

(b) At Tier 3 where the packet moves from IDB to GC.




(c) At Tier 4 where packet moves from GC to MCS.

We estimate both best case and worst case delay ateach of the above three stages.

Tier 1 and Tier 2The best case situation is the situation where the DTN is

about to reach the nearest IDB and the packet in question isthe first packet to be uploaded to the IDB, yielding the bestcase waiting time in this stage = B1 ’ 0.

The worst case situation is the situation where the packetis generated when the DTN is just leaving IDB and when itcomes back there, all other DTNs queued before IDB.

The service time required for each DTN node = the timeto upload the packets generated during the time they wereaway from the IDB =

FDTN �ge

n� jSj � p� ��

rBT min ð4Þ

Thus the worst-case waiting time at Tiers 1–2,

W1 ¼ FDTN þðn� 1Þ � FDTN � ge � p

n� jSj � rBT

� �min ð5Þ

Tier 3The best case time at Tier 3 = travel time of a DM from

IDB to GC + Service Time for each IDB down the line by DM.Service time for each IDB by DM

mij ¼TrndðmijÞ � ge � pjSj � rSWiFi

6 MSTMax; say ð6Þ

where MSTMax refers to maximum service time by any DMin the system to any IDB.

Therefore, the best case waiting in Tier-3 =

TtrvlðmijÞ þX

xik2IðmijÞðTservðmijÞÞ

6 TtrvlðmijÞ þ jIðmijj �MSTMax ¼ B2; say: ð7Þ

The worst case time in Tier 3 =

TrndðmijÞ þ TtrvlðmijÞ þ 2�X

xik2IðmijÞðTservðmijÞÞ

þX

mip2ðMðGiÞnfmijgÞðTservðmijÞ � CservðmijÞÞ

6 TrndðmijÞ þ TtrvlðmijÞ þ 2� jIðmijÞj �MSTMax

þ ðjMðGiÞj � 1Þ � ðjGij � jIðmijÞjÞ �MSTMax

¼W2; say: ð8Þ

Fig. 3. Disaster area scenario map


Tier 4The best case for the packet in question at Tier 4 = Hop

Count � the connection set up time at each hop ¼ B3, say.The worst case for packet in question at Tier 4.= Hop count � (Maximum time to transfer packets per

Hop).Now, if a group Gi gets an opportunity after a time Ti,

Then the total time to transfer the load of Gi to MCS=

jGij � ge � p� Ti

jSj � rLWiFið9Þ

We assume, for stability, the above time is boundedabove by some Maximum time, LSTMax say.

Worst case for packet in question at Tier 4.Hop Count �LSTMax ¼W3, say. In both cases the hop

count refers to the depth of the tree of LWCs.Combining the above three cases, the best case waiting

time for a packet from ith group Bi ¼ B1 þ B2 þ B3, and theworst case waiting time for a packet from ith groupWi ¼W1 þW2 þW3.

Now, we can say packet generated in the given group ican neither be delivered earlier than time Bi nor be deliv-ered after time Wi.

If we assume the delivery times of the packet from thegroup Gi are uniformly distributed in time span between Bi

and Wi, then we can write,

piðxÞ ¼1

Wi�Bi; if Bi 6 x 6Wi

0; otherwise

(

where piðxÞ refers to probability of a packet generated atthe ith group be delivered at an age x.

Now, we consider the system as a whole and let p(x)represents the probability that a packet from anywherein the system be delivered at an age x. If L denotes the max-imum latency of a packet then the above probability p(x)can be written as:

pðxÞ ¼

Xi

1jGjpiðxÞ; 0 6 x 6 L

0; otherwise

8<:Then, the average latency of a packet from the system =Z L

0xpðxÞdx

with connectivity matrix.




6. A case study

As a case study we have taken sundarban area mapwhich is one of the disaster-prone area from eastern Indiafor our post-disaster management network modeling andanalysis.

6.1. Area map and disaster scenario specification

Assumed disaster-affected area is spanned over225 km2 and comprising of 19 existing shelter pointswhich is shown in Fig. 3, each shelter point covers almost12 km2. This area is intersected by a complex network oftidal waterways, mudflats and small islands full of denseforests. Possible connections between these islands aremainly through boats. As a sample connectivity matrixbetween these shelter points are shown in Fig. 3 in table.

6.2. Deployment plan under constrained delay

When we are planning to deploy a hybrid ad hoc net-work infrastructure for a particular disaster-hit area witha given latency constraint, a pre-planned tier-wise mathe-matical formulation is very much desirable. A plannedapproach is preferred over an unplanned one as the lateris very much prone to be biased. An optimal sharing of net-work resources satisfying the delay constraint is possiblewith a planned approach. Fig. 4 depicts an actual deploy-ment of network resources following a planned approachand we can ensure this distribution is a near-optimal one.

7. Simulation results

7.1. ONE Simulator overview

The Opportunistic Network Simulator (ONE) [22] is awidely used simulator for DTN protocol evaluation. It hasnumerous in-built features to enhance its expertise. It sup-ports: (i) different routing algorithms such as Epidemic [1],Prophet [23], Spray and Wait, Spray and Focus, andMaxProp; (ii) different mobility models such as Shortest-PathMapBased, RandomWalk, and RandomWaypoint. Inaddition to these, there is a scope for applying real tracesin a text file named as external movement, which can be

Fig. 4. Group (picture) and DM trajectory (table) information forL = 200 min.


modified by any user in ONE; hence, any practical scenariocan be deployed with its real traces in ONE instead ofrandom simulation; (iii) visualization tool and GUI to setthe simulation parameters (dynamically in some cases)and observe the simulation progress, with user-friendlygraphical representation; (iv) it is an open source package,providing the flexibility to modify different modulesaccording to specific requirements; (v) every new releaseof ONE adds some new routing algorithms and mobilitymodels – several new features have been added by thecommunity working with ONE; and (vi) ONE also providesseveral metrics to analyze simulations, like latency, deliv-ery probability, and overhead, which are echoed in a reportfile, considered as output. Map of any location can also beused as the play field for simulations and the simulationcan be designed accordingly; (vii) the extensivedocumentation provided makes ONE developer-friendly.

7.2. ONE Simulator with enhanced features

We have used ONE Simulator [13] for our analysis.Although it is quite an effective simulator, but it still hasa lot of ground to cover before being apt for the real worldscenario. It requires several major modifications todifferent modules in order for them to work with morerealistic scenarios.

There is a lack of several heterogeneous/smartinterfaces, such as the satellite phone interface and otherslike long-range WiFi, and Mesh, for supporting infrastruc-ture nodes. Moreover, it requires inclusion of hybrid appli-cation-specific routing strategies since different resourcesuse different technologies to work. The mobility of nodesalso needs to be modified in order to match with the realworld requirements, such as group mobility, and differenttypes of path based movements. Here, we describe how wehave enhanced the different features of e-ONE [24].

7.2.1. Mobility modelsEnhancements are made to consider both DTN node and

DM mobility.DTN Mobility Model: No mobility model from DTN lit-

erature fits the mobility patterns in post-disaster situation.To design a suitable DTN movement model in the abovementioned condition, we have to keep the following thingsin mind: (i) the AA is usually divided into subregions(mostly around shelter-points) within which a group ofvolunteers work and (ii) usually DTN nodes carried by localvolunteers or rescue/relief workers are very few in numberas compared to the area of the subregion, forcing the net-work to be a sparse one. As a consequence, our desiredmovement model must be a cluster type movement modelwhere a node’s movement is restricted to a specific regionwithin AA. But to ensure a bounded delivery latency, itmust have some kind of bias towards the IDB location inthe sense that the DTN nodes must visit the IDB at periodicintervals to deliver the messages they have collected. Thisrestriction is not in contradiction to the real-life post-disaster situation movement patterns, as volunteers oftenneed to visit the SP (local co-ordination center) wherethe IDB is located. For this reason, we have designed a spe-cial class of cluster movement model that we refer as Post-



Fig. 5. Interrelation between Postoffice and Cluster Movement Models.


office Cluster Movement model (PCM). It is implemented bymodifying the Cluster Movement Model9 (CM) [25] availablein ONE with the above mentioned bias towards the IDB.

The PCM has been implemented as a two parametermovement model where parameters h1 and h2 are twopositive integers such that the node makes a randomnumber of hops in the integer range ½h1;h2�, between twoconsecutive visits to an IDB. To make these models lookrealistic, we have designed a variant of PCM in such away that the inter-arrival time gaps between two consecu-tive visits of a DTN node to the nearest IDB follows somestandard probability distribution.

The first variant uses Poisson-distributed time gaps. Wecall this movement a Poisson Postoffice Cluster MovementModel (PPCM). We introduce a parameter k which repre-sents the average time between two consecutive visits tothe IDB. It is worth pointing out that more biased theDTN nodes are towards the IDB, less explored are theregions of the cluster away from the IDB. On the otherhand, if the movement is less biased towards the IDB, itcovers more area, however, packet delivery probabilityfalls.

To strike a balance, we introduce the second variant ofPostoffice Cluster Movement called Multi-mode PostofficeCluster Movement (MPCM). Here the DTN nodes are dividedinto multiple classes each of which moves following PCM,but with a different set of values for the relevantparameters k;h1;h2. The MPCM reflects the real life factthat the movement patterns of different class of volunteerslike local volunteers, rescue-relief workers from thegovernment agencies/NGOs, each may have different biastowards the SP, i.e. IDB.

Lastly, to include both randomness and bias together,we have designed another multi-mode movement modelwhere some proportion of nodes follows PCM (includesbias) model and the rest follows CM (includes randomness)model. We called the movement Mixed-mode Cluster Move-ment Model (MCM). Fig. 5 shows the inter-relation betweenthe different movement models we have used in our study.

DM Mobility Model: Another important class of mobiledevices is carried by DMs which periodically visits the IDBsto collect Re messages and drop Rs messages. We have usedthe feature of External Movements in ONE Simulator whichenables specifying the movement for each DMs so thatthey move within the region along a predefined trajectorywith a predefined speed.

7.2.2. Traffic modelWe have considered mainly two kinds of packet gener-

ation model, namely Uniform Packet Generation Model(UPG) and Poisson Packet Generation Model (PPG). In formermodel the packets created by a DTN node with a constantinter-creation interval, whereas in the later model, thepackets are created with an exponential inter-creation inter-vals. Re packets are generated by randomly selecting a DTNnode as its source and MCS as destination. Rs packets aregenerated with MCS as its source and randomly selectinga DTN node as its destination.

9 Here DTN nodes moves randomly within its cluster.


Considering a disaster scenario, there might be a smallfraction of emergency messages that need to be deliveredto the MCS at the earliest. Such packets are assigned higherpriority (Prioritized Packets) over the normal ones during itscreation. Message queues are maintained as priorityqueues to deal with prioritized packets.

7.2.3. Restricted routing strategySince our network is a sparse network, epidemic routing

is possibly the best routing strategy in this situation. Inorder to reduce overall system load, we impose few restric-tions on epidemic routing strategy being used. If a packet isdelivered to IDB then it will try to restrict the further prop-agation of the same packet to other DTN within the samecluster by introducing some suitable acknowledgmentmechanism. Similarly the DMs will deliver a packet onlyto that IDB to whose cluster the packet is meant for.

7.2.4. Buffer schedulingWhen a node delivers a packet to its immediate next

tier then it clears the packet from its buffer in order torestrict further delivery of same packet as well as reducingthe chance of buffer overflow.

7.2.5. Device schedulingTwo types of devices encounter IDBs at each SP. One is

the class of DTNs and other is the class of DMs. Messagesare uploaded to and downloaded from both of them atIDB. But a proper scheduling mechanism should be fol-lowed when a DM appears to IDB when it is serving aDTN. In this case, DM should be served immediately witha higher priority than a DTN node.

7.3. Simulation plan

Simulation is carried out using the enhanced ONE Simu-lator for the area of Sundarban, India; an area of 225 km2 isdivided into 19 SPs with a density of 10 smart phones per SP,each having a data rate of 8Mbps and coverage range of10 m. These nodes follow the restricted epidemic routingstrategy for message transfer. They only interact with eitherother smart phones or the IDB at the center. The speed ofDMs is restricted to an average of 10 km/h. List of simulationparameters for our modeling as shown in Table 3.

In order to illustrate the performance of our work wecompare our model with an unplanned approach whichis described below.



Fig. 6. (%) Of cumulative packet delivery for L = 200 using PCM Move-ment Model with UPG Traffic Model.

Table 3List of simulation parameters used to modeled the architecture.

Parameter name Value

No of DTN Nodes (n) 10/shelter PointRe Packet Generation Rate 10 Packets/h/DTN/Rs Packet Generation Rate 3 Packets/minPacket Size (p) 1 MB(Data Rate,Range) of DTN

Interface(2 Mbps/10 Mbps, 10 m/100 m)

(Data Rate,Range) of DM Interface (20 Mbps, 100 m)(Data Rate,Range) of LWC

Interface(8 Mbps, 8 km)

Speed of DM (s) 10 km/hMCS ID 5Number of IDBs 19Area 225 km2

Simulation Time 24 h


7.3.1. Outline of an unplanned approachLet us assume that the average value for FDTN is t1

(=20 min, say) for each DTN node to the nearest IDB; andthe latency at the Tier-4 is t2 (=20 min, too) for LWC toMCS. We also assume that the Service Time at each IDBby the any DM is same and is equal to t3 (=5 min, say).Keeping the above assumptions in mind, we start ourgroup formation as follows:

1. We place the MCS first at the most convenient place.2. Keeping the TTL constraint in mind we allow a finite

number of DMs to roll along different possible pathsand calculate the worst-case travel time for each ofthe above DMs as 3 � (The time needed by the corre-sponding DM to the intended IDB + 3 � (Service timefor each IDB in the trajectory of the DM). In other words,the maximum allowed distance from MCS for placingan IDB is (TTL� t1 � t2)/3.

3. If the above collection covers the whole affected area,we are through. Else we add more DMs, if possible.

4. If the set of all possible DM trajectory spans over thewhole affected area, we are through. Otherwise, wedeploy LWCs within the range of MCS LWC or in therange of some previously placed LWC. We set the IDBsat the location of the LWCs as our GCs.

5. Starting at each GC, we repeat the process of allocatingDMs in the same fashion described in steps 2 and 3. Ifabove spans over the whole affected area, we are through.Else we add more DMs or more LWCs maintaining thedelay constraints, until whole affected area is covered.

In Sections 3 and 4, we have described our plannedapproach elaborately. Here we summarize the key stepsto be followed in the planned approach.

7.3.2. Outline of our planned approach

1. We start with a map of the affected area and pinpointthe candidate SP locations.

2. Depending on the availability of the IDB devices, wechoose the best SP locations for placing IDBs.

3. Apply Algorithm 1 through 4 to see if the affected areacan be covered using DMs only.

4. If unsuccessful, Tier-4 devices (LWC) are essential.


5. Apply Algorithm 5 to find a group formation. This willdivide the collection SPs in AA in few groups clearlypointing out the GC for each group.

6. For each group, apply Algorithm 1 through 4 to findnumber of DMs and their trajectories.

7. Apply an optimization algorithm over the deploymentplan thus obtained to find if any of the LWCs/DMs canbe removed without hampering the overallperformance of the system.

The following two subsections compares theperformances of both the Planned and the Unplannedapproach in order to justify the effectiveness of ourPlanned approach.

7.4. Performance benefits of planned deployment

As seen from Fig. 6, the planned approach actuallyyields 100% packet delivery as being predicted from ourtheoretical study; whereas, the unplanned approachdescribed earlier failed to deliver 100% packets withinthe desired L. The Figure also shows that nearly 30% ofall the packets from the system are delivered nearly within25 min. We feel that these are from the DTN nodes near theGCs as well as the MCS. Again we observe that nearly 70%packets are delivered within the time equal to half of L.That can also be predicted from theoretical study.

Fig. 7 illustrates mean delay at every tier with corre-sponding error bar highlighted in them. The observationsuggests that majority of the delay is contributed by thetraversal of the packets from IDBs to the respective GCsthrough the DMs. The above can also be well predictedfrom our theoretical study of our planned model. Combingall the above observations, we can say that plannedapproach supersedes the unplanned one in all respect.

7.5. Cost benefits of planned deployment

Through Figs. 8 and 9, we illustrate the effect of L con-straints on the process of group formation and the totalcost of the system deployment. From Fig. 8 we see thatthe total cost ($) for the system deployment decreases aswe increase the value of L. It is well-expected, as higher



Fig. 7. Mean Latency with error bar for L = 200 using PCM Movementm Model with UPG Traffic Model.

Fig. 8. Number of different devices, total cost, group information for latency 60 and 300 min.


the value of L, lesser the number of groups, and lesser thecostlier equipments. For example, required number ofLWCs is higher for lower L compared to the same in higherL case. Fig. 9 shows nearly the same pattern of cumulative(%) packet delivery for different L values.

Fig. 10(a) shows the total solution cost under unplannedapproach for varying latency scenario. Fig. 10(b) reflects thefact that at lower latency the total deployment cost underthe unplanned approach is higher than the same underthe planned approach, whereas at higher latency they arealmost identical. This is mainly because it is very difficultto find optimal group formation in low latency without aproper planning. Again, at higher latency, although we aregetting almost the same solution cost both under plannedand unplanned approach, around 10–20% packet loss isobserved in every case under unplanned solution, as seenfrom Fig. 10(c). Thus we have observed how our plannedapproach supersedes an unplanned approach.


Now we would like to focus completely on the perfor-mance of our planned deployment only. Different ingredi-ents that may affect the performance of a post-disastercommunication network are the mobility models for themobile devices, the traffic generation pattern, and the val-ues of different communication parameters like ranges anddata rates of the interfaces used in communicating devices,and speed of DM. In next three subsections, we concentratein studying the effects of these parameter values in a sys-tematic fashion.

7.6. The effect of mobility model

Postoffice Cluster Movement models PCM offer significantimprovement in both latency as well as in packet deliveryprobability performances over ordinary Cluster MovementModel CM. The result shows that if we design a Mixed-modeCluster Movement Model MCM as Two-mode Cluster Move-



Fig. 9. Delivery probability analysis in equal interval for all latency in planned simulation and theory using PCM Movement Model with UPG Traffic Model.


ment Model in which one class of nodes follow CM and the restfollow PCM and use that as our DTN movement model, thenthe packet delivery probability falls of critically (Fig. 11).Fig. 11 also shows that if we increase the ratio of nodes that fol-lows PCM, the packet delivery performance improves dramat-ically. The reason behind this is the fact that our network is asparse network and chance of intermediate contact amongnodes is very low. So, if one has to rely on the random/free-form movement of the nodes, either we have to accept lowdelivery probability or we have to allow high latency value,both of which are unacceptable for a real deployment.

Other PCM Movement Models like the Poisson Postof-fice Cluster Movement PPCM or the Multi-mode PostofficeCluster Movements MPCM are equally useful. Figs. 12 and13 respectively shows the performances of PPCM and Two-mode Poisson Postoffice Cluster Movement Model (MPCM)for different parameters. Results show DTN nodes may moverandomly but should have a bias towards visiting the nearestIDB at periodic intervals to ensure packet delivery withindesired latency constraints.

7.7. Effect of traffic model

This subsection studies (a) the performance of the archi-tecture for two packet generation models (Uniform PacketGeneration (UPG) and Poisson Packet Generation (PPG))and (b) how the packet priorities can influence performance.

Fig. 14 shows the overall performances for both thepacket generation models UPG and PPG under differentpacket load conditions. The simulation results suggest thatboth the traffic models performs almost identically in terms ofboth delivery probability and overall (DTN to MCS and viceversa) packet delivery delay. The reason behind this behav-ior is that the packet delivery in our case largely depend onthe movement pattern of the DMs whose periodic durationof visit to an IDB is very large as compared to averagepacket inter-creation time. Since the network works in astore and forward manner in Tiers 1–3, when DM receives


the packets from the packet buffer of the IDB, it has littleeffect on way they are created at DTN nodes.

The effect of increase in the rate of generation of packetskeeping every other system parameter values unchangedmay end up in buffer overflow and hence loss of packets.This observation is illustrated by Fig. 14(a). It is alsoobserved from Fig. 14(b) that increase in packet generationrate increases the mean latency of the packets. However, atwice increase in rate degrades the delivery probability only to70% shows the robustness of the planned deployment.

In our study, we have simulated prioritized messages,too. These type of messages in our case is handled bypushing them towards the front of the buffer wheneverthey arrive. However, Fig. 15 shows that the overall perfor-mance of the emergency packets is no better than that ofnormal packets. This is because the waiting time for apacket in IDB as well as in DM is too large even for anemergency message because of store and forward strategy.One possible way to reduce mean latency for emergencymessages is to use satellite communication. It is possibleif few or all the DMs are equipped with communicationdevices like Very Small Aperture Terminals (VSATs).Fig. 15 shows the packet delivery performance for prioritizedmessages increases significantly (nearly 90% delivered in0.5 L) with the use of satellite phones.

7.8. Effect of communication parameters and DM speed

We know, WiFi devices have longer range and higherdata rate, but at the same time higher power consumption.If we change the DTN interface from Bluetooth to WiFi, Fig. 16shows an overall improvement in performance of the system,both the delivery probability and the average delay. But at thesame time, there is a risk of fast drainage of battery powerwhich in turn may deteriorate the performance sincepower source may be scarce after disaster.

Though we do not have much control over the speed ofthe DM, the change in road conditions in AA, like a new



Fig. 10. Performance evaluation of unplanned approach using PCM Movement Model with UPG Traffic Model.


collapse in a bridge or road or a temporary repairment of aroad or bridge may significantly improve the averagespeed and hence the roundtrip time of a DM. Since DMmovement is a bottleneck in our design, our performance


may improve significantly due to increase in average DMspeed. In Fig. 17, we try to capture the impact of changesin DM speed over performance of the system. Fig. 17 showsthat packet delivery rate increases as DM speed increases.



Fig. 11. Performance of Mixed-mode Cluster Movement Model (MCM) forvarying ratio of PCM and CM Movement Components with PPG trafficmodel.


At Tier-4 we use high-speed LWC devices. But the datarate of these devices may change due to landscape interfer-ence.10 In Fig. 18 shows how average packet delay increasesand hence overall delivery probability decreases as data ratefor Tier-4 devices decreases. It is worth noting that with asmall decrease the performance degradation is graceful,however, when data rate drops to as low as half (4 Mbps) withother simulation parameters remain standard, then averagepacket delay between GC-LWC and MCS-LWC increasessignificantly and hence overall packet delivery probability fallsoff drastically. This is mainly due to queueing effect at Tier-4 device buffers.

7.9. Cost-benefit analysis

Given L, so far we are successful in obtaining the nearoptimal design plan, since the formulated problems areNP-complete in nature. With a near-optimal solution, theiralways have chances of getting a design comprising ofredundant resources. Moreover, for developing regions,rescue agencies in most cases have to function under bud-getary constraints which is far less than that is required fora near-optimal deployment. Hence, it is worth studyinghow the reduction of cost impact the performance, i.e.delivery probability.

We observe that, for a reasonably large affected area,Tiers-1 and -2 devices are mandatory, but either or bothof Tiers-3 and -4 devices may be present, depending onthe latency constraint. If the desired latency is very low(�1 h.), then DMs are useless and only the use of LWCdevices can provide a feasible solution, which however isa very costly option. On the other hand, if the situationcan tolerate large latency value (�24 day), then use ofLWC devices can be reduced to a minimum and feasiblesolution may be obtained mostly by the use of DMs. At thispoint, in case when the underlying graph is a connectedone, for a sufficiently large latency value, one can get ridof LWC devices completely in deployment plan. In case of

10 It’s the interference due to obstacles. Tall buildings, trees and forestsattenuate the microwave signal, and hills make it difficult to establish line-of-sight propagation of long range WiFi.


disconnected graphs, the minimum is equal to the numberof connected components of the graph. Fig. 19(a) showsthe change in quantum of critical devices for varyinglatency constraints.

We know LWCs are costly devices and deployment ofLWCs carry a lot of overhead. Hence reduction of LWCsfrom the near-optimal plan with 6 LWCs, may reduce sig-nificant deployment cost and overhead. But their removalmay reduce the performance as well. Fig. 19(b) showshow overall packet delivery probability changes with thenumber of LWCs and hence with deployment cost. TheFigure also suggests that, a reduction of LWC count from6 to 4 may substantially (25%) decrease cost withouthampering the performance much, but a further reductionincreases the packet loss by more than 20% or so. For the sce-nario, if we go on reducing LWCs by merging correspondinggroups, the packet delivery probability is tolerable up toremoval of two LWCs, and then falls off sharply. Hence, froma cost-benefit perspective, a deployment plan with 4 LWCs isthe best choice.

One can reduce DMs from their design, too. But impactof removal of DMs from the design is much higher thanthat of the LWCs; however, cost benefit due to reductionof a DM is much less than that of an LWC.

7.10. Key observations

The conclusions we can draw from the results obtainedthrough extensive simulations described above may besummarized as follows:

� A planned deployment may supersede an unplannedone in performance as well as in cost. An unplanneddeployment mostly rely on individuals’ experiencesand prediction capabilities, whereas the planneddeployment analyzes the overall situation in a step bystep fashion by simulating the situation for the possibleperformances and finally can come up with a near-opti-mal deployment plan that optimally serve the victimsas well as can reduce cost.� Postoffice Cluster Movement Model and its variants

may be a good alternative for designing DTN mobilityin a post-disaster situation analysis.� Increasing Packet generation rate may severely impair

the performance. As seen from Fig. 14(a), the cumula-tive packet delivery falls from nearly 100% to as lowas 70% when packet generation rate is doubled to thestandard packet load.� Due to the store-n-forward mechanism, the emergency/

prioritized messages do not get additional benefit fromthe ad hoc infrastructure, however use of VSAT withDMs equipped with satellite phones can drasticallyreduce the mean latency to half.� As observed from Fig. 18, the data rate of LWC devices is

a very important design parameter. Because of theinternal queueing effect, the delivery probability fallsfrom 98% to 90% when we reduce the LWC data ratefrom 0.6R to 0.5R (R = 8 Mbps). In other words, if theLWC data rate falls due to some reason, the packetgeneration rate needs to be reduced.



Fig. 12. Performance of Poisson Postoffice Cluster Movement Model (PPCM) for varying k, L = 200 with PPG traffic Model.

Fig. 13. Performance of Two-mode Poisson Postoffice Cluster Movement Model (MPCM) for varying k, L = 200 with PPG traffic Model.


� Reduction of Tier-4 devices (LWC) by smaller amountmay help huge saving (�25%) in deployment cost withvery little sacrifice (�5%) in performance (deliveryprobability), but it may severely degrade the perfor-mance (�20%) if reduced beyond. So, although not theoptimal, the design at point of sharp decline may bethe best cost-effective option.

8. Conclusion and future work

This paper proposes a deployment plan of a 4-Tier adhoc hybrid network architecture consisting of DTN nodes,IDBs, DMs and LWC devices to enable communication inthe aftermath of a disaster. Noting that packet deliverydelay is one of the key performance parameter in thispaper we formulated novel problems related to the deploy-


ment so that overall cost of the solution is minimizedensuring guaranteed delivery within a constrained delay.Finding optimal trajectory of DMs and placement of LWCsare NP Complete problems.

Suitable heuristic algorithms are designed to ensurebetter utilization of the resources. The effectiveness ofthe plan has been tested through extensive simulationsusing enhanced ONE Simulator and validated through ana-lytical results. As a case study, a portion of the Sunderbans,one of the areas highly prone to natural disasters isconsidered. A cost-benefit analysis suggests that for agiven disaster scenario, traffic load, and delay constrainta planned deployment significantly outperforms anunplanned deployment. The packet delivery probabilitydegrades gracefully with an increase in traffic load (packetgeneration rate).



Fig. 14. Effect of increasing packet generation rate in system performance using PPCM Movement Model with k ¼ 20.

Fig. 15. Performance of prioritized messages using PPCM Movement Model with k ¼ 30.


Please cite this article in press as: S. Saha et al., Designing delay constrained hybrid ad hoc network infrastructure for post-disaster com-munication, Ad Hoc Netw. (2014), http://dx.doi.org/10.1016/j.adhoc.2014.08.009


Fig. 16. Performance Analysis for varying DTN interface using PPCM Movement Model with PGM Traffic Model.

Fig. 17. Effect of change in DM speed over performance using PPCMMovement Model with k ¼ 20 and PGM Traffic Model.

Fig. 18. Impact of changing LWC data rate over system performance us



This paper presents analysis based on some simplifiedassumptions on traffic model, movement model, etc. How-ever, given performance constraints it is probably the firstattempt to formalize problems and finding solutions tomaximize utilization of communication resources duringdisaster management through proper planning rather thanusing mere human expertise/intuition. Some regions likecoastal areas, and terrains, are highly prone to cyclones,and floods. Here disasters are recurrent, hence, the extentof damage can be predicted from prior reports. In suchsituation, different alternative deployment plans can bechalked out that will improve preparedness of the disastermanagement authorities so as to maximize utilization ofavailable budget. To meet this goal we have developed asoftware tool with a Google map-based user-interface[26] that enables a user to mark the disaster affected areaand feed the different inputs parameters.

ing PPCM Movement Model with k ¼ 20 and PGM Traffic Model.



Fig. 19. Effect of performance on repeated reduction of LWCs from the near-optimal plan corresponding AA in Fig. 4 using PPCM Movement Model withk ¼ 20 and PGM Traffic Model.


It will be interesting to study (a) how the dynamics of dif-ferent parameters affect the performance of the deployedinfrastructure and (b) how the existing plan may be mini-mally changed (say, sub-groups and DM trajectories bereconstructed), to adapt to the dynamics. Moreover, a com-parative study of performance of aerial DMs (UAV) with vehi-cle mounted mules and impact of using a small fraction ofVSAT devices in the infrastructure will be interesting. Pres-ently we are developing a prototype system to check effec-tiveness of the 4-Tier infrastructure under dummy disasterscenarios. To enable the use to the prosed solution severalother networking issues like universal device addressing(identification), multicast routing (say, among a set of volun-teers from a rescue group) need to be resolved. Moreoversome system level challenges also need to be addressed todeal with the issues like enabling communication betweenhandheld devices like smart phone, tabs, etc. in ad hoc mode[27] and also to implement the DTN protocol stack.


Acknowledgments

The authors are grateful to the anonymous reviewers forconstructive suggestions and insightful comments whichgreatly helped to improve the quality of the manuscript.This Publication is an outcome of the R&D work undertakenin the ITRA project of Media Lab Asia entitled ‘‘Post-DisasterSituation Analysis and Resource Management Using Delay-Tolerant Peer-to-Peer Wireless Networks (DISARM)’’. Thework of S. K. Das was partially supported by a grant fromthe Intelligent Systems Center (ISC) at Missouri Universityof Science and Technology, NSF grants under award num-bers CNS-1404677 and CNS-1355505.

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[27] M. Raj, K. Kant, S.K. Das, E-DARWIN: Energy aware disaster recoverynetwork using WiFi tethering, in: Proceedings of 23rd InternationalConference on Computer Communications and Networks (ICCCN),Shanghai, China, 2014.

Sujoy Saha is presently a faculty member inthe Department of Computer Applications,National Institute of Technology, Durgapur,India. He has completed B. Tech (ComputerScience and Engineering) from NIT Calicut andM.Tech (Computer Science And Engineering),Jadavpur University in 2005. He is presentlypursuing his Ph.D in ‘‘Designing SecureResource Constrained DTN Architecture forChallenged Schenario’’ under Dr. SubrataNandi, Department of CSE, NIT. His areas ofresearch are Mobile Ad-hoc Network, Net-

work Modeling, Delay Tolerant Networks and Network Security. He haspublished 9 papers in different International Conferences/Journals whichincludes leading conferences ACM Mobicom, ACM DEV, ICDCN, etc. He
has significant expertise of working at the ground level of real lifeimplementation with WiFi & WiMax devices while working as a researchfellow in the project titled as ‘‘Secured Decentralized Disaster Manage-ment Information Network using Rapidly Deployable Wireless Network-ing and Mobile Computing Technologies’’ funded by Department ofInformation Technology, (DIT) Ministry of Information Technology, Govt.of India conducted by Centre for Distributed Computing, Department ofComputer Science & Engineering, Jadavpur University under the superv-ison of Prof. Chandan Majumder.
Subrata Nandi is presently working as Asso-ciate Professor in the Department of Com-puter Science & Engineering, NIT, Durgapur,India. He has completed B.Tech (ComputerScience & Engineering) from University ofCalcutta and M.Tech (Computer Science &Engineering) from Jadavpur University. He hascompleted PhD (Titled: Information Manage-ment in Large Scale Networks) from theDepartment of Computer Science And Engi-neering, Indian Institute of Technology,Kharagpur in 2011. He worked as a project

fellow in the Indo-German (DST-BMBF) project during 2008–2010. Hevisited Dept. of High Performance Computing, TU Dresden, Germany asvisiting research scientist and received ACM SIGCOMM travel grant in
2008. He has published 25 papers in different International Conferences/Journals which includes Physical Review E, ACM Mobicom, ACM SIG-COMM, ACM DEV, etc. His broad interest lies in developing technologiesfor developing regions with specific focus on Peer to Peer Network,Mobile Ad-hoc Network, Delay Tolerant Network, Service-orientedArchitecture, etc.
Partha Sarathi Paul is presently working as aSenior Research Fellow in the Department ofComputer science & Engineering at NationalInstitute of Technology Durgapur, India. Hehas received his B.Sc. degree from Universityof Calcutta with honours in Mathematics inthe year of 1998. He then got his M.Sc. degreefrom the same university in pure mathemat-ics in the year of 2000. After that he com-pleted his M.Tech. in Computer Scienceprogram form Indian Statistical Institute,Calcutta in the year of 2003. Presently he is

pursuing Ph.D. in the area of Modeling, Design & Analysis of HybridNetworks Infrastructures, under Dr. Subrata Nandi at NIT Duragpur. Hisresearch interests include Mobile Ad-hoc Network, Network Modeling,
Delay Tolerant Networks, etc.

http://refhub.elsevier.com/S1570-8705(14)00180-2/h0010






















http://nethope.org/images/uploads/casestudies/ictin2010pakistanfloods.pdf

http://nethope.org/images/uploads/casestudies/ictin2010pakistanfloods.pdf







http://drupal.airjaldi.com
















































Vijay Kumar Shah is presently working atBelzabar Software Design. He has got hisB.Tech. in Computer Science and Engineeringfrom National Institute of Technology,Durgapur, India in the year of 2013. Heworked as a young researcher in a projectentitled ‘‘Post-Disaster Situation Analysis andResource Management Using Delay-TolerantPeer-to-Peer Wireless Networks (DISARM)‘‘which is funded by ITRA, Media Lab Asia,Government of India in 2013. He has pub-lished three international conference papers

like MOBICOM CHANTS workshop.

Akash Roy is an undergradute student purs-ing his B.Tech. Degree in Computer Scienceand Engineering from National Institute ofTechnology, Durgapur, India. He has workedas a young researcher in a project entitled as‘‘Post-Disaster Situation Analysis andResource Management Using Delay-TolerantPeer-to-Peer Wireless Networks (DISARM)‘‘which is funded by ITRA, Media Lab Asia,Government of India in 2013. He has workedat Indian Statistical Institute, Calcutta as aSummer Intern (2013) where he studied on

various multiple instances learning algorithms and using fuzzy logic.


Sajal K. Das is the chair of the Computer Sci-ence department and the Daniel St. ClairEndowed Chair Professor at the MissouriUniversity of Science and Technology (MST).He has completed B.S. degree in ComputerScience from Calcutta University in 1983 andM.S. degree in Computer Science from IndianInstitute of Science in Bangalore in1984. Hehas completed Ph.D. degree in Computer Sci-ence from University of Central Florida in1988. During 2008–2011 he served the USNational Science Foundation as a Program

Director in the division of Computer Networks and Systems. His researchinterests include wireless and sensor networks, mobile and pervasivecomputing, smart environments and smart health care, pervasive secu-
rity, biological networking, applied graph theory and game theory. Dashas published over 650 papers, gathering 15,500 + citations according toGoogle Scholar, and 50 invited book chapters.


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