An Energy Entropy-Based Power-conserving Multicast Routing of Ad Hoc Networks

8/2/2019 An Energy Entropy-Based Power-conserving Multicast Routing of Ad Hoc Networks

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An Energy Entropy-based Power-conserving

Multicast Routing of Ad Hoc Networks

Rui Yang

Department of Electrical Engineering,

Hubei University of Economics

Wuhan 430205, P. R. China

[email protected]

Baolin Sun, Zhuanhua Deng, Qifei Zhang

School of Computing,

Hubei University of Economics

Wuhan 430205, P. R. China

[email protected], [email protected],

[email protected]

AbstractA Mobile Ad Hoc Network (MANET) is a dynamic

wireless network that can be formed without the need for any

pre-existing infrastructure in which each node can act as a

router. Due to energy constraint and dynamic topology ofmobile ad hoc networks, multicast supported routing is a very

important research issue. MAODV (Multicast Ad hoc On-

demand Distance Vector) routing protocol provides fast and

efficient route establishment between mobile nodes that need to

communicate with each other in MANET. By analyzing the

operation mechanism of MAODV, this paper presents an

Energy Entropy-based multicast routing protocol in MAODV

(EEMAODV). The key idea of the protocol is to find the

minimal nodal residual energy of each route in the process of

selecting path by descending nodal residual energy. It can

balance individual nodes battery power utilization and hence

prolong the entire networks lifetime. Simulation results show

that the proposed EEMAODV routing protocol performed

better than MAODV.

Keywords- MANET; Multicast Routing; MAODV; Energy

Entropy

I. INTRODUCTION

A Mobile Ad Hoc Network (MANET) consists of agroup of mobile nodes collectively form a wireless networkwithout the aid of any infrastructure. The mobile nodes self-organize via peer-to-peer multi-hop routing protocols to formcomputer networks instantly without support from networkinfrastructure. MANETs have many potential applications ina variety of fields, like military tactical communication,disaster rescue and recovery, and collaborative group

meetings. For such networks, an effective routing protocol iscritical for adapting to node mobility as well as possiblechannel error to provide a feasible path for data transmission[1-10].

Multicast routing is a type of communication in whichdata packets sent by a single source or multiple sources aredelivered to a group of hosts [1-10]. To support multicast ina network, intermediate nodes need to maintain stateinformation for a multicast session among all nodes. Formulticasting, a multicast group is constructed with one or

more group members, which should receive and handle anyinformation sent to that group. A unique multicast identifier,namely its multicast address, is assigned to each group. At

any time, each node may join a multicast group, and eachgroup member may leave the multicast group. In a MANET,the group members randomly spread and frequently move inthe whole network, which causes more difficulty in packetdelivery and group maintenance. In addition, multicast givesrobust communication whereby the receiver address isunknown or modifiable without the knowledge of the sourcewithin the wireless environment.

In MANETs, each mobile node has limited battery power.In order to maximize the lifetime of ad hoc networks, trafficshould be sent via routes that can avoid nodes with low

power while minimizing the total transmission power.Because of the severe battery power and transmission

bandwidth limitations in wireless ad hoc networks, energyefficient multicast routing can significantly improve thenetwork performance. Various energy conservation schemeshave been introduced to reduce energy consumption of anode through operation optimization. Energy-efficientmulticast schemes [9] minimize the total energy cost byadaptively adjusting the radio transmission power levels toconstruct a minimum-energy multicast tree, whereasmaximum lifetime multicast schemes [10] rely on balancingenergy dissipation among nodes to maximize the operationallifetime of the network. The objective of energy conservationis to help extend the lifetime of a network with energy-limited nodes. It is desirable to balance energy dissipation ofthese nodes so that they would not run out of energy early in

some area, resulting in a disconnection of the entire network.Entropy [11-13] presents the uncertainty and a measure

of the disorder in a system. There are some commoncharacteristics among self-organization, entropy, and thelocation uncertainty in mobile ad hoc wireless networks.These common characteristics have motivated our work indeveloping an analytical modeling framework using entropyconcepts and utilizing mobility information as thecorresponding variable features, in order to support routestability in self-organizing mobile ad hoc wireless networks.

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H. Kawahigashi [14] propose a new measure, urgency indexand urgency entropy, to evaluate the effectiveness of codingin urgent communication and discuss its usefulness fordesigns of bandwidth constrained, control-based ad-hocnetworks from a viewpoint of utility functions and elasticity.

In this paper, we present an Energy Entropy-based

Multicast Routing Protocol in MAODV (EEMAODV). Thekey idea of the protocol is to find the minimal nodal residualenergy of each route in the process of selecting path bydescending nodal residual energy. It can balance individualnodes battery power utilization and hence prolong the entirenetworks lifetime.

The rest of the paper is organized as follows. In section IIabstracts the MAODV protocol. Section III presents theconcept of energy entropy metric. Some simulation resultsare provided in section IV. Finally, Section V describes theconclusion.

II. MAODV PROTOCOL ROUTING MECHANISM

MAODV[6] is an on-demand routing protocol based ondistance vector, consisting of mainly two parts: route requestand route maintains.

A. Route Discovery

Route discovery with MAODV[6] is on-demand andoccurs when a node requires a route to a destination forwhich it does not already have a recorded route. Such a nodeinitiates route discovery by broadcasting a RREQ (RoutingRequest) packet. Fig. 1(a) illustrates the broadcasting ofRREQs.

Figure 1. The RREQ/RREP message cycle.

A node receiving a RREQ first updates its route table torecord the sequence number and next hop information for thesource node. This reverse route entry may later be used torelay a RREP (Routing Reply) back to source. The node thenchecks this table to see whether it has a route to the requesteddestination. When a node fulfills these requirements, it sendsa RREP back to the source node. Fig. 1(b) is an example ofthe destination node responding by sending a RREP back tothe source.

B. The Multicast Algorithm

A node sends a RREQ message when it wishes to join amulticast group and it does not have a route to that group. Ifthe node wishes to join the multicast group, it sets the J_flag

of the RREQ; otherwise, it leaves the flag unset. Fig. 2(a)illustrates the propagation of a broadcast RREQ. Fig. 2(b)illustrates the path of the RREPs to the source node. Themulticast algorithm uses the same RREQ/RREP messages as

previously described. Only one new message, the MulticastActivation (MACT), is needed. Fig. 2(c) illustrates amulticast tree created in the described manner. For further

details, please see Ref. [6].

Figure 2. Multicast join operation.

III. ENERGY ENTROPY METRIC

In this section, the mathematical background of robotgrouping is introduced and a node is represented as a mobilerobot and a route is represented as a robot group. In this

paper, the route stability of a MANET is redefined to perform the robot grouping since a route is composed ofseveral intermediate nodes between source and destinationnodes and each node in a route has its own entropy value.

Information theory developed by Shannon is afundamental field in mathematical sciences to deal withtransmission of information through communication systems[11]. In information theory, the standard and basic quantityto deal with information is entropy. There are some commoncharacteristics among self-organization, entropy, and thelocation uncertainty in mobile ad hoc wireless networks [12,13]. The energy entropy was defined [13] to optimize

clusterhead power use when electing cluster members. Thecorresponding methodology, results and observations can beused by the routing protocols to select the most stable route

between a source and a destination, in an environment wheremultiple paths are available, as well as to create a convenient

performance measure to be used for the evaluation of thestability and connectivity in mobile ad hoc networks.

The main idea behind the proposed formulation is toexplore a broad variety of legitimate routes by quantifying

(a)

Destination

Source

(b)

Destination

Source

R

R

?

Group Leader

(a) R ationREQ Message Propag

R

R

?

Group Leader

(b) RREPs sent back to source

Group Leader

R

R

R

Prospective Group Member

Multicast Group Member

Multicast Tree Member

Non-Tree Member

Multicast Tree Link

?

R

(c) Multicast Tree Branch Addition

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B. Simulation Results

We will compare the performance of EEMAODV andMAODV under the same movement models andcommunication models. We evaluate the performanceaccording to the following metrics:

1. Packet delivery ratio the packet delivery ratio is the

ratio of the correctly delivered data packets, and is obtainedas follows.

Packet delivery ratio =

The number of delivered data packets is the summationof total numbers of delivered data packets received by eachnode. The number of sent data packets is the summation oftotal numbers of sent data packets of each node. The packetdelivery ratio shows the transmission efficiency of thenetwork with the given protocol.

2. Routing overhead ratio the routing overhead ratio is

a ratio of the control packets to the correctly delivered datapackets, and is obtained as.

Packet overhead ratio =

The control packets are route discovery packets (RREQ,RREP) and route maintenance packets (RERR, HELLO,Keep-alive packet).

3. Average end-to-end delay of data packets itrepresents the average value of the time that the receiveddata packets take to reach the destination from their origin.This parameter includes all possible delays caused by

buffering during route discovery latency, queuing at theinterface queue, retransmission delays at the MAC,

propagation and transfer times.

4. Network lifetime is defined as the duration from thebeginning of the simulation to the first time a node runs outof energy.

In order to evaluate the performances of our QoS routing protocol, we simulate the proposed mechanisms using NS-2[15] extended by a complete implementation of IEEE802.11. NS-2 is a discrete event simulator targeted atnetworking research. NS-2 provides substantial support forsimulation of TCP, routing, and multicast protocols overwired and wireless networks.

Fig. 3 and Fig. 4 depicts the comparison of success rateto find the path and packet delivery rate under nodeschanging movement speed for MAODV and EEMAODV.From the Fig. 3 and Fig. 4 we can see that when the nodesmovement speed increases, EEMAODV success rate and

packet delivery rate is still higher than that of MAODV, dueto the fact that when the movement speed increases for thenodes, the networks topology structure changes faster. Thereason is that multicast tree can select the most stablemulticast routing between source node and destination node.

This proves that EEMAODV outperforms MAODV protocolin packet delivery ratio because EEMAODV can balance thetraffic load among different nodes depending on their nodalresidual energy and prolong the individual nodes lifetime.

The control packets are route discovery packets (RREQ,RREP) and route maintenance packets (RERR, HELLO).

The routing overhead ratio displays the level of transmissionoverhead expenses of the network with the given protocol.Fig. 5 shows a comparison of routing overhead of the tworouting protocols by taking the routing overhead and themobility speed. From the Fig. 5 we can see that when thenodes movement speed increases, EEMAODV routingoverhead ratio is lower than that of MAODV.

No. of packets delivered

No. of packets sent

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Figure 3. Comparison of success rate to find the multicast tree.

Figure 4. Comparison of packet delivery rate.

Figure 5. Comparison of routing overhead ratio.

Fig. 6 depicts the comparison of average end-to-enddelay under nodes changing movement speed for both

protocols: the faster the nodes movement speed, the higherthe protocols end-to-end delay. From the Fig. 6 we can seethat when the nodes movement speed increases,

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MAODVEEMAODV

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No. of packets delivered

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EEMAODV data transmission rate is lower than that ofMAODV.

ACKNOWLEDGMENT

This work is supported by The Young and Middle-agedElitists Scientific and Technological Innovation TeamProject of the Institutions of Higher Education in HubeiProvince (No. T200902), Key Scientific Research Project ofHubei Education Department (No. D20081904, Q20091903,

B20091904).

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EEMAODV

REFERENCES

[1] B. L. Sun, S. C. Pi, C. Gui, et al, Multiple Constraints QoS MulticastRouting Optimization Algorithm in MANET based on GA, Progressin Natural Science, Vol. 18, No. 3, 2008, pp. 331336.

[2] B. L. Sun, L. Y. Li, Reliable Adaptive Multicast Protocol inWireless Ad Hoc Networks, Journal of Systems Engineering andElectronics, Vol. 17, No. 1, 2006, pp. 187-192.

Figure 6. Comparison of average end-to-end delay.

Fig.7 shows the networks lifetime. Under all the maxspeeds, EEMAODV gives much longer lifetime thanMAODV approach. EEMAODV get nearly lifetime 20%higher than MAODV. This justifies that EEMAODV can

balance the traffic load among different nodes and prolongthe individual nodes lifetime and hence the entire systemlifetime.

[3] B. L. Sun, C. Gui, and H. Chen, et al, An Entropy-Based StabilityQoS Routing with Priority Scheduler in MANET using FuzzyControllers, Lecture Notes in Artificial Intelligence, Vol. 4223,Springer Verlag, 2006, pp. 735738.

[4] B. L. Sun, C. Gui, Q. F. Zhang, et al, A Multipath on-DemandRouting with Path Selection Entropy for Ad Hoc Networks, The 9th

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[5] C. E. Perkins, and E. M. Royer, Ad hoc On-Demand DistanceVector Routing, In: Proceedings of the 2nd IEEE Workshop onMobile Computing Systems and Applications, New Orleans, LA,February 1999, pp.90100.

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Figure 7. Comparison of Networks lifetime.

V. CONCLUSION AND FUTURE WORK

In this paper, we present an Energy Entropy-basedMulticast Routing Protocol in MAODV (EEMAODV). Thekey idea of the protocol is to find the minimal nodal residualenergy of each route in the process of selecting path bydescending nodal residual energy. It can balance individualnodes battery power utilization and hence prolong the entirenetworks lifetime. The performance evaluation of our

proposed methods is accomplished via modeling andsimulation. The simulation results demonstrate that the

proposed approach and parameters provide an accurate and

efficient method of estimating and evaluating the routestability in dynamic mobile networks.

As further work, we intend to study the delay jitter andQoS characteristic of entropy metric in varying conditionssuch as node density. We also consider enhancing our

protocol with a global congestion control mechanism to slowthe data rate of the senders when the network is highlyloaded.

[6] E. M. Royer, and C. E. Perkins, Multicast Ad Hoc On-DemandDistance Vector (MAODV) Routing, Internet Draft, draft-ietf-manet-maodv-00.txt, work in progress, July 2000.

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[8] C. R. Lin, On-demand QoS Routing in Multihop Mobile Networks,In: Proceedings of IEEE INFOCOM 2001, April 2001, pp. 17351744.

[9] W. Liang, Xiaoxing Guo, Online Multicasting for Network CapacityMaximization in Energy-Constrained Ad Hoc Networks, IEEETransactions on Mobile Computing, Vol. 5, No. 4, April 2006, pp.1215-1227

[10] P. Floreen, P. Kaski, et al, Lifetime maximization for multicasting inenergy-constrained wireless networks, IEEE Journal on SelectedAreas in Communications, Vol. 23, No. 1, January 2005, pp. 117-126

[11] C. E. Shannon, The mathematical theory of communication, TheBell System Technical Journal, Vol. 27, 1948, pp. 379423, 623656

[12] B. An, and S. Papavassiliou, An Entropy-Based Model forSupporting and Evaluating Route Stability in Mobile Ad hoc Wireless

Networks, IEEE Communications Letters, Vol. 6, No. 8, 2002, pp.328330.

[13] K. Robinson, D. Turgut, and M. Chatterjee, An Entropy-basedClustering in Mobile Ad hoc Networks, Proceedings of the IEEE

Conference on Networking, Sensing and Control (ICNSC06), Florida,USA, 23-25 April, 2006, pp.1-5.

[14] H. Kawahigashi, Y. Terashima, N. Miyauchi, A Proposal for a NewMeasure Analogous to Entropy for Bandwidth Constrained, Control-Based Ad-Hoc Network Design, Military CommunicationsConference, 2006. MILCOM 2006, Washington, DC, USA, 23-25Oct. 2006, 7 Pages

[15] The Network Simulator - NS-2: http://www.isi.edu/ nsnam/ns/.

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An Energy Entropy-Based Power-conserving Multicast Routing of Ad Hoc Networks

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