G-FANET: an ambient network formation between ground and...

Telecommun SystDOI 10.1007/s11235-016-0210-2

G-FANET: an ambient network formation between groundand flying ad hoc networks

Vishal Sharma1 · Rajesh Kumar1

© Springer Science+Business Media New York 2016

Abstract Networking with aerial vehicles has evolved con-siderably over a period of time. Its applications range acrossa wide spectrum covering areas of military and civilian activ-ities. Connectivity between aerial vehicles in ad hoc modeallows formation of multiple control units in the sky whichhave an ability to handle complex tasks. One of the majorapplications of these aerial vehicles is to coordinate simul-taneously with another ad hoc network operating on theground. This formation is termed as cooperative ad hoc net-working. These networks operate on multiple data-sharingin form of cognitive maps. Thus, an efficient traffic man-agement strategy is required to form a robust network. Inthis paper, an ambient network framework for coordinationbetween ground and flying ad hoc network is presented. Afault-tolerant and robust connectivity strategy is proposedusing neural, fuzzy and genetic modules. quaternion Kalmanfilter and its variant α − β − γ filter is used to form theneural and decision system for guided aerial network. Effec-tiveness of the proposed traffic management framework foraerial vehicles is presented using mathematical simulations.

Keywords Quaternion neural model · Traffic management ·Ambient networks · Decision support system

B Vishal [email protected]

Rajesh [email protected]

1 Computer Science and Engineering Department,Thapar University, Patiala, Punjab, India

1 Introduction

Flying ad hoc networks (FANETs) are the ad hoc units oper-ating in air comprising of unmanned vehicles which act asnetwork nodes. Networking with aerial vehicles is an exi-gent task as it involves uncertain dynamics and operatingconditions which are difficult for a network to sustain. Thesevehicles, if used for networking, have a crucial advantageof formation of an aerial mesh. This will resolve the issuesinvolving civilian-military operations, and will reduce riskof any loss of lives that may occur due to use of mannedvehicles. One of the major applications of aerial vehicles isformation of a guided system by collaborating with anothernetwork operating in ad hoc mode but with different con-figurations [1]. Such collaborative formation can extend theutility of ad hoc networks. For networks operating in col-laboration, the major issue of connectivity is the trafficmanagement and flow control. A sustainable framework isrequired that can be incorporated into existing frameworks toallow effective traffic management amongst these networks.Guided network formation involves search, tracking and datagathering as major tasks. For such tasks, data is transferredin form of cognitive maps. The utility of cognitive is derivedfrom neural network that allows formation of guidance mapsbetween the nodes of diversified networks operating in coor-dination with each other. Another network considered forcollaboration is the ad hoc network operating on ground i.e.mobile ad hoc network or vehicular ad hoc network. Suchassociationwill allow cognitive based knowledge representa-tion of each node depicting its environment and connectivity.These networks will sustain only if effective collaborativedata sharing is provided between them. Such network for-mation is presented in Fig. 1.

In this paper, a cooperative traffic management frame-work is presented for ground-aerial ad hoc formation which

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V. Sharma, R. Kumar

Fig. 1 Flying ad hoc network in collaboration with ground ad hocnetwork

provides continuous data transfers and network stabilizationbetween the aerial and the ground nodes. This allows theformation of an ambient network that provides connectivitywith any type of network device, thus, providing the facilityof “network to all”. Ambient network allows provision of ser-vice to all the available components in the network area [2,3].In telecommunications, ambient networks are attained by theformation of heterogeneous or the hybrid networks that mayinvolve two or more types of networks. In case of flyingad hoc networks, ambient formations is attained either bythe formation of the high altitude platform systems (HAPS),capacity enhancers instead of small cells, or in traditional cel-lular networks by acting as intermediatemulti-relaying nodesbetween the macro base station and the user equipments.UAVs also play a key role in the current internet of things(IoT) scenario. UAVs are capable of providing embedded,context-aware, personalized, adaptive, and anticipated para-digms by enhancing the application scenario of the existingnetwork environments [4–6]. Further, UAVs also provides along vision on intelligent systems that can facilitate the enduser development. Also, the network between the UAVs andthe ground nodes can be used as a facility manager in variousapplications such as user tracking, searching, and data acqui-sition [7]. Theproposed ambient framework canbe integratedas amodule with any other network based on the requirementand the application. It provides a reliable and fault-tolerant

support that can provide facilities like “network to all andcan also provide a solid backbone to applications requiringambient intelligence.

The proposed framework includes formation of feedbacknetwork, neural derivative to control cognitive map deriva-tion, fuzzy based decision support system, and finally agenetic modeler that controls the traffic flow. For an efficientnetwork formation, it is better to identify the sub targets andthen, improve them to increase the overall performance of thenetwork. Similar strategy is opted in case of proposed frame-work. Neural network is used to provide self-localization,estimation and feedback that helps network to attain a stateof equilibrium which allows continuous network trackingfor enhanced communication. This continuous connectivityis subjected to certain changes in network parameters. Thus,to maintain network stabilization, a decision support systemis required that not only considers the direct value set butalso considers a range of values before arriving at a conver-gence. This is possible to achieve using fuzzy logic. In thenext process, it is required to regulate the traffic while keep-ing intact the network stabilization parameters. This could beattained if all parameters affecting the network are subjectedto a ceratinminimaormaxima corresponding to their control-ling function. Hence, a genetic based solution is selected thatnot only regulates the traffic based on the fitness derivativeof the network, but also supports fuzzy for maintaining sta-bilization. Further, transmission strategies for the proposedframework use quaternion Kalman filter and α − β − γ Fil-ter for neural computations. The framework is capable ofproviding feedback, learning and updating with provision tocompute error correction over the ambient network forma-tion.Another aspect of this formation is the inter-network andintra-network connectivity between the network nodes. Theinter-network connectivity deals with nodes of the differentnetworks and is termed as cross network formation whereasintra-network connectivity deals with network nodes of thesimilar network and is termed as group network formation.Two strategies for robust and fault-tolerant connectivity arealso provided in this framework. Despite being trivial, thesestrategies provide effective solution for formation of a sta-ble network. Another aim of inter-network formation is toattain the minimum state of equilibrium that do not furtherallows any variations in feedback rate and learning rate. Atequilibrium state, network error rate is minimized, thus pro-vides smooth combination of cognitive data to form searchand tracking maps. The stabilization parameters which con-trols the network activities are termed as neural agents. Theseagents operate as a separate hidden layer over the neuralschema to evolve as a major controlling factor for such ambi-ent network formations.Motivation Formation of ad hoc network in the air has begunin the recent years.Using aerial nodes, it is possible to achievecomplex tasks such as navigation and surveillance. These net-

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G-FANET: an ambient network formation between ground and flying ad hoc networks

works allow enhancement of border security, civilian areamanagement, rescue operations, telecommunications, andagricultural activities, etc. Thus, keeping in view these appli-cations, it is necessary to form networks between aerial andground nodes that can provide continuous transmissionwhilekeeping fault-tolerance and stabilization intact.

The remaining structure of the paper is as follows: Sect. 2presents the related work in field of collaborative networkingwith aerial vehicles and their networking issues. Section 3presents the detailed proposed ground and flying ad hocnetwork (G-FANET) with preface to existing cooperativenetwork framework. It also presents the neural, fuzzy andgenetic controllers for the ambient network formation withaerial and ground nodes. Simulations and result discussionsare given in Section 4, and Section 5 finally concludes thepaper.

2 Related work

Multi-UAV network formation is a special ad hoc forma-tion which is the extension of the mobile and vehicular adhoc networks with difference in types of nodes involved andtheir operational environment. It has been introduced as sep-arate area of research by Bekmezci et al. [8]. Flying networkformation has been taken up a as research area by manyof the researchers around the world. In the initial steps ofresearch evolution, certain applications ofmulti-UAV systemhave been developedwhich dealswith analysis and formationof ad hoc network with the aerial nodes. Temel and Bek-mezci [9] have presented the scalability analysis of flyingnetworks for different possible search paths. Possibility offlying ad hoc network formation and its variants have alsobeen presented by sharma and kumar [1,10]. Authors havedeveloped a specialized cooperative framework that allowsformation of guided aerial ad hoc network. Authors havetaken into account a special application of such networks forthe purpose of search and tracking i.e. formation of a col-laborative network between the aerial nodes and the groundnodes such that both operate in ad hoc manner. Collisionbetween nodes is one of the major issues as the nodes oper-ates autonomously. Thus, they requires collision avoidancestrategies for enhanced and continuous functioning. For this,a solution has been provided by Chiaramonte et al. [11].Authors have used received signal strength as a possiblemetric to avoid any collision between the aerial vehicles.Positioning of single UAVs has already been presented andimplemented by Luo et al. [12]. But this positioning esti-mated the presence of a single UAV only. This model can betaken as one of the initial steps and can be extended furtherto form a position estimation system involving aerial swarm.

Another solution for collaboration of UAVs has been sug-gested by Mohamed et al. [13]. Authors have given an idea

of using service oriented architecture as one of the solutionsfor collaboration of UAVs. In this schema, UAVs are treatedas a service, and a special broker architecture has been sug-gested to handle the queries between them. Perez et al. [14]have designed a ground control system for aerial collabo-rative network. A fully functional ground station has beendesigned that is capable of handling the network formationbetween the aerial vehicles. However, it is not capable of pro-viding any data sharing facility directly amongst the aerialnodes. In another approach, Cevik et al. [15] have introducedthe swarming with UAVs to form a collaborative network tosolve complex tasks. An electronic warfare has been formedusing aerial vehicle and their utility for complex missionshas been presented. Shanmugavel et al. [16] have workedon cooperative path formation for multiple UAVs. Clothoidarc solution is provided to multiple UAVs arriving at sameinstance of time. With an aim to discuss FANETs, Sahin-goz [17] has presented a study of certain network modelsand possible routing strategies that can be used amongst thecollaborative aerial nodes. However, the utility and the actualimplementation of the study is still a major research gap inFANETs implementation. Capitn et al. [18] have designeda decentralized solution for surveillance using UAVs. Aninformation matrix is used as a possible solution to providedata to UAVs for reconnaissance and path planning. Lilienet al. [19] have suggested the usage of opportunistic net-work strategies as a possible solution for network involvinghybrid nodes. A virtual machine based framework has beendeveloped to handle the transmission and provide continu-ous connectivity amongst the specialized ad hoc formations.The importance of guidance system andmap formation usingcognitive data has been presented by Minjia et al. [20].Authors have given a cooperation strategy amongst aerialvehicles and their possible self organization to sustain thenetwork formation. Certain other possible UAV collabora-tions have been studied by various authors: Li et al. [21],Moon and Prasad [22], Paw and Balas [23], and Samar andRehman [24]. Jawhar et al. [25] have focussed over datagathering using aerial vehicles. Authors have developed aframework for collecting data over wireless sensor networkwhich is linearly placed on ground. The framework is lim-ited to collection of data from static network and does notsupport data gathering over dynamic ground nodes. Ad hocnetworking using aerial vehicles is still a constraint with theautonomous strategies suggested by these authors.

Sharma et al. [26] has considered the coordinationbetween the ground and the UAV ad hoc networks. Theauthors have provided a solution for broadcast storm issues inthese types of networks and have focussed on the end to enduser based application of these networks. Qingwen et al. [27]has considered the aerial part only, and has developed a rout-ing strategy for 3D environments of flying nodes. Their workfocuses on the data dissemination over the aerial nodes. Sim-

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ilar routing applications have been considered in the workby Hasan et al. [28]. Considering the vast range of appli-cations of these coordinated networks, Zaouche et al. [29]has developed an approach for the target tracking and thevideo transmission using aerial nodes. Their work providesintelligence in the form of target location network. Further,based on these applications, an opportunistic network hasbeen developed by Sharma and Kumar [30]. The networkprovides service facility over the flying networks. Despite ofso many approaches on the coordination between the aerialand the ground nodes, none has focussed on the versatileapplications by these networks. A generic network environ-ment with these nodes is required that can be customizedbased on the requirement and the specific applications.

From the related work, it can be analyzed that there hasbeen progress in the formation of aerial ad hoc network. A lotof open research issues are present that are still to be handledto form a fully functional aerial ad hoc network. The possiblesolution to these can be taken from other fields of research.Metaheuristics can be one of the solutions to task solvingability of the UAVs [31]. However, no study is directly avail-able that provides site-through the utility of these modelsover the collaborative aerial and the ground network.Anotherpossibility can be the use of single UAV systems and thenfurther, extend their operational model to form a network byintegrating them with certain network frameworks to handlethe issues involved in aerial networking. Traffic managementforms the backbone of any network. The utility of a net-work depends upon the efficient management of data sharingbetween the nodes. In this paper, a guidance system betweenthe aerial and the groundnetwork is suggested as a frameworkwith facility to provide flow control and traffic managementusing enhanced feedback mechanism.

Advantages of using neural, fuzzy and geneticalgorithms

Neural networks are the sub-category of cognitive sciencesthat can form an output generating function based on theapproximations and input values. Neural networks are reli-able to use in environments that are dependent on thenon-linear approach such as telecommunications, robotics,etc; and can be used in independent environmentswith almostno causal relationship. The ability to be trained and learn-ing from failures are the other major advantages of neuralnetworks. Rigourous stability can be achieved with efficienttraining and correct data inputs. Further, neural networksallow modelling of networks that can detect complex as wellas non-linear relationship between the variables [32]. Also,neural networks provide a strong platform for the predic-tion and estimation of the desired output based on the inputstates [33].

Fuzzy approaches are good in handling the uncertainties.But, there is a possible chance of a flaw in the formation ofthe inference rules that maps the fuzzy input to the fuzzyoutput [33]. This flaw can be improved by integrating theinference system of fuzzy with intelligent learning that canbe attained using the neural networks. Neural networks incombination with the fuzzy inference rules allow modellingof the physical aspect of the complex system without anydependency over the mathematical representation.

For the systems that are bound to evolve over the courseof time, or that may undergo some unproductive changes, anefficient approach is required that can handle these predic-tions and mutated states. Such features can be obtained fromthe genetic algorithms that provides an efficient approach forselection, mutations, and crossovers. Further, genetic algo-rithms are advantageous in networks that operates over alarge number of parameters as it can provide a multiple localoptima with lesser number of iterations. Also, for noisy orstochastic convergence of neural-fuzzy model, genetic algo-rithms can provide a smooth convergence by the formationof an efficient fitness function [34].

Paliwal and Kumar [32] in their analytical survey overneural networks and statistical approaches have stated theutility of neural networks especially for dynamic environ-ments. The comparative study presented by the authors iswell in alignment to the type of model proposed in this paper.Further, neural networks, fuzzy and genetic algorithms havebeen tested earlier over their applications in ad hoc networks.Kumar et al. [35] has presented a storage solution in the cloudusing neural networks. Their approach aims at the selectionof optimal servers for data coordination. This coordinationis achieved using the neural networks approach.

Elleuch et al. [33] has used the neuro-fuzzy combinationfor the prediction of mobility in wireless ad hoc networks.Neural-fuzzy model used by the authors, allows the selec-tion of optimal paths by prediction of the node movementwhich lowers the network overheads, thus improving net-work performance. The authors presented their analysesover estimation of paths along with the time series prob-lem. Ghouti et al. [36] also considered the similar problemand used extreme machine learning models by implicatingthe fitness functions for generating predictive mobility out-puts. Islam and Raghunathan [37] took the utility of neuralnetworks in wireless communications to the next level byproposing an intelligent transmission control protocol forpreventing congestions in multi-hop networks. The authorsdesigned an artificial neural network model that can followthe mesh like networks to perform end-to-end transmissionwith efficient control over congestions.

The advantages and the study presented above justifiesthe utility of neural, fuzzy and genetic algorithms in ad hocnetworks that comprises of two or more networks operatingin coordination with each other. Further, better stability can

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be attained in the algorithms by learning and training overthe desired output using these approaches.

3 Proposed framework

Multi-UAV ad hoc network in collaboration with groundad hoc network allows ambient network formation withenhanced situational awareness and decision making facil-ities. The proposed framework “G-FANET” provides trafficmanagement as well stabilization during network formationbetween the ground and aerial ad hoc networks. The titleis derived from combination of G-FANETs. The proposedframework allows formation of multi-functional networkwith collaboration between the aerial and the ground adhoc networks. Such network provides varied applicationswith improved activity than the isolated ground networkthat operates without any collaboration with other networksof different domains (independent aerial ad hoc networks).Keeping intact the original working of framework, the pro-posed framework G-FANET provides further regulation tonetwork traffic. G-FANET also aims at formation of deci-sion support system for efficient transmission with improvedself organization of nodes. G-FANET is a multi-agent frame-work with its functionality derived from neural-fuzzy baseddecision support system which is used by genetic modeler toform a traffic management policy between the two networks.Further, G-FANET aims at ambient network formation keep-ing intact the responsiveness, organization and situationalawareness of the cooperative network framework.

3.1 Ambient network framework (G-FANET)

G-FANET aims at formation of a fault tolerant networkbetween the two ad hoc networks operating over differ-ent environments with diversified network configurations.The proposed framework provides regulated flow controlbetween the ad hoc network with enhanced traffic manage-ment by formation of multiple network protection protocolsthat provide traffic regulations in case of network failuresas well as during normal network operations. G-FANET’sobjective is to organize multiple UAVs in accordance toground nodes such that a situation-aware network is for-mulated. The functionality of basic framework operating inbackground of G-FANET is not affected and supports similarnon-redundant search and tracking with enhanced cognitivetransfers. G-FANET is a multi-agent framework that opti-mizes the network formation using a decision support system.G-FANET is a neural-fuzzy model integrated with geneticmodeler to form a traffic management system. The depen-dency chart showing functional dependency of G-FANETover various parameters is presented in Fig. 2.

The proposed framework uses three core dependent mod-ules that provide support for hybrid network formation.Neural schema based on the quaternion algebra allows theformation of self organized cognitive topology maps forsituational awareness, fuzzy inference engine provides deci-sion support system based on the group and cross networkformation with hybrid network unit. Finally, a genetic mod-eler uses the control parameters from the neural schemaand decision support system to form a fault tolerant traf-fic management system. The linked module integration ofthe proposed framework is shown in Fig. 3. The proposedframework operates in two parts. One acts as an integratedmodule and the other is the ambient module. The integratedmodule handles the G-FANET’s interaction with the existingcooperative network framework [1] and further integrates itwith situational and self-organizational modules to form afault tolerant network model. Ambient module is the core ofthe proposed framework that monitors and regulates the net-work traffic to provide efficient flow control between the twoad hoc units. Ambient module comprises of network coordi-nator and traffic manager that operate over the logged datafrom the fuzzy/genetic module which provides regulations tohandle the enhanced traffic pressure over the diversified net-works. This sub-module uses agent based activity to controlthe ambient network formation. Network coordinator oper-ates over two protocols especially designed for such crossnetwork. These protocols control the activity of the nodesoperating in a similar group or interacting with nodes ofgroups formed for another network. These control protocolsare termed as group network control protocol and cross net-work control protocol. The principle of these protocols is toform a reserved corridor of connectivity during initial stageand use them in accordance with the priority that can bedefined on the basis of network dependency over a partic-ular parameter. However, it is to be taken into care that theselected parameter for control protocol dependency must beincluded in quaternion neural formation to effectively resolvethe issues of similar priorities association to multiple corri-dors. These control protocols allow formation of robust andfault tolerant network with high reliability and protectionagainst any network loss; be it parameterized or hop based.

Network comprising of two different ad hoc units operatesin two formations. One of them is the group based networkformation, and the other is corridor based cross network for-mation.The selectivity of the best possible group and corridoris performed using the neural schema of the cooperativenetwork framework. However, in case of G-FANET, theseselections are not performed to select a single controllingentity, rather a set of parameters are selected. These set ofparameters affect the functionality of the network in order oftheir priority. This priority order allows selection of anotherpossible group and corridor in case of unavailability or lossof originally selected group and corridor. This phenomenon

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V. Sharma, R. Kumar

Fig. 2 G-FANET’s functional modules and controlling parameters

of multi-support amongst the groups is termed as group con-trol network formation and for inter-networks is termed ascross network control formation. In Fig. 4, three groups G1,G2, G3 are formulated. Localization of nodes using existingframework is realized in such a manner that priority orderformation is allowed. Thus, a node is provided with possi-ble connection to one or more group leader at same instancewith operability of only one. Similarly, corridors C1-C4 areallocated with two corridors to each of the nodes with facilityof multiple group connectivity. This forms a reliable sourcedestination network with availability of alternate path in caseof network losses. For a network with complex corridor, andgroup availability, minimum number of network corridorsfor a group Nc_min is given as:

Nc_min = 1

2Min

(Ai_g,Gi_g

), (1)

where Ai_g and Gi_g are the number of nodes in the selectedgroup from aerial and ground network respectively that willunite together to form a possible network corridor. Duringthis corridor formation, it is to be noted that from a group

of single ad hoc networks, each group must have a provisionfor providingminimumof two corridor connectivitywith twodifferent group nodes. Also, for group control protection, agroup must have a node which is a member of atleast two ormore network groups.

3.2 Quaternion topology maps and G-FANET neuralschema

G-FANET is amulti-modular framework having dependencyon the efficient operability of neural schema. The underly-ing cooperative network framework provides the support forparameter selection based on the topology organizing maps.In the proposed framework, a new quaternion based neuralfeedbackmodel is developed that allows formation of quater-nion topology maps with enhanced utility and performance.These quaternion topologymaps provide feedback rate, errorcorrection and update rate to all the connected modules toform a decision system that can regulate the functioningof the cross network formation. Multi-set polynomial equa-tions form the input of this quaternion topology map which

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Fig. 3 G-FANET’s operational units

Fig. 4 Group network control formation and control network controlformation

in collaboration with the decision support system forms aquaternion neural schema for the whole network. The pro-posed model is based on the set of five polynomials: networklocalization NL , network connectivity NC , network transferrate NT , network group trust value NGT , and network crosstrust value NCT . The quaternion so formed is fed intoKalmanFilter and α − β − γ filter, that finally deduces the requiredfeedback, update and error rate for the overall network. Thedependency and controlling parameters for the quaternionalgebra are shown in Fig. 5. These quaternion maps con-trol the network operations and provide robust situationalawareness to network with reliable localization. The finallyformed topology maps are evaluated by the network agentsof the G-FANET to provide facility for group network con-trols and cross network controls. The mathematical modelusing quaternion kalman filter and α − β − γ filter decidesthe inter-network relation and interaction between the nodes.For defined quaternion dependencies,

NL −→ f((AL ,GL) , (AV ,GV ) ,CS

L

), (2)

where AL , GL defines the aerial and ground localization interms of x, y, z coordinates. (AV ,GV ) defines the velocityfunction for the aerial and ground networks.CS

L is the cooper-ative network time derivative for rate of change of UAVs [1],such that

CSL =H,S w(t) (3)

It also denotes the collision avoidance matrix for the guidedaerial network. For network connectivity, functional depen-dency is given as:

NC −→ f((

DAC , DG

C

), (NCO , NCH ) , NR

), (4)

where DAC , D

GC denotes the degree of connectivity between

the aerial and the ground nodes. NCO represents the numberof corridors for inter network formation with NCH numberof supported channels. NR is the number of relays in groupnetwork formation. For theG-FANET, equal number of chan-nel availability is considered for selected corridors. Networktransfer rate is dependent on the parameterized value derivedfrom the neural schema of the underlying cooperative net-work framework. According to its neural schema [1],

NT −→ f ((MR, SD) , NCH , LS) , (5)

where MR is the minimum achievable rate, SD is the numberof network seeds available and LS is the link speed for thenetwork considered. G-FANET’s dependency over the groupnetwork controls and cross network controls is given as:

NGT −→ f ((CGV ,GCV ) ,GF , NLJ ) , (6)

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V. Sharma, R. Kumar

Fig. 5 Quaternion dependencyparameters

and

NCT −→ f ((DIC , NF ) , MFR, TD) , (7)

whereCGV ,GCV are the coordinated value and group coher-ent value respectively. GF denotes the number of groupfailures, and NLJ is the network joining and leaving rate.DIC denotes the degree of inter-connectivity, MFR is mapformation rate, NF is the number of node failures, and TDdefines the traffic density for the overall network. Thus, foroverall self-organized quaternion map formation, quaterniontopology map dependency is given as:

QTM −→ f ((NL , NC ) , NT , (NGT , NCT )) . (8)

Now, using definition of quaternion [38],

NL = a1 (AL ,GL) + a2 (AV ,GV ) + a3CSL + a4, (9)

NC = b1(DAC , DG

C

)+ b2 (NCO , NCH ) + b3NR + b4,

(10)

NT = c1 (MR, SD) + c2NCH + c3LS + c4, (11)

NGT = d1 (CGV ,GCV ) + d2GF + d3NLJ + d4, (12)

NCT = e1 (DIC , NF ) + e2MFR + e3TD + e4. (13)

Therefore, based on the dependency defined in equation(8),topology map for quaternion derivative will be given as:

QTM = x1 (NL , NC ) + x2NT + x3 (NGT , NCT ) + x4,

(14)

and

QTM =[QTM

x4

](15)

such that updated collision avoidance matrix is given as:

CSL =L C (t).

(S,L Q

HTM

).SC

(t). (16)

Here, x1, x2, x3 and x4 are the hyper-imaginary numbers.f (a1, b1), f (a2, b2), f (a3, b3) are the unit vectors for net-work localization and network connectivity.

f (d1, e1), f (d2, e2), f (d3, e3) are the unit vectors forgroup trust value and cross trust value. c1, c2, c3 are theunit vectors for network transfer rate. Therefore, for groupnetwork control and cross network control,

QTM =⎡

⎣θ1 θ2 θ3 1

c1 f (NGT , NCT ) c2 f (NGT , NCT ) c3 f (NGT , NCT ) 1f (d1, e1) f (d2, e2) f (d3, e3) 1

⎤

⎦

(17)

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where

θ1 = f (a1, b1) f (NGT , NCT ),

θ2 = f (a2, b2) f (NGT , NCT ),

and

θ3 = f (a3, b3) f (NGT , NCT ).

For independent analysis of group network connectiv-ity and cross network connectivity, the quaternion will bedenoted as:

QGTM =

⎡

⎢⎢⎣

f (a1, b1) f (NGT ) f (a2, b2) f (NGT ) f (a3, b3) f (NGT ) 1c1 f (NGT ) c2 f (NGT ) c3 f (NGT ) 1

f (d1, e1) f (NCT ) f (d2, e2) f (NCT ) f (d3, e3) f (NCT ) 1,

⎤

⎥⎥⎦

(18)

and

QCTM =

⎡

⎣f (a1, b1) f (NCT ) f (a2, b2) f (NCT ) f (a3, b3) f (NCT ) 1

c1 f (NCT ) c2 f (NCT ) c3 f (NCT ) 1f (d1, e1) f (NGT ) f (d2, e2) f (NGT ) f (d3, e3) f (NGT ) 1

⎤

⎦

(19)

respectively.WhereQGTM andQC

TM are defined for group andcross connectivity respectively. The unit vectors f (d, e) areoperated for alternate network to allow dependent analysisof one network over the other. Now, the multiple quaternionmatrix expressing the overall state of the network connectiv-ity will be given as:

SY =⎡

⎣(NL , NC ) NT (NGT , NCT )

CGV GCV GF

NCO NCH TD

⎤

⎦ (20)

For the defined state, over all rotational quaternion framewillbe given using [1] as:

S′Y (t) =

[SY

QTM

], (21)

Integrating with cooperative network framework, situationalawareness for G-FANET will be computed as:

SUA (t) =[S

′Y

Z(t)

], (22)

where Z(t) is derived from cooperation amongst two ad hocnetworks and it denotes the state equation over rotationalframe matrix. Thus, for overall connectivity, network gov-erning equation for ith UAV will be given as:

Q′U,i (t) = 1

2

(SUA (t) × γU × TD

), (23)

where

γU =n∑

i=1

NCO∑

j=1

⎛

⎝∑NCH

k=1(Do

IC×NoF)(D

tIC×Nt

F)MFR

∑NCHk=1

(DIC×NF )2

MFR

⎞

⎠ , (24)

is obtained using Gauss–Markov loss model [39]. Networkfeedback rate, error rate and update rate are the driving met-rics for the neural schema of the G-FANET. These can becomputed by subjecting quaternion topology map equationsto Kalman Filter and evaluate them over the group and crossnetwork controls.

UsingKalman Filter [40], the state equations will be givenas:

GNC = GQ′U (t) (NL , NC ) + EO , (25)

CNC = H Q′U (t) (NGT , NCT ) + E

′O, (26)

where GNC , CNC are the group network connection withaerial dependency and cross network connectionwith groundvehicle dependency with EO and E

′O

the observed errors,respectively. Now,

GQ′U (t) = GS

′Y (t − 1) + Oe, (27)

H Q′U (t) = AS

′Y (t − 1) + O

′e, (28)

For system of interconnected nodes, error correction ECO

for system state will be given as:

ECO =[E

′t

Et

], (29)

where

E′t =

(SZT

QUSC

)GNC , (30)

and

Et =(SZT

QUSC

)CNC , (31)

where S is the covariance matrix for system state and ZTQU

is

derived over Q′U (t). Now the feedback rate FR for the above

network is given as:

FR = ||IR − ECO ||, (32)

where IR is the initial rate dependent on the probabilistictransmission error rate TER computed for aerial and crossconnectivity such that

IR ←− P(T tER |

(GQ

′U ,H Q

′U

)). (33)

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Fig. 6 Neural schema : feedback model for G-FANET

It is to be noted that P(T tER |

(GQ

′U ,H Q

′U

))should not

be greater than 0.5 as it would lead to multiple network fail-ures that would hinder the network operations. The value isdecided on the basis that atleast half of the network nodesshould perform during network operations to support internetwork transmissions. For update rate UR , α − β − γ filteris used to allow self organization and enhanced situationalawareness by controlling the operations of the G-FANET’sneural schema. Using definition of filter [41],

UtR = αt NL J + β t (DIC , NF ) + γ t TD + εt , (34)

where inverse dependency is followed by the filter with α

metric dependent on the β metric such that

β t =(eNF − 1

)

βo, (35)

αt = αoeβ t (

β t)NCH

NCH ! , (36)

and

γ t = αtβ t

(αt − 1) (β t − 1). (37)

Here, εt is the residue which follows standard normallydistribution such that εt ∼ N (0, 1). This quaternion formsthe base for formation of neural schema for the G-FANET.This neural schema operates on the feed backward principle.The neural schema formulated for the quaternion network isshown in Fig. 6.

The neural model uses multi hierarchical approach withfour level sequence before generating the actual output forthe network. In the initial stage, the model operates overquaternion by feeding the parameters as input to the model.In the second stage, bypassing the updating metrics, deci-sion support system evaluates the parameters over the hiddenlayer and then generates a final output by further evaluat-ing the hidden layer using quaternion. In the next process,before achieving a stable stage, the initial output is passedto intermediate agent layer. This layer estimates the networkperformance and adjusts the parameter values before generat-ing the actual output. The working of the neural schema canbe understood from the flowchart as shown in Fig. 7. Themodel in the initial stage does not operate on the updatesrather it produces a single output. In the later stage, updatesare performed to form a highly reliable self-aware systemhaving fault-tolerance and robustness. This schema allowsformation of a recurring system that keeps on learning and

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Fig. 7 Flow chart for neural schema operations

updating with each iteration until it stabilizes, hence, it is selflearning.

3.3 Decision support system using fuzzy inferenceengine

Network stabilization and decision for continuation of trans-mission process is controlled by the decision support systemwhich is based on the fuzzy inference engine. The fuzzyinference engine is operated using multi set values to finallyconclude the network coordinated value. Higher coordinatedvalue denotes better connectivity, thus, improves map forma-tion for various applications involving UAV guided nodes.The dependency involves the group trust value with higherperformance measuring over 0.7. Cross trust value requiresmeasurement greater than 0.5 for better inter-connectivity. Itis to be noted that this cross trust value is computed on thebasis of probabilistic values used for computation of feed-back rate. The overall fuzzy states that network coordinatedvalue should be greater than 0.4 for network to commencenode identification and search process. With the increase inthe inference value, network performance would increase.The overall fuzzy inference chart for decision support sys-tem is shown in Fig. 8.

Algorithm 1 Genetic Modeler Algorithm For Traffic Man-agement

Require: f i t St , f i tMt , f i tCt , NA, NG

Ensure: NCH −→ active, NCO −→ activeNCH > NCO

1: while {Map! = complete} do2: initialize ←− connectivity_matrix3: Nt

CH ←− ACT (NCH )

4: NtCO ←− ACT (NCO )

5: f orm_quat ←− (QTM ,FR , UR , LR)6: regulate

(f i t St , f i t Mt , f i tCt

)

7: if {(GTV ≥ CTV ) && (CT H ≥ T H)} then8: initialize cog ←− map_data9: while {cog �= complete} do10: if {NTV ≥ P (QTM )} then11: Qt ←− Queue (cog)12: else13: reset NTV14: end if15: transmit Qt ←− scheduler16: end while17: end if18: counter ←− increment19: end while

3.4 Genetic modeler for traffic management

Neural network allows self-localization based feedback net-work formation between the aerial and the ground nodes.This network formation is further stabilized using fuzzylogic. The decision support system requires traffic regulationequations to allow continuous support for stabilized trans-mission. Thus, objective defined is to regulate and managethe traffic that can be achieved efficiently using fitness deriv-ative defined over network parameters. The fitness derivativeallows identification of network selections, mutations andcross-overs. This genetic modeler is used to evaluate the net-work performance to allow traffic management for enhancedcommunication between the two diverse networks. The oper-ation of the genetic modeler is controlled by the computationof the genetic based fitness derivative. For cross networkcommunication, fitness f i tt at time t is given as

f i ti −→ f (TR, FR, LR, ECO , NTV ) , (38)

such that for trivial solution, fitness derivative will be givenas

f i ti = αTR + β(FR,LR ,ER

) + γ NTV + error. (39)

Here, α, β, γ are derived over standard variate in the similarmanner as derived for situational awareness. The above func-tion f i tt computes the functional minima for the derivativeover TR and NTV with major functional dependency overFR , LR and ER for accuracy and fault tolerance. Variationsin values for FR , LR , and ER are to be minimized; here mini-

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V. Sharma, R. Kumar

Fig. 8 Decision support system using fuzzy inference engine

mum defines lower fluctuations and better stability. Learningrate for such network will be increased to a higher range ifmore updates are required. To counter any network unbal-ancing, learning rate must be greater than certain thresholdvalue. The traffic regulations for the inter network connectiv-ity will be handled using selection, mutation and cross-overstrategies of genetic modeler. The probabilistic fitness deriv-ative for selectivity f i t St is computed usingBayesmodel [40]as:

P(f i t St | (T t

R, NtT V

))

=P

(UR | f i t St−1,

(T t−1R , Nt−1

T V

))× P

(f i t St |

(T t−1R , Nt−1

T V

))

∑NCOi=1

∑NCHj=1 P

(UR | f i t St−1,

(T t−1R , Nt−1

T V

)) .

(40)

The mutation f i t Mt for the inter network is dependent on thefunctional dependency as shown below

||X || ←− f (TR, (FR, LR, ER)) , (41)

such that

P(f i t Mt | (||X ||t)

)

= P(UR | f i t Mt−1,

(||X ||t−1)) × P

(f i t Mt | (||X ||t−1

))

∑NCOi=1

∑NCHj=1 P

(UR | f i t Mt−1,

(||X ||t−1)) .

(42)

For achieving stabilization, cross over might be requiredover the selection andmutation. This cross over hybridizationwill be computed on the basis of mutation with alternatevalues of parameters from fitness function such that

f i tCt ∈(f i t St , f i t Mt

). (43)

To regulate the traffic and manage the flow, the minimum offunction is required such that

FR = min (FR, QTM ) (44)

LR = min (LR, QTM ) (45)

ER = min (ER, QTM ) (46)

For UAV coordinated network, selectivity, mutation, andcross over algorithm for traffic management in G-FANETis shown in Algorithm 1. The detailed integrated operationalactivity of the proposed G-FANET framework to provide

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Fig. 9 Neural-fuzzy-genetic flow chart for G-FANET’s traffic management

support for ambient network formation with diverse nodes isshown in Fig. 9.

4 Simulations and results

The simulation set up for proposed framework was evaluatedusing MAT LABTM . Multiple user modules were coded tomake the systems behave as virtual nodes for the aerial andthe ground networks. A separate user program was codedto interact with the cooperative network framework to over-look the topology organizing maps and then update themusing quaternion maps for enhanced localization and situ-ational awareness. The fuzzy inference engine was initiallyfeatured using Matlab tools, later it was induced in the deci-sion support system that operated on an analysis machine.Interferencemodel is an important aspect of applications thatdeal with the physical deployment of network nodes. Sincethe proposed approach exploits the traffic management in thecoordinated ground-flying ad hoc networks, the interferencemodel is relaxed and the UAVs are assumed to be operatingover the frequency with relaxed channel gains. The flow andarrangement of the simulation set for analysis of G-FANETare shown in Fig. 10.

Fig. 10 Simulation setup for analysis of G-FANET

Separate network analyzer was coded in .NET for analy-sis of inter and group network connectivity, also it performedtrace analysis for performance evaluations. For simulations,area of 5000 × 5000 sq.m. was considered with equivalenttheatre size for search and tracking. Number of obstacleswere kept fixed, and ground nodes in range between 30 and

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V. Sharma, R. Kumar

100. Based on the requirement, number of UAVs for acquisi-tion ranges between 5 and 15. During simulations, minimumassumptions were made to allow rigourous analysis of theproposed framework. Neural feedback model based on thequaternion was also evaluated separately for learning rate,feedback and error rate during network operations. Fixedwing UAVs were used for simulations that were able toattain maximum cruising speed of 30 m/s with an averagemaintained around 25 m/s. The flight time supported by eachof the UAVs was 35 min (approx.). The data/logs from theUAVs and the ground nodes are stored on the local server.The connectivity provided over this channel was long enoughto allow ad hoc formation. Using similar channel and radiorange, robust connectivity was attained between the groundand the aerial nodes. For analysis, 50 simulation runs wereperformed with nodes being initialized in the defined range.The simulations were randomized with capability to restoreand rerun the previous simulations based on the log files thatis updated after each simulation run. Several other parame-ters and device values configured for analysis are presentedin Table 1.

4.1 Neural and traffic analysis for G-FANET

Neural analysis was carried by recording the network learn-ing rate and feedback rate w.r.t. simulation time. For anetwork operating with multiple facilities, the learning ratewill become constant as the network stabilizes. With thedecrease in learning rate, feedback rate for the over all net-work also decreases. The decrease shows the utility of theframework. Figure 11 shows the plot for learning rate andfeedback rate. The simulations conducted in this paper, sug-gests that both the curves follow the decreasing indent, thus,showing that with passage of time, the network errors are lowenough. Thus, signifies that no updates are required and anequilibrium state is achieved with no further learning untiloccurrence of faults. However, the curve trend may changein the real-time scenarios because of the varying radio range,network path loss and interference. Learning rate of a net-work will be minimized if no errors are recorded during thetransmission process. The results traced in the simulationssuggest that the network errors followed a decreasing slopewith range varying from 0.6 to 0.14 (approx.). The plot fornetwork learning ratew.r.t to error rate versus simulation timeis shown in Fig. 12.

With extensive application of the learning rate, feedbackrate and minimum error; network performance increaseswith increasing traffic rate, thus, offering better connectiv-ity. Network performance was evaluated in comparison withnormalized network traffic rate over the simulation time, asshown in Fig. 13. The genetic modeler controlled the trafficflow of the network. Both intra- and inter network commu-nications were handled using fitness values. The range of

Table 1 Parameter configurations

Parameter Value

Dimensions 5000 × 5000 sq. m

Number of ground nodes 30, 50, 70, 100

Number of UAVs 5, 8, 10, 15

Traffic type CBR

Packet size 1024 bytes

Mac layer 802.11 b

Buffer size 15,000–20,000

Propagation radio model Two ray ground

Attained aerial speed 27 m/s

UAVs radio link range 0.8–1 Km

Flight time 35 min

Ground pause time 2 s

Ground node mobility range 0–2.5 m

Initial transmission 512 kbps

Maximum receiver mapping 30–10

Parameter count 2–10

Mobility model (ground) Random way point

Mobility model (aerial) Fixed way point

αini tial 0.2

βini tial 0.2

Polynomial dependency 5

Simulation time 100 s

Simulation runs 50

Default coordinated value 0.4

Inter-network trust 0.7

Group-network trust 0.7

Map trust value minima 0.2

DSS dependency value 3

0 20 40 60 80 1000

0.2

0.4

0.6

0.8

1

Network Trasnfer Time (seconds)

Ra

te

Network Feedback RateNetwork Learning Rate

Fig. 11 Network learning rate, feedback rate versus simulation time

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20 40 60 80 1000

0.2

0.4

0.6

0.8

1

Network Trasnfer Time (seconds)

Rate

Network Learning RateNetwork Error Rate

Fig. 12 Network learning rate, error rate versus simulation time

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0.1

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0.3

0.4

0.5

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0.7

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0.9

Network Transfer Time (seconds)

Perf

orm

ance

Rate

Network Performance RateNormalized Traffic Rate

Fig. 13 Network performance w.r.t. normalized traffic versus simula-tion time

fitness function were defined as search minima and maxima,as shown in Fig. 14. The proposed G-FANET for ambientnetwork operates over neural-genetic model with its deci-sion support system being controlled using fuzzy inferenceengine. The fuzzy inference rules are the major role play-ers and act as a pivot to the process of traffic managementbetween the two inter connected networks. Thus, the evalua-tion and formation of inference model, and its rule derivationto support the network was required. This evaluation allowedanalysis of the over all networks initializing time. The vari-ation in decision support system (DSS) formation time withaverage transfer time over available number of channels ispresented in Fig. 15. Other metrics considered to evaluate thenetwork performance were the network responsiveness andnetwork coordinated value. This coordination was the trustvalue that identified the inter-network connectivity. Larger

150 200 250 300 350 400 4500.1

0.2

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0.9

Maximum Number of Channels

Fitn

ess

Valu

e

Min. Fitness ValueMax. Fitness Value

Fig. 14 Fitness value range versus number of channels

150 200 250 300 350 400 4500

0.1

0.2

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0.5


Tim

e (

ms)

Average Transfer Time

DSS Formation Time

Fig. 15 Decision support system formation time variation versus num-ber of channels

value denotes higher transmission rates whereas higher net-work responsiveness denotes greater reaction time of thenetwork nodes to sudden changes; be it in traffic or the nodelocalization. Both these were evaluated for aerial network asground ad hoc connectivity is dependent upon the efficientlocalization of the aerial nodes. The proposed G-FANEToffers higher network responsiveness as well as higher coor-dinated trust value, thus, making it a robust and fault-tolerantnetwork. The coordinated trust value and responsivenessw.r.taerial nodes is shown in Fig. 16. In a network, operatingwith twodiverse network nodeswith different configurations,inter-network communication identifies the performance ofthe network. For such network formation, cognitive trans-fer evaluates the network efficiency on basis of the numberof successful transfers over a sustained network. The pro-posed G-FANET was evaluated using 5, 8, 10 and 15 UAVs

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V. Sharma, R. Kumar

5 6 7 8 9 10 11 12 13 14 1560

65

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90

95

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Co

ord

ina

ted

Tru

st (

%)

Ne

two

rk R

esp

on

sive

ne

ss (

%)

Coordinated Trust (%)Network Responsiveness (%)

Fig. 16 Network responsiveness, coordinated trust value versus aerialnodes

30 40 50 60 70 80 90 10040

50

60

70

80

90

100

Ground Nodes

Inte

r N

etw

ork

Tra

nsf

er

Ra

te (

%)

Transfer Rate − 5 UAVsTransfer Rate − 8 UAVsTransfer Rate − 10 UAVsTransfer Rate − 15 UAVs

Fig. 17 Inter network transfer rate (cognitive delivery percentage)

that maneuvered over an area of 25,000,000 sq.m. with 30,50, 70 and 100 ground nodes for data acquisition. Cognitivedelivery for aerial guided network during inter-connectivityis shown in Fig. 17. With higher number of network nodes,more number of corridors were provided with higher numberof channels for inter-connectivity. Higher value of cognitivetransfers make G-FANET scalable. For group connectivityanalysis, intra network formation amongst the nodes of thesimilar network were evaluated for receiver identificationand mapping accuracy and cognitive delivery with commonchannels and similar configurations. Higher value of theseparameters allowed better connectivity amongst nodes ofsame network, thus, making cognitive map formation rel-atively efficient and effective. Figure 18 presents the groupconnectivity analysis ofG-FANETfor cognitive transfers andreceiver mapping defined in terms of percentage of accuracy.

0

50

100 5

10

15

20

40

60

80

100

UAVsGround Nodes

Mappin

g A

ccura

cy (

%)

Vs

Tra

nsf

er

Rate

(%

)

Receiver Mapping Accuracy (%)Average Transfer Rate (%)

Fig. 18 Group mapping accuracy and group network transfer (cogni-tive delivery percentage)

150 240 300 360 4500

10

20

30

40

50

60

70

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100


Age

nt C

onne

ctiv

ity (

%)

Vs

Cog

nitiv

e D

eliv

ery

Rat

io (

%)

Agent ConnectivityCognitive Delivery

Fig. 19 Agent connectivity (%) and cognitive delivery (%)

The complete network model is supported by the neuralagents which map the parametric values to the expected out-put values and then provide a feedback to the agent layerthat evaluates the update rate, learning rate and error rate toform a self-aware systemwith higher tolerance to sudden net-work changes. These changes are managed by the agents thatoperate over these feedback and helps to stabilize the networkwith enhanced connectivity. Agent Connectivity refers to thenumber of agents connected during transmission betweenthe aerial and the ground nodes. A bar chart is formed for theanalysis of the G-FANET agent’s activity w.r.t to networkcognitive transfer (%) as shown in Fig. 19. Figure shows theaverage delivery (%) acquired during agent connectivity overthe configured number of channels. For example, from figureit can be traced that if 73% of total agents available over 450channels are connected, then the network attains a cognitivedelivery of 92%. Large number of channels utilization, pro-

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Group−1

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vides higher connectivity to the network nodes that furtherhelps to sustain the process of continuous transmission.

4.2 Group and cross network protection usingG-FANET

The proposed framework is capable of providing continuousand fault-tolerant network formation between the aerial andthe ground ad hoc networks. The fault tolerant strategies forgroup and cross network protectionwere separately analyzedfor G-FANET. Figures 20 and 21 present the initial networkformation using aerial and ground nodes. Group network for-mation and cross network formation is shown in Figs. 22 and23, respectively. The proposed ambient formation aims atreliable intra-connectivity between the nodes of similar net-work. Such independent cluster formation and independentnetwork connectivity is presented in Figs. 24 and 25, respec-tively.

The utility of group network protection is presented dia-grammatically in Figs. 26, 27, and 28. The plots present that

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Fig. 22 Group network formation in G-FANET

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Fig. 24 Independent cluster formation

in a network with uncontrolled group formation, a node fail-ure may result in network breakdown, as shown in Fig. 26.However, using a strategic group formation, a fault-tolerantnetwork is possible with better connectivity and networklifetime, as shown in Figs. 27 and 28, respectively. Simi-

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Fig. 26 Node failures leading to network breakdown

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up N

etw

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imat

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and

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dici

ton

durin

g In

itial

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nect

ivity

Fig. 27 Group estimation and network prediction during initial con-nectivity

lar approach is used to present the ideology of cross networkformation as shown in Figs. 29, 30, and 31. Cross networkformation deals with ideal selection of network corridors forcontinuousmap building and collaborative planning between

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Fig. 28 Group connection protection using G-FANET

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Fig. 29 Corridor failures leading to network failures

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Fig. 30 Cross estimation and network prediction during initial con-nectivity

the aerial and the ground adhocnetworks.Randomized selec-tion of corridors for inter-networking may lead to networkbreakdown in case of corridor failures, as shown in Fig. 29.This can be resolved by strategic selection of corridors by

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Miltiple Corridors for Same Group

Fig. 31 Cross connection protection using G-FANET

Fig. 32 Component importance in neural interface

means of estimation and prediction using system equations,as shown in Figs. 30 and 31, respectively. Using group andcross network protection strategies, G-FANET is capable ofproviding continuous and enhanced cognitive transmissionin a network involving hybrid nodes operating in differentnetwork environment.

4.3 Statistical analysis

Further, to validate the proposed model, statistical analysisof the network and its components were performed. The sta-tistical analysis of the network model was performed usingSPSST M . The network importance for neural schema pro-posed was derived using multi-layer perceptron model. Theimportance plot for the neural schema is shown in Fig. 32.The statistical analysis showed that in the considered simula-tive scenario, feedback rate was the major driving factor forsuch cross network formation in diversified ad hoc networks.An equilibrium state in the network can be attained based on

Fig. 33 Initial cluster centers For G-FANET

Fig. 34 Final cluster centers for G-FANET

the feedback rate of the neural schema. Higher value of net-work denotes better connectivity amongst network nodes.

To check the ambiguity and error in network values dur-ing operations, k-means clustering was used to identify anybug in the network formation. Figures 33, 34, 35, and 36presents the details of the test and its output in form ofcluster centralization proving that network was capable ofoperating without any hinderance and no bug or maliciousactivity was traced during its operations. Cluster centers arethe controlling nodes that regulate the network formationand controls network localization and stabilization. It is tobe noted that ground ad hoc network is subjected to clusterformation whereas aerial network is subjected to group for-mation. The statistical analysis over the simulations showedthat this cluster and group formation was stable enough tomaintain continuous connectivity between the two variedlyoperating networks. Figs. 33 and 34 present the initial andfinal cluster centers for G-FANET, respectively. Accordingto these figures, the variation in the initial and final clus-

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Fig. 35 Iteration history for K-means clustering analysis of G-FANET

Fig. 36 Cluster-cases for K-means clustering analysis of G-FANET

ter centers was minimum, thus, proves that lesser number ofiterations were required to attain equilibrium. The numberof iterations for clustering analysis is presented in Fig. 35.Also, no traces of ambiguity in network connections wererecorded for cluster-cases, as shown in Fig. 36, thus, provingG-FANET to be a completely fault-tolerant solution for internetwork connectivity.

The network formation was also tested for cross-correla-tions and correlations between the driving components of theproposed framework. The dependency of network on theseindependent parameters shows the robustness of the frame-work. Also, the trending curve formation with learning rate,which in actual sustains the completion of feedback rate,makes the network operations continuous. The two-tailed

Fig. 37 Cross co-relation: error rate versus learning rate

Fig. 38 Cross co-relation: learning rate versus feedback rate

bivariance test for correlations further validates the pro-posed framework. Figure 37 presents the cross-correlationsfor error rate in comparison with learning rate. Error correla-tions offered higher confidence limit with effective learningrate. Upper confidence limit was traced for cross-correlationcomparison between the learning rate and feedback rate, asshown in Fig. 38. Traffic rate varies with transmission due tonetwork uncertainties. Thus, to attain equilibrium, network

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Fig. 39 Cross co-relation: learning rate versus traffic rate

learning rate shuffles between the lower andupper confidencelimit in contrast with traffic rate, as shown in Fig. 39. Further,confidence interval and cross-correlation for packet deliveryratio (PDR) and network throughput was also recorded.Withincrease in number of UAVs, PDR also increase as more cor-ridors are available for connectivity as shown in Fig. 40.Network throughput is directly influenced by the number ofchannels. Higher confidence value was traced during simu-lation analysis for configured network as shown in Fig. 41.Figure 42 presents the completeG-FANET correlations com-parison for error rate, learning rate, feedback rate and trafficrate. Upper confidence bars show that error rate, learningrate and feedback rate was capable of sustaining the varyingaffects of traffic rate. Thus, the overall connectivity is leastaffected by the uncertain changes in the network as learningrate and feedback rate adjust in accordance with the networkrequirements to maintain the stabilization.

5 Real time analyses of the proposed approach

The proposed G-FANET formation was also analyzed overreal scenario which comprised of three rotor wing UAVs.These UAVs were pre-programmed to maneuver over thetwenty ground nodes. The network was analyzed for identi-fication of user stations by the ground nodes, and then sharingof data alongwith the learning of the network. TheUAVs con-sidered for evaluation allowed data recording for 35min overa 2.5 GHz channel with logs maintained at the local groundserver. Arduino-uno was used to control the manoeuvrability

Fig. 40 Cross co-relation: packet delivery ratio versus UAVs

Fig. 41 Cross co-relation: throughput versus UAVs

of the UAVs, and raspberry-pi provided the support for theonboard processor. Other configurations and devices usedfor real time analysis are shown in Table 2. The data sharedbetween the nodes were analyzed for different parametersover the simulation study was also evaluated.

Analyseswere traced for the neuralmappingwhich identi-fied the networks’ learning rate, error rate, mapping with theground nodes, and the cognitive/packet delivery ratio. Out of

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Fig. 42 Complete G-FANETcorrelations

Table 2 Configurations for real time analysis

Parameter Value

UAVs 3

Ground nodes (PDAs & laptops) 20

Flight time 35 min

UAV type Rotor wing

Radio frequency 2.5GHz

Onboard process Raspberry Pi(Model B+)

IMU MPU 6050

Data analyzer Dell precision T15610

GPS GPS-10710

On-board memory 512 MB

the total run time of 35 min, the results were taken for the2000 s. Results show that the proposed network formationoperated over an average learning rate of 0.53, with a verylow error rate of 0.40, as shown in Fig. 43. The network con-verged towards more stabilized formation with more timeof connectivity, thus, decreasing the overall network error.Also, with more number of connections available, networklearning also improved.

Further, the proposed G-FANET relies on accurate con-nections between the ground nodes and the aerial nodes.WithUAVs maneuvering over the ground devices over real time,

200 400 600 800 1000 1200 1400 1600 1800 20000

0.2

0.4

0.6

0.8

1

Network Connectivity Time (sec)

Rat

e

Network Learning Rate

Network Error Rate

Fig. 43 Network learning and error rate versus total connectivity time

the proposed approach allowed an average of 73.1% accu-racy in successfully mapping the UAVs to the ground nodes,as shown in Fig. 44. This effect of accurate mapping wastraced over the cognitive/packet delivery ratio of the overallnetwork. The proposed network model provided an averageof 80% of packet delivery ratio during the complete connec-tivity, as shown in Fig. 45. The real time analyses show thatthe proposed G-FANET is capable of handling large num-ber of nodes. It can be concluded that with more number

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200 400 600 800 1000 1200 1400 1600 1800 200060

65

70

75

80

85

90

95


Ma

pp

ing

Acc

ura

cy (

%)

Mapping Accuracy vs. Time

Fig. 44 Accuracy in mapping versus total connectivity time

200 400 600 800 1000 1200 1400 1600 1800 200055

60

65

70

75

80

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Cogniti

ve/P

ack

et D

eliv

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Ratio

(%

)

Cognitive Delivery Ratio vs. Time

Fig. 45 Cognitive/packet delivery ratio versus total connectivity time

of nodes, number of channels for connectivity will increasewhich will certainly enhance the network connectivity andthe transfer rate. The detailed result traces are provided aturl: http://bit.ly/groundfanet.

6 Conclusion

Cooperation amongst multiple ad hoc networks enhances theapplication and utility of these networks. In this paper, anambient network formation is presented between the aerialand ground nodes with effective flow control and capabilityto handle larger traffic. The proposed G-FANET allows con-nectivity between the aerial and the ground networks to forma search and tracking based guidance system. Adding to thisframework, a quaternion neural feedback model has beendeveloped that operates over the fuzzy based decision sup-port system. This decision system allows genetic modeler

to regulate the traffic flow amongst the nodes operating indifferent transmission conditions using inference rules. Theproposed schema is validated using mathematical and sta-tistical simulation analysis. Analysis proved that G-FANETis capable of providing continuous and fault-toleration con-nectivity amongst the aerial and ground ad hoc network withenhanced traffic management and flow control.

Acknowledgments Weare very grateful to theEiC,AE, and the anony-mous reviewers for their constructive comments and encouragementwhich helped in improving the overall quality of the paper. We are alsohighly obliged to the computer science and engineering department of“Thapar University”, Patiala for rendering their incessant help in pro-viding infrastructure and work environment.

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Vishal Sharma received hisPh.D. from Thapar University,Patiala, Punjab, India andB.Tech.from Punjab Technical Univer-sity, India in Computer Scienceand Engineering. He is workingas a Lecturer in the ComputerScience and Engineering depart-ment, Thapar University, Patiala,Punjab, India. He is a memberof various professional bodiesand past Chair for ACM StudentChapter-TU. His area of researchand interest are Wireless Net-works, Estimation Theory, andArtificial Intelligence.

Rajesh Kumar is currentlyanAssociateProfessor-ComputerScience and EngineeringDepart-ment, in Thapar University,Patiala. He obtained his M.Sc.,M.Phil. and Ph.D. degree fromIIT Roorkee. He has more than18 years of UG & PG teachingand research experience. He hasmore than 70 research papers tohis credit in various internationaland national Journals and confer-ences. He is a member of variousprofessional bodies/societies ofrepute. His area of research and

interest includesMathematicalModeling,WirelessNetworks, and Soft-ware Engineering.

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