An Opportunistic Cross Layer Design for Efficient Service ...

IEEE SPONSORED SECOND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS(ICECS ‘2015)

An Opportunistic Cross Layer Design for Efficient Service Dissemination over Flying Ad Hoc Networks (FANETs) Vishal Sharma1 and Rajesh Kumar2 1

Research Lab-CSED, Thapar University, Patiala-147004, Punjab, India [email protected]@gmail.com

2

Associate Professor-SMCA, Thapar University, Patiala -147004, Punjab, India [email protected]

Abstract: Collaborative networking involves multinetworks to unite together to form a network that can provide reliable connectivity amongst the ad hoc nodes. Flying ad hoc networks involves the aerial nodes that are capable to form the aerial ad hoc that can identify the ground nodes for potential data transmission between them. This allows formation of guidance map that can aloof any chances of network failures. Opportunistic network formation provides continuous connectivity between the aerial and the ground nodes. This can be realized using cross layer formation that operates over the parameters from multiple layers, and then finally a neural schema helps in attaining stabilization over the hybrid network. Cross layer design allows efficient service dissemination over the flying networks. In this paper, a cross layer based opportunistic network formation for flying network is presented that can provide continuous data sharing with efficient utilization of network resources between the aerial and the ground network. The Effectiveness of the proposed opportunistic network formation is presented using simulations. Keywords: FANETs, Service Dissemination, Opportunistic Relaying, Cross Layer Design.

1. Introduction Flying ad hoc networks (FANETs) are specialized form of ad hoc network with nodes involving aerial vehicles that have capability of forming an aerial network with coordination of supported ground network. The network formation is autonomous that can be used to solve complex tasks involving military and civilian applications. FANETs are the derived version of mobile and vehicular ad hoc network [1]. These networks provide wide range of applications in area tracking and searching. Civilian and border security are one of the major applications of these networks as shown in Fig1. Another aspect of these networks are the cooperative network formation to provide look ahead information of unidentified terrain to localize the ground nodes [2]. Such dynamic formation is subjected to certain network problems that hinder the operations of aerial network to form a guidance system. One of the area for improvement is service handling amongst the network nodes

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involving aerial and ground vehicles. Such networks are termed as hybrid networks. These hybrid networks can solve the complex task effectively if the services running onboard and in collaboration are handled efficiently.

Fig 1: Cooperative Network Formation: FANETs, MANETs, VANETs

Another aspect of such network is the opportunistic relay formation. This provides continuous and an effective solution to service handling amongst the network nodes with fault-tolerance. The opportunistic relaying is possible if network is aware about its functionality and state. This can be handled by providing certain parameters to accumulate at a common identifier and then, investigate the network state to provide effective solution for maintaining continuous transmission. This multi-layer parameter accumulation and selectivity formation at layer, other than the parameter dependency layer is termed as cross layer. In this paper, a cross layer design is proposed over the existing service model for UAV guided network to form an efficient flying network that is capable to provide continuous transmission between the aerial and the ground network without affecting the actual operations of the network. The proposed model is controlled by the neural based service handling algorithm which efficiently disseminates the services over this hybrid network formation.

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The remaining paper is structured as follows: Section 2 presents the preface to history of aerial network; section 3 presents the proposed cross layer design with its mathematical dependency with simulations and results in section 4. Finally, section 5 concluded the paper with future scope in section 6.

2. Related Work Flying ad hoc networks are the set of aerial nodes operating over the defined area to perform search and tracking operations. In the recent years, work has been carried to form the network with aerial vehicles. Ad hoc networking with UAVs was initially recolonized by Bekmezci et al. [3] [1]. Authors highlighted the importance of flying ad hoc networks and their applications and issues in their survey. Sharma and Rajesh have formulated one of the initial frameworks for the formation of ad hoc network with UAVs. Authors have provided efficient non-redundant searching using aerial vehicles that maneuvers over the ground vehicles to form a coordinated cognitive maps for enhanced tracking [2]. Jensen and Chen used the collaboration of aerial vehicles as swarm to identify the location of tagged fish. Kalman filtering was used with potential fields to identify and estimate the location [4]. Opportunistic network formation with aerial vehicles in collaboration with other terrestrial vehicles can solve the issue of network failures and can provide a fault tolerant network with better connectivity. Also, such networks are efficient enough to handle the resources without affecting the operational activity of the overall network [5]. Ryan and Madey have presented simulation study for the formation of cooperative network involving UAVs as nodes. Authors have focused in target tracking with their approach [6]. Bing et al. have also focused on the aerial swarming to form the network which is capable to solve the complex tasks [7]. Sahingoz have further discussed the importance of flying networks with the challenges involved and open issues to be targeted [8]. Wang et al. have used a strategy to place the aerial vehicles amongst mobile nodes [9]. Another aspect of networking with aerial vehicles is the service coordination. Service distribution over the network can be used to re-distribute the task amongst the aerial nodes to solve the issue of the data dissemination. Service collaboration can provide effective solution to cooperative rendezvous and task handling with efficient traffic management and cooperative map formation [10] [11] [12]. In the process of attaining data accuracy over the ad hoc network, certain design issues were integrated over the separate network layers to form a collaborative cross layer structure for ad hoc networks. Cross layer allows inter parametric analysis of network to form a stabilized network with improved efficiency. This can also solve the issue of multi-networking involving hybrid nodes [13] [14] [15] [16].

3. Proposed Strategy Opportunistic network operates for efficient handling of services to enhance the network lifetime and performance. Opportunistic relaying over flying ad hoc networks provides reliable service distribution amongst the network nodes such that an enhanced connectivity is established between the aerial and the ground nodes. The network operates in collaboration with the pre-existing ground network that coordinates the operations of the aerial networks. The application suggested for the proposed service operating over the flying network is to provide efficient task distribution amongst the network nodes in form of services based on the computational resources of each node. The proposed model is a cross layer approach. The operational layers of the network model for guided UAV ad hoc network are cross-layered on basis of certain utility parameters to handle the service distribution. Tasks involving aerial networks are configured into series of services. These services are handled using data handling algorithm which uses resources available as its decision metric for dissemination. The proposed model uses the service coordination layered structure for service dissemination [12]. 3.1 Mathematical Dependency of Data Dissemination Model The mathematical formation for service dissemination operates by forming the service model which is evaluated over the neural schema that handles the input, updations and output of the flying ad hoc networks. The model operates over the network connectivity time, probabilistic associativity between the aerial and the ground network, services for number of tasks and the resources available. For opportunistic cross layer formation over flying ad hoc network, probability of connectivity P ck,t at time t for kth node will be given as: Pck,t =

R ∈(𝐶𝐻 𝐶𝑂 ) k D c ,t i=1 T Aso CH CO (N C ∈(A,G)) i=1 j=1

,

(1)

where CHco is the number of channels available for connectivity between the aerial and ground network, Dkc is the average connectivity of the kth node, TAso is the total associativity of the network, NC is the total network connectivity at time t. For a network operating over data sharing, service dissemination time Si for ith service will be computed as: Si =

𝐶𝐻 𝐶𝑂 𝑘=1

(Tsg) - Treq ,

(2)

where Tsg is the time of generation of the service, known as first response time, and Treq is the request time for particular service. For a network operating in coordination with another network, services are to be handled based on the number of resources available that can efficie ffntly

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distribute the requested services without any loss over the mutual network. For aerial network, resources available RAH are given as functional dependency such that: RAH ← f (NCH, NCO)A

(3)

Similarly, for ground network, resources available RGH are given as: RGH ← f (NCH, NCO)G ,

(4)

where NCH, NCO are the number of channels and corridors available for network connectivity. Now, resource handler RM H for the mutually coordinated network will be computed as: G A RM H = Min (R H , R H )

(5)

The network service density ratio SDR will be computed as: SDR =

Sreq CH ser

,

S DR N i=1

(7)

N CO S A _G j=1

Here, SA_G are the services operating between the aerial and the ground network. The parameterized dependency of the cross layer formation is subjected to neural schema for analysis of its input, output and updations. The neural model can be used depending upon the amount and flow regulations of the network. For the proposed model, the output I/O at time t is mapped as: I/O =

W tS + D AG C (S i )

T DR + ||S DR || r tmin

The data handling algorithm uses flow control methodologies to manage the network transfer rate, thus, providing an effective solution to service dissemination over FANETs. Algorithmic selection is based on the neural schema that balances the network in case network stabilization is disturbed. The state of stabilization is decided by the neural network itself based on the number of parameters and learning rate of the network. The algorithm for managing the flow and forming an opportunistic relaying network using cross layer design over FANETs is shown in Algorithm 1. Algorithm 1: Algorithm for Opportunistic Secure Cross Layer Formation in FANETs

(6)

where Sreq are the number of service requests, and CHser is the number of channels available to handle service requests. Now, traffic density ratio TDR will be given as: TDR =

network is shown in Fig 2. The operational activities of the layers are derived from [12]. Each layer provides a driving parameter value which is subjected to the neural schema that disintegrates the services requests and efficiently distributes them amongst network nodes using data handling algorithm.

Require: NA, NG, S Ensure: Min(TAso) = Avg (DA,G C ) While t ≤ total_time do While service_handled ≠ complete do set CO ← corridors set CH ← channels map (CO, CH) j ← count (map (CO, CH)) initialize i while i ≠ j set map_counter = true if map_network ≠ complete then count Sn ← services RAH = Min (RAH , RGH ) Pck,t =

,

(8)

where rmin=Min(R' ϵ (NA, NG)) ,

(9)

and WSt = rtmin f(RAH , PCt , RGH ) + DAG (10) C 3.2 Functioning of Proposed Model The proposed model provides opportunistic network formation by modifying the functioning of the service layers of UAV network to form a cross layer design. The cross layer allows hybridization of parameters that allows decision making for service handlers easy and efficient. The operational model of cross layer design for flying ad hoc

R ∈(𝐶𝐻 𝐶𝑂 ) k D c ,t i=1 T Aso CH CO (N C ∈(A,G)) i=1 j=1 𝐶𝐻 𝐶𝑂

Si = 𝑘=1 (Tsg) - Treq if SDR && TDR ≥ 0.5 then continue counter=counter+1 else reset endif else reset endif i=i+1 EndWhile Service_handled= S+1 EndWhile t=t+count(time_tick) EndWhile

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Fig 2: Cross Layer Design for FANETs

Table 1: Simulation Configurations Parameters Terrain Type Area Number of aerial vehicles Number of ground nodes Number of services Flight Record Time Capacity Simulation Time Air band Type Frequency Range Data Rate Aircraft Mobility Rate Load Factor Transmission Range Minimum Altitude Between the Aerial objects Wind Speed Turbulence Recovery Time

Values Moderate Hilly 2500x2500 sq. m. 5,8,10 30,40,50,70 500-2000 3 hours 1500 seconds VHF/UHF Radio 110-145 MHz 4800-9600 bps 150-200 knots 2 5.0 Km 1000 Feet 50 Kmph 5-10 seconds

4. Simulations and Results Network simulations for FANETs are a complex task. For generation of traffic, NS-2 was used that was integrated with PlotTM for traffic analysis. MATLABTM was used to analyze the performance of the network by evaluating it over the cooperative network framework given by [2] [12]. The number of nodes for aerial network range between 5 to 10, and that for ground network range between 30 to 70 with area of 2500 x 2500 sq. m. The services available for dissemination range between 500 to 2000. Other parameters configured for network analysis are presented in Table 1. For analysis, results were traced for cognitive delivery ratio, overall network performance, average hit ratio, number of services handled with average time in service dissemination.

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Fig 6: Average Service Dissemination Time Fig 3: Message Hit Ratio

Fig 4: Overall Network Performance

Fig. 3 presents the analysis for number of hits over the aerial network for number of cognitive transfers. The plot presents the average number of data received at each node involved in formation of aerial ad hoc network. Message delivery ratio with variation in number of vehicles is presented in Fig. 4. The plot presents the overall network performance w.r.t network losses that were induced during simulations. Further, the proposed cross layer design was evaluated for number of services that were handled during formation of guided network between the aerial network and the ground network. Fig. 5 shows the comparison for number of services handled in the network formation with the ground nodes. The variation is presented for 5, 8, 10 UAVs maneuvering over 30, 40, 50 and 70 ground nodes. For an efficient cross layer formation, service dissemination time provides analysis for average time consumed by the algorithm to effectively distribute the services amongst the network nodes based on the availability of the resources. Fig. 6 presents the comparison of average service dissemination time with variation in number of UAVs against the number of service request induced by UAV group at a given instance of time.

5. Conclusion

Fig 5: Service Handled (%) Over Ground Network

In this paper, an opportunistic network formation using cross layer design is presented for flying ad hoc network. The network model proposed in the paper uses the service layers of the flying network to provide parameterized input to neural setup. The neural setup combines the network parameters to realize a cross layered network formation which is capable to provide stabilization during internetwork transmission between the hybrid ad hoc networks. The simulations were used to present the operability of the proposed model. Analysis showed that the proposed cross layer design for FANETs is capable to offer high data delivery ratio, provides efficient service coordination with

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effective utilization of network resources, higher scalability with lower time for service dissemination.

6. Future Scope In Future, the service dissemination can be integrated with other frameworks to provide efficient collaboration between the aerial and the ground network. Also, more fault-tolerant feature with algorithmic solution to service collaboration can be provided with the proposed cross layer schema.

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