Energy Optimization For UAV Communication Using ...

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 9, Number 23 (2014) pp. 23311-23319 © Research India Publications http://www.ripublication.com

Energy Optimization For UAV Communication Using Grid Configured ADHOC Network M. Suresh Kumar Assistant Professor, Department of Electronics and Communication Engineering, Vel Tech Technical University, Chennai, India. Email: [email protected] R.Vasantharaj Assistant Professor Department of Electronics and Communication Engineering, Vel Tech Technical University, Chennai, India. Email:[email protected] R.Prabhakaran Assistant Professor Department of Eletronics and Communication Engineering, Vel Tech Technical University, Chennai , India Email: [email protected]

Abstract The wireless communication between UAVs (Unmanned Aerial Vehicle) has significant and wide range of application that helps to enhance the SWARM feature in UAV platform. To establish the Ad-hoc network communication in UAV each UAV is considered as a communication node. In this research the parameters like energy efficiency, throughput, PDR (packet delivery ratio) and bit rate were taken into the account for the analysis. Wireless networks experience frequent link failss caused by channel interference, dynamic obstacles, and bandwidth demands. These failures cause performance degradation in group behaviour of UAV. It is highly complex in using manual network management. This paper presents an ARS (Autonomous network

Paper code 28441-IJAER

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Reconfiguration System) that enables a multi radio adhoc network to autonomously recover from local link failures and preserve network performance. This function can be realized with use of AODV (Ad-hoc Ondemand Distance Vector) routing protocol and implemented in NS2-based simulation for evaluation. Keywords- UAV, AODV routing protocol, ARS,NS2

I. INTRODUCTION UAV networking is a relatively new approach through ad-hoc networks. It provides high quality monitoring for large geographical areas with relatively low power utilization and it establishes the communication between two entities. Each UAV node is equipped with several sensors such as camera, IMU(Inertial measurement unit) and etc.... with the help of advantages in integrated circuits. It is also possible to embed communication modules in UAV When it is work as communication relays to build a wireless aerial backbone network. A short range radio communication is used for transfer the information between the entities. The UAV nodes form an ad-hoc network capable of sending the sensed data to other UAVs and to the base stations that further forward the data using a long link to the monitoring/control center. The Unmanned aerial vehicle(UAVs), capable of performing autonomous coordinated actions due to the Development of lithium polymer batteries, carbon fiber-reinforce plastic materials and light weight on board autopilot . However, the cooperative operation between multiple autonomous unmanned aerial vehicles is usually constrained by sensor range, communication limits, and operational environments.The topology(GRID) of the UAV ad-hoc network plays an important role in the system performance.

II. UAV ARS ARCHITECTURE A. FEATURES OF ARS ARS is a distributed system that is easily deployable in UAV node. ARS has following distinct features such as; i) It generates reconfiguration plans based on multiple channels and radio associations which is used for network configuration changes. ii) It effectively identifies QoS(Quality of service) reconfiguration plans by estimating the QoS by generating reconfiguration plans and the channel utilization. iii) It accurately monitors the quality of each UAV node in a distributed manner based on the measurements and the given links. It detects local link failures and autonomously initiates the network reconfiguration. iv) ARS continuously monitor the link layers and reroute the path across the whole network. It can also maintain connectivity during the recovery period with the help of a routing protocol. Thus the above feature of ARS helps to establish the communication for group behaviour of UAV’s

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III. ALGORITHM DESCRIPTION A. operation of ARS: The steps to overcome the link failure using ARS are Step1: ARS in every Sensor UAV node monitors the quality of its outgoing wireless links at desired time interval (10 sec) and reports the results to a ground control station. Step2: Once it detects a link failure(s), ARS in the detector UAV node(s) triggers the formation of a group among local Sensor routers that use a faulty channel, and one of the group members is elected as a leader using the well-known bully algorithm for coordinating the reconfiguration. Step3: The leader UAV node sends a planning-request message to a ground control station. Then, the ground control station synchronizes the planning requests—if there are multiple requests—and generates a reconfiguration plan for the request. The ground control station sends a reconfiguration plan to the leader UAV node and the group members. Finally, all UAV nodes in the group execute the corresponding command from a ground control station to freeze all UAV node positions. B. ARS implementation using AODV AODV is the simplest and widely used algorithm either for wireless network. It is one of the most efficient routing protocols in terms of establishing shortest path and lowest power consumption. It is mainly used for ad-hoc and wireless sensor networks. It uses the concepts of path discovery and maintenance. However, AODV builds routes between UAV nodes on-demand i.e. only as needed. The primary objectives of AODV are; 1) To broadcast discovery packets only when necessary, 2) To distinguish between local connectivity management (neighbourhood detection) and general topology maintenance, 3) To misestimate information about changes in local connectivity to those neighbouring mobiles UAV nodes that are likely to need the information. AODV does not depend on network-wide periodic advertisements of identifying messages to other UAV nodes in the network. It periodically broadcasts messages to the neighbouring UAV nodes. Then it uses the neighbours in routing. Whenever any UAV node needs to send a message to some UAV node that is not its neighbour, the source UAV node initiates a path discovery by sending a Route REQuest (RREQ) message to its neighbours. UAV nodes receiving the RREQ and update their information from the source.

IV. EXISTING SYSTEMS A. Greedy Forwarding Greedy forwarding is a simple yet efficient technique employed by many routing protocols. It is ideal to realize point-to-point routing because packets can be delivered by only maintaining a small set of neighbours’ information regardless of network size. It has been successfully employed by geographic routing, which assumes that a packet can be moved closer to the destination in the network topology. This assumption,

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however, may lead packets to the local minimum where no neighbours of the sender are closer to the destination or low quality routes that generates long distance hopping and the low packet reception ratio. B.Reroute Technique Rerouting is a technique that can be used in both Circuit Switching and Packet Switching networks. When a link in the network fails, the failed link must change its path in order to reach its destination (ie) it is rerouted from a primary path to a backup path. The primary and the backup path can be totally disjoint or partially merged. Figure(1) represents where a source UAV node A sends information to a destination UAV node F, and when a link on the primary path fails, then the rerouting operation has been followed . A complete rerouting technique consists in seven steps. The first four concern rerouting after a link has failed and switch information from the primary to the backup path, while the last three concern rerouting after the failed link has been repaired to bring back information from backup to the primary path.

Fig.1. Rerouting Terminology

The network must be able to detect link failures. Link failure detection can be performed by dedicated hardware or software by the end UAV nodes C and D of the failed link. UAV nodes that detect the link failure must notify certain UAV nodes in the network of the failure. A backup path must be computed. In pre-planned rerouting schemes, however, this step is performed before link failure detection. Instead of sending traffic on the primary, failed path, a UAV node called Path switching UAV node must send information on the backup path. This step in the rerouting process is called switchover. Switchover completes the repairing of the network after a link failure. C. Limitation of Existing Systems The wireless communications consisted of a single wireless network might have the following problem. First, the physical characteristics of wireless frequency may cause lack of QoS. Moreover, in the link failure, there is certain possibility that power energy cannot be supplied to those communication network devices, eventually those wireless network cannot be functioned. In order to solve this problem, the

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Autonomous Network Reconfiguration System is used to forms Never Die Network (NDN).

V. ARS ROUTING FOR UAV COMMUNICATION The Autonomous network Reconfiguration System (ARS) based routing method that allows a multi-radio WSN to autonomously reconfigure its local network setting such as channel, radio, and route assignment for real-time recovery from link failures. In its core, ARS is equipped with a reconfiguration planning algorithm that identifies local configuration changes to the recovery while minimizing changes of healthy network settings. ARS first searches for feasible local configuration changes available around a faulty area, based on current channel and radio associations. Then, by imposing current network settings as constraints, ARS identifies reconfiguration plans that require the minimum number of changes for the healthy network settings. A.ARS Usage ARS include a monitoring protocol that enables a wireless network to perform realtime failure recovery in conjunction with the planning algorithm. The accurate linkquality information from the monitoring protocol is used to identify network changes that satisfy applications. It avoids propagation of QoS failures to neighbouring links (or “ripple effects”). Running on every sensor UAV node, the monitoring protocol periodically measures wireless link conditions via a hybrid link-quality measurement technique. Based on the measurement information, ARS detects link failures and generates QoS aware network reconfiguration plans upon detection of a link failure. B. Advantages of Using ARS ARS have been implemented and evaluated extensively via experimentation on ns2based simulation. Our evaluation results show that ARS outperforms the existing failure-recovery methods, such as static or greedy channel assignments, and local rerouting. First, ARS’s planning algorithm effectively identifies reconfiguration plans that maximally satisfy the applications such as QoS demands, accommodating twice more flows than static assignment. Next, ARS avoids the ripple effect via QoS-aware reconfiguration planning, unlike the greedy approach. Third, ARS’s local reconfiguration improves network throughput and Energy efficiency, over the local rerouting scheme.

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VI. RESULTS AND ANALYSIS

Fig. 2. Simulation of lossy transmission

Fig.3.Simulation of lossless transmission

Fig.4.Energy Efficiency Vs Time

Energy Optimization For UAV Communication

Fig.5.Throughput Vs Time

Fig. 6. Packet Delivery Ratio Vs Time

Fig.7. Bit Rate Vs Time

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The simulation results clearly show that energy efficiency, Throughput, Packet delivery ratio and bit rate are increasing in ARS method when the time elapses

VII.CONCLUSION AND FUTURE WORK A. Conclusion An ARS, which forms the Never Die Network with a combination of Cluster formation and the routing protocol that helps to share the position and other information between the UAVs. The figure (4) illustrate ARS method is energy efficient than Greedy and Re-Route. ARS generates an effective reconfiguration plan that requires only changes in the local network configuration using exploiting channel, radio, and path diversity. B. Future Work Generally classic proactive and reactive routing protocols apply a simple additive cost metric (often the hop-count) to compute shortest paths towards destinations. Often, shortest paths are not reliable when the network topology changes dynamically. Finding more stable routes is an important goal in dynamic multi-hop sensor networks. Identifying the stable paths permits to decrease control traffic and the number of route interruptions. Stability based routing aims at choosing routes which are more stable in time. So, these latter can be more resilient to dynamic changes in the network topology. If the events (such that the exact trajectory of the UAV nodes, the power battery level, the associated user behaviour, the network failures, etc.) are predictable, then the best route can be computed to satisfy a communication request. Practical observation based and more sophisticated, statistical and probability based routing models exist to deal with long-life routing. Objectives of route Stability a) Minimize the number of unstable link - If links can be classified as stable and unstable, the number of unstable links along the path can be used as a simple stability metric; the objective being to minimize this number. b) Maximize the expected residual lifetime - The residual lifetime of a path is equal to the lifetime of its more critical link. The expected residual lifetime of a link may be calculated from collecting statistical data. c) Maximize the persistence probability - Similarly to the expected residual lifetime, the computation of the persistence probability of a link is proposed based on statistical data. d) Avoid Instable Links - Here the weakest link rule is applied: the stability of a path is the stability of the most instable link along the path.

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