EPLAODV: Energy Priority and Link Aware Ad-hoc On-demand Distance Vector Routing Protocol for Post-disaster Communication Networks

In Mobile Ad-hoc Networks (MANETs) the major issue between nodes for data transfer is link quality. The majority of the MANET routing protocols try to maintain the link quality between nodes but still need improvement. This paper focuses on link quality between nodes in an ad hoc network in emergency scenarios through the design of the Energy Priority and Link Aware Ad-hoc On-demand Distance Vector (EPLAODV) routing protocol. EPLAODV improves the link quality between nodes through SNR. For performance analysis, the NS2.35 network simulator was used. Simulation time and traffic load were the major simulation parameters. From the simulation results, it is observed that EPLAODV performs better than AODV. Keywords-mobility; SNR; EPLAODV; link-quality; lifetime

INTRODUCTION Many users use wireless communication devices or channels for the exchange of information. In an MANET, mobile nodes are connected through wireless links. An MANET lacks pre-deployed infrastructure for environment monitoring and disaster scenarios [1][2][3]. RF frequencies are used from mobile nodes to flood data packets from source to destination nodes. An MANET has flexible and dynamic topology. In dynamic topology, the nodes of the network frequently move with different speeds within the transmission range. Nodes may join or leave the network thus changing its topology. Node mobility is a main cause of frequent link failure. To prolong network lifetime and to get maximum throughput, MANETs use reactive, proactive and hybrid routing protocols to discover shortest, stable, and efficient routes. The reactive routing protocols AODV [4], DSR [5] and TORA [6] are on demand routing protocols which discover routes when required, avoid traffic congestion, reduce routing overhead, minimize end-to-end delay, and reduce traffic collision [7]. Generally, MANET nodes have battery constraints. In a network, the nodes with very low energy level are disconnected. To discover a route, the conventional AODV routing protocol does not consider the fairness of node energy consumption. MANETs are used in various real time applications such as environment monitoring control, air traffic control, in battle fields, and in many emergency scenarios like major accidents and disaster relief operations [8]. In real time scenarios, where the replacement or recharging of batteries is almost not possible, the lifetime of the network is important. To prolong the network lifetime link various energy efficient and link aware routing protocols have been proposed. These routing protocols avoid link failures and consider less node energy consumption [9][10][11][12], although the current status in maximizing network lifetime still needs improvement.
In disaster-affected areas, the telecommunication infrastructures are seriously damaged or even collapsed. MANETs can be deployed in critical areas to carry out the rescue operations. Rescue workers or first responders are equipped with mobile devices to share important information about operations at the disaster site. The first responders share videos, make voice calls and send text messages to their supervisors to inform about the situation in the area. Wireless communication is used to support the operational analysis of disaster response. To avoid communication interruption among the rescue works, and to reduce the losses of infrastructure and human lives, critical information travels through high powered nodes or stable links. The proposed energy, priority and link aware ad hoc on demand distance vector (EPLAODV) routing protocol discovers energy efficient and link aware routes from source to destination for the exchange of critical information in disaster operations.
II. LITERATURE REVIEW MANET routing protocols are used to discover routes from source to destination. Node mobility and unfair consumption of node energy and frequent link failure are inherent in an MANET, which causes routing overhead and end-to-end delay. Conventional AODV reactive routing protocol does not consider link stability and energy consumption, while energy consumption is the main issue of MANETs. Different routing protocols based on the node energy consumption and link quality parameters to prolong network life time, have been proposed. Authors in [13] proposed MAODV routing protocol which is based on AODV. The algorithm considers two parameters, node energy level and node distance. The distances among the nodes are measured and a node with a good energy level and with small distances is considered in route selection. MAODV selects an energy efficient route in which all nodes possess good energy level. This protocol consumes less energy and maximizes packet delivery ratio and network lifetime. In [14], authors suggested a local repair method to avoid link breakage. The protocol takes preemptive measures based on the residual energy, and uses three state modes to avoid link failure: normal, selfish, and sleep. The algorithm selects stable and energy efficient routes. It minimizes routing overheads by avoiding link breakages and maximizes network lifetime. Authors in [15] proposed the energy efficient routing protocol Ad-hoc On-demand Distance Vector Energy Aware (AODVEA) which utilizes a min-max algorithm to take routing decisions. A node must have a minimum remaining energy to participate in route selection. The algorithm computes the minimum remaining energy of all possible routes. To prolong the network lifetime and minimize end-to-end delay, the protocol selects the path having maximum value of minimum remaining energy. Authors in [16] proposed the Dynamic Energy Ad-hoc On-demand Distance Vector (DE-AODV) routing protocol. Route selection is based on node energy level. The node energy level is compared with a threshold. A node having more energy is selected in a route. To avoid link failures, the algorithm provides external batteries to the nodes with less energy. The protocol maximizes the network lifetime by minimizing energy consumption. Authors in [17] suggested a new method to improve link quality. The suggested method is based on ant colony optimization (ACO) algorithm. The algorithm considers received signal strength (RSS) as a link quality parameter. The protocol selects nodes in a route that have stable links with good RSS values. The protocol minimizes routing overhead and node energy consumption by avoiding link failure. Authors in [18] proposed the Route Stability and Energy Aware (RSEA)-AODV routing protocol, which considers RSS, drain rate of nodes, remaining energy, and delay as route metrics to select a stable route. In the route discovery process, these parameters are compared with respective thresholds. A less congested node, having good energy level and good RSS values is selected in an energy efficient and stable route. The protocol minimizes energy consumption, end-to-end delay and maximizes network lifetime and packet delivery ratio.
III. THE PROPOSED EPLAODV In the proposed EPLAODV routing protocol, the discovered routes are based on traffic priority, node energy, and link quality. The RREQ and RREP control packets are modified by adding new priority fields. High priority 0 is assigned to time critical traffic for exchange of information in the form of voice and video. Low priority 1 is assigned to normal traffic for the exchange of information in the form of text and data. When the source node wants to discover a route to a destination, it sets the traffic priority in the RREQ control packet and broadcasts it to all its neighbors. In the following section the route discovery process is discussed in detail.

A. Route Discovery
Assume that node N s is the source and node N d is the destination, as shown in Figure 1. The source node initiates route discovery process, N s assigns a traffic priority 0 in the RREQ control packet, and broadcasts RREQ packets to neighbor nodes n 1 , n 3 , and n 5 . The intermediate nodes n 1 , n 3 and n 5 compare their residual energy with the energy threshold Th e . If their residual energy is greater than Th e , they make a reverse path entry and broadcast RREQ packets to their neighbors. If any node among n 1 , n 3 or n 5 has a residual energy level lower than threshold Th e , it drops the RREQ packet. Assuming that these nodes have sufficient residual energy level, node n 1 rebroadcasts the RREQ packet to node n 2 , which does not accept the packet due to its insufficient energy level. Similarly, node n 3 rebroadcasts RREQ packet to node n 4 and node n 5 rebroadcasts RREQ packet to node n 6 . This process is continued until RREQ packet reaches the destination. When the RREQ control packet reaches the destination node N d , it updates the priority field in the RREP control packets for assigning traffic priority, and the RREP packets are unicast to nodes n 4 and n 7 . When nodes n 4 , and n 7 receive the RREP packets, they check their traffic priority value, and their own residual energy and signal-to-noise ratio (SNR) value. If the RREP packet contains priority value of 0, the nodes assume that the route is going to be established for high priority traffic, otherwise low priority traffic is established. Intermediate nodes do not accept any RREP control packet until they check their node energy level and SNR value. If the residual energy of node n 4 and n 7 is greater than Th e , and the SNR value of node n 4 or n 7 is greater than Th snro , then node n 4 unicasts the RREP packet to nodes n 3 and node n 7 unicasts RREP packet to node n 6 . If node n 4 or n 7 have residual energy level lower than Th e and less SNR than Th snro , they drop the RREP packet. Node n 5 drops RREP control packet due to insufficient energy or insufficient SNR value. This discovery process continues until RREP control packet reaches the source node N s . Finally, node n 3 unicasts the RREP packet to the source node N s and the route N s − n 3  discover low priority routes, priority 1 is assigned in RREQ and RREP control packets and the same steps are taken in route discovery process to establish energy efficient and stable routes. Once the route has been established, the source node forwards data packets to the destination node.

B. EPLAODV Algorithm
In Table I, the route setup process of the EPLAODV protocol is described. Let Ns denote the source node and Nd denote the destination node.

Let N represent the set of intermediate nodes between Ns and Nd,
where N={n0,n1, n2,……., nm} and ni represent the current node.

3.
Let nie represent the residual energy level of the current node.
Let Thsnr0 represent the SNR threshold for real time traffic 6.
Let Thsnr1 represent the SNR threshold for normal traffic

7.
Node Ns sets the traffic priority value in the priority field in the RREQ packet and broadcasts to its neighbors.

8.
Node ni receives the RREQ packet, where ni Є N.

IV. PERFORMANCE ANALYSIS
The proposed EPLAODV routing protocol is implemented in the open source network simulator NS2 [19]. The simulator helps in evaluating the performance of MANET routing protocols. The proposed EPLAODV is compared with AODV. To measure the performance of the proposed EPLAODV routing protocol, we run different emergency scenarios (simulation) in NS2 environment with varying traffic load and simulation time. The simulation results show that the proposed EPLAODV performs better than the original AODV. The simulation parameters are presented in Table II. The simulation results are benchmarked with AODV routing protocol with keeping in mind that energy performance is crucial in MANETs because node mobility drains the energy of the node. Results show that the proposed EPLAODV controls the node mobility in efficient way and prolongs the energy of the node as compared to AODV. Figure 3 shows the simulation results of packet delivery ratio versus simulation time. EPLAODV and AODV are compared for high and low priority traffic. Initially at 200s the packet delivery ratio is less because less number of data packets are generated , and less packets are reaching their destination. As the simulation time increases, packet delivery ratio also increases. In conclusion, EPLAODV performs better for high priority and low priority traffic. Figure 4 shows the simulation results of routing overhead versus simulation time for proposed EPLAODV and AODV. As the simulation time increases, EPLAODV selects more stable routes on the basis of link quality. The protocol generates less number of control packets, which causes less routing overhead. It can be seen that EPLAODV performs better than AODV.  Figure 5 shows the simulation results of network energy consumption versus simulation time for the proposed EPLAODV and AODV. Initially at 200s, the network consumes less energy. As the simulation time increases, EPLAODV generates more control and data packets, causing more energy consumption. EPLAODV discovers energy efficient and stable routes, which minimizes the flooding of control packets and energy consumption. Figure 6 shows the simulation results of end-to-end delay versus simulation time. The proposed EPLAODV and AODV are compared for both high priority traffic and low priority traffic. Initially, at 200s the end-to-end delay is maximum, because more packets are generated, due to congestion or link breakage. As the simulation time of the network increases, end-to-end delay decreases, because EPLAODV selects stable paths based on link quality which helps avoiding link breakage and congested traffic. The proposed EPLAODV performs better for both high priority and low priority traffic. End-to-End delay vs. simulation time Figure 7 shows the simulation results of packet delivery ratio versus traffic load for high and low priority traffic. Initially, the packet delivery ratio is maximum. As the traffic load increases, the network becomes more congested, which reduces packet delivery ratio. The proposed protocol EPLAODV performs better than AODV in both cases. Packet delivery ratio vs. traffic load Figure 8 shows the simulation results of routing overhead versus traffic load. Initially the routing overhead of the network minimizes. As the traffic load increases, the congestion of the network also increases, which causes link breakage and increased routing overhead. Again, the proposed EPLAODV routing protocol performs better than AODV. Routing overhead vs. traffic load Figure 9 shows the simulation results of network energy consumption versus traffic load. Initially, with minimum traffic load, the energy consumption of the network is reduced. As the traffic load increases, the network becomes more congested. AODV routing protocol generates more control and data packets, causing more energy consumption. The proposed EPLAODV protocol reduces the broadcasting of control packets, in order to minimize energy consumption. The proposed EPLAODV routing protocol performs better than AODV. Figure 10 shows the simulation results of end-to-end delay versus traffic load. EPLAODV and AODV are compared for both high and low priority traffic. Initially, with minimum traffic load, end-to-end delay is reduced. As the traffic load increases, the congestion of the network also increases, which causes more end-to-end delay. The proposed EPLAODV performs better for both cases of high priority and low priority traffic. An energy, link and traffic aware extension of AODV routing protocol named EPLAODV is introduced in this paper. The EPLAODV selects energy efficient and link aware routes for time critical and normal traffic. Some emergency scenarios have been simulated by varying simulation time and network traffic load. The simulation results show that the proposed scheme performs better than the traditional AODV for real time and textual data in terms of packet delivery ratio, energy consumption, routing overhead, and end-to-end delay for all simulated scenarios.