In this paper, an Ant Colony Optimization (ACO) based Energy Efficient Shortest Path Routing (AESR) algorithm is developed for Mobile Ad hoc Network (MANET). The Mobile Ad hoc Network consists of a group of mobile nodes that can communicate with each other without any predefined infrastructure. The routing process is critical for this type of network due to its dynamic topology, limited resources and wireless channel. The technique incorporated in this paper for optimizing the routing in a Mobile ad hoc network is Ant Colony Optimization. The ACO algorithm is used to solve network problems related to routing, security, etc. in wired and wireless networks. This proposed work is used for selecting the energy efficient shortest route between the source and the destination of the network. This routing technique uses important parameters such as residual energy of the nodes, minimum residual energy of the path, the distance between the nodes, trip time and hop count to select the optimal route for communication to extend the lifetime of the network by reducing the energy consumption. The proposed routing protocol is compared with other conventional routing protocols. The experimental results demonstrate that the proposed AESR routing protocol outperforms the existing protocols in terms of performance metrics such as Packet Delivery Ratio, Average Path Length, Network Lifetime, Number of Dead nodes and Average Energy Consumption in high mobility networks.
Mobile Ad hoc Network (MANET) is a complex distributed system formed by mobile nodes that can move freely and can be self-organized for creating temporary networks [
Energy conservation is one of the important parameters that can influence to a greater extent for improving the performance of Mobile hoc Ad Networks. Normally, energy can be conserved in any network in two ways: by reducing the communication range of the node and by controlling the transmission and the reception power. Generally, the mobile nodes have limited power, since they operate on battery power. Moreover, the mobile nodes can function as a router by forwarding the data packets to other nodes. This in turn results in more power consumption in Mobile Ad hoc Networks which may reduce the Network Lifetime. To overcome this problem, a new energy efficient routing protocol was suggested which is mainly based on the concept of the ACO algorithm. ACO refers to Ant Colony Optimization which solves many problems in networking applications [
The remainder of this paper is structured as follows: Section 2 deals with the existing techniques related to the proposed work. The merits and demerits of the existing methods are also discussed. Then, Section 3 describes the model of Mobile Ad hoc Network. Section 4 describes the proposed Ant Colony Optimization based Energy Efficient Shortest Path Routing (AESR) algorithm. Section 5 presents the performance metrics used to analyze the performance of the proposed method. Section 6 discusses the results obtained from the simulation conducted to analyze the performance of the AESR and the last section deals with the conclusion and future scope.
Several ant-based routing protocols are developed for wired and wireless networks [
The ARA is a highly adaptive and scalable ant colony-based routing algorithm that is efficient and reactive in nature [
The Improved Energy and Mobility Ant Colony Optimization (IEMACO) routing algorithm select the good quality path based on the remaining lifetime of the nodes and the link [
Mohajerani et al. [
Sarkar et al. [
The purpose of this work is to develop an energy efficient routing for Mobile Ad hoc Network. The nodes in the Mobile Ad hoc Network have limited battery capacity. So it is important to consider the energy efficient routing in Mobile Ad hoc Network so that the available energy can be used efficiently by reducing the energy consumption which in turn increases the lifetime of the network. Several energy efficient routing algorithms have been proposed for Mobile Ad hoc Network. The swarm intelligence based energy efficient routing performs well compared to the traditional routing protocols. In the proposed routing protocol, for selecting the energy efficient optimal path the parameters consider both the residual energy of the nodes and the distance of the path which leads to the selection of energy efficient shortest path for data transmission.
Any Mobile Ad hoc Network (MANET) can be represented in the form of a graph
In this section, the proposed Ant Colony Optimization based Energy Efficient Shortest Path Routing (AESR) algorithm for Mobile Ad hoc Network is presented in detail. The methodology consists of two different phases of routing protocol namely: Path discovery Data transmission and Route maintenance
The path discovery gives the optimal path between the source and the destination nodes of the network. The optimal path is identified using Ant Colony Optimization based Energy efficient Shortest path Routing (AESR) algorithm. It is a reactive routing protocol based on the ACO algorithm. In the path discovery phase, two control ants such as REQUEST Ant
When a source node (
The different fields present in the
Initially the hop count, distance and trip time fields of the
In this step, first, the source node (
Now, the hop count field (
In addition to that, the first intermediate node (
In the above equation,
When the destination node (
When the second
In the above equation,
When an intermediate node receives the
When the source node receives the
Once the
The random parameters employed in this algorithm include pheromone updation constant (
The performance of the proposed routing protocol is evaluated by considering five performance metrics such as Packet Delivery Ratio (PDR), Average Path Length (APL), Network Lifetime (NL), Number of Dead nodes (ND) and Average Energy Consumption (AEC) [
Packet Delivery Ratio refers to the total number of packets received by the destination to the total number of packets transmitted by the source. The Packet Delivery Ratio must be high for a good routing protocol. In other words, it is desirable that the maximum number of data packets has to be reached to the destination.
It is the average length of the path traveled by the packet between the source node and the destination node. The data packets must take the shortest path to reach the destination node from the source node. The Average Path Length taken by the data must be less for an energy efficient routing protocol.
Network Lifetime is the time when the first node in the network dies. In other words, it is the time when the first node’s battery power is exhausted. The Network Lifetime is an essential parameter for representing the performance of the energy efficient routing protocol for MANET. It depends on the battery power of the mobile node in the network. The energy efficient routing protocol must prolong the Network Lifetime by reducing the Average Energy Consumption.
A Dead node is one whose residual energy is zero or battery power is completely exhausted.
It is the ratio of the total amount of energy consumed for transmission and reception of packets from source to destination to the total number of nodes in the network. The energy efficient routing protocol must reduce the Average Energy Consumption and enhance the overall lifetime of the network.
This section explains the details of the simulation results attained for the proposed methodology. In this work, the Network Simulator NS2 (Version 2) is used for constructing the network and computing the performance metrics of the network. It is the commonly used simulation tool for the research in the networking field. The two languages used in NS2 are C++ and OTcl. The C++ language is efficient for the implementation of a design but visualization is difficult. At the same time, OTcl is an object-oriented extension of Tcl script language where visualization is easy. Two cases of Mobile Ad hoc Network is constructed for simulation purpose. In the first case, the Mobile Ad hoc Network is constructed with 50 nodes and in the second case, Mobile Ad hoc Network is constructed with the variable number of nodes (20, 40, 60, 80 and 100 nodes).
The nodes are distributed in a random manner within the simulation area. The random waypoint mobility model is considered for node movement in which the nodes move with a constant velocity of 20 m/s. The pause time of the nodes is varied from 0 to 300 s. The initial energy of the mobile node is taken as 20 J. The power required for transmission is set to 0.3 mW and power for the reception of a packet is set to 0.2 mW. In this work, each experiment runs for about 300 s. The performance of the proposed technique is analyzed by comparing it with three different algorithms (AOMDV, ARA and ACECR). The performance metrics are computed for different networks and the results are discussed in the subsequent sections.
Parameters | Values | |
---|---|---|
Simulation area | 1500 m × 300 m | |
Channel type | Wireless channel | |
Propagation model | Two ray ground propagation model | |
Transmission range | 250 m | |
Mobility model |
Random waypoint mobility model |
|
Pause time | 0, 50, 100, 150, 200, 250, 300 s | |
Node speed | 0, 50, 100, 150, 200, 250, 300 m/s | |
Traffic type | Constant bit rate traffic | |
Data rate | 1 packet/sec | |
Packet size | 512 bytes | |
Initial energy | 20 J | |
Transmit power | 3 mW | |
Receive power | 2 mW | |
Protocols | ACECR, AESR, AOMDV and ARA | |
Simulation time | 300 s |
First, the Mobile Ad hoc network is created with the interconnection of 50 nodes which are distributed randomly within the area 1500 m × 300 m. Moreover, the constructed network is analyzed by considering two distinct scenarios. In the first scenario, the performance of the network is evaluated by varying the pause time of the mobile nodes and in the second scenario, the performance is evaluated by varying the mobility of the nodes.
The impact of the pause time of the nodes in the network is analyzed by varying the pause time. The pause time of the nodes is varied from 0 to 300 s. The mobile nodes move with a constant speed of about 20 m/s.
The performance result demonstrates that the proposed AESR method provides better performance than ACECR, AOMDV and ARA at all the different pause times considered in this simulation.
The performance of the proposed protocol is also evaluated under different node mobility, i.e., the speed of the network nodes is changed from 10 to 300 m/s. The pause time of the nodes is kept constant as 20 s. The effect of the performance metrics due to the varying speed of the mobile node is shown below.
From the simulation results discussed in the above two cases, it is concluded that the AESR routing technique has performed well in all aspects. Compared to the ACECR, AOMDV and ARA in all the investigated cases, the experimental results reveal that the proposed ACO based energy efficient shortest path routing technique provides the best performance in terms of Packet Delivery Ratio, Average Path Length, Network Life time, Dead nodes and Average Energy Consumption.
The performance evaluations are also carried out for the networks with different nodes (20, 40, 60, 80 and 100 nodes). The performance of the proposed protocol is compared with the existing protocol ACECR, AOMDV and ARA by varying the size of the network, i.e., the number of nodes in the network is varied as 20, 40, 60, 80 and 100.
In this paper, an ACO based Energy efficient Shortest path Routing method is presented. From the simulation results, we found that the proposed routing technique has a high Packet Delivery Ratio and low Average Path Length. In addition to that, the proposed technique extends the lifetime of the network by reducing energy consumption which in turn reduces the number of dead nodes in the network. In the future, the proposed approach can be analyzed by modifying the ACO algorithm by applying different types of pheromone update and pheromone evaporation methods. The technique proposed in this paper can also be analyzed by varying the mobility model and data rate. In addition to this, the proposed ACO based Energy efficient Shortest path Routing algorithm can be analyzed by using other optimization algorithms such as Bee Colony Optimization, Bat Algorithm etc instead of ACO. Though they have different dimensions, they are suited for Mobile Ad hoc Networks due to their dynamic characteristics. The foraging behavior of honey bees and the echolocation behavior of bats can be used for finding the energy efficient route for Mobile Ad hoc Networks.