The mobile ad hoc network (MANET) is a multi-hop, non-central network composed of mobile terminals with self-organizing features. Aiming at the problem of extra energy consumption caused by node motion in MANETs, this paper proposes an improved energy and mobility ant colony optimization (IEMACO) routing algorithm. Firstly, the algorithm accelerates the convergence speed of the routing algorithm and reduces the number of route discovery packets by introducing an offset coefficient of the transition probability. Then, based on the energy consumption rate, the remaining lifetime of nodes (RLTn) is considered. The position and velocity information predicts the remaining lifetime of the link (RLTl). The algorithm combines RLTn and RLTl to design the pheromone generation method, which selects the better quality path according to the transition probability to ensure continuous data transmission. As a result, the energy consumption in the network is balanced. The simulation results show that compared to the Ad Hoc on-demand multipath distance vector (AOMDV) algorithm with multipath routing and the Ant Hoc Max-Min-Path (AntHocMMP) algorithm in consideration of node energy consumption and mobility, the IEMACO algorithm can reduce the frequency of route discovery and has lower end-to-end delay as well as packet loss rate especially when nodes move, and can extend the network lifetime.
To mitigate the frequent link breakage and node death caused by node mobility and energy constraints in mobile ad-hoc networks, we propose an energy-balanced routing algorithm for energy and mobility greedy perimeter stateless routing (EM-GPSR) based on geographical location. In the proposed algorithm, the forward region is divided into four sub-regions. Then, according to the remaining lifetime of each node and the distance between the source node and the destination node, we select the next-hop node in the candidate sub-regions. Since the energy consumption rate of the node is taken into account, the next-hop selection favors the nodes with longer remaining lifetimes. Simulation results show that compared with conventional greedy perimeter stateless routing (GPSR) and speed up-greedy perimeter stateless routing (SU-GPSR) routing algorithms, the proposed algorithm can lead to a lower end-to-end delay, longer service time, and higher transmission efficiency for the network.
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