Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

Abstract

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

“…In [176] the authors proposed an improved artificial bee colony (iABC) metaheuristic with an improved search equation to enhance its exploitation capabilities, the proposed clustering protocol outperforms other algorithms based protocols on the basis of packet delivery, throughput, energy consumption and prolong the network lifetime. A novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address the energy consumption problem [177]. In [178] the authors proposed a next hop selection mechanism for VANETs which takes the heterogeneous environment into consideration, the minimum hop count prediction method is firstly proposed to help the current packet-carrying vehicle node to estimate the minimum hop counts required from each neighbor to the destination.…”

A comprehensive survey on nature‐inspired algorithms and their applications in edge computing: Challenges and future directions

“…Another PSO-aware routing protocol has been developed by Yang et al in a multiaccess network for efficient data transmission between two edge devices. 134 The main purpose of the work is to minimize the latency and energy usage of the network. Li et al have introduced a PSO-aware fuzzy clustering strategy in edge networks for utilizing the network parameters and edge resources efficiently.…”

“…The outcomes of this strategy are to reduce the communication overhead while utilizing the edge resources and network bandwidth efficiently. Another PSO‐aware routing protocol has been developed by Yang et al in a multiaccess network for efficient data transmission between two edge devices 134 . The main purpose of the work is to minimize the latency and energy usage of the network.…”

A Social Potential Fields Approach for Self-Deployment and Self-Healing in Hierarchical Mobile Wireless Sensor Networks

“…However, when nodes start to die, some node clusters might get disconnected from the sink node. Nodes close to the sink node usually have a higher traffic load and fail first, whereas nodes on the boundaries may have a longer life [ 19 ]. However, if they are not compelled to move closer to the sink node after part of the network has failed, they might get disconnected from the rest of the MWSN.…”

“…Some approaches for deployment in WSN are based on deliberative algorithms to optimize efficiency [ 15 , 16 , 17 , 18 ] and also for self-healing [ 3 , 19 ]. Similarly, self-healing may rely on the deliberative relocation of nodes in the network [ 20 ].…”