An Adaptive Fish Swarm‐Based Mobile Coverage in WSNs

Abstract

Swarm intelligent algorithms are embedded into sensor networks to achieve perfect coverage with minimal cost. However, these methods are often highly complex and easily fall into the local optimum when balancing coverage and resource consumption. We introduce adaptive improved fish swarm optimization (AIFS) that extricates each node from the local optimum and reduces overlap and overflow coverage. Drawing on the habits of fish, AFIS ensures node mobility with respect to the food concentration at a certain point. Node dispersion shows good compromise under coordination by two presented parameters, namely, food concentration and crowd density. In addition to inheriting properties from traditional fish swarm, the initial random nodes become dispersed without overflow in assisting the proposed jumping and dodging behavior. The resulting network avoids potential local optima and improves the network boundary coverage efficiency. The convergence speed and efficiency of AIFS are verified. Extensive simulation experiments reveal that an improved coverage gain is obtained, and computation cost and overflow waste are reduced.

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

“…It can recognize coverage holes and label the border nodes of coverage holes effectively. The authors in [129] introduced the adaptive improved fish swarm optimization algorithm (AIFS) that extricated each node from the local optimum and reduced the overlap and overflow coverage. Drawing on the habits of fish, AFIS ensured the node mobility with respect to the food concentration at a certain point.…”

Wireless Sensor Network Deployment Optimization Based on Two Flower Pollination Algorithms

“…This scheme only addresses the deployment strategy under ideal conditions, but not the complex environment. In [10], the coverage rate is the optimization goal, and the adaptive improved fish swarm algorithm (AIFS) is used to optimize the sensor deployment, which significantly improves the network coverage area and reduces the energy consumption, but only for homogeneous sensors and the ideal deployment environment.…”

“…In this paper, the LAPJV algorithm in [27] is adopted for optimal assignment, so that the sum of the moving distance of all nodes is minimum, that is, the total energy consumption is minimum. Reference [9] mentioned that the energy consumed by the node's moving distance can be described as: (10) Assuming that the total moving distance is D all , l is the energy consumed per meter of movement, so the total energy consumed under the model is the product of l and D all .…”

“…Reference [10] mentions the calculation of node radiation overflow area, but it only discusses the situation in which node radiation overflows monitoring area. On this basis, this paper adds the discussion of node radiation overflowing to obstacles.…”

“…3) The proposed greedy crossover strategy optimizes the search with the current individual, surrounding individuals and optimal individuals, and effectively improving the precision of solution. For the second deployment circumstance, in [10], a model of node radiation overflow rate was proposed, but it only discusses the area of the node radiation overflow. Reference [12] proposed a network model with network coverage and energy consumption, but only considered a part of energy consumption.…”