Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment

Abstract

In recent years, wireless sensor networks (WSNs) have been widely applied to sense the physical environment, especially some difficult environment due to their ad-hoc nature with self-organization and local collaboration characteristics. Meanwhile, the rapid development of intelligent vehicles makes it possible to adopt mobile devices to collect information in WSNs. Although network performance can be greatly improved by those mobile devices, it is difficult to plan a reasonable travel route for efficient data gathering. In this paper, we present a travel route planning schema with a mobile collector (TRP-MC) to find a short route that covers as many sensors as possible. In order to conserve energy, sensors prefer to utilize single hop communication for data uploading within their communication range. Sojourn points (SPs) are firstly defined for a mobile collector to gather information, and then their number is determined according to the maximal coverage rate. Next, the particle swarm optimization (PSO) algorithm is used to search the optimal positions for those SPs with maximal coverage rate and minimal overlapped coverage rate. Finally, we schedule the shortest loop for those SPs by using ant colony optimization (ACO) algorithm. Plenty of simulations are performed and the results show that our presented schema owns a better performance compared to Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-hop Weighted Revenue (MWR) algorithm and Single-hop Data-gathering Procedure (SHDGP).

A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

“…In [12], the authors proposed a deployment of wireless sensor network nodes based on dynamic sensing distance and found the combination of sensor nodes and radius through traversal algorithm. In [13], the authors proposed a travel route planning schema with a mobile collector (TRP-MC) to find a short route that covers as many sensors as possible for WSNs; the proposed algorithm had a high coverage rate and prolonged the network's lifetime.…”

“…In equation (13), ξ(T k+1 ) is the receiving probability when the temperature is T k+1 . (3) Judge whether it is completed according to the constraints.…”

“…(3) Calculate the fitness value of each grey wolf individual in the initial population, select the top three individuals of fitness value, and set them as X α , X β , and X δ , respectively. (4) Update the position of the grey wolf through equations (13) and (14), recompare the adaptive value, calculate the value of the control parameter α, and update the convergence factor A and the swing factor C according to equations (8) and (9). (5) Calculate the fitness value of each grey wolf again, and update X α , X β , X δ , then select the grey wolf individuals with the better fitness value.…”

“…e problem of energy consumption of the network has always been a hot issue in WSNs research, which also restricts the research progress of the coverage of the network. e common methods are reasonable deployment of the sensor nodes, sleep/wake-up control, and power control in the network to Update each grey wolf individual position according to equations (13) and 14Calculate the fitness of each grey wolf, and update the optimal solution, suboptimal solution, and third optimal solution Computational Intelligence and Neuroscience maximize the energy utilization of the network and realize the optimal coverage of the network. e energy consumption model of WSNs uses the same wireless communication model as the LEACH algorithm, and the formula for energy consumption of sensor nodes sending data is shown in the following equation:…”

Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments

“…With the progressive adoption of wireless sensor networks, radio-planning tasks have been adapted to optimize network configuration [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. In this case, the specific considerations given by the wireless sensor network (WSN) operation have to be taken into account, such as the random node location, the existence of predominant non-line-of-sight operation conditions or the inherent energy consumption and processing constraints, among others.…”