H. Jamali Rad, B. Abolhassani, M. Abdizadeh,
Volume 8, Issue 3 (9-2012)
Abstract
In this paper, we study the problem of power efficient tracking interval management for distributed target tracking wireless sensor networks (WSNs). We first analyze the performance of a distributed target tracking network with one moving object, using a quantitative mathematical analysis. We show that previously proposed algorithms are efficient only for constant average velocity objects however, they do not ensure an optimal performance for moving objects with acceleration. Towards an optimal performance, first, we derive a mathematical equation for the estimation of the minimal achievable power consumption by an optimal adaptive tracking interval management algorithm. This can be used as a benchmark for energy efficiency of these adaptive algorithms. Second, we describe our recently proposed energy efficient blind adaptive time interval management algorithm called Adaptive Hill Climbing (AHC) in more detail and explain how it tries to get closer to the derived optimal performance. Finally, we provide a comprehensive performance evaluation for the recent similar adaptive time interval management algorithms using computer simulations. The simulation results show that using the AHC algorithm, the network has a very good performance with the added advantage of getting 9 % closer to the calculated minimal achievable power consumption compared with that of the best previously proposed energy efficient adaptive time interval management algorithm.
A. Pathak,
Volume 16, Issue 4 (12-2020)
Abstract
It is very difficult and expensive to replace sensor node battery in wireless sensor network in many critical conditions such as bridge supervising, resource exploration in hostile locations, and wildlife safety, etc. The natural choice in such situations is to maximize network lifetime. One such approach is to divide the sensing area of wireless sensor network into clusters to achieve high energy efficiency and to prolong network lifetime. In this paper, an Artificial Bee Colony Inspired Clustering Solution (ABCICS) is introduced. The proposed protocol selects the head of the cluster with optimal fitness function. The fitness function comprises the residual energy of node, node degree, node centrality, and distance from base station to node. When cluster-head with high energy node transmits the data to the base station, it further minimizes the energy consumption of the sensor network. The presented protocol is compared with LEACH, HSA-PSO, and MHACO-UC. Simulation experiments show the effectiveness of our approach to enhance the network lifetime.
M. H. Adhami, R. Ghazizadeh,
Volume 19, Issue 1 (3-2023)
Abstract
A novel hybrid method for tracking multiple indistinguishable maneuvering targets using a wireless sensor network is introduced in this paper. The problem of tracking the location of targets is formulated as a Maximum Likelihood Estimation. We propose a hybrid optimization method, which consists of an iterative and a heuristic search method, for finding the location of targets simultaneously. The Levenberg-Marquardt (LM) algorithm is used for iterative search, while the Particle Swarm Optimization (PSO) is used for the heuristic search. We use the maximum sensors separating distance-grouping algorithm (G-MSSD), which was introduced in our previous work, to generate initial guesses for search algorithms. The estimates of both methods are compared and the best one is selected as the final estimation. We demonstrate the accuracy and performance of our new tracking method via simulations and compare our results with the Gauss-Newton (GN) method.
