Abstract-Wireless sensor networks are widely adopted in many location-sensitive applications including disaster management, environmental monitoring, military applications where the precise estimation of each node position is inevitably important when the absolute positions of a relatively small portion as anchor nodes of the underlying network were predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance/path measures. Most of the existing localization methods focus on using different heuristic-based or mathematical techniques to increase the precision in position estimation. However, there were recent studies showing that nature-inspired algorithms like the ant-based or genetic algorithms can effectively solve many complex optimization problems. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm, as a post-optimizer into some existing localization methods such as the Ad-hoc Positioning System (APS) to further improve the accuracy of their position estimation. Obviously, our proposed MGA is highly adaptable and easily integrated into other localization methods. Furthermore, the remarkable improvements attained by our proposed MGA on both isotropic and anisotropic topologies of our simulation tests prompt for several interesting directions for further investigation.
Abstract-Recently, a time synchronization algorithm called Pairwise Broadcast Synchronization (PBS) is proposed. With PBS, a sensor can be synchronized by overhearing synchronization packet exchange among its neighbouring sensors without sending out any packet itself. In an one-hop sensor network where every node is a neighbour of each other, a single PBS message exchange between two nodes would facilitate all nodes to synchronize. However, in a multi-hop sensor network, PBS message exchanges in several node pairs are needed in order to achieve network-wide synchronization. To reduce the number of message exchanges, these node pairs should be carefully chosen. In this paper, we investigate how to choose these "appropriate" sensors aiming at reducing the number of PBS message exchanges while allowing every node to synchronize. This selection problem is shown to be NP-complete, for which the greedy heuristic is a good polynomial-time approximation algorithm. Nevertheless, a centralized algorithm is not suitable for wireless sensor networks. Therefore, we develop a distributed heuristic algorithm allowing a sensor to determine how to synchronize itself based on its neighbourhood information only. The protocol is tested through extensive simulations. The simulation results reveal that the proposed protocol gives consistent performance under different conditions with its performance comparable to that of the centralized algorithm.
To determine the geographical positions of sensors, numerous localization algorithms have been proposed in recent years. The positions of sensors are inferred from the connectivity between sensors and a set of nodes called anchors which know their precise locations. We investigate the effect of adverse placement and density of anchors on the accuracies of different algorithms. We develop an algorithm called HyBrid Localization (HyBloc) to provide reliable localization service with a limited number of clustered anchors. HyBloc is distributed in nature with reasonable message overhead. Through simulations, we demonstrate that HyBloc provides more accurate location estimates than some existing distributed algorithms when there are only a few anchors. HyBloc also performs well when anchors are clustered together.
Wireless sensor networks have wide applicability to many important applications including environmental monitoring and military applications. Typically with the absolute positions of only a small portion of sensors predetermined, localization works for the precise estimation of the remaining sensor positions on which most locationsensitive applications rely. Intrinsically, localization can be formulated as an unconstrained optimization problem based on various distance/path measures, for which most of the existing work focus on increasing its precision through different heuristic or mathematical techniques. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm (MGA), and its variant as postoptimizers to enhance the precision of existing localization methods including the Ad-hoc Positioning System. Our adapted MGA and its variants can easily be integrated into different localization methods. Besides, the prototypes of our evolutionary approach gained remarkable results on both uniform and anisotropic topologies of the simulation tests, thus prompting for many interesting directions for future investigation. Our Adapted Micro-Genetic AlgorithmEvolutionary algorithms (EA) [4,14] are local search methods capable of efficiently solving complex constrained or unconstrained optimization problems (OPT) [1]. Localization [3,5,6,7,8,9,10,11,12,13] in wireless sensor networks is intrinsically an unconstrained OPT. However, in handling relatively sizable (with ≥ 500 variables) or complicated OPTs, the total computational costs can be drastically reduced by focusing the search only on a reasonably small population of chromosomes without much impact on the search efficiency. In fact, it has been reported that MGAs can find solutions with fewer iterations than evolutionary algorithms (EAs) with larger population sizes for some problems [4]. Therefore, in this paper, we consider a micro-genetic algorithm (MGA) based on a small population size (usually < 30) to tackle the challenging localization problems in wireless sensor networks. Basically, a variable in an OPT is usually represented by a gene in a MGA. A chromosome, consisting of all the genes, is used to denote a valuation for all the variables. As inspired by the nature, an EA or MGA basically performs a parallel local search in different parts of the search space through maintaining a population of chromosomes for iterative improvements over the successive generations. Figure 1 shows the pseudo-code MGA(P Z, MZ, fitness()) initialize a small P opulation of P Z chromosomes repeat select the best MZ chromosomes ∈ P opulation P opulation := ∅ repeat produce {offspring} by descend-based genetic opr. P opulation := P opulation ∪ {offspring} until (sizeof (P opulation) = P Z) until (P opulation is converged or resource limit is exceeded) Figure 1. The Convergence Procedure of our Adapted MGA of our adapted MGA as a post-optimizer for further position refinement in any localization method. Given the population size P Z, the size M Z of the...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.