Time-Bounded Essential Localization for Wireless Sensor Networks

Conference PaperinIEEE/ACM Transactions on Networking 21(2):3-12 · July 2010with6 Reads
DOI: 10.1109/TNET.2012.2200107 · Source: DBLP
Conference: Fifth International Conference on Networking, Architecture, and Storage, NAS 2010, Macau, China, July 15-17, 2010
Abstract
In many practical applications of wireless sensor networks, it is crucial to accomplish the localization of sensors within a given time bound. We find that the traditional definition of relative localization is inappropriate for evaluating its actual overhead in localization time. To address this issue, we define a novel problem called essential localization and present the first rigorous study on the essential localizability of a wireless sensor network within a given time bound. Additionally, we propose an efficient distributed algorithm for time-bounded essential localization over a sensor network and evaluate the performance of the algorithm with analysis and extensive simulation studies.
    • "Additionally, it provides a complete parameter study that was formulated to enhance the performance of the multi-objective PSO (MOPSO). The key novelty of this paper is the optimization of the power consumption of the whole network without the need to cluster or build any small sensor islands such as in [9] or in [5]. This study takes advantage of the functionalities of toady's WSNs nodes to enhance the performance of the whole network, the ZigBee technology of transceivers in wireless nodes made that possible by allowing us to use multiple transmission power levels, where the different variants of PSO were used to programmically change the transmission level after the evaluation of the designed fitness functions. "
    [Show abstract] [Hide abstract] ABSTRACT: Trilateration-based localization (TBL) has become a corner stone of modern technology. This study formulates the concern on how wireless sensor networks can take advantage of the computational intelligent techniques using both single- and multi-objective particle swarm optimization (PSO) with an overall aim of concurrently minimizing the required time for localization, minimizing energy consumed during localization, and maximizing the number of nodes fully localized through the adjustment of wireless sensor transmission ranges while using TBL process. A parameter-study of the applied PSO variants is performed, leading to results that show algorithmic improvements of up to 32% in the evaluated objectives.
    Full-text · Article · Jul 2014
    • "We implement D-MDS by using MATLAB simulator. Then, we evaluate the proposed algorithm by comparing it to the time-bound localization algorithm based on Trilateration method [7]. "
    [Show abstract] [Hide abstract] ABSTRACT: Many applications of Wireless Sensor Networks (WSN) require to achieve the positions of the sensor nodes within a given time bound. In this paper we study the relative and physical localizability of WSN in a given time bound. We propose a new distributed and time bounded localization algorithm based on Multidimensional Scaling (MDS) method in WSN called D-MDS localization time algorithm. We compare the proposed algorithm to the existing algorithm based on the well-known Trilateration method. The simulation results show that the proposed algorithm outperforms the existing approach based on Trilateration method in terms of the number of localized nodes in the network and the number of anchors required to physically localize the sensors. The D-MDS localization time algorithm localizes a large number of nodes for a low node degree in a time bound. Moreover it is able to physically localize the network with a low number of anchors compared with the algorithm based on Trilateration method.
    Full-text · Conference Paper · Jun 2014 · International Journal of Distributed Sensor Networks
    • "where (x j , y j ) denotes the Cartesian coordinates of node j. We can work out E i j-result With H result and some other parameters as follows: (19) where α is the data compression ratio. When E i j-storage , E i j-query , and E i j-result are all worked out, E i j in Formula (6) can be worked out. "
    [Show abstract] [Hide abstract] ABSTRACT: In-network data storage and retrieval is one of the most important problems in wireless sensor networks. Many schemes have been proposed to solve this problem. However, most of them do not take the frequencies of event and query into consideration. In fact, the frequencies of event and query are very important factors in real applications of in-network data storage and retrieval. In this paper, we introduce a virtual-ring-based data storage and retrieval scheme, which is called VRS, to solve the problem. VRS divides the whole sensor network field into some virtual rings. According to the frequencies, one of the virtual rings is selected out as the rendezvous ring, which plays the role of a bridge between the information consumers and the information producers. Extensive experiments have been done to evaluate the performance of the proposed scheme, VRS. Simulation results show that VRS outperforms the existing work in load balance, delay of data storage and retrieval, and the lifetime of the sensor networks.
    Full-text · Article · Nov 2012
Show more