Particle Swarm Optimized Power Consumption of Trilateration
📝 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.
💡 Analysis
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.
📄 Content
International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014 DOI:10.5121/ijfcst.2014.4401 1
PARTICLE SWARM OPTIMIZED POWER CONSUMPTION OF TRILATERATION
Hussein S. Al-Olimat1, Robert C. Green II2, Mansoor Alam1,
Vijay Devabhaktuni1 and Wei Cheng3
1EECS Department, College of Engineering, University of Toledo, Toledo, OH, USA 2Department of Computer Science, Bowling Green State University, Bowling Green, OH, USA 3Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA, USA
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.
KEYWORDS
WSN, Trilateration, Localization, PSO, MOPSO, ZigBee, RSSI.
- INTRODUCTION
Wireless sensor networks (WSN) consist of many sensing devices which are distributed inside of a given area. Sensors in the network carry out different tasks such as recording weather conditions, sensing motion, or recording sounds in addition to many other tasks. In WSNs, sensors cooperate with each other to formulate a fully connected network to allow information sharing between the network nodes. Such networks have many applications both for civilian and military purposes, the position of sensing devices that record the humidity of a place or the position of a military vehicle in a war zone are two examples of such applications where knowing the location of the information source is very important.
Wireless sensor nodes in WSNs may be positioned permanently or dynamically in a field depending on the localization protocol and nodes functionalities as thoroughly discussed in [1]. For permanent localization scenarios, knowing the location of the sensor is not a problem throughout the life time of the network; but in dynamic networks, localizing nodes can be time and power consuming and, in some scenarios, a lack of accuracy may occur. To solve problems of localization accuracy and increase the number of localized nodes in a time critical localization scenarios, meta-heuristic solutions and novel range-based iterative localization algorithms have previously been proposed in [2–6]. Additionally, to allow mapping localization solutions into real world scenarios relaxations to the localization problem regarding the nodes ordering, anchor nodes distribution, or global information sharing were also discussed in [7, 8]. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
2 Trilateration-based localization (TBL) and Multilateration-based localization (MBL) techniques are among the well-known and most used methods for localization. In this study, the various performance aspects of the TBL algorithm are examined through the application of single and multi-objective variants of particle swarm optimization (PSO). We implemented three version of PSO in this study to allow nodes to vary the transmission power level when broadcasting messages during localization. Trade-offs between multiple objectives — the number of transmitted messages, number of localized nodes, power consumption and the time needed to localize as many nodes as possible — are studied.
However, for the sake of demonstrating the applicability of our methods, ranging and location estimations were both assumed as being correctly calculated with minimal errors, which means that this study do not really discuss the localization accuracy or signal noises. Instead, the methods of this study try to allow WSNs to reduce the overall power consumption of the localization process without affecting the localization time or localizability (i.e. the number of localized nodes). So the meta-heuristic methods implemented in this paper allow one to find optimal and balanced solutions in terms of energy consumption by minimizing the number of messages sent and localization time while trying not to negatively affect the localizability.
The paper present the results of three implemented versions of the particle swarm optimization and clearly show the performance of them while trying to optimize the WSN work. Additionally, it provides a complete parameter study th
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