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Rajesh, G.
- Localization for Mobile Sensor Networks Based on MCL Method using HW Prediction
Authors
1 Madras Institute of Technology, Anna University, Chennai, IN
Source
Wireless Communication, Vol 1, No 1 (2009), Pagination: 21-26Abstract
Awareness of the physical location for each node is required by many wireless sensor network applications. The location awareness is required by many sensor network applications, but it is often too expensive to include GPS receiver in a sensor network node. Therefore, Localization schemes make use of the Seed nodes that knows their location and protocols, thereby other nodes estimate their positions from the messages they received from it. The crux part is estimating the location of the mobile nodes and seeds keeping in mind all the constraints of the sensor nodes (energy, network, memory etc.,). Although mobility appears to make localization difficult, we adapt sequential Monte carlo Localization method along with minimum spanning tree concept to prove efficient localization. This approach does not need any extra hardware on the nodes and seeds even when the movement of seeds are uncontrollable.Keywords
Localization, Wireless Sensor Networks, Mobility, Monte-Carlo Method, Minimum Spanning Tree, Holt-Winters Prediction, Robot Localization, Range Free Algorithms, Prediction Phase, Exponentially Weighted Moving Average.
- Low Power Localization with Location Verification for Wireless Sensor Networks
Authors
1 Department of Information Technology, MIT Campus, Anna University, Chennai, IN
Source
Wireless Communication, Vol 1, No 1 (2009), Pagination: 45-51Abstract
The purpose of localization in Wireless Sensor Network is to establish the position of the nodes at a particular point of time as the nodes are mobile. Locating information is not only used to realize it s position but it can also be used to increase outing capabilities and load balancing of network. This paper proposes the combination of clock algorithm and rectangle truncation (RT) localization algorithm for locating objects even in a fading-signal sensor environment. The paper further presents a location verification mechanism to increase the accuracy and consistency of the estimated object location. It can be classified as low power algorithm as it does not need any special hardware and the computations are minimized.