4-MASCLE - Improved Coverage Aware Clustering with Self Healing Abilities

IEEE 5th International Symposium on Wireless Pervasive Computing 2010

2010

Self Cite

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspects of WSN research is location estimation. A good solution of fine grained localization is the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation. The latter is performed on constrained sensor nodes, finalizing the localization by adding locale knowledge. This approach lacks for large WSNs, because cost of communication and computation theoretically increases with network size. In practice the approach is even unusable for large WSNs. An important assumption of DLS is that each blind node is able to communicate with each beacon node to receive the precalculation and to determine distances to beacon nodes. This restriction have been overcome by scalable DLS (sDLS), which enabled to use the idea of DLS in large WSN for the first time.Although, sDLS has lower cost of computation than DLS, for large networks, this cost, caused by matrix updates, is pretty high. In this work an adaptation of sDLS is presented, which dramatically reduces cost of computation by circumventing matrix updates as often as possible.

Section: Discussionmentioning

confidence: 99%

Section: A Arithmetic Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improvements on scalable Distributed Least Squares localization for large Wireless Sensor Networks

IEEE 5th International Symposium on Wireless Pervasive Computing 2010

2010

Self Cite

“…Another energy-aware WSN organization strategy is investigated in [3] and [4] with the cluster-based redundancy exploiting MASCLE (Mutual Assistance in a Cluster Environment) approach.…”

Section: Introductionmentioning

confidence: 99%

Hex-MASCLE – hexagon based clustering with self healing abilities

2011 IEEE Wireless Communications and Networking Conference

2011

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In large wireless sensor networks, low energy consumption is a major challenge. Hence, deployed nodes have to organize themselves as energy efficient as possible to avoid unnecessary sensor and transceiver operations. The energy conserving operations are limited by the task of the network, usually the network has to guarantee complete functionality during its lifetime.The contribution of this paper completes the functionality-aware and energy-efficient clustering algorithm family MASCLE by two innovative algorithms. As already given by the MASCLEalgorithms, the proposed Hex-MASCLE algorithms combine advantages of temporal and spatial network fragmentation. In contrast to previous approaches, the shapes of the basic cells are given by regular hexagons, similar to honeycombs. In the present work, two possible versions for hexagon-based clustering with self-healing abilities are proposed and evaluated.As result, the applying sensor network achieve a significant improve of network lifetime. Additionally, the algorithms are more fault-tolerant against localization errors.

“…In many applications of WSN, knowledge of nodes' locations is mandatory for a meaningful interpretation of the data sensed. Location-awareness is not only necessary to assign a location to measured values but also to perform geographic routing [1] [2] or location based clustering [3]. Due to existing limitations in terms of size and energy consumption, local positioning within the network is preferred over common positioning systems like GPS.…”

Section: Introductionmentioning

confidence: 99%

sDLSne - Improved scalable Distributed Least Squares localization with minimized communication

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

2010

Self Cite

Wireless Sensor Networks (WSNs) have been of high interest during the past couple of years. One of the most important aspects of WSN research is location estimation. As a good solution of fine grained localization Reichenbach et al. introduced the Distributed Least Squares (DLS) algorithm, which splits the costly localization process in a complex precalculation and a simple postcalculation which is performed on constrained sensor nodes to finalize the localization by adding local knowledge. This approach lacks for large WSNs, because cost of communication and computation theoretically increases with the network size. In practice the approach is even unusable for large WSNs. This restriction have been overcome by scalable DLS (sDLS), which enabled to use the idea of DLS in large WSNs for the first time. Although, sDLS outperforms DLS for large networks, cost of communication and computation is initially higher for small networks, caused by data updates. The approach, presented in this work, dramatically reduces cost of communication of sDLS. Additionally, a new approach of distance estimation is applied to original DLS. In contrast to earlier simulations, this leads to improved localization, which is used for fairer comparison.