Localization algorithm based on difference estimation for wireless sensor networks in Pervasive Computing Environment

Deng, Zhongliang; Ren, Weizheng; Xu, Lianming

doi:10.1109/icpca.2008.4783635

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Upon receipt of this message each node in the network estimates its distance to base station (D tobs ) using RSSI. Due to work in [21] [22] and [23] it is fairly assumed that the nodes can estimate their distances to transmitter, using RSSI. Again it is assumed that base station knows its location with respect to the network and its distance from the network.…”

Section: A Base Station Distance Estimationmentioning

confidence: 99%

Distributed Clustering with Restricted Number of Cluster heads for Energy Efficient Data Gathering in Wireless Sensor Networks

Deosarkar¹,

Yadav²,

Yadav³

2010

IJET

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Energy being major constraint and data gathering the core operation, considerable attention has been given to energy efficient data gathering in wireless sensor network research and clustering has emerged as an efficient architecture to achieve it. Low energy adaptive clustering hierarchy (LEACH), proposed in past, guided significant portion of this research and is used as a benchmark in this area. However, use of probabilistic and randomized approach for clusterhead selection results in their variable number, during different rounds of data gathering. Use of spread spectrum technique, to allow interference free parallel communication in different clusters, results in increased data size. The variable number of clusterheads requires the spreading factor to be set corresponding to probable maximum number of clusterheads, to avoid interference. This additional increase in data size increases amount of energy required to transmit it to the base station, directly affecting network lifetime and energy efficiency, both. To reduce the spreading factor and to improve data gathering efficiency a distributed clustering scheme is proposed in this paper, which restricts the number of clusterheads whenever more than the desired numbers of nodes select them to acquire the role. Results of the simulations, carried out in NS2, show that with the proposed scheme the amount of data received at base station per unit of energy consumed is significantly more than the benchmark scheme used. Proposed scheme is observed to provide improvement of 58% in network lifetime and 48.76% in energy efficiency over LEACH.

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Section: A Base Station Distance Estimationmentioning

confidence: 99%

Distributed Clustering with Restricted Number of Cluster heads for Energy Efficient Data Gathering in Wireless Sensor Networks

Deosarkar¹,

Yadav²,

Yadav³

2010

IJET

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“…In [18] experimental work was carried out to analyze the principle of RSSI based node localization, with two different node modules, using trilateration and Maximum Likelihood Estimation (MLE), which claims to have the accuracy of MLE estimation to be comparatively better and highlights that the multihop accuracy is greatly affected by the error in distance measurement. Localization algorithm in [19] combines difference correction method with MLE and claims the localization error to be less than 2.5 meters. However, these techniques [18] [19] use beaconing nodes to estimate the locations.…”

Section: Related Workmentioning

confidence: 99%

“…Most economical and energy efficient solution among the localization techniques is the Received signal strength (RSS) method which determines the location of unknown node by measuring the received signal strength between the two communicating devices but the determined locations are said to be inaccurate. However, the recent work in [18], [19] and [20] motivates its use. In [18] experimental work was carried out to analyze the principle of RSSI based node localization, with two different node modules, using trilateration and Maximum Likelihood Estimation (MLE), which claims to have the accuracy of MLE estimation to be comparatively better and highlights that the multihop accuracy is greatly affected by the error in distance measurement.…”

Section: Related Workmentioning

confidence: 99%

“…Each of these types has its own merits and demerits, but where energy is the major constraint RSSI based schemes is a better solution but the distances estimated with RSSI measurements are not said to be accurate. But the recent study in [18] motivates the use of Received Signal Strength Indicator (RSSI) as distance estimator and the correction mechanisms proposed in [19] and [20] further biases the view in its favor. Further the localization schemes in the literature may be classified as: with and without anchor nodes.…”

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confidence: 99%

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Uniform Cluster Distribution with Anchor Free Node Localization for Energy Efficient Data Gathering in Wireless Sensor Networks

Deosarkar¹,

Yadav²

2009

IJCEE

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Abstract-Data gathering is an important research area in wireless sensor networks, being it the core operation. As an efficient topology management approach clustering is shown to have advantage, over other network architectures, due to its capability to reduce the communication burden and utilization of the processing features of the sensor nodes for local data aggregation. The algorithms proposed for this purpose may be classified as centralized or distributed based on the place of decision making. Centralized algorithms are revealed to lack the scalability in terms node density and distributed algorithms have an edge over them in such scenario. For distributed algorithms, it is needed to distribute the clusters uniformly over the network area, for them to be more energy efficient. Acquisition of the knowledge about their locations, by the sensor nodes, is expected to prove to be useful for this distribution but due to their limited energy resources and the associated cost providing location finding capabilities, like GPS or anchor nodes, is not advisable. If provided such facility, the error if occurs, due to its own limitations, may get propagated over the entire network. So, a scheme is proposed in this paper, for uniform distribution of the clusters over the network area in which the sensor nodes, utilizing anchor free and distributed localization mechanism, estimate their relative positions and based on these estimates assign to different clusters with an expectation from RSSI to provide the necessary support for required distance estimation. Sensor nodes, with knowledge of their distances to the base station, estimate their locations through collaborative efforts of small percentage of total nodes, with minimum of communication burden. Results of the simulations carried out in NS2, show that the proposed scheme has a potential to distribute the clusters uniformly over network area.

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Enhancement of the QoS in an OFDMA/VLC system

Seguel

Soto

Iturralde

et al. 2016

2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)

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Localization algorithm based on difference estimation for wireless sensor networks in Pervasive Computing Environment

Cited by 4 publications

References 4 publications

Distributed Clustering with Restricted Number of Cluster heads for Energy Efficient Data Gathering in Wireless Sensor Networks

Distributed Clustering with Restricted Number of Cluster heads for Energy Efficient Data Gathering in Wireless Sensor Networks

Uniform Cluster Distribution with Anchor Free Node Localization for Energy Efficient Data Gathering in Wireless Sensor Networks

Enhancement of the QoS in an OFDMA/VLC system

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