Self-organizing sensor networks for integrated target surveillance

Biswas, Pradipta; Phoha, Shashi

doi:10.1109/tc.2006.130

Cited by 52 publications

(38 citation statements)

References 20 publications

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“…Clustering in ad-hoc sensor networks is an effective technique for achieving scalability and prolonged network lifetime [4][5][6][7][8][9]. A well-known clustering algorithm for continuous data centric application gathering sensor networks is the Low Energy Adaptive Clustering Hierarchy (LEACH) mechanism [5].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the above mentioned schemes [5][6][7][8][9], offer disadvantages by incorporating task driven criteria for sensor management. In [5], CHs are rotated according to metrics such as, battery energy level, transmission power or network connectivity, while in [6] CH's with the greatest received energy are selected, according to the defined signal threshold.…”

Section: Related Workmentioning

confidence: 99%

“…Such criteria offer drawbacks due to random rotation of cluster heads (CHs), by introducing considerable re-setup delay of the clusters, increasing communication overhead (energy consumption) and congestion (latency). This can also lead to degradation in surveillance performance by depleting network resources rapidly as in [5] or introducing higher target location errors, as in [6][7][8][9], in conditions where the sensing environment is corrupted with high levels of noise, leading to greater group instability.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, [5][6][7][8][9] do not consider the potential resource benefit savings that can be achieved through networking according to the derived understanding of the external operating environment, from captured sensor data. Recent initiatives have begun to address the problem of organising nodes according to the level of understanding about their environment, so that further improvements in operational and network performance can be achieved [10][11][12], however this view is mostly taken towards the network and medium access control layers.…”

Section: Related Workmentioning

confidence: 99%

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VIGILANT+: mission objective interest groups for wireless sensor network surveillance applications

Ghataoura

Mitchell²,

Matich

2011

IET Wireless Sensor Systems

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In this paper a system termed VIGILANT + is outlined, which utilises situation awareness for the purposes of enabling distributed, autonomic, sensor management, so that savings on consumption of network resources can be achieved. VIGILANT + is a novel proposition allowing deployed, unattended, wireless sensor nodes to selforganise into dynamic groups and self-manage their transmissions efficiently, according to a current common mission objective. Firstly, a distributed situation assessment system named PORTENT models, detects and characterises potential situations occurring within an uncertain environment, using the metric, quality of surveillance information (QoSI). Secondly, a Bayesian Belief Network (BBN) is utilised to understand and analyse the significance associated with the potential situation, primarily to enable deployed sensors to selforganise and assign themselves to mission objectives autonomously. Thirdly, a system is introduced for distributed autonomic transmission control, which enables the efficient management of sensor network resource consumption. Simulations have been undertaken to verify the integrated VIGILANT + concepts and to demonstrate the effectiveness of the proposed approach in improving network efficiency, without compromising the presentation of mission surveillance utility.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

VIGILANT+: mission objective interest groups for wireless sensor network surveillance applications

Ghataoura

Mitchell²,

Matich

2011

IET Wireless Sensor Systems

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“…Given an event appearing in a WSN, the event detection latency is to model the time that it takes for the WSN to be aware of the event. Such latency is an important metric to measure the monitoring capability of a WSN's deployment for real-time applications such as surveillance [8][9][10] or object tracking [11][12][13].…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Using event detection latency to evaluate the coverage of a wireless sensor network

Wang¹,

Cheng²,

Tseng³

2007

Computer Communications

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A wireless sensor network (WSN) consists of many tiny and low-power devices deployed in a sensing field. One of the major tasks of a WSN is to monitor the surrounding environment and to detect events occurring in the sensing field. Given an event appearing in a WSN, the event detection latency is to model the time that it takes for the WSN to be aware of the event.In this work, we analyze the latency using a probabilistic approach under an any-sensor-detection and a k-sensor-detection models, where k > 1 is an integer. Such an analysis can be used as an index to evaluate a WSN's coverage and thus can help guide the deployment of a WSN. We also develop simulations to verify our analytical results.

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An Efficient Data Extraction Framework for Mining Wireless Sensor Networks

Rashid

Gondal

2016

Neural Information Processing

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A wide range of Sensor Networks (SNs) are deployed in real world applications which generate large amount of raw sensory data. Data mining technique to extract useful knowledge from these applications is an emerging research area due to its crucial importance but still it's a challenge to discover knowledge efficiently from the sensor network data. In this paper we proposed a Distributed Data Extraction (DDE) method to extract data from sensor networks by applying rules based clustering and association rule mining techniques. A significant amount of sensor readings sent from the sensors to the data processing point(s) may be lost or corrupted. DDE is also estimating these missing values from available sensor reading instead of requesting the sensor node to resend lost reading. DDE also apply data reduction which is able to reduce the data size while transmitting to sink. Results show our proposed approach exhibits the maximum data accuracy and efficient data extraction in term of the entire network's energy consumption.

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Self-organizing sensor networks for integrated target surveillance

Cited by 52 publications

References 20 publications

VIGILANT+: mission objective interest groups for wireless sensor network surveillance applications

VIGILANT+: mission objective interest groups for wireless sensor network surveillance applications

Using event detection latency to evaluate the coverage of a wireless sensor network

An Efficient Data Extraction Framework for Mining Wireless Sensor Networks

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