Mobile Target Detection in Wireless Sensor Networks With Adjustable Sensing Frequency

Hu, Yanling; Dong, Mianxiong; Ota, Kaoru; Liu, Anfeng; Guo, Minyi

doi:10.1109/jsyst.2014.2308391

Cited by 81 publications

(78 citation statements)

References 20 publications

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“…In addition, the value of path simplification coefficient λ is 1.5 through calculation. At this time, according to Formula (25), in the example, the value of δ 1 , δ 2 are 0.7, 0.3 respectively, the calculation process is as shown in the table below. In the table, CCP stands for the cost of creating a path, including the delay and energy consumption.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of stationary target or event monitoring [5,25], as shown in the network model shown in Figure 1, in this kind of research, sensor nodes are deployed in advance in the network, the event or target can be sensed by nodes nearby when a pre-defined event or target appears in the network. The sensing data is then sent to the sink through the shortest routing path.…”

Section: Target Detection In Stationary Sink Networkmentioning

confidence: 99%

“…Such monitoring is mainly applied to monitoring mobile targets [5,6,[23][24][25]. In such studies, when the mobile target is monitored by a node, the node will notify nodes of the next region that the target may move to.…”

Section: Tracking Mobile Targetmentioning

confidence: 99%

“…In the condition that hopCount source p reaches a threshold η, the cost of creating a path from source to p can be calculated according to Formula (24), and the cost of transmitting data from source to sink can also be caculated according to Formula (23). Therefore, the total cost is C tot = C qr δ 1 + C nqr δ 2 according to Formula (25). However, it is unnecessary to create a new path if transmitting data through the original path, and its cost is C old = C qr δ 2 .…”

Section: Theoremmentioning

confidence: 99%

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Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Zhao

Liu

et al. 2017

Symmetry

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Abstract:Transferring emergent target tracking data to sinks is a major challenge in the Industrial Internet of Things (IIoT), because inefficient data transmission can cause significant personnel and property loss. For tracking a constantly moving mobile target, sensing data should be delivered to sinks continuously and quickly. Although there is some related research, the end to end tracking delay is still unsatisfying. In this paper, we propose a Fast and Efficient Data Forwarding (FEDF) scheme for tracking mobile targets in sensor networks to reduce tracking delay and maintain a long lifetime. Innovations of the FEDF scheme that differ from traditional scheme are as follows: firstly, we propose a scheme to transmit sensing data through a Quickly Reacted Routing (QRR) path which can reduce delay efficiently. Duty cycles of most nodes on a QRR path are set to 1, so that sleep delay of most nodes turn 0. In this way, end to end delay can be reduced significantly. Secondly, we propose a perfect method to build QRR path and optimize it, which can make QRR path work more efficiently. Target sensing data routing scheme in this paper belongs to a kind of trail-based routing scheme, so as the target moves, the routing path becomes increasingly long, reducing the working efficiency. We propose a QRR path optimization algorithm, in which the ratio of the routing path length to the optimal path is maintained at a smaller constant in the worst case. Thirdly, it has a long lifetime. In FEDF scheme duty cycles of nodes near sink in a QRR path are the same as that in traditional scheme, but duty cycles of nodes in an energy-rich area are 1. Therefore, not only is the rest energy of network fully made use of, but also the network lifetime stays relatively long. Finally, comprehensive performance analysis shows that the FEDF scheme can realize an optimal end to end delay and energy utilization at the same time, reduce end to end delay by 87.4%, improve network energy utilization by 2.65%, and ensure that network lifetime is not less than previous research.

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Section: Resultsmentioning

confidence: 99%

Section: Target Detection In Stationary Sink Networkmentioning

confidence: 99%

Section: Tracking Mobile Targetmentioning

confidence: 99%

Section: Theoremmentioning

confidence: 99%

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Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Zhao

Liu

et al. 2017

Symmetry

Self Cite

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“…Every node in the network will be given a preferred bandwidth. Euclidian distance concept is used to identify the neighbours for a particular node [3]. Every node is assigned with a threshold value of the trust.…”

Section: System Modelmentioning

confidence: 99%

Reliable and Secured Routing in Wireless Sensor Networks using Active Trust

Sneha¹,

Akhila²

2017

IJCA

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A Wireless Sensor Network (WSN) comprises of devices that are autonomously distributed in space used to supervise the physical and environmental conditions with the help of sensors. They are mainly used in security critical applications such as monitoring the environment, tracking the surroundings and controlling areas. Security is an important requirement in WSNs. Because of the resource limitation and computational constraints the WSNs are transparent to various security attacks. Black hole attack is a serious attack which badly affects the packet transfer from source to destination. Here the Active Trust scheme is used to avoid the Black hole attack. The Active Trust scheme avoids the black holes through Active detection routing and data routing which improves the route security. The results obtained will indicate that the Active Trust scheme can achieve the same throughput as that of the normal Wireless Sensor Network without black holes. This shows the importance of Active Trust scheme in black hole attacks.

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A comprehensive analysis for fair probability marking based traceback approach in WSNs

Liu

2016

Security Comm Networks

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Currently, the analysis result of fair probability marking approach can only be got in the single‐source linear and single‐source tree network, which cannot meet the requirement of applications. This paper fills in this gap by providing an accurate marking probability analysis model for linear network, tree network, and planar network. Compare with the marking packets mechanism with equal marking probability, the analysis result of this paper shows that FPM mechanisms cannot only reduce convergence time up to 20%–60% for medium‐sized plane sensor networks, but also reduces the amount of data, so the network lifetime is 1.2 times to 3.37 times than previous methods. The mark probability τ of each node is given and its performance is comprehensively analyzed for FPM approach. The results have important guiding significance for designing traceback scheme. Copyright © 2016 John Wiley & Sons, Ltd.

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Mobile Target Detection in Wireless Sensor Networks With Adjustable Sensing Frequency

Cited by 81 publications

References 20 publications

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Reliable and Secured Routing in Wireless Sensor Networks using Active Trust

A comprehensive analysis for fair probability marking based traceback approach in WSNs

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