An online approach for wireless network repair in partially-known environments

Truong, Thuy T.; Brown, Kenneth N.; Sreenan, Cormac J.

doi:10.1016/j.adhoc.2016.02.021

Cited by 3 publications

(3 citation statements)

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“…Although this approach reduces the energy consumption by optimizing the number and size of communication messages to the sink, the accuracy of the boundary detection and tracking of a continuous object is compromised. The COBOM algorithm proposed in [ 7 ] is similar to that in [ 14 ] in that it presents an energy-efficient algorithm that detects and tracks the boundary of a continuous object. Each boundary node maintains a BN array to keep the neighboring nodes’ IDs and their detection values.…”

Section: Related Workmentioning

confidence: 99%

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A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks

Imran

2017

Sensors

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Abstract:In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status. The sink performs computations on the received data to estimate the object boundary. For accurate boundary estimation, nodes at the phenomenon boundary need to be carefully selected. Failure of one or multiple boundary nodes (BNs) can significantly affect the object detection and boundary estimation accuracy at the sink. We develop an efficient failure-prone object detection approach that not only detects and recovers from BN failures but also reduces the number and size of transmissions without compromising the boundary estimation accuracy. The proposed approach utilizes the spatial and temporal features of sensor nodes to detect object BNs. A Voronoi diagram-based network clustering, and failure detection and recovery scheme is used to increase boundary estimation accuracy. Simulation results show the significance of our approach in terms of energy efficiency, communication overhead, and boundary accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, it is important to detect and recover these failures. Different failure detection and recovery schemes are available in the literature [ 9 , 13 , 14 , 15 , 16 ]; however, there is no scheme available in the literature that considers and analyzes node failure in continuous object tracking.…”

Section: Introductionmentioning

confidence: 99%

A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks

Imran

2017

Sensors

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“…In the Wireless Sensor Networks (WSN) community, where it is commonly assumed that ample amounts of hopping nodes are available, the problem of repairing a connectivity failure is viewed differently. In this case, mobile robots can be used as sensor nodes which can be repositioned or added to replace failed nodes [20], [21].…”

Section: Related Workmentioning

confidence: 99%

RCAMP: A resilient communication-aware motion planner for mobile robots with autonomous repair of wireless connectivity

Caccamo

Parasuraman

Freda

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network.In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.

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