Application-Oriented Fault Detection and Recovery Algorithm for Wireless Sensor and Actor Networks

Du, Jun; Xie, Lihua; Sun, Xiaoyan; Zheng, Ronghao

doi:10.1155/2012/273792

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…The rule evaluation process is illustrated in Figure 8. One of the mostly used de-fuzzification functions used with Sugeno method is the "center of gravity for singleton" as shown in (5)…”

Section: Figure 7 Fuzzy Logic Module Basic Operationsmentioning

confidence: 99%

“…Different data prediction and replacement strategies have been proposed in recent literature. Some depend on using the history of collected data from the failing node, like those in [4], or moving a neighboring node to do the work of this failing node [5], or changing the route transmitted messages using a 'life time aware routing protocols' [6]. Other techniques involve assigning a backup node for those nodes that are under the threat of failing [7].…”

Section: Introductionmentioning

confidence: 99%

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A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

Al-Azzam¹,

Sharieh²

2020

IJECE

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Continuous data transmission in wireless sensor networks (WSNs) is one of the most important characteristics which makes sensors prone to failure. a backup strategy needs to co-exist with the infrastructure of the network to assure that no data is missing. The proposed system relies on a backup strategy of building a history file that stores all collected data from these nodes. This file is used later on by fuzzy logic to estimate missing data in case of failure. An easily programmable microcontroller unit is equipped with a data storage mechanism used as cost worthy storage media for these data. An error in estimation is calculated constantly and used for updating a reference “optimal table” that is used in the estimation of missing data. The error values also assure that the system doesn’t go into an incremental error state. This paper presents a system integrated of optimal data table, microcontroller, and fuzzy logic to estimate missing data of failing sensors. The adapted approach is guided by the minimum error calculated from previously collected data. Experimental findings show that the system has great potentials of continuing to function with a failing node, with very low processing capabilities and storage requirements.

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“…The rule evaluation process is illustrated in Figure 8. One of the mostly used de-fuzzification functions used with Sugeno method is the "center of gravity for singleton" as shown in (5)…”

Section: Figure 7 Fuzzy Logic Module Basic Operationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

Al-Azzam¹,

Sharieh²

2020

IJECE

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“…A number of localization techniques were studied and introduced for WSANs. Some techniques focus on nodes positioning are provided in [ 6 ], while some other studies focus on failure node recovery process [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Related Workmentioning

confidence: 99%

Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks

Mahjoub

Elleithy

2017

Sensors

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The actor nodes are the spine of wireless sensor and actor networks (WSANs) that collaborate to perform a specific task in an unverified and uneven environment. Thus, there is a possibility of high failure rate in such unfriendly scenarios due to several factors such as power consumption of devices, electronic circuit failure, software errors in nodes or physical impairment of the actor nodes and inter-actor connectivity problem. Therefore, it is extremely important to discover the failure of a cut-vertex actor and network-disjoint in order to improve the Quality-of-Service (QoS). In this paper, we propose an Efficient Actor Recovery (EAR) paradigm to guarantee the contention-free traffic-forwarding capacity. The EAR paradigm consists of a Node Monitoring and Critical Node Detection (NMCND) algorithm that monitors the activities of the nodes to determine the critical node. In addition, it replaces the critical node with backup node prior to complete node-failure which helps balancing the network performance. The packets are handled using Network Integration and Message Forwarding (NIMF) algorithm that determines the source of forwarding the packets; either from actor or sensor. This decision-making capability of the algorithm controls the packet forwarding rate to maintain the network for a longer time. Furthermore, for handling the proper routing strategy, Priority-Based Routing for Node Failure Avoidance (PRNFA) algorithm is deployed to decide the priority of the packets to be forwarded based on the significance of information available in the packet. To validate the effectiveness of the proposed EAR paradigm, the proposed algorithms were tested using OMNET++ simulation.

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“…In [ 38 ], the authors proposed a distributed faulty detection mechanism that analyzes neighbor data in order to identify disconnected nodes. In the same way, the work in [ 39 ] proposes a distributed failure detection mechanism, also addressing automatic recovery from hardware failures. Although relevant, those works are concerned with identification of connection losses.…”

Section: Node Failuresmentioning

confidence: 99%

Availability Issues in Wireless Visual Sensor Networks

Costa

Silva

Guedes

et al. 2014

Sensors

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Wireless visual sensor networks have been considered for a large set of monitoring applications related with surveillance, tracking and multipurpose visual monitoring. When sensors are deployed over a monitored field, permanent faults may happen during the network lifetime, reducing the monitoring quality or rendering parts or the entire network unavailable. In a different way from scalar sensor networks, camera-enabled sensors collect information following a directional sensing model, which changes the notions of vicinity and redundancy. Moreover, visual source nodes may have different relevancies for the applications, according to the monitoring requirements and cameras' poses. In this paper we discuss the most relevant availability issues related to wireless visual sensor networks, addressing availability evaluation and enhancement. Such discussions are valuable when designing, deploying and managing wireless visual sensor networks, bringing significant contributions to these networks.

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Application-Oriented Fault Detection and Recovery Algorithm for Wireless Sensor and Actor Networks

Cited by 18 publications

References 16 publications

A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

A data estimation for failing nodes using fuzzy logic with integrated microcontroller in wireless sensor networks

Efficient Actor Recovery Paradigm for Wireless Sensor and Actor Networks

Availability Issues in Wireless Visual Sensor Networks

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