FDRA: Fault Detection and Recovery Algorithm for Wireless Sensor Networks

Titouna, Chafiq; Ari, Ado Adamou Abba; Moumen, Hamouma

doi:10.1007/978-3-319-97163-6_7

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…A WSN is an ad hoc network consisting of a set of sensor nodes, randomly fixed or dispersed in a given geographical area, communicating via a wireless link to autonomously collect, process, and transmit data in their environment to a special node, considered the collection point, called a sink [16,22,30,31,32]. The geographical area where the sensor nodes operate is called the field or area of interest.…”

Section: Overview Of Wsnsmentioning

confidence: 99%

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Big Data Collection in Large-Scale Wireless Sensor Networks

Djedouboum

Ari

Guéroui

et al. 2018

Sensors

105

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Data collection is one of the main operations performed in Wireless Sensor Networks (WSNs). Even if several interesting approaches on data collection have been proposed during the last decade, it remains a research focus in full swing with a number of important challenges. Indeed, the continuous reduction in sensor size and cost, the variety of sensors available on the market, and the tremendous advances in wireless communication technology have potentially broadened the impact of WSNs. The range of application of WSNs now extends from health to the military field through home automation, environmental monitoring and tracking, as well as other areas of human activity. Moreover, the expansion of the Internet of Things (IoT) has resulted in an important amount of heterogeneous data that are produced at an exponential rate. Furthermore, these data are of interest to both industry and in research. This fact makes their collection and analysis imperative for many purposes. In view of the characteristics of these data, we believe that very large-scale and heterogeneous WSNs can be very useful for collecting and processing these Big Data. However, the scaling up of WSNs presents several challenges that are of interest in both network architecture to be proposed, and the design of data-routing protocols. This paper reviews the background and state of the art of Big Data collection in Large-Scale WSNs (LS-WSNs), compares and discusses on challenges of Big Data collection in LS-WSNs, and proposes possible directions for the future.

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Section: Overview Of Wsnsmentioning

confidence: 99%

“…Therefore, WSNs are known to be a fundamental component that allowed the development of the IoT system. In such networks, a large number of sensors are deployed in the order of thousands, collaborating to collect and transfer information to the base station [15,16].…”

Section: Introductionmentioning

confidence: 99%

Big Data Collection in Large-Scale Wireless Sensor Networks

Djedouboum

Ari

Guéroui

et al. 2018

Sensors

105

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“…The ratio of total energy consumed by sensor nodes while sending and receiving sensed information used radio model is same as discussed in Ref. [24]. In this model, based on actual connecting space among source and recipient, free space or multi-path channels are used.…”

Section: Network Model and Terminologymentioning

confidence: 99%

Energy Attentive Pre-fault Detection Mechanism with Multilevel Transmission for Distributed Wireless Sensor Network

Talmale¹,

Mundukur²,

Thakare³

2019

RIA

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To recognize the vision of ambient intelligence, wireless sensor networks (WSNs) are broadly used to invent real time and smart applications. Energy has been a major concern in sensor networks. Another challenging issue of wireless sensor network is to discover the imperfect node and recover them to maintain network topology. Enhancement of network lifetime, reliability and scalability also depends on fault detection and recovery mechanism. Distributed Energy Attentive Pre Fault Detection Mechanism is proposed in this work for pre fault detection and multipath routing. The novel idea behind this approach is to recognize the sensor fault in advanced and performed the improvement action based on notification parameter like acknowledgement not received, residual energy is below threshold value and hope count distance. It enables a predesor node to rapidly identify and efficiently categories the next energy aware shortest path, upon steering failure, rather than relying from the starting place. Extensive simulations are performed and result demonstrates that the proposed algorithms perform well on various performance indicator metrics such as throughput, energy consumption, packet drop ratio, and communication delay.

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“…For WSNs dealing with a large number of sensors, several research works have been made to obtain an effective data prediction approach. Some of the works were directed toward solving the problem of energy consumption during data transmission [14]- [16] while other researchers where focusing on data reconstruction [17].…”

Section: Introductionmentioning

confidence: 99%

Data Prediction Based Encoder-Decoder Learning in Wireless Sensor Networks

et al. 2022

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Wireless sensor networks are typically characterized by large network size and sensor nodes with low energy capacity and a limited bandwidth for data transmission. Over-activity of the sensor nodes will therefore cause many issues to the network, such as an increase in the network depletion rate and poor data transmission. Data prediction methods that exploit the inter-relationship between sensor nodes can be used to reduce data traffic across wireless networks. Several related work in data prediction do not consider the time-series distribution of the sensing data. However, exploiting sequential features of the historical observations can improve prediction accuracy, and increase the number of sensing data predicted per sequence. This work propose a sequence to sequence data prediction model, using a one dimensional layer convolutional neural network to extract spatial features from the pre-processed sensing data, and an encoderdecoder model to predict the next two outputs in the sequence by exploiting the temporal distribution of the data. The aforementioned approach has the capacity of generating more accurate information, which can reduce network traffic and energy expenditure in WSNs. Furthermore, the experimental results reveals that, based on a suitable choice of nodes, our proposed model perform accurate predictions, with reduced root mean squared error as compared to related work. We also propose an approach to regulate and control the data traffic toward the base station.

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FDRA: Fault Detection and Recovery Algorithm for Wireless Sensor Networks

Cited by 19 publications

References 23 publications

Big Data Collection in Large-Scale Wireless Sensor Networks

Big Data Collection in Large-Scale Wireless Sensor Networks

Energy Attentive Pre-fault Detection Mechanism with Multilevel Transmission for Distributed Wireless Sensor Network

Data Prediction Based Encoder-Decoder Learning in Wireless Sensor Networks

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