A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

Goyal, Nitin; Dave, Mayank; Verma, Anil Kumar

doi:10.1002/dac.3485

Cited by 43 publications

(22 citation statements)

References 47 publications

(51 reference statements)

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“…The CHs collect sensor data from its cluster members, aggregate, and send it to the remote BS for the end user. Therefore, the CHs play an important role 8 in a clustering algorithm with CH selection being the key component of a clustering algorithm 9 . Cluster‐based WSNs used algorithms are mostly deals with stationary sensor nodes 10 to address network performance.…”

Section: Literature Surveymentioning

confidence: 99%

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

Arulanantham¹,

Palanisamy

2020

Int J Communication

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SummaryThe Internet of Things (IoT) is a structure with sensors, base station, gateway, and network servers. Users can access the information contained in it and use it as they need. These sensors are embedded in several data collection locations in a uniform or random manner to collect data. The users in many parts of the world obtain such data that fit their requirements. For example, sensors are used in remote areas to monitor various areas such as forests, wildlife sanctuaries, fields, industrial needs, human condition, and changes in the ocean. The changes like these that take place in the open spaces require the selection of the best routing method and seamless delay reduction in data collection helps to immediately reach those involved in it. In the proposed protocol, dynamic ant routing‐based channel accessing cognitive sensor network (DACASN) in the IoT, cognitive sensors are used. It is made with the knowledge of choosing a channel for communication as well as selecting the parameters for path selection and setting the most accurate path with the channel knowledge and communicating with the internet server. The sensors set the paths for this route selection using the centrifugal method and the anticolonial method. As a result, it can be seen in the results and discussion section that the quality of the routing information is high because it is delivered to the destination in the shortest possible time.

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Section: Literature Surveymentioning

confidence: 99%

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

Arulanantham¹,

Palanisamy

2020

Int J Communication

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“…Clustering is the most effective method for improving the energy efficiency and network lifetime 5 . Clustering divides sensor nodes into some clusters, consisting of a cluster head (CH) and some members.…”

Section: Introductionmentioning

confidence: 99%

A method for routing and data aggregating in cluster‐based wireless sensor networks

Sharifi

Barati

2021

Int J Communication

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Summary Wireless sensor networks (WSNs) consist of a large number of small nodes, containing sensors; these sensors serve as powerful devices to gather data in different environments. One of the major challenges facing WSNs is their limited energy resources, which affects network lifetime directly. This paper provides a method of hierarchical routing and data aggregating for WSNs. In the proposed method, the network is clustered, and some nodes are selected as cluster heads. On constructing a rendezvous region and selecting backbone nodes, a tree is formed within backbone‐tree nodes. The aggregated data are sent to the sink through backbone‐tree nodes and cluster heads. Here, there are two modes of data transmission. The proposed method is simulated by Network Simulator (NS‐2) software and compared with based data aggregation and rendezvous‐based routing protocol. The results of simulation reveal that our method causes a decrease in end‐to‐end delay and energy consumption as well as an increase in the number of alive nodes and packet delivery rate.

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“…This monitoring process, commonly referred to as the sensor outlier detection, is known to detect any anomaly or deviation in the sensor's readings and is usually one of the key processes that influence the quality of the data collected by the sensor. Of late, within the research communities, the detection of outliers is of great interest [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

et al. 2020

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The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.

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A novel fault detection and recovery technique for cluster‐based underwater wireless sensor networks

Cited by 43 publications

References 47 publications

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

DACASN: Dynamic ant routing‐based channel accessing cognitive sensor network in Internet of Things

A method for routing and data aggregating in cluster‐based wireless sensor networks

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

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