Dynamic duty cycle-based Wireless Sensor Network for underground pipeline monitoring

Emerging technologies, such as the Internet of things (IoT), machine learning (ML) and machine-to-machine networks encourage deployment of large-scale wireless sensor networks (WSNs). The major problem in WSN is the limited energy of node batteries. Therefore, the efficient use of node energy for data sensing, processing and communication operations is important to maintain a fully operational network for longest period of time. Literature presents a wide range of lifetime maximization techniques for WSN such as resource allocation algorithm, clustering and routing, sleep–wake scheduling, energy harvesting, MIMO technique, Distributed source coding, genetic algorithm and sink mobility. These techniques effectively lessen the energy consumption and enhance the lifetime of the entire wireless sensor network in various applications. Besides energy consumption, the characterization parameters such as coverage and connectivity, communication and modulation schemes, operational environment, network parameters, node parameters and service parameters also have great impact on WSN performance. This paper presents a comprehensive survey of state-of-the-art research works that improves the performance of WSN by optimizing various network characterization parameters and lifetime maximization techniques. These results highlight the key issues which affects WSN performance and provide a roadmap for WSN designers for effective implementation of novel WSN strategies.

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A survey on the characterization parameters and lifetime improvement techniques of wireless sensor network

Singh

Amin

Choudhary

2021

Frequenz

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Dynamic duty cycle-based Wireless Sensor Network for underground pipeline monitoring

Cited by 1 publication

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A survey on the characterization parameters and lifetime improvement techniques of wireless sensor network

A survey on the characterization parameters and lifetime improvement techniques of wireless sensor network

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