Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm: Fundamentals and Applications

Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm: Fundamentals and Applications

Ahona Ghosh (Brainware University, Kolkata, India), Chiung Ching Ho (Department of Computing and Information Systems, School of Science and Technology, Sunway University, Malaysia) and Robert Bestak (Department of Telecommunication Engineering, Czech Technical University in Prague, Czech Republic)
DOI: 10.4018/978-1-7998-5068-7.ch002
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Abstract

Wireless sensor networks consist of unattended small sensor nodes having low energy and low range of communication. It has been observed that if there is any system to periodically start and stop the sensors sensing activities, then it saves some energy, and thus, the network lifetime gets extended. According to the current literature, security and energy efficiency are the two main concerns to improve the quality of service during transmission of data in wireless sensor networks. Machine learning has proved its efficiency in developing efficient processes to handle complex problems in various network aspects. Routing in wireless sensor network is the process of finding the route for transmitting data among different sensor nodes according to the requirement. Machine learning has been used in a broad way for designing energy efficient routing protocols, and this chapter reviews the existing works in the said domain, which can be the guide to someone who wants to explore the area further.
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Introduction

The rise of artificial intelligence (AI) has influenced every field largely; from entertainment to education or from agriculture to manufacturing. Healthcare and computer network are not outside the list which have witnessed a great impact with the magical touch of AI and Machine Learning (ML). In computer network, Wireless Sensor Network (WSN) denotes a collection of spatially isolated and dedicated sensors for monitoring and recording the physical situations of the circumstances and shaping the collected data at a central position. Apart from the different factors like time consumption, energy efficiency, security and cost which contribute to the process of routing in WSN, the active research areas include different Quality of service parameters like Packet Delivery Ratio (PDR), efficiency, robustness, reliability etc. The process of data transmission between different sensor nodes and communication between them is called routing and the goal always remains to reduce the energy consumption during this routing (Pathan et al., 2007) and increase the lifetime of the sensor nodes as much as possible. In this chapter, the design issues of WSN have been addressed first and then the applications of machine learning are described in the concerned domain.

In the next section, related background study and currently available applications of machine learning algorithms in the concerned domain are highlighted. In section 3, the advantages and drawbacks for the existing ML approaches in Energy efficient routing scenario are discussed. Section 4 describes the recent algorithms or techniques used to develop energy efficient routing in WSN and compares them after performance evaluation. Segment 5 provides an overall discussion about the scope and limitation of the chapter and future direction.

Motivation of the Chapter

This chapter collects views of different researchers worldwide from different perspectives. The survey outline presented here is definitely going to help and guide the present researchers in the concerned domain. WSNs can be used to track and monitor the dangerous and unreachable areas where exploration of locations having irregular behaviours like volcanic eruption, forest fire etc. The initial configurations should have the capability of changing its nature to adopt with circumstances, because anytime anything can happen. Machine learning algorithms are capable of calibrating itself to newly acquired knowledge, so application of machine learning in these types of systems will be really useful. The sensor devices are often capable of collecting large data, but sometimes they cannot find the correlation between them. Machine learning can be applied to them for exploring the correlation for better deployment and wide area of coverage which is always desired for the systems. This chapter summarizes the existing systems where limited resource and diversity in the learning patterns have been considered. However, areas like development of distributed and lightweight message transmission system and using machine learning in resource scheduling and management have still remained unexplored. Further experiments and researches can be undertaken in the domain mentioned.

Background of WSN

Wireless Sensor Networks control and monitor rapidly changing environments efficiently. This dynamic nature is sometimes due to some external factors affecting the system and sometimes it is due to some requirement from designer’s perspective. To cope up with such ever changing scenarios, machine learning is applied to WSN implementation so that the network learns the nature and trend by its own.

Contribution of the Chapter

In this chapter, we have presented a review of the machine learning methods applied in various literatures over the period 2004-2020 related to WSN, especially in security and energy efficient routing methods of WSN. Various problems of the existing works have been addressed along with experimental finding comparison and advantages of the methods. We have also provided a guidance to current and future WSN designers for their concerned application challenges aiming to maximize resource utilization and lifespan of sensor nodes.

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