Preface

Wireless sensor networks (WSNs) are new wireless networks used in numerous applications to sense and monitor various physical and environmental parameters. This adaptation is made possible by the advances made in semiconductors, networking, and material science technologies. Due to these advancements, WSNs are being adapted into various applications such as military sensing, physical security, air traffic control, traffic surveillance, video surveillance, and more.

This book focuses on understanding the design, deployment, and applications of WSNs. The book is divided into three sections. Section 1 provides an overview of WSNs and their various applications. Section 2 focuses on various design and deployment techniques and challenges. Section 3 discusses some specialized applications and challenges in adapting WSNs for different purposes.

Section I: Overview

Chapter 1: Wireless Sensor Networks: Applications

This chapter presents an overview of WSNs. It describes in detail various components of WSNs and their main characteristics. The chapter also gives an overview of the various WSN architectures along with the characteristics to optimize when designing a WSN for various applications.

Chapter 2: Wireless Sensor Networks: Applications and Challenges

This chapter discusses issues of WSNs ranging from applications to challenges. It examines various features such as signal processing techniques, topology, approaches, and others, which that are needed for WSN design. The chapter also covers the limitations of a WSN along with various technologies available for deploying WSNs.

Section 2: Design and Deployment

Chapter 3: Design Model and Deployment Fashion of Wireless Sensor Networks

This chapter discusses the manner of WSN deployment. It also discusses the communication of sensors over a wireless link to unite the necessities of a specific application.

Chapter 4: An Algorithmic Approach to the Node Selection Problem in Industrial Wireless Sensor Networks

This chapter talks about the problem of placing a minimum number of sink nodes in a weighted topology such that each sink node should have a maximum number of sensor nodes within the given capacity, which is known as Capacitated Sink

Node Placement Problem. It also proposes a heuristic-based approach to solve this problem.

This chapter addresses the fundamentals of distributed inference problems in WSNs and provides statistical theoretical foundations to several applications. It adopts a statistical signal processing perspective and focuses on the distributed version of the binary-hypothesis test for detecting an event as correctly as possible.

This chapter proposes a similarity between the problem of selecting indexes and materialized views using the Knapsack algorithm. The contributions of the work are the use of the backpack algorithm to present this problem as well as mathematical modeling, followed by the use of machine learning to reduce the execution time of

This chapter addresses deployment-signal interference of Internet of Things (IoT) devices occupying the same spectrum and signal fading due to the environment. It discusses the effects of fading and interference from other IoT devices working in

Chapter 14: Applications of Prediction Approaches in Wireless sensor Networks

This chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in the sleep/

Chapter 15: Innovative Wearable Sensors Based on Hybrid Materials for Real-Time

This chapter covers different aspects of the design and implementation of wearable biosensors woven into fabric. Weaving the sensors into fabrics gives flexibility to the user, allowing 24 hours of medical observation without causing discomfort. The focus of this chapter is on measuring the breathing pattern of

Chapter 16: An Evolutionary Perspective for Network Centric Therapy through Wearable and Wireless Systems for Reflex, Gait, and Movement Disorder

This chapter discusses Network Centric Therapy for assessing reflex, gait, and movement disorders. Inherent aspects pertaining to Network Centric Therapy involve wearable and wireless inertial sensor systems, machine learning, and Cloud

This chapter discusses the various challenges of integrating WSNs into the IoT, such as security, topologies, and so on. The limited resources of WSNs are the main

computing access for the acquired inertial sensor signal data.

Chapter 13: Interference Mapping in 3D for High-Density Indoor IoT

The chapter also examines a reference WSN scenario-MIMO.

Chapter 12: Queries Processing in Wireless Sensor Network

the workload.

Deployments

the same frequency range.

wake-up periods of sensor nodes.

Assessment with Machine Learning

Chapter 17: Challenges of WSNs in IOT

Section 3: Applications

Breath Monitoring

individuals.

Chapter 5: Data Aggregation Scheme Using Multiple Mobile Agents in Wireless Sensor Network

This chapter presents an efficient data aggregation scheme based on an itinerary approach using multiple mobile agents aimed at accumulating and transferring data to the sink. It also presents a predefined set of experiments and compares the outcome for the proposed data aggregation scheme with existing ones.

Chapter 6: Data Collection Protocols in Wireless Sensor Networks

This chapter covers the classification of data collection protocols. The classification is done based on various parameters such as network lifetime, energy, fault tolerance, and latency. The chapter analyses different techniques to achieve these parameters.

Chapter 7: WSN for Event Detection Applications: Deployment, Routing, and Data Mapping Using AI

This chapter describes a query-processing framework for WSN that addresses many of the requirements associated with the vision of WSN as a database. By considering WSN as a database, a significant reduction in the cost of software engineering that implements a data collection program for the WSN can be achieved.

Chapter 8: Swarm Intelligence-Based Bio-Inspired Framework for Wireless Sensor Networks

This chapter examines swarm intelligence- and social insects-based approaches to deal with a bio-inspired networking framework. The chapter looks into various challenges and issues in the WSN field.

Chapter 9: Energy Saving Hierarchical Routing Protocol in WSN

This chapter examines and compares five protocols for extending the life of WSNs by minimizing energy consumption without affecting packet transmission. It begins with a discussion of the background of the WSN and the factors associated with energy utilization in sensor nodes.

Chapter 10: Research on Polling Control System in Wireless Sensor Networks

This chapter proposes a prioritized polling system that combines exhaustive service with gated service. The gated service is used in ordinary sites with low priority and exhaustive service is used at central sites with high priority. The average queue length and average waiting delay of the service model are accurately analysed by using embedded Markov chain and probability generating functions.

Chapter 11: Cross-Layer Inference in WSN: From Methods to Experimental Validation

This chapter addresses the fundamentals of distributed inference problems in WSNs and provides statistical theoretical foundations to several applications. It adopts a statistical signal processing perspective and focuses on the distributed version of the binary-hypothesis test for detecting an event as correctly as possible. The chapter also examines a reference WSN scenario-MIMO.

Chapter 12: Queries Processing in Wireless Sensor Network

This chapter proposes a similarity between the problem of selecting indexes and materialized views using the Knapsack algorithm. The contributions of the work are the use of the backpack algorithm to present this problem as well as mathematical modeling, followed by the use of machine learning to reduce the execution time of the workload.

Chapter 13: Interference Mapping in 3D for High-Density Indoor IoT Deployments

This chapter addresses deployment-signal interference of Internet of Things (IoT) devices occupying the same spectrum and signal fading due to the environment. It discusses the effects of fading and interference from other IoT devices working in the same frequency range.

Chapter 14: Applications of Prediction Approaches in Wireless sensor Networks

This chapter discusses WSN deployment environment, energy conservation techniques, mobility in WSN, prediction approaches and their applications in the sleep/ wake-up periods of sensor nodes.

Section 3: Applications

Chapter 15: Innovative Wearable Sensors Based on Hybrid Materials for Real-Time Breath Monitoring

This chapter covers different aspects of the design and implementation of wearable biosensors woven into fabric. Weaving the sensors into fabrics gives flexibility to the user, allowing 24 hours of medical observation without causing discomfort. The focus of this chapter is on measuring the breathing pattern of individuals.

Chapter 16: An Evolutionary Perspective for Network Centric Therapy through Wearable and Wireless Systems for Reflex, Gait, and Movement Disorder Assessment with Machine Learning

This chapter discusses Network Centric Therapy for assessing reflex, gait, and movement disorders. Inherent aspects pertaining to Network Centric Therapy involve wearable and wireless inertial sensor systems, machine learning, and Cloud computing access for the acquired inertial sensor signal data.

Chapter 17: Challenges of WSNs in IOT

This chapter discusses the various challenges of integrating WSNs into the IoT, such as security, topologies, and so on. The limited resources of WSNs are the main concern when it comes to integrating them with the IoT. Integration makes it possible to access a sensor node from anywhere in the world. It implies that the sensor node is now open for any heterogeneous Internet user in the world.

> **Siva S. Yellampalli,** SRM University AP, Neerukonda, India

Acknowledgments

I would like to thank the chapter authors for their excellent contributions. I would also like to my wife, Suma, and son, Gangadhar, for their patience and understand-

ing during the process.
