Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications

A comprehensive survey of wireless body area networks on PHY, MAC, and network layers solutions

Recent advances in microelectronics and integrated circuits, system-on-chip design, wireless communication and intelligent low-power sensors have allowed the realization of a Wireless Body Area Network (WBAN). A WBAN is a collection of low-power, miniaturized, invasive/non-invasive lightweight wireless sensor nodes that monitor the human body functions and the surrounding environment. In addition, it supports a number of innovative and interesting applications such as ubiquitous healthcare, entertainment, interactive gaming, and military applications. In this paper, the fundamental mechanisms of WBAN including architecture and topology, wireless implant communication, low-power Medium Access Control (MAC) and routing protocols are reviewed. A comprehensive study of the proposed technologies for WBAN at Physical (PHY), MAC, and Network layers is presented and many useful solutions are discussed for each layer. Finally, numerous WBAN applications are highlighted

Evaluation of various MAC Protocols for Node Density in Wireless Sensor Networks based on QoS

A wireless sensor network is a collection of sensor nodes that communicate with one another to gather data and send it to a base station. The quality of service provided by sensor networks determines their efficiency and lifetime.  Energy, channel capacity, packet transmission, packet drop, and latency are all factors in QoS. In WSNs, routing protocols are designed to discover the shortest route to a network's destination, whereas MAC protocols are designed to transmit data through a communication channel. To increase the network's life span, the best routing and MAC protocols are required for communication. In this research, we examined the performance of different MAC protocols for a variety of QoS measures as node density increased. Future researchers will benefit from this research in establishing the best hybrid protocols for wireless sensor networks. The results demonstrate that CSMA is the best communication protocol among the others

QoS-aware Energy Efficient Cooperative Scheme for Cluster-based IoT Systems

The Internet of Things (IoT) technology with huge number power-constrained devices has been heralded to improve the operational efficiency of many industrial applications. It is vital to reduce the energy consumption of each device, however, this could also degrade the Quality of Service (QoS) provisioning. In this paper, we study the problem of how to achieve the tradeoff between the QoS provisioning and the energy efficiency for the industrial IoT systems. We first formulate the multi-objective optimization problem to achieve the objective of balancing the outage performance and the network lifetime. Then we propose to combine the Quantum Particle Swarm Optimization (QPSO) with the improved Non-dominated Sorting Genetic algorithm (NSGA-II) to obtain the Pareto optimal front. In particular, NSGA-II is applied to solve the formulated multi-objective optimization problem and QPSO algorithm is used to obtain the optimum cooperative coalition. The simulation results suggest that the proposed algorithm can achieve the tradeoff between the energy efficiency and QoS provisioning by sacrificing about 10% network lifetime but improving about 15% outage performance

A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

Dynamic Sleep Scheduling on Air Pollution Levels Monitoring with Wireless Sensor Network

Wireless Sensor Network (WSN) can be applied for Air Pollution Level Monitoring System that have been determined by the Environmental Impact Management Agency which is  PM10, SO2, O3, NO2 and CO. In WSN, node system is constrained to a limited power supply, so that the node system has a lifetime. To doing lifetime maximization, power management scheme is required and sensor nodes should use energy efficiently. This paper proposes dynamic sleep scheduling using Time Category-Fuzzy Logic (Time-Fuzzy) Scheduling as a reference for calculating time interval for sleep and activated node system to support power management scheme. This research contributed in power management design to be applied to the WSN system to reduce energy expenditure. From the test result in real hardware node system, it can be seen that Time-Fuzzy Scheduling is better in terms of using the battery and it is better in terms of energy consumption too because it is more efficient 51.85% when it is compared with Fuzzy Scheduling, it is more efficient 68.81% when it is compared with Standard Scheduling and it is more efficient 85.03% when compared with No Scheduling