An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

Y. Savaria Mohammadreza Binesh Marvasti Ali Forghani Elah Abadi + 3 lainnya

2022

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

Sangkeum Lee Dongsoo Har L. Vecchietti + 2 lainnya

1 Juni 2021

Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures.

A. Haque F. Blaabjerg Himanshu Sharma

23 April 2021

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.

J. F. Jurado Letizia Marchegiani A. Abu-Mahfouz + 2 lainnya

3 Februari 2022

Wireless Sensor Network (WSN), which are enablers of the Internet of Things (IoT) technology, are typically used en-masse in widely physically distributed applications to monitor the dynamic conditions of the environment. They collect raw sensor data that is processed centralised. With the current traditional techniques of state-of-art WSN programmed for specific tasks, it is hard to react to any dynamic change in the conditions of the environment beyond the scope of the intended task. To solve this problem, a synergy between Software-Defined Networking (SDN) and WSN has been proposed. This paper aims to present the current status of Software-Defined Wireless Sensor Network (SDWSN) proposals and introduce the readers to the emerging research topic that combines Machine Learning (ML) and SDWSN concepts, also called ML-SDWSNs. ML-SDWSN grants an intelligent, centralised and resource-aware architecture to achieve improved network performance and solve the challenges currently found in the practical implementation of SDWSNs. This survey provides helpful information and insights to the scientific and industrial communities, and professional organisations interested in SDWSN, mainly the current state-of-art, ML techniques, and open issues.

Tariq Shahzad Umair Ahmad Salaria Tehseen Mazhar + 4 lainnya

2024

Energy efficiency and safety are two essential factors that play a significant role in operating a wireless sensor network. However, it is claimed that these two factors are naturally conflicting. The level of electrical consumption required by a security system is directly proportional to its degree of complexity. Wireless sensor networks require additional security measures above the capabilities of conventional network security protocols, such as encryption and key management. The potential application of machine learning techniques to address network security concerns is frequently discussed. These devices will have complete artificial intelligence capabilities, enabling them to understand their environment and respond. During the training phase, machine-learning systems may face challenges due to the large amount of data required and the complex nature of the training procedure. The main objective of the article is to know about different machine learning algorithms that are used to solve the security issues of wireless sensor networks. This study also focuses on the use of wireless sensor networks in different fields. Furthermore, this study also focuses on different Machine learning algorithms that are used to secure wireless sensor networks. Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. Furthermore, this article also focuses on open issues in this field that must be solved.