Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A group-based architecture and protocol for wireless sensor networks

There are many works related to wireless sensor networks (WSNs) where authors present new protocols with better or enhanced features, others just compare their performance or present an application, but this work tries to provide a different perspective. Why don¿t we see the network as a whole and split it into groups to give better network performance regardless of the routing protocol? For this reason, in this thesis we demonstrate through simulations that node¿s grouping feature in WSN improves the network¿s behavior. We propose the creation of a group-based architecture, where nodes have the same functionality within the network. Each group has a head node, which defines the area in which the nodes of such group are located. Each node has a unique node identifier (nodeID). First group¿s node makes a group identifier (groupID). New nodes will know their groupID and nodeID of their neighbors. End nodes are, physically, the nodes that define a group. When there is an event on a node, this event is sent to all nodes in its group in order to take an appropriate action. End nodes have connections to other end nodes of neighboring groups and they will be used to send data to other groups or to receive information from other groups and to distribute it within their group. Links between end nodes of different groups are established mainly depending on their position, but if there are multiple possibilities, neighbor nodes could be selected based on their ability ¿, being ¿ a choice parameter taking into account several network and nodes parameters. In order to set group¿s boundaries, we can consider two options, namely: i) limiting the group¿s diameter of a maximum number of hops, and ii) establishing boundaries of covered area. In order to improve the proposed group-based architecture, we add collaboration between groups. A collaborative group-based network gives better performance to the group and to the whole system, thereby avoiding unnecessary message forwarding and additional overheads while saving energy. Grouping nodes also diminishes the average network delay while allowing scaling the network considerably. In order to offer an optimized monitoring process, and in order to offer the best reply in particular environments, group-based collaborative systems are needed. They will simplify the monitoring needs while offering direct control. Finally, we propose a marine application where a variant of this groupbased architecture could be applied and deployed.García Pineda, M. (2013). A group-based architecture and protocol for wireless sensor networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/27599TESISPremios Extraordinarios de tesis doctorale

A Survey on Efficient Routing Strategies For The Internet of Underwater Things (IoUT)

The Internet of Underwater Things (IoUT) is an emerging technology that promised to connect the underwater world to the land internet. It is enabled via the usage of the Underwater Acoustic Sensor Network (UASN). Therefore, it is affected by the challenges faced by UASNs such as the high dynamics of the underwater environment, the high transmission delays, low bandwidth, high-power consumption, and high bit error ratio. Due to these challenges, designing an efficient routing protocol for the IoUT is still a trade-off issue. In this paper, we discuss the specific challenges imposed by using UASN for enabling IoUT, we list and explain the general requirements for routing in the IoUT and we discuss how these challenges and requirements are addressed in literature routing protocols. Thus, the presented information lays a foundation for further investigations and futuristic proposals for efficient routing approaches in the IoUT

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Impact Analysis of Different Scheduling and Retransmission Techniques on an Underwater Routing Protocol

Despite many advances in the area of Underwater Wireless Sensor Networks (UWSN) during the last years, still many challenges need to be successfully tackled before large-scale deployment of underwater sensor networks becomes a reality. UWSNs usually employ acoustic channels for communications, which compared with radio-frequency channels, allow much lower bandwidths and have longer propagation delays. In the past, different methods have been proposed to define how a node must acquire the channel in order to start a transmission. Given the large propagation delays of underwater communication channels, a TDMA-based approach may need big time-guards. On the other hand, the very same large propagation delay increases the occurrence of the hidden terminal problem in a CSMA-based approach. In this paper, impacts of utilization of different scheduling and retransmission techniques on an underwater routing protocol will be analyzed. This analysis, in which energy consumption, packet delay, number of duplicate packets, and packet loss are considered, will be carried out by means of simulation using the Network Simulator 3 and a subset of EDETA (Energy-efficient aDaptive hiErarchical and robusT Architecture) routing protocol recently adapted to UWSN