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

Wireless Sensor Network Exploiting High Altitude Platform in 5G Network [Jaringan Sensor Nirkabel Menggunakan High Altitude Platform pada Jaringan 5G]

Technology development and socio-economic transformation have increased the demand for 5G cellular networks. They are expected to send information quickly and support many use cases emerging from a variety of applications. One of the use cases on the 5G network is the massive MTC (Machine Type Communication), wherein wireless sensor network (WSN) is a typical application. Challenges faced by a 5G cellular network are how to model an architecture/topology to support WSN and to solve energy consumption efficiency problem in WSN. So, to overcome these challenges, a HAP system integrated with WSN which uses Low Energy Adaptive Hierarchy routing protocol is implemented. The HAP system is designed to be used at a 20-km altitude, and the topologies used are those with and without clustering. It uses 1,000 sensor nodes and Low Energy Adaptive Clustering Hierarchy protocol. This system was simulated using MATLAB. Simulations were performed to analyze the energy consumption, the number of dead nodes, and the average total packets which were sent to HAP for non-clustered topology and clustered topology. Simulation results showed that the clustered topology could reduce energy consumption and the number of dead nodes while increasing the total packet sent to HAP.*****Perkembangan teknologi dan transformasi sosial-ekonomi telah menyebabkan bisnis jaringan seluler 5G mengalami perubahan, sehingga jaringan seluler 5G diharapkan dapat mengirim informasi dengan cepat dan mendukung kasus penggunaan yang banyak bermunculan dari berbagai aplikasi. Salah satu kasus penggunaan pada jaringan 5G adalah massive Machine Type Communication (MTC). Salah satu aplikasi massive MTC adalah jaringan sensor nirkabel (JSN). Tantangan bagi jaringan seluler 5G ini adalah bagaimana memodelkan arsitektur/topologi untuk mendukung JSN dan bagaimana mengatasi masalah efisiensi konsumsi energi di JSN. Untuk menjawab tantangan ini, maka diterapkan sistem HAP yang terintegrasi JSN dan menggunakan protokol routing Low Energy Adaptive Clustering Hierarchy. Sistem HAP dirancang untuk digunakan di ketinggian 20 km dengan topologi tanpa dan dengan clustering, menggunakan 1.000 node sensor. Sistem ini telah disimulasikan dengan menggunakan MATLAB. Simulasi dilakukan untuk melihat konsumsi energi, jumlah node yang mati dan rata-rata total paket yang dikirim ke HAP untuk topologi tanpa dan dengan clustering. Dari serangkaian simulasi, terlihat bahwa topologi dengan clustering dapat mengurangi konsumsi energi dan jumlah node yang mati, sekaligus meningkatkan total paket yang dikirimkan ke HAP

A Comprehensive Approach to WSN-Based ITS Applications: A Survey

In order to perform sensing tasks, most current Intelligent Transportation Systems (ITS) rely on expensive sensors, which offer only limited functionality. A more recent trend consists of using Wireless Sensor Networks (WSN) for such purpose, which reduces the required investment and enables the development of new collaborative and intelligent applications that further contribute to improve both driving safety and traffic efficiency. This paper surveys the application of WSNs to such ITS scenarios, tackling the main issues that may arise when developing these systems. The paper is divided into sections which address different matters including vehicle detection and classification as well as the selection of appropriate communication protocols, network architecture, topology and some important design parameters. In addition, in line with the multiplicity of different technologies that take part in ITS, it does not consider WSNs just as stand-alone systems, but also as key components of heterogeneous systems cooperating along with other technologies employed in vehicular scenarios