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

Energy harvesting and wireless transfer in sensor network applications: Concepts and experiences

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite, yet intermittent, energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about

A group-based wireless body sensors network using energy harvesting for soccer team monitoring

[EN] In team-based sports, it is difficult to monitor physical state of each athlete during the match. Wearable body sensors with wireless connections allow having low-power and low-size devices, that may use energy harvesting, but with low radio coverage area but the main issue comes from the mobility. This paper presents a wireless body sensors network for soccer team players' monitoring. Each player has a body sensor network that use energy harvesting and each player will be a node in the wireless sensor network. This proposal is based on the zone mobility of the players and their dynamism. It allows knowing the physical state of each player during the whole match. Having fast updates and larger connection times to the gateways, the information can be routed through players of both teams, thus a secure system has been added. Simulations show that the proposed system has very good performance in high mobility.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by Government of Russian Federation, Grant 074-U01, by National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2013 Project.Lloret, J.; García Pineda, M.; Catala Monzo, A.; Rodrigues, JJPC. (2016). A group-based wireless body sensors network using energy harvesting for soccer team monitoring. International Journal of Sensor Networks. 21(4):208-225. https://doi.org/10.1504/IJSNET.2016.079172S20822521

An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running

A hybrid sensor network for watershed monitoring

This thesis discusses the Hydrological Hybrid Communication Sensor Network (HHCSN), which is designed for in situ measurement of various hydrological properties of a watershed. HHCSN is comprised of a network of sensor strings, each of which connects up to 100 sensing nodes on a communication line as long as 100 m. Each node includes sensors that measure soil attributes of interest, as well as a microcontroller with basic communication and processing capabilities. A relay point at the surface compresses data from the nodes and wirelessly transmits it to a base station that serves as a gateway to the outside world. The base station compresses data from multiple strings and utilizes the GSM cellular infrastructure to communicate the data to a remote server and to receive software updates to be disseminated to the sensor strings. Ultra-low power design and remote maintenance result in an unattended eld life of over ve years. The system is scalable in area and sensor design modality, as covering a larger area would only entail the addition of sensor strings, and the nodes are designed to facilitate the interfacing of additional sensors. The system is robust, as the only exposed portion is the relay point. Data collection and transmission can be event-driven or time-driven. Battery power, which is supplemented by solar harvesting, and wireless short- and long-range communication, eliminate the need for surface wiring, signicantly reducing the cost of system deployment. Currently, the estimate is a cost of less than $40 for each sensor string, which compares very favorably to the price of existing systems, most of which oer very limited in situ measurement capabilities, yet cost tens of thousands of dollars --Abstract, page iii

Energy harvesting schemes for radio technologies used in IoT: overview and suitability study

The number of devices connected to the Internet increases day by day. Moreover people start using the network in their everyday life to shopping, to control the house by remote, to check news or the weather forecast, to check the traffic, to call their friend or their family and so on. Their phones are interconnected all the time with other devices and sensors to gather all the information the users need. This network of object and the exchange of data are described with the Internet of Things idea. With the Internet of Thing concept all object are connected to the Internet and they are able to transmit data to each other. Thanks to sensors, inanimate object are able to understand the environment around them and to make decision and to interact with it. With this scenario the amount of data exchanged is huge. The main two challenges of the Internet of Things concept are the energy consumption and the portability of a given sensor or node in the network. In this way all the object and people can be connected everywhere and all the time. To reach those aims it is important that the devices implement specific communication standards that require low energy to work and that guaranty, at the same time, quality and security to the transmission of the data. Batteries or cable are not suitable to satisfy the IoT requirements and new energy sources using energy harvesting schemes, are needed to power the devices. Moreover the communication protocols have to be faster and have to use as less power as possible to work. In this thesis an overview on multiple energy harvesting schemes given and different communication standards used in the Wireless Sensor Networks are analyzed. The main focus is on the energy consumption of the Wireless Sensor Networks that implements the communication standard IEEE 802.11ah. The aim was to understand whether it could be possible to power one node network or even a more complex one, only with the energy harvesting schemes described in the thesis. Networks of different sizes are simulated and analyzed. All the networks present only one AP but they differentiate from each other by the number of nodes (STAs). Moreover two different scenarios are simulated to better understand the energy consumption in different traffic case. Both saturated and non-saturated traffic scenario were simulated and analyzed. To enhance the throughput and to decrease the energy needed to power the sensors, different Modulation and Code Schemes where implemented. To assess the performance of simulated scenarios, the throughput and the energy consumption where analyzed. The results have showed that different networks required a small amount of energy to send and receive data. Therefore it is technically possible to power them only with some existing energy harvesting schemes