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

Localization in Long-range Ultra Narrow Band IoT Networks using RSSI

Internet of things wireless networking with long range, low power and low throughput is raising as a new paradigm enabling to connect trillions of devices efficiently. In such networks with low power and bandwidth devices, localization becomes more challenging. In this work we take a closer look at the underlying aspects of received signal strength indicator (RSSI) based localization in UNB long-range IoT networks such as Sigfox. Firstly, the RSSI has been used for fingerprinting localization where RSSI measurements of GPS anchor nodes have been used as landmarks to classify other nodes into one of the GPS nodes classes. Through measurements we show that a location classification accuracy of 100% is achieved when the classes of nodes are isolated. When classes are approaching each other, our measurements show that we can still achieve an accuracy of 85%. Furthermore, when the density of the GPS nodes is increasing, we can rely on peer-to-peer triangulation and thus improve the possibility of localizing nodes with an error less than 20m from 20% to more than 60% of the nodes in our measurement scenario. 90% of the nodes is localized with an error of less than 50m in our experiment with non-optimized anchor node locations.Comment: Accepted in ICC 17. To be presented in IEEE International Conference on Communications (ICC), Paris, France, 201

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Improving a wireless localization system via machine learning techniques and security protocols

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community

Practical and Robust Power Management for Wireless Sensor Networks

Wireless Sensor Networks: WSNs) consist of tens or hundreds of small, inexpensive computers equipped with sensors and wireless communication capabilities. Because WSNs can be deployed without fixed infrastructure, they promise to enable sensing applications in environments where installing such infrastructure is not feasible. However, the lack of fixed infrastructure also presents a key challenge for application developers: sensor nodes must often operate for months or years at a time from fixed or limited energy sources. The focus of this dissertation is on reusable power management techniques designed to facilitate sensor network developers in achieving their systems\u27 required lifetimes. Broadly speaking, power management techniques fall into two categories. Many power management protocols developed within the WSN community target specific hardware subsystems in isolation, such as sensor or radio hardware. The first part of this dissertation describes the Adaptive and Robust Topology control protocol: ART), a representative hardware-specific technique for conserving energy used by packet transmissions. In addition to these single-subsystem approaches, many applications can benefit greatly from holistic power management techniques that jointly consider the sensing, computation, and communication costs of potential application configurations. The second part of this dissertation extends this holistic power management approach to two families of structural health monitoring applications. By applying a partially-decentralized architecture, the cost of collecting vibration data for analysis at a centralized base station is greatly reduced. Finally, the last part of this dissertation discusses work toward a system for clinical early warning and intervention. The feasibility of this approach is demonstrated through preliminary study of an early warning component based on historical clinical data. An ongoing clinical trial of a real-time monitoring component also provides important guidelines for future clinical deployments based on WSNs

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

With the rapid proliferation of wireless sensor networks, different network topologies are likely to exist in the same geographical region, each of which is able to perform its own functions individually. However, these networks are prone to cause interference to neighbor networks, such as data duplication or interception. How to detect, determine, and locate the unknown wireless topologies in a given geographical area has become a significant issue in the wireless industry. This problem is especially acute in military use, such as spy-nodes detection and communication orientation systems. In this paper, three different clustering methods are applied to classify the RSSI and LQI data recorded from the unknown wireless topology into a certain number of groups in order to determine the number of active sensor nodes in the unknown wireless topology. The results show that RSSI and LQI data are capable of determining the number of active communication nodes in wireless topologies