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

Cattle-powered nodes experience in a heterogeneous network for localization of herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize is presented and its use demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondarynode transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system

A Heterogeneous Wireless Identification Network for the Localization of Animals Based on Stochastic Movements

The improvement in the transmission range in wireless applications without the use of batteries remains a significant challenge in identification applications. In this paper, we describe a heterogeneous wireless identification network mostly powered by kinetic energy, which allows the localization of animals in open environments. The system relies on radio communications and a global positioning system. It is made up of primary and secondary nodes. Secondary nodes are kinetic-powered and take advantage of animal movements to activate the node and transmit a specific identifier, reducing the number of batteries of the system. Primary nodes are battery-powered and gather secondary-node transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. The system allows tracking based on contextual information obtained from statistical data

Methods and Tools for Battery-free Wireless Networks

Embedding small wireless sensors into the environment allows for monitoring physical processes with high spatio-temporal resolutions. Today, these devices are equipped with a battery to supply them with power. Despite technological advances, the high maintenance cost and environmental impact of batteries prevent the widespread adoption of wireless sensors. Battery-free devices that store energy harvested from light, vibrations, and other ambient sources in a capacitor promise to overcome the drawbacks of (rechargeable) batteries, such as bulkiness, wear-out and toxicity. Because of low energy input and low storage capacity, battery-free devices operate intermittently; they are forced to remain inactive for most of the time charging their capacitor before being able to operate for a short time. While it is known how to deal with intermittency on a single device, the coordination and communication among groups of multiple battery-free devices remain largely unexplored. For the first time, the present thesis addresses this problem by proposing new methods and tools to investigate and overcome several fundamental challenges

Long-term tracking and monitoring of mobile entities in the outdoors using wireless sensors

There is an emerging class of applications that require long-term tracking and monitoring of mobile entities for characterising their contexts and behaviours using data from wireless sensors. Examples include monitoring animals in their natural habitat over the annual cycle; tracking shipping containers and their handling during transit; and monitoring air quality using sensors attached to bicycles used in public sharing schemes. All applications within this class require the acquisition of sensor data tagged with spatio-temporal information and uploaded wirelessly. Currently there is no solution targeting the entire class of applications, only point solutions focused on specific scenarios. This thesis presents a complete solution (firmware and hardware) for applications within this class that consists of attaching mobile sensor nodes to the entities for tracking and monitoring their behaviour, and deploying an infrastructure of base-stations for collecting the data wirelessly. The proposed solution is more energy efficient compared to the existing solutions that target specific scenarios, offering a longer deployment lifetime with a reduced size and weight of the devices. This is achieved mainly by using the VB-TDMA low-power data upload protocol proposed in this thesis. The mobile sensor nodes, consisting of the GPS and radio modules among others, and the base-stations are powered by batteries, and the optimisation of their energy usage is of primary concern. The presence of the GPS module, in particular its acquisition of accurate time, is used by the VB-TDMA protocol to synchronise the communication between nodes at no additional energy costs, resulting in an energy-efficient data upload protocol for sparse networks of mobile nodes, that can potentially be out of range of base-stations for extended periods of time. The VB-TDMA and an asynchronous data upload protocol were implemented on the custom-designed Prospeckz-5-based wireless sensor nodes. The protocols’ performances were simulated in the SpeckSim simulator and validated in real-world deployments of tracking and monitoring thirty-two Retuerta wild horses in the Doñana National Park in Spain, and a herd of domesticated horses in Edinburgh. The chosen test scenario of long-term wildlife tracking and monitoring is representative for the targeted class of applications. The VB-TDMA protocol showed a significantly lower power consumption than other comparable MAC protocols, effectively doubling the battery lifetime. The main contributions of the thesis are the development of the VB-TDMA data upload protocol and its performance evaluation, along with the development of simulation models for performance analysis of wireless sensor networks, validated using data from the two real-world deployments

UAV Aided Data Collection for Wildlife Monitoring using Cache-enabled Mobile Ad-hoc Wireless Sensor Nodes

Unmanned aerial vehicle (UAV) assisted data collection is not a new concept and has been used in various mobile ad hoc networks. In this paper, we propose a caching assisted scheme alternative to routing in MANETs for the purpose of wildlife monitoring. Rather than deploying a routing protocol, data is collected and transported to and from a base station using a UAV. Although some literature exists on such an approach, we propose the use of intermediate caching between the mobile nodes and compare it to a baseline scenario where no caching is used. The paper puts forward our communication design where we have simulated the movement of multiple mobile sensor nodes in a field that move according to the Levy walk model imitating wildlife animal foraging and a UAV that makes regular trips across the field to collect data from them. The unmanned aerial vehicle can collect data not only from the current node it is communicating with but also data of other nodes that this node came into contact with. Simulations show that exchanging cached data is highly advantages as the drone can indirectly communicate with many more mobile nodes