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

Indoor localisation by using wireless sensor nodes

This study is devoted to investigating and developing WSN based localisation approaches with high position accuracies indoors. The study initially summarises the design and implementation of localisation systems and WSN architecture together with the characteristics of LQI and RSSI values. A fingerprint localisation approach is utilised for indoor positioning applications. A k-nearest neighbourhood algorithm (k-NN) is deployed, using Euclidean distances between the fingerprint database and the object fingerprints, to estimate unknown object positions. Weighted LQI and RSSI values are calculated and the k-NN algorithm with different weights is utilised to improve the position detection accuracy. Different weight functions are investigated with the fingerprint localisation technique. A novel weight function which produced the maximum position accuracy is determined and employed in calculations. The study covered designing and developing the centroid localisation (CL) and weighted centroid localisation (WCL) approaches by using LQI values. A reference node localisation approach is proposed. A star topology of reference nodes are to be utilized and a 3-NN algorithm is employed to determine the nearest reference nodes to the object location. The closest reference nodes are employed to each nearest reference nodes and the object locations are calculated by using the differences between the closest and nearest reference nodes. A neighbourhood weighted localisation approach is proposed between the nearest reference nodes in star topology. Weights between nearest reference nodes are calculated by using Euclidean and physical distances. The physical distances between the object and the nearest reference nodes are calculated and the trigonometric techniques are employed to derive the object coordinates. An environmentally adaptive centroid localisation approach is proposed.Weighted standard deviation (STD) techniques are employed adaptively to estimate the unknown object positions. WSNs with minimum RSSI mean values are considered as reference nodes across the sensing area. The object localisation is carried out in two phases with respect to these reference nodes. Calculated object coordinates are later translated into the universal coordinate system to determine the actual object coordinates. Virtual fingerprint localisation technique is introduced to determine the object locations by using virtual fingerprint database. A physical fingerprint database is organised in the form of virtual database by using LQI distribution functions. Virtual database elements are generated among the physical database elements with linear and exponential distribution functions between the fingerprint points. Localisation procedures are repeated with virtual database and localisation accuracies are improved compared to the basic fingerprint approach. In order to reduce the computation time and effort, segmentation of the sensing area is introduced. Static and dynamic segmentation techniques are deployed. Segments are defined by RSS ranges and the unknown object is localised in one of these segments. Fingerprint techniques are applied only in the relevant segment to find the object location. Finally, graphical user interfaces (GUI) are utilised with application program interfaces (API), in all calculations to visualise unknown object locations indoors

Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

Sensory fusion of UBW-TOF-based location systems for mobile robotics

With the increasing need for mobile robots in industrial applications, real-time location systems, which is a crucial point in these applications, has attracted attention from many researchers around the world. Thus, robot location is the process of determining the robot position and orientation in its environment. Location systems using Ultra-WideBand (UWB) have been widely used in complex urban and indoor environments. Consequently, a moving UWB tag can be located by measuring the distances to fixed UWB anchors whose positions are known in advance. The difficulty of this approach remains in the fact that the measurements are not perfect. There will always be some noise in the measurements, and because of this, position determination could contain some errors that may result in decreased accuracy. In this work, the Pozyx performance, a low-cost Ultra-WideBand (UWB) Time-of-flight (TOF) technology solution, is studied and implemented on a mobile robot, through a beacon-based location scheme. In order to reduce the impact of measurement noise and system disturbances, the readings of odometry, Pozyx measures and the information of the lines of a known navigation path are fused to improve the estimated location of the mobile robot. Therefore, the goal of this integration is to improve the accuracy of location for indoor autonomous robots. Firstly, was studied the characterisation of the Pozyx measurement error among several test conditions. Then, an Extended Kalman Filter (EKF) algorithm is implemented using two heuristics that allow the release of the filter so that it converges to the correct robot pose after it has started to diverge. Consequently, the results obtained from the different location tests performed are presented and compared, to present the precision achieved and proving the several advantages of using heuristics. Overall, this work with Pozyx system showed that it is a proper and effective tool to improve the robot location in a challenging indoor environment given its good cost/accuracy trade-off

Cooperative localisation in underwater robotic swarms for ocean bottom seismic imaging.

Spatial information must be collected alongside the data modality of interest in wide variety of sub-sea applications, such as deep sea exploration, environmental monitoring, geological and ecological research, and samples collection. Ocean-bottom seismic surveys are vital for oil and gas exploration, and for productivity enhancement of an existing production facility. Ocean-bottom seismic sensors are deployed on the seabed to acquire those surveys. Node deployment methods used in industry today are costly, time-consuming and unusable in deep oceans. This study proposes the autonomous deployment of ocean-bottom seismic nodes, implemented by a swarm of Autonomous Underwater Vehicles (AUVs). In autonomous deployment of ocean-bottom seismic nodes, a swarm of sensor-equipped AUVs are deployed to achieve ocean-bottom seismic imaging through collaboration and communication. However, the severely limited bandwidth of underwater acoustic communications and the high cost of maritime assets limit the number of AUVs that can be deployed for experiments. A holistic fuzzy-based localisation framework for large underwater robotic swarms (i.e. with hundreds of AUVs) to dynamically fuse multiple position estimates of an autonomous underwater vehicle is proposed. Simplicity, exibility and scalability are the main three advantages inherent in the proposed localisation framework, when compared to other traditional and commonly adopted underwater localisation methods, such as the Extended Kalman Filter. The proposed fuzzy-based localisation algorithm improves the entire swarm mean localisation error and standard deviation (by 16.53% and 35.17% respectively) at a swarm size of 150 AUVs when compared to the Extended Kalman Filter based localisation with round-robin scheduling. The proposed fuzzy based localisation method requires fuzzy rules and fuzzy set parameters tuning, if the deployment scenario is changed. Therefore a cooperative localisation scheme that relies on a scalar localisation confidence value is proposed. A swarm subset is navigationally aided by ultra-short baseline and a swarm subset (i.e. navigation beacons) is configured to broadcast navigation aids (i.e. range-only), once their confidence values are higher than a predetermined confidence threshold. The confidence value and navigation beacons subset size are two key parameters for the proposed algorithm, so that they are optimised using the evolutionary multi-objective optimisation algorithm NSGA-II to enhance its localisation performance. Confidence value-based localisation is proposed to control the cooperation dynamics among the swarm agents, in terms of aiding acoustic exteroceptive sensors. Given the error characteristics of a commercially available ultra-short baseline system and the covariance matrix of a trilaterated underwater vehicle position, dead reckoning navigation - aided by Extended Kalman Filter-based acoustic exteroceptive sensors - is performed and controlled by the vehicle's confidence value. The proposed confidence-based localisation algorithm has significantly improved the entire swarm mean localisation error when compared to the fuzzy-based and round-robin Extended Kalman Filter-based localisation methods (by 67.10% and 59.28% respectively, at a swarm size of 150 AUVs). The proposed fuzzy-based and confidence-based localisation algorithms for cooperative underwater robotic swarms are validated on a co-simulation platform. A physics-based co-simulation platform that considers an environment's hydrodynamics, industrial grade inertial measurement unit and underwater acoustic communications characteristics is implemented for validation and optimisation purposes

Information Fusion for 5G IoT: An Improved 3D Localisation Approach Using K-DNN and Multi-Layered Hybrid Radiomap

Indoor positioning is a core enabler for various 5G identity and context-aware applications requiring precise and real-time simultaneous localisation and mapping (SLAM). In this work, we propose a K-nearest neighbours and deep neural network (K-DNN) algorithm to improve 3D indoor positioning. Our implementation uses a novel data-augmentation concept for the received signal strength (RSS)-based fingerprint technique to produce a 3D fused hybrid. In the offline phase, a machine learning (ML) approach is used to train a model on a radiomap dataset that is collected during the offline phase. The proposed algorithm is implemented on the constructed hybrid multi-layered radiomap to improve the 3D localisation accuracy. In our implementation, the proposed approach is based on the fusion of the prominent 5G IoT signals of Bluetooth Low Energy (BLE) and the ubiquitous WLAN. As a result, we achieved a 91% classification accuracy in 1D and a submeter accuracy in 2D

Audio Fingerprinting for Multi-Device Self-Localization

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Multi-modal probabilistic indoor localization on a smartphone

The satellite-based Global Positioning System (GPS) provides robust localization on smartphones outdoors. In indoor environments, however, no system is close to achieving a similar level of ubiquity, with existing solutions offering different trade-offs in terms of accuracy, robustness and cost. In this paper, we develop a multi-modal positioning system, targeted at smartphones, which aims to get the best out of each of its constituent modalities. More precisely, we combine Bluetooth low energy (BLE) beacons, round-trip-time (RTT) enabled WiFi access points and the smartphone’s inertial measurement unit (IMU) to provide a cheap robust localization system that, unlike fingerprinting methods, requires no pre-training. To do this, we use a probabilistic algorithm based on a conditional random field (CRF). We show how to incorporate sparse visual information to improve the accuracy of our system, using pose estimation from pre-scanned visual landmarks, to calibrate the system online. Our method achieves an accuracy of around 2 meters on two realistic datasets, outperforming other distance-based localization approaches. We also compare our approach with an ultra-wideband (UWB) system. While we do not match the performance of UWB, our system is cheap, smartphone compatible and provides satisfactory performance for many applications

Train Localisation using Wireless Sensor Networks

Safety and reliability have always been concerns for railway transportation. Knowing the exact location of a train enables the railway system to react to an unusual situation for the safety of human lives and properties. Generally, the accuracy of localisation systems is related with their deployment and maintenance costs, which can be on the order of millions of dollars a year. Despite a lot of research efforts, existing localisation systems based on different technologies are still limited because most of them either require expensive infrastructure (ultrasound and laser), have high database maintenance, computational costs or accumulate errors (vision), offer limited coverage (GPS-dark regions, Wi-Fi, RFID) or provide low accuracy (audible sound). On the other hand, wireless sensor networks (WSNs) offer the potential for a cheap, reliable and accurate solutions for the train localisation system. This thesis proposes a WSN-based train localisation system, in which train location is estimated based on the information gathered through the communication between the anchor sensors deployed along the track and the gateway sensor installed on the train, such as anchor sensors' geographic coordinates and the Received Signal Strength Indicator (RSSI). In the proposed system, timely anchor-gateway communication implies accurate localisation. How to guarantee effective communication between anchor sensors along the track and the gateway sensor on the train is a challenging problem for WSN-based train localisation. I propose a beacon driven sensors wake-up scheme (BWS) to address this problem. BWS allows each anchor sensor to run an asynchronous duty-cycling protocol to conserve energy and establishes an upper bound on the sleep time in one duty cycle to guarantee their timely wake-up once a train approaches. Simulation results show that the BWS scheme can timely wake up the anchor sensors at a very low energy consumption cost. To design an accurate scheme for train localisation, I conducted on-site experiments in an open field, a railway station and a tunnel, and the results show that RSSI can be used as an estimator for train localisation and its applicability increases with the incorporation of another type of data such as location information of anchor sensors. By combining the advantages of RSSI-based distance estimation and Particle Filtering techniques, I designed a Particle-Filter-based train localisation scheme and propose a novel Weighted RSSI Likelihood Function (WRLF) for particle update. The proposed localisation scheme is evaluated through extensive simulations using the data obtained from the on-site measurements. Simulation results demonstrate that the proposed scheme can achieve significant accuracy, where average localisation error stays under 30 cm at the train speed of 40 m=s, 40% anchor sensors failure rate and sparse deployment. In addition, the proposed train localisation scheme is robust to changes in train speed, the deployment density and reliability of anchor sensors. Anchor sensors are prone to hardware and software deterioration such as battery outage and dislocation. Therefore, in order to reduce the negative impacts of these problems, I designed a novel Consensus-based Anchor sensor Management Scheme (CAMS), in which each anchor sensor performs a self-diagnostics and reports the detected faults in the neighbourhood. CAMS can assist the gateway sensor to exclude the input from the faulty anchor sensors. In CAMS, anchor sensors update each other about their opinions on other neighbours and develops consensus to mark faulty sensors. In addition, CAMS also reports the system information such as signal path loss ratio and allows anchor sensors to re-calibrate and verify their geographic coordinates. CAMS is evaluated through extensive simulations based on real data collected from field experiments. This evaluation also incorporated the simulated node failure model in simulations. Though there are no existing WSN-based train localisation systems available to directly compare our results with, the proposed schemes are evaluated with real datasets, theoretical models and existing work wherever it was possible. Overall, the WSN-based train localisation system enables the use of RSSI, with combination of location coordinates of anchor sensors, as location estimator. Due to low cost of sensor devices, the cost of overall system remains low. Further, with duty-cycling operation, energy of the sensor nodes and system is conserved