Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Optimizing Indoor Location Based Tracking through Proper Filter Selection and Wireless Sensor Network Design

Indoor positioning system (IPS) is a topic that is coming up more and more for various reasons, such as allowing companies to track important objects using radio frequency identification (RFID) and employees with Bluetooth devices inside a facility. Geofencing is one of the biggest topics with IPS and is meant to limit access to a network in specified areas. Devices that incorporate indoor tracking are not initially precise when objects and employees are on the move. This movement requires devices to have a reliable filter for noise and package lose. For this paper, the comparison between extended Kalman filters and unscented Kalman filter in a controlled environment will help indicate which is ideal for IPS tracking. Both filters will be applied and compared on location accuracy metrics. The proper design of the wireless network is also crucial for having an effective IPS method. This will show the difference in wireless networks and how the initial design will lead to greater chance of success for IPS

Minimal Infrastructure Radio Frequency Home Localisation Systems

The ability to track the location of a subject in their home allows the provision of a number of location based services, such as remote activity monitoring, context sensitive prompts and detection of safety critical situations such as falls. Such pervasive monitoring functionality offers the potential for elders to live at home for longer periods of their lives with minimal human supervision. The focus of this thesis is on the investigation and development of a home roomlevel localisation technique which can be readily deployed in a realistic home environment with minimal hardware requirements. A conveniently deployed Bluetooth ® localisation platform is designed and experimentally validated throughout the thesis. The platform adopts the convenience of a mobile phone and the processing power of a remote location calculation computer. The use of Bluetooth ® also ensures the extensibility of the platform to other home health supervision scenarios such as wireless body sensor monitoring. Central contributions of this work include the comparison of probabilistic and nonprobabilistic classifiers for location prediction accuracy and the extension of probabilistic classifiers to a Hidden Markov Model Bayesian filtering framework. New location prediction performance metrics are developed and signicant performance improvements are demonstrated with the novel extension of Hidden Markov Models to higher-order Markov movement models. With the simple probabilistic classifiers, location is correctly predicted 80% of the time. This increases to 86% with the application of the Hidden Markov Models and 88% when high-order Hidden Markov Models are employed. Further novelty is exhibited in the derivation of a real-time Hidden Markov Model Viterbi decoding algorithm which presents all the advantages of the original algorithm, while producing location estimates in real-time. Significant contributions are also made to the field of human gait-recognition by applying Bayesian filtering to the task of motion detection from accelerometers which are already present in many mobile phones. Bayesian filtering is demonstrated to enable a 35% improvement in motion recognition rate and even enables a floor recognition rate of 68% using only accelerometers. The unique application of time-varying Hidden Markov Models demonstrates the effect of integrating these freely available motion predictions on long-term location predictions

Enabling wireless sensors localization in dynamic indoor environments

Wireless sensors networks localization is an important area that attracts significant research interest. Localization is a fundamental problem that must be solved in order to support location-aware applications. The growing demand of location-aware applications requires the development of application-oriented localization solutions with appropriate trade offs between accuracy and costs. The present thesis seeks to enhance the performance of simple and low-cost propagation based localization solutions in dynamic indoor environments. First, an overview of the different approaches in wireless sensors networks localization is provided. Next, sources of received signal strength variability are investigated. Then, the problems of the distance-dependant path loss estimation caused by the radio channel of dynamic indoor situations are empirically analyzed. Based on these previous theoretical and empirical analysis, the solution uses spatial and frequency diversity techniques, in addition to time diversity, in order to create a better estimator of the distance-dependent path loss by counteracting the random multipath effect. Furthermore, the solution attempts to account for the random shadow fading by using "shadowing-independent" path loss estimations in order to deduce distances. In order to find the unknown sensor's positions based on the distance estimates, the solution implements a weighted least-squares algorithm that reduces the impact of the distance estimates errors in the location estimate

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Ph.DDOCTOR OF PHILOSOPH