A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods

This research presents a distributed and formula-based bilateration algorithm that can be used to provide initial set of locations. In this scheme each node uses distance estimates to anchors to solve a set of circle-circle intersection (CCI) problems, solved through a purely geometric formulation. The resulting CCIs are processed to pick those that cluster together and then take the average to produce an initial node location. The algorithm is compared in terms of accuracy and computational complexity with a Least-Squares localization algorithm, based on the Levenberg–Marquardt methodology. Results in accuracy vs. computational performance show that the bilateration algorithm is competitive compared with well known optimized localization algorithms

Probabilistic Time of Arrival Localization

In this letter, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement

Time-based Location Techniques Using Inexpensive, Unsynchronized Clocks in Wireless Networks

The ability to measure location using time of flight in IEEE 802.11 networks is impeded by the standard clock resolution, imprecise synchronization of the 802.11 protocol, and the inaccuracy of available clocks. To achieve real-time location with accuracy goals of a few meters, we derive new consensus synchronization techniques for free-running clocks. Using consensus synchronization, we improve existing time of arrival (TOA) techniques and introduce new time difference of arrival (TDOA) techniques. With this common basis, we show how TOA is theoretically superior to TDOA. Using TOA measurements, we can locate wireless nodes that participate in the location system, and using TDOA measurements, we can locate nodes that do not participate. We demonstrate applications using off-the-shelf 802.11 hardware that can determine location to within 3m using simple, existing optimization methods. The synchronization techniques extend existing ones providing distributed synchronization for free-running clocks to cases where send times cannot be controlled and adjusted precisely, as in 802.11 networks. These location and synchronization techniques may be applied to transmitting wireless nodes using any communication protocol where cooperating nodes can produce send and receive timestamps

EM Algorithm for Multiple Wideband Source Localization

A computationally efficient algorithm using the expectation-maximization (EM) algorithm for multiple wideband source localization in the near field of a sensor array/area is addressed in this thesis. Our idea is to decompose the observed sensor data, which is a superimposition of multiple sources, into the individual components in the frequency domain and then estimate the corresponding location parameters associated with each component separately. Instead of the conventional alternating projection (AP) method, we propose to adopt the EM algorithm in this work; our new method involves two steps, namely Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum likelihood estimates of the source location parameters are obtained. These two steps are executed iteratively and alternatively until the pre-defined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing AP scheme is investigated. It is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the existing AP algorithm

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

Robust Inference in Wireless Sensor Networks

This dissertation presents a systematic approach to obtain robust statistical inference schemes in unreliable networks. Statistical inference offers mechanisms for deducing the statistical properties of unknown parameters from the data. In Wireless Sensor Networks (WSNs), sensor outputs are transmitted across a wireless communication network to the fusion center (FC) for final decision-making. The sensor data are not always reliable. Some factors may cause anomaly in network operations, such as malfunction, corruption, or compromised due to some unknown source of contamination or adversarial attacks. Two standard component failure models are adopted in this study to describe the system vulnerability: the probabilistic and static models. In probabilistic models, we consider a widely known ε−contamination model, where each node has ε probability of malfunctioning or being compromised. In contrast, the static model assumes there is up to a certain number of malfunctioning nodes. It is assumed that the decision center/network operator is aware of the presence of anomaly nodes and can adjust the operation rule to counter the impact of the anomaly. The anomaly node is assumed to know that the network operator is taking some defensive actions to improve its performance. Considering both the decision center (network operator) and compromised (anomalous) nodes and their possible actions, the problem is formulated as a two-player zero-sum game. Under this setting, we attempt to discover the worst possible failure models and best possible operating strategies. First, the effect of sensor unreliability on detection performance is investigated, and robust detection schemes are proposed. The aim is to design robust detectors when some observation nodes malfunction. The detection problem is relatively well known under the probabilistic model in simple binary hypotheses testing with known saddle-point solutions. The detection problem is investigated under the mini-max framework for the static settings as no such saddle point solutions are shown to exist under these settings. In the robust estimation, results in estimation theory are presented to measure system robustness and performance. The estimation theory covers probabilistic and static component failure models. Besides the standard approaches of robust estimation under the frequentist settings where the interesting parameters are fixed but unknown, the estimation problem under the Bayes settings is considered where the prior probability distribution is known. After first establishing the general framework, comprehensive results on the particular case of a single node network are presented under the probabilistic settings. Based on the insights from the single node network, we investigate the robust estimation problem for the general network for both failure models. A few robust localization methods are presented as an extension of robust estimation theory at the end