In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches. In this work, we present an RFID-based system, Tagoram, for object localization and tracking using COTS RFID tags and readers. Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. Our system achieves these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. In Tagoram, we exploit the tag's mobility to build a virtual antenna array by using readings from a few physical antennas over a time window. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tag's moving trajectories and its locations, relaxing the assumption of knowing tag's track function in advance. We have implemented the Tagoram system using COTS RFID tags and readers. The system has been tested extensively in the lab environment and used for more than a year in real airline applications. For lab environment, we can track the mobile tags in real time with a millimeter accuracy to a median of 5mm and 7.29mm using linear and circular track respectively. In our year- long large scale baggage sortation systems deployed in two airports, our results from real deployments show that Tagoram can achieve a centimeter-level accuracy to a median of 6.35cm in these real deployments.

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Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices

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.

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

Opportunistic spectrum access (OSA) has been regarded as the most promising approach to solve the paradox between spectrum scarcity and waste. Intelligent decision making is key to OSA and differentiates it from previous wireless technologies. In this article, a survey of decision-theoretic solutions for channel selection and access strategies for OSA system is presented. We analyze the challenges facing OSA systems globally, which mainly include interactions among multiple users, dynamic spectrum opportunity, tradeoff between sequential sensing cost and expected reward, and tradeoff between exploitation and exploration in the absence of prior statistical information. We provide comprehensive review and comparison of each kind of existing decision-theoretic solution, i.e., game models, Markovian decision process, optimal stopping problem and multi-armed bandit problem. We analyze their strengths and limitations and outline further research for both technical contents and methodologies. In particular, these solutions are critically analyzed in terms of information, cost and convergence speed, which are key concerns for practical implementation. Moreover, it is noted that each kind of existing decision-theoretic solution mainly addresses one aspect of the challenges, which implies that two or more kinds of decision-theoretic solutions should be incorporated to address more challenges simultaneously.

Decision-Theoretic Distributed Channel Selection for Opportunistic Spectrum Access: Strategies, Challenges and Solutions

Different from online shopping, in-store shopping has few ways to collect the customer behaviors before purchase. In this paper, we present the design and implementation of an on-site Customer Behavior IDentification system based on passive RFID tags, named CBID. By collecting and analyzing wireless signal features, CBID can detect and track tag movements and further infer corresponding customer behaviors. We model three main objectives of behavior identification by concrete problems and solve them using novel protocols and algorithms. The design innovations of this work include a Doppler effect based protocol to detect tag movements, an accurate Doppler frequency estimation algorithm, an image-based human count estimation protocol and a tag clustering algorithm using cosine similarity. We have implemented a prototype of CBID in which all components are built by off-the-shelf devices. We have deployed CBID in real environments and conducted extensive experiments to demonstrate the accuracy and efficiency of CBID in customer behavior identification.

CBID: A Customer Behavior Identification System Using Passive Tags

Wi-Fi and magnetic field fingerprinting have been a hot topic in indoor positioning researches because of their ubiquity and location-related features. Wi-Fi signals can provide rough initial positions, and magnetic fields can further improve the positioning accuracies, therefore many researchers have tried to combine the two signals for high-accuracy indoor localization. Currently, state-of-the-art solutions design separate algorithms to process different indoor signals. Outputs of these algorithms are generally used as inputs of data fusion strategies. These methods rely on computationally expensive particle filters, labor-intensive feature analysis, and time-consuming parameter tuning to achieve better accuracies. Besides, particle filters need to estimate the moving directions of particles, limiting smartphone orientation to be stable, and aligned with the user’s moving directions. In this paper, we adopted a convolutional neural network (CNN) to implement an accurate and orientation-free positioning system. Inspired by the state-of-the-art image classification methods, we design a novel hybrid location image using Wi-Fi and magnetic field fingerprints, and then a CNN is employed to classify the locations of the fingerprint images. In order to prevent the overfitting problem of the positioning CNN on limited training datasets, we also propose to divide the learning process into two steps to adopt proper learning strategies for different network branches. We show that the CNN solution is able to automatically learn location patterns, thus significantly lower the workforce burden of designing a localization system. Our experimental results convincingly reveal that the proposed positioning method achieves an accuracy of about 1 m under different smartphone orientations, users, and use patterns.

Indoor Positioning Based on Fingerprint-Image and Deep Learning

For cognitive wireless networks, one challenge is that the status and statistics of the channels’ availability are difficult to predict. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed order. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem , and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conduct extensive simulations to compare the performance of our method with traditional MAB-based approach. Simulation results show that the proposed scheme improves the throughput by more than 30% and speeds up the learning process by more than 100%.

Almost Optimal Dynamically-Ordered Channel Sensing and Accessing for Cognitive Networks

Indoor navigation has received much attention in academics and industry in recent years. Previous methods often attempt to locate users with various localization algorithms in combination with an indoor map, so they need expensive infrastructures deployed in advance. In this study, we propose to utilize existing indoor objects attached RFID tags and the reader to navigate the user to the destination, without need of any extra hardware. The key insight is that the personal movement takes an impact on the Doppler frequency shift values collected from the indoor objects when getting close to the tag. Such local humanitem spatial relation is leveraged to infer the users position and further navigate user to destination step by step. We implement a prototype navigation system, called RollCaller, and conduct comprehensive experiments to examine its performance. Keywords—RFID, Doppler Frequency Shift, Human-Item Spatial Relation, Indoor Navigation

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RollCaller: User-friendly indoor navigation system using human-item spatial relation

In multichannel system, user could keep transmitting over an instantaneous “on peak” channel by opportunistically accessing and switching among channels Previous studies rely on constant transmission duration, which would fail to leverage more opportunities in time and frequency domain In this paper, we consider opportunistic channel accessing/releasing scheme in multichannel system with Rayleigh fading channels Our main goal is to derive a throughput-optimal strategy for determining when and which channel to access and when to release it We formulate this real-time decision-making process as a two-dimensional optimal stopping problem We prove that the two-dimensional optimal stopping rule can be reduced to a simple threshold-based policy Leveraging the absorbing Markov chain theory, we obtain the optimal threshold as well as the maximum achievable throughput with computational efficiency Numerical and simulation results show that our proposed channel utilization scheme achieves up to 140 percent throughput gain over opportunistic transmission with a single channel and up to 60 percent throughput gain over opportunistic channel access with constant transmission duration

Optimal Frequency-Temporal Opportunity Exploitation for Multichannel Ad Hoc Networks

In opportunistic channel access, the user needs to make real time decisions on when and which channel to access with uncertainty. Assuming perfect channel statistics, several studies have applied optimal stopping theory to derive control strategy for sequential sensing/probing based opportunistically accessing ( s -SPA), exploiting temporary opportunities among multiple channels. Meanwhile, numerous multi-arm bandit (MAB)-based approaches have been proposed for online learning of channel selection in periodical sensing/accessing system, however, these schemes fail to exploit the opportunistic diversity in short term. In this paper, we investigate online learning of optimal control in s -SPA systems, where both statistics learning and temporary opportunity utilization are jointly considered. An effective and efficient online policy, so called IE-OSP, is proposed, which theoretically guarantees system converges to the optimal s -SPA strategy with bounded probability. Experimental results further show that, the regret of IE-OSP is almost in optimal logarithmic increasing rate over time, and is sub-linear with the increasing number of channels. Compared with existing solutions, our proposed algorithm achieves 25 ∼ 30% throughput gain in typical scenarios.

Online Sequential Channel Accessing Control: A Double Exploration vs. Exploitation Problem

For cognitive wireless networks, one challenge is that the status of the channels' availability and quality is difficult to predict and quantify. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed ordering. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem, and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conducted extensive simulations to compare the performance of our method with traditional MAB-based approach. Our simulation results show that our scheme improves the throughput by more than 30% and speed up the learning process by more than 100%.

Bowen Li

Papers

Almost Optimal Dynamically-Ordered Channel Sensing and Accessing for Cognitive Networks

RollCaller: User-friendly indoor navigation system using human-item spatial relation

Optimal Frequency-Temporal Opportunity Exploitation for Multichannel Ad Hoc Networks

Online Sequential Channel Accessing Control: A Double Exploration vs. Exploitation Problem

Almost optimal dynamically-ordered multi-channel accessing for cognitive networks