Neurological gait disorders affect a large population, significantly reducing life quality. This article brings a foundational understanding to the potentials of emerging sensing modalities (e.g., WiFi) for gait disorder assessment, via conducting a one-year-long clinical trial in collaboration with the Neurology Associates of Santa Barbara. Our medical campaign encompasses 114 real subjects and a wide spectrum of disorders (e.g., Parkinson’s, Neuropathy, Post Stroke, Dementia, Arthritis). We then develop the first WiFi-based gait disorder sensing system of its kind, distinguished by its scope of validation with a large and diverse patient cohort. To ensure generalizability, we mainly leverage publicly accessible online videos of gait disorders for training, and develop a video-to-RF pipeline to convert them to synthetic RF training data. We then extensively test the system in a neurology center (i.e., the Neurology Associates of Santa Barbara). Additionally, we provide a 1-1 comparison with a vision-based system, by developing a vision-based gait assessment system under identical conditions, a first-of-its-kind comparison to our knowledge. We finally contrast both systems with neurologists’ accuracy when basing evaluation solely on visual gait inspection, by designing/distributing a large survey to 70 neurologists, offering the first apples-to-apples comparison of these three sensing modalities. Our findings can help integrate these sensing systems into medical practice, working toward equitable healthcare.

Uncrewed vehicles (UVs) functioning as autonomous agents are anticipated to play a crucial role in the sixth generation (6G) of wireless networks. Their seamless integration, cost-effectiveness, and additional controllability through motion planning make them an attractive deployment option for a wide range of applications, both as assets in the network e.g., mobile base stations (BSs) and as consumers of network services (e.g., autonomous delivery systems). However, despite their potential, the convergence of UVs and wireless systems brings forth numerous challenges that require attention from both academia and industry. This article then aims to offer a comprehensive overview, encompassing the transformative possibilities as well as the significant challenges associated with UV-assisted next-generation wireless communications. Considering the diverse landscape of possible application scenarios, problem formulations, and mathematical tools related to UV-assisted wireless systems, the underlying core theme of this article is the unification of the problem space, providing a structured framework to understand the use cases, problem formulations, and necessary mathematical tools. Overall, this article sets forth a clear understanding of how UVs can be integrated in the 6G ecosystem, paving the way toward harnessing the full potential at this intersection.

The advances of 6G systems have prompted the study of new communication paradigms, including relay networks enabled by wireless power transfer (WPT). While existing literature focuses on the cooperative case, this paper examines a non-cooperative scenario in which a source must incentivize one of several battery-powered user equipments (UEs) to act as a relay by offering payment in the form of WPT, while the utility-maximizing UEs seeking to extract as much energy from the source as possible. We propose a protocol based on a reverse auction that enables the source to determine which candidate UE to select as the relay and the amount of energy to be transferred as payment, even when the channel quality between the candidates and the destination is unknown to the source. We first examine the performance of the system under the classical Vickrey auction. We prove that our protocol achieves the best possible outage probability and point out ways to mitigate the gap in energy efficiency when compared to a cooperative baseline. We then analyze system performance under a Myerson auction, which maximizes the auctioneer’s utility. To ensure computational tractability, we extend the forward auction regularity condition to the reverse setting, providing a mathematical characterization of regularity and the associated pricing mechanism. We then prove the regularity of the WPT-based auction under both lognormal and Rayleigh fading. To validate our analytical findings, we present extensive numerical results demonstrating how system parameters affect energy efficiency and outage probability. Our results show that auction-based protocols can reduce both outage probability and communication energy by more than 50% with as few as two relay candidates, compared to direct transmission by the source. Additionally, they demonstrate exponential convergence to the cooperative lower performance bound and indicate conditions under which one auction type is preferred over the other. Overall, the auction-based system offers a foundation for improved energy efficiency and communication reliability in non-cooperative environments.

This paper addresses the challenge of incentivizing energy-constrained, non-cooperative user equipment (UE) to serve as cooperative relays. We consider a source UE with a non-line-of-sight channel to an access point (AP), where direct communication may be infeasible or may necessitate a substantial transmit power. Other UEs in the vicinity are viewed as relay candidates, and our aim is to enable energy-efficient connectivity for the source, while accounting for the self-interested behavior and private channel state information of these candidates, by allowing the source to "pay" the candidates via wireless power transfer (WPT). We propose a cooperation-inducing protocol, inspired by Myerson auction theory, which ensures that candidates truthfully report power requirements while minimizing the expected power used by the source. Through rigorous analysis, we establish the regularity of valuations for lognormal fading channels, which allows for the efficient determination of the optimal source transmit power. Extensive simulation experiments, employing real-world communication and WPT parameters, validate our theoretical framework. Our results demonstrate over 71% reduction in outage probability with as few as 4 relay candidates, compared to the non-cooperative scenario, and as much as 70% source power savings compared to a baseline approach, highlighting the efficacy of our proposed methodology.

Recent advances in robotics present new paradigms in communication systems, particularly the use of autonomous vehicles to enhance communication capabilities in a number of settings. Polling systems, in which a single server services multiple queues and incurs some idle time when switching between these, provide useful models for a number of scenarios encountered in such systems. In this paper, we extend traditional polling systems to model these mobility-enabled communication scenarios by embedding the polling systems in 2D Euclidean space and defining service regions in this space where the queues' requests may be serviced. We show that this model can be used to capture the Age of Information (AoI) in the system as well. We pose the fundamental problem of minimizing the average wait time for a request, a key metric of the system's performance, and we provide lower and upper bounds on the optimal solution. Focusing on a cyclic visit order, we find provably optimal trajectories for the low- and high-traffic cases. Finally, we present numerical results from an IoT data collection scenario which illustrate the derived theory and show significant improvements in average wait time when compared to a move-stop-communicate baseline.

This paper presents a novel approach for crowd analytics using a single monostatic mmWave radar. We propose a new mathematical model that infers the crowd size for dynamic and quasi-dynamic crowd behaviors. More specifically, we derive a novel closed-form mathematical expression that describes the statistical dynamics of undercounting due to crowd shadowing. This new methodical finding allows for significantly improved crowd density estimates. For spatially-patterned crowds where the mathematical solution does not extend, we then develop a Temporal Convolutional Network (TCN) which is purely trained on simulated data. We perform extensive testing over a total of 22 experiments, with up to (and including) 21 people and in 4 different areas, including indoors, and the proposed mathematical solution achieves a Mean Absolute Error (MAE) of 1.53. Lastly, we show how our framework can infer anomalies, bottlenecks, and crowd engagement level. Overall, the paper can have a significant impact on crowd management and urban planning.

In this paper, we propose a new paradigm in intelligent surface design for field programming and multi-point focusing. We approach this problem from an entirely different vantage point by leveraging edges and the corresponding Geometrical Theory of Diffraction (GTD), allowing us to avoid the use of highly specialized and expensive element designs. More specifically, we show that a lattice of edge elements (i.e., cheap, thin, rectangular metal plates with length long enough as compared to width) can provide a rich repertoire for programming the RF field. When a wave is incident on an edge, a cone of outgoing rays emerges, known as a Keller cone. When considering a lattice of such edge elements, we then have a rich set of "knobs" for RF field programming, via changing the orientation of the edge elements and exploiting the exiting Keller cones. We then show how to electromagnetically model and design a practical edge element. We further propose an efficient algorithm to configure the orientations of the edges to achieve the desired multi-point focusing. We build sample prototypes of our proposed paradigm, using off-the-shelf material (i.e., 7 cent metal plates). We then show several real-world experiments in three different indoor areas, where an edge lattice focuses the transmitted wave of a WiFi card of a laptop on up to and including 4 focal points (maximum achieved in the literature albeit with much more expensive element designs). Overall, the paper shows the rich potential of edges for RF field programming.

This paper considers trajectory planning for a mobile robot which persistently relays data between pairs of far-away communication nodes. Data accumulates stochastically at each source, and the robot must move to appropriate positions to enable data offload to the corresponding destination. The robot needs to minimize the average time that data waits at a source before being serviced. We are interested in finding optimal robotic routing policies consisting of 1) locations where the robot stops to relay (relay positions) and 2) conditional transition probabilities that determine the sequence in which the pairs are serviced. We first pose this problem as a non-convex problem that optimizes over both relay positions and transition probabilities. To find approximate solutions, we propose a novel algorithm which alternately optimizes relay positions and transition probabilities. For the former, we find efficient convex partitions of the non-convex relay regions, then formulate a mixed-integer second-order cone problem. For the latter, we find optimal transition probabilities via sequential least squares programming. We extensively analyze the proposed approach and mathematically characterize important system properties related to the robot’s long-term energy consumption and service rate. Finally, through extensive simulation with real channel parameters, we verify the efficacy of our approach.

In this paper, we are interested in high-quality imaging of still objects with only received power measurements of off-the-shelf WiFi transceivers. We show that the scattered WiFi signals off of objects carry much richer information about the edges of the objects than the surface points. Based on this observation, we then propose a completely different way of thinking about this imaging problem. More specifically, we propose Wiffract, a new foundation for imaging objects via edge tracing. Our approach uses the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones to image edges of the object. We extensively validate our approach with 37 experiments in three different areas, including through-wall scenarios. We take developing a WiFi Reader as one example application to showcase the capabilities of our proposed pipeline. More specifically, we show how our approach can successfully image several alphabet-shaped objects. We further show that our approach enables WiFi to read, i.e., correctly classify the letters, with an accuracy of 86.7%. Finally, we show how our approach enables WiFi to image and read through walls, by imaging the details and further reading the letters of the word "BELIEVE" through walls. Overall, our proposed approach can open up new directions for RF imaging.

The detection of nocturnal seizures in epilepsy patients is essential, both for the quick management of the seizure complications, and for the assessment of the ongoing seizure treatment. Traditional seizure detection products (e.g., wearables), however, are either very costly, uncomfortable, or unreliable. In this article, we then propose to utilize everyday WiFi signals for robust, fast, and noninvasive detection of nocturnal seizures. We first present a new and rigorous mathematical characterization for the spectral content/bandwidth of the WiFi signal, measured on a WiFi device placed near a sleeping patient, during different kinds of sleep motions: seizures, normal movements (e.g., posture adjustments), and breathing. Based on this mathematical modeling, we propose a novel pipeline for processing the received WiFi signals to robustly detect all nocturnal nonbreathing movements, and then classify them into normal body movements or seizures. In order to validate this, we carry out extensive experiments in seven different typical bedroom locations, where a set of 20 actors simulate the state of having seizures (a total of 260 instances), as well as normal sleep movements (a total of 410 instances). Our proposed system detects 93.85% of the seizures with a mean response time (MRT) of only 5.69 s since the onset of the seizure. Moreover, our proposed system achieves a probability of false alarm of only 0.0097, when classifying normal sleep movements. Overall, our new mathematical modeling and experimental results show the great potential the ubiquitous WiFi signals have for detecting nocturnal seizures, which can provide better support for epilepsy patients and their caregivers.

In this paper, we are interested in path optimization for robotic communication operations in obstacle environments. Consider a robot that needs to perform a given communication task (e.g., data uploading, broadcasting, or relaying) while navigating from a start position to a designated final position, avoiding obstacles, and minimizing its total motion and communication costs. Our goal is to develop a general path planning algorithm applicable to various robotic communication scenarios in which a robot operates in realistic channel fading environments and in the presence of obstacles. We show how we can adapt the traditional Rapidly-Exploring Random Tree Search Star (RRT*) path planning algorithm to jointly consider both communication and motion objectives in realistic channel environments that contain obstacles. We further show that our proposed approach can provide theoretical optimality guarantees while being computationally efficient. We extensively evaluate our proposed approach in realistic wireless channel environments for various transmission settings and communication tasks. The results demonstrate the efficacy of our proposed approach.

In this paper, we are interested in the problem of counting a crowd of stationary people (i.e., seated) using a pair of WiFi transceivers. While the people in the crowd are stationary, i.e. with no major body motion except breathing, people do not stay still for a long period of time and frequently engage in small in-place body motions called fidgets (e.g., adjusting their seating position, crossing their legs, checking their phones, etc). In this paper, we propose that the aggregate natural fidgeting and in-place motions of a stationary crowd carry crucial information on the crowd count. We then mathematically characterize the Probability Distribution Function (PDF) of the crowd fidgeting and silent periods (which we can extract from the received WiFi signal) and show their dependency on the total number of people in the area. In developing our mathematical models, we show how our problem of interest resembles a several-decade-old M/G/∞ queuing theory problem, which allows us to borrow mathematical tools from the literature on M/G/∞ queues. We extensively validate our proposed approach with a total of 47 experiments in four different environments (including through-wall settings), in which up to and including N = 10 people are seated. We further test our system in different scenarios, and with different activities, representing various engagement levels of the crowd, such as attending a lecture, watching a movie, and reading. Moreover, we test our proposed system with different number of people seated in several different configurations. Our evaluation results show that our proposed approach achieves a very high counting accuracy, with the estimated number of people being only 0 or 1 off from the true number 96.3% of the time in non-through-wall settings, and 90% of the time in through-wall settings. Our results show the potential of our proposed framework for crowd counting in real-world scenarios.

In this article, we are interested in through-wall gait-based identification of multiple people who are simultaneously walking in an area, using only the WiFi magnitude measurements of a small number of transceivers. This is a considerably challenging problem as the gait signatures of the walking people are mixed up in the WiFi measurements. In order to solve this problem, we propose a novel multidimensional framework, spanning time, frequency, and space domains, that can separate the signal reflected from each walking person and extract its corresponding gait content, in order to identify multiple people through walls. To the best of our knowledge, this is the first time that WiFi signals can identify multiple people in an area. We extensively validate our proposed system with 92 test experiments conducted in four different areas, where the WiFi transceivers are placed behind walls, and where two or three people (randomly selected from a pool of six test subjects) are walking in the area. Our system achieves an overall average accuracy of 82% in correctly identifying whether a person walking in the test experiment (referred to as a query) is the same as a candidate person, based on 6404 query-candidate test pairs. It is noteworthy that none of the test subjects/areas has been seen in the training phase.

We are interested in robotic visual object classification using a deep convolutional neural network (DCNN) classifier. We show that the correlation coefficient of the automatically learned DCNN features of two object images carries robust information on their similarity, and can be utilized to significantly improve the robot's classification accuracy, without additional training. More specifically, we first probabilistically analyze how the feature correlation carries vital similarity information and build a correlation-based Markov random field (CoMRF) for joint object labeling. Given query and motion budgets, we then propose an optimization framework to plan the robot's query and path based on our CoMRF. This gives the robot a new way to optimally decide which object sites to move close to for better sensing and for which objects to ask a remote human for help with classification, which considerably improves the overall classification. We extensively evaluate our proposed approach on two large datasets (e.g., drone imagery and indoor scenes) and several real-world robotic experiments. The results show that our proposed approach significantly outperforms the benchmarks.

In this paper, we propose a methodology for estimating the crowd speed using WiFi devices without relying on people to carry any device. Our approach not only enables speed estimation in the region where WiFi links are, but also in the adjacent possibly WiFi-free regions. More specifically, we use a pair of WiFi links in one region, whose RSSI measurements are then used to estimate the crowd speed, not only in this region, but also in adjacent WiFi-free regions. We first prove how the cross-correlation and the probability of crossing the two links implicitly carry key information about the pedestrian speeds and develop a mathematical model to relate them to pedestrian speeds. We then validate our approach with 108 experiments, in both indoor and outdoor, where up to 10 people walk in two adjacent areas, with a variety of speeds per region, showing that our framework can accurately estimate these speeds with only a pair of WiFi links in one region. For instance, the NMSE over all experiments is 0.18. We also evaluate our framework in a museum-type setting and estimate the popularity of different exhibits. We finally run experiments in an aisle in Costco, estimating key attributes of buyers' behaviors.

In this paper, we are interested in passively tracking multiple people walking in an area, using only the magnitude of WiFi signals from one WiFi transmitter and a small number of receivers (configured as an array) located on one side of the area. Past works on RF-based tracking either track only a single moving person, use a large number of transceivers surrounding the area to track multiple people, or use additional resources like ultra-wideband signals. Furthermore, magnitude-based tracking provides an attractive feature that additional receiver antennas can easily be added to the antenna array as needed, without the need for phase synchronization, since the magnitude can be measured independently on the different antennas. In this paper, we then propose a new framework that uses only the magnitude of WiFi signals and expresses it in terms of the angles of arrival of signal paths at the receivers as well as the motion parameters of the virtual arrays emulated by the moving people. We then use a two-dimensional MUltiple SIgnal Classification (MUSIC) algorithm to estimate the aforementioned parameters, and further utilize a Particle Filter with a Joint Probabilistic Data Association Filter to track multiple people walking in the area. We extensively validate our proposed framework in both indoor and outdoor areas, through 40 experiments of tracking 1 to 3 people, using only one transmit antenna and three laptops as receivers (a total of four off-the-shelf Intel 5300 WiFi Network Interface Cards (NICs)). Our results show highly accurate tracking (mean error of 38 cm in outdoor areas/closed parking lots, and 55 cm in indoor areas) using minimal WiFi resources on only one side of the area.

In this paper, we propose XModal-ID, a novel WiFi-video cross-modal gait-based person identification system. Given the WiFi signal measured when an unknown person walks in an unknown area and a video footage of a walking person in another area, XModal-ID can determine whether it is the same person in both cases or not. XModal-ID only uses the Channel State Information (CSI) magnitude measurements of a pair of off-the-shelf WiFi transceivers. It does not need any prior wireless or video measurement of the person to be identified. Similarly, it does not need any knowledge of the operation area or person's track. Finally, it can identify people through walls. XModal-ID utilizes the video footage to simulate the WiFi signal that would be generated if the person in the video walked near a pair of WiFi transceivers. It then uses a new processing approach to robustly extract key gait features from both the real WiFi signal and the video-based simulated one, and compares them to determine if the person in the WiFi area is the same person in the video. We extensively evaluate XModal-ID by building a large test set with $8$ subjects, $2$ video areas, and $5$ WiFi areas, including 3 through-wall areas as well as complex walking paths, all of which are not seen during the training phase. Overall, we have a total of 2,256 WiFi-video test pairs. XModal-ID then achieves an $85%$ accuracy in predicting whether a pair of WiFi and video samples belong to the same person or not. Furthermore, in a ranking scenario where XModal-ID compares a WiFi sample to $8$ candidate video samples, it obtains top-1, top-2, and top-3 accuracies of $75%$, $90%$, and $97%$. These results show that XModal-ID can robustly identify new people walking in new environments, in various practical scenarios.

In this paper, we propose a new framework to estimate the occupancy dynamics of the shoppers over a whole retail store, based on the received power measurements of wireless links that are installed in only a small number of aisles, and without relying on people to carry any device. More specifically, we utilize the received power measurements collected by a small number of wireless links installed in only a few aisles of a retail store and show that we can estimate the rate of arrival of people in all the aisles of the retail store. We first show how a pair of wireless links in an aisle can estimate the rate of arrival of people into that aisle for the general case where people can have a bi-directional flow. We then propose a new framework to estimate the rate of arrival of people into all the aisles of the retail store, using the received power measurements of a number of wireless links that are installed in only a few aisles. Our proposed approach utilizes the sparsity in the spatial and temporal gradient of the occupancy dynamics and poses an optimization problem to estimate the arrival rates over the whole store based only on a very small number of wireless measurements. We thoroughly validate our framework with several experiments in three different retail stores - Kmart and two anonymous retail stores (Store-2 and Store-3), using the RSSI measurements of Bluetooth Low Energy (BLE) Chips. Our results confirm that our framework can accurately estimate the rate of arrival of people into different aisles of a retail store with minimal wireless sensing. More specifically, we show that our approach can estimate the rate of arrival of people in different aisles of a store, with an average root mean square of 0.03 people/minute, when averaged over all the aisles and all the time, and while reducing the number of required wireless links by 57%.

This paper is on human-robot collaborative site inspection and target classification. We consider the realistic case that human visual performance is imperfect (depending on the sensory input quality), and that the robot has constraints in communication with human (e.g., limited chances for query, poor channel quality). The robot has limited onboard motion and communication energy and operates in realistic channel environments experiencing path loss, shadowing, and multipath. We then show how to co-optimize motion, sensing, and human queries. Given a probabilistic assessment of human visual performance and a probabilistic channel prediction, we pose the co-optimization as multiple-choice multidimensional knapsack problems. We then propose a linear program-based efficient near-optimal solution, mathematically characterize the optimality gap, showing it to be very small, and mathematically characterize properties of the optimum solution. We then comprehensively validated the proposed approach with extensive real human data (from Amazon MTurk) and real channel data (from downtown San Francisco), confirming that the proposed approach significantly outperforms benchmark methodologies.

Consider a general path planning problem of a robot on a graph with edge costs, where each node has a Boolean value of success or failure (with respect to some task) with a given probability. The objective is to plan a path for the robot on the graph that minimizes the expected cost until success. In this paper, it is our goal to bring a foundational understanding to this problem. We start by showing how this problem can be optimally solved by formulating it as an infinite-horizon Markov decision process, but with an exponential space complexity. We then formally prove its NP-hardness. To address the space complexity, we then propose a path planner, using a game-theoretic framework, that asymptotically gets arbitrarily close to the optimal solution. Moreover, we also propose two fast and nonmyopic path planners. To show the performance of our framework, we do extensive simulations for two scenarios: a rover on Mars searching for an object for scientific studies, and a robot looking for a connected spot to a remote station (with real data from downtown San Francisco). Our numerical results show a considerable performance improvement over existing state-of-the-art approaches.

The ability to estimate the perceptual error between images is an important problem in computer vision with many applications. Although it has been studied extensively, however, no method currently exists that can robustly predict visual differences like humans. Some previous approaches used hand-coded models, but they fail to model the complexity of the human visual system. Others used machine learning to train models on human-labeled datasets, but creating large, high-quality datasets is difficult because people are unable to assign consistent error labels to distorted images. In this paper, we present a new learning-based method that is the first to predict perceptual image error like human observers. Since it is much easier for people to compare two given images and identify the one more similar to a reference than to assign quality scores to each, we propose a new, large-scale dataset labeled with the probability that humans will prefer one image over another. We then train a deep-learning model using a novel, pairwise-learning framework to predict the preference of one distorted image over the other. Our key observation is that our trained network can then be used separately with only one distorted image and a reference to predict its perceptual error, without ever being trained on explicit human perceptual-error labels. The perceptual error estimated by our new metric, PieAPP, is well-correlated with human opinion. Furthermore, it significantly outperforms existing algorithms, beating the state-of-the-art by almost 3Ã - on our test set in terms of binary error rate, while also generalizing to new kinds of distortions, unlike previous learning-based methods.

In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.

In this paper, we propose a framework to sense occupancy attributes of an area, such as speed of a crowd traversing through the area, the total number of people in the area, and the rate of arrival of people into the area, using only the received power measurements (RSSI) of two WiFi links, and without relying on people to carry any device. We first show that the cross-correlation between the two WiFi link measurements and the probability of crossing a link implicitly carry key information about the occupancy attributes and develop a mathematical model to relate these parameters to the occupancy attributes of interest. Based on this, we then propose a system to estimate the occupancy attributes and validate it with 51 experiments in both indoor and outdoor areas, where up to (and including) 20 people walk in the area with different possible speeds, and show that our framework can accurately estimate the occupancy attributes. For instance, our framework achieves a Normalized Mean Square Error (NMSE) of 0.047 (4.7%) when estimating the speed of a crowd, an NMSE of 0.034 (3.4%) when estimating the arrival rate to the area, and a Mean Absolute Error (MAE) of 1.3 when counting the total number of people. We finally run experiments in an aisle in Costco, showing how we can estimate the key attributes of buyers' motion behaviors.

In this paper, we are interested in the 3D through-wall imaging of a completely unknown area, using WiFi RSSI and Unmanned Aerial Vehicles (UAVs) that move outside of the area of interest to collect WiFi measurements. It is challenging to estimate a volume represented by an extremely high number of voxels with a small number of measurements. Yet many applications are time-critical and/or limited on resources, precluding extensive measurement collection. In this paper, we then propose an approach based on Markov random field modeling, loopy belief propagation, and sparse signal processing for 3D imaging based on wireless power measurements. Furthermore, we show how to design efficient aerial routes that are informative for 3D imaging. Finally, we design and implement a complete experimental testbed and show high-quality 3D robotic through-wall imaging of unknown areas with less than 4% of measurements.

We consider the case where a team of unmanned vehicles are tasked with distributed beamforming in order to cooperatively transmit a message to a remote station. We propose a joint motion and communication optimization framework where the robots move in order to find locations that satisfy the given reception quality requirement while minimizing the overall motion energy consumption. For the case where the channel is perfectly known, we show that this problem can be posed as a knapsack problem and show the underlying trends of the optimum solution. We then extend our approach to the case where the channel is not known over the space. We show how the previously proposed channel prediction framework can be integrated with path planning for distributed robotic beamforming under motion energy constraints. Finally, we present extensive simulation results in realistic communication environments.

In this paper, we are interested in counting the total number of people walking in an area based on only WiFi received signal strength indicator (RSSI) measurements between a pair of stationary transmitter/receiver antennas. We propose a framework based on understanding two important ways that people leave their signature on the transmitted signal: blocking the line of sight (LOS) and scattering effects. By developing a simple motion model, we first mathematically characterize the impact of the crowd on blocking the LOS. We next probabilistically characterize the impact of the total number of people on the scattering effects and the resulting multipath fading component. By putting the two components together, we then develop a mathematical expression for the probability distribution of the received signal amplitude as a function of the total number of occupants, which will be the base for our estimation using Kullback-Leibler divergence. To confirm our framework, we run extensive indoor and outdoor experiments with up to and including nine people and show that the proposed framework can estimate the total number of people with a good accuracy with only a pair of WiFi cards and the corresponding RSSI measurements.

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.

The papers in this special issue on location awareness will continue with the state-of-the-art in technology, regulation, and theory for the emerging field of localization. This second part issue continues from the July 2015, Part I, issue which discusses location awareness for radio and networks.

In this article, we study the problem of dynamic coverage of a set of points of interest (POIs) in a time-varying environment. We consider the scenario where a physical quantity is constantly growing at certain rates at the POIs. A number of mobile agents are then deployed to periodically cover (sense or service) the POIs and keep the physical quantity under control bounded at all the POIs. We assume a communication-constrained operation, where the mobile agents need to communicate to a fixed remote station over realistic wireless links to complete their coverage task. We then propose novel mixed-integer linear programs (MILPs) to design periodic trajectories and TX power policies for the mobile agents that minimize the total energy (the summation of motion and communication energy) consumption of the mobile agents in each period, while (1) guaranteeing the boundedness of the quantity of interest at all the POIs, and (2) meeting the constraints on the connectivity of the mobile agents, the frequency of covering the POIs, and the total energy budget of the mobile agents. We furthermore provide a probabilistic analysis of the problem. Our results show the superior performance of the proposed framework for dynamic coverage in realistic fading environments.

In this paper, we develop a theoretical and experimental framework for the mapping of obstacles (including occluded ones), in a robotic cooperative network, based on a small number of wireless channel measurements. This would allow the robots to map an area before entering it. We consider three approaches based on coordinated space, random space, and frequency sampling, and show how the robots can exploit the sparse representation of the map in space, wavelet or spatial variations, in order to build it with minimal sensing. We then show the underlying tradeoffs of all the possible sampling, sparsity and reconstruction techniques. Our simulation and experimental results show the feasibility and performance of the proposed framework. More specifically, using our experimental robotic platform, we show preliminary results in successfully mapping a number of real obstacles and having see-through capabilities with real structures, despite the practical challenges presented by multipath fading.

We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a pre-defined trajectory, and while minimizing its motion and communication energy costs. We propose a co-optimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavy-task load and the light-task load cases. We also propose an additional stop-time online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement.

In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality.

In this paper, we are interested in fundamentally understanding the spatial predictability of wireless channels. We propose a probabilistic channel prediction framework for predicting the spatial variations of a wireless channel, based on a small number of measurements. By using this framework, we then develop a mathematical foundation for understanding the spatial predictability of wireless channels. More specifically, we characterize the impact of different environments, in terms of their underlying parameters, on wireless channel predictability. We furthermore show how sampling positions can be optimized to improve the prediction quality. Finally, we show the performance of the proposed framework in predicting (and justifying the predictability of) the spatial variations of real channels, using several measurements in our building.

In this paper, we propose to use binary consensus algorithms for distributed cooperative spectrum sensing in cog-nitive radio networks. We propose to use two binary approaches, namely diversity and fusion binary consensus spectrum sensing. The performance of these algorithms is analyzed over fading channels. The probability of networked detection and false alarm are characterized for the diversity case. We then show that binary consensus cooperative spectrum sensing is superior to quantized average consensus in terms of agility, given the same number of transmitted bits.

In this paper, we consider a mobile cooperative network that is tasked with building a map of the spatial variations of a parameter of interest, such as an obstacle map or an aerial map. We propose a new framework that allows the nodes to build a map of the parameter of interest with a small number of measurements. By using the recent results in the area of compressive sensing, we show how the nodes can exploit the sparse representation of the parameter of interest in the transform domain in order to build a map with minimal sensing. The proposed work allows the nodes to efficiently map the areas that are not sensed directly. We consider three main areas essential to the cooperative operation of a mobile network: building a map of the spatial variations of a field of interest such as aerial mapping, mapping of the obstacles based on only wireless measurements, and mapping of the communication signal strength. For the case of obstacle mapping, we show how our framework enables a novel noninvasive mapping approach (without direct sensing), by using wireless channel measurements. Overall, our results demonstrate the potentials of this framework.

In this paper we consider estimating the spatial variations of a wireless channel based on a small number of measurements in a robotic network. We use a multi-scale probabilistic model in order to characterize the channel and develop an estimator based on this model. We show that our model-based approach can estimate the channel well for several scenarios, with only a small number of gathered measurements. We furthermore consider a sparsity-based channel estimation approach, in which we utilize the compressibility of the channel in the frequency domain. Our results show that this approach can also be effective in several scenarios. We then discuss the underlying tradeoffs between the two approaches. For the model-based approach, we show the impact of the error in the underlying model as well as the error in the estimation of the parameters of the model on the overall performance. For the sparsity-based approach, we show the impact of channel compressibility on the performance. Overall, the proposed framework can be utilized for communication-aware motion planning in robotic networks, where a prediction of the link qualities is needed.

In this paper we consider a network of distributed sensors that are trying to measure a parameter of interest cooperatively, by exchanging their acquired information repeatedly over fading channels. We consider different ways of using the available bandwidth, in terms of what each node can send to its neighbors. More specifically, we characterize the impact of local fusion and show how it is a suitable policy when graph connectivity is low. When poor link qualities are the main bottleneck, on the other hand, we show how a diversity approach can be more beneficial. The proposed framework highlights the underlying tradeoffs between fusion and diversity approaches in cooperative networks. It furthermore explores the impact of multiple sensing on the overall performance.

The goal of this paper is to serve as a reference for researchers in robotics and control that are interested in realistic modeling, theoretical analysis and simulation of wireless links. To realize the full potentials of networked robotic systems, an integration of communication issues with motion planning/control is necessary. While considerable progress has been made in the area of networked robotic systems, communication channels are typically considered ideal or ideal within a certain radius of the transmitter, both considerable oversimplifications of wireless channels. It is the goal of this paper to provide a comprehensive overview of the key characteristics of wireless channels, as relevant to networked robotic operations. In particular, we provide a probabilistic framework for characterization of the underlying multi-scale dynamics of a wireless link: small-scale fading, large-scale fading and path loss. We furthermore confirm these mathematical models with channel measurements made in our building. We also discuss channel characterization based on the knowledge available on the geometry and dielectric properties of the environment.

In this paper we consider a cooperative network that is trying to reach binary consensus over fading channels. We first characterize the impact of fading on network consensus by upper bounding the second largest eigenvalue of the underlying probability transition matrix in fading environments. Using the information of link qualities, we then propose a novel consensus-seeking protocol based on the best affine estimation of network state. We characterize the performance of our proposed strategy mathematically. We derive an approximated expression for the second largest eigenvalue in order to characterize the convergence rate. Our results show the impact of fading on network consensus. They furthermore indicate that the proposed technique can improve the consensus performance considerably.

In this paper we create a framework to model and characterize the impact of time-varying fading communication links on the performance of a mobile sensor network. We propose communication-aware motion-planning strategies, where each node incorporates statistical learning of communication link qualities, such as Signal to Noise Ratio (SNR) and correlation characteristics, into its motion-planning function. We show that while uncorrelated fading channels can ruin the overall performance, the introduced natural randomization can potentially help the nodes leave deep fade spots. We furthermore show that highly correlated deep fades, on the other hand, can degrade the performance drastically for a long period of time. We then propose a randomizing motion-planning strategy that can help the nodes leave highly correlated deep fades.

In this paper we are interested in designing a robust timing synchronization algorithm for OFDM systems that utilize pilot-aided channel estimation. We first characterize the impact of timing errors on the performance of a pilot-aided OFDM system. We derive analytical expressions for average channel estimation error variance in the presence of timing errors in high delay spread fading environments. The derived expressions show that pilot-aided channel estimators are considerably sensitive to timing synchronization errors due to the impact of rotations in different bases. We then show how to utilize this sensitivity to design a robust timing synchronization algorithm, without training overhead. The proposed algorithm is a cross-block design that uses channel estimation information to improve timing synchronization. We confirm our analytical results by simulating the proposed algorithm in high delay spread fading environments. In this paper, i.e. part I, we focus on timing synchronization for low-mobility cases. The analysis and results are then extended to high-mobility applications in part II.

In this paper we consider the design of a robust timing synchronization algorithm for pilot-aided OFDM systems in high-mobility fading environments. We first analyze the impact of both mobility and timing errors on the performance of a pilot-aided OFDM system for frequency selective fading channels, by deriving an expression for channel estimation error variance. The analysis will show that, even for high levels of mobility, a pilot-aided channel estimator is considerably sensitive to timing errors, due to the impact of rotations in different bases. We then show how this sensitivity can be utilized to design a robust timing synchronization algorithm for mobile OFDM systems, without relying on synchronization training information. Theoretical results are then confirmed by simulating the performance of an OFDM system in high delay and Doppler spread fading environments. Finally, we show how the proposed mathematical framework and algorithm can be used to address timing synchronization in the presence of a frequency offset as well. The analysis of this paper is the extension of the derivations of Part I [8], the accompanying paper on the design of a robust timing synchronizer for low-mobility OFDM systems.

This letter addresses the effect of timing synchronization errors that are introduced by an erroneous detection of the start of an orthogonal frequency-division multiplexing (OFDM) symbol. Throughout this letter, the term "timing error" would refer to this type of error. Such errors degrade the performance of an OFDM receiver by introducing intercarrier interference (ICI) and intersymbol interference (ISI). They can occur due to either an erroneous initial frame synchronization or a change in the power delay profile of the channel. In this letter, we provide a mathematical analysis of the effect of timing errors on the performance of an OFDM receiver in a frequency-selective fading environment. The analysis presented in this letter is for the case that no equalization technique has been used to mitigate the introduced ICI and ISI. We find exact formulas for the power of interference terms and the resulting average signal-to-interference ratio. We further extend the analysis to the subsample level. Our results show the nonsymmetric effect of timing errors on the performance of an OFDM system. Finally, simulation results confirm the analysis. The results of this letter can be easily extended to address the effect of such errors on DMT modems.

In this paper we characterize the impact of imperfect communication on the performance of a decentralized mobile sensor network. We first examine and demonstrate the trade-offs between communication and sensing objectives, by determining the optimal sensor configurations when introducing imperfect communication. We further illustrate the performance degradation caused by non-ideal communication links in a decentralized mobile sensor network. To address this, we propose a decentralized motion-planning algorithm that considers communication effects. The algorithm is a cross-layer design based on the proper interface of physical and application layers. Simulation results will show the performance improvement attained by utilizing this algorithm.

Orthogonal frequency-division multiplexing (OFDM) is robust against frequency selective fading due to the increase of the symbol duration. However, for mobile applications channel time-variations in one OFDM symbol introduce intercarrier-interference (ICI) which degrades the performance. This becomes more severe as mobile speed, carrier frequency or OFDM symbol duration increases. As delay spread increases, symbol duration should also increase in order to maintain a near-constant channel in every frequency subband. Also, due to the high demand for bandwidth, there is a trend toward higher carrier frequencies. Therefore, to have an acceptable reception quality for the applications that experience high delay and Doppler spread, there is a need for ICI mitigation within one OFDM symbol. We introduce two new methods to mitigate ICI in an OFDM system with coherent channel estimation. Both methods use a piece-wise linear model to approximate channel time-variations. The first method extracts channel time-variations information from the cyclic prefix. The second method estimates these variations using the next symbol. We find a closed-form expression for the improvement in average signal-to-interference ratio (SIR) when our mitigation methods are applied for a narrowband time-variant channel. Finally, our simulation results show how these methods would improve the performance in a highly time-variant environment with high delay spread.

OFDM systems are sensitive to timing synchronization errors. Utilizing pilot-aided channel estimators in OFDM systems can further increase this sensitivity. This is shown in Y. Mostofi et al. (Oct. 2002) where authors have analyzed the-effect of such errors on a pilot-aided channel estimator in a fixed wireless environment. They proposed an algorithm that exploits this sensitivity to improve timing synchronization without additional training overhead. The effect of these errors and design of suitable synchronization algorithms for high mobility applications, however, have not been studied before. In this paper we extend the analysis in Y. Mostofi et al. (Oct. 2002) to high mobility environments. Timing synchronization becomes more challenging for mobile applications since power-delay profile of the channel may change-rapidly due to the sporadic birth and death of the channel paths. We find analytical expressions for channel estimation error in the presence of timing synchronization errors and mobility. We show that the sensitivity of the channel estimator can still be exploited to improve timing synchronization in high mobility environments. Then we extend the algorithm proposed in Y. Mostofi et al. (Oct. 2002) to high mobility applications. Finally simulation results show the performance of the algorithm in high delay and Doppler spread environments.

In mobile sensor networks, sensor measurements as well as control commands are transmitted over wireless time-varying links. It then becomes considerably important to address the impact of imperfect communication on the overall performance. In this paper, we study the effect of time-varying communication links on the control performance of a mobile sensor node. In particular, we investigate the impact of fading. We derive a key performance measure parameter to evaluate the overall feedback control performance over narrowband channels. We show that fading can result in considerable delay and/or poor performance of the mobile sensor depending on the system requirements. To improve the performance, we then show how the application layer can use the channel status information of the physical layer to adapt control commands accordingly. We show that sharing information across layers can improve the overall performance considerably. We verify our analytical results by simulating a wireless location and speed control problem.

In OFDM receivers, pilot tones should be inserted among the sub-carriers in order to estimate the channel in high delay spread environments. To estimate the channel at all the sub-carriers, using the pilot tones, different frequency-domain interpolators can be used. For a given number of pilot tones, these interpolators will have different levels of complexity depending on the number of adjacent pilot tones that they use to estimate the channel at each sub-carrier. For high delay-spread channels, as the number of adjacent pilot tones used by an interpolator increases, channel estimation improves resulting in a tradeoff between performance and complexity. In this paper, we first find an analytical average error rate formula for a pilot-aided OFDM receiver in a delay spread fading environment. This analysis will provide a framework to evaluate the performance of different interpolators. As an example, we then evaluate and compare the performance of two interpolators with different levels of complexity: a trigonometric interpolator and a linear one. We derive the average error rate formulas of the interpolators and confirm that the former one has a considerably better performance as the delay spread increases. However, for flat fading channels, we show that the linear interpolator has a 3 dB gain. Finally, our simulation results support the mathematical analysis and comparison.

Orthogonal frequency division multiplexing (OFDM) is robust against inter-symbol interference (ISI) due to the increase of the symbol duration. However, for mobile applications, channel variations during one OFDM symbol introduce inter-carrier-interference (ICI), which degrades the performance. This gets more severe as mobile speed, carrier frequency or OFDM symbol duration increases. We introduce two new methods to mitigate ICI in an OFDM system with coherent channel estimation. Both methods use a piece-wise linear model to approximate channel variations. The first method extracts channel variations information from the cyclic prefix. The second method estimates these variations utilizing the next symbol. Since mathematical analysis of the whole system becomes intractable in a long delay spread environment, we provide a mathematical analysis that investigates the effect of linearization for the case of a time-variant one path channel. Then our simulation results show how these methods would improve the performance in a highly time-variant environment with a long delay spread.

In an outdoor communication environment, a percentage of bandwidth is wasted sending a training sequence for channel estimation and equalization. For instance in GSM, 17.93% of bandwidth is dedicated to the transmission of such a known sequence. Therefore, if a blind algorithm, using only the knowledge of input constellation and/or correlation, can achieve an acceptable performance, it will save bandwidth considerably. If the receiver does not have this information, blind adaptation would not be feasible with reasonable complexity. We present a robust blind adaptation structure for TDMA-based communication systems. Also, we include implementation issues such as differential coding and oversampling in our system modeling.