Extending the range of RF energy harvesting can revolutionize battery-free/low-power sensing and networking. This paper explores the design space for RF infrastructure to charge battery-free devices (e.g. RFID) or devices with coin-cell batteries (e.g. water and security sensors) over much longer range than the state-of-the-art. Rather than rely completely on ambient RF (e.g. TV towers) or dedicated infrastructure (e.g. RFID readers), we explore a middle path - combine RF energy from (nearly) all available major wireless frequency bands and then supplement this with low-cost specially designed RF charging infrastructure to fill in any gaps.

This paper introduces RFIMap, a system that aims to inexpensively characterize the spatial and temporal distribution of RF spectrum occupancy of any indoor space at fine granularity (tens of centimeters). RFIMap builds rich wide-band indoor spectrum occupancy maps using low-cost and battery-free commodity RFID tags. RFIMap's spectrum maps have wide-ranging applications such as monitoring ambient interference in smart manufacturing, and smart hospitals. RFIMap relies on the observation that commodity RFID tags naturally reflect ambient transmission at other frequency bands, without any modification. RFIMap uses these reflections to estimate the ambient signal power originally received at these tags. RFIMap further performs a careful modeling of indoor multipath to build a dense spectrum map with fine spatial granularity. Our experiments demonstrate spatial spectrum measurement with 2.15 dB of median error at 2.4 GHz, 4.45 dB of median error at 470-700 MHz TV whitespace band, 2.1 dB of median error at 1.8-1.9 GHz in diverse industrial and university settings.

In this paper, we present Vi-Fi, a multi-modal system that leverages a user's smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e.g. WiFi MAC address). Our approach uses a recurrent multi-modal deep neural network that exploits FTM and IMU measurements along with distance between user and camera (depth information) to learn affinity matrices. As a baseline method for comparison, we also present a traditional non deep learning approach that uses bipartite graph matching. To facilitate evaluation, we collected a multi-modal dataset that comprises camera videos with depth information (RGB-D), WiFi FTM and IMU measurements for multiple participants at diverse real-world settings. Using association accuracy as the key metric for evaluating the fidelity of Vi-Fi in associating human users on camera feed with their phone IDs, we show that Vi-Fi achieves between 81% (real-time) to 91% (offline) association accuracy.

We demonstrate an application of finding target persons on a surveillance video. Each visually detected participant is tagged with a smartphone ID and the target person with the query ID is highlighted. This work is motivated by the fact that establishing associations between subjects observed in camera images and messages transmitted from their wireless devices can enable fast and reliable tagging. This is particularly helpful when target pedestrians need to be found on public surveillance footage, without the reliance on facial recognition. The underlying system uses a multi-modal approach that leverages WiFi Fine Timing Measurements (FTM) and inertial sensor (IMU) data to associate each visually detected individual with a corresponding smartphone identifier. These smartphone measurements are combined strategically with RGB-D information from the camera, to learn affinity matrices using a multi-modal deep learning network.

Driver assistance and vehicular automation would greatly benefit from uninterrupted lane-level vehicle positioning, especially in challenging environments like metropolitan cities. In this paper, we explore whether the WiFi Fine Time Measurement (FTM) protocol, with its robust, accurate ranging capability, can complement current GPS and odometry systems to achieve lane-level positioning in urban canyons. We introduce Wi-Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by coherently fusing WiFi FTMs, GPS, and vehicle odometry information together. Wi-Go also adaptively controls the FTM messaging rate from clients to prevent high bandwidth usage and congestion, while maximizing the tracking accuracy. Wi-Go achieves lane-level vehicle positioning (1.3 m median and 2.9 m 90-percentile error), an order of magnitude improvement over vehicle built-in GPS, through vehicle experiments in the urban canyons of Manhattan, New York City, as well as in suburban areas (0.8 m median and 3.2 m 90-percentile error).

Routing protocols in multi-hop cognitive radio networks (CRNs) can be classified into two main categories: local and global routing. Local routing protocols aim at decreasing the overhead of the routing process while exploring the route by choosing, in a greedy manner, one of the direct neighbors. On the contrary, global routing protocols choose the optimal route by exploring the whole network to the destination paying the flooding overhead cost. In this paper, we propose a primary user-aware$k$-hop routing scheme where$k$is the discovery radius. This scheme can be plugged into any CRN routing protocol to adapt, in real time, to network dynamics like the number and activity of primary users. The aim of this scheme is to cover the gap between local and global routing protocols for CRNs. It is based on balancing the routing overhead and the route optimality, in terms of primary users avoidance, according to a user-defined utility function. We analytically derive the optimal discovery radius ($k$) that achieves this target. Evaluations on NS2 with a side-by-side comparison with traditional CRNs protocols show that our scheme can achieve the user-defined balance between the route optimality, which in turn reflected on throughput and packet delivery ratio, and the routing overhead in real time.

This paper explores the feasibility of localizing and detecting activities of building occupants using visible light sensing across a mesh of light bulbs. Existing Visible Light activity sensing (VLS) techniques require either light sensors to be deployed on the floor or a person to carry a device. Our approach integrates photosensors with light bulbs and exploits the light reflected off the floor to achieve an entirely device-free and light source based system. This forms a mesh of virtual light barriers across networked lights to track shadows cast by occupants. The design employs a synchronization circuit that implements a time division signaling scheme to differentiate between light sources and a sensitive sensing circuit to detect small changes in weak reflections. Sensor readings are fed into indoor supervised tracking algorithms as well as occupancy and activity recognition classifiers. Our prototype uses modified off-the-shelf LED flood light bulbs and is installed in a typical office conference room. We evaluate the performance of our system in terms of localization, occupancy estimation and activity classification, and find a 0.89m median localization error as well as 93.7% and 93.78% occupancy and activity classification accuracy, respectively.

Academic and industry research has argued for supporting WiFi time-of-flight measurements to improve WiFi localization. The IEEE 802.11-2016 now includes a Fine Time Measurement (FTM) protocol for WiFi ranging, and several WiFi chipsets offer hardware support albeit without fully functional open software. This paper introduces an open platform for experimenting with fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems. We analyze the key factors and parameters that affect the ranging performance and revisit standard error correction techniques for WiFi time-based ranging system. The results confirm that meter-level ranging accuracy is possible as promised, but the measurements also show that this can only be consistently achieved in low-multipath environments such as open outdoor spaces or with denser access point deployments to enable ranging at or above 80 MHz bandwidth.

The growing number of devices we interact with require a convenient yet secure solution for user identification, authorization and authentication. Current approaches are cumbersome, susceptible to eavesdropping and relay attacks, or energy inefficient. In this paper, we propose a body-guided communication mechanism to secure every touch when users interact with a variety of devices and objects. The method is implemented in a hardware token worn on user's body, for example in the form of a wristband, which interacts with a receiver embedded inside the touched device through a body-guided channel established when the user touches the device. Experiments show low-power (uJ/bit) operation while achieving superior resilience to attacks, with the received signal at the intended receiver through the body channel being at least 20dB higher than that of an adversary in cm range.

We introduce a mobile sensing technique to detect a nearby active television, the channel it is tuned to, and whether it is receiving this channel over the air or not. This technique can find applications in tracking TV viewership, second screen services and advertising, as well as improving the efficiency of TV white space spectrum usage. The technique uses a three-stage detection process: It first uses a Gaussian mixture model on audio recordings from mobile phones to detect likely TV sounds in the area. It then correlates the recording with known TV channel audio to identify the channel and improve detection robustness. Finally, it applies a latency analysis to determine whether programming is received over-the-air or through alternate means such as cable or satellite TV. Our system is evaluated using diverse datasets that take into account different realistic scenarios of indoor environments for several users. The results show that the system can achieve an area under the curve (AUC) of 0.9979 and a false negative rate of 0.0132.

We propose a primary user-aware k-hop routing scheme that can be plugged into any cognitive radio network routing protocol to adapt, in real time, to the environmental changes. The main use of this scheme is to make the compromise required between the route overhead and its optimality based on a user-defined utility function. We analytically derive the optimal discovery radius (k) that achieves this target. Evaluations on NS2 show that our scheme can enhance the current routing protocols in terms of throughput with minimal overhead.

Wide deployment of GSM based location determination systems is a critical step towards moving existing systems to the real world. The main barrier towards this critical step is the heterogeneity of existing types of cell phones which results in different readings of received signal strength. Specially, in the context of fingerprinting localization where offline phases are needed for system training and different types of phones may be used in the offline and the online phases. Therefore, a mapping function, that maps the RSSI values between different types of cell phones, is inevitably needed. A trivial solution is to build a radio map for each type of phone. Obviously, this solution can neither scale in terms of number of phone types nor fingerprint size. In this paper, we address this problem by proposing the following two-way approach: A mathematical approach that maps RSSI values of different types of phones using linear transformation with regression, or logging ratios of readings instead of absolute values. We have empirically evaluated the proposed approach on Android-based phones. Our experimental results show that applying our approach can improve location accuracy with at least 127.84% in multiple cell tower configuration and at least 22.11% in the single cell tower configuration compared to the state-of-the-art GSM localization systems.

Cognitive radio networks (CRNs) provide a solution to increase the utilization of the scarce radio frequency spectrum. Building testbeds for CRNs is one of the main challenges that can affect the wide deployability of such networks. In this paper, we present the design, implementation, and evaluation of CogFrame: a framework that facilitates the development of cost-efficient large-scale CRNs routing protocols testbeds. The framework allows the designers to focus on the CRNs routing protocols by abstracting the PHY and MAC layers while providing the necessary cross layer functionalities. CogFrame works with standard computers and WiFi cards to reduce the cost while allowing integration with other special hardware for more flexibility. In addition, CogFrame provides different modules for implementing and emulating complex scenarios such as regulatory authority policies, mobility management, and topology management. We benchmark the performance of CogFrame and compare it to standard ns-2 simulations and USRP2 implementations. In addition, we case study a location-aided routing protocol for CRNs using both CogFrame and ns-2 simulations. Our results highlight the ease of implementation, low-cost, and realistic replication of the CRN environment, showing the promise of CogFrame as a testbed for future CRNs implementations.

Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Android-based phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems.

Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. To evaluate our proposed system, we implemented CellSense on Android-based phones. Results for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSM localization systems. This comes with a minimal increase in computational requirements. We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff.