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With the rising popularity of wearable devices and sensors, shielding Body Area Networks (BANs) from eavesdroppers has become an urgent problem to solve. Since the conventional key distribution systems are too onerous for resource-constrained wearable sensors, researchers are pursuing a new light-weight key generation approach that enables two wearable devices attached at different locations of the user body to generate an identical key simultaneously simply from their independent observations of user gait. A key challenge for such gait-based key generation lies in matching the bits of the keys generated by independent devices despite the noisy sensor measurements, especially when the devices are located far apart on the body affected by different sources of noise. To address the challenge, we propose a novel machine learning framework, called Auto-Key, that uses an autoencoder to help one device predict the gait observations at another distant device attached to the same body and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using a public acceleration dataset collected from 15 real subjects wearing accelerometers attached to seven different locations of the body. Our results show that, on average, Auto-Key increases the matching rate of independently generated bits from two sensors attached at two different locations by 16.5%, which speeds up the successful generation of fully-matching symmetric keys at independent wearable sensors by a factor of 1.9. In the proposed framework, a subject-specific model can be trained with 50% fewer data and 88% less time by retraining a pre-trained general model when compared to training a new model from scratch. The reduced training complexity makes Auto-Key more practical for edge computing, which provides better privacy protection to biometric and behavioral data compared to cloud-based training.

For kinetic-powered body area networks, we explore the feasibility of converting energy harvesting patterns for device authentication and symmetric secret keys generation continuously. The intuition is that at any given time, multiple wearable devices harvest kinetic energy from the same user activity, such as walking, which allows them to independently observe a common secret energy harvesting pattern not accessible to outside devices. Such continuous KEH-based authentication and key generation is expected to be highly power efficient as it obviates the need to employ any extra sensors, such as accelerometer, to precisely track the walking patterns. Unfortunately, lack of precise activity tracking introduces bit mismatches between the independently generated keys, which makes KEH-based authentication and symmetric key generation a challenging problem. We propose KEHKey, a KEH-based authentication and key generation system that employs a compressive sensing-based information reconciliation protocol for wearable devices to effectively correct any mismatches in generated keys. We implement KEHKey using off-the-shelf piezoelectric energy harvesting products and evaluate its performance with data collected from 24 subjects wearing the devices on different body locations including head, torso and hands. Our results show that KEHKey is able to generate the same key for two KEH-embedded devices at a speed of 12.57 bps while reducing energy consumption by 59% compared to accelerometer-based methods, which makes it suitable for continuous operation. Finally, we demonstrate that KEHKey can successfully defend against typical adversarial attacks. In particular, KEHKey is found to be more resilient to video side channel attacks than its accelerometer-based counterparts.

IoT enables a new set of novel applications that promise to transform the physical world as we know it today. Current IoT applications have demonstrated the feasibility and promise of this technology, but there is still a large space open for innovation. We present our vision for the next generation of IoT devices: IoT devices that are autonomous, in terms of energy and communication, as well as cost-efficient to deploy. We present enabling-technologies to realize our vision and open research challenges.

Advancements in nanotechnology promise new capabilities for the Internet of Things (IoT) to monitor extremely fine-grained events with sensors as small as a hundred nanometers. Researchers predict that such tiny sensors can be connected to the Internet using graphene-based nano-antenna radiating in the terahertz band, giving rise to the so-called Internet of Nano-Things (IoNT). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. Since in many application domains, different types of events discharge different amounts of energy to the environment, we propose an energy-neutral event monitoring framework, called eNEUTRAL IoNT, that allows the sensors to transmit event information using only the amount of energy harvested from the events. We design and analyze two implementation options for this framework. The first option uses a single pulse containing the entire energy harvested from the event but manipulates its time duration to create a unique pulse amplitude for a given combination of event type and its location. In the second option, the harvested event energy is divided into two pulses so that the energy of the first pulse uniquely defines a location and the second pulse uses the remaining energy to identify event types. To minimize classification error at the receiver, we optimize pulse durations in the single-pulse option and pulse energies in the dual-pulse option. Feasibility of eNEUTRAL IoNT is demonstrated using extensive numerical experiments involving terahertz channels. We find that the dual-pulse approach significantly outperforms the single-pulse approach achieving 99% accuracy for detecting both location and event type in 10-node network monitoring two different event types for a radius of 28 mm.

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learning models have been proposed to achieve high sensing accuracy over a large population and activity set, as well as in unseen environments. Deep learning has also enabled detection of novel human sensing phenomena that were previously not possible. In this survey, we provide a comprehensive review and taxonomy of recent research efforts on deep learning based RF sensing. We also identify and compare several publicly released labeled RF sensing datasets that can facilitate such deep learning research. Finally, we summarize the lessons learned and discuss the current limitations and future directions of deep learning based RF sensing.

Switchable glass, which can electronically change its state between opaque and transparent, is finding applications in modern buildings as a replacement for traditional curtains or blinds to enable dynamic privacy control in physical spaces. In this work, we propose to exploit the electronic configurability of switchable glass to modulate natural light, which can be demodulated by a nearby receiver with the light-sensing capability to realise data communication over natural light. We call this natural light communication (NLC). A key advantage of NLC is that no energy is used to generate light, as it simply modulates the existing light in nature. The slow response rate of the switchable glass, however, limits the achievable data rates for NLC. We propose and optimise an ON-OFF modulation/demodulation technique for such slow-response switchable glass and implement it using off-the-shelf Polymer Dispersed Liquid Crystal (PDLC) glass. We demonstrate that the proposed NLC can achieve 33.33 bits per second communication with a bit error rate below 1% under a wide range of ambient luminance.

With the rapid development of sensor networks and embedded computing technologies, miniaturized wearable healthcare monitoring devices have become practically feasible. For many of these devices, accelerometer-based user authentication systems by gait analysis are becoming a hot research topic. However, a major bottleneck of such system is it requires continuous sampling of accelerometer, which reduces battery life of wearable sensors. In this paper, we present KEH-Gait , which advocates use of output voltage signal from kinetic energy harvester (KEH) as the source for gait recognition. KEH-Gait is motivated by the prospect of significant power saving by not having to sample the accelerometer at all. Indeed, our measurements show that, compared to conventional accelerometer-based gait detection, KEH-Gait can reduce energy consumption by 82.15 percent. The feasibility of KEH-Gait is based on the fact that human gait has distinctive movement patterns for different individuals, which is expected to leave distinctive patterns for KEH as well. We evaluate the performance of KEH-Gait using two different types of KEH hardware on a data set of 20 subjects. Our experiments demonstrate that, although KEH-Gait yields slightly lower accuracy than accelerometer-based gait detection when single step is used, the accuracy problem can be overcome by the proposed Probability-based Multi-Step Sparse Representation Classification (PMSSRC). Moreover, the security analysis shows that the EER of KEH-Gait against an active spoofing attacker is 11.2 and 14.1 percent using two different types of KEH hardware, respectively.

Recent work has shown that two wearable devices worn on the same user can exploit gait as a secret source to generate a common key for secure pairing. The main threat of using gait comes from side-channel attackers who can use cameras to record the walking user and extract accelerations from the video to pair with legitimate devices. We propose a novel pre-step that uses a CNN-LSTM deep learning model to classify the acceleration measurement devices, i.e., between IMU vs. Camera. We prototype the pre-step and evaluate it using real subjects. Our results show that the proposed pre-step can achieve high classification success rates. The experiments with different cut-off frequencies show that the higher acceleration frequencies appear to contain more distinguishable features to classify camera from IMU.

On-body health monitoring involves continuous sensing and wireless data transmissions, which severely impacts the battery life of wearable devices. However, rapid advances in kinetic energy harvesting (KEH) soon might obviate the need for battery recharging or replacement by harnessing human motion energy. The authors discuss the challenges and opportunities of KEH technology to help realize the ultimate vision of fully autonomous IoT-based healthcare.

Floating car data (FCD) refers to the motion and sensor data produced by moving vehicles on the road. Given that traffic management authorities and drivers can benefit from FCD enormously, there is an urgency to develop efficient FCD collection and dissemination techniques that scale with peak road traffic. In this paper, we present CarAgent, a message exchange protocol that periodically collects and uploads FCD to data centres with the minimal utilisation of existing mobile network resources. We also show how CarAgent can be easily extended to efficiently disseminate FCD to all vehicles in a target area using Dedicated Short Range Communication (DSRC), a recently released wireless communication standard for vehicles. We analytically derive the key performance metrics of CarAgent and validate them with simulations. We evaluate CarAgent using microscopic simulation of road traffic in real street maps while incorporating wireless protocol details of Long Term Evolution (LTE) and DSRC. Simulation results confirm that, compared to the state-of-the-art, CarAgent consumes 50% less LTE resources for FCD collection. For FCD dissemination, CarAgent consumes 45% less DSRC resources while improving the speed of dissemination significantly.

We study a scenario where multiple drone-mounted base stations cruise freely over a macro hotspot to serve mobile users on the ground. The drone base stations move constantly and update their moving directions following our proposed mobility control algorithm. The constant movement of drones reduces the distance between the base stations and users, which in turn improves the probability of having a line of sight connection. We consider a practical user association scheme for the moving base stations, which enables user equipments to switch their serving base stations based only on the received signal strength. via extensive simulations, we demonstrate that the drone base stations moving according to our proposed algorithms can improve the average packet throughput by 82% and the 5th-percentile packet throughput by 430% compared to a baseline scenario, where drones hover over fixed locations. These improvements can be realized regardless of users’ and base stations’ density. The constant movement of the drones also helps reduce the total number of drones required to cover the macro hotspot.

A conventional wireless sensor network node consists of a number of components: microprocessor, memory, sensor, and radio. Advances in nanotechnology have enabled the miniaturization of these components, thus enabling wireless nanoscale sensor networks (WNSN). Due to their small size, WNSN nodes are expected to be powered by harvesting energy from the environment. Unfortunately, there is a mismatch in the energy that can be harvested and the energy required to power all the aforementioned components in a WNSN node. In this article, we propose a simplified sensor node architecture for event detection. We call our architecture Sensorless Event MONitoring in self-powered WNSNs (SEMON). A SEMON node consists of only an energy harvester and a radio with minimal processing capacity. We assume that each event to be monitored will generate a different amount of energy, and we can therefore use this amount of energy as the signature of an event. When an event occurs, a SEMON node harvests the energy released by the event and turns it into a radio pulse with an amplitude proportional to the harvested energy. A remote station is used to decode the amplitude of the pulse to recognize the event that has occurred. We propose two methods for the remote station to decode the events that have occurred. The first method is based on thresholds. The second method makes use of an event model that gives the probability that a sequence of events will occur. This enables us to formulate the decoding problem using Hidden Markov Models. We study the decoding performance of both methods. Finally, we provide a case study on using the SEMON architecture to monitor the chemical reactions inside a reactor.

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we propose Skin-MIMO, a deep learning based CSI acquisition technique to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope) measurements at the transmitter, thus obviating the need for channel sounding. Based on experimental vibration data, we show that Skin-MIMO can improve MIMO capacity by a factor of 2.3 compared to Single-Input-Single-Output (SISO) or open-loop MIMO, which do not have access to CSI. A surprising finding is that gyroscope, which measures the angular velocity, is found to be superior in predicting skin vibrations than accelerometer, which measures linear acceleration and used widely in previous research for vibration communications over solid objects.

In this paper, we study the application of unmanned aerial vehicle (UAV) base stations (BSs) in order to improve the cellular network capacity. We consider flying BSs where BS equipments are mounted on UAVs, making it possible to move BSs freely in space. We study the optimization of UAVs' trajectory in a network with mobile users to improve the system throughput. We consider practical two-hop communications, i.e., the access link between a user and the UAV BS, and the backhaul link between the UAV BS and a macrocell BS plugged into the core network. We propose a reinforcement learning based algorithm to control the UAVs' mobility. Additionally, the proposed algorithm is subject to physical constraints of UAV mobility. Simulation results show that considering both the backhaul and access links in the UAV mobility optimization is highly effective in improving the system performance than only focusing on the access link.

Intelligent reflective surfaces (IRSs) capable of reconfiguring their electromagnetic absorption and reflection properties in real-time are offering unprecedented opportunities to enhance wireless communication experience in challenging environments. In this paper, we analyze the potential of IRS in enhancing cellular communications for UAVs, which currently suffers from poor signal strength due to the down-tilt of base station antennas optimized to serve ground users. We consider deployment of IRS on building walls, which can be remotely configured by cellular base stations to coherently direct the reflected radio waves towards specific UAVs in order to increase their received signal strengths. Using the recently released 3GPP ground-to-air channel models, we analyze the signal gains at UAVs due to the IRS deployments as a function of UAV height as well as various IRS parameters including size, altitude, and distance from base station. Our analysis suggests that even with a small IRS, we can achieve significant signal gain for UAVs flying above the cellular base station. We also find that the maximum gain can be achieved by optimizing the location of IRS including its altitude and distance to BS., 6 pages, 6 figures

This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth&rsquo, s magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

Time difference of arrival (TDOA) is a widely used technique for localizing a radio transmitter from the difference in signal arrival times at multiple receivers. For TDOA to work, the individual receivers must estimate the respective signal arrival times precisely, which requires sampling the signal at least double the rate of its highest frequency content, commonly known as the Nyquist rate. Such sampling is less practical for the millimeter wave band comprising of frequencies in 30-300 GHz range. In this paper, we propose an energy detection architecture for accurately estimating the time of arrival from a single picosecond Gaussian pulse, which enables TDOA localization without sampling at the receiver. We derive the closed form expression of the estimated time of arrival and validate it via simulation. We demonstrate that the proposed sampling-free TDOA can localize millimeter wave transmitters as accurately as the conventional sampling-based TDOA., 6 Pages, 12 Figures, Camera Ready for IEEE WCNC 2019

In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

Advances in nanotechnology are paving the way for a new generation of sensor networks at the nanoscale, promising radically new applications in medical, biological, and chemical fields. At the nanoscale, it is difficult to use large batteries for prolonged operations, necessitating energy harvesting as the most viable solution for these nanosensor networks. Unfortunately, the rate at which energy can be harvested at the nanoscale may not be sufficient to simultaneously power all of the conventional device components, including the microprocessor, memory, sensor, and the communication radio. We propose a simplified architecture for energy-harvesting nanosensor networks that uses the energy signatures of monitored events for efficient event recognition at the sink. Using the instantaneous harvested energy to transmit a short pulse of proportional amplitude, the proposed architecture obviates the need for several conventional sensor node elements, including the microprocessor, memory, and sensor. In this article, we validate the feasibility of the architecture using a novel application of nanosensor networks for microscopic monitoring of chemical reactors, and we demonstrate that different types of reactions can be detected accurately at a remote sink.

We characterize terahertz wireless channels for extracting data from nanoscale sensors deployed within human lungs. We discover that the inhalation and exhalation of oxygen and carbon dioxide causes periodic variation of the absorption coefficient of the terahertz channel. Channel absorption drops to its minimum near the end of inhalation, providing a window of opportunity to extract data with minimum transmission power. We propose an algorithm for nanosensors to estimate the periodic channel by observing signal-to-noise ratio of the beacons transmitted from the data sink. Using real respiration data from multiple subjects, we demonstrate that the proposed algorithm can estimate the minimum absorption interval of the periodic channel with 98.5% accuracy. Our analysis shows that by confining all data collections during the estimated low-absorption window of the periodic channel, nanosensors can reduce power consumption by six orders of magnitude. Finally, we demonstrate that for wireless communications within human lungs, 0.1–0.12 THz is the least absorbing spectrum within the terahertz band.

Internet browsing over cellular networks is a major source of power consumption in mobile devices. A device could potentially reduce its browsing power consumption by downloading a page from the browser cache of a proximate device over the short-range low-power non-cellular device-to-device (D2D) wireless link, such as Bluetooth. Using an Android-based cooperative browser prototype, this paper experimentally characterizes the power saving opportunity of such D2D cache cooperation. In particular, we model the impact of cellular network power management protocol and the dynamic components of web pages on the power saving of D2D cooperation. We find that the power saving is limited (upper bounded) by the power management parameter values of cellular networks, which could vary significantly from operator to operator. Based on previously reported cellular power management data and the data obtained from our own experiments, the upper bound can vary from as low as 15-51%. Our model also shows that the existence of dynamic components in the web page decreases the power saving further proportional to the amount of dynamic components. We validate the model with power measurement data from smartphones using real cellular networks for Internet access and Bluetooth connectivity for D2D cache access. By downloading most popular web pages and studying the distribution of their dynamic components, we find that the expected amount of dynamic component in a popular page is rather small, allowing D2D cooperation to achieve device power saving close to (95% of) its upper bound.

We study a networking scenario for 5G and beyond, called DroneCells, where miniaturized base stations (BSs) are mounted on flying drones to serve mobile users. We propose that the drones never stop, and move continuously within the cell in a way that reduces the distance between the BS and the serving users, thus potentially improving the spectral efficiency of the network. By considering the practical mobility constraints of commercial drones, we design drone mobility algorithms to improve the spectral efficiency of DroneCells. We propose a range of practically realizable heuristics with varying complexity and performance. Simulations show that, using the existing consumer drones, the proposed algorithms can readily improve spectral efficiency by 34% and the 5-percentile packet throughput by 50% compared to the scenario, where drones hover over fixed locations. More significant gains can be expected with more agile drones in the future. A surprising outcome is that the drones need to fly only at minimal speeds to achieve these gains, avoiding any negative effect on drone battery lifetime. We further demonstrate that the optimal solution provides only modest improvements over the best heuristic algorithm, which employs Game Theory to make mobility decisions for drone BSs.

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has an overwhelming pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient.

With the growing number of deployments of Internet of Things (IoT) infrastructure for a wide variety of applications, the battery maintenance has become a major limitation for the sustainability of such infrastructure. To overcome this problem, energy harvesting offers a viable alternative to autonomously power IoT devices, resulting in a number of battery-less energy harvesting IoTs (or EH-IoTs) appearing in the market in recent years. Standards activities are also underway, which involve wireless protocol design suitable for EH-IoTs as well as testing procedures for various energy harvesting methods. Despite the early commercial and standards activities, IoT sensing, computing and communications under unpredictable power supply still face significant research challenges. This paper systematically surveys recent advances in EH-IoTs from several perspectives. First, it reviews the recent commercial developments for EH-IoT in terms of both products and services, followed by initial standards activities in this space. Then it surveys methods that enable the use of energy harvesting hardware as a proxy for conventional sensors to detect contexts in energy efficient manner. Next it reviews the advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices. We also survey recent research in novel wireless communication techniques for EH-IoTs, such as the applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions, and packet-less IoT communications and backscatter communication techniques for energy impoverished environments. The paper is concluded with a discussion of future research directions., Comment: Accpeted for publication in IEEE Communications Surveys & Tutorials (2019)

Advancements in nanotechnology promises new capabilities for Internet of Things (IoT) to monitor extremely fine-grained events by deploying sensors as small as a few hundred nanometers. Researchers predict that such tiny sensors can transmit wireless data using graphene-based nano-antenna radiating in the terahertz band (0.1-10 THz). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. In this paper, we propose an energy efficient event monitoring framework for nano IoT that enables nanosensors to update a remote base station about the location and type of the detected event using only a single short pulse. Nanosensors encode different events using different center frequencies with non overlapping half power bandwidth over the entire terahertz band. Using uniform linear array (ULA) antenna, the base station localizes the events by estimating the direction of arrival of the pulse and classifies them from the center frequency estimated by spectral centroid of the received signal. Simulation results confirm that, from a distance of 1 meter, a 6th derivative Gaussian pulse consuming only 1 atto Joule can achieve localization and classification accuracies of 1.58 degree and 98.8%, respectively., 6 pages, 18 Figures, accepted for publication in IEEE GLOBECOM Conference 2018

Reliable command and control channels to unmanned aerial vehicles (UAVs) are needed to allow beyond visual line of sight (LoS) operations. Cellular networks, with their almost ubiquitous coverage, are an obvious candidate to provide such conditions. However, up to which extent the current networks designed for ground users can support UAV communications is an open question. In this paper, we provide a comprehensive theoretical analysis, using stochastic geometry, of the performance that operators could expect from traditional cellular networks with omnidirectional antennas when supporting UAV downlink command and control channels. Our study employs the latest UAV height-dependent path loss model defined by the 3GPP, with LoS and non-LoS transmissions and a probabilistic model to switch between them. We derive analytical expressions for the coverage probability and area spectral efficiency, while accounting for base stations with idle mode capabilities, a practical finite UAV density, and different UAV heights. Results show that networks based on base stations with omnidirectional coverage can support low-height UAVs but will struggle with high-height ones. Network densification helps to provide a better performance.

Millimeter Wave (MmWave) based communication standards including IEEE 802.11ad WiFi show great promise in supporting next generation service demands such as real-time streaming of ultra-high-definition video. However, MmWave signals suffers from high path loss and often required to have unobstructed line-of-sight. Narrow beamwidth antenna arrays along with adaptive beamforming is currently deployed to mitigate those issues. Nevertheless, the impact of device population on beam alignment has not yet been well studied in the literature. In this paper, we quantify the loss of antenna gain when there are multiple devices in the communication range completing for channel access to perform beamforming. We further study the link outage probability for devices with angular displacement similar to change in orientation of hand-held or head-worn devices in home wireless networks. The simulation results reveal that loss of antenna gain can be as high as 7dB and link outage probability increases up to 90% for 16 antenna sectors and 10 competing devices.

The 30-300GHz millimeter wave (mmWave) band is currently being pursued to combat the rising capacity demands in 5G, WiFi, and IoT networks. Due to the high frequency, impulse radio (IR) in this band is better suited for positioning than other existing low-frequency bands. Besides precision positioning, the exceptionally wide bandwidth also enables concurrent use of multiple center frequencies in the same application, which opens up additional avenues of information encoding in IR mmWave networks. In this paper, we propose a new mmWave IR framework that can simultaneously detect direction of arrival (DOA) as well as the center frequency of the transmitted pulse. Based on the emerging graphene-based transceivers, we evaluate the performance of the proposed framework in the higher frequency region of mmWave band (100-300GHz). Numerical experiments demonstrate that the proposed framework can detect the DOA of a 0.1 $\mu$Watt mmWave pulse within 1 degree of precision at 20 meters, and classify three different center frequencies with 100% accuracy from a distance of 10 meters. These performances could be further improved by trading off the pulse rate of the system., Comment: 6 pages, 35 figures, Camera Ready version, Accepted for publication in 2nd ACM Workshop on Millimeter Wave Networks and Sensing Systems

Transparent solar cell is an emerging solar energy harvesting technology that allows us to see through these cells. This revolutionary discovery is creating unique opportunities to turn any mobile device screen into solar energy harvester. In this paper, we consider the possibility of using such energy harvesting screens as a sensor to detect hand gestures. As different gestures impact the incident light on the screen in a different way, they are expected to create unique energy generation patterns for the transparent solar cell. Our goal is to recognize gestures by detecting these solar energy patterns. A key uncertainty we face with transparent solar cell is that, to provide transparency, they cannot harvest from the visible spectra, which may lead to weaker energy patterns for the gestures. To study gesture recognition feasibility of transparent solar cell, we develop a 1cmx1cm organic see-through solar cell which provides high level of content visibility when placed on mobile phone screen. We then use the output current of the organic cell as the source signal for gesture pattern recognition using machine learning. Experimental results demonstrate that we can detect five hand gestures with average accuracies of 95%. We also compare gesture recognition accuracies of our prototype organic cell with those obtained from a conventional ceramic opaque solar cell, which reveals that organic solar cell can recognize some of these gestures almost as good as the opaque cells.

The rapid growth of consumer Unmanned Aerial Vehicles (UAVs) is creating promising new business opportunities for cellular operators. On the one hand, UAVs can be connected to cellular networks as new types of user equipment, therefore generating significant revenues for the operators that can guarantee their stringent service requirements. On the other hand, UAVs offer the unprecedented opportunity to realize UAV-mounted flying base stations that can dynamically reposition themselves to boost coverage, spectral efficiency, and user quality of experience. Indeed, the standardization bodies are currently exploring possibilities for serving commercial UAVs with cellular networks. Industries are beginning to trial early prototypes of flying base stations or user equipments, while academia is in full swing researching mathematical and algorithmic solutions to address interesting new problems arising from flying nodes in cellular networks. In this article, we provide a comprehensive survey of all of these developments promoting smooth integration of UAVs into cellular networks. Specifically, we survey (i) the types of consumer UAVs currently available off-the-shelf, (ii) the interference issues and potential solutions addressed by standardization bodies for serving aerial users with the existing terrestrial base stations, (iii) the challenges and opportunities for assisting cellular communications with UAV-based flying relays and base stations, (iv) the ongoing prototyping and test bed activities, (v) the new regulations being developed to manage the commercial use of UAVs, and (vi) the cyber-physical security of UAV-assisted cellular communications., Accepted in the JOURNAL OF COMMUNICATIONS SURVEYS AND TUTORIALS, Feb 2019, 26 pages, 9 Tables, 9 Figures Submitted to JOURNAL OF COMMUNICATIONS SURVEYS AND TUTORIALS, June 2018, 20 pages, 9 Tables, 9 Figures

Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts the sensing signal. To achieve high quality sensing from PEH, the state-of-the-art uses separate PEHs for sensing and energy harvesting, which increases system complexity, form factor, and cost. In this paper, we propose a novel SEHS architecture, which combines energy harvesting and sensing in the same piece of PEH, and minimizes distortion in the sensing signal by applying a special filtering algorithm. We prototype the SEHS concept in the form factor of a shoe, and evaluate its energy harvesting as well as sensing performance with 20 subjects using gait recognition as a case study. We demonstrate that the SEHS prototype harvests up to 127% more energy and detects human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art.

The feasibility of using vibration energy harvesting (VEH) as an energy-efficient receiver for short-range acoustic data communication has been investigated recently. When data was encoded in acoustic signal within the energy harvesting frequency band and transmitted through a speaker, a VEH receiver was capable of decoding the data by processing the harvested energy signal. Although previous work created new opportunities for simultaneous energy harvesting and communication using the same hardware, the communication makes annoying sounds as the energy harvesting frequency band lies within the sensitive region of human auditory system. In this work, we present a novel modulation scheme to completely hide all communications within background music sound. The proposed modulation exploits sound masking theory to maximize signal to noise ratio of data communication without being audible to the music listener. We capitalize on the existence of repetitive sound patterns within popular music to realize synchronization between the transmitter and the receiver. We implement the proposed modulation within multiple hit songs and demonstrate its efficacy using a real VEH prototype made from off-the-shelf hardware. A user study involving 30 subjects confirms that the proposed modulation can completely hide VEH-based data communication from human perception while achieving up to 14 bps data rate, which is sufficient to transmit short codes or coupons of practical use.

It is reported that mobile users spend most of their time on texting SMS, Social Networking, Emailing, or sending instant messaging (IM), all of which involve text input. There are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each one of them engages a different set of hardware and consequently consumes different amounts of battery energy. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three input modalities. We find that the length of interaction, or the message length, determines the most energy efficient modality. For short interactions, less than 14-30 characters, SK is the most energy efficient. For longer interactions, however, STT significantly outperforms both SK and Swype. When message length distributions of popular text activities are considered, STT provides near optimal energy consumption without requiring the user to predict the message length and decide between SK and STT. In terms of battery life, the choice of input modality makes significant differences. If users always choose SK for all their text activities, they will consume nearly 50% of the phone battery each day. Choosing STT over SK can save 30%-40% of the battery depending on the choice of STT software.

We consider a growing research trend of using personal mobile devices for forwarding opportunistic network data. Because personal device storage is meant to support user applications, opportunistic networks must use it in a manner that remains completely transparent to the user. One way to make a device's storage use transparent is to allow priority access to the storage to user applications, even if the storage is currently occupied by network data yet to be forwarded. This means that data given to a device waiting to be forwarded can be overwritten by application data and may, thus, be lost. In this paper we consider random access memory (RAM) as the primary storage location in a mobile device. We propose three algorithms of different sophistications to answer the question of how much data should be moved when a contact opportunity arises between two devices in such a way to first maximise the data transferred while minimising the probability that this data will be overwritten when applications claim priority access. We collect 33 h of high-resolution RAM usage traces of two real smartphones over a 3-day period under a variety of usage scenarios to evaluate and compare the performances of the proposed algorithms. Surprisingly, we find that autoregression forecasting of RAM usage cannot outperform the simplest algorithm that greedily occupies all of the RAM that is found unused at the time of contact. We show that Bayesian inference is very effective in minimising the risk of data loss in such uncertain environments and significantly outperforms the greedy approach as well as autoregression forecasting.

The terahertz band is considered the last frontier for wireless communications and expected to play a significant role in beyond 5G networks. Besides supporting extremely high data rates for existing devices, the terahertz band is also expected to connect future nanoscale devices using graphene-based nano-antenna, which happens to radiate in 0.1-10 THz band. In this band, higher order derivatives of Gaussian pulses can provide energy-efficient communication for nanodevices. In this paper, we propose a metric, called root mean square (RMS) frequency spread, to detect the derivative order of the pulse at the receiving base station using uniform linear array antennas. Simulation experiments demonstrate that RMS frequency spread can be used to detect the derivative order of Gaussian pulses with 99% accuracy for distances up to 50 cm. This finding opens up a new design space for nanoscale terahertz communication, which can encode information in time derivative order of the transmitted pulse., Comment: 6 pages, 17 figure, accepted for publication in 2018 IEEE Global Communications Conference: Workshops: International Workshop on Emerging Technologies for 5G and Beyond Wireless and Mobile Networks (GC'18 WS - ET5GB). arXiv admin note: text overlap with arXiv:1808.06764

Nanoscale wireless sensor networks (NWSNs) could be within reach soon using graphene-based antennas, which resonate in 0.1-10 terahertz band. To conserve the limited energy available at nanoscale, it is expected that NWSNs will communicate using extremely short pulses on the order of femtoseconds. Accurate estimation of direction of arrival (DOA) for such terahertz pulses will help realize many useful applications for NWSNs. In this paper, using the well-known MUltiple SIgnal Classification (MUSIC) algorithm, we study DOA estimation for NWSNs for different energy levels, distances, pulse shapes, and frequencies. Our analyses reveal that the best DOA estimation is achieved with the first order Gaussian pulses, which emit their peak energy at 6 THz. Based on Monte Carlo simulations, we demonstrate that MUSIC algorithm is capable of estimating DOA with root mean square error less than one degree from a distance of around 6 meter for pulse energy as little as 1 atto Joule., Comment: 6 Pages, 9 figures, Camera Ready Version, NANOCOM '18: ACM The Fifth Annual International Conference on Nanoscale Computing and Communication, September 5--7, 2018, Reykjavik, Iceland

In this paper, the deployment of drone base stations to provide higher performance in the cellular networks is analyzed. In particular, we investigate a new mobility model for drone base stations where they can move freely in the network, ignoring the cell boundaries. Free movement model for drones bring out new challenges such as user association and physical collision among drones. We consider two user association schemes and evaluate their performance through simulation. We show that by deploying a smart user association scheme in the free movement model, the obtained results are greatly better than those restricting each drone base station to fly over a certain small cell area, and serving local users. Additionally, the impact of drones' movement on the load balance, signal strength and interference are studied. Moreover, we show that our proposed algorithm can maintain a comfortable distance among the drones to avoid physical collision., Comment: to appear in IEEE Globecom 2017

In this paper, we present a new theoretical discovery that the multiple-input and multiple-output (MIMO) capacity can be influenced by atmosphere molecules. In more detail, some common atmosphere molecules, such as Oxygen and water, can absorb and re-radiate energy in their natural resonance frequencies, such as 60GHz, 120GHz, and 180GHz, which belong to the millimeter wave (mmWave) spectrum. Such phenomenon can provide equivalent non-line-of-sight (NLoS) paths in an environment that lacks scatterers, and thus greatly improve the spatial multiplexing and diversity of a MIMO system. This kind of performance improvement is particularly useful for most mmWave communications that heavily rely on line-of-sight (LoS) transmissions. To sum up, our study concludes that since the molecular re-radiation happens at certain mmWave frequency bands, the MIMO capacity becomes highly frequency selective and enjoys a considerable boosting at those mmWave frequency bands. The impact of our new discovery is significant, which fundamentally changes our understanding on the relationship between the MIMO capacity and the frequency spectrum. In particular, our results predict that several mmWave bands can serve as valuable spectrum windows for high-efficiency MIMO communications, which in turn may shift the paradigm of research, standardization, and implementation in the field of mmWave communications., arXiv admin note: text overlap with arXiv:1710.09031

We propose a new activity sensing method, CapSense, which detects activities of daily living (ADL) by sampling the voltage of the kinetic energy harvesting (KEH) capacitor at an ultra low sampling rate. Unlike conventional sensors that generate only instantaneous motion information of the subject, KEH capacitors accumulate and store human generated energy over time. Given that humans produce kinetic energy at distinct rates for different ADL, the KEH capacitor can be sampled only once in a while to observe the energy generation rate and identify the current activity. Thus, with CapSense, it is possible to avoid collecting time series motion data at high frequency, which promises significant power saving for the sensing device. We prototype a shoe-mounted KEH-powered wearable device and conduct experiments with 10 subjects for detecting 5 different activities. Our results show that compared to the existing time-series-based activity recognition, CapSense reduces sampling-induced power consumption by 99% and the overall system power, after considering wireless transmissions, by 75%. CapSense recognizes activities with up to 90%.

In this paper, we compare the performance of two main MIMO techniques, beamforming and multiplexing, in the Terahertz (THz) band. The main problem with the THz band is its huge propagation loss, which is caused by the tremendous signal attenuation due to molecule absorption of the electro-magnetic wave. To overcome the path loss issue, massive MIMO has been suggested to be employed in the network and is expected to provide Tbps for a distance within a few meters. In this context, beamforming is studied recently as the main technique to take advantage of MIMO in THz and overcome the very high path loss with the assumption that the THz communication channel is Line-of- Sight (LoS) and there are not significant multipath rays. On the other hand, recent studies also showed that the well- known absorbed energy by molecules can be re-radiated immediately in the same frequency. Such re-radiated signal is correlated with the main signal and can provide rich scattering paths for the communication channel. This means that a significant MIMO multiplexing gain can be achieved even in a LoS scenario for the THz band. Our simulation results reveal a surprising observation that the MIMO multiplexing could be a better choice than the MIMO beamforming under certain conditions in THz communications.

© 1998-2012 IEEE. As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices-wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accessories and clothing that people wear. Although small in size, they are often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put significant demand on improving communication security and reducing power consumption of the system, fueling new research in these areas. In this paper, we first provide a comprehensive survey and classification of commercially available wearables and research prototypes. We then examine the communication security issues facing the popular wearables followed by a survey of solutions studied in the literature. We also categorize and explain the techniques for improving the power efficiency of wearables. Next, we survey the research literature in wearable computing. We conclude with future directions in wearable market and research.

Increasing sensing and communication capabilities combined with falling prices have made drones very attractive for Internet of Things applications. A key requirement of these applications is that the drones should be autonomously maneuvered by computer programs. It is therefore important to understand the practical limitations of autonomous drone maneuverability to ensure that target application performance is met. In this paper, we first analyze drone maneuverability using theory to shed light on the tradeoff between the flying speed and the turning agility of the drone. To investigate the practical maneuverability performance, we then emulate as well as fly a commercial drone under the control of an Android program. We reveal some practical maneuverability factors that must be considered for the applications that require frequent changes of direction for the drone.

This paper investigates the possibility of using a vibration energy harvesting (VEH) device as a communication receiver. By modulating the ambient vibration energy using a transmitting speaker, and demodulating the harvested power at the receiving VEH, we aim to transmit small amounts of data at low rates between two proximate devices. The key advantage of using VEH as a receiver is that the modulated sound waves can be successfully demodulated directly from the harvested power without employing the power-consuming digital signal processing (DSP), which makes a VEH receiver significantly more power efficient than a conventional microphone-based decoder. To address the extremely narrow bandwidth of VEH, we design a simple ON-OFF keying modulation, but optimized for VEH hardware. Experiments with a real VEH device shows that, at a distance of 2 cm, a laptop speaker with the proposed modulation scheme can achieve 30 bps communication for a target bit error rate of less than 1%, which would enable many emerging short range applications, such as mobile payment. The communication range of a laptop can be extended to 80 cm for 5 bps, allowing a range of other audio-based device-to-device communications, such as a web advertisement on a laptop browser transferring tokens to a nearby smartphone. We also demonstrate that the proposed VEH-based sound decoding is resilient to background noise, thanks to its extremely narrow power harvesting bandwidth, which works as a natural noise filter.

Digital out-of-home (DOOH) advertising networks, comprised of pervasive distributed digital signages (screens), are rapidly growing. It is reported that more than 70% of DOOH revenue comes from local ads, while it is especially challenging to decide when and where to deliver the most suitable ad due to the spatio-temporal dynamics of human mobility and preferences. Understanding urban geo-social connectivity in terms of people movement would greatly benefit ad content distribution, and could potentially be utilized by a large number of mobile applications and geo-social services. However, existing DOOH ad distribution systems are designed to target individuals, which might not be the best choice in public spaces, and do not consider the preferences of "cohort of users". In this paper, we propose an alternative approach to target cohort of users extracting urban geo-social connectivity through large-scale mobile network data and existing geo-social service data. We construct a dynamic urban geo-social connectivity graph, and formulate the problem of distributing ads for maximum exposure to the "right" users under a constrained budget. Hence, we propose a heuristic algorithm. Simulation results show that our system targeting "cohort of users" achieves a maximum 300% improvement compared to naive distributing method in displaying ads to the "right people" when user preferences are completely known, while a minimum of 25% improvement when the knowledge of user preferences is limited.

With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100\% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption., Comment: Accepted at IEEE WoWMoM 2017 - 9 pages, 2 tables, 4 figures

Thanks to the recent advancements in drone technology, it has become viable and cost-effective to quickly deploy small cells in areas of urgent needs by using a drone as a cellular base station. In this paper, we explore the benefit of dynamically repositioning the drone base station in the air to reduce the distance between the BS and the mobile user equipment, thereby improving the spectral efficiency of the small cell. In particular, we propose algorithms to autonomously control the repositioning of the drone in response to users activities and movements. We demonstrate that, compared to a drone hovering at a fixed location, dynamic repositioning of the drone moving with high speed can increase spectral efficiency by 15\%. However, considering the tradeoff between spectral efficiency and energy efficiency of the drone, we show that 10.5\% spectral efficiency gain can be obtained without negatively affecting energy consumption of the drone., Comment: 6 pages, to appear in IEEE Globecom Workshop 2016

Location privacy is an important concern for the users of mobile devices and this private information can be identified using the embedded motion sensors in these devices. This paper highlights the privacy leakage resulting from unrestricted access to seemingly innocuous generated patterns of vibration energy harvesting (VEH) circuits in mobile devices. We investigate that by monitoring the VEH generated patterns, a malware application can infer which train route the passenger has taken, violating her privacy. We collected real-world motion data for 4 distinct train routes in the Sydney metropolitan area. Our results show that the passenger's train route can be inferred with an average accuracy of 97.2% for a journey of 7 stations, confirming the rich contextual information embedded in VEH data and to light the associated privacy risks in releasing this data.

Vibration energy harvesting (VEH) is a promising source of renewable energy that can be used to extend battery life of next generation mobile devices. In this paper, we study the feasibility and accuracy of VEH for detecting hotwords, such as “OK Google”, used by popular voice control applications to distinguish user commands from other conversations. The idea of using power signals of VEH to detect hotwords is based on the fact that human voice creates vibrations in the air, which could be potentially picked up by the VEH hardware inside a mobile device. Using off-the-shelf VEH product, we conduct a comprehensive experimental study involving 8 subjects. We analyse two possible usage scenarios for the VEH hardware. In the first scenario, the user is not required to talk directly to the device (indirect), but the VEH is expected to pick up the ambient vibrations caused by user-generated sound waves. In the second, the user is expected to direct his voice to the VEH (direct) and talk to it from a close distance. For both usage scenarios, we evaluate two types of hotword detection, speaker-independent and speaker-dependent. We find that VEH can detect hotwords more accurately in the direct scenario compared to the indirect. For the direct scenario, our results show that a simple Decision Tree classifier can detect hotwords from VEH signals with accuracies of 73% and 85%, respectively, for speaker-independent and speaker-dependent detections. Finally, we show that these accuracies are comparable to what could be achieved with an accelerometer sampled at 200 Hz.

Bandwidth fluctuation in mobile networks severely effects the quality of service (QoS) of bandwidth-sensitive applications such as video streaming. Using bandwidth statistics it is possible to predict the network behaviour and take proactive actions to counter network fluctuations, which in turn can improve the QoS. In this paper, we present comprehensive bandwidth datasets from extensive measurement campaigns conducted in Sydney on both 3G and 4G networks under vehicular driving conditions. A particularly distinguishing feature of our dataset is that we have collected data from repeated trips along a few routes. Thus our data can be useful to obtain statistically significant results on network performance in an urban setting. We outline the measurement methodology and present key insights obtained from the collected traces. We have made our dataset available to the wider research community.