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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.

Pulse-based terahertz transmission offers opportunities to realise high-speed wireless communication with low-power and low-complexity transceivers. However, at ultra-low-power, reliable decoding of terahertz pulses becomes extremely challenging due to high attenuation and molecular absorption at the terahertz band. Although the reflected pulses in multipath propagation environment create an opportunity to combine them for improved reliability, it requires complex circuitry at the receiver to adjust the unwanted phase shifts of the reflections caused by the natural surface. Given that the emerging intelligent reflective surfaces (IRSs) can eliminate the unwanted phase shifts of the reflected signals, we propose IRS as a means to boost signal reception and improve pulse decoding without requiring any complex receiver hardware. We analyze the performance of IRS-assisted pulse decoding for Gaussian pulses centered at different center frequencies to represent different symbols. Simulation results reveal that for 4-ary frequency modulation with pulse transmission power as little as 0.01μW, IRS assisted multipath diversity can keep symbol error rate below 10−2 for distances up to 1.4 meter, which is 75% longer compared to that achievable under conventional surfaces.

We address the problem of human posture detection with casual loose-fitting smart garments by fabricating a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor enabled by uniquely designed microcracks within a hybrid conductive thin film. In terms of sensitivity and stretchability, the developed sensor outperformed most of the works reported in recent literature, and has a gauge factor of 103 at the high strain of 58%. By attaching these sensors to an off-the-self casual jacket, we implement E-Jacket, a smart loose-fitting sensing garment prototype. To detect postures from sensor data, we implement a conventional deep learning model, CNN-LSTM, capable of overcoming the noise induced by the loose-fitting of the sensors to the human skin. To evaluate E-Jacket, we conducted three case studies in experimental environments: recognition of daily activities, recognition of stationary postures with random hand movements, and slouch detection. Our evaluation results demonstrate the feasibility of the proposed E-Jacket smart garment system for different posture recognition applications.

We demonstrate a human activity detection with casual loose-fitting smart garment system. By employing a new type of highly sensitive, stretchable, optical transparent and low-cost strain sensor and a deep learning model enabled by CNN-LSTM, the loose-fitting jacket is able to recognize 5 activities with 90.9% accuracy when the system is trained with the user data, and 73.5% accuracy when an unseen user wears the smart jacket, which is comparable with tight-fitting smart garment system. In the demonstration, we will showcase activity recognition of three activities: walk, sit, and stand.

Switchable privacy glass can electronically change its state between opaque and transparent. 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 light sensing capability to realise data communication over natural light. A key advantage is that no energy is used to generate light, as it simply modulates the existing light in the nature. We demonstrate that the proposed data communication using switchable glass modulation can achieve 33.33 bits per second communication with a bit rate below 1% under a wide range of ambient luminance.

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.

Sensitivity of WiFi channel measurements to the transceiver placement is a major limitation for on-demand deployment of device-free WiFi sensing in environments where the transmitting/receiving devices may move. Using publicly available datasets, we show that even slight deviations of transmitter/receiver placements from the reference values can degrade device-free gesture recognition accuracy significantly. We design a convolutional autoencoder to translate WiFi spectrograms from arbitrary receiver placements to a reference placement configuration in a given area of interest with minimal human effort. Our experiments with the public datasets reveal that the proposed autoencoder can successfully reduce WiFi measurement variability caused by transmitter/receiver movement, which ultimately increases gesture recognition accuracy by up to 58%.

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.

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.

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

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.

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve battery-less wearable system. 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, which suggests that energy harvesting and sensing can be performed concurrently. However, realization of simultaneous energy harvesting and sensing (SEHS) is challenging as the energy harvesting process distorts the sensing signal. In this demo, we propose a novel SEHS architecture prototyped in the form factor of an insole, which combines energy harvesting and sensing in the same piece of PEH. Meanwhile, a special filtering algorithm is applied to minimize the distortion in the sensing signal. 'is demo complements the paper "SEHS: Simultaneous Energy Harvesting and Sensing using Piezoelectric Energy Harvester" to be presented at IoTDI'18.

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.

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

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.

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.

In HTTP-based adaptive streaming systems, media server simply stores video content segmented into a series of small chunks coded in different qualities and sizes. The decision for next chunk's quality level to achieve a high quality viewing experience is left to the client which is a challenging task, especially in mobile environment due to unexpected changes in network bandwidth. Using computer simulations, previous work has demonstrated that Markov decision process (MDP) is very effective for such decision making and that it can reduce video freezing or re-buffering events drastically compared to other methods of adaptation. However, to date there has been no practical implementation and evaluation of MDP-based DASH players. In this work, we extend a publicly available DASH player recently released by DASH industry forum to realise a real DASH player that implements MDP-based video adaptation. We implement two alternative MDP optimisation algorithms, value iteration and Q learning and evaluate their performances in real driving conditions under 300 minutes of video streaming. Our results show that value iteration and Q learning reduce video freezing by a factor of 8 and 11, respectively, compared to the default decision making algorithm implemented in the public DASH player.

5G is planning to exploit millimeter wave (mmWave) channels to achieve the massive capacity increase needed for future applications. Unfortunately, wireless signal attenuation and noise in this high frequency band is very sensitive to atmospheric conditions, such as temperature and humidity. Given that such atmospheric conditions at any given location could vary throughout the day, mobile communication systems seeking to exploit this band must expect some sort of diurnal variations in attenuation and noise. In this paper, we study the extent of diurnal variation in mmWave channels in three largest cities of Australia by accessing the hourly air quality and weather data over 12 months in 2015. We find that all planned mmWave bands, with the exception of 60 GHz, experiences significant diurnal variation in attenuation and noise. The attenuation and noise generally drops in the middle of the day when temperature rises and humidity falls, but remains high during the night. The diurnal variation is found to be more significant in summer compared to winter. The 60 GHz band remains stable throughout the day as this band is mainly affected by the amount of oxygen in the air, which does not fluctuate much for a given location.

Achieving energy-efficiency is a challenging task in human activity monitoring. The continuous activity sensing using accelerometer and the burdensome on-node classification rapidly deplete the limited battery resource of the wearable nodes. To reduce the energy overhead and achieve the system energy-neutrality, we present a novel Bayesian framework for human activity monitoring using the energy-harvesting wearable sensors. The proposed framework utilizes a capacitor to store the harvested kinetic energy and uses all the stored energy to transmit an unmodulated signal, called an activity pulse. Our framework can infer the human activity directly from the received signal strength of the activity pulse at a remote server. Neither accelerometer nor classifier is required on the wearable devices, and therefore, our framework guarantees the system energy-neutrality. Using a real dataset collected from a kinetic energy harvester coupled with a Bluetooth prototype, an overall accuracy of 91% is achieved when the distance between the transmitter and the receiver is set to 30 cm.

Detecting the transportation mode of an individual's everyday travel provides useful information in urban design, real-time journey planning, and activity monitoring. In existing systems, accelerometer and GPS are the dominantly used signal sources which quickly drain the limited battery life of the wearable devices. In this paper, we investigate the feasibility of using the output voltage from the kinetic energy harvesting device as the signal source to achieve transportation mode detection. The proposed idea is based on the intuition that the vibrations experienced by the passenger during motoring of different transportation modes are different. Thus, voltage generated by the energy harvesting devices should contain distinctive features to distinguish different transportation modes. Using the dataset collected from a real energy harvesting device, we present the initial demonstration of the proposed method. We can achieve 98.84% of accuracy in determining whether the user is traveling by pedestrian or motorized modes, and in a fine-grained classification of three different motorized modes (car, bus, and train), an overall accuracy over 85% is achieved.

Energy-harvesting wearable devices generate power by converting natural phenomena such as human motion into usable electricity. We conduct an experimental study to validate the feasibility of detecting steps from the power generation patterns of a wearable piezoelectric energy harvester (PEH). Four healthy adults took part in the study, which includes walking along straight and turning walkways as well as descending and ascending stairs. We find that power generation exhibits distinctive peaks for each step, making it possible to accurately detect steps using widely used peak detection algorithms. Using our PEH prototype, we successfully detected 550 steps out of 570, achieving a step detection accuracy of 96%.

The nonuniform sampling in human visual system (HVS) is used in a video compression technique called foveation in which the region of interest (ROI) is given a higher bitrate. This technique can significantly reduce the network traffic or provide higher quality with similar bitrate. ROI or fovea region can be predicted using offline algorithms with a considerable prediction error. In real-time video streaming scenario, although fovea region can be detected precisely using an eye-tracker device, accessing to this data is not possible on real-time basis due to the network latency. In this paper, we propose a prediction model which uses streaming client's gaze locations on a set of frames to predict the fovea region on future frames. With this method we achieved 10x higher prediction accuracy compared to the offline model.

Dynamic Adaptive Streaming over HTTP (DASH) is one of the most widely used adaptive streaming technique for watching online video content. DASH adapts to the varying network conditions by selecting the appropriate bitrate of the video stream. The bitrate adaptation is typically done by monitoring the playback buffer level and/or the network condition on client side. In this paper we empirically evaluate our JavaScript DASH player in which, Markov Decision Process (MDP) has been considered as the underlying optimization framework. This player uses Q-learning algorithm to learn the model and optimize the Quality of Service (QoS) after multiple streaming sessions. The basic JavaScript DASH player developed by DASH Industry Forum (DASHIF) is used as a benchmarking model in our evaluations. We use Google Chrome's 3G and 4G network emulators in our experiments and show that our MDP-based DASH player significantly outperforms the DASHIF player which uses buffer control and rate adaptation techniques simultaneously. Using real-time experiments, we show that for similar picture quality we can achieve about 18x fewer deadline misses and 5x fewer quality switches over a 3G network and 32x fewer deadline misses and 1.6x fewer quality switches over 4G.

Wireless Nano-scale Sensor Networks (WNSNs) are very simple and energy restricted networks that operate over terahertz band ranging from 0.1–10 THz, which faces significant molecular absorption noise and attenuation. Given these challenges, reliability, energy efficiency, and simplicity constitute the main criteria in designing communication protocols for WNSNs. Due to its simplicity and energy efficiency, carrier-less pulse based modulation is considered the best candidate for WNSNs. In this paper, we compare the performance of four different carrier-less modulations, PAM, OOK, PPM, and BPSK, in the context of WNSNs operating within the terahertz band. Our study shows that although BPSK is relatively more complex in terms of decoding logic at the receiver, it provides the highest reliability and energy efficiency among all the contenders. PAM has the worst performance in terms of reliability as well as energy efficiency. OOK and PPM have simpler decoding logic, but perform worse than BPSK in both reliability and energy efficiency.

Advances in nanotechnology is paving the way for wireless nanoscale sensor networks (WNSNs), promising radically new applications in medical, biological, and chemical fields. The small nanomaterial-based antennas communicate in the terahertz band, which coincide with the natural resonance frequencies of many molecule species causing severe molecular absorption and noise. The problem is particularly more complicated when the channel condition (composition, pressure and temperature) changes over time, causing time-varying absorption. This paper aims to characterise the time-varying property of terahertz communication over a channel whose condition varies over time. Using existing propagation models for WNSNs, we then investigate the reliability of communication over composition varying channels as a major class of time-varying WNSNs. We use WNSN for chemical reactor monitoring and health monitoring as two case studies where channel compositions vary over time. Our simulation results show that for a given transmitted power, the Signal-To-Noise (SNR) and Bit Error Rate (BER) vary over time in a given distance from the transmitter and that is highly sensitive to the composition of the channel.

Conventional human activity recognition (HAR) relies on accelerometers to frequently sample human motion (acceleration). Unfortunately, power consumption of accelerometers becomes a bottleneck for realising pervasive self-powering HAR as the amount of power that can be practically harvested from the environment is very small. Instead of using accelerometer, this paper advocates the use of energy harvesting power signal as the source of HAR when motion (kinetic) energy is being harvested to power the device. The proposed use of harvested power for classifying human activities is motivated by the fact that different activities produce kinetic energy in a different way leaving their signatures in the harvested power signal. Using information theoretic analysis of experimental data, we show that many standard statistical features provide significant information gain when the kinetic power signal is used for discriminating between different activities, confirming its potential use for HAR. We have evaluated activity recognition accuracy for kinetic power signal based HAR using 14 different sets of common activities each containing between 2–10 different activities to be classified. HAR accuracies varied between 68% to 100% depending on the set of activities. The average accuracy over all activity sets is 83%, which is within 13% of what could be achieved with an accelerometer without any power constraints.

Mobile device users use applications that require text input. Today there are three primary text input modalities, soft keyboard (SK), speech to text (STT) and Swype. Each of these input modalities have different energy demands, and as a result, their use will have a significant impact on the battery life of the mobile device. Using high-precision power measurement hardware and systematically taking into account the user context, we characterize and compare the energy consumption of these three text input modalities. We show that the length of interaction determines the most energy efficient modality. If the interactions is short, on average less than 30 characters, using the device SK is the most energy efficient. For longer interactions, the use of a STT applications is more energy efficient. Swype is more energy efficient than STT for very short interactions, less than 5 characters on average, but is never as efficient as SK. This is primarily due to STT enabling the users to complete tasks more quickly than when using SK or Swype. We also show that these results are independent of “user style”, the experience of using different input modalities and device characteristics. Finally we show that STT energy efficiency is dependent on application logic of whether speech samples are for a given period of time before transmitting to a server for analysis as opposed to streaming the speech to a sever for analysis. Based on these observations we recommend that the users should use SK for short interactions of less than 30 characters, and STT for longer interactions. In addition, they should use STT applications which uses storing and transmit logic, if they are willing to trade off battery life to QoE. Finally we proposed the development of an adaptive storing and analyze STT to improve the energy efficiency of it.

Pedestrian dead reckoning (PDR) is widely used for indoor localisation. Its principle is to recursively update the location of the pedestrian by using step length and step heading. A common method to estimate the heading in PDR is to use magnetometer measurements. However, unlike outdoor environments, the Earth's magnetic field is strongly perturbed inside buildings making the magnetometer measurements unreliable for heading estimation. This paper presents a new method to reduce heading estimation errors when magnetometers are used. The method consists of two components. The first component uses a machine learning algorithm to detect whether a heading estimate is within a specific error margin. Only heading estimates within the error margin are retained and passed to the second component, while the other estimates are discarded. The second component uses data fusion to average the heading estimates from multiple people walking in the same direction. The rationale of this component is based on the observation that magnetic perturbations are often highly localised in space and if multiple people are walking in the same direction, then only some of their magnetometers are likely to be perturbed. Data fusion between users can be carried out in a distributed manner by using a consensus algorithm with information sharing over wireless links. We tested the performance of our method using 92 datasets. The method is shown to provide an average heading estimate error of approximately 2°, which is more than 6-fold lower than the error of the heading estimate based only on raw magnetometer measurements (without any filtering and fusion). Assuming highly accurate step-length observation, the improved heading estimation leads to an average localisation accuracy of 55cm, which is an 80% improvement over PDR localisation using only raw magnetometer measurements.

The terahertz band is an unlicensed frequency range that is expected to be exploited in the near future for many different types of communications, including wireless communication in nano-scale sensor networks. However, as terahertz band is the resonance frequency of many molecules, communication in this band is severely affected by molecular absorption noise and attenuation. In this paper, we consider a nano-scale terahertz sensor network (TSN) where the chemical composition of the medium varies over time causing absorption for different frequency regions at different times. We propose frequency hopping as a means to overcome the problem of dynamic molecular absorption in composition varying channels. We formulate the frequency selection problem as a Markov Decision Process (MDP), which allows us to adjust the rate of frequency switching for the nano sensors because resource constrained TSN nodes may not be able to switch frequency rapidly. We show that, compared to non-hopping channel selection, frequency hopping can significantly improve capacity and bit error rate when nano sensors have severe power constraints. We propose practically realizable offline policies that obviate the need for observing the channel states, yet perform close to the MDP-based solutions.

The ongoing success of smartphones and tablet computers, combined with the widespread deployment of cellular network infrastructure, has paved the way for ubiquitous Internet access. Access to mobile services has become a commodity for many commuters on public transport vehicles. On their daily trips to work and back, however, people often experience varying throughput rates due to the different capacities of network cells and the channel quality to the cell site. Links with reduced or no throughput are clearly unfavorable when users need to download large files or engage in synchronous communication activities. We thus introduce the notion of opportunistic personal bandwidth maps (OPBMs) in this paper. OPBMs allow the user to schedule activities with high throughput demand to parts of their journey where the bandwidth requirements are likely to be met. Users create their own OPBM by means of opportunistically monitoring their throughput during access to the cellular network and consolidating these individual measurements. Due to the opportunistic nature of our approach, no additional data transfers are required. Our measurements for more than 70 commutes show that the achievable throughput for road segments is highly variable across different trips. Still, the availability of OPBMs allows users to make decisions (e.g. to download a large file) when traveling along the segment with highest expected throughput.

In large shopping malls and airports, pedestrians often change floors using conveniently located lifts and escalators. Floor changing activity recognition (FCAR) therefore can be a vital aid to multi-floor pedestrian navigation systems. The focus of this paper is to achieve accurate FCAR with the minimal number of features. Using experimental data, we compare the performance of various feature selection methods and classifiers trained to detect whether the user is using an escalator or a lift. The results show that an accelerometer embedded in a smartphone can achieve 94% recognition accuracy using only 5 features.

Due to its simplicity at the server side, HTTP-based adaptive streaming has become a popular choice for streaming on-line contents to a wide range of user devices. In HTTP-based streaming systems, the server simply stores the video segmented into a series of small chunks coded in many different qualities and sizes, and leaves the decision of which chunk to download next to achieve a high quality viewing experience to the client. This decision making is a challenging task, especially in mobile environment due to unexpected changes in network bandwidth as the user moves through different regions. In this paper, we consider Markov Decision Process (MDP) to derive the optimum chunk selection strategy that maximizes streaming quality and propose three approaches to reduce the computational cost of MDP. The first approach recomputes the solution after downloading every k chunks. The second approach computes the solution once using global network statistics of a given region. The third approach recomputes the solution every x meters using offline statistics for each x meters of the road. The three approaches are compared using real-world 3G bandwidth and mobility traces. The best performance is achieved with x-MDP.

A pedestrian activity classification (PAC) system classifies pedestrian motion data into activities related to the usage of specific building facilities, such as going up on an escalator or descending a staircase. Recent studies confirm that use of PAC significantly reduces indoor localization errors of a pedestrian dead reckoning (PDR) system as exact facility locations in the building can be retrieved from the floor map. However, classification complexity may become an issue for resource constraint mobile devices. We propose a novel PAC system that, instead of using a single complex classifier based on a large set of features, employs multiple simple classifiers each trained to classify only a subset of the activities using a small number of features. As the pedestrian moves around inside a building, the proposed adaptive-PAC dynamically switches to the right (simple) classifier based on the facilities that exist within the immediate proximity. By always using a simple classifier, adaptive-PAC has the potential to drastically reduce the average classification complexity for PAC-aided PDR systems. Using experimental data, we quantify and compare the performance of the proposed adaptive-PAC against the conventional PAC. We find that for typical shopping centers, adaptive-PAC reduces classification complexity by 91–97% without any degradation in classification accuracy rates.

Advanced computing and sensing capabilities of smartphones provide new opportunities for personal indoor positioning. A particular trend is to employ human activity recognition for autonomous calibration of pedestrian dead reckoning systems thereby achieving accurate indoor positioning even in the absence of any positioning infrastructure. The basic idea is that the activity context, such as switching from a walking to a stair climbing activity gives clues about pedestrian's current position. In this paper, we have made a first attempt in developing a performance model for such systems. For an unbiased random walk, we have obtained two interesting results in closed-form expressions. First, we have demonstrated that the distance a pedestrian is expected to travel before the PDR is recalibrated is reciprocal of the density of activity switching points (ASPs) in the indoor environment. The implication of this finding is that the continuous unaided use of PDR can be curbed drastically by identifying more ASPs in a given environmental setting. Second, we have shown that false negatives of the activity detection algorithms do not have a major impact as long as they are within a reasonable range of 0–30%. The system performance however degrades rapidly if false negatives continue to grow beyond 30%.

If users are known to perform specific activities at specific locations within a building, then indoor positioning could be achieved by monitoring user activities and matching them to specific locations in a preloaded floor map. This is the fundamental idea behind activity-based map matching (AMM). For example, the user's smartphone could use the accelerometer readings to detect whether a user is using an escalator, and then match the current location of the user to the nearest escalator. AMM therefore could be used for frequently recalibrating location estimators to ground-truth values. This is especially useful for recalibrating pedestrian dead reckoning (PDR), which can estimate indoor position if started from a known location, but error grows unboundedly with time or distance traveled. However, AMM is not perfect and could potentially cause mismatches by matching the current location of the user to a wrong location. In this paper we propose a methodology and derive a closed-form expression for mismatch probability as a function of PDR sensor error and proximity between two facilities. By applying our methodology to a practical indoor complex (Sydney airport) we find several interesting results: (1) that mismatch probability is spatially non-uniform, i.e., it can be different in different parts of the floor, (2) for some specific facilities, mismatch probability can be very high (up to 80%), and (3) if escalators could be distinguished from lifts with high accuracy, we could reduce mismatch probability significantly (by up to 68%).

While mobile broadband performance measured from moving vehicles in metropolitan areas has drawn significant attentions in recent studies, similar investigations have not been conducted for regional areas. Compared to metropolitan cities, regional suburbs are often serviced by wireless technologies with significantly lower data rates and less dense deployments. Conversely, vehicle speeds are usually much higher in the regional areas. In this paper, we seek to provide some insights to user experience of mobile broadband in terms of TCP throughput when travelling in a regional train. We find that (1) using a single broadband provider may lead to a large number of blackouts, which could be reduced drastically by simultaneously subscribing to multiple providers (provider blackouts are not highly correlated), (2) the choice of train route may have a more significant effect on broadband experience than the time-of-day of a particular trip, and (3) the speed of the train itself has no deterministic effect on TCP throughput.

Rapidly repeating the original packet multiple times has been found to improve the probability of successful reception of a packet in error-prone vehicular communication environment. However, since each repetition increases channel load, performance of repetition-based loss recovery starts to deteriorate with increasing number of vehicles on the road. This paper explores the benefit of Network Coding in improving the performance of repetition-based loss recovery for vehicular safety communications. A simple network coded repetition scheme is proposed, which combines (XORs) packets from close-by neighbours and repeats the XORed packets instead of original packets, thereby creating the possibility of an increased number of packet recovery per repetition. An analytical study is conducted to evaluate the performance of the proposed network coded repetition, which is validated by simulation experiments. Conditions under which network coding can outperform the original repetition scheme are discussed.

Mobile handset manufacturers are introducing new features that allow a user to configure the same handset for seamless operation with multiple wireless network providers. As the competitiveness in the wireless network service market intensifies, such products will deliver greater freedom for the mobile users to switch providers dynamically for a better price or quality of experience. For example, when faced with an unexpected wireless link quality problem, the user could choose to physically switch the provider, or she could be more strategic and use her freedom of switching provider as a ‘psychological weapon’ to force the current provider upgrading the link quality without delay. In this paper, we explore the latter option where users threaten to quit the current provider unless he (the provider) takes immediate actions to improve the link quality. By threatening the provider, the user will have to accept the risk of having to disconnect from the current provider and reconnect to another in the middle of a communication session, should the provider defies the threat. The user therefore will have to carefully assess the merit of issuing such threats. To analyze the dynamics of this scenario, we formulate the problem as a brinkmanship game theory model. As a function of user's and provider's payoff or utility values, we derive conditions under which the user could expect to gain from adopting the brinkmanship strategy. The effect of uncertainties in payoff values are analyzed using Monte Carlo simulation, which confirms that brinkmanship can be an effective strategy under a wide range of scenarios. Since user threats must be credible to the provider for the brinkmanship model to work, we discuss possible avenues in achieving threat credibility in the context of mobile communications.

This paper examines the spatial correlation of packet loss events in IEEE 802.11 wireless networks for broadcast communications. We discuss limitations of previously used metrics to measure spatial loss correlation and show that the entropy correlation coefficient, which is based on the mutual information concept of Information Theory, can overcome these limitations. In our experiments, we find that the packet losses among closely located receivers are highly correlated when signal strength is high, but the correlation decreases with decreasing signal strength. The losses become totally independent once the signal strength falls below a threshold. As a first step to model the relationship between spatial loss correlation and signal strength, we find that the relationship can be approximated as a Gaussian cumulative distribution function.

The increasing processing power, storage and support of multiple network interfaces are promising the mobile devices to host services and participate in service discovery network. A few efforts have been taken to facilitate provisioning mobile Web services. However they have not addressed the issue about how to host heavy-duty services on mobile devices with limited computing resources in terms of processing power and memory. In this paper, we propose a framework which partitions the workload of complex services in a distributed environment and keeps the Web service interfaces on mobile devices. The mobile device is the integration point with the support of backend nodes and other Web services. The functions which require the resources of the mobile device and interaction with the mobile user are executed locally. The framework provides support for hosting mobile Web services involving complex business processes by partitioning the tasks and delegating the heavy-duty tasks to remote servers. We have analyzed the proposed framework using a sample prototype. The experimental results have shown a significant performance improvement by deploying the proposed framework in hosting mobile Web services.