Underground mines require continuous airflow monitoring, yet cabling or frequent battery replacement for distributed sensors is costly and hazardous. In this study, we present a piezoelectric wind energy harvester (PWEH) that also functions as a self-powered sensor for monitoring ventilation airflow conditions in underground mines. A shallow groove machined into the windward face of a D‑shaped bluff body boosts harvested power by 29.3% at a wind speed of 4.5 m/s compared with an ungrooved benchmark. The resulting voltage waveform supports two embedded sensing functions without external power: (i) ventilation fault diagnosis based on a stacking ensemble that integrates one-dimensional convolutional neural network (1D CNN), random forest (RF) and transfer-learned AlexNet classifier, enhanced by manifold‑based data augmentation, to identify 16 fault scenarios in a laboratory mine ventilation layout with up to 97.4% accuracy; and (ii) wind speed estimation using Random Forest that delivers a median mean absolute error below 0.12 m/s and worst‑case error under 0.5 m/s across 1.5–5.5 m/s airflow speeds, matching the performance of commercial anemometers. Laboratory trials in a scaled mine ventilation layout validate these capabilities across 16 duct networks with fan speeds ranging from 200–700 rpm, while energy measurements show the device produces a root mean square power output of 5.2 μW at a 4.5 m/s wind speed, which is sufficient to run a Bluetooth Low Energy-based SensorTag for fog data transmission every 50 seconds. By combining wind energy harvesting and airflow sensing in a single device, the proposed PWEH offers a battery-free and low-maintenance path to safer and smarter Mine Internet of Things deployments.

We propose LightLLM, a model that fine tunes pre-trained large language models (LLMs) for light-based sensing tasks. It integrates a sensor data encoder to extract key features, a contextual prompt to provide environmental information, and a fusion layer to combine these inputs into a unified representation. This combined input is then processed by the pre-trained LLM, which remains frozen while being fine-tuned through the addition of lightweight, trainable components, allowing the model to adapt to new tasks without altering its original parameters. This approach enables flexible adaptation of LLM to specialized light sensing tasks with minimal computational overhead and retraining effort. We have implemented LightLLM for three light sensing tasks: light-based localization, outdoor solar forecasting, and indoor solar estimation. Using real-world experimental datasets, we demonstrate that LightLLM significantly outperforms state-of-the-art methods, achieving 4.4x improvement in localization accuracy and 3.4x improvement in indoor solar estimation when tested in previously unseen environments. We further demonstrate that LightLLM outperforms ChatGPT-4 with direct prompting, highlighting the advantages of LightLLM's specialized architecture for sensor data fusion with textual prompts.

Intelligent Reflective Surfaces (IRS) are an emerging research focus aimed at enhancing non-line-of-sight wireless communications by manipulating radio reflections. However, when embedded within objects, IRS may disrupt mmWave radar object classification by altering reflected features. In this study, we explore the adverse effects of a misconfigured IRS on radar classification. We prototyped an IRS with configurations that can either induce destructive interference with the object's reflected signals or deflect these reflections away from the radar using beamforming techniques. Experiments using a 24 GHz radar to detect four everyday objects revealed a significant drop in classification accuracy due to this interference. These findings underscore a significant vulnerability in the increasingly pervasive deployment of mmWave radar for object classification, highlighting the urgent need for robust countermeasures.

We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a 6-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m---over 5 times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30° away from the boresight, exhibiting a modest 5% drop in accuracy.

Bearings are critical components in industrial machinery, yet their vulnerability to faults often leads to costly breakdowns. Conventional fault detection methods depend on continuous, high-frequency vibration sensing, digitizing, and wireless transmission to the cloud—an approach that significantly drains the limited energy reserves of battery-powered sensors, accelerating their depletion and increasing maintenance costs. This work proposes a fundamentally different approach: rather than using instantaneous vibration data, we employ piezoelectric energy harvesters (PEHs) tuned to specific frequencies and leverage the cumulative harvested energy over time as the key diagnostic feature. By directly utilizing the energy generated from the machinery’s vibrations, we eliminate the need for frequent analog-to-digital conversions and data transmission, thereby reducing energy consumption at the sensor node and extending its operational lifetime. To validate this approach, we use a numerical PEH model and publicly available acceleration datasets, examining various PEH designs with different natural frequencies. We also consider the influence of the classification algorithm, the number of devices, and the observation window duration. The results demonstrate that the harvested energy reliably indicates bearing faults across a range of conditions and severities. By leveraging the proposed framework instead of high-frequency vibration signals, the system significantly reduces the energy requirements for data acquisition and transmission—by over 70% and 99%, respectively. This makes the methodology a highly promising solution for long-term, self-powered condition monitoring in industrial applications. By converting vibration energy into both a power source and a diagnostic feature, our solution offers a more sustainable, low-maintenance strategy for fault detection in smart machinery.

Vibration energy harvesting (VEH) has emerged as a viable option for mobile devices that serves the dual purpose of generating power and sensing ambient vibrations. This paper highlights the location privacy leakage resulting from unrestricted access to seemingly innocuous VEH data on mobile devices. We present VEH-Attack, a side-channel attack that exploits an inference model and VEH data patterns generated from train vibrations, enabling precise tracking of train passengers. VEH-Attack achieves an accuracy of 97% and 83.13% for VEH derived data and actual VEH data, respectively, for trip length of 6 stations with the accuracy reaching 100% for longer trip lengths.

We introduce LiDARSpectra, a novel approach utilizing mobile-integrated commodity Light Detection and Ranging (LiDAR) signals for synthetic indoor light spectral mapping. Our method incorporates an innovative material estimation algorithm into the LiDAR signal processing pipeline, accurately simulating reflected wavelengths from indoor surfaces. Utilizing low-resolution LiDAR scans enriched with material information, it eliminates the need for deploying dedicated spectral sensors, greatly simplifying the spectral mapping process. We validate our synthetic spectral maps against real sensor data and demonstrate their utility in applications such as indoor localization and solar energy provisioning. This presents an efficient solution for indoor spectral mapping with wide-ranging potential across fields like lighting design, indoor planting, environmental monitoring, and location-based services.

Passive visible light communication (VLC) modulates light propagation or reflection to transmit data without directly modulating the light source. Thus, passive VLC provides an alternative to conventional VLC, enabling communication where the light source cannot be directly controlled. There have been ongoing efforts to explore new methods and devices for modulating light propagation or reflection. The state-of-the-art has broken the 100 kbps data rate barrier for passive VLC by using a digital micro-mirror device (DMD) as the light modulating platform, or transmitter, and a photo-diode as the receiver. We significantly extend this work by proposing a massive spatial data channel framework for DMDs, where individual channels can be decoded in parallel using an event camera at the receiver. For the event camera, we introduce event processing algorithms to detect numerous channels and decode bits from individual channels with high reliability. Our prototype, built with off-the-shelf event cameras and DMDs, can decode up to ~2,000 parallel channels, achieving a data transmission rate of 1.6 Mbps, markedly surpassing current benchmarks by 16x.

Recent research has demonstrated the huge potential of WiFi for contactless sensing of human activities. Unfortunately, such sensing is highly sensitive to the relative orientation between the user and the WiFi receivers. To overcome this problem, existing solutions deploy multiple WiFi receivers at precise positions to capture orientation-independent view of the human activity. Orientation-independent single-receiver WiFi sensing is still considered an open problem. In this article, we propose a deep neural network architecture that uses radar data during training to learn high-precision Doppler features of human activities from the noisy channel states observed by a single WiFi receiver. Once trained with radars, the network can be used to detect human activities at any arbitrary orientations based only on WiFi signals. Using extensive experiments with millimeter-wave radars, we demonstrate that the proposed approach, called WiFi2Radar in this article, significantly outperforms state-of-the-art for detecting human activities in untrained orientations using only a single WiFi receiver. Our results show that WiFi2Radar can detect orientation-independent human activities with up to 91% accuracy, which outperforms the state of the art by 19%.

Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed in this paper: a vibration-based milk spoilage detection method called VibMilk that utilizes the ubiquitous vibration motor and Inertial Measurement Unit (IMU) of off-the-shelf smartphones. The method detects spoilage based on the fact that the milk’s physical properties change, inducing different vibration responses at various stages of degradation. Using the InceptionTime deep learning model, VibMilk achieves 98.35% accuracy in detecting milk spoilage across 23 different stages, from fresh (pH = 6.6) to fully spoiled (pH = 4.4).

Piezoelectric Energy Harvesters (PEHs) are typically employed to provide additional source of energy for a sensing system. However, studies show that a PEH can be also used as a sensor to acquire information about the source of vibration by analysing the produced voltage signal. This opens a possibility to create Simultaneous Energy Harvesting and Sensing (SEHS) system, where a single piece of hardware, a PEH, acts as both, a harvester and as a sensor. This raises a question if it is possible to design a bi-functional PEH device with optimal harvesting and sensing performance. In this work, we propose a bi-objective PEH design optimisation framework and show that there is a trade-off between energy harvesting efficiency and sensing accuracy within a PEH design space. The proposed framework is based on an extensive vibration (strain and acceleration) dataset collected from a real-world operational cable-stayed bridge in New South Wales, Australia. The bridge acceleration data is used as an input for a PEH numerical model to simulate a voltage signal and estimate the amount of produced energy. The numerical PEH model is based on the Kirchhoff-Love plate and isogeometric analysis. For sensing, convolutional neural network AlexNet is trained to identify traffic speed labels from voltage CWT (Continuous Wavelet Transform) images. In order to improve computational efficiency of the approach, a kriging metamodel is built and genetic algorithm is used as an optimisation method. The results are presented in the form of Pareto fronts in three design spaces.

Visible light communications (VLC) have gained significant attention as a potential solution for the radio spectrum crunch. To achieve high data rates, emerging transmitter devices like 2D digital micro-mirror devices (DMD) have been proposed, offering significantly faster state flipping rates compared to conventional liquid crystalline shutters. However, previous approaches utilizing DMD suffered from a lack of spatial diversity, as they used all micro-mirrors in the same state. This paper introduces EV-DMD, a novel approach that utilizes DMD as a 2D transmitter, working in tandem with an event-based vision (EV) camera. In this method, multiple bit streams are transmitted in parallel through different mirror blocks of the DMD, while an EV camera simultaneously decodes multiple light blocks, enabling a truly 2D high-speed VLC system. To the best of our knowledge, this is the first implementation of a 2D VLC system that achieves an order-of-magnitude improvement in bit rate compared to state-of-the-art solutions.

Internet of Things (IoT) technology has been widely used for real-time monitoring of the environment, safety and production in underground mines. This paper presents the basic structure of a Mine Internet of Things (MIoT) system based on a widely used three-layer IoT architecture, classifies types of sensors commonly used in underground mines by specific application, and introduces available wired and wireless communication technologies and network topologies that can be applied in underground mines. This paper provides a comprehensive review of recent developments in IoT applications in underground mines to monitor various environmental parameters, including mine gas and dust concentrations, temperature, humidity and airflow, groundwater, ground support and seismic activity. MIoT applications for fire and hazard detection, personnel and equipment positioning, and production safety management have also been investigated. This paper highlights key challenges for the broad application of IoT technology in underground mines such as operation disruption, additional investment, limited battery life, poor quality of underground communication, and difficulty in data management. Further research on novel advanced techniques, such as self-powered sensors, MIoT standardization and underground wireless communication technologies, is essential to improve the applicability and effectiveness of IoT applications in underground mines.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a$2\times 2$solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

The ability to recognize and detect changes in human posture is important in a wide range of applications such as health care and human–computer interaction. Achieving this goal using loose-fit garments instrumented with sensors is particularly challenging, due to the complex interaction between garments and human body. Herein we present a method to detect and recognize human posture with casual loose-fitting smart garments integrated with highly sensitive, stretchable, optical transparent, and low-cost strain sensors. By attaching these sensors to an off-the-shelf casual jacket, we developed a smart loose-fitting sensing garment that enables posture recognition using a deep learning model, domain-adaptive Convolutional Neural Networks–Long Short-Term Memory (CNN-LSTM). This deep learning model overcame the noise and variation due to the complex interaction between loose-fitting garments and human body. Considering that users’ labeled data are usually not available in the training stage, an additional domain discriminator path on the conventional CNN-LSTM model has been introduced to further improve the adaptability. To evaluate the potential of this loose-fitting smart garment, three case studies were conducted under realistic conditions: recognitions of human activities, stationary postures with random hand movements and slouch. Our results demonstrate the potential of the proposed smart garment system for practical applications.

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can localize the user at sub-meter accuracy in a real indoor environment.

We propose a novel Visible Light Positioning (VLP) method, called Iris, that uses light spectral information (LSI) to localize humans completely passively in the sense that it neither requires the user to carry any device, nor does it require any modifications to existing lighting infrastructure. Iris localizes a user based on the interference they produce on the LSI recorded at an array of spectral sensors embedded in the environment. We design a deep neural network that can effectively learn location fingerprints directly from the sensor LSI data and predict locations accurately under varying lighting conditions. We prototype Iris using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength channels. We benchmark Iris against the state-of-the-art passive VLPs that rely on conventional photo-sensors capable of measuring only a single light intensity value aggregated over the entire visible spectrum. Our evaluations over two typical indoor environments, a 25 m2 one-bedroom apartment and a 13m × 8m office space, demonstrate that Iris can significantly reduce both the localization errors and the number of required sensors, while increasing robustness against changes in environmental lighting.

In this paper, we investigate the impacts of location on the spectral distribution of received light, i.e., the intensity of light for different wavelengths, in indoor environments. Our findings show that, even when using the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. Based on this observation, we present Spectral-Loc, a novel indoor localization method that employs light spectrum information to detect the device's position. Because spectrum sensors are increasingly being used in new products and applications, such as white balance in smartphone photography, Spectral-Loc can be quickly implemented without the need for extra hardware or infrastructure. We used a commercially available light spectrum sensor, the AS7265x, to prototype Spectral-Loc, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations in two indoor areas, a meeting room and a large office, show that using light spectral information considerably decreases the localization error for different percentiles.

Kinetic energy harvesting technologies have been progressively used to power wearable devices and to sense the context through energy generation patterns. However, detecting human activities with signals from kinetic harvesters still needs improvement due to the use of approaches based on handcrafted features and the overfitting to device location or subjects. Hence, in this article, we present a deep learning architecture that leverages the feature extraction capability of the convolutional neural networks and the construction of the temporal sequences of recurrent neural networks to improve existing classification results. To provide sufficient data for the deep learning classifier, we propose three data augmentation methods to increase intraclass variance simulating new users performing the same activities. The proposed architecture outperforms existing approaches of kinetic harvesting-based human activity recognition by 13% of accuracy when the training data are augmented with the proposed methods. Finally, given the dependency of kinetic harvesting signals on device location and subjects, we employ transfer learning to improve the classification performance when the system is exposed to new subjects and locations. Transfer learning helps to increase classification performance by 30% when the device location is changed and 35% when the data come from a new subject.

Piezoelectric energy harvester (PEH), which generates electricity from stress or vibrations, is attracting tremendous attention as a viable solution to extend battery life of wearable devices. More interestingly, besides the energy harvesting capability, recent research has demonstrated the feasibility of leveraging PEH as an power-free sensor for gait recognition as its stress or vibration patters are significantly influenced by the gait. However, as PEHs are not designed for precise motion sensing, the gait recognition accuracy remains low with conventional classification algorithms. The accuracy deteriorates further when the generated electricity is stored simultaneously. In this work, to achieve high performance gait recognition and efficient energy harvesting at the same time, we make two distinct contributions. First, we propose a preprocessing algorithm to filter out the effect of energy storage on PEH electricity signals. Second, we propose long short-term memory (LSTM) network-based classifiers to accurately capture temporal information in gait-induced electricity generation. We prototype the proposed gait recognition architecture in the form factor of an insole and evaluate its gait recognition as well as energy harvesting performance with 20 subjects. Our results show that the proposed architecture detects human gait with 12 percent higher recall and harvests up to 127 percent more energy while consuming 38 percent less power compared to the state-of-the-art.

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.

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.

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.

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%.

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.

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.

We propose the use of the conventional energy storage component, i.e., capacitor, in the kinetic-powered wearable IoTs as the sensor to detect human activities. Since activities accumulate energy in the capacitor at different rates, the charging rate of the capacitor can be used to detect the activities. The key advantage of the proposed capacitor-based activity sensing mechanism, called CapSense, is that it obviates the need for sampling the motion signal at a high rate, and thus, significantly reduces power consumption of the wearable device. The challenge we face is that capacitors are inherently non-linear energy accumulators, which leads to significant variations in the charging rates. We solve this problem by jointly configuring the parameters of the capacitor and the associated energy harvesting circuits, which allows us to operate in the charging cycles that are approximately linear. We design and implement a kinetic-powered shoe and conduct experiments with 10 subjects. Our results show that CapSense can classify five different daily activities with 95% accuracy while consuming 57% less system power compared to conventional motion-sensor-based approaches.

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, and providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by the passenger during traveling with different transportation modes are distinctive. Thus, voltage signal generated by the energy harvesting devices should contain sufficient features to distinguish different transportation modes. We evaluate our system using over 28 h of data, which is collected by eight individuals using a practical energy harvesting prototype. The evaluation results demonstrate that EnTrans is able to achieve an overall accuracy over 92% in classifying five different modes while saving more than 34% of the system power compared to conventional accelerometer-based approaches.

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 a 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.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.

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.

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.

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.

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.

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.

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.

Recent work has shown that it is possible for two wearable devices worn by the same user to generate a common key for secure pairing by exploiting gait as a common secret. A key challenge for such device pairing lies in matching the bits of the keys generated by two independent devices despite the noisy on-board sensor measurements. We propose a novel machine learning framework that uses an autoencoder to help one device predict the sensor observations at another device and generate the key using the predicted sensor data. We prototype the proposed method and evaluate it using real subjects. Our results show that the proposed method achieves a 10% increase in bit agreement rate between two keys generated independently by two different wearable devices.

Kinetic energy harvesting (KEH) may help combat battery issues in wearable devices. While the primary objective of KEH is to generate energy from human activities, the harvested energy itself contains information about human activities that most wearable devices try to detect using motion sensors. In principle, it is therefore possible to use KEH both as a power generator and a sensor for human activity recognition (HAR), saving sensor-related power consumption. Our aim is to quantify the potential of human activity recognition from kinetic energy harvesting (HARKE). We evaluate the performance of HARKE using two independent datasets: (i) a public accelerometer dataset converted into KEH data through theoretical modeling; and (ii) a real KEH dataset collected from volunteers performing activities of daily living while wearing a data-logger that we built of a piezoelectric energy harvester. Our results show that HARKE achieves an accuracy of 80 to 95 percent, depending on the dataset and the placement of the device on the human body. We conduct detailed power consumption measurements to understand and quantify the power saving opportunity of HARKE. The results demonstrate that HARKE can save 79 percent of the overall system power consumption of conventional accelerometer-based HAR.

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 60 GHz, 120 GHz and 180 GHz, 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.

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%.

With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve self-powered wearable system. Apart from generating energy to power the wearable devices, the output electricity signal of the PEH can also be used as an information source as it reflects the activity or motion patterns of the user. In this paper, we have designed and built an insole-based user authentication system by leveraging the AC voltage generated by the PEH during human walking. Meanwhile, the generated power is also collected and stored, which could be later used as the power source of the mobile system. By using a dataset of 20 subjects, we have demonstrated that our system can achieve 89.76% of human recognition accuracy when using only one gait cycle signal, and the accuracy can be further increased to 95.86% when two gait cycles are utilized.

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.

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.

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.

The papers in this special issue focus on the deployment of video over future networks. The past decade has seen how major improvements in broadband and mobile networks have led to widespread popularity of video streaming applications, and how the latter now becomes the major driving force behind exponentially growing Internet traffic. This special issue seeks to investigate these future Internet technologies through the prism of its most prevalent application, that of video communications. video.

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.

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.

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.

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.

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.

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.

Hypertext transfer protocol (HTTP) is the fundamental mechanics supporting web browsing on the Internet. An HTTP server stores large volumes of contents and delivers specific pieces to the clients when requested. There is a recent move to use HTTP for video streaming as well, which promises seamless integration of video delivery to existing HTTP-based server platforms. This is achieved by segmenting the video into many small chunks and storing these chunks as separate files on the server. For adaptive streaming, the server stores different quality versions of the same chunk in different files to allow real-time quality adaptation of the video due to network bandwidth variation experienced by a client. For each chunk of the video, which quality version to download, therefore, becomes a major decision-making challenge for the streaming client, especially in vehicular environment with significant uncertainty in mobile bandwidth. In this paper, we demonstrate that for such decision making, the Markov decision process (MDP) is superior to previously proposed non-MDP solutions. Using publicly available video and bandwidth datasets, we show that the MDP achieves up to a 15x reduction in playback deadline miss compared to a well-known non-MDP solution when the MDP has the prior knowledge of the bandwidth model. We also consider a model-free MDP implementation that uses Q-learning to gradually learn the optimal decisions by continuously observing the outcome of its decision making. We find that the MDP with Q-learning significantly outperforms the MDP that uses bandwidth models.

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.

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.

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.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbors for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbor positions. We contend and demonstrate that periodic beaconing regardless of the node mobility and traffic patterns in the network is not attractive from both update cost and routing performance points of view. We propose the Adaptive Position Update (APU) strategy for geographic routing, which dynamically adjusts the frequency of position updates based on the mobility dynamics of the nodes and the forwarding patterns in the network. APU is based on two simple principles: 1) nodes whose movements are harder to predict update their positions more frequently (and vice versa), and (ii) nodes closer to forwarding paths update their positions more frequently (and vice versa). Our theoretical analysis, which is validated by NS2 simulations of a well-known geographic routing protocol, Greedy Perimeter Stateless Routing Protocol (GPSR), shows that APU can significantly reduce the update cost and improve the routing performance in terms of packet delivery ratio and average end-to-end delay in comparison with periodic beaconing and other recently proposed updating schemes. The benefits of APU are further confirmed by undertaking evaluations in realistic network scenarios, which account for localization error, realistic radio propagation, and sparse network.

ABSTRACT In multihop wireless networks, delivering a packet to all nodes within a specified geographic distance from the source is a packet forwarding primitive (geography‐limited broadcasting), which has a wide range of applications including disaster recovery, environment monitoring, intelligent transportation, battlefield communications, and location‐based services. Geography‐limited broadcasting, however, relies on all nodes having continuous access to precise location information, which may not be always achievable. In this paper, we consider achieving geography‐limited broadcasting by means of the time‐to‐live (TTL) forwarding, which limits the propagation of a packet within a specified number of hops from the source. Because TTL operation does not require location information, it can be used universally under all conditions. Our analytical results, which are validated by simulations, confirm that TTL‐based forwarding can match the performance of the traditional location‐based geography‐limited broadcasting in terms of the area coverage as well as the broadcasting overhead. It is shown that the TTL‐based approach provides a practical trade‐off between geographic coverage and broadcast overhead. By not delivering the packet to a tiny fraction of the total node population, all of which are located near the boundary of the target area, TTL‐based approach reduces the broadcast overhead significantly. This coverage‐overhead trade‐off is useful if the significance of packet delivery reduces proportionally to the distance from the source. Copyright © 2011 John Wiley & Sons, Ltd.

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%).

We model the user's quality of experience (QoE) in a wireless voice over IP (VoIP) service as a function of the amount of effort the user has to put to continue her conversation. We assume that users would quit or terminate an ongoing call if they have to put more efforts than they could tolerate. Not knowing the tolerance threshold of each individual user, the service provider faces a decision dilemma of whether to fix the network problem immediately whenever he detects a user effort in the VoIP system, or ignore it with the hope that the user may still continue the call anyway. In this paper, we formulate the provider's dilemma as a non-cooperative game between the provider and the VoIP user experiencing a deteriorating QoE. We demonstrate that providers implementing the equilibrium solutions can expect to not only increase their revenues, but also reduce the number of cases when users quit out of frustration thus minimizing potential churning. We also discuss conditions under which a sophisticated user may or may not benefit from faking unwarranted efforts with a goal of receiving a better service from the provider. Finally, we conduct a subjective experiment of VoIP over WiFi, which verifies the key model assumption that perceptual quality is negatively correlated to the amount of effort the user has to put to continue the call.

It is widely evidenced that location has a significant influence on the actual bandwidth that can be expected from Wireless Wide Area Networks (WWANs), e.g., 3G. Because a fast-moving vehicle continuously changes its location, vehicular mobile computing is confronted with the possibility of significant variations in available network bandwidth. While it is difficult for providers to eliminate bandwidth disparity over a large service area, it may be possible to map network bandwidth to the road network through repeated measurements. In this paper, we report results of an extensive measurement campaign to demonstrate the viability of such bandwidth maps. We show how bandwidth maps can be interfaced with adaptive multimedia servers and the emerging vehicular communication systems that use on-board mobile routers to deliver Internet services to the passengers. Using simulation experiments driven by our measurement data, we quantify the improvement in Quality of Service (QoS) that can be achieved by taking advantage of the geographical knowledge of bandwidth provided by the bandwidth maps. We find that our approach reduces the frequency of disruptions in perceived QoS for both audio and video applications in high-speed vehicular mobility by several orders of magnitude.

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.

Spatial packet loss correlation is important for error control protocols in wireless multicast and broadcast communications. This paper quantitatively studies the spatial packet loss correlation in 802.11 wireless networks using a series of experiments conducted in different environments with different impact of multipath fading on wireless links. It is found that environments with more multipath opportunities exhibit less spatial correlation. It is also observed that spatial correlation is strongly dependent on the packet reception rate. Based on the experimental data, an empirical model is proposed to estimate the level of spatial loss correlation as a function of packet reception rate. It is shown that the empirical model yields accurate estimates of spatial packet loss correlation for different environments.

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.

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.

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.

Wireless voice over IP (VoIP) is subject to unpredictable link conditions which directly contribute to user irritation. Standard technological means available to the network provider to remedy wireless link problems require additional radio resources to be allocated. Given that radio resource is limited, it is not clear whether such allocation strategies to reduce user irritation are economically rewarding for the wireless providers. We model the resource allocation dilemma to reduce user irritation as a noncooperative game between the provider and the VoIP user suffering from a link quality problem. We demonstrate that providers implementing Nash equilibrium can expect to optimize their revenues and avoid potential churning.

Before vehicle-to-vehicle-communication-based road safety becomes a reality, one of the challenges that needs to be resolved is efficient and timely recovery of safety packets that may be lost due to reception failure. Various types of retransmission-based recovery protocols have been proposed in the literature to address this problem. Although, retransmission can help recover some of the lost packets, it consumes significant bandwidth. In this paper, we apply the concept of network coding to achieve efficient loss recovery by combining multiple packets that were transmitted by different vehicles into a single retransmission. Through an analytical study of coding gain, we show how to optimize the performance of our proposed network coding based recovery scheme. Using simulation, we demonstrate that the use of network coding improves the bandwidth efficiency of the recovery protocols, which in turn improves the performance of vehicular safety communication.

Adaptive video is a popular technique to continuously deliver a video stream to a user in the best quality possible when the underlying network bandwidth cannot be guaranteed. As such, quality of adaptive video depends critically on the agility of the rate adaptation algorithms in tracking the varying bandwidth. In this paper, we investigate the performance of a popular rate adaptation algorithm, namely, TCP-friendly rate control (TFRC), in vehicular environments. Our results show that TFRC cannot cope well with the pattern of bandwidth changes faced by a user travelling in a fast moving vehicle, resulting in poor viewing experience. Motivated by the observation that bandwidth changes in vehicular environment is significantly influenced by the rapid change of user's geographic location, we propose Geo-TFRC, which empowers TFRC with a street-level bandwidth map that holds summary of past bandwidth observations for each segment of the street. We conduct simulation experiments which are driven by the real High-Speed Downlink Packet Access (HSDPA) bandwidth traces collected from a vehicle traveling along a route in Sydney. Our results reveal that Geo-TFRC can track the bandwidth changes much more effectively, which in turn improves the quality of the mobile video. We find our proactive approach can significantly reduce the time that a user suffers from pixelated viewing experience by up to five folds as compared to TFRC.

A vehicle area network (VAN) is a local area network deployed onboard a moving vehicle, e.g., a train, bus, or a private car, to provide high-speed Internet access to the passengers. A VAN may face temporary network disconnection when the vehicle passes through challenging radio environments, e.g., deep tunnels. Such disconnections disrupt the ongoing network services causing passenger dissatisfaction. Given that tunnel locations are known, we propose to use geographical knowledge in the admission control function of the VAN to reduce the probability of service disruption. A geographic admission controller (GAC) rejects a new call request based on the vehicle's current location if it can be determined that the vehicle would enter a tunnel within a short period of time. By rejecting a new call prior to entering a tunnel, GAC reduces the probability of an admitted call to be disrupted, but it does so at the expense of increasing probability that a new request would be blocked. For a quantitative investigation of the trade-off between these two important probabilities, we propose a 2-D Markov Chain model. Using the model, we derive both probabilities as a function of the time interval from the moment the GAC starts rejecting new requests until the vehicle actually enters the tunnel. This time interval is a configurable parameter which controls both probabilities. The model is validated using simulation. Our model reveals that the disruption probability can be reduced quadratically with a linear increase in the blocking probability.

There is a promising development in wireless communications technology which indicates that road accidents could be significantly reduced if vehicles were allowed to communicate with each other. By regularly exchanging their current position, velocity, etc., vehicles could predict an upcoming accident and alert the human drivers in time or proactively take precautionary actions to avoid the accident. The realization of this vision would require the design of wireless channel access schemes that can guarantee a high level of message reliability as well as a minimum throughput for vehicles to communicate frequently enough. In this paper, we propose a Markov chain model to analytically derive the throughput and reliability of the popular 802.11 protocol which has been adopted for vehicular communications. The model reveals the existence of a throughput-reliability tradeoff influenced by the contention window, the parameter which coordinates the access to the underlying wireless channel. By adjusting this window, it is possible to gain an extra level of reliability at the expense of some throughput. Through numerical experiments, we show that, for low to medium vehicular traffic density, it is possible to achieve a very high message reliability by trading off the "excess" throughput not required for vehicular communications. Our study also reveals that, the tradeoff capability of 802.11 diminishes with increasing traffic density. For extremely dense traffic, it may not be possible to meet the requirements of vehicular safety communications by simply adjusting the 802.11 parameters.

Frequent loss of network connectivity makes media streaming very challenging in vehicular mobile communication scenarios. It has been shown earlier that a streaming proxy onboard a vehicle can be effective in mitigating the adverse effects of temporary network disconnections. During the connected period, a vehicular proxy pre-fetches as much media as possible ahead of the client's playback time. This pre-fetching is done by utilizing any excess bandwidth in the network connection. The pre-fetched contents are stored in a local storage and played out when the connectivity is temporarily lost. A number of research studies have been conducted to understand the performance dynamics of such streaming proxy systems in vehicular networks, however, no analytical model has been proposed yet. In this paper, we propose a 3-D Markov Chain model to analytically study the performance of such streaming proxies as a function of the system load. The model is validated by means of discrete- event simulation of a realistic networking scenario. The proposed model can be effectively used to dimension the streaming proxy systems in next-generation vehicular networks.

The energy expended by sensor nodes in data communication makes up a significant quantum of their total energy consumption. Consequently, a mathematical model that can accurately predict the communication traffic load of a sensor node is critical for designing efficient sensor network protocols. In this paper, we present an analytical model for estimating the per-node traffic load in a multi-hop wireless sensor network. We consider a typical scenario wherein, the sensor nodes periodically sense the environment and forward the collected samples to a sink using greedy geographic routing. The analysis incorporates the idealistic circular coverage radio model as well as a realistic model, log-normal shadowing. Our results confirm that irrespective of the radio model, the traffic load generally increases as a function of the node's proximity to the sink. However, in the immediate vicinity of the sink, the two radio models yield quite contrasting results. The ideal radio model reveals the existence of a volcano region near the sink, where the traffic load drops significantly. On the contrary, with the log-normal shadowing model, the opposite effect is observed, wherein the traffic load actually increases at a much higher rate as one approaches the sink, resulting in the formation of a mountain peak. The results from our analysis are validated by extensive simulations.

Wireless mesh networks (WMN) have attracted considerable interest in years as a convenient, flexible and low-cost alternative to wired communication infrastructures in many contexts. However, the great majority of research on metropolitan-scale WMN has been centered around maximization of available bandwidth, suitable for non-real-time applications such as Internet access for the general public. On the other hand, the suitability of WMN for mission-critical infrastructure applications remains by and large unknown, as protocols typically employed in WMN are, for the most part, not designed for real-time communications. In this paper, we describe the smart transport and roads communications (STaRComm) project at National ICT Australia (NICTA), which sets a goal of designing a wireless mesh network architecture to solve the communication needs of the traffic control system in Sydney, Australia. This system, known as SCATS (Sydney coordinated adaptive traffic system) and used in over 100 cities around the world, connects a hierarchy of several thousand devices - from individual traffic light controllers to regional computers and the central traffic management centre (TMC) - and places stringent requirements on the reliability and latency of the data exchanges. We discuss our experience in the deployment of an initial testbed consisting of 7 mesh nodes placed at intersections with traffic lights, and share the results and insights learned from our measurements and initial trials in the process.

Communication solutions for passengers on public transport vehicles, in the form of on-board networks connected to the Internet via a mobile router (MR) and a wireless link, are increasingly offered by public transport providers in many countries. An important challenge in such networks is to guarantee a fair access to the scarce wireless bandwidth by all on-board users, particularly in networks with multiple wireless interfaces. Accordingly, we consider an active fairness control scheme, where the MR transparently overrides the TCP advertised receiver window size to limit the transmission rates of on-board TCP connections to their fair share. We focus on the overhead that this scheme places on the mobile router and the wireless interface, due to the need for the MR to estimate the round-trip time (RTT) of each on-board connection. We investigate the performance of three strategies that differ in the timing of RTT estimations, and show that a fairness target-based strategy, which continuously monitors the ongoing fairness index and invokes an RTT reestimation whenever it drops below a predetermined value, consistently achieves a significant improvement in fairness for only a mild overhead cost.

The concept of providing mobile Internet connectivity for passengers in public transport vehicles, where users connect to a local network that attaches to the Internet via a mobile router and a wireless link, has become increasingly popular in recent years, as evidenced by the growing amount of commercially available systems and associated research and standardization activities. The challenge of providing wireless connectivity to networks in motion is compounded by the highly dynamic nature of the user population and the strict quality-of-service (QoS) requirements of many applications typical of such environments. As a result, several protocols extending Internet QoS support approaches to on-board mobile networks have been proposed in the past. In this paper, we focus on modeling and performance evaluation of periodical aggregation of resource reservation messages, which forms the basis of the on-board RSVP protocol. We present a model consisting of a discrete-time, multiple-server and finite-capacity queueing system with bulk arrivals and departures, conduct a detailed analysis of the model, and use it to evaluate the performance of the resource reservation aggregation scheme in a practical scenario. The validity of our model is also backed by extensive simulation results.

Geocasting uses location information to disseminate messages within a specified geographic area. However, in some applications, it is not feasible for the nodes to be able to determine their location coordinates. In this paper, we propose a novel distanced-based approach for local geocasting to address this problem. In local geocasting, source node is interested in spread messages within a local area around itself. We exploit the relationship between radius of local area and the expected hop count in a multi-hop wireless network with uniformly distributed nodes. We estimate the minimum number of hops required to cover all of the nodes within the given local geocasting area. The hop count is then used as hop limit to restrict flooding. We theoretically analyze the average number of rebroadcast messages in the proposed approach and the analytical model is validated by the statistic results. We further conduct a simulation-based comparison between distance-based local geocasting and traditional local geocasting. The results show that our approach can achieve a similar performance as that of traditional local geocasting.

We investigate the effects of using dual transmission power to achieve quality-of-service (QoS) differentiation in IEEE 802.11 wireless LANs. Specifically, we assume all stations employ the standard IEEE 802.11 distributed coordination function (DCF), with the exception that some stations transmit at a higher power than others. Consequently, in the event of a collision involving frames from both power levels, a high-power frame is more likely to be received correctly as a result of the so-called capture effect (i.e. received at a power sufficiently higher than that of the interference, allowing it to be decoded correctly). This effectively leads to QoS differentiation between two classes, corresponding to the high-power and low-power stations. We develop a Markov model for the IEEE 802.11 DCF in a network with dual transmission power over a Rayleigh-fading channel, and use it to evaluate the resulting performance, in terms of the key metrics of throughput and delay. We explore how the performance of the service classes depends on the proportion of stations in each class and the transmission power ratio, focusing in particular on the bounds achieved in the limit case of 'ideal differentiation', characterised by a perfect capture probability of high-power frames. We find that significant performance differentiation between high-power and low-power stations is achieved even with transmission power ratios that are not very high, leading us to conclude that employing multiple transmission power levels is a viable and efficient approach for service quality differentiation in wireless LANs.

In geographic routing, nodes need to maintain up-to-date positions of their immediate neighbours for making effective forwarding decisions. Periodic broadcasting of beacon packets that contain the geographic location coordinates of the nodes is a popular method used by most geographic routing protocols to maintain neighbour positions. We contend that periodic beaconing regardless of network mobility and traffic pattern does not make optimal ulilisation of the wireless medium and node energy. For example, if the beacon interval is too small compared to the rate at which a node changes its current position, periodic beaconing will create many redundant position updates. Similarly, when only a few nodes in a large network are involved in data forwarding, resources spent by all other nodes in maintaining their neighbour positions are greatly wasted. To address these problems, we propose the Adaptive Position Update (APU) strategy for geographic routing. Based on mobility prediction, APU enables nodes to update their position adaptively to the node mobility and traffic pattern. We embed APU into the well known Greedy Perimeter Stateless Routing Protocol (GPSR), and compare it with original GPSR in the ns-2 simulation platform. We conducted several experiments with randomly generated network topologies and mobility patterns. The results confirm that APU significantly reduces beacon overhead without having any noticeable impact on the data throughput of the network. This result is further validated through a trace driven simulation of a practical vehicular ad-hoc network topology that exhibits realistic movement patterns of public transport buses in a metropolitan city.

Increasing demand on the scarce radio spectrum indicates that availability of required radio resources (e.g. wireless bandwidth) at a given time and location will be less certain in the future. Resource uncertainty inhibits many future applications that require large amount of resources to be guaranteed at specific times and locations. In order to address the problem of resource uncertainty, these applications may need to reserve network resources in advance. However, in order to support percall advance reservation, the network must store, process, and access large volume of reservation data efficiently. In particular, the admission control functions must have fast and scalable access to these reservation data for making effective decisions regarding the acceptance and rejection of both immediate and future calls. In this paper, we propose a distributed reservation database architecture that features proactive processing and delivery of reservation data to admission control functions located in each radio base-station. To demonstrate the effectiveness of our architecture, we present results from a prototype experiment that compared the proposed proactive approach with the traditional query-based data delivery approach. With proactive approach, the response time of admission control was 20 times or more faster than traditional query-based approach in our experiments.

A research area that has become increasingly important in recent years is that of on-board mobile communication, where users on a vehicle are connected to a local network that attaches to the Internet via a mobile router and a wireless link. In this architecture, link disruptions (e.g., due to signal degradation) may have an immediate impact on a potentially large number of connections. We argue that the advance knowledge of public transport routes, and their repetitive nature, allows a certain degree of prediction of impending link disruptions, which can be used to offset their catastrophic impact. Focusing on the transmission control protocol (TCP) and its extension known as Freeze-TCP, we present a detailed analysis of the performance improvement of TCP connections in the presence of disruption prediction. In particular, we propose a Markov model of Freeze-TCP that captures both the TCP behavior and the prediction+"freezing" feature and, using simulations, show that it accurately predicts the performance of the protocol. Our results demonstrate the significant throughput improvement that can be gained by disruption prediction, even with random packet losses or imperfect timing of the predicted disruptions

We investigate the effects of using a dual transmission power scheme for quality-of-service (QoS) differentiation in IEEE 802.11 wireless LANs. By setting hosts to transmit at different power levels, we enhance the likelihood that a high-power frame gets received correctly when a collision involving frames from both power levels occurs due to the capture effect. We develop a Markov model for the IEEE 802.11 DCF in a dual transmission power system over a Rayleigh-fading channel, and use it to evaluate the resulting performance, in terms of the key metrics of throughput and delay. Specifically, we study how the performance of the service classes depends on the proportion of hosts from each service class and the transmission power ratio, and demonstrate that, counter-intuitively, this dependence may be non-monotonic

Broadband Internet access in mobile hotspots (e.g. public transport vehicles) through high-speed on-board local area networks and mobile routers is becoming an increasingly popular area of research and development. Mobile hotspot operators can provide faster, cheaper, and more stable communication services to on-board passengers using the multi-homing technique; whereby the mobile router is connected to a diverse array of wireless access technologies (e.g., GPRS, UMTS, 802.11) through a multiplicity of wireless service providers. As the set of available access networks may change frequently during each trip, the challenge for the mobile hotspot operators is to decide on how to "best" distribute the user data traffic among the multiple access networks. In this paper, we proposed a new business model and a user traffic distribution algorithm that aims to maximize the profit for the mobile hotspot operator while providing an acceptable level of service. We also provide results from a detailed simulation study of this algorithm under various usage probability distributions.

The inherent difficulty in faithfully modeling wireless channel characteristics in simulators has prompted researchers to build wireless network testbeds for realistic testing of protocols. While previous testbeds are mostly designed to provide a research environment of static wireless networks, our work is aimed to assess protocols used for mobile wireless networks (such as an on-board network on public transport vehicles). In this work, we describe our on-going efforts in designing and implementing a network mobility testbed for network mobility (NEMO) protocol. This paper attempts to provide an initial reference to identify the feature set necessary for a network mobility testbed. We first describe the architecture of our testbed. Next, we present some preliminary results to demonstrate the use of our testbed in evaluating the performance of NEMO protocol under different scenarios.

In on-board mobile networks, such as those proposed for (and employed in) public transport vehicles, users are connected to a local network that attaches to the Internet via a mobile router and a wireless link. Central and coordinated management of mobility in a single router, rather than by each user device individually, has numerous advantages; however, it also means that link outages, e.g. due to signal degradation or handoff failure, may have an immediate impact on a potentially large number of connections. We argue that the advance knowledge of public transport routes, and their repetitive nature, allows a certain degree of prediction of impending link outages, which can be used to offset their catastrophic impact. Focusing on the TCP protocol and its extension known as Freeze-TCP, we study how the performance of the protocol depends on the outage prediction probability. In particular, we propose a Markov model of Freeze-TCP and, using simulations, show that it accurately predicts the performance improvement gained by outage prediction.

Useless packet transmission (UPT) arises when a multimedia stream becomes unintelligible because too many packets, in aggregate, are dropped by fair queuing algorithms at routers, even with low drop rates at individual routers. We propose two UPT avoidance (UPTA) schemes for networks with multiple congested links: partial UPTA (P-UPTA), which is core-stateless, and centralized UPTA (C-UPTA), which employs a bottleneck fairshare discovery (BFD) protocol to determine a flow's global fairshare. Our simulation study shows that P-UPTA eventually detects UPT, but bandwidth may be wasted on upstream links before UPT is detected, whereas C-UPTA avoids UPT in all situations as it always drops useless packets at the network edge. We quantitatively analyse the performance of C-UPTA in conjunction with WFQ, in terms of TCP throughput, file download time, MPEG-2 video intelligibility, and fairness. The results reveal that, for the scenarios simulated, the TCP throughput is improved by up to 50% without any significant impact on the intelligibility of the MPEG-2 video and the fairness of the scheduling algorithm.

The paper is concerned with optimizing end-to-end window controls that achieve proportional fairness in the long run, and then investigating their transient or short-term fairness. An abstracted stochastic model of a bottlenecked connection is employed and the window control is designed to minimize the buffer queue variance while keeping the mean queue level at a target value, taking the round-trip delay into account. A generalized window control aimed at reducing window size fluctuations is also derived. The effects of both the target queue length and a weighting parameter on the short-term fairness, as measured by short-term fairness indices calculated over short time intervals, are investigated using ns-2 simulations.

As wireless networking products and ad-hoc networks become more popular, the resource usage in routing packets for other network users will become an issue for users and developers of ad-hoc wireless products. In this paper, we propose a novel monitoring tool called WANMon (wireless ad-hoc network monitoring tool). WANMon can be installed on a wireless node to monitor resource usage (such as network usage, power usage, memory usage, and CPU usage) at the node, in the context of how much of resource is used for supporting the node's own applications versus the usage for routing data of other network users in an ad-hoc wireless network. We discuss design of WANMon, and present our prototype implementation of WANMon in Linux, which provides a starting point for further research and a basis for developing a fully featured ad-hoc wireless network monitoring tool.

The recent auctioning of wireless bandwidth in various countries indicates that the service provided by Telcos over these networks will be very expensive. A new type of architecture is currently evolving as a low cost alternative cellular wireless service by allowing the users to play the role of a wireless network operator. Some users of the current Internet may install a low cost base station based on their needs and interconnection between these base stations will form a grid of wireless Internet. This paper provides a survey on related work that either uses ad-hoc mobile networking or a combination of fixed and wireless networking. Finally we present an alternate architecture and describe new issues that arise from this new type of architecture.

The design of rate allocation and queue length control in computer networks is treated as a stochastic optimal control problem. The performance index is chosen to achieve the twin objectives of minimising queue length fluctuations and fully utilising the available bandwidth. Simple, practically realisable optimal control schemes are obtained for both LANs and WANs. An adaptive scheme is proposed where the auto-regressive parameters of the traffic, needed for gain calculations, are estimated by an LMS algorithm. Discrete event simulations are carried out to verify the fluid-flow models used in developing the controllers, to compare their performance against PI controllers proposed previously, and to study the effect of self-similar traffic. Two key results are obtained. First, queue-length fluctuations, and hence potentially packet losses, are much smaller for the optimal feedforward controller than for the PI controller. Second, in contrast to uncontrolled queues, the queue length variance decreases with increasing Hurst parameter for self-similar traffic.

We describe a Java implementation of a policy based bandwidth management system using the standard policy protocols and an interface to the Linux DiffServ implementation and demonstrate the capability of our implementation in supporting policy based dynamic resource allocations in enterprise networks.

This paper proposes an extension to the standard subnet bandwidth manager (SBM). The extension controls the best effort traffic on the subnet to minimize its impact on the QoS of the RSVP (resource reservation protocol) flows. The extension is achieved by adding extra messages to the standard RSVP that enables the SBM to signal the hosts about the available bandwidth. The hosts then control the rate of best effort traffic accordingly to avoid excessive collisions on the subnet. The implementation of the proposed extension on a shared Ethernet LAN demonstrates the benefits of this extension.

In absence of any link layer traffic controls or priority-queuing mechanism in LAN infrastructure (such as shared media LAN), the subnet bandwidth management based approach of managing bandwidth is limited to only the total amount of traffic load imposed by RSVP associated flows. In such cases no mechanism is available to separate RSVP flows from best effort traffic. This brings the usefulness of the subnet bandwidth manager into question. This uses a combination of integrated services of a specific link layer model based on RSVP and an IP rate-control approach for best effort traffic to manage intra-domain traffic in a differentiated services network.