This paper presents CARTS, an adaptive 5G uplink sensing scheduling scheme designed to provide Integrated Communication and Localization services. The performance of both communication and localization fundamentally depends on the availability of accurate and up-to-date channel state information (CSI). In modern 5G networks, uplink CSI is derived from two reference signals: the demodulation reference signal (DMRS) and the sounding reference signal (SRS). However, current base station implementations treat these CSI measurements as separate information streams. The key innovation of CARTS is to fuse these two CSI streams to increase the frequency of CSI updates and to extend sensing opportunities to more users. CARTS addresses two key challenges: (i) a novel channel stitching and compensation method that integrates asynchronous CSI estimates from DMRS and SRS, despite their different time and frequency allocations, and (ii) a real-time SRS triggering algorithm that complements the inherently uncontrollable DMRS schedule, ensuring sufficient and non-redundant sensing opportunities for all users. Our trace-driven evaluation shows that CARTS significantly improves scalability, achieving a channel estimation error (NMSE) of 0.167 and UE tracking accuracy of 85 cm while supporting twice the number of users as a periodic SRS-only baseline with similar performance. By opportunistically combining DMRS and SRS, CARTS therefore provides a practical, standard-compliant solution to improve CSI availability for localization and communication without requiring additional radio resources.

The accuracy of traditional localization methods significantly degrades when the direct path between the wireless transmitter and the target is blocked or non-penetrable. This paper proposesN LoS, a novel approach for precise non-line-of-sight (NLoS) localization using a single mmWave radar and a backscatter tag.N LoSleverages multipath reflections from both the tag and surrounding reflectors to accurately estimate the target's position.N LoSintroduces several key innovations. First, we designHFD(Hybrid Frequency-Hopping and Direct Sequence Spread Spectrum) to detect and differentiate reflectors from the target. Second, we enhance signal-to-noise ratio (SNR) by exploiting the correlation properties of the designed signals, improving detection robustness in complex environments. Third, we proposeFS-MUSIC(Frequency-Spatial Multiple Signal Classification), a super-resolution algorithm that extends the traditional MUSIC method by constructing a higher-rank signal matrix, enabling the resolution of additional multipath components. We evaluateN LoSusing a 24 GHz mmWave radar with 250 MHz bandwidth in three diverse environments: a laboratory, an office, and an around-the-corner corridor. Experimental results demonstrate thatN LoSachieves median localization errors of10.69 cm (X)and11.98 cm (Y)at a 5 m range in the laboratory setting, showcasing its effectiveness for real-world NLoS localization.

This poster presents CARTS, an adaptive 5G uplink sensing scheme that jointly uses the estimated CSI from both data channel reference signal (DMRS) and channel sounding reference signal (SRS) to improve the UE sensing capacity with minimal degradation in communication performance. In order to efficiently combine the estimated CSIs from these two reference signals, CARTS features a real-time SRS triggering algorithm to complement the channel estimations from the DMRS. Besides, to address asynchornization issues caused by DMRS and SRS, which are sampled at different time and frequency bands, CARTS applies a new channel stitching and compensation method.

Plant sensing plays an important role in modern smart agriculture and the farming industry. Remote radio sensing allows for monitoring essential indicators of plant health, such as leaf water content (WC). While recent studies have shown the potential of using millimeter-wave (mmWave) radar for plant sensing, many overlook crucial factors, such as leaf structure and surface roughness, which can impact the accuracy of the measurements. In this article, we introduce Leafeon, which leverages mmWave radar to measure leaf WC noninvasively. Utilizing electronic beam steering, multiple leaf perspectives are sent to a custom deep neural network, which discerns unique reflection patterns from subtle antenna variations, ensuring accurate and robust leaf WC estimations. We implement a prototype of Leafeon using a Commercial Off-The-Shelf mmWave radar and evaluate its performance with a variety of different leaf types. Leafeon was trained in-lab using high-resolution destructive leaf measurements, achieving a mean absolute error (MAE) of leaf WC as low as 3.17% for the Avocado leaf, significantly outperforming the state-of-the-art approaches with an MAE reduction of up to 55.7%. Furthermore, we conducted experiments on live plants in both indoor and glasshouse experimental farm environments. Our results showed a strong correlation between predicted leaf WC levels and drought events.

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.

Molecular communication is a bio-inspired communication paradigm where molecules are used as the information carrier. This paper considers a molecular communication network where the transmitter uses concentration modulated signals for communication. Our focus is to design receivers that can demodulate these signals. We want the receivers to use enzymatic cycles as their building blocks and can work approximately as a maximum a posteriori (MAP) demodulator. No receivers with all these features exist in the current molecular communication literature. We consider enzymatic cycles because they are a very common class of chemical reactions that are found in living cells. In addition, a MAP receiver has good statistical performance. In this paper, we study the operating regime of an enzymatic cycle and how the parameters of the enzymatic cycles can be chosen so that the receiver can approximately implement a MAP demodulator. We use simulation to study the performance of this receiver. We show that we can reduce the bit-error ratio of the demodulator if the enzymatic cycle operates in specific parameter regimes.

The accuracy of the current localization methods degrades significantly when the direct path between the wireless transmitter and the target is blocked. This paper considers the problem of using a single mmWave radar and a tag to facilitate localization in the non-penetrable non-line-of-sight (NLoS) scenario. We present mN2LoS (short for mmWave based Non-penetrable NLoS LOCalization), which accurately localizes the tag by using the multipath reflections. mN2LoS has a few novel features. First, we design HTRD for detecting reflectors and surroundings while distinguishing them from the tag, using Hybrid utilization of Tag localization code and Reflector localization code based on Direct sequence spread spectrum techniques. Second, we enhance the signal-to-noise ratio by exploiting the correlation features of the designed signal. Evaluation results demonstrate that the developed mN2LoS can achieve median errors (at 5m range) of 12.9cm and 3.8° for distance and AOA estimations for the office configuration, respectively.

Non-line-of-sight (NLoS) sensing represents a significant advancement in sensor technology. Unlike traditional sensing methods that rely on direct line-of-sight, NLoS sensing allows for the detection and localization of objects obscured from the sensor's view. In this paper, we introduce mmMirror, a novel Van Atta Array based millimetre-wave (mmWave) reconfigurable intelligent reflecting surface (IRS) that provides: (i) NLoS localization at a range of approximately 3 meters, (ii) seamless communication between radar and IRS using existing frequency-modulated continuous-wave (FMCW) signals, and (iii) support for multiple targets. The mmMirror system is implemented on commodity 24 GHz radars, and the IRS is prototyped on printed circuit boards (PCBs).

Wearable devices-based biometrics has become mainstream in the biometric domain, especially in mobile computing, due to its convenience, flexibility, and potentially high user acceptance. Among various modalities, wearable devices-based gait recognition has been recognized as an effective user authentication method and employed in various applications, such as automated entry systems for home, school, work, vehicles, and automated ticket payment/validation for public transport. However, how secure wearable gait remains an open research question. In this study, we conduct a comprehensive security analysis of the wearable gait. Then, we demonstrate that gait itself is not robust against some attacking methods, such as spoofing or forgery. Therefore, we argue that an anti-spoofing mechanism is important for enhancing the security of wearable gait biometric systems. To this end, we proposed a novel authentication protocol called$Pistis$that embedded gait biometrics and a liveness detection mechanism that is aiming to detect various attacks of gait authentication systems. Our extensive experiments based on 50 subjects demonstrate that$Pistis$is effective in liveness detection and authentication performance enhancement, providing 100% accuracy for human and nonhuman detection, and 99.53% accuracy for user authentication. Pistis can be used as a liveness detection method for wearable devices-based biometrics, significantly for wearable gait.

As the human hand makes direct physical contact with smartphones, significant efforts have recently been made to study the behavioral information of hand gripping of smartphones for user authentication purposes. Most existing methods leverage hand gripping behavior (e.g., gripping gesture, gripping position, gripping strength) of smartphones as biometrics to identify users. However, behavioral-based biometric authentication approaches may suffer from two problems: authentication performance (accuracy) degradation due to high-intra class variations arising from changes in user behavior over time, and vulnerability under spoofing attacks. To address these issues, we propose HoldPass, which is a behavior-independent in-hand user authentication method using vibration. HoldPass is able to adapt to the changes of hand gripping behavior of smartphones by extracting unique and stable physical features of human hands and eliminating the behavior-related prior information. Specifically, in HoldPass, we propose an adversarial neural network to achieve authentication based on unique physical features. Experiments with 10 users show that HoldPass can authenticate users with 97.39% accuracy while keeping False Accepted Rates (FAR) at a minimum of 2.1%.

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAI). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this work, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of≈67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.

Each year, hundreds of thousands of people contract Healthcare Associated Infections (HAIs). Poor hand hygiene compliance among healthcare workers is thought to be the leading cause of HAIs and methods were developed to measure compliance. Surprisingly, human observation is still considered the gold standard for measuring compliance by World Health Organization (WHO). Moreover, no automated solutions exist for monitoring hand hygiene techniques, such as "how to hand rub" technique by WHO. In this paper, we introduce RFWash; the first radio-based device-free system for monitoring Hand Hygiene (HH) technique. On the technical level, HH gestures are performed back-to-back in a continuous sequence and pose a significant challenge to conventional two-stage gesture detection and recognition approaches. We propose a deep model that can be trained on unsegmented naturally-performed HH gesture sequences. RFWash evaluation demonstrates promising results for tracking HH gestures, achieving gesture error rate of < 8% when trained on 10-second segments, which reduces manual labelling overhead by ≈ 67% compared to fully supervised approach. The work is a step towards practical RF sensing that can reliably operate inside future healthcare facilities.

This work builds on our earlier work on designing demodulators for diffusion-based molecular communications using a Markovian approach. The demodulation filters take the form of an ordinary differential equation (ODE) which computes the log-posteriori probability of observing a transmission symbol given the continuous history of receptor activities. A limitation of our earlier work is that the receiver is assumed to be a small cubic volume called a voxel. In this work, we extend the maximum a-posteriori demodulation to the case where the receiver may consist of multiple voxels and derive the ODE for log-posteriori probability calculation. This extension allows us to study receiver behaviour of different volumes and shapes. In particular, it also allows us to consider spatially partitioned receivers where the chemicals in the receiver are not allowed to mix. The key result of this paper is that spatial partitioning can be used to reduce bit-error rate in diffusion-based molecular communications.

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.

Motivated by the fact that living cells use molecular circuits (i.e., a set of chemical reactions) for information processing, this paper investigates the problem of designing molecular circuits for demodulation. In our earlier work, we use a Markovian approach to derive a demodulator for diffusion-based molecular communication. The demodulation filters take the form of an ordinary differential equation, which computes the log-posteriori probability of a transmission symbol being sent. This paper considers the realization of these demodulation filters using molecular circuits assuming the transmission symbols are rectangular pulses of the same duration but different amplitudes, i.e., concentration modulation. This paper makes a number of contributions. First, we use time-scale separation and renewal theory to analytically derive an approximation of the demodulation filter from our earlier work. Second, we present a method to turn this approximation into a molecular circuit. By using simulations, we show that the output of the derived molecular circuit is approximately equal to the log-posteriori probability calculated by the exact demodulation filter if the log-posteriori probability is positive. Third, we demonstrate that a biochemical circuit in yeast behaves similarly to the derived molecular demodulation filter and is therefore a candidate for implementing the derived filter.

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

Heart rate is one of the most important vital signals for personal health tracking. A number of approaches were proposed to monitor heart rate, ranging from wearables to device-less systems. While WiFi has been shown to track heart rate accurately, existing solutions rely on directional antennas to improve the signal quality and ultimately the accuracy of heart rate estimation. Special hardware used in these approaches limits their applicability and truly device-less and ubiquitous heart rate monitoring is yet to be achieved.

This paper presents VeinDeep, a system for using vein patterns to secure smartphones from opportunistic access, e.g. a device left unattended. VeinDeep takes advantage of infrared depth sensors, which at the time of writing have recently started to appear in smartphones for 3D indoor mapping and localisation. We find these sensors can be re-purposed to capture images of the unique vein patterns on the back of each person's hand. We simulate a depth sensor equipped smartphone by developing VeinDeep on a low power Compute Stick. We use Kinect V2 depth sensor to collect 240 recordings from 40 hands belonging to 20 test subjects. Then use this data to compare VeinDeep to one older but popular vein pattern recognition algorithm which uses Hausdorff distance and one recently developed algorithm which uses Kernel distance. We achieve a precision of 0.98, compared to 0.9 for Kernel distance and 0.5 for Hausdorff distance when recall is approximately at 0.83. In addition VeinDeep does all this while taking an average of 6 MiB of memory and 466 milliseconds per comparison. This is an average of 1/6 run time, 2/3 the memory of Hausdorff distance and 1/3 the run time, 1/2 the memory of Kernel distance.

This paper considers a diffusion-based molecular communication system, where the transmitter uses reaction shift keying (RSK) as the modulation scheme. We focus on the demodulation of RSK signal at the receiver. The receiver consists of a front-end molecular circuit and a back-end demodulator. The front-end molecular circuit is a set of chemical reactions consisting of multiple chemical species. The optimal demodulator computes the posteriori probability of the transmitted symbols given the history of the observation. The derivation of the optimal demodulator requires the solution to a specific Bayesian filtering problem. The solution to this Bayesian filtering problem had been derived for a few specific molecular circuits and specific choice(s) of observed chemical species. The derivation of such solution is also lengthy. The key contribution of this paper is to present a general solution to this Bayesian filtering problem, which can be applied to any molecular circuit and any choice of observed species.

Face recognition is one of the important components of many smart devices apps, e.g., face unlocking, people tagging and games on smart phones, tablets, or smart glasses. Sparse Representation Classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV, e.g., Eigenface algorithm. The success of SRC is due to its use of 21 optimization, which makes SRC robust to noise and occlusions. Since 21 optimization is computationally intensive, SRC uses random projection matrices to reduce the dimension of the 21 problem. However, random projection matrices do not give consistent classification accuracy as they ignored the prior knowledge of the training set. In this paper, we propose to exploit the prior knowlege of the training set to improve the recognition accuracy. It first learns the optimized projection matrix from the training set to produce consistent recognition performance then applies 21-based classification based on the group sparsity structure of SRC to further improve the recognition accuracy. Our evaluations, based on publicly available databases and real experiment, show that face recognition using optimized projection matrix is 8-17 percent more accurate than its random counterpart and Eigenface algorithm, and the recognition accuracy can be further improved by up to 5 percent by exploiting group sparsity structure. Furthermore, the optimized projection matrix does not have to be re-calculated even if new faces are added to the training set. We implement the SRC with optimized projection matrix on Android smartphones and find that the computation of residuals in SRC is a severe bottleneck, taking up 85-90 percent of the computation time. To address this problem, we propose a method to compute the residuals approximately, which is 50 times faster with little sacrificing recognition accuracy. Lastly, we demonstrate the feasibility of our new algorithm by the implementation and evaluation of a new face unlocking app and show its robustness to variation of poses, facial expressions, lighting changes, and occlusions.

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.

Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing WashInDepth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the gesture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS.

This paper is concerned with the performance of diffusion-based molecular communication when both the transmitter and receiver are molecular circuit based or chemical reaction based. On the transmitter side, we use Reaction Shift Keying (RSK) which is a recently proposed modulation scheme. RSK uses different chemical reactions to generate different emission patterns for different transmission symbols. On the receiver side, we use a molecular circuit found in living organisms. This circuit uses multiple intermediate chemical species and negative feedback. The information on the transmitted symbol is contained in the time series of the counts of the intermediate species. If the receiver measures the counts of some of these intermediate species, it can use these counts to infer the symbol sent by the transmitter. We are interested to study how the choice of measurements and negative feedback affect the communication performance in terms of symbol error rate. We address these questions by first deriving the optimal demodulator using the maximum a posteriori framework and Bayesian filtering. We learnt two lessons from this study. First, there is a pattern in the form of the optimal demodulation filters where the rates of reactions leading to the production or consumption of the measured chemical species play a strong role. Second, negative feedback can impact on the communication performance in different ways depending on which chemical species are measured.

Real-time target tracking is an important service provided by embedded camera networks. The first step in target tracking is to extract the moving targets from the video frames, which can be realised by using background subtraction. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computationally efficient. We propose a baseline version which uses luminance only and then extend it to use colour information. The key idea is to use random projection matrics to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, to show the computational efficiency of our methods is not platform specific, we implement it on various platforms. The real implementation shows that our proposed method is consistently better and is up to six times faster, and consume significantly less resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

Activity recognition is an important component of many pervasive computing applications. Device-free activity recognition has the advantage that it does not have the privacy concern of using cameras and the subjects do not have to carry a device on them. Recently, it has been shown that channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this paper, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We conduct experiments in environments without and with RFI. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier and activity recognition also becomes harder. Our extensive experiments shows that the performance of state-of-the-art classification methods may degrade significantly with RFI. We then propose a number of counter measures to mitigate the impact of RFI and improve the location-oriented activity recognition performance. Our evaluation shows the proposed method can improve up to 10% true detection rate in the presence of RFI. We also study the impact of bandwidth on activity recognition performance. We show that with a channel bandwidth of 20 MHz (which is used by WiFi), it is possible to achieve a good activity recognition accuracy when RFI is present.

Radio tomographic imaging (RTI) enables device free localisation of people and objects in many challenging environments and situations. Its basic principle is to detect the changes in the statistics of radio signals due to the radio link obstruction by people or objects. However, the localisation accuracy of RTI suffers from complicated multipath propagation behaviours in radio links. We propose to use inexpensive and energy efficient electronically switched directional (ESD) antennas to improve the quality of radio link behaviour observations, and therefore, the localisation accuracy of RTI. We implement a directional RTI (dRTI) system to understand how directional antennas can be used to improve RTI localisation accuracy. We also study the impact of the choice of antenna directions on the localisation accuracy of dRTI and propose methods to effectively choose informative antenna directions to improve localisation accuracy while reducing overhead. Furthermore, we analyse radio link obstruction performance in both theory and simulation, as well as false positives and false negatives of the obstruction measurements to show the superiority of the directional communication for RTI. We evaluate the performance of dRTI in diverse indoor environments and show that dRTI significantly outperforms the existing RTI localisation methods based on omni-directional antennas.

In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper assumes that the transmitter uses different chemical reactions to generate different emission patterns of signalling molecules to represent different transmission symbols, and the receiver consists of receptors. When the signalling molecules arrive at the receiver, they may react with the receptors to form ligand-receptor complexes. Our goal is to study the demodulation in this setup assuming that the transmitter and receiver are synchronised. We derive an optimal demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum a posteriori framework and use Bayesian filtering to derive the optimal demodulator. We use numerical examples to illustrate the properties of this optimal demodulator.

This paper presents a cross-layer approach for enabling high-throughput reliable multicast in multi-hop wireless mesh networks. The building block of our approach is a multicast routing metric, called the expected multicast transmission count (EMTX). EMTX is designed to capture the combined effects of MAC-layer retransmission-based reliability, wireless broadcast advantage, and link quality awareness. The EMTX of single-hop transmission of a multicast packet from a sender is the expected number of multicast transmissions (including retransmissions) required for its next-hop recipients to receive the packet successfully. We formulate the EMTX-based multicast problem with the objective of minimizing the sum of EMTX over all forwarding nodes in the multicast tree, aiming to reduce network bandwidth consumption while ensure high end-to-end packet delivery ratio for the multicast traffic. We provide rigorous mathematical formulations and methods to find near-optimal solutions of the problem computationally efficiently. We present centralized and distributed algorithms, and demonstrate their effectiveness in tackling the EMTX-based multicast problem with a combination of theoretical and numerical results. Simulation experiments show that, in comparison with two baseline approaches, EMTX-based multicast routing reduces the number of hop-by-hop transmissions per packet by up to 40 percent and yet improves the multicast throughput by up to 24 percent.

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

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

The information-preserving sampling properties of compressive sensing have found a number of successful applications, such as sensor scheduling, localisation and tracking to deal with the resource constraints of the embedded systems. In this paper, we investigate an approach to improve the performance of compressive sensing applications through a novel strategy for optimising the projection matrix. We formulate the projection matrix optimisation problem and apply greedy algorithm to solve the optimisation problem efficiently. We evaluate the proposed approach by an emerging background subtraction method designed specifically for the embedded systems and show the proposed approach outperforms existing approaches significantly with little overhead.

Acoustic Sensor Networks (ASNs) have a wide range of applications in natural and urban environment monitoring, as well as indoor activity monitoring. In-network classification is critically important in ASNs because wireless transmission costs several orders of magnitude more energy than computation. The main challenges of in-network classification in ASNs include effective feature selection, intensive computation requirement and high noise levels. To address these challenges, we propose a sparse representation based feature-less, low computational cost, and noise resilient framework for in-network classification in ASNs. The key component of Sparse Approximation based Classification (SAC), ℓ1 minimization, is a convex optimization problem, and is known to be computationally expensive. Furthermore, SAC algorithms assumes that the test samples are a linear combination of a few training samples in the training sets. For acoustic applications, this results in a very large training dictionary, making the computation infeasible to be performed on resource constrained ASN platforms. Therefore, we propose several techniques to reduce the size of the problem, so as to fit SAC for in-network classification in ASNs. Our extensive evaluation using two real-life datasets (consisting of calls from 14 frog species and 20 cricket species respectively) shows that the proposed SAC framework outperforms conventional approaches such as Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) in terms of classification accuracy and robustness. Moreover, our SAC approach can deal with multi-label classification which is common in ASNs. Finally, we explore the system design spaces and demonstrate the real-time feasibility of the proposed framework by the implementation and evaluation of an acoustic classification application on an embedded ASN testbed.

Wireless sensor networks (WSNs) enable the collection of physical measurements over a large geographic area. It is often the case that we are interested in computing and tracking the spatial-average of the sensor measurements over a region of the WSN. Unfortunately, the standard average operation is not robust because it is highly susceptible to sensor faults and heterogeneous measurement noise. In this paper, we propose a computational efficient method to compute a weighted average (which we will call robust average) of sensor measurements, which appropriately takes sensor faults and sensor noise into consideration. We assume that the sensors in the WSN use random projections to compress the data and send the compressed data to the data fusion centre. Computational efficiency of our method is achieved by having the data fusion centre work directly with the compressed data streams. The key advantage of our proposed method is that the data fusion centre only needs to perform decompression once to compute the robust average, thus greatly reducing the computational requirements. We apply our proposed method to the data collected from two WSN deployments to demonstrate its efficiency and accuracy.

Background subtraction is often the first step in many computer vision applications such as object localisation and tracking. It aims to segment out moving parts of a scene that represent object of interests. In the field of computer vision, researchers have dedicated their efforts to improve the robustness and accuracy of such segmentations but most of their methods are computationally intensive, making them non-viable options for our targeted embedded camera platform whose energy and processing power is significantly more constrained. To address this problem as well as maintain an acceptable level of performance, we introduce Compressive Sensing (CS) to the widely used Mixture of Gaussian to create a new background subtraction method. The results show that our method not only can decrease the computation significantly (a factor of 7 in a DSP setting) but remains comparably accurate.

Sparse approximation has now become a buzzword for classification in numerous research domains. We propose a distributed sparse approximation method based on l1 minimization for frog sound classification, which is tailored to the resource constrained wireless sensor networks. Our pilot study demonstrates that l1 minimization can run on wireless sensor nodes producing satisfactory classification accuracy.

Opportunistic routing has been shown to outperform traditional routing protocols which use a pre-determined next hop. Most of the opportunistic routing protocols proposed to-date are for unicast while few multicast opportunistic routing protocols have been proposed. This paper proposes an opportunistic multicast routing protocol. The protocol addresses the problem of selection and prioritisation of relay candidates to reduce the overall number of transmissions. It also addresses the problem of reducing protocol overheads. We compare the performance of our proposed protocol against an existing opportunistic multicast routing protocol, and find that our proposed protocol has a higher packet delivery rate and lower number of transmissions per packet delivered.

Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

This paper studies video multicasting in large-scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy and low-capacity multihop wireless paths. A set of heuristic-based algorithms is described that together aim to maximize reliable network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the dynamic group management algorithm. These algorithms use different approaches to arrange nodes involved in video multicasting into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are presented, showing that our multicasting algorithms can achieve up to 40 percent more throughput than other related published approaches.

This paper aims to reduce the prebuffering requirements, while maintaining continuity, for video streaming. Current approaches do this by making use of adaptive media playout (AMP) to reduce the playout rate. However, this introduces playout distortion to the viewers and increases the viewing latency. We approach this by proposing a frame rate optimization framework that adjusts both the encoder frame generation rate and the decoder playout frame rate. Firstly, we model this problem as the joint adjustment of the encoder frame generation interval and the decoder playout frame interval. This model is used with a discontinuity penalty virtual buffer to track the accumulated difference between the receiving frame interval and the playout frame interval. We then apply Lyapunov optimization to the model to systematically derive a pair of decoupled optimization policies. We show that the occupancy of the discontinuity penalty virtual buffer is correlated to the video discontinuity and that this framework produces a very low playout distortion in addition to a significant reduction in the prebuffering requirements compared to existing approaches. Secondly, we introduced a delay constraint into the framework by using a delay accumulator virtual buffer. Simulation results show that the delay constrained framework provides a superior tradeoff between the video quality and the delay introduced compared to the existing approach. Finally, we analyzed the impact of delayed feedback between the receiver and the sender on the optimization policies. We show that the delayed feedbacks have a minimal impact on the optimization policies.

We propose a routing metric for enabling high-throughput reliable multicast in multi-rate wireless mesh networks. This new multicast routing metric, called expected multicast transmission time (EMTT), captures the combined effects of 1) MAC-layer retransmission-based reliability, 2) transmission rate diversity, 3) wireless broadcast advantage, and 4) link quality awareness. The EMTT of one-hop transmission of a multicast packet minimizes the amount of expected transmission time (including that required for retransmissions). This is achieved by allowing the sender to adapt its bit-rate for each ongoing transmission/retransmission, optimized exclusively for its next-hop receivers that have not yet received the multicast packet. We model the rate adaptation process as a Markov decision process (MDP) and derive an efficient procedure for computing EMTT from the theory of MDP. We present receiver-initiated algorithms and describe protocol implementation for the EMTT-based multicast routing problem. Numerical results are presented to demonstrate the accuracy of the proposed algorithms against optimal solutions to the multicast routing problem. Simulation experiments confirm that, in comparison with single-rate multicast, multi-rate multicast using the EMTT metric effectively reduces the overall multicast transmission time while yielding higher packet delivery ratio and lower end-to-end latency.

This paper aims to reduce the amount of prebuffering required to ensure a maximum video continuity in streaming. Current approaches do this by slowing the playout frame rate of the decoder, this is known as adaptive media playout (AMP). However, doing this introduces playout distortion to the viewers as the video is played slower than its natural playout rate. We approach this by proposing a frame rate control scheme that jointly adjusts the encoder frame generation rate of the encoder and the playout frame rate of the decoder. This is done such that the scheme can choose to sacrifice the frame quality or continuity of the video to reduce the playout distortion introduced. This implies that the scheme needs to find the optimal trade-off between frame quality, playout distortion and video continuity. To do that, we characterize the frame rate control problem using Lyapunov optimization. We then systematically derive the optimization policies. We also show that these policies can be decoupled into separate encoder and decoder optimization policies, thus allowing for a distributed implementation. Simulation results show significant reductions in the prebuffering requirements over a scheme that perform no frame rate control and lower playout distortions compared to the AMP schemes, while exhibiting a modest drop in frame quality.

In this paper, we propose an adaptive cross layer technique that optimally enhance the QoS of wireless video transmission in an IEEE 802.11e WLAN. The optimization takes into account the unequal error protection characteristics of video streaming, the IEEE 802.11e EDCA parameters and the lossy nature of wireless channel. Our proposed technique makes use of two analytical models: a video distortion model and a channel throughput estimation model. The first model predicts the video quality in term of average PSNR of all decoded video frames. The second model estimates the channel throughput and packet loss rates of each MAC layers queue, which are then fed into the first model as inputs. The optimal EDCA parameters are selected by an optimization module based on the information from the analytical models. The accuracy of our optimized EDCA parameters is verified through the extensive simulation.

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps.

This paper proposes an integrated model to predict the quality of video, expressed in terms of mean square error (MSE) of the received video frames, in an IEEE 802.1 le wireless network. The proposed system takes into account contention at the MAC layer, wireless channel error, queueing at the MAC layer, parameters of different 802.1 le access categories (ACs), and video characteristics of different H.264 data partitions (DPs). To the best of the authors' knowledge, this is the first system that takes these network and video characteristics into consideration to predict video quality in an IEEE 802.1 le network. The proposed system consists of two components. The first component predicts the packet loss rate of each H.264 data partition by using a multi-dimensional discrete-time Markov chain (DTMC) coupled to a M/G/l queue. The second component uses these packet loss rates and the video characteristics to predict the MSE of each received video frames. We verify the accuracy of our combination system by using discrete event simulation and real H.264 coded video sequences.

We consider the problem of using wireless sensor networks (WSNs) to measure the temporal-spatial field of some scalar physical quantities. Our goal is to obtain a sufficiently accurate approximation of the temporal-spatial field with as little energy as possible. We propose an adaptive algorithm, based on the recently developed theory of adaptive compressive sensing, to collect information from WSNs in an energy efficient manner. The key idea of the algorithm is to perform ¿projections¿ iteratively to maximise the amount of information gain per energy expenditure. We prove that this maximisation problem is NP-hard and propose a number of heuristics to solve this problem. We evaluate the performance of our proposed algorithms using data from both simulation and an outdoor WSN testbed. The results show that our proposed algorithms are able to give a more accurate approximation of the temporal-spatial field for a given energy expenditure.

Welcome to the 34thIEEE Conference on Local Computer Networks (LCN). It has been our privilege to coordinate the review process and put together a high quality technical program. This year LCN received 188 regular full-length (up to 8 pages) and 24 short (up to 4 pages) submissions. Every submitted paper received at least three reviews. Most papers have been evaluated by 4 TPC members.

The increasing number of independent IEEE 802.11 WLANs owned and managed by autonomous users has led to increased interference, resulting in performance degradation and unfairness. Performance can be improved by allowing these networks to operate on different channels. Due to the autonomous nature of the networks, a suitable channel selection scheme should be distributed, adaptive and require no explicit coordination. In this paper, we model the channel selection of WLANs as a non-cooperative game in a learning setting. Using a novel method of acquiring a disruption factor value, we propose a class of socially conscious channel selection schemes based on game-theoretic learning. These schemes are distributed, adaptive and are able to improve fairness without explicit inter-network communication. These features allow the WLANs to coexist in an interference-limited but non-cooperative environment. They also have the advantage of not requiring any modification to the existing 802.11 standards. Simulations show improved fairness and aggregate throughput compared with two existing schemes.

Noise map can provide useful information to control noise pollution. We propose a people-centric noise collection system called the Ear-Phone. Due to the voluntary participation of people, the number and location of samples cannot be guaranteed. We propose and study two methods, based on compressive sensing, to reconstruct the missing samples.

This paper addresses the problem of "efficientrdquo broadcast in a multiradio, multichannel, multirate wireless mesh network (MR2-MC WMN). In such an MR2-MC WMN, nodes are equipped with multiple radio interfaces, tuned to orthogonal channels, that can dynamically adjust their transmission rate by choosing a modulation scheme appropriate for the channel conditions. We choose "broadcast latency,rdquo defined as the maximum delay between a packet's network-wide broadcast at the source and its eventual reception at all network nodes, as the ldquoefficiencyrdquo metric of broadcast performance. We study in this paper how the availability of multirate transmission capability and multiple radio interfaces tuned to orthogonal channels in MR2-MC WMN nodes can be exploited, in addition to the medium's ldquowireless broadcast advantagerdquo (WBA), to improve the ldquobroadcast latencyrdquo performance. In this paper, we present four heuristic solutions to our considered problem. We present detailed simulation results for these algorithms for an idealized scheduler, as well as for a practical 802.11-based scheduler. We also study the effect of channel assignment on broadcast performance and show that channel assignment can affect the broadcast performance substantially. More importantly, we show that a channel assignment that performs well for unicast does not necessarily perform well for broadcast/multicast.

This article investigates a wireless acoustic sensor network application—monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing, wide area sensing coverage and long-lived unattended operation. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to ninth frog species found in northern Australia. Our first prototype consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates four algorithms designed to account for the sampling, processing, energy, and communication bottlenecks of the Mica2s (1) high frequency sampling, (2) thresholding and noise reduction, to reduce data transmission by up to 90%, (3) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate, and (4) harvesting-aware energy management, which exploits sensor energy harvesting capabilities to extend the system lifetime. Our evaluation shows the performance of our systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

This paper studies video multicasting in large scale areas using wireless mesh networks. The focus is on the use of Internet access gateways that allow a choice of alternative routes to avoid potentially lengthy multi-hop wireless paths with low capacity. A set of heuristic-based algorithms are described that together aim to maximize network capacity: the two-tier integrated architecture algorithm, the weighted gateway uploading algorithm, the link-controlled routing tree algorithm, and the alternative channel assignment algorithm. These algorithms use different approaches to arrange multicast group members into a clustered and two-tier integrated architecture in which network protocols can make use of multiple gateways to improve system throughput. Simulation results are used to determine the performance of the different approaches.

In wireless environments, video quality can be severely degraded due to channel errors. Improving error robustness towards the impact of packet loss in error-prone network is considered as a critical concern in wireless video networking research. Data partitioning (DP) is an efficient error-resilient tool in video codec that is capable of reducing the effect of transmission errors by reorganizing the coded video bitstream into different partitions with different levels of importance. Significant video performance improvement can be achieved if DP is jointly optimized with unequal error protection (UEP). This paper proposes a fast and accurate frame-recursive block-based distortion estimation model for the DP tool in H.264.AVC. The accuracy of our model comes from appropriately approximating the error-concealment cross-correlation term (which is neglected in earlier work in order to reduce computation burden) as a function of the first moment of decoded pixels.Without increasing computation complexity, our proposed distortion model can be applied to both fixed and variable block size intra-prediction and motion compensation. Extensive simulation results are presented to show the accuracy of our estimation algorithm.

We address the problem of minimizing the worst-case broadcast delay in ldquomulti-radio multi-channel multi-rate wireless mesh networksrdquo (MR2-MC WMN) in a distributed and localized fashion. Efficient broadcasting in such networks is especially challenging due to the desirability of exploiting the ldquowireless broadcast advantagerdquo (WBA), the interface-diversity, the channel-diversity and the rate-diversity offered by these networks. We propose a framework that calculates a set of forwarding nodes and transmission rate at these forwarding nodes irrespective of the broadcast source. Thereafter, a forwarding tree is constructed taking into consideration the source of broadcast. Our broadcasting algorithms are distributed and utilize locally available information. We present a detailed performance evaluation of our distributed and localized algorithm and demonstrate that our algorithm can greatly improve broadcast performance by exploiting the rate, interface and channel diversity of MR2-MC WMNs and match the performance of centralized algorithms proposed in literature while utilizing only limited two-hop neighborhood information.

This paper studies probabilistically reliable multicast in wireless mesh networks (WMNs), utilizing MAC layer re-transmission and wireless broadcast advantage to improve both the multicast throughput and the delivery rate. We first present a new multicast routing metric which we call the expected multicast transmissions (EMT). EMT captures the effect of link packet delivery ratio, MAC layer retransmission and wireless broadcast advantage at the same time. The EMT of a MAC layer multicast transmission is the expected number of data transmissions (including retransmissions) required for a packet to reach all the recipients. The EMT of a multicast tree is the sum over the EMT of each forwarding node. Then, we propose a probabilistically reliable on-demand (PROD) multicast protocol with the objective of minimizing the EMT of the multicast tree. Simulation results show that, in comparison with existing approaches, PROD reduces the end-to-end packet loss ratio by up to 30% and improves the multicast throughput by up to 25%. In addition, it reduces the number of transmissions per packet by up to 40% and thus significantly reduces the network overhead of the multicast session.

In this paper, we proposed a probabilistically reliable multicast routing (PRMR) which uses link packet delivery rate as the link-quality-based metric to find paths from source to destinations. In order to increase the reliability of the multicast session, we use a pair of disjoint paths to connect the source to each destination. We reduce the total number of forwarding nodes by exploiting the wireless broadcast advantage. Our experiment results show that PRMR can increase the average reliability by up to 25% and the node with the lowest reliability by up to 50% in comparison with ODMRP.

As more wireless networks are being deployed in a given geographic area, the problem of interference and coexistence of these independently operated networks is becoming an increasing problem. This paper looks at the coexistence of independent multihop wireless mesh networks (WMNs). We argue that cooperation is difficult in such scenarios. We define a coexistence game model and apply it to study channel assignment in co-located WMNs. In addition, we propose using no-regret learning algorithms that allow WMNs to iteratively arrive at Nash equilibrium outcomes. Simulation results show that the informed no-regret learning algorithms we have tested converge to a set of Nash equilibrium strategy profiles. We also show that network information is not critical for games with large numbers of players.

A wireless mesh network (WMN) that utilizes multiple interfaces and multiple channels has been shown to improve network performance by reducing the interference and increasing the available bandwidth. However, the contention delay experienced by a frame along every hop of the WMN can still limit the performance. In addition, cross-layer delay occurs when a frame has to travel up and down the protocol stack to access different interfaces. This paper motivates the need for a MAC in a multi-interface backhaul WMN and proposed a cut-through MAC that is able to reduce the end-to-end delay of data frames in the network. Preliminary simulation results show that this MAC scheme gives higher good put and lower end-to-end delay in a chain topology, when compared to IEEE 802.11 DCF MAC. The paper also highlights key challenges that need to be addressed in the design of a cut-through MAC for multihop wireless networks.

This paper studies some of the fundamental challenges and opportunities associated with the network-layer broadcast and multicast in a multihop multirate wireless mesh network (WMN). In particular, we focus on exploiting the ability of nodes to perform link-layer broadcasts at different rates (with correspondingly different coverage areas). We first show how, in the broadcast wireless medium, the available capacity at a mesh node for a multicast transmission is not just a function of the aggregate pre-existing traffic load of other interfering nodes, but intricately coupled to the actual (sender, receiver) set and the link-layer rate of each individual transmission. We then present and study four alternative heuristic strategies for computing a broadcast tree that not only factors in a flow's traffic rate but also exploits the wireless broadcast advantage (WBA). Finally, we demonstrate how our insights can be extended to multicast routing in a WMN, and present results that show how a tree-formation algorithm that combines contention awareness with transmission rate diversity can significantly increase the total amount of admissible multicast traffic load in a WMN.

We address the problem of minimizing the worst-case broadcast delay in multi-rate wireless mesh networks (WMN) in a distributed and localized fashion. Efficient broadcasting in such networks is especially challenging due to the multi-rate transmission capability and the interference between wireless transmissions of WMN nodes. We propose connecting dominating set (CDS) based broadcast routing approach which calculates the set of forwarding nodes and the transmission rate at each forwarding node independent of the broadcast source. Thereafter, a forwarding tree is constructed taking into consideration the source of the broadcast. In this paper, we propose three distributed and localized rate-aware broadcast algorithms. We compare the performance of our distributed and localized algorithms with previously proposed centralized algorithms and observe that the performance gap is not large. We show that our algorithms greatly improve performance of rate-unaware broadcasting algorithms by incorporating rate-awareness into the broadcast tree construction algorithm process.

As most proxy caches for streaming media cache only a part of the video, the most important issue for a caching algorithm is to determine which portion of the video should be cached. Due to different part of the video can have different popularity, the studying of the internal popularity characteristics of streaming videos is very important for the development of efficient cache mechanisms. In this paper, we showed that the internal popularity of the majority of the most popular videos obeys a k-transformed Zipf-like distribution through analyzing two 7-month long traces recorded at different streaming video servers of an entertainment video-on-demand provider. Based on this empirical internal popularity distribution model, we then proposed a new dynamical caching algorithm. The results of simulations proved that this algorithm can have better performance than algorithms based on other models and it has similar performance compare with fine-grained caching but requires significantly less state information.

A multi-rate capable IEEE 802.11a/b/g node can utilize different link-layer transmission rates. Interestingly, multi-rate capability is defined by IEEE802.11 standards only for unicast transmissions. In this paper, we consider a novel type of multi-radio multi-channel wireless mesh network (WMN) where a radio can multicast at different link-layer transmission rates to its neighbors. Such link-layer multi-rate multicast capability will enable low-latency network-layer broadcast/multicast for multimedia. In our previous work, we assumed a "fully multi-rate multicast" (EMM) framework in which nodes can adjust link-layer multicast transmission rate for each link-layer frame. We propose a new framework called "single best-rate multicast" (SBM) that exploits the link-layer rate diversity by enabling each WMN to decide, depending on its topological properties, a single transmission rate for all its link-layer data multicasts. Although, EMM improves performance significantly, employing SBM is attractive since it can eliminate some undesirable features of practical multi-rate media access control (MAC) protocols. We propose methods to determine the "best" link-layer transmission rate for the SBM framework. We also propose two heuristic broadcast solutions, using SBM framework that can realize low-latency broadcast by exploiting inherent rate and interface diversity in multi-radio multi-channel WMN. Simulation results indicate that SBM broadcast heuristics give comparable performance to EMM broadcast heuristics, especially in dense networks

To support reliable multicast routing in wireless mesh networks, it is important to protect multicast sessions against link or node failures. The issue of protecting multicast sessions in wireless mesh networks is a new problem to the best of our knowledge. In this paper, we propose a resilient forwarding mesh approach for protecting a multicast session in wireless mesh networks. Utilizing the wireless broadcast advantage, a resilient forwarding mesh effectively establishes two node disjoint paths for each source-destination pair. This allows a multicast session to be immune from any single link or intermediate node failure. We introduce four heuristic algorithms to obtain approximate solutions that seek to minimize the number of required broadcast transmissions. We evaluate the performance of these heuristic algorithms against the optimal resilient forwarding mesh (ORFM) obtained by solving an integer linear programming (ILP) formulation of the problem. Experimental results demonstrate that one of these heuristic algorithms, which we call the minimal disjoint mesh algorithm (MDM), performs sufficiently close to ORFM. Besides, we find that the resilient forwarding mesh approach provides efficient 1+ 1 protection (Pioro and Medhi, 2004) to the multicast session without incurring much additional overhead on a single minimal cost multicast tree

Most ad hoc and sensor network research assumes idealized radio propagation models without considering fading and shadowing effects. Experimental results have shown that many well-designed protocols will fail simply because of fading and shadowing experienced in a realistic wireless environment. While fading and shadowing for radio propagation are well understood in wireless communication community, they are rarely studied in network level research for wireless sensor networks. This paper studies the fading and shadowing effects on the performance of different MAC protocols for wireless sensor networks. We show that fading and shadowing can have a significant influence on network performance. We study and compare network performance for three different systems: 1) a multi-channel CDMA system; 2) a pure CDMA system; 3) a contention based system. Through discrete event simulation (using ns-2), we show that the multi-channel CDMA system outperforms the pure CDMA system as well as the contention based system under fading and shadowing environments

We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study the effect of channel assignment on broadcast performance and show that channel assignment can affect the broadcast performance substantially. More importantly, we show that a channel assignment that performs well for unicast does not necessarily perform well for broadcast/multicast. To the best of our knowledge, this work constitutes the first contribution in the area of broadcast routing for MR2-MC WMN

In a multirate wireless network, a node can dynamically adjust its link transmission rate by switching between different modulation schemes. In the current IEEE802.11a/b/g standards, this rate adjustment is defined for unicast traffic only. In this paper, we consider a wireless mesh network (WMN), where a node can dynamically adjust its link-layer multicast rates to its neighbors, and address the problem of realizing low-latency network-wide broadcast in such a mesh. We first show that the multirate broadcast problem is significantly different from the single-rate case. We will then present an algorithm for achieving low-latency broadcast in a multirate mesh which exploits both the wireless multicast advantage and the multirate nature of the network. Simulations based on current IEEE802.11 parameters show that multirate multicast can reduce broadcast latency by 3-5 times compared with using the lowest rate alone. In addition, we show the significance of the product of transmission rate and transmission coverage area in designing multirate WMNs for broadcast

In this paper, we propose a novel approach for reducing the download time of large files over the Internet. Our approach, known as Parallelized File Transport Protocol (P-FTP), proposes simultaneous downloads of disjoint file portions from multiple file servers. P-FTP server selects file servers for the requesting client on the basis of a variety of QoS parameters, such as available bandwidth and server utilization. The sensitivity analysis of our file server selection technique shows that it performs significantly better than random selection. During the file transfer, P-FTP client monitors the file transfer flows to detect slow servers and congested links and adjusts the file distributions accordingly. P-FTP is evaluated with simulations and real-world implementation. The results show at least 50 percent reduction in download time when compared to the traditional file-transfer approach. Moreover, we have also carried out a simulation-based study to investigate the issues related to large scale deployment of our approach on the Internet. Our results demonstrate that a large number of P-FTP users has no adverse effect on the performance perceived by non-P-FTP users. In addition, the file servers and network are not significantly affected by large scale deployment of P-FTP.

The parallelized file transfer protocol (P-FTP) is a novel network resource aware parallel technique for improving file transfer performance on the Internet. Before starling the parallel file transfer sessions, it considers the available resources in the network (available bandwidth) and at the file servers (memory and CPU utilization). The client dynamically changes the file portions being downloaded from different file servers by monitoring the FTP flows and detecting slow servers and congested links. Early experimentation on Planet-Lab (2004) for a single P-FTP client suggests that the download time can be reduced by more than 50% for large files. In this paper, our goal is to evaluate whether P-FTP can be widely adopted within the Internet, To this end, we have carried out a simulation-based study to investigate the performance of P-FTP when it is adopted by a large user base. We find that, by virtue of its self-tuning capability, P-FTP continues to exhibit improved performance even with many simultaneous clients. Our results also demonstrate that introducing a large number of P-FTP users has no adverse effect on the performance perceived by users of the traditional single server file transfer. We attribute this improvement to the fact that P-FTP dynamically adapts the parallel sessions in response to changes in network state and server resources. This illustrates that P-FTP is highly scalable and is hence suitable for widespread deployment in the Internet.

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

In this paper we consider dynamic routing of restorable quality of service (QoS) connections in multi-protocol label switched (MPLS) networks under a single link failure model. To route a restorable QoS connection, two link disjoint label switched paths from the ingress to the egress node need to be computed such that both paths comply with the QoS constraints. When looking at QoS constraints like bandwidth guarantee and end-to-end delay bound, the current approach described in the literature converts the end-to-end delay bound into an effective bandwidth in a pre-processing step before computing two link disjoint bandwidth guaranteed paths. We argue that the current approach results in poor network performance and suggest a new approach that exploits the dependency between the end-to-end delay, chosen path and provisioned bandwidth. Thus, the two link disjoint paths are computed dynamically with their respective bandwidths so that they meet both the bandwidth guarantee and the end-to-end delay bound. In this paper, we present a new generic algorithm and two new linear programming formulations that implement the new approach. The two linear programming formulations are not intended to be used in a production environment due to their extensive running times, up to 2.5 minutes on average per request, but rather to benchmark approximation algorithms that in turn can be used in production. Furthermore our experiments show that the network performance improvement when exploiting the dependency between end-to-end delay, chosen path and provisioned bandwidth is substantial.

This demonstration shows a wireless, acoustic sensor network application--- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our system uses automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage [2]. Our prototype consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica motes operating in concert. The Mica motes are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks.

The performance of direct sequence code division multiple access (DS-CDMA) sensor networks is limited by multiple access interference (MAI). The paper proposes using frequency division to reduce the MAI in a DS-CDMA sensor network. We provide theoretical characterization of the mean MAI at a given node and show that a small number of frequency channels can reduce the MAI significantly. In addition, we provide a comparison of our proposed system to systems which do not use frequency division or which employ contention based protocols. Our study found that, by using only a small number of frequency channels, our system has less channel contention, lower packet latency, higher packet delivery ratio and lower energy consumption.

The DiffServ Assured Forwarding (AF) service provides a scalable solution to QoS guarantee. Currently, AF only provides qualitative differentiation of delay or loss between classes of service, but not quantitative guarantee. By studying the quantitative behaviour of the steady state operating point for RIO, we propose two adaptive RIO algorithms for per-hop QoS provisioning in AF service. These two algorithms, ARIO-D and ARIO-L, work by dynamically adjusting the RIO parameters so that the packet delay and the packet loss are, respectively, kept at their target level. Simulation results show that they can provide both high link utilization as well as stable and differentiated delay or loss for different AF classes on a single router.

RIO is the primary queue management mechanism proposed for assured forwarding in the DiffServ framework. Although RIO can generally provide bandwidth guarantee, its performance in terms of both delay and loss is sensitive to traffic level. In this paper, we demonstrate this sensitivity problem by simulation and present a qualitative explanation for its origin. We propose two adaptive algorithms to overcome this problem. Simulation results show that they can effectively improve the robustness of RIO under different and dynamic traffic, and provide stable and quantitative performance of delay or loss.

Most of the current proxy caching algorithms for streaming video media assume that users favor the beginning of the media object. However, this assumption is questionable in highly interactive scenarios, such as e-learning, where some parts of the video other than the prefix can also be popular. A new segment-based proxy caching algorithm, named popularity-wise caching, is proposed for highly interactive streaming. It is designed to deal with arbitrary popularity distribution of media content. Simulations are performed using synthetic traces with different kinds and levels of user interactivity. The results show that the performance of current segment-based caching (such as exponential caching and soccer caching) degrade with increasing user interactivity, while popularity-wise caching can provide the lowest user startup latency for interactive requests and highest bandwidth saving for the backbone network.

We propose two location-aware select optimal neighbor (SON) algorithms that are suitable for CSMA/CA based MAC protocols for wireless ad hoc networks. Both algorithms optimize the energy efficiency by reducing the effective number of neighbors and thus reduce the transmission power as well as the overhearing power consumption at irrelevant receivers. NS-2 simulations show that our algorithms can achieve about 28% and 38% average energy savings per node compared to CSMA/CA based MAC protocols such as IEEE 802.11.

Optical burst switches (OBSes) have been proposed to improve the utilization of a network of optical cross connect (OXCs). Current studies on OBS assume a network consisting of OBSes alone. While this is a reasonable assumption for evaluating a new technology, the question of how a network of OXCs can be evolved to a network of OBSes has not been studied. In this paper, we propose a hybrid architecture consisting of OBSes at the network edge and OXCs in the network core. This architecture allows carriers to gradually migrate from an OXC-based network to an OBS-based network with an improved network utilization. In addition, we use queueing analysis to study the performance of this new architecture.

This paper presents an analytical model for saturation throughput of IEEE 802.11 distributed coordination function (DCF) with multiple classes of service based on varying the parameters used in medium access control. In particular, we show that relative service differentiation can easily be achieved by varying the initial contention window alone. The simulation results show the validity of this model.

Enterprise-wide distributed computing systems are inherently stochastic and the performance management of distributed applications running on the top of DCS is a complex and computationally hard task, in this paper, we define the enterprise distributed application and provide queueing analysis on the relationship between the application level performance parameters and the network and computer system parameters. We also provide a binary integer programming (BIP) model and a case study on how to allocate application components across an enterprise local area network.

This paper considers the traffic engineering of MPLS-based virtual private networks (VPNs) with multiple classes of service. We focus on two main issues. Firstly, we point out that the one LSP per ingress-egress pair constraint can be relaxed for the case of MPLS-based VPNs due to the ease in classifying flows on a per-VPN basis. This allows us to use LSP with finer granularity and thus better load balancing. Secondly, we point out that the single objective traffic engineering formulations proposed in literature address only one particular aspect of the traffic engineering problem. We propose a multiobjective traffic engineering problem which takes both resource usage and link utilisation into account. This optimisation problem is NP-complete and involves a large number of variables. We propose an heuristic to solve this problem.