WiFi devices are ubiquitous in modern environments, from smartphones and laptops to IoT sensors and AR/VR headsets. Identifying device types/models within these populations enables crowd analysis, network optimization, and detection of unusual devices. Current identification methods struggle with MAC address randomization, require large training datasets, and perform poorly in real-world deployments. This paper introduces a device identification method based on Information Element (IE) attributes extracted from WiFi probe requests. We evaluate the approach using probe requests captured in the 2.4 GHz band. Evaluation across 70+ device types yields 99% precision, 98% recall, and 99% F1 score, exceeding deep learning approaches (92% F1 score) under similar training conditions. Our approach maintains accuracy despite MAC randomization and requires minimal training data. We demonstrate practical applicability through an operational dashboard tested in real-world scenarios for urban planning and network management. Case studies across diverse environments confirm the effectiveness of the method for operational use.

The ubiquity of WiFi-enabled devices raises the need for advanced network monitoring and management due to security and privacy issues associated with wireless networks. One method is Network Traffic Classification (NTC). However, robust and resilient NTC when traffic is encrypted and without compromising privacy is challenging. One possibility is to use WiFi Channel State Information (CSI), as it will change depending on the characteristics of the information being transmitted. In this article, we show that it is possible to use CSI for NTC by extracting CSI amplitudes from different network traffic streams, and then using this information to create a feature set that can be used with machine learning classifiers to develop a novel NTC mechanism. We show the robustness of our CSI-based NTC by considering real-world scenarios under different wireless interference, namely overlapping frequencies, location-based interference, and interference generated by various network streams. Consequently, our proposed NTC scheme achieved 0.84 NTC F-score as a baseline, and we identify that spectrally overlapping interference reduces the overall F-score of the CSI-based NTC classifier by upto 0.6. For traffic classes with similar characteristics, the proposed framework achieved an NTC F-score above 0.95, corroborating the scalability of our non-intrusive sensing technology. 1

The rapid proliferation of devices brought about by the Internet of Things (IoT) has underpinned the inclusion of sensing capabilities in wireless systems. Therefore, integrated sensing and communication (ISAC) is emerging as a key use case for next-generation systems. The dual functionality of ISAC systems increases interference, which multiple-input multipleoutput (MIMO) arrays can mitigate through spatial diversity. However, achieving substantial diversity gains with MIMO requires large antenna arrays, which could amplify the system complexity. Fluid antennas (FAs) provide an efficient alternative, delivering comparable spatial gains without the need for massive arrays, making them a promising candidate for ISAC systems. In this paper, we propose a quality-of-service (QoS) aware powerefficient transceiver design for an FA-ISAC system. We jointly design the antenna position vector, transmit beamformers, radar signal and receive combiners to meet the sensing and communication performance constraints. Our results demonstrate a 2 dB improvement compared to conventional MIMO systems.

With the mass inclusion of machines in network architectures, integrated sensing and communication (ISAC) is emerging as a key use-case of 6 G communication systems. The dual functionality of ISAC introduces prohibitive energy and spectrum costs, which are further exacerbated by the increasing scale of modern networks. This escalating demand highlights the urgent need for green network architectures that minimize power consumption and improve energy efficiency. Backscatter technology offers a promising avenue for alleviating resource constraints, as it utilizes existing radio-frequency signals, enhancing both spectrum and energy efficiency. However, the interference between the sensing and communication signals can further increase resource consumption. Hence, green communication, with a directed focus on power minimisation and energy efficiency, is essential for next-generation systems. In this paper, we present a green, quality-of-service (QoS) -aware transceiver design for integrated sensing and backscatter communication (ISABC) systems. The sensing and communication QoS constraints ensure the functional operation of target sensing and backscatter communication. We jointly optimize the radar signal, precoding vector, and receive combiners to minimize power consumption at the dual-function base station while meeting both sensing and communication requirements. The proposed green ISABC design demonstrates a 12 dB performance improvement compared to equivalent radar sensing and uplink communication systems.

Ensuring the integrity and safety of perishable goods within cold storage facilities has become a paramount concern for industries ranging from pharmaceuticals to food production. Meanwhile, as Integrated Sensing and Communication (ISaC) capable WiFi communication is on the horizon, We examine the feasibility of wireless sensing for cold storage monitoring. To this end, we aim to leverage the ubiquitous WiFi signals from commercial Internet-of-Things (IoT) devices, combined with lightweight Artificial Intelligence (AI), to monitor cold storage temperature without using dedicated IoT temperature sensors. Specifically, we build a sensing framework implemented on WiFi-enabled IoT hardware devices. The proposed framework uses the channel state information (CSI) of WiFi signals to monitor temperature variations within cold storage facilities by adopting a classification-based machine learning approach. Our experimental results empirically confirm the correlation between CSI and ambient temperature and evaluate the framework's performance at different locations within cold storage. Furthermore, the classification models have been successfully employed to accurately categorise temperature ranges with an accuracy of 92%, demonstrating the feasibility of wireless sensing techniques in monitoring cold storage facilities.

The integration of sensing capabilities into emerging wireless standards, such as 802.11 bf, presents an increasing threat to public privacy. Recent studies have demonstrated that even minor activities, such as finger movements on a keyboard, can be detected by exploiting Wi-Fi Channel State Information (CSI). To mitigate the privacy risks associated with Wi-Fi sensing, prior research has explored methods to disrupt the CSI measurement process by injecting interference into the wireless channel. However, current techniques often inundate the channel with excessive interference, resulting in a significant degradation of the wireless communication link. This paper proposes a spectrally efficient approach by investigating the sensitivity of Wi-Fi sensing to the CSI measurement rate, and designing interference that reduces sensing accuracy while preserving the integrity of the communication link. First, we quantify the accuracy of Wi-Fi-based keystroke recognition in relation to the CSI data rate. We then present theoretical justifications for the use of interference and demonstrate its impact on CSI data rates. Then, a method of adversarial interference is applied, reducing Wi-Fi sensing accuracy by 70 % in a keystroke detection scenario. Finally, a comprehensive trade-off study is conducted to demonstrate how interference can be optimized to protect privacy with savings of up to 21 % in bandwidth, ensuring minimal degradation of service quality.

Accurate occupancy counting is essential for effective space management, safety protocols, and resource optimisation in various environments. In recent years, the rapid development of the Internet of Things (IoT) has advanced traditional WiFi sensing into a new domain of research known as Integrated Sensing and Communication (ISAC). Such technology enables simultaneous communication and sensing, allowing for efficient data transmission while gathering environmental information. In this paper, we conduct a novel experimental demonstration and robustness validation of ISAC for occupancy counting. Compared to traditional WiFi sensing technology, we first demonstrated that human presence can be detected by sensing applications using communication packets. Specifically, using our Machine Learning (ML) algorithm, we got a sensing accuracy of more than 96 %. However, we found that this accuracy decreases with increased communication rates and higher occupancy levels, as they introduce noise and interference into the WiFi channel. We also experimentally investigated edge communication, which reveals a tradeoff between occupancy monitoring and communication rate regarding the number of Transmission Control Protocol (TCP) retransmission requests for different distances. We observed that improved accuracy results in an increase in TCP retransmission requests and a reduced communication quantity. Both sensing and communication performance lie in an optimal deployment of the transmit and receive with devices. Further tests at various distances revealed novel insight that sensing performance is better at both short and long distances. Specifically, we present that sensing benefits from diverse reflections, whereas communication relies on a balance best achieved at intermediate distances.

Advanced security solutions are essential for sustainable and autonomous networking in 6G systems. Secure backscatter communications (BSC) are crucial to protecting data integrity and confidentiality against evolving threats in green communication networks. This form of wireless communication involves devices reflecting signals to the source and extends beyond existing security solutions. Physical layer security (PLS) solutions have emerged as a promising avenue for developing adaptive, robust, and lightweight security measures. We propose a unique approach involving beamforming at the multiantenna transceiver, known as the reader, to enhance data confidentiality in multi-tag monostatic backscattering systems. Our innovative framework protects against security breaches due to eavesdroppers and optimizes the sum-secrecy rate among the tags by integrating transceiver beamforming and artificial noise techniques. Given the non-convex nature of the original problem, we propose two efficient solutions: a transmitter design for a given combiner using fractional programming and a closed-form optimal receiver design for a specified precoder configuration. Comprehensive numerical analyses validate the proposed solutions and verify key analytical claims while also quantifying the significant performance gains achieved over benchmark schemes across various system parameters.

Unmanned aerial vehicles (UAVs) are now being used to efficiently collect data from passive backscattering tags and support existing terrestrial links in the Internet of Things (IoT) when these links become overloaded. However, securing UAV-aided backscatter communication (BSC) in non-terrestrial networks is challenging due to the hardware limitations of passive tags. This paper introduces a secure BSC system utilizing a UAV for radio frequency (RF) signal transmission and data collection. Batteryless tags or backscatter devices (BDs) harness these RF signals to communicate with the UAV, even under untrusted scenarios where the BDs are mutually untrusted. We enhance the uplink fair-secrecy rate of the BDs by jointly optimizing the transmit and received beamforming vectors, artificial noise (AN), and power allocation while achieving the energy harvesting requirements and UAV flight constraints. Block coordinate descent (BCD) and fractional programming (FP) algorithms are used to address the non-convexity and obtain a fast converging solution. Simulation results verify the analysis, provide valuable insights, and demonstrate the substantial performance gains of our design for UAV-aided secure BSC in improving the fair-secrecy rate. Specifically, our proposed design achieves 0.19%, 2.08%, and 69.78% higher performance compared to three benchmarks.

Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa’s low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently utilize the limited available network bandwidth in these low-data-rate networks due to excessive control overhead, acknowledgments, and redundant retransmissions. In this paper, we introduce a novel position- and energy-aware routing strategy tailored for subterranean LoRa mesh networks aimed at enhancing maximum throughput and power efficiency while also maintaining high packet delivery ratios. Our mechanism begins with a lightweight position learning phase, during which LoRa repeaters ascertain their relative positions and gather routing information. Afterwards, the network becomes fully operational with adaptive routing, leveraging standby LoRa repeaters for recovery from packet collisions and losses, and energy-aware route switching to balance battery depletion across repeaters. The simulation results on a representative subterranean network demonstrate a 185% increase in maximum throughput and a 75% reduction in energy consumption compared to a previously optimized flooding-based approach for high traffic.

With the rapid growth of Lithium-ion (Li-ion) battery usage in modern lifestyles, the increasing risk of fire hazards demands practical temperature monitoring techniques that are massively deployable. The goal of this work is to explore the use of wireless sensing in conjunction with Artificial Intelligence (AI) to enable the detection of the surface temperature of a target object. Our framework for detecting the target surface temperature based on the channel state information (CSI) of WiFi signals utilises a low-complexity classifier algorithm, specifically the linear support vector machine. With a prototype constructed with commodity embedded systems, we experimentally validated the feasibility of our proposed framework for detecting surface temperature levels with more than 93% accuracy at a resolution of 2°C. In addition, we evaluated the robustness of our embedded AI-based WiFI sensing technology. We identified that accuracy is subject to degradation with deployment conditions such as over-range, imbalanced positioning, and inadequate transmit power. Overall, with adequate accuracy, our WiFi CSI-based surface temperature detection framework provides a low-cost, massively deployable, and non-invasive solution for practical temperature surveillance applications involving heat-sensitive products.

With the accelerated global urbanization and industrialization, air pollution due to aerosol has become one of the main challenges facing society. The aim of this work is to explore the use of artificial intelligence (AI) and WiFi-based wireless sensing technologies to enable the detection of aerosols and to assess their potential application in environmental monitoring. We proposed a framework that detects the aerosol concentration levels via the channel state information (CSI) of the ambient WiFi signal with the help of low-complexity machine learning (ML) classifiers that can process CSI data on the embedded devices. We experimentally validated the feasibility of the proposed framework with a prototype system implemented with wide-deployable commodity hardware. We demonstrated empirically that the proposed framework can predict aerosol concentration levels with 99% average accuracy via edge computing on an embedded system with tailored pre-processing. We evaluated the robustness of our proposed technology with practical aerosol materials and achieved more than 97% accuracy for both graffiti paint and air refresher. Overall, our edge-AI-based wireless sensing offers a low-cost, low-complexity, non-invasive solution to practical aerosol monitoring applications, such as indoor air quality monitoring and autonomous graffiti vandalism alerting.

Despite regulatory efforts to establish reliable content-rating guidelines for mobile apps, the process of assigning content ratings in the Google Play Store remains self-regulated by the app developers. There is no straightforward method of verifying developer-assigned content ratings manually due to the overwhelming scale or automatically due to the challenging problem of interpreting textual and visual data and correlating them with content ratings. We propose and evaluate a vision-language approach to predict the content ratings of mobile game applications and detect content rating violations, using a dataset of metadata of popular Android games.Our method achieves ∼6% better relative accuracy compared to the state-of-the-art CLIP-fine-tuned model in a multi-modal setting. Applying our classifier in the wild, we detected more than 70 possible cases of content rating violations, including nine instances with the ‘Teacher Approved’ badge. Additionally, our findings indicate that 34.5% of the apps identified by our classifier as violating content ratings were later removed from the Play Store. In contrast, the removal rate for correctly classified apps was only 27%. This discrepancy highlights the practical effectiveness of our classifier in identifying apps likely to be removed based on user complaints.

The rapid surge in network sizes, driven by the proliferation of wireless applications, has placed unprecedented demands on wireless systems leading to high interference, spectrum bottlenecks, and increased power utilization. Reconfigurable intelligent surfaces (RISs) have emerged as a promising candidate for these challenges owing to their passive beamforming action, but have limited gains due to independently functioning elements. Consequently, beyond-diagonal RISs (BDRISs) are proposed as the key-enabler of next-generation systems. In this paper, we propose a fairness-aware design for BDRIS-assisted fluid antenna systems. We jointly optimize the antenna position vector, beamforming vectors, and BDRIS configuration matrix to address service fairness while meeting the power budget in a multi-user downlink system. To tackle this non-convex problem, we utilize proximal policy optimization and obtain a fast-converging solution. Our results demonstrate a 2.8 bps/Hz mean rate improvement as compared to conventional RIS systems.

Backscatter communication (BackCom) is a promising technology that enables ultra-low-power wireless communication by reflecting RF signals. We propose novel Binary polarization Shift Keying (BPolSK) and Differential polarization Shift Keying (DPolSK) in Bistatic BackCom. Here, the backscatter tag modulates the information by changing the polarization state of the incident RF carrier. We derive the closed-form expression for Bit Error Rate (BER) and analyze the performance of BPolSK and DPolSK. Our results compare the performance of BPolSK and DPolSK and verify that they can achieve low BER, demonstrating their reliability for BackCom applications.

Backscatter tag-to-tag networks offer a sustainable and energy-efficient solution for large-scale Internet-of-Things (IoT) applications. In this paper, we propose a novel backscatter tag-to-multiple-tag network protocol based on a dual-phase system, partitioning the operational time into energy-harvesting and backscatter-communication phases. By utilising a multi-antenna reader and jointly optimising the beamforming vectors for the dual-phase problem, our design maximises the sum-throughput and substantially enhances overall system performance. To solve the non-convex transceiver optimisation problem, we derive closed-form solutions for the first phase and employ fractional programming with semidefinite relaxation for the second phase. Simulation results validate the effectiveness of the proposed solution and provide valuable insights for practical deployment, achieving an enhancement of around 3 dB over the benchmarks.

As mobile networks increasingly support sustainable and green Internet of Things (IoT) applications, energy-efficient solutions that address coverage constraints have become paramount. Although backscatter communication (BSC) offers a low-power option for IoT devices, it can suffer from limited coverage. To overcome this, we leverage unmanned aerial vehicles (UAVs) and non-orthogonal multiple access (NOMA) to enhance both coverage and spectral efficiency. Motivated by vehicular communication applications, this paper investigates a NOMA-enabled UAV-assisted BSC framework to maximise system throughput by jointly optimising power allocation and trajectory scheduling. We derive a closed-form solution for the UAV's optimal collection location and apply the Karush-Kuhn-Tucker (KKT) conditions to obtain the power allocation. The numerical and simulation results demonstrate sum-throughput improvements of 620.278% and 7.795% compared to two benchmark schemes, underscoring the potential of our approach for large-scale IoT deployments.

Recent cyberattacks have increasingly targeted distributed networking environments like IoT networks. To detect these attacks, hidden under network traffic encryption, many centralized Machine Learning (ML) based solutions have been introduced, which are not well suited for IoT networks. This work proposes PIFL a practical approach to secure IoT networks by combining federated learning, in-network ML using P4-enabled devices, software-defined networks, and binarized neural networks. PIFL detects compromised edge devices and isolates them into separate network slices based on trust parameters derived from their behavior. We demonstrate the feasibility of PIFL using an experimental testbed with three intelligent network devices and seven IoT devices implemented on Raspberry Pi devices.

Abstract.Traffic infrastructure safety is vital to society, and critical traffic corridor breakdowns could halt cities. Particularly, vehicular tunnels are notable due to the constrained space and challenges to emergency response. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of Internet-of-Things (IoT) enabled WiFi sensing technology for environmental safety monitoring using versatile, low-cost embedded devices. As a proof-of-concept, we deployed our WiFi sensing devices in an urban underwater tunnel to monitor the tunnel temperature and fire accidents. Specifically, we set up one long-term deployment for temperature monitoring and conducted two fire detection experiments in the tunnel using a small-scaled bonfire and an actual vehicle fire. Our experiments show that the proposed WiFi sensing system could accurately monitor the ambient tunnel temperature to an error of≤0.4∘C during the long-term deployment. In the smallscaled bonfire experiment, we developed a novel empirical model to characterise fire intensity using WiFi signals. This proposed linear regression model has a coefficient of determination of 0.76. Finally, we validate the potential of our wireless environment vision system for detecting fire events from ambient environments using a low-complexity machine learning classifier enabling in situ processing locally on IoT devices.

The rise of Wi-Fi sensing applications leveraging Channel State Information (CSI) from ambient wireless signals has opened up extensive opportunities for human activity and identity recognition. However, this advancement raises serious privacy concerns, as sensitive personal data can be inferred by applying advanced Machine Learning (ML) algorithms to CSI data. In response, researchers have explored adversarial techniques to degrade Wi-Fi sensing accuracy and protect privacy, often by interfering with or corrupting CSI. This paper introduces Wi-Spoof, a novel approach for spoofing CSI to deceive Wi-Fi-based Human Activity Recognition (HAR) systems. Wi-Spoof manipulates Wi-Fi transmission power to inject noise into the CSI and employs a pseudo-Pulse Width Modulation (PWM) scheme to generate controlled, adversarial CSI. Using commercially available hardware, we experimentally demonstrate that Wi-Spoof can achieve targeted misclassification in a state-of-the-art HAR system with a 93% success rate. Our approach is validated on a widely recognised public dataset and further supported by extensive local experiments, underscoring Wi-Spoof’s effectiveness in steering HAR predictions to specified outcomes.

This paper studies backscatter communication (BackCom) systems with a passive backscatter tag. The effectiveness of these tags is limited by the amount of energy they can harness from incident radio signals, which are used to backscatter information through the modulation of reflections. To address this limitation, we adopt a practical Constant-Linear-Constant (CLC) energy harvesting model that accounts for the harvester’s sensitivity and saturation threshold, both of which depend on the input power. This paper aims to maximize this harvested power at a passive tag by optimally designing the underlying M-ary amplitude-shift keying (ASK) modulator in a monostatic BackCom system. Specifically, we derive the closed-form expression for the global optimal reflection coefficients that maximize the tag’s harvested power while satisfying the minimum symbol error rate (SER) requirement, tag sensitivity, and reader sensitivity constraints. We also proposed optimal binary-ASK modulation design to gain novel design insights on practical BackCom systems with readers having superior sensitivity. We have validated these nontrivial analytical claims via extensive simulations. The numerical results provide insight into the impact of the transmit symbol probability, tag sensitivity constraint, and SER on the maximum average harvested power. Remarkably, our design achieves an overall gain of around 13% over the benchmark, signifying its utility in improving the efficiency of BackCom systems. Moreover, our proposed solution methodology for determining the maximum average harvested power is applicable to any type of energy harvesting model that exhibits a monotonic increasing relationship with the input power.

App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements and issue resolution, some apps deviate from this norm by undergoing significant transformations in their use cases or market positioning. We define this previously unstudied phenomenon as ‘app metamorphosis'. In this paper, we propose a novel and efficient multi-modal search methodology to identify apps undergoing metamorphosis and apply it to analyse two snapshots of the Google Play Store taken five years apart. Our methodology uncovers various metamorphosis scenarios, including re-births, re-branding, re-purposing, and others, enabling comprehensive characterisation. Although these transformations may register as successful for app developers based on our defined success score metric (e.g., re-branded apps performing approximately 11.3% better than an average top app), we shed light on the concealed security and privacy risks that lurk within, potentially impacting even tech-savvy end-users.

Backscatter communication (BackCom) systems play a crucial role in low-cost and low-data-rate Internet of Things (IoT) applications. However, existing research predominantly focuses on idealized scenarios using minimum scattering antennas, resulting in performance discrepancies between simulations and practical implementations. To address this limitation, we investigate the impact of the antenna-dependent parameter associated with structural mode scattering on the amplitude and phase of the backscattered signal.We reveal that the conventional assumption of a minimum scattering antenna negatively affects BackCom system performance. To overcome this challenge, we propose an innovative signal subtraction technique (SST) that effectively mitigates the issues arising from this assumption. Our proposedSSTnot only preserves the minimum scattering antenna assumption but also enhances BackCom systems. Specifically, it improves the signal-to-noise ratio (SNR) in amplitude-shift keying (ASK)-modulated BackCom systems and introduces a trade-off between SNR and bit error rate (BER) in phase-shift keying (PSK)-modulated BackCom systems. Our extensive simulations highlight the practical application ofSST, demonstrating zero performance degradation when applying optimized designs tailored for minimum scattering antennas to any tag antenna. These findings underscore the pivotal role ofSSTin optimizing ASK- and PSK-modulated BackCom systems and offer valuable insights for enhancing overall system performance.

Road tunnel safety is vital to society, and break-downs due to fire accidents could halt cities. Existing tunnel monitoring solutions, such as cameras and thermal sensors, are prone to false positives and usually expensive. To address these problems, we investigate the efficacy of our WiFi sensing technology for environmental safety monitoring in a versatile and low-cost manner using commercial WiFi-enabled IoT devices. As a proof-of-concept, we conducted both a small-scale bonfire experiment and a real vehicle fire detection experiment in a traffic tunnel using our proposed WiFi environment vision system. As a result, we verified that WiFi sensing can be used to monitor fire accidents from the bonfire experiment, and developed a novel empirical model to characterise car fire intensity using WiFi signals with a coefficient of determination of 0.319. Additionally, we also demonstrated the potential for detecting fire outbreaks using a low-complexity machine learning-based classifier, capable of identifying the occurrence of fire accidents on the edge.

Thermal source localization is crucial in detecting fires and other anomalies, allowing emergency responders to quickly locate the source of the hazard to minimize damage. With recent advancements in WiFi sensing technology for thermal detection and the emergence of Integrated Sensing and Communication (ISAC) on WiFi systems, we are interested in exploring the potential of using WiFi sensing technology for thermal source localization using commercially available Internet of Things (IoT) WiFi hardware. Our study investigates the potential of using WiFi Channel State Information (CSI) to locate thermal sources. Using the cost-effective and power-efficient ESP32 microcontroller, we propose a WiFi sensing-based system to predict the location of a heat source. We demonstrate that the WiFi CSI signatures can be used to identify localized temperature changes in a timely manner. Our key contribution is effectively filtering out undesired components from the CSI and using smart subcarrier selection to form the feature set for the machine learning algorithm. We present a Support Vector Machine (SVM)–based classifier that achieves up to 99% accuracy in predicting the relevant heat source location. We also identify the optimal WiFi sensing device configuration, demonstrate our technology’s fast response time, and shed insights on the impact of heat source distance on localization performance. Our findings offer promising solutions for low-cost, environmentally friendly monitoring systems.

Backscatter communication (BackCom) is a wireless technology that is highly cost-effective and power-efficient. It uses passive tags to reflect incoming radio frequency (RF) signals to transmit data instead of actively generating their own RF signals. Our study aims to improve the performance of the BackCom system by using Quadrature Amplitude Modulation (QAM), which can make it more useful in various applications. Our primary goal is to increase the distance between the emitter and the tag, which is essential in determining the practicality of BackCom systems. We achieve this by optimizing the reflection coefficients while considering quality of service (QoS) requirements such as tag sensitivity, receiver sensitivity, and the occurrence of symbol error rate. We apply a successive convex approximation method to transform the non-convex problem of maximizing the range into a convex one, enabling us to find the optimal solution using a proposed low-complexity maximization algorithm. The simulation results verify the key analytical claims and provide novel insights into the maximum emitter-to-tag distance for different BackCom applications and tag design specifications, including the near-optimal QAM constellation design.

In traditional Internet-of-Things (IoT) networks, devices generate their own signals to transmit data, which consumes more power. However, monostatic backscatter communications (BSC) can perform modulation and signal processing using an external signal from a reader, rather than generating signals from the device itself. Radio Frequency Identification (RFID) systems operate on this monostatic backscattering technology. Despite its benefits, BSC is vulnerable to exploitation by cyber attackers, primarily due to the limited hardware capabilities of passive RFID tags. Jamming attacks can disrupt legitimate BSC systems, enabling illegal activities or allowing competitors to gain advantages. This novel empirical investigation proposes two methods, power control and location control, to enable a typical RFID system to read data even in the presence of a powerful jamming attack. The study analyzed the relationships among reader power, the reader-to-tag (R2T) distance, the jammer-to-tag (A2T) distance, and power gain. The performance was analytically characterized and evaluated through computer simulations and hardware experiments. To the best of our knowledge, this is the first work to empirically quantify the impact of jamming attacks on RFID read rates at the reader and to assess the efficacy of physical layer security techniques in mitigating their impact. Lastly, experimental validation utilizing commodity hardware provides novel insights into optimal power and topology control for securing passive tags in sustainable IoT environments against jamming attacks.

The modern world is characterized by the Internet of Things (IoT), which requires efficient and eco-friendly energy solutions like backscattering communication. However, wireless communication vulnerabilities and high sensitivity expose backscattering systems to risks, particularly from proactive eavesdropping attacks due to the hardware limitations of the passive tags. This empirical study explores the effects of such attacks on radio frequency identification (RFID) backscattering communication in practical settings using commodity hardware. Specifically, we investigate the two-fold impact of proactive eavesdropping attacks, which decrease legitimate read rates due to jamming and data leakage caused by eavesdropping activities. The novel experimental demonstration proposes two defence mechanisms, power and location control, to protect RFID backscattering against proactive eavesdropping. Our nontrivial findings reveal that the impact of jamming is more challenging to eliminate than eavesdropping in the case of RFID backscattering. Moreover, location control is more effective in reducing data leakage than power control. This innovative research extends its analysis from single-tag scenarios to multiple-tag scenarios by using off-the-shelf hardware, which supports our proposed backscattering framework. Overall, this paper provides substantive insights that contribute to advancing green, energy-efficient security solutions for IoT communication networks.

Backscatter Communication (BackCom) is gaining popularity due to its potential for sustainable and low-cost Internet of Things (IoT) applications. However, due to the limited resources of passive tags, optimizing the backscatter modulation is critical for the widespread use of this technology. Current backscatter modulation designs ignore the impact of the tag’s antenna structure, which we show in this paper to have a negative effect on system performance and lead to design discrepancies. We investigate the impact of the antenna structure parameter on the backscattered signal characteristics. Then, we propose a novel signal subtraction technique that effectively calibrates the received signal based on the tag’s antenna structure to enable accurate detection. Our simulation results demonstrate that different values of this critical parameter result in different backscattered signals, which influence the signal decoding efficiency at the receiver. Furthermore, our work provides insights for optimized system design and enhanced BackCom performance.

Collecting reliable data over extended areas in rural environments for surveillance purposes requires low-cost and effective technologies. This paper proposes a backscattering data collection system that uses unmanned aerial vehicles (UAVs) to overcome wireless coverage challenges in rural areas. The proposed system provides physical-layer security during autonomous data collection, and we optimize the UAV’s trajectory to manage data leakage while taking into account the limited battery of the UAV. Specifically, we aim to maximize the ratio of secrecy across all tags to the UAV’s power consumption while considering constraints such as the UAV’s maximum speed, secrecy rate fairness among the tags and energy budget of the UAV. Since the problem is non-convex, we apply convex transformation by relaxing certain constraints to obtain a locally optimal trajectory with low complexity. We evaluate the performance of our proposed optimization scheme by comparing it with relevant benchmarks and quantify its complexity through simulations.

The Internet-of-things (IoT) for environmental sensing has attracted substantial interest and has great potential in many applications for both industry and lifestyle. Our research aims to sense the wetness of textiles via wireless transmission, which could provide a non-invasive alternative to the existing wearable sensing techniques. We proposed a sensing framework that uses the channel state information (CSI) signature of ambient WiFi signals to reflect the wetness of the targeted textile with a classification-based machine learning approach. As a proof of concept, we experimentally implemented the proposed frame-work with commodity hardware to affirm the wide deployability. Our embedded system-based design empirically demonstrated the proposed WiFi textile wetness sensing was feasible and could predict the wetness levels of Oml to 20ml with a resolution of 5ml for the target textile with more than 99% accuracy for indoor environments. We tested the robustness and scalability of our wireless wetness sensing technology via this real-world implementation and achieved over 90% accuracy with up to a 6m working range in an open outdoor environment. Overall, our prototype implementation provides an affordable, non-invasive, and device-free solution that is perfect for many household healthcare applications, such as monitoring sweat conditions and detecting body fluid leakage for infants and elderly care.

As our cities become more intelligent and more connected with new technologies like 6G, improving communication between vehicles and infrastructure is essential while reducing energy consumption. This study proposes a secure framework for vehicle-to-infrastructure (V2I) backscattering near an eaves-dropping vehicle to maximize the sum secrecy rate of V2I backscatter communication over multiple coherence slots. This sustainable framework aims to jointly optimize the reflection coefficients at the backscattering vehicle, carrier emitter power, and artificial noise at the infrastructure, along with the target vehicle's linear trajectory in the presence of an eavesdropping vehicle in the parallel lane. To achieve this optimization, we separated the problem into three parts: backscattering coefficient, power allocation, and trajectory design problems. We respectively adopted parallel computing, fractional programming, and finding all the candidates for the global optimal solution to obtain the global optimal solution for these three problems. Our simulations verified the fast convergence of our alternating optimization algorithm and showed that our proposed secure V2I backscattering outperforms the existing benchmark by over 4.7 times in terms of secrecy rate for 50 slots. Overall, this fundamental research on V2I backscattering provided insights to improve vehicular communication's connectivity, efficiency, and security.

WiFi has emerged as the standard method for local connectivity across various devices, including smart assistants, IoT devices, smart TVs, and AR/VR devices. Identifying WiFi devices in neighborhoods has implications for law enforcement, urban planning, and socio-economic analysis. This paper introduces a novel approach to constructing WiFi device-type signatures using Information Element attributes from wildcard WiFi probe requests. Our method accurately identifies device types even when dealing with randomized MAC addresses and requires minimal training data, thus addressing limitations of existing machine learning and deep learning approaches. We evaluate our approach using a dataset of 51,726 probe requests across 50 device types, achieving an average F1 score of 99%, precision of 99%, and recall of 98% in device-type identification. Importantly, our method outperforms deep learning methods with significantly less training data, achieving a 92% F1 score with only one training sample per device type.

Although LoRa (Long Range) wireless communication technology is most commonly used in the form of LoRaWAN, a single-hop Low Power Wide Area Network (LPWAN) protocol, recent advancements have seen the emergence of multi-hop and mesh LoRa networks. Among these, research on linear LoRa mesh networks for subterranean environments, where traditional LoRaWAN networks face challenges due to limited telecommunication infrastructure, insufficient range, and environmental constraints, is notable. In a linear LoRa mesh network, LoRa repeaters are arranged in a line to provide extended coverage in lengthy environments such as underground mines, pipelines, and tunnels. In this paper, we present a comprehensive analysis of the scalability of these networks, based on a series of simulation experiments conducted using LoRaMeshSim, the first-ever LoRa mesh network simulator, developed by us. We propose optimal selections for LoRa configurations and network properties, including the optimal repeater density, the optimal waiting period in our presented repeater algorithm featuring carrier sensing, and a strategic method of utilizing two frequency channels to enhance network performance. Employing our proposed optimizations, we demonstrate how linear LoRa mesh networks can effectively scale up to support numerous repeaters, end devices, and high-traffic loads.

Wireless technology has been used to locate and track people in real time using active sensors attached to the subject. Camera systems can be contact-free, but they are also invasive in terms of privacy. Backscattering and deep learning algorithms have enabled low-cost sustainable tracking, but this also needs to be worn by the subject. We present a novel solution that utilises backscattering from radio frequency identification (RFID) tags placed in the environment, not on the person, to detect the underlying location. These battery-less, sub-dollar energy harvesting tags are sustainable and enable passive contactfree localisation, acting as multiple sensors. To come up with a timely, real-time green, noninvasive wireless localisation system, we use a pre-trained linear machine learning algorithm to predict a person’s location based on a Received Signal Strength Indicator (RSSI) measured from an array of tags placed in the target environment. Our experiments demonstrate that we can accurately predict an individual’s location with high precision on commodity hardware, achieving a resolution of $0.4 \times 0.4 \mathbf{m}^{2}$ in a $6 \mathbf{m}^{2}$ area, with an accuracy of 81.26% in real-time.

In response to growing privacy concerns, many websites have implemented privacy controls that aim to enhance user autonomy and compliance with data protection regulations. While existing literature has evaluated individual privacy controls such as cookie consent interfaces, less attention has been given to how combining multiple privacy controls in realistic online environments affects the overall user experience. We conducted an online user study with 75 participants to explore the usability of privacy controls offered by websites across four widely used categories. Participants were asked to interact with website prototypes that differed in terms of where and how the privacy control variants were presented, then answer a survey about their experience. Our findings revealed the usability impact of design parameters on privacy controls and highlighted how user expectations vary across different demographics and website categories. We provide design recommendations that combine informative elements (privacy notices and policies) with actionable elements (privacy nudges and settings) to enhance the usability of website privacy controls for users.

As the Internet of Things (IoT) becomes more integrated with everyday life, Vehicle-to-Vehicle (V2V) communication is becoming increasingly crucial for intelligent transportation systems (ITSs). However, ensuring secure V2V communication is crucial to fully realize the potential and usefulness of IoT-enabled ITS. Therefore, this article proposes a novel joint optimization framework for securing orthogonal frequency division multiple access (OFDMA)-based V2V communications in untrusted vehicle platooning networks. To address this timely nonconvex optimization problem in cooperative vehicular IoT systems, we divide it into two subproblems: 1) power control and 2) subcarrier allocation. For each subproblem, we propose dual solution strategies, prioritizing low-computational cost and emphasizing high accuracy, albeit at a higher computational expense. The low-complexity approach for subcarrier allocation utilizes channel power gains within platoons. On the other hand, the high-accuracy strategy involves a branch-and-bound (BNB) algorithm to obtain the near-global optimal solution for this nondeterministic polynomial-time (NP)-hard problem. Similarly, we introduce a low-complexity strategy based on a high-signal-to-interference-plus-noise ratio (SINR) transformation, enabling closed-form solutions through fractional programming (FP) for optimal power control, which is ideal for automated vehicles. The alternative approach adopts a direct FP transformation for precise power distribution, attaining the near-global optimum numerically but with increased computational demands. Numerical simulations are conducted to validate our theoretical assertions and to offer nontrivial vital design insights. Our policy of jointly allocating BNB subcarriers and directly controlling FP power significantly increases the sum secrecy rate by more than 58% compared to conventional schemes in typical intelligent vehicle settings for IoT environments.

Deep learning (DL) has become a powerful tool in many areas of research and industry, ranging from computer vision to natural language processing. Nonetheless, as DL models are trained on large amounts of sensitive data, concerns about data privacy have emerged. In light of this, differential privacy (DP) has emerged as a promising technique that provides strong privacy guarantees while allowing useful information to be extracted from the data. DP involves adding random noise to the training data or model parameters, which makes it difficult for an attacker to identify the contribution of any single data point to the final model. Despite the promising results, DP can significantly degrade the performance of DL models, especially when dealing with large datasets or complex models. To improve the balance between privacy and utility, this paper proposes a novel modification to the vanilla DP algorithm that uses a Haar wavelet transform. The proposed method achieves better utility while maintaining the same ($\varepsilon, \delta$) privacy guarantees as vanilla DP algorithms. The paper provides an analytical demonstration of the improved noise variance bounds compared to previous methods. The paper also provides a detailed analysis of the convergence performance of the proposed algorithm and shows that the Haar wavelet transform improves the accuracy and efficiency of the training process. The experimental evaluation demonstrates that the proposed method outperforms state-of-the-art algorithms on four widely used scientific benchmark datasets making this a significant contribution to DP techniques’ practical applications in DL.

Backscatter communication (BackCom) has been gaining a lot of interest as a low-energy consumption energy harvesting solution. Here, the limited transmission range of BackCom systems constraint can now be resolved by using mobile data collectors or readers such as unmanned aerial vehicles (UAVs). In this study, we investigate a monostatic UAV-assisted BackCom system where backscatter devices (BDs) are served in a time-division multiple access (TDMA) fashion. Here we solve an energy efficiency (EE) maximization problem by jointly optimizing the transmit power allocation and the UAV's trajectory while adhering to the quality of service constraints. Since the problem is non-convex and combinatorial in nature, we employ a reinforcement learning framework that utilizes a finite-state Markov decision process. We introduce a low-complexity Ex-pected State-Action-Reward-State-Action (ESARSA) algorithm to determine the UAV's optimal trajectory with power allocation. A closed-form solution is proposed for global power optimization. In simulations, we compare the implemented ESARSA against the State-Action-Reward-State-Action (SARSA) and Q-learning algorithms and show that the proposed ESARSA algorithm can provide a 24% gain over the fixed allocation benchmark.

With the increasing popularity of WiFi in recent years, WiFi-based wireless sensing technologies have attracted tremendous research. As commercial WiFi networks transition from the 2.4GHz band into the 5GHz band, no prior work has explicitly investigated the impact that this choice of frequency has on sensing outcomes. On both frequency bands, this paper uses the frequency selective behaviour of CSI and a support vector machine classifier to verify the accuracy of human surveillance applications. These experiments demonstrate that 5GHz WiFi offers superior sensing outcomes, with a 6% increase in human occupancy counting accuracy. By restricting the domain of Orthogonal Frequency Division Multiplexing (OFDM) subcarriers, we perform further experiments to conclude that the increase in accuracy is primarily a result of having the higher number of subcarriers or the larger bandwidth for 5GHz WiFi. To corroborate the robustness of our sensing system, we conduct the experiments over multiple trials for two very different environments; one being a controlled elevator setup, while the other being a more realistic and uncontrolled, office space. This novel experimental investigation motivates the need for conducting more comparison studies across the electromagnetic spectrum to identify the application specific best frequency spectrum for the next generation of wireless sensing technologies.

This paper proposes a novel cooperative transmission protocol for a three-user backscatter communication (BackCom) in the Internet of Things. Specifically, the optimal time-division multiple access based transmission protocols for the 2-hop and 3-hop cooperative schemes in the BackCom system have been developed to maximize the underlying system throughput. In the investigated monostatic BackCom system, a reader simultaneously transmits the radio frequency signal to the backscatter tags in the downlink and receives the backscattered signals in the uplink. In the 2-hop cooperative scheme, either one of the near-apart tags serves as a cooperative decode-and-forward agent that relays the far-apart tag’s information to the reader. On the other hand, the farthest tag collaborates with the two near-apart tags to relay its information to the reader in the 3-hop cooperative scheme. Since we aim to maximize the system throughput of the cooperative BackCom system, we formulate the maximization problem of minimum throughput among the tags in both 2-hop and 3-hop cooperative schemes. Then, we investigate the optimal time allocation for maximizing the minimum system throughput for both 2-hop and 3-hop cooperative schemes. Subsequently, we introduce auxiliary variables to the original problems and convert them into equivalent linear programming problems. The numerical results verify the utility of the cooperative schemes in different transmission channels and tags’ placement. In our investigation, the proposed 3-hop cooperative scheme obtained an average gain above 30% over the non-cooperative scheme.

The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme for a blockchain-based MEC system where each edge device not only handles data tasks but also deals with block mining for improving the system utility. To address the latency issues caused by the blockchain operation in MEC, we develop a new Proof-of-Reputation consensus mechanism based on a lightweight block verification strategy. A multi-objective function is then formulated to maximize the system utility of the blockchain-based MEC system, by jointly optimizing offloading decision, channel selection, transmit power allocation, and computational resource allocation. We propose a novel distributed deep reinforcement learning-based approach by using a multi-agent deep deterministic policy gradient algorithm. We then develop a game-theoretic solution to model the offloading and mining competition among edge devices as a potential game, and prove the existence of a pure Nash equilibrium. Simulation results demonstrate the significant system utility improvements of our proposed scheme over baseline approaches.

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.

WiFi sensing has gained tremendous traction due to its inherent advantages in terms of privacy and ubiquity. Recent work has shown the ability to sense physical environments, such as counting the number of human occupants. These results have traditionally been achieved using statistical features on WiFi Channel State Information (CSI) amplitude, however more recently there has been interest in exploiting Image based Machine Learning (ML) techniques to achieve better outcomes. In this work, we produce Probability Mass Function (PMF) Images on WiFi CSI, to create spectral maps which clearly distinguish between different human occupancies. We validate our PMF images with common default CNN architectures such as GoogleNet, ResNet and ShuffleNet. By changing the filter size and training parameters, we improve the performance of ShuffleNet from 84% to 98%. Furthermore, we demonstrate how the PMF images can be optimised for sensing outcomes, by controlling the image resolution.

With the emergence of next generation networks, Network Traffic Classification (NTC) has seen greater importance in network management and security. Recently, Channel State Information (CSI) based WiFi sensing techniques have shown their potential for NTC applications [1], [2] as a privacy-preserving yet effective tool. As CSI could be prone to interference, this paper examines the performance of CSI-based NTC models under interference-induced adversarial attacks. Specifically, the impact of spectral allocation of the interference, underlying interfering network traffic type, and physical location of the interference are studied and quantified. We conducted experiments using off-the-shelf devices to test the NTC performance, with and without the adversarial interference attack of ping, buffered video streaming, and live video streaming network traffics. Subsequently, we found that spectral allocation of the attacking interference and the underlying traffic types of interference could be used to deceive the established NTC model, and different network traffic types show different robustness across the interference cases. Namely, ping suffers the most in the spectrally manipulated attack, with the classification accuracy down to as low as 23.2%, whereas Twitch might be completely misidentified as other traffic in underlying traffic type controlled attack.

With the Internet of Things becoming an essential part of our lives, Vehicle-to-vehicle (V2V) communication is becoming very popular to meet the growing demands of having an Intelligent Transportation System (ITS). However, ensuring secure V2V communication is the key to realizing the full potential and utility of ITS. Therefore, this paper develops a secure framework for orthogonal frequency division multiple access (OFDMA) based V2V communication among the untrusted platoons. Specifically, we propose optimal subcarrier and power allocation policies to maximize the sum secrecy rate across the vehicles which share the subcarriers with the vehicles outside their platoons. We start with allocating sub-carriers to the vehicles inside each platoon based on their underlying channel power gains. After that, noting the non-convexity of the power allocation problem, we propose a high signal-to-interference-plus-noise ratio-based transformation to develop two optimal power control policies. While both are based on fractional programming (FP), one yield closed-form power allocation and the other provides the optimal global solution numerically at a higher computational complexity. Lastly, the numerical simulations verify the analytical claims, provide key design insights and demonstrate that our proposed power control policies can improve the sum secrecy rate of benchmark schemes by over 4dB for the eight vehicles setting.

Tag-to-tag backscattering is emerging as a promising technology to realise cooperative symbiotic radio communications. This paper aims to maximise the system throughput and backscattering range for multiple and single antenna readers. Specifically, we consider optimal transceiver designs for the multiantenna reader and inter-node location between the tags to maximise the sum of tag-to-tag (T2T) and monostatic backscattering throughput. Whereas, for the single-antenna scenario, we obtain the quality-of-service-aware maximum backscattering range that satisfies the minimum throughput requirements for T2T and tag-to-reader communications. For both scenarios, we have proved the global optimality of the underlying proposed numerical solutions. Apart from numerical solutions, we also proposed low complexity semi-closed-form solutions for sum throughput maximisation in multiantenna reader case and analytical approximations for range maximisation in single antenna case. Finally, simulations validate the proposed solution’s effectiveness and provide nontrivial design insights while demonstrating around 90% improvement over the benchmark.

This paper explores a novel cooperative timing protocol in two-user backscatter communication (BSC) network, where one Reader transmits a wireless energy signal to two collaborative backscatter tags. These tags modulate the incident signal and backscatter its information to the Reader. Specifically, the tag closer to the Reader uses its resources to help relay the far tag’s information to the Reader to reduce the effect of the doubly near-far problem in BSC. We aim to maximize the transmission range of the farther tag while satisfying the Quality of Service (QoS) requirement in terms of throughput and energy threshold. First, we derive the necessary conditions for the proposed problem’s feasibility for throughput and energy constraints. Then, we solve this non-convex range maximization problem by alternatively optimizing the time allocation for maximizing minimum throughput and energy, respectively, and the transmission range. The numerical simulations have been conducted to provide insight into the impact of energy and throughput base QoS demand on the achievable transmission range. The average gain of the proposed cooperative BSC over the non-cooperative one is $\gt 20$%.

The sixth-generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) toward a future of fully intelligent and autonomous systems. In this article, we explore the emerging opportunities brought by 6G technologies in IoT networks and applications, by conducting a holistic survey on the convergence of 6G and IoT. We first shed light on some of the most fundamental 6G technologies that are expected to empower future IoT networks, including edge intelligence, reconfigurable intelligent surfaces, space–air–ground–underwater communications, Terahertz communications, massive ultrareliable and low-latency communications, and blockchain. Particularly, compared to the other related survey papers, we provide an in-depth discussion of the roles of 6G in a wide range of prospective IoT applications via five key domains, namely, healthcare IoTs, Vehicular IoTs and Autonomous Driving, Unmanned Aerial Vehicles, Satellite IoTs, and Industrial IoTs. Finally, we highlight interesting research challenges and point out potential directions to spur further research in this promising area.

COVID-19 has spread rapidly across the globe and become a deadly pandemic. Recently, many artificial intelligence-based approaches have been used for COVID-19 detection, but they often require public data sharing with cloud datacentres and thus remain privacy concerns. This paper proposes a new federated learning scheme, called FedGAN, to generate realistic COVID-19 images for facilitating privacy-enhanced COVID-19 detection with generative adversarial networks (GANs) in edge cloud computing. Particularly, we first propose a GAN where a discriminator and a generator based on convolutional neural networks (CNNs) at each edge-based medical institution alternatively are trained to mimic the real COVID-19 data distribution. Then, we propose a new federated learning solution which allows local GANs to collaborate and exchange learned parameters with a cloud server, aiming to enrich the global GAN model for generating realistic COVID-19 images without the need for sharing actual data. To enhance the privacy in federated COVID-19 data analytics, we integrate a differential privacy solution at each hospital institution. Moreover, we propose a new blockchain-based FedGAN framework for secure COVID-19 data analytics, by decentralizing the FL process with a new mining solution for low running latency. Simulations results demonstrate the superiority of our approach for COVID-19 detection over the state-of-the-art schemes.

Mobile Edge Computing (MEC) has been envisioned as an emerging paradigm to handle the overwhelming explosion of mobile applications and services, by allowing edge devices (EDs) to offload their computationally-intensive tasks to heterogeneous MEC servers. Most of the existing works focus mostly on a centralized agent or an independent multi-agent setting which cannot work well in distributed edge networks with heterogeneous computation tasks. This paper considers a more realistic setting consisting of multiple cooperative EDs and multiple MEC servers in heterogeneous edge networks (HENs). We propose a new collaborative offloading framework in a HEN where each ED acts as an intelligent agent to make offloading decisions collaboratively, aiming to achieve the optimal system utility. To this end, we formulate the collaborative offloading problem as a Markov game which is then solved by a novel multi-agent deep reinforcement learning (MADRL) approach based on a multi-agent deep deterministic policy gradient (MA-DDPG) algorithm. Numerical simulations with real-life mobile wireless datasets show that the proposed cooperative multi-agent scheme can improve the system utility by 43.6% compared to the non-cooperative offloading schemes.

WiFi Sensing has received tremendous attention in Recent Literature, demonstrating the ability to leverage ubiq-uitous commercial WiFi devices to sense Human activities and environmental occupancy. We identify that in all environments fire-safety is vital, and this paper demonstrates the suitability for using WiFi to sense fire. Using commodity Raspberry Pi devices on the 5GHz WiFi band we demonstrate a temporal shift in WiFi Channel State Information (CSI) Amplitude, before, during, and after the ignition of a flame. We further emphasise the presence of fire by observing the spread of CSI Amplitudes, noting that CSI takes much more diverse values in the presence of fire. This result is exacerbated by the frequency selective behaviour of OFDM subcarriers, where some subcarriers displayed larger variation in CSI amplitude due to the fire. The WiFi Fire sensing model was evaluated in an ideal setup with a gas flame to remove material deformation as a variable, and subsequently in a real-world scenario with the ignition of building cladding.

Extensive literature has shown the possibility of using WiFi to sense large scale environmental features such as people, movement, and human gestures. To our best knowledge, there has been no investigation on identifying the microscopic changes in a channel due to atmospheric temperature variations. We identify this as a real world use case, since there are scenarios such as Data Centres where WiFi traffic is omnipresent and temperature monitoring is important. We develop a framework for sensing temperature using WiFi Channel State Information (CSI), proposing that the increased kinetic energy of ambient gas particles will affect the wireless link. To validate this, our paper uses low wavelength 5GHz WiFi CSI from commodity hardware to measure how the channel changes as the ambient temperature is raised. Empirically, we demonstrate that the CSI amplitude value drops at a rate of 13 per degree Celsius rise in the ambient temperature based on the testing platform, and developed regressions models with ± 1°C accuracy in the majority of cases. Moreover, we have shown that WiFi subcarriers exhibit a frequency-selective behaviour in their varying responses to the rise in ambient temperature.

The combination of mobile edge computing (MEC) and blockchain is transforming the current computing services in Internet of Things networks, by offering task offloading solutions with security enhancement enabled by blockchain mining. Nevertheless, these important enabling technologies have been studied separately in most existing works. This article proposes a novel cooperative task offloading and block mining (TOBM) scheme to optimize the system utility in blockchain-empowered MEC. Herein, each edge device (ED) not only handles data tasks but also deals with block mining which makes the system design and optimization highly complex. Therefore, we develop a novel cooperative deep reinforcement learning (DRL) approach which allows EDs to cooperatively offload their data tasks to the MEC server and perform block mining based on a Proof-of-Reputation consensus mechanism. Simulation results demonstrate that the proposed scheme significantly improves offloading utility, reduces blockchain mining latency, and achieves better system utility, compared to other non-cooperative and cooperative schemes.

Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the massive volume of data generated from ubiquitous mobile devices for enabling intelligent services with the help of artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training in a single entity, e.g., an MEC server, which is now becoming a weak point due to data privacy concerns and high overhead of raw data communications. In this context, federated learning (FL) has been proposed to provide collaborative data training solutions, by coordinating multiple mobile devices to train a shared AI model without directly exposing their underlying data, which enjoys considerable privacy enhancement. To improve the security and scalability of FL implementation, blockchain as a ledger technology is attractive for realizing decentralized FL training without the need for any central server. Particularly, the integration of FL and blockchain leads to a new paradigm, called FLchain, which potentially transforms intelligent MEC networks into decentralized, secure, and privacy-enhancing systems. This article presents an overview of the fundamental concepts and explores the opportunities of FLchain in MEC networks. We identify several main issues in FLchain design, including communication cost, resource allocation, incentive mechanism, security and privacy protection. The key solutions and the lessons learned along with the outlooks are also discussed. Then, we investigate the applications of FLchain in popular MEC domains, such as edge data sharing, edge content caching and edge crowdsensing. Finally, important research challenges and future directions are also highlighted.

The healthcare industry has witnessed significant transformations in e-health services by using mobile-edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low Quality of Services (QoS), data privacy, and system security vulnerabilities. In this article, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data-sharing scheme, which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contract-based authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the Cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this work, we propose Kryptein, a compressive-sensing-based lightweight encryption scheme for Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Kryptein supports random compressed encryption, statistical computation over cipher, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices. After comparing with other state-of-the-art lightweight ciphers on IoT (Simon and Speck), IoT system with Kryptein is expected to have a much more longevity with about 35 percent extended lifetime. Further, experiments illustrated IoT data variance will not affect Kryptein's accuracy in a long term usage, and Krpytein is also able to support basic analytics tasks like machine learning (e.g., classification).

Blockchain and Mobile Edge Computing (MEC) are newly emerging technologies with great potential to revolutionize healthcare. This paper proposes a new decentralized healthcare architecture for distributed Electronic Medical Records (EMRs) sharing among federated hospitals based on blockchain and MEC. Unlike the existing schemes that often rely on a third-party for healthcare management, we focus on a fully decentralized access control solution by using smart contracts that enable EMRs access verification at the edge of the network without requiring any central authority. Moreover, a decentralized interplanetary file system (IPFS) platform is also integrated with smart contracts over the MEC network, which significantly reduces data retrieval latency and enhances security for EMRs sharing. The experimental results and analysis show the superior performance of the proposed scheme over the existing ones in terms of reduced data retrieval latency, enhanced blockchain performance, and security guarantees.

Due to the increased proliferation of WiFi in public and private spaces, there is interest in exploiting WiFi for spatial monitoring. In this paper, we aim to characterize movement or objects in a channel using Channel State Information (CSI). Channel state information represents the degree to which a wireless signal has been attenuated and delayed, and hence we hope to characterize different objects and multipath channel characteristics from CSI. We place different static objects and moving humans in a channel and inspect the CSI for each channel condition. From the variations in CSI Amplitude we can accurately distinguish between a person walking, squatting, or standing still in the channel. To identify static objects, we present a novel approach by inspecting the CSI of different Orthogonal Frequency Division Multiplexing (OFDM) subcarriers. This paper makes a novel contribution, by observing frequency selective behavior of CSI for different channel stimuli. This can be used to improve channel detection accuracy.

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.

Bloom filter encoding has widely been used as an efficient masking technique for privacy-preserving matching functions. The existing matching techniques, however, are limited to relatively simple types such as string, categorical and signal numerical values. In this paper, we propose a new scheme that significantly extends the class of matching primitives that are based on privacy-preserving Bloom filter mechanism. These primitives include sequence data matching and popular distance-based machine learning algorithms such as KNN and SVM. Our scheme hash-maps a sequence data vector into the Bloom filter space while checking the similarity of the data points efficiently with negligible utility loss by adding a timestamp (bit) for each element in the data represented with its neighboring values. Furthermore, it includes a Laplace-like perturbation method on the constructed Bloom filters to address the weakness of deterministic probability led by encoding techniques. As a result, the proposed work guarantee the private data records are difficult to be discriminated due to collisions and differential privacy. The experimental results on three real-scenario based datasets illustrate that our method can achieve a significantly better trade-off between utility and privacy than the state-of-the-art differential privacy-based method by adding Laplace noise to the data directly.

Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications. However, the process of executing extensive tasks such as computation-intensive data applications and blockchain mining requires high computational and storage capability of mobile devices, which would hinder blockchain applications in mobile systems. To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their data processing tasks and mining tasks to a nearby MEC server via wireless channels. Specially, we formulate task offloading, user privacy preservation and mining profit as a joint optimization problem which is modelled as a Markov decision process, where our objective is to minimize the long-term system offloading utility and maximize the privacy levels for all blockchain users. We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states, wireless channel qualities between MUs and MEC server and user's power hash states. To further improve the offloading performances for larger-scale blockchain scenarios, we then develop a deep RL algorithm by using deep Q-network which can efficiently solve large state space without any prior knowledge of the system dynamics. Experiment and simulation results show that the proposed RL-based offloading schemes significantly enhance user privacy, and reduce the energy consumption as well as computation latency with minimum offloading costs in comparison with the benchmark offloading schemes.

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.

Mobile vision technologies have paved the way for augmented (AR) and mixed reality (MR) applications to be realizable on mobile devices. Mobile platforms such as Android and iOS have recently demonstrated the early opportunities for AR/MR applications using their devices. Now, while these technologies can still be considered in its infancy, it is opportune to start thinking about privacy and security while their functionalities are slowly being revealed to us. In this work, we present a visual access control mechanism in the form of object-level abstraction. Using readily-available object detection algorithms, we are able to demonstrate a proof-of-concept object-level abstraction for fine-grained access control in a mobile device. Furthermore, aside from the inherent confidentiality and content awareness guarantee of abstraction, reduction in execution times from visual processing resource sharing is another consequential benefit of abstraction without any energy consumption impact.

The world is embracing the presence of connected autonomous vehicles which are expected to play a major role in the future of intelligent transport systems. Given such connectivity, vehicles in the networks are vulnerable to making incorrect decisions due to anomalous data. No sophisticated attacks are required; just a vehicle reporting anomalous speeds would be sufficient to disrupt the entire traffic flow. Detection of such anomalies is vital for a secured vehicular network. Nevertheless, the attention given for the use of physics of traffic flow to secure vehicular networks is relatively less. We propose to integrate traffic flow phenomena within anomalous data detection techniques to improve the evaluation of threats in vehicular networks. We apply traffic flow theory under steady state assumptions to identify anomalous data. The numerical results indicate the proposed method to provide reliable and consistent predictions.

Summary In this paper, we explore the problem of providing selective read/write access to the outsourced data for clients using mobile devices in an environment that supports users from multiple domains and where attributes are generated by multiple authorities. We consider Ciphertext‐Policy Attribute‐based Encryption (CP‐ABE) scheme as it can provide access control on encrypted outsourced data. One limitation of CP‐ABE is that the users can modify the access policy specified by the data owner if write operations are introduced in the scheme. We propose a protocol for providing different levels of access to outsourced data that permits the authorized users to perform write operation without altering the access policy specified by the data owner. Our scheme provides fine‐grained read/write access to the users, accompanied with a light weight signature scheme and computationally inexpensive user revocation mechanism suitable for resource‐constrained mobile devices. We provide a theoretical analysis of the security of the proposed protocol and the experimental results measured from a real‐world testbed.

Mobile data traffic is growing exponentially and it is even more challenging to distribute content efficiently while users are “on the move” such as in public transport. The use of mobile devices for accessing content (e.g., videos) while commuting are both expensive and unreliable, although it is becoming common practice worldwide. Leveraging on the spatial and temporal correlation of content popularity and users' diverse network connectivity, we propose a novel content distribution system, uStash, which guarantees better QoE with regards to access delays and cost of usage. The proposed collaborative download and content stashing schemes provide the uStash provider the flexibility to control the cost of content access via cellular networks. We model the uStash system in a probabilistic framework and thereby analytically derive the optimal portions for collaborative downloading. Then, we validate the proposed models using real-life trace driven simulations. In particular, we use dataset from 22 inter-city buses running on six different routes and from a mobile VoD service provider to show that uStash reduces the cost of monthly cellular data by approximately 50 percent and the expected delay for content access by 60 percent compared to content downloaded via users' cellular network connections.

The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile apps are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that app developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design Application Function Virtualization (AFV), an architecture enabling automated dynamic function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device, and network context, to enable the adaptive invocation of functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

Trust has played a crucial role in enhancing the security of IoT systems over their lifecycles from creation to retirement. Particularly, in a personal space IoT system where devices join and leave the system dynamically, it is important to evaluate the device's behavior in the form of trust on its admission to the system to reduce the risk and uncertainty of the overall system. Currently, proposed trust evaluation models primarily rely on the historical knowledge or trusted recommendations. However, in many situations, such information is not available at the first encounter between the system and the device. The challenge tackled by this paper is how to establish whether a device can be trusted to a level that merits further evaluation for admission into an IoT system when it encounters the system for the first time. We propose a Dirichlet-based trust assessment model to establish the initial trust that the system places on a device in a mobile and dynamic environment called personal space IoT. The proposed scheme can also be used to affirm the trust of a device during its operation or when it is being re-admitted to the system after an interruption. We describe and evaluate our proposed model theoretically and by simulation.

The smart devices usage is growing rapidly driven by innovative new smart wearables and service offerings. This has led to applications that utilize multiple devices around the body to provide immersive environments such as mixed reality that rely on a number of different types of functions and require considerable resources. Thus one of the major challenges in supporting these applications is dependent on the battery lifetime of devices that provide the necessary functionality. The focus of this paper is to improve the battery efficiency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, it is possible to consider them as a resource pool and further utilize them intelligently to improve the system lifetime via function virtualization. We formulate the intelligent function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an efficient heuristic solution. Next, we demonstrate the orchestration of the virtualized functions in order to achieve specific functionalities. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper function allocation and orchestration.

Cash-less payment via a variety of credit, debit or prepaid cards is pervasive in our interconnected society, but not so ubiquitous in remote rural regions where network connectivity is intermittent. We proposed a cash-less payment scheme for remote villages based on blockchains that allow maintaining a record of verifiable transactions in a distributed manner. We overcome the limitations of intermittent network connectivity by solely relying on blockchain mining nodes in the village for transaction processing and verification. The bank joins as a peer and monitors node behaviors, rewards miners and processes currency exchanges whenever the connectivity is available. We take advantage of the Ethereum network to develop our solution and demonstrate the feasibility of the proposed system on off-the-shelf computing devices. We emulate a remote village scenario with intermittent network connectivity and show the robustness and reliability of the proposed system.

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.

Outsourcing data to the cloud for computation and storage has been on the rise in recent years. In this paper we investigate the problem of supporting write operation on the outsourced data for clients using mobile devices. We consider the Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme as it is well suited to support access control in outsourced cloud environments. One shortcoming of CP-ABE is that users can modify the access policy specified by the data owner if write operations are incorporated in the scheme. We propose a protocol for collaborative processing of outsourced data that enables the authorized users to perform write operation without being able to alter the access policy specified by the data owner. Our scheme is accompanied with a light weight signature scheme and simple, inexpensive user revocation mechanism to make it suitable for processing on resource-constrained mobile devices. The implementation and detailed performance analysis of the scheme indicate the suitability of the proposed scheme for real mobile applications. Moreover, the security analysis demonstrates that the security properties of the system are not compromised.

Accurate estimation of crowd dynamics is difficult, especially when it comes to fine-grained spatial and temporal predictions. A deep understanding of these fine-grained dynamics is crucial during a major disaster, as it guides efficient disaster managements. However, it is particularly challenging as these fine-grained dynamics are mainly caused by high-dimensional individual movement and evacuation. Furthermore, abnormal user behavior during disasters makes the problem of accurate prediction even more acute. Traditional models have difficulties in dealing with these high dimensional patterns caused by disruptive events. For example, the 2016 Kumamoto earthquakes disrupted normal crowd dynamics patterns significantly in the affected regions. We first perform a thorough analysis of a crowd population distribution dataset during Kumamoto earthquakes collected by a major mobile network operator in Japan, which shows strong fine-grained temporal autocorrelation and spatial correlation among geographically neighboring grids. It is also demonstrated that temporal autocorrelation during disasters is more than simple diurnal patterns. Moreover, there are many factors that could potentially influence spatial correlations and affect the dynamics patterns. Then, we illustrate how a spatial-temporal Long-Short-Term-Memory (LSTM) deep neural network could be applied to boost the prediction power. It is shown that the error in terms of Mean Square Error (MSE) is reduced by as much as 55.1-69.4% compared to regressive models such as AR, ARIMA and SVR. Furthermore, LSTM outperforms the aforementioned models significantly even when little training data is available right after the mainshock. Finally, we also show a Region-aware LSTM does not necessarily outperform a regular LSTM.

Internet of Things (IoT) is flourishing and has penetrated deeply into people's daily life. With the seamless connection to the physical world, IoT provides tremendous opportunities to a wide range of applications. However, potential risks exist when the IoT system collects sensor data and uploads it to the cloud. The leakage of private data can be severe with curious database administrator or malicious hackers who compromise the cloud. In this work, we propose Kryptein, a compressive-sensing-based encryption scheme for cloud-enabled IoT systems to secure the interaction between the IoT devices and the cloud. Kryptein supports random compressed encryption, statistical decryption, and accurate raw data decryption. According to our evaluation based on two real datasets, Kryptein provides strong protection to the data. It is 250 times faster than other state-of-the-art systems and incurs 120 times less energy consumption. The performance of Kryptein is also measured on off-the-shelf IoT devices, and the result shows Kryptein can run efficiently on IoT devices.

Nowadays cloud services have become a cost-effective platform for sharing data and performing large scale computations. As outsourcing data to a third party involves security and privacy concerns, data should be sent to the cloud in encrypted form. To handle access control on encrypted cloud data, Ciphertext-Policy Attribute-based Encryption (CP-ABE) has become very popular. In this scheme data can be encrypted once and can be read multiple times by the authorized users. However, CP-ABE currently has no mechanism to protect against a user of data being able to alter the access policy originally specified by the owner of the data when s/he re-encrypts the modified data (write operation). In this work, we propose a scheme that extends CP-ABE without compromising the security to preserve data owner defined access policy when multiple write operations are performed along with a user revocation mechanism in a setting where multiple authorities generate decryption keys.

The majority of available wearable computing devices require communication with Internet servers for data analysis and storage, and rely on a paired smartphone to enable secure communication. However, many wearables are equipped with WiFi network interfaces, enabling direct communication with the Internet. Secure communication protocols could then run on these wearables themselves, yet it is not clear if they can be efficiently supported.,,,,In this paper, we show that wearables are ready for direct and secure Internet communication by means of experiments with both controlled local web servers and Internet servers. We observe that the overall energy consumption and communication delay can be reduced with direct Internet connection via WiFi from wearables compared to using smartphones as relays via Bluetooth. We also show that the additional HTTPS cost caused by TLS handshake and encryption is closely related to the number of parallel connections, and has the same relative impact on wearables and smartphones.

We propose BreathPrint, a new behavioural biometric signature based on audio features derived from an individual's commonplace breathing gestures. Specifically, BreathPrint uses the audio signatures associated with the three individual gestures: sniff, normal, and deep breathing, which are sufficiently different across individuals. Using these three breathing gestures, we develop the processing pipeline that identifies users via the microphone sensor on smartphones and wearable devices. In BreathPrint, a user performs breathing gestures while holding the device very close to their nose. Using off-the-shelf hardware, we experimentally evaluate the BreathPrint prototype with 10 users, observed over seven days. We show that users can be authenticated reliably with an accuracy of over 94% for all the three breathing gestures in intra-sessions and deep breathing gesture provides the best overall balance between true positives (successful authentication) and false positives (resiliency to directed impersonation and replay attacks). Moreover, we show that this breathing sound based biometric is also robust to some typical changes in both physiological and environmental context, and that it can be applied on multiple smartphone platforms. Early results suggest that breathing based biometrics show promise as either to be used as a secondary authentication modality in a multimodal biometric authentication system or as a user disambiguation technique for some daily lifestyle scenarios.

Attribute-based encryption schemes provide read access to data based on users' attributes. In these schemes, user privacy is compromised as the access policies are visible. This privacy issue has been addressed in literature by enabling the data owner to obfuscate the policy in a setting where a single authority generates decryption keys. However, a single authority can figure out the hidden access policy which violates user privacy. We present PPDAS, a scheme which overcomes these limitations and makes two contributions. Firstly, we present a mechanism which supports fine-grained read and write operations in a setting where decryption keys are generated by multiple attribute authorities, and the access policy is hidden from all unauthorized entities including the attribute authorities. Our scheme is also accompanied with a user revocation mechanism. Secondly, we show that it is possible to adapt the scheme for accessing data through resource-constrained devices such as smart watches and IoT devices through extensive experimental evaluations.

Leveraging the path diversity in heterogeneous wireless networks by Multi-path TCP (MPTCP) not only depends on end-user's decisions but also on other competitors who are looking for maximizing their benefits. The incomplete information repeated Bayesian game is proposed to enhance the MPTCP throughput in a resource-shared wireless network context. Mobile nodes establish the initial path in the first stage by choosing the best opening sub-flows connection. Moreover, by receiving feedbacks regarding the preference of other opponents the repeated Bayesian game in the second stage improves the achievable throughput via selecting the best combination of networks. The numerical and simulation results demonstrate that the proposed algorithm could at least achieve (17%-24%) more throughput than WiFi in the initial path selection and (11%-14%) more throughput than MPTCP.

Dynamic Adaptive Streaming over HTTP (DASH) is one of the most popular ways to stream videos at present. In this work, we propose a DASH player energy-aware plugin (eDASH) for mobile devices which help reduce the battery consumption of the device. The eDASH player utilises a novel bitrate and video brightness adaptation algorithm to determine the next chunk to download. This algorithm utilises an energy-aware QoE model which factors in power consumption of the device in conjunction with existing bitrate adaptation logic to determine the next chunk. We also propose a new DASH architecture which could be easily integrated with the existing one. Macro-benchmarking of energy consumption of a mobile device while streaming and playing back video is conducted to obtain energy profiles of various video qualities. This energy data is then used along with real world network traces to drive simulations to evaluate energy savings that could be achieved using eDASH. We observe that up to 45% energy savings could be achieved with minimal reduction is QoE. We also find that up to 80% data transfer savings could also be achieved with an eDASH client.

An ongoing challenge in the rapidly evolving app market ecosystem is to maintain the integrity of app categories. At the time of registration, app developers have to select, what they believe, is the most appropriate category for their apps. Besides the inherent ambiguity of selecting the right category, the approach leaves open the possibility of misuse and potential gaming by the registrant. Periodically, the app store will refine the list of categories available and potentially reassign the apps. However, it has been observed that the mismatch between the description of the app and the category it belongs to, continues to persist. Although some common mechanisms (e.g., a complaint-driven or manual checking) exist, they limit the response time to detect miscategorized apps and still open the challenge on categorization. We introduce FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following salient features: (i) it is based on a data-driven topic model and automatically suggests the categories appropriate for the app store, and (ii) it can detect miscategorizated apps. Extensive experiments attest to the performance of FRAC+. Experiments on GOOGLE Play shows that FRAC+'s topics are more aligned with GOOGLE's new categories and 0.35-1.10 percent game apps are detected to be miscategorized.

Opportunistic communication, off-loading, and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security, and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends' devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends' devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones.

The increased popularity of smartphones has attracted a large number of developers to offer various applications for the different smartphone platforms via the respective app markets. One consequence of this popularity is that the app markets are also becoming populated with spam apps. These spam apps reduce the users’ quality of experience and increase the workload of app market operators to identify these apps and remove them. Spam apps can come in many forms such as apps not having a specific functionality, those having unrelated app descriptions or unrelated keywords, or similar apps being made available several times and across diverse categories. Market operators maintain antispam policies and apps are removed through continuous monitoring. Through a systematic crawl of a popular app market and by identifying apps that were removed over a period of time, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. We build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy of over 95% with precision varying between 85% and 95% and recall varying between 38% and 98%. We further show that a limited number of features, in the range of 10--30, generated from app metadata is sufficient to achieve a satisfactory level of performance. On a set of 180,627 apps that were present at the app market during our crawl, our classifier predicts 2.7% of the apps as potential spam. Finally, we perform additional manual verification and show that human reviewers agree with 82% of our classifier predictions.

Smart wearable devices are widely available today and changing the way mobile applications are being developed. Applications can dynamically leverage the capabilities of wearable devices worn by the user for optimal resource usage and information accuracy, depending on the user/device context and application requirements. However, application developers are not yet taking advantage of these cross-device capabilities.

Mobile video traffic is exploding and it is particularly challenging to stream video when high density of users are "on the move", e.g., in public transport systems. It becomes increasingly problematic as video traffic is predicted to account for more than 80% of Internet traffic by 2019. This will be exacerbated by factors such as cellular network coverage issues and unstable network throughput due to high speed mobility. By exploiting the predictable public transport mobility patterns, spatio-temporal correlation of user interests and users' video viewing behaviors, we proposed TransFetch which uses intelligent caching on-board the public transport vehicles as well as a novel video chunk placement algorithm. We show through extensive simulations, that TransFetch reduces the system cellular data usage by up to 45% and improves the quality of video streaming by up to 35%. Finally, we demonstrate the practical feasibility of TransFetch by implementing caching units on a Raspberry-Pi and a mobile app on an Android device.

Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.

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.

Outsourcing data to the cloud for computation and storage has been on rise in recent years. In this paper we investigate the problem of supporting write operation on the outsourced data for clients using mobile devices. We consider the Attribute-based Encryption (ABE) scheme as it is well suited to support access control in outsourced cloud environment. Currently there is a gap in the literature on providing write access on the data encrypted with ABE. Moreover, since ABE is computationally expensive, it imposes processing burden on resource constrained mobile devices. Our work has two fold advantages. Firstly, we extend the single authority Ciphertext-Policy Attribute-based Encryption (CP-ABE) scheme to support write operations. Secondly, in achieving this goal, we move some of the expensive computations to a manager and remote cloud server by exploiting their high-end computational power. Our security analysis demonstrates that the security properties of system are not compromised.

WiFi networks are becoming increasingly ubiquitous. In addition to providing network connectivity, WiFi finds applications in areas such as indoor and outdoor localisation, home automation, and physical analytics. In this paper, we explore the semantics of one key attribute of a WiFi network, SSID name. Using a dataset of approximately 120,000 WiFi access points and their corresponding geo-locations, we use a set of similarity metrics to relate SSID names to known business venues such as cafes, theatres, and shopping centres. Such correlations can be exploited by an adversary who has access to smartphone users preferred networks lists to build an accurate profile of the user and thus can be a potential privacy risk to the users.

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.

Smartphone usage is tightly coupled with the use of apps that can be either free or paid. Numerous studies have investigated the tracking libraries associated with free apps. Only a limited number of these have focused on paid apps. As expected, these investigations indicate that tracking is happening to a lesser extent in paid apps, yet there is no conclusive evidence. This paper provides the first large-scale study of paid apps. We analyse top paid apps obtained from four different countries: Australia, Brazil, Germany, and US, and quantify the level of tracking taking place in paid apps in comparison to free apps. Our analysis shows that 60% of the paid apps are connected to trackers that collect personal information compared to 85%--95% in free apps. We further show that approximately 20% of the paid apps are connected to more than three trackers. With tracking being pervasive in both free and paid apps, we then quantify the aggregated privacy leakages associated with individual users. Using the data of user installed apps of over 300 smartphone users, we show that 50% of the users are exposed to more than 25 trackers which can result in significant leakages of privacy.

Increased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam apps, which reduce the users' quality of experience and increase the workload of app market operators. Apps can be "spammy" in multiple ways including not having a specific functionality, unrelated app description or unrelated keywords and publishing similar apps several times and across diverse categories. Market operators maintain anti-spam policies and apps are removed through continuous human intervention. Through a systematic crawl of a popular app market and by identifying a set of removed apps, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. Finally, we build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy over 95% with precision varying between 85%-95% and recall varying between 38%-98%. By applying the classifier on a set of apps present at the app market during our crawl, we estimate that at least 2.7% of them are spam apps.

Wi-Fi based fingerprinting systems, mostly utilize the Received Signal Strength Indicator (RSSI), which is known to be unreliable due to environmental and hardware effects. In this paper, we present a novel Wi-Fi fingerprinting system, exploiting the fine-grained information known as Channel State Information (CSI). The frequency diversity of CSI can be effectively utilized to represent a location in both frequency and spatial domain resulting in more accurate indoor localization. We propose a novel location signature CSI-MIMO that incorporates Multiple Input Multiple Output (MIMO) information and use both the magnitude and the phase of CSI of each sub-carrier. We experimentally evaluate the performance of CSI-MIMO fingerprinting using the k-nearest neighbor and the Bayes algorithm. The accuracy of the proposed CSI-MIMO is compared with Finegrained Indoor Fingerprinting System (FIFS) and a simple CSI-based system. The experimental result shows an accuracy improvement of 57% over FIFS with an accuracy of 0.95 meters.

Yalut is a novel user-centric hybrid content sharing overlay for social networking. Yalut enables the users to retain control over their own data and preserve their privacy, whilst still using the popular centralized services. In this demonstration, we show the feasibility of Yalut by integrating the service with the popular social networking apps on Android devices, Mac and Windows desktop platforms. We show that it is possible to provide the benefits of distributed content sharing on top of the existing centralized services with minimal changes to the content sharing process.

Crowd-Cache is a novel crowd-sourced content caching system which provides cheap and convenient content access for mobile users. Our system exploits both transient colocation of devices and the spatial temporal correlation of content popularity, where users in a particular location and at specific times would be likely interested in similar content. We demonstrate the feasibility of Crowd-Cache system through a prototype implementation on Android smartphones.

Distributed social networking services show promise to solve data ownership and privacy problems associated with centralized approaches. Smartphones could be used for hosting and sharing users data in a distributed manner, if the associated high communication costs and battery usage issues of the distributed systems could be mitigated. We propose a novel mechanism for reducing these costs to a level comparable with centralized systems by using a connectivity aware replication strategy. We develop an algorithm for grouping devices into tribes for content replication among intended content consumers and serve it using low-cost network connections. We evaluate the performance of the algorithm using three real world trace data sets. The results show that a persistent low-cost network availability can be achieved with an average of two replicas per content. Additionally, cellular bandwidth consumption and energy consumption of users are evaluated analytically using user content creation and consumption modeling. The results show that the proposed mechanism lowers monetary and energy costs for users compared to non-mobile-optimized distributed systems irrespective of the content demand model.

Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms.

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.

Received signal strength based fingerprinting approaches have been widely exploited for localization. The received signal strength (RSS) plays a very crucial role in determining the nature and characteristics of location fingerprints stored in a radio-map. The received signal strength is a function of distance between the transmitter and receiving device, which varies due to various in-path interferences. A detailed analysis of factors affecting the received signal for indoor localization is presented in this paper. The paper discusses the effect of factors such as spatial, temporal, environmental, hardware and human presence on the received signal strength through extensive measurements in a typical IEEE 802.11b/g/n network. It also presents the statistical analysis of the measured data that defines the reliability of RSS-based location fingerprints for indoor localization.

Summary form only given. Mobile devices already produce about 600 terabytes of data every month, through more than 1.5 million cellular base stations and 5 billion mobile phones. Moreover, the rate of data that is being produced is expected to grow exponentially over time. Mobile broadband is fast becoming an essential part of modern life. The ability to cater to this demand is severely hampered by the shortage of radio frequency spectrum. So apart from developing new technologies that use the spectrum efficiently, it is also necessary to make the mobile systems smarter. This talk will describe the techniques that are making systems smarter through the provision of user centered/personalized services and applications, and highlight one of the major challenges of designing such systems, namely preservation of privacy of the users. It will examine some of the recent recommender systems, personalized content delivery systems, and mobile applications to highlight the potential privacy threat these applications and services pose to users. Then it will outline some of the current solutions that are being proposed by the research community and provide discussion of one such approach - mobile service overlays - by focusing on the principles and practical considerations that went into the design of a privacy-preserving user generated content distribution system called mobitribe.

We focus on dissemination of content for delay tolerant applications, (i.e. content sharing, advertisement propagation, etc.) where users are geographically clustered into communities. We propose a novel architecture that addresses the issues of lack of trust, delivery latency, loss of user control, and privacy-aware distributed mobile social networking by combining the advantages of decentralized storage and opportunistic communications. The content is to be replicated on friends' devices who are likely to consume the content. The fundamental challenge is to minimize the number of replicas whilst ensuring high and timely availability. We propose a greedy heuristic algorithm for computationally hard content replication problem to replicate content in well-selected users, to maximize the content dissemination with limited number of replication. Using both real world and synthetic traces, we show the viability of the proposed scheme.

We proposed MobiTribe which enables device centric social networking on smart mobile devices while reducing high communication costs and battery usage normally associated with mobile distributed systems. In this paper, we demonstrate the feasibility of MobiTribe by integrating the service with the popular social networking application Facebook. We show that it is possible to provide the benefits of distributed content sharing on top of the existing centralized social networking services with minimal changes to the content sharing process.

Distributed social networking services show promise to solve data ownership and privacy problems associated with centralised approaches. Smartphones could be used for hosting and sharing users data in a distributed manner, if the associated high communication costs and battery usage issues of the distributed systems could be mitigated. We propose a novel mechanism for reducing these costs to a level comparable with centralised systems by using a connectivity aware replication strategy. To this end, we develop an algorithm based on a combination of bipartite b-matching and a greedy heuristics for grouping devices into tribes among intended content consumers. The tribes replicate content and serve it using low-cost network connections by exploiting time elasticity of user generated content sharing. The performance is evaluated using three real world trace data sets. The results show that a persistent low-cost network availability can be achieved with an average of two replicas per content. Additionally, a content creator can reduce 3G traffic by up to 43% and device energy use by up to 41% on average compared to content sharing in non-mobile-optimised distributed social networking approaches. Moreover, the results show that the proposed mechanism can provide the benefits of a distributed content sharing system for monetary and energy costs comparable to those of a centralised server based system.

Users today have come to expect constant connectivity through their mobile devices even when they are on the move, as most urban areas are heavily blanketed with Access Points and GSM Base Stations. Network selection poses a problem however as it is driven primarily by physical layer information, such as Received Signal Strength, which is a poor indicator of actual network performance. Thus there is a need for a more sophisticated method of discovering network resources that goes beyond radio interface characteristics. A way to address this is to use information not only from within the node, but also externally, for instance by enabling nodes to exchange information in a peer to peer manner. One of the biggest challenges of adopting this approach stems from the fact that nodes participating in such systems are not equally trustworthy. Therefore it is necessary to develop mechanisms that make the decision making process robust against dishonest information. In this work we evaluate data fusion techniques in the context of reconciling conflicting information due to the presence of dishonest or malfunctioning nodes. We show that it is possible to adapt classical data fusion techniques to develop a more robust decision-making mechanism, and moreover that Dempster-Shafer Theory is the optimal choice.

An increasing portion of traffic in mobile networks conies from users creating content and uploading it to the Internet to share it. The capacity of mobile networks is a limited resource and uploading high resolution content consumes a large part of it. We introduce MobiTribe, a distributed storage cloud consisting of mobile devices for storing the content created on the phones. It can serve requests for content and take advantage of networks with spare capacity to deliver the content at a lower cost. We propose a content distribution and replication algorithm which achieves this goal. The performance of the algorithm is evaluated using empirical data traces of WLAN availability patterns of mobile devices, showing that it is possible to achieve 99.98% availability of a content via WLAN while minimising content distribution to an average of 2.69 replicas.

We propose a novel trust and probabilistic node selection mechanism for content distribution in mobile ad hoc networks, which aims to achieve trustworthy node selection and to preserve mobile node resources. The proposed mechanism is evaluated against selected alternative trust schemes, with the results showing that our proposal achieves its goals.

In Mobile Social Networks(MSN) individuals with similar interests or commonalities, connect to each other using the mobile phones. Validation of protocols for these networks relies almost exclusively on simulations. Thus a simulation using a mobility model that captures the behavior of nodes in the real world is needed. The current simulations techniques use random models to generate dynamic MSN. However, the random models are not suitable for MSN simulations. In this paper we use Second Life(SL)1 as a simulation environment, which can support dynamics in simulation models by allowing real users to participate using their avatars. SL is a virtual world simulator accessible via the Internet having more than five hundred thousand active users. SL can capture dynamics of movement models as avatars have different movement speed, different movement patterns and different neighbors. Therefore, in this paper we propose the Virtual Social Simulated Environment (VSSE). VSSE consists of basic simulation using SL Bots (computer controlled SL agents) and protocols to allow avatars to participate in the simulation. Thus making it a participatory MSN simulation environment. We present the design of our state-driven grid region, a prototype implementation based on the daily mobility patterns and compare our system with a similar real-world experiment. To the best of our knowledge this is the first time that a participatory MSN simulations environment has been proposed, which allows anyone, including non-experts, to experiment and experience the technology. Moreover, this approach allows to validate protocols by providing a close to real life simulation environment for researchers.

Multi-interfaced mobile devices can connect to heterogeneous wireless access networks with different capabilities and constraints. Additionally, many bandwidth intensive applications have rather relaxed real time constraints allowing for alternative scheduling mechanisms which can take into account user preferences, network characteristics as well as future network resource availability to better exploit network heterogeneity. The current approaches either simply react to changes, or assume that availability predictions are perfect. In this paper, we propose a scheduling scheme based on stochastic modeling to account for prediction errors. The scheme optimizes overall user utility gain considering imperfect predictions taken over realistic time intervals while catering for different applications' needs. We use 60 days of real user data of many users to demonstrate that it consistently out-performs other non-stochastic and greedy approaches in typical networking environments.

Semantic web is gaining popularity as the candidate for next generation World Wide Web. Distribution of the data across number of physical information stores and proliferation of semantic web data brings variety of non-trivial challenges. One of such challenge is to identify information stores for a given query. This paper presents a framework to address this problem probabilistically and by exchanging the summaries of actual contents. Experimental evaluation shows promising results with high recall for probabilistic approach and lower response time for pre-processed summary exchanges.

Current devices use a network selection policy that is mostly driven by the physical layer, choosing the point of attachment with the highest Received Signal Strength Indicator (RSSI). Unfortunately for 802.11 networks, RSSI is not a good indicator of actual network performance as it is normally the bandwidth to the Internet and not the wireless signal conditions which dictates the quality of service a user might experience. Worse, the AP may belong to a pay service which renders it inaccessible to the user. MOBIX is a system which leverages on the fact that nodes on the move will meet other nodes who will be able to share conditions of networks they have recently used. MOBIX exchanges reports with other nodes it encounters using a short-range communication channel such as Bluetooth. Our simulation results show that exchanging throughput information resulted in 70% success rate over relying on RSSI measurements alone. Using our power measurements, we show that we can achieve energy savings of more than 80%.

Efficient Information retrieval is the key goal of semantic web. This technology ameliorates the documents with meta-data in a machine readable markup. Well known semantic search engines such as swoogle1or SHOE2use pre-fetched static indexing for efficiently retrieving the requested runtime documents. This paper presents a novel application dependent approach for improving the efficiency and retrieving the content documents based on an ASK type of SPARQL query in a distributed environment. We developed an initial prototype of the system and the performance curve for this approach is discussed in this paper. Observation on the results clearly shows that our approach receives favourable preliminary results.

It is evident that mobile devices of the future will have multiple wireless interfaces. For small, energy-constrained devices, determining network availability by keeping all radio interfaces turned on at all times will negatively impact battery lifetime even when these interfaces are idle. Predicti

In the future mobile world, users will be carrying devices with multiple radio interfaces. Despite rapid progress in battery technology, small, mobile devices of the future will still be energy constrained. Thus, turning on all wireless interfaces all the time to detect available network points of a

Modern mobile devices are increasingly capable of simultaneously connecting to multiple access networks with different characteristics. Restricted coverage combined with user mobility will vary the availability of networks for a mobile device. Most proposed solutions for such an environment are reactive in nature, such as performing a vertical handover to the network that offers the highest bandwidth. But the cost of the handover may not be justified if that network is only available for a short time. Knowledge of future network availability and their capabilities are the basis for proactive schemes which will improve network selection and utilization. We have previously proposed a prediction model that can use any available context such as GSM Location Area, WLAN presence or even whether the power cable is plugged in, to predict network availability.

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.

The rate at which the Internet is becoming mobile is unprecedented. This has increased the demand for continuous connectivity even while moving from one network to another at very high speeds. Moving from one network to another gives rise to a handoff process which often incurs packet losses and severe end to end transport protocol performance degradations for the Mobile Node. Most research on IP mobility has focused on minimizing the delays of the handoff process with network infrastructure based approaches. A different way of minimizing the impact of the handoff is to enable the Mobile Node to connect to multiple access networks simultaneously, allowing it to perform Make-Before-Break handoffs. In this paper, we compare the performance of these two alternatives, focusing on the use of Fast Handovers for Mobile IPv6 framework on the infrastructure side and on the other hand Make-Before-Break handoffs using two network interfaces. Both of these schemes require proactive handoffs for optimal performance. The results show that the use of two interfaces for Make-Before-Break handoffs provides increased handoff performance over Fast Handovers for Mobile IPv6.

In the next generation Internet, the network should evolve from a plain communication medium into an endless source of services available to the end-systems. We name these services "overlay applications". They would be composed of multiple cooperative distributed application elements that would build a dynamic communication mesh, namely "overlay association". In a former contribution, we proposed an unstructured super-peer architecture (SPAD) that provides enhanced quality of service (QoS) between end-points within an overlay association. This architecture aims at discovering and utilizing composite alternate end-to-end paths that experience better QoS than the path given by the default IP routing mechanisms. This paper presents a proactive information dissemination scheme that complements SPAD's mechanisms and significantly improves its performances.

Personalised networks (PN) introduce new mobility management challenges since individual devices participating in a PN may exhibit different mobility behaviour relative to one another and to human users. Mobility events may result in devices either leaving or joining the PN. In this paper we propose that PN mobility be managed by redirecting ongoing application data streams between endpoint devices in the PN. We describe a scheme to facilitate application level inter-device mobility by extending the PN to include composed network based adaptation services that adapt and reroute ongoing data streams to new endpoint devices. In order to cope with device heterogeneity and mobility, services are composed on demand and adapted in response to changes in the availability of endpoint devices. We analyse simulation results which show that our approach enables enhanced mobility handling over a range of conditions

We present arguments to support the co-existence of two or more mobility management schemes, and detail a novel approach to integrate the mobility and multi-homing support provided by a higher level mobility mechanism, the host identity protocol (HIP), with a lower level mobility mechanism, network mobility (NEMO, i.e. mobile IPv6 enabled routers). Our design integrates a context aware handoff system that triggers the dynamic switching of the HIP host identity binding between a private address managed by a mobile router and a topologically correct address, in order to simultaneously take advantage of the efficient handover offered by vehicular networks with NEMO support, and the infrastructure-less operability of HIP when mobile routers become unavailable. We discuss implementation and detail how our scheme uses context cues such as vehicle speed to switch between NEMO and HIP mobility handling. We present results of emulation experiments that support our design

Measuring the performance of an implementation of a set of protocols and analyzing the results is crucial to understanding the performance and limitations of the protocols in a real network environment. Based on this information, the protocols and their interactions can be improved to enhance the performance of the whole system. To this end, we have developed a network mobility testbed and implemented the network mobility (NEMO) basic support protocol and have identified problems in the architecture which affect the handoff and routing performance. To address the identified handoff performance issues, we have proposed the use of make-before-break handoffs with two network interfaces for NEMO. We have carried out a comparison study of handoffs with NEMO and have shown that the proposed scheme provides near-optimal performance. Further, we have extended a previously proposed route optimization (RO) scheme, OptiNets. We have compared the routing and header overheads using experiments and analysis and shown that the use of the extended OptiNets scheme reduces these overheads of NEMO to a level comparable with Mobile IPv6 RO. Finally, this paper shows that the proposed handoff and RO schemes enable NEMO protocol to be used in applications sensitive to delay and packet loss

Summary form only given. One of the underlying assumptions on the design of peer-to-peer (P2P) network is that each peer trusts each other for forwarding transit messages. However, with the growth of P2P application, especially in business model, some peers may behave as selfish, irrational nodes due to conflict of interest or for conservation bandwidth. In our model, the P2P network is considered as a virtual market place where peers can find, provide, and use services. Since peers are owned and operated by different entities, they do not necessarily share the same goals, but rather, to serve their own interests. In this paper, incentive mechanism to motivate peers to forward transit messages is proposed. We have implemented the monetary/token as an incentive mechanism, in which peers can setup their cost to charge for transiting a message. We also show that a peer may setup transiting cost higher or lower than other peers, but the strategy on setup the cost reflects its benefit.

Advances in ubiquitous computing applications depend heavily on developments in supporting technologies, including network communications. Recent research in Internet addressing has pointed towards a need for a more generic approach to the definition of endpoint identity and a change in the traditional communication model. This change defines one or more thin layers of resolution between network, transport, or application level identifiers. In this paper we advocate the concept of endpoint identity location decoupling in the context of ubiquitous computing. We provide an analysis of the general network security issues affecting ubiquitous computing in terms of the identity/location decoupled naming scheme approach.

One of the underlying assumptions on the design of peer-to-peer (P2P) network is that each peer trusts each other for forwarding transit messages. However, with the growth of P2P application, especially in business model, some peers may behave as selfish, irrational nodes due to conflict of interest or for conservation bandwidth. In our model, the P2P network is considered as a virtual market-place where peers can find, provide, and use services. Since peers are owned and operated by different entities, they do not necessarily share the same goals, but rather, to serve their own interests. In this paper, incentive mechanism to motivate peers to forward messages is proposed. We have implemented the monetary as an incentive mechanism, in which peers can setup their cost to charge for transiting a message. However, with only monetary incentive, peers may misbehave. Therefore, the decentralized reputation has been implemented with monetary mechanism. This will allow an application with dramatically improved utility, co-operate among peers and hence enable whole new domains of use.

The limited IPv4 address space has driven the cellular industry to start using IPv6 addresses for mobile users in 3G-networks. However, there is a potential threat to the success of 3G-network deployment, as the success will depend on the services offering to the end users. Currently, the overwhelming proportion of services resides in the IPv4 address space, which makes them inaccessible to users in the IPv6 address space. Thus, users cannot directly communicate with and access services without an intermediate translation mechanism. Previous studies on network address translation methods have shown that REBEKAH-IP with Port Extension, RPX is promising in that it provides excellent theoretical maximum scalability while supporting all types of services without limitations for the users in the network. However, the initial RPX proposal does not support host mobility to different networks, despite the fact that mobility is the most important feature of a wireless communication system. In this paper, we propose to extend the RPX scheme with a mobility support scheme based on mobile IP. RPX allows more than one host to use a single IPv4 address and therefore we have augmented Mobile IP with a new tunneling mechanism called IP-in-FQDN tunneling. The mechanism allows for unique mapping despite the sharing of IP addresses while maintaining the scalability of RPX. In addition, we present simulation results that indicate that the proposed scheme performs well in terms of scalability, connection request delay and packet transmission delay compared to mobile IP

At the core of any peer-to-peer network is the primitive of routing, whereby peers assist each other by forwarding transit traffic. However, some nodes may adopt selfish behavior and drop transit traffic due to reasons such as competing commercial interest or conservation of bandwidth. Thus nodes must be able to detect misbehavior as well as use this information to avoid misbehaving nodes. We propose two reputation schemes to address this problem, focusing on the Chord routing protocol. In the first scheme, nodes submit keyspace ranges that they suspect to contain a misbehaving node, and all the ranges are intersected to narrow the keyspace. The second scheme introduces a new routing reputation metric that is allocated to each finger of chord nodes' routing table. This allows nodes to route to fingers with better reputation in order to have a greater chance of successful delivery. We also present simulation results to evaluate the performances of the two schemes.

The architecture proposed in our previous paper uses mobile agents to support the use of peer-to-peer applications by mobile devices. In order to improve the efficacy and scalability of the number of mobile agents supported by the agent execution environments, a suitable coordination model between the mobile agents is needed. The coordination model adopted is facilitated by the logical entity of a blackboard within the execution environment, where all mobile agents register their existence. In addition, the mobile agents form a peer-to-peer network within the execution environment, with one agent acting as the gateway to the outside network. The network topology adopted within the execution environment is a logical ring, and this choice is justified with some intuitive analysis.

A tremendous proliferation of mobile computing devices is made for mobile users demanding a network environment with continuous connectivity as wireless network matures. However, with current wireless technologies, when mobile users traverse into adjacent networks, connectivity is compromised due to handoff latency. Moreover, there is no connectivity provided while mobile users remain in areas not covered by wireless network infrastructure. In this paper, we present an architecture that uses mobile location management as the basic platform to improve connectivity for mobile users in both connected and disconnected areas. Seamless connectivity is provided when mobile user is traveling between adjacent networks and virtual connectivity is provided while mobile user is located in areas without any wireless network coverage.

The most intuitive way to build a service directory application that allows for service entities to register or search for services on top of a structured peer-to-peer network is to build reverse indices at appropriate nodes on the network. However, this implies trust on the reliability and integrity of other nodes on the network, which may be too risky an assumption for businesses. This paper proposes a service directory that groups service entities of the same category together; this is achieved by dedicating part of the node identifiers to correspond to their service category semantic. Using chord as the peer-to-peer substrate, this scheme logically divides the chord circle into equidistant arcs; each arc is called an island. This scheme results in the formation of islands of varying population, and thus changing the uniformly spread topology of the original chord. Simulations are used to investigate the path length and message load of the changed topology. An additional routing scheme is also proposed and simulated to exploit the new topology to gain better path length.

We present the idea of a programmable structured P2P architecture. Our proposed system allows the key-based routing infrastructure, which is common to all structured P2P overlays, to be shared by multiple applications. Furthermore, our architecture allows the dynamic and on-demand deployment of new applications and services on top of the shared routing layer.

As the number of Mobile IP (MIP) [C. Perkins, (1996)] users grow, so will the demand for delay sensitive real-time applications, such as audio streaming, that require seamless handoff, namely, a packet lossless Quality-of-Service guarantee during a handoff. Two well-known approaches in reducing the MIP handoff latency have been proposed in the literature. One aims to reduce the (home) network registration time through a hierarchical management structure, while the other tries to minimize the lengthy address resolution delay by address pre-configuration through what is known as the fast-handoff mechanism. We present a novel seamless handoff architecture, S-MIP, that builds on top of the hierarchical approach [H. Soliman et al. (2002)] and the fast-handoff mechanism [G. Dommety et al. (2002)], in conjunction with a newly developed handoff algorithm based on pure software-based movement tracking techniques [Z.-G. Zhou et al. (2002)]. Using a combination of simulation and mathematical analysis, we argue that our architecture is capable of providing packet lossless handoff with latency similar to that of L2 handoff delay when using the 802.11 access technology. More importantly, S-MIP has a signaling overhead equal to that of the well-known 'integrated' hierarchical MIP with fast-handoff scheme [H. Soliman et al. (2002)], within the portion of the network that uses wireless links. In relation to our S-MIP architecture, we discuss issues regarding the construction of network architecture, movement tracking, registration, address resolution, handoff algorithm and data handling.

The use of peer to peer file sharing networks such as Gnutella is proliferating on the Internet, but its use is bandwidth consuming due to the broadcast nature of some peer to peer protocols. This is undesirable for mobile devices due to their bandwidth and power constraints. This paper proposes an architecture that uses mobile agents to participate in the Gnutella network on behalf of mobile devices in order to reduce the amount of traffic for the mobile device, as well as providing support for device mobility. Using real Gnutella traffic characteristics, analysis on the viability of the architecture is presented.

Handoff latency results in packet losses and severe End-to-End TCP performance degradation as TCP, perceiving these losses as congestion, causes source throttling or retransmission. In order to mitigate these effects, various Mobile IP(v6) extensions have been designed to augment the base Mobile IP with hierarchical registration management, address pre-fetching and local retransmission mechanisms. While these methods have reduced the impact of losses on TCP goodput and improved handoff latency, no comparative studies have been done regarding the relative performance amongst them. In this paper, we comprehensively evaluated the impact of layer-3 handoff latency on End-to-End TCP for various Mobile IP(v6) extensions. Five such frameworks are compared with the base Mobile IPv6 framework, namely, i) Hierarchical Mobile IPv6, ii) Hierarchical Mobile IPv6 with Fast-handover, iii) (Flat) Mobile IPv6 with Fast-handover, iv) Simultaneous Bindings, and v) Seamless handoff architecture for Mobile IP (S-MIP). We propose an evaluation model examining the effect of linear and ping-pong movement on handoff latency and TCP goodput, for all above frameworks. Our results show that S-MIP performs best under both ping-pong and linear movements during a handoff, with latency comparable to a layer-2 (access layer) handoff. All other frameworks suffer from packet losses and performance degradation of some sort. We also proposed an optimization for S-MIP which improves the performance by further eliminating the possibility of packets out of order, caused by the local packet forwarding mechanisms of S-MIP.

Mobile IPv4 has been considered as the de facto standard in providing Internet mobility. However, as the demand for wireless mobile devices capable of executing real-time applications increases, it is necessary to provide superior handoff latency and quality of service (QoS). Mobile IPv6 is designed to resolve these issues, and has numerous applicable optimization techniques. Two ways of reducing the handoff latency, in both IPv4 and IPv6, have been proposed in the literature. One aims to reduce the (home) network registration time while the other aims to reduce the lengthy address resolution time when in a visiting network. We present a performance analysis of the current IETF proposals, namely, the hierarchical Mobile IPv6 architecture and the fast-handoff mechanism. The former is aimed at reducing the registration time while the later in reducing the address resolution time. We show through simulation that managing the registration process in a hierarchical fashion greatly reduces the overall handoff latency. Comparatively, the fast-handoff mechanism is even more capable of reducing the handoff latency. The simple superimposition of these two frameworks produces the best overall handoff latency result. However, the overall improvement is not a simple aggregation of the individual handoff latency gains. In fact, we discovered some rather non-trivial traffic behavior when these two frameworks are combined. We identify the causes which hinder the handoff performance and hence devise a set of design guidelines to improve the handoff latency further.

Recently, there has been a growing interest in peer-to-peer networks such as Gnutella. A typical characteristic of Gnutella is that it is a 'pure' peer-to-peer system, with all nodes being equal participants in the network. Due to its decentralized nature, Gnutella implements services such as searching and peer discovery via flooding-based application-level broadcast. In this paper, we study the cost of Gnutella's version of broadcast, based on the total number of messages generated and forwarded as the metric of cost. We further propose the use of Rumor Mongering (or Gossip) as an alternative routing method in decentralized peer-to-peer networks. Using simulation, we show that this probabilistic protocol significantly reduces the cost of broadcast.

Integrated Personal Mobility Architecture (IPMoA) is a personal mobility framework that supports personality in both the areas of personal communications and personalising the user's operation environments and services. In this paper, we describe how we use mobile agents in this architecture to achieve the results of providing personal, mobility support in the area of personalising the user's operational environments and services. In particular, we describe the functionality of the Personal Service Assistant (PSA). The services that a PSA can provide includes executing user's applications that cannot be "migrated" with the user, such as applications implemented in native code, and customised Internet services, namely HTTP and e-mail, based on the user's terminal and network characteristics. This paper then illustrates the viability of the proposed scheme through a prototype implementation.

SRM (scalable reliable multicast) is a good example of a robust design intended to work across a wide range of group sizes and dynamics topologies, even though it does not address the integration of the multicast with the mechanisms supporting mobility. In this paper, we propose a reliable mobile multicast scheme called "Multi-Level Reliability Mobile Multicast Supporting SRM" (Scalable Reliable Multicast), MR MoM. MR MoM is designed to reasonably support SRM. The scheme consists of mobile multicast support without error recovery and multi-level reliability support. For our mobile multicast support, we adopt Mobile IP regional registration and low latency handoff in Mobile IPv4 to provide the efficient delivery of multicast packets to MH (mobile hosts), less frequent re-computation of the multicast trees and lower packet loss during handoff. For the multi-level reliability support, MR MoM is a receiver-based protocol in which the receivers themselves are responsible for loss detection and error recovery. In addition, original ADU (application data unit) naming is slightly modified to seamlessly cooperate with SRM. We have simulated our reliable mobile multicast protocol using the NS Network Simulator to compare our scheme to the reliable multicast protocol (RMMP). The performance results show that our proposed scheme is highly efficient. We also show the average number of requests and repairs, which show how scalable our scheme is.

The high expectations and demand for users to access the Internet from anywhere at anytime has made user mobility an important part of the design and development of the next generation mobile communications and computing. Traditionally user mobility has been divided into two areas: terminal mobility and personal mobility. Terminal mobility has been the focus of researchers, with personal mobility somewhat lagging behind. Although there are research in personal mobility, researchers have taken a very discrete approach of the two directions in this area: personal mobility in communications and personalisation of operating environments. We introduce a new personal mobility framework called IPMoA (Integrated Personal Mobility Architecture), with the use of mobile agents, integrates both aspects of personal mobility to provide a complete personal mobility solution.

Today information can be accessed from the Internet using a variety of devices and via different types of networks. With such diversity, it is impossible for a server on the Internet to contain information for all different types of clients. A possible solution to this problem is to use proxies to alter the content and to provide network enhancements to suit client's individual requirements. The current proxy-based solutions rely on static implementations which have several disadvantages. We believe that it will be possible to overcome these disadvantages by making the proxies transportable and active, i.e. the use of proxy agents. In this paper we present the architecture of such a proxy agent system, and the implementation of a prototype to evaluate its viability.

This paper describes a novel framework for managing connections to mobile hosts in the Internet. The framework, SLM, integrates the notions of quality of service management and mobility management and forms a base for overall session management. We compare SLM with the currently most widely adopted mobility management framework, Mobile IP, and show how some of Mobile IP's deficiencies are overcome. The paper further presents some initial experimental results and future research.

The term lip synchronisation applies to synchronising audio with the movement of a person's lips. If the data is out of synchronisation then human perception tends to identify the presentation as artificial, strange and annoying. We propose metrics to evaluate the degree of synchronisation for interactive multimedia communications. Through experimental results we demonstrate that such metrics are quite useful in providing quality of service (QoS) feedback to end users. A quantitative evaluation of the degree of synchronisation between loosely coupled multimedia tools is performed based on mean absolute deviation calculations and a subjective evaluation.

The wide acceptance of ATM (asynchronous transfer mode) as a backbone technology in both LAN and WAN environments and the demand of mobile computing with QoS supports have led to the deployment of mobile wireless networks with ATM cores. This paper presents the architecture, implementation and performance evaluation of a mobile-enabled hybrid ATM backbone wireless network. In this architecture, software switching is used to create separate control channels for supporting mobility and QoS. Common applications such as Telnet, Netscape, and MPEG video have been successfully tested in this prototype. Test performance shows that the proposed architecture has acceptable handover latency and location management cost.

Location management is one of the most important aspects in the realization of mobility in wireless networks. Following developments of ATM (asynchronous transfer mode) technology, more core networks are becoming ATM-based. Therefore research into location management schemes for wireless networks with ATM cores is attracting more attention. Several location management schemes have been previously proposed for classical wireless ATM networks, which have end-to-end native ATM connections. Most of these proposals assume that a wireless ATM network is built using end-user mobile ATM switches (EMAS). However, current ATM networks only consist of normal ATM switches that are not mobility enabled. We present a location management scheme for hybrid wireless ATM networks, which have an ATM based core and IP based wireless tails. From a performance analysis, we show that the proposed scheme has a better cost performance than the conventional GSM/IS-41 based location management scheme.

Location management is one of the most important aspects in the realization of mobility in wireless networks. Recent developments in asynchronous transfer mode (ATM) technology have resulted in many core networks becoming ATM-based. Therefore the requirement for mobility on wireless networks with ATM cores is increasing. In recent years several location management schemes have been proposed for classical wireless ATM networks. Most of these proposals assume that end-user mobile ATM switches (EMASs) are used to construct a wireless ATM network. However, current ATM networks only consist of normal ATM switches that are not mobility enabled. In this paper, we propose a location management scheme for hybrid wireless ATM networks. The proposed scheme can be applied to hybrid wireless ATM networks consisting of both normal ATM switches and EMASs. A detailed description of the proposed location management scheme and a comparative analysis of its cost are presented. Numerical results show that the proposed scheme has better cost performance than a generic GSM/IS-41 based location management scheme.

Multimedia services, such as videoconferencing require network and systems performance guarantees in terms of throughput, delay, jitter etc. These parameters, also known as quality of service (QoS), factors vary from user to user and application to application. Existing networking infrastructures such as the Internet do not yet support guarantees of QoS. However we can expect such support in the future. This paper presents our research in experimentation and modelling of schemes for providing multimedia services with guaranteed QoS.

Abstract It has been shown that protocol processing represents a severe bottle‐neck for high speed computer networks. The disadvantages of currently proposed solutions are their incompatibility with existing standardised protocol implementations, their complexity and/or their inflexibility. One method of alleviating these limitation is to have an adaptable protocol stack, as proposed in the paper. Preliminary results are presented which show that significant gains in throughput can be achieved while creating a framework suitable for future applications.

It has been shown that protocol processing represents a severe bottle-neck for high speed computer networks. The disadvantage of proposed solutions are their incompatibility with existing standardised protocol implementations and/or their complexity. One method of alleviating this limitation is to have an adaptable protocol stack, as proposed in this paper. Preliminary results are presented which show that significant gains in throughput can be achieved while still maintaining compatibility with existing standard protocol stacks.>

An efficient approach for implementing a method of minimizing data copying developed by M.L. Woodside et al. (1989) is advocated. The method, referred to as buffer cut through (BCT), allows the passing of data buffer through layers without copying, relying on the principle of passing responsibility for the buffer from one layer to another. The method proposed for the implementation of BCT is the upcall approach of D. Clark (1985) which permits the use of efficient synchronous procedure calls. Arguments are presented to justify the idea that an upcall approach can support the implementation of BCT. A proposed BCT implementation scheme within the PC transmission control protocol/internet protocol (TCP/IP) using upcalls is presented. It is also concluded that uniform layer interface with m-map upcall can be a general solution to software communications between network layers.>