Intelligent Transportation Systems (ITS) optimize road network capacity, monitor traffic flow, and enhance overall road safety by analyzing real-time trajectory data. However, the utilization of such data raises privacy concerns, enabling potential attackers to gain insights into users’ real-time activities and personal information. Furthermore, existing privacy preservation methods have multiple limitations, particularly in low-traffic density environments. To address these issues, this paper presents a novel approach for generating realistic trajectories that evade tracking. Existing trajectory generation mechanisms are coarse-grained and cannot adequately preserve the quality of location-based services while safeguarding individual privacy. To overcome this limitation, we first use differential privacy to determine a location near the actual destination and employ a path search algorithm to extract relevant road information. Subsequently, by leveraging our hybrid reinforcement learning model, we generate trajectories leading to this fictitious point. The comparison conducted on real-world maps with other trajectory generation methods reveals its superior ability to preserve spatio-temporal features. Finally, we propose two approaches that use the generated trajectories to protect privacy, ensuring both individual privacy protection and the utility of data.

In this article, the predefined-time cooperative platoon control problem with constrained communication range for uncertain networked aerial vehicles is considered. The uncertain networked aerial vehicles are subjected to external disturbance and parameter uncertainties. A novel adaptive sliding-mode disturbance observer is first designed for single uncertain networked aerial vehicle with an radial basis function of neural network estimator to guarantee the control performance. By embedding a dynamic control gain associated with the predefined convergence time, the proposed disturbance observer is proved to be uniformly ultimate boundedness stable by the time transformation approach. Then the dynamic gain technology is combined with the prescribed performance control to design the platoon controllers for uncertain networked aerial vehicles. With the proposed disturbance observer and the distribute controller, the platoon can be achieved within the predefined time. The proposed approach can simultaneously guarantee the system stabile without any initial conditions and system parameters. Finally, some simulation experiments are given to verify the effectiveness of the proposed protocols.

Vehicular Ad-Hoc Networks (VANETs) are a significant part of Intelligent Transportation Systems (ITS), and they are used to enhance the road safety and improve the traffic efficiency through the communication between vehicles and roadside units. However, some malicious adversaries can use the periodically broadcast beacons in VANETs to track vehicles. To mitigate this threat and protect privacy, existing research primarily suggests the use of pseudonyms as variable identities for each vehicle, and has explored different pseudonym mix strategies. These mix strategies often introduce latent traffic accident risks by requiring the vehicles to change their driving behavior or stop broadcasting. In this paper, we present a flickering context-based mix strategy, which can reduce such hidden dangers and provide higher privacy level than traditional context-based strategy. Besides, we employ a passive global adversary to evaluate the proposed strategy and conduct simulations in both virtual maps and real city maps to measure the protection level of the proposed privacy scheme. Finally, the influences of different parameters in our new method are explored.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

Indoor positioning plays an important role in a variety of applications under Internet of Things (IoT). Conventional WiFi fingerprinting-based indoor positioning systems (IPSs) usually require extensive manual calibrations to construct radio maps. This process severely limits the system scalability and adaptiveness. Pedestrian dead reckoning (PDR) is a popular method that can avoid the calibration process. However, PDR-based IPSs typically suffer from accumulated errors. To tackle this problem, many refinement methods require map information or floorplans which may not be available or up-to-date in practice. With the development of IoT, various types of crowdsourced data become available. In this work, we propose GraphIPS, a calibration-free and map-free IPS which dynamically generates accurate radio maps by utilizing smartphone crowdsourced WiFi and inertial measurement unit (IMU) data. GraphIPS fuses the crowdsourced data into a graph-based formulation and applies the multidimensional scaling (MDS) algorithm to compute the positions of the user's steps. The experimental results show that GraphIPS achieves comparable accuracy to the calibration-based method in a significantly shorter run time than optimization-based methods. In addition to smartphones, GraphIPS is also potentially applicable for the smart wearables with embedded WiFi modules and IMUs.

With the development of Internet of Things (IoT), a variety of crowdsourced data becomes available. In this article, we propose to utilize the crowdsourced WiFi received signal strength (RSS) data to perform indoor positioning. In many instances, the size of crowdsourced WiFi RSS data is potentially large, and the ground truths of the corresponding RSS fingerprints are unavailable. Therefore, it is challenging to construct the associated radio map. In the proposed method, a heuristic geometrical algorithm is developed to convert the RSS data into pairwise distances among the fingerprints. Based on these pairwise distances, multidimensional scaling (MDS) is then applied to compute the positions of all the fingerprints, thereby building a radio map. We show that the accuracy of the proposed method is only reasonably lower than a state-of-the-art calibration-based method. Further, we present a k -means clustering-based region partitioning method that partitions the large crowdsourced data set effectively. The parallel processing on the partitioned data and computational simplicity of MDS result in significantly shorter runtime for the proposed method than the previous optimization-based methods.

Indoor positioning plays an important role in various location-based services (LBSs). In conventional systems, the process of constructing radio maps for positioning usually involves labor-intensive manual calibrations, which seriously limits the system's scalability and adaptiveness. In this paper, we propose an efficient calibration-free method by leveraging on crowdsourced WiFi signal data that are captured passively through a WiFi sensing testbed. Since the ground truths of the crowdsourced data are unavailable, the radio maps cannot be directly constructed. In the proposed method, we adopt the multidimensional scaling (MDS) technique to compute the positions of the unlabeled data thereby generating radio maps. In order to enable MDS, we estimate the pairwise distances among the unlabeled data by using an improved trilateration method and a law of cosine (LoC)-based geometrical algorithm without online pairwise measurements. Experimental results show that the accuracy of the proposed method is higher than trilateration-based method and reasonably lower than that of calibration-based method. Meanwhile, the run time of the proposed method is shorter than previous optimization-based methods. The short run time allows the radio maps to be dynamically updated against the environmental variations.

Indoor position information plays an important role in a wide variety of Internet of Things (IoT) applications. In recent studies, Wi-Fi channel state information (CSI) is becoming a popular tool for indoor positioning and environmental sensing due to the richer information it contains in multiple subcarriers. Deep neural networks (DNNs) and convolutional neural networks (CNNs) have been applied in training CSI data for indoor positioning and are shown to have fine performances. However, the potential sequential correlations among the subcarriers and consecutive CSI packets are yet to be exploited in those networks. In this study, we propose a two-level nested long short-term memory (LSTM) network named IPRIC to recognize the indoor positions and classify the type of interferences of Wi-Fi CSI data by analyzing the sequential correlations among the subcarriers and consecutive packets. We apply the discrete wavelet transform (DWT) for data preprocessing to minimize noise and improve classification. As evaluated in an office environment, IPRIC achieves higher classification accuracy than the DNN and CNN-based methods.

Pedestrian dead reckoning (PDR) is a popular localization approach. However, PDR is error prone at turning points. During a turn, a small error in heading estimation can cause severe accumulated positioning errors thereafter. In this paper, we propose a turn-based correction approach which aims to reduce the potential accumulated errors caused by inaccurate heading estimations during turns. We consider a crowdsourcing environment in which previous users' data can help in the positioning of current users. For previous and current users moving in reverse directions and turn at the same position, we match the previous user's turning position with the current user's using Kullback-Leibler (KL) divergence based on the received signal strength (RSS) scans collected. Upon a match, the previous user's pre-turn positions can be used to improve the post-turn positions of the current user. Tests are done for different types of paths and the results show that the proposed approach can effectively reduce the PDR errors caused by inaccurate heading estimations at turning points.

A compensation based indoor localization method is proposed for localizing mobile stations utilizing Received Signal Strength Indicator (RSSI). The method consists of two subsystems, a data collection subsystem and a decision making subsystem based on log-distance path loss model. The main contribution of the method proposed is that localization errors caused by multipath fading are estimated and compensated in a vector space. The accuracy of traditional log-distance path loss model based localization method is improved. A new measure of multipath fading is proposed and analysed in comparison to existing measures. Experiments are conducted and results show that an average error of 2.98m and a 31% improvement compared to the traditional log-distance path loss model based algorithms can be achieved.

Mobile crowd sensing is an emerging sensing paradigm where sensing applications buy sensor data from mobile smartphone users (workers) instead of deploying their own sensor networks to estimate some statistics of a spatial event. In many spatial monitoring applications, the crowdsourcer needs to incentivize smartphone users to contribute sensing data so that the collected dataset has good spatial coverage. To further incentivize privacy-concerned workers to contribute, we propose a Stackelberg incentive framework that allows workers to specify their location privacy requirements while also increasing the spatial coverage of the collected dataset. We then derive a unique Stackelberg equilibrium which demonstrates the stability of our approach. Our simulation results show that our approach is significantly better in terms of data utility than the non-location-aware and uniform-reward approaches.

To avoid substantial manual calibration effort, some WiFi-based indoor localization methods attempt to achieve calibration-free feature. However, many of these methods have additional requirements which limit their practical applicabilities, e.g. lateration-based methods require the target locations to be covered by at least three APs, some methods require specific relationships between the training locations and APs. In this paper, we propose an indoor localization system named SimpLoc, which does not require any manual calibration effort nor any strict condition of use. The system applies the multi-dimensional scaling technique by converting the crowd-sourced RSS data to pseudo pairwise distances between the unknown locations. In a 36 × 23m2area, SimpLoc achieves a mean distance error of 1.93m. Experimental results show that the error can still be maintained at 2.14m when only 10 percent of the training data are used.

Privacy-preserving routing protocols in wireless networks frequently utilize additional artificial traffic to hide the identities of communicating source-destination pairs. Usually, the addition of artificial traffic is done heuristically with no guarantees that the transmission cost, latency, and so on, are optimized in every network topology. In this paper, we explicitly examine the privacy-utility tradeoff problem for wireless networks and develop a novel privacy-preserving routing algorithm called optimal privacy enhancing routing algorithm (OPERA). OPERA uses a statistical decision-making framework to optimize the privacy of the routing protocol given a utility (or cost) constraint. We consider global adversaries with both lossless and lossy observations that use the Bayesian maximum-a-posteriori (MAP) estimation strategy. We formulate the privacy-utility tradeoff problem as a linear program, which can be efficiently solved. Our simulation results demonstrate that OPERA reduces the adversary's detection probability by up to 50% compared to the random Uniform and Greedy heuristics, and up to five times compared to a baseline scheme. In addition, OPERA also outperforms the conventional information-theoretic mutual information approach.

We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called enhanced location spoofing detection using audibility (ELSA), which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user's device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit audibility information (or outage information) to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device's location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements' spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

We contend that context information of Internet clients can help to efficiently manage a variety of underlying resources for different Internet services and systems. We therefore propose a resource distribution framework that provides quality of experience (QoE) aware service differentiation, which means that starving clients are prioritized in resource allocation to enhance the corresponding end-user's QoE. The framework also actively motivates each Internet client to consistently provide its actual context information and to adopt moderate competition policies, given that all clients are selfish but rational in nature. We analyze the Internet client's behavior by formulating a non-cooperative game and prove that the framework guides all clients (game players) towards a unique Nash equilibrium. Furthermore, we prove that the distribution results computed by the framework maximize a social welfare function. Throughout this paper, we demonstrate the motivation, operation and performance of the framework by presenting a Web system example, which leverages on the advanced context information deduced by a context-aware system.

We present a phantom-receiver-based routing scheme to enhance the anonymity of each source-destination pair (or contextual privacy) while using an adjustable amount of overhead. We also study how traditional network coding and opportunistic routing can leak contextual privacy. We then incorporated both network coding and opportunistic routing into our scheme for better network performance and show how we mitigate its vulnerability. Contrary to prior works, we allow the destination to anonymously submit an acknowledgment to the source for enhanced reliability. Performance analysis and simulations are used to demonstrate the efficacy of the proposed scheme against commonly considered traffic analysis attacks.

This paper uses a multi-channel spatial Aloha model to describe a distributed autonomous wireless network where a group of transmit-receive pairs (users) share multiple collision channels via slotted-Aloha-like random access. The design objective is to enable each autonomous user i to select a channel c_i and decide a medium access probability (MAP) q_i to improve its throughput, while providing a certain degree of fairness among the users. Game theoretic approaches are applied, where each user i is a player who chooses the strategy (c_i,q_i) to improve its own throughput. To search for a Nash Equilibrium (NE), a Multi-Leader Stackelberg Game (MLSG) is formulated to iteratively obtain a solution on each dimension of the (c_i,q_i) strategy. Initially, multiple Stackelberg leaders are elected to manage the MAPs of all players. Then under the resulting MAP profile, each player iteratively chooses its channel to improve its throughput. An Oscillation Resolving Mechanism (ORM) is further proposed to stabilize the design in some special cases where the operating points of some players in a local region would oscillate between the two dimensions of the myopic search. Compared to existing methods of pre-allocating MAPs, the MLSG game further improves the overall network throughput by iteratively tuning the MAPs toward max-min throughput in each subnet. Simulation results show that the MLSG game gradually improves the total throughput until reaching a NE, which also provides good throughput fairness for the players.

Inter-user interference deteriorates reliable communication in body sensor networks (BSNs) when multiple BSNs are transmitting simultaneously in close proximity to each other. Stochastic geometry provides a natural way of modeling the placement of interferers, by averaging over all potential geometrical realizations for the interferers. This paper presents a stochastic geometry analysis of inter-user interference in IEEE 802.15.6 BSNs. The analysis evaluates the performance of BSNs in terms of outage probability and spatial throughput. The effects of medium access control (MAC) design in IEEE 802.15.6 are considered where both contention-free and contention-based MAC operations co-exist in the BSN deployment area. Based on the analysis, the interference detection range is optimized to achieve the maximum spatial throughput while the reliable transmission requirement is met. Moreover, insights are provided on the design of MAC for BSNs depending on the specific BSN applications.

Since both transmission power and modulation method can be varied in Mobile Ad hoc Networks (MANETs), it is necessary to take power control and rate adaptation into account when dealing with routing problems. In this paper, we propose a Q-Learning-based Rate-aware Power-Controlled Routing scheme (QLRPCR) to address the joint optimization of power, rate and routing in an on-demand and distributed manner. An evaluation considering traffic load and node mobility shows that QLRPCR improves the average latency and the packet delivery ratio, while maintaining an acceptable overhead level compared with other Q-learning or power-controlled-routing protocols.

Step counting based dead-reckoning has been widely accepted as a cheap and effective solution for indoor pedestrian tracking using hand held device equipped with motion sensors. To calibrate the accumulating error in a dead-reckoning tracking system, extra techniques are always fused to form a hybrid system. Currently various particle filter based map filtering algorithms have been proposed in the literature. However, more improvement can be achieved by using map information. In this paper, a map matching involved step counting algorithm is proposed. It is shown that by applying map matching in an indoor environment with corridors, not only can the estimated location be calibrated, but also the estimated sensor's orientation and the walking direction, which reduces the error in the ensuing location update process. The computational complexity is also less than the particle filter. Experimental results show that map matching algorithm returns more accurate results than the particle filter given the same map information. Further experiments indicate that when incomplete map information is given, the particle filter is much easier in returning a drifted path compared to map matching. This indicates that less map information is required by map matching to achieve a robust performance. This reduced reliance on accurate map information implies less labour cost and error in map maintenance.

In this paper, we propose a novel mobile terminal (MT) tracking approach performed at the fixed terminal (FT). The method involves three steps: motion-dependent parameters estimation, extended Kalman filter (EKF) based tracking and enhanced estimation accuracy through additional partial location information. An algorithm based on the space-time correlation of the received signal is first proposed to estimate the radial velocity (both speed and direction) of the MT. The EKF-based tracking is next adopted to estimate the current location of the MT by using the estimated parameters and the previous location estimate. Finally, additional high-resolution angle-of-arrival (HR-AoA) estimate is applied and we show how this partial location information can be fused with the tracking results to further improve the tracking accuracy. The performances of the algorithms are studied through simulations.

Aloha games with spatial reuse study the interactions among a group of selfish transmit-receive pairs which share a common collision channel using slotted-Aloha-like protocols. These Tx-Rx pairs are allowed to reuse the channel if they cause negligible interference to each other. Our work in [1] has proved the existence of a Least Fixed Point (LFP) which is the most energy-efficient operating point as well as the unique Nash Equilibrium (NE) in such games. Based on the earlier derived conditions for the stability of this NE and the way to converge to this NE, it is possible to design a self-adaptive algorithm for the players to self-adjust their target rates based on a set of pre-installed rules so that the network always achieves Pareto optimal bandwidth utilization. In this paper, we implement such an algorithm in a fully distributed manner, which requires no information exchange among the players. Each player repeatedly measures its current throughput and uses the measured value to make myopic best response to the current channel idle rate. Our simulations show that the system indeed achieves close to Pareto optimal performance while guaranteeing a certain degree of fairness. The algorithm is robust and can handle various practical issues such as the dynamic arrival/departure of players, parameter estimation errors, etc.

In Wireless Mesh Networks (WMNs), Mesh Access Points (MAPs) forward the packets from their associated mobile stations (STAs) to other stations or to the portal through a wireless multi-hop backhaul. The wireless backhaul has limited bandwidth and may become the bottleneck of the WMN easily when network load is heavy. Inspired by the fact that transmission from MAPs with good backhaul condition consumes less backhaul network resource, we propose a Mobility-Aware ReAssociation (MARA) control scheme that makes better use of the network resource by prolonging mobile stations association period with good backhaul MAPs. In MARA, STAs adjust their scan intervals and make association decisions based on the estimated moving directions and the association cost of nearby MAPs. Our simulation results show that the proposed scheme achieves improved end-to-end performance consistently under different network scenarios.

In this paper, we consider the medium access control (MAC) protocol design for random access cognitive radio network (CRN). Based on asynchronous spectrum sensing technique and RTS/CTS mechanism, a new MAC protocol, namely, cognitive-radio-based carrier sense medium access with collision avoidance (CR-CSMA/CA) is proposed to coordinate the channel access of secondary network as well as protect the operation of primary network, which applies to both single and multiple channel models. Using the G/G/1 queuing model with consideration of unsaturated and saturated network condition, we develop a framework to analyze the proposed MAC protocol and also derive closed-form expressions of specific performance metrics such as normalized throughput, average packet service time, etc. Performance evaluations illustrate and validate that the performance of CR-CSMA/CA varies with the offered traffic load of secondary network and the spectrum utilization rate of primary network, respectively, and also show that CR-CSMA/CA outperforms other relevant MAC protocols.

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.

Aloha games study the transmission probabilities of a group of non-cooperative users which share a channel to transmit via the slotted Aloha protocol. This paper extends the Aloha games to spatial reuse scenarios, and studies the system equilibrium and performance. Specifically, fixed point theory and order theory are used to prove the existence of a least fixed point as the unique Nash equilibrium (NE) of the game and the optimal choice of all players. The Krasovskii's method is used to construct a Lyapunov function and obtain the conditions to examine the stability of the NE. Simulations show that the theories derived are applicable to large-scale distributed systems of complicated network topologies. An empirical relationship between the network connectivity and the achievable total throughput is finally obtained through simulations.

Body Sensor Networks (BSNs) enable continuous and remote health monitoring in medical applications. In realistic deployments, inter-user interference is the major cause that deteriorates reliable data transmissions in BSNs when multiple BSNs are transmitting simultaneously in close proximity to one another. In this paper, we evaluate the performance of BSNs in terms of Packet Error Rate (PER) in the presence of such inter-user interference. Our study considers multiple factors that affect the BSN performance, including BSN density, traffic load, and transmission power in a realistic moderate-scale deployment case in hospital. Our results show that with 20% duty cycle, only 68.5% of data transmission can achieve the targeted reliability requirement (PER<;0.05) even in the off-peak period. We then suggest inter-user interference mitigation schemes based on the performance results in specific BSN deployment scenarios.

Above water reflectance measurements are usually made laboriously with handheld sensors. In recent years, autonomous robotic systems or fixed spectro-radiometers are used. In this work, we used off-the shelf optical systems with standard netbooks to make autonomous measurements of water reflectance. The optical systems are calibrated radiometrically and spectrally.

Body Sensor Networks (BSNs) enable continuous and remote health monitoring in medical applications. Inter-user interference is the major cause that deteriorates the reliable data transmission in BSNs when multiple BSNs are transmitting simultaneously in close proximity to one another. This paper presents an inter-user interference mitigation (IIM) method, which enables BSNs to adaptively reschedule their transmissions or switch to another channel in a distributed manner based on the detected information from neighboring BSNs. By taking advantage of the general property of the low channel utilization of BSNs in typical medical applications, the proposed IIM method increases system utilization and thus alleviates inter-user interference. In addition, our IIM method operates in a distributed manner without the need of extra infrastructure as compared to other approaches. In this paper, we describe the detailed design of the IIM method, and provide performance comparisons through simulations. The simulation results indicate that with the proposed IIM method, the network throughput is improved by 30% on average, and the energy consumption for data transmission is reduced by 16%.

This paper proposes an ambulatory algorithm to estimate Center of Mass (CoM) displacement of the subjects for Micro-sensor Motion Capture (MMoCap) system. Estimation of the CoM displacement is based on gait analysis and segmental kinematics. In our MMoCap system, the human body is modeled as a kinematic chain of rigid segments linked by joints, and the pelvis is considered as the root joint in the model. Each joint's relative positions are obtained from hierarchical transformations. The CoM displacement is imported into the root joint of a human skeleton model to track human locomotion with reference to the global coordinate system. Performance of the algorithm is evaluated using simulated sensor data obtained from an optical motion capture database and also by our ambulatory MMoCap system. The experimental results show robust and continuous tracking of CoM movements while a subject is walking.

In this paper, we consider the medium access control (MAC) protocol design for cognitive radio networks. An opportunistic spectrum access protocol named Slotted CR-ALOHA is proposed, and its performances in terms of normalized throughput and average packet delay are evaluated. Simulation results show that for various frame lengths and number of SUs, the optimal performance can be achieved at an appropriate spectrum sensing time, and there also exists a tradeoff between the achievable performance of secondary network and the protection effect on primary network.

The emerging vehicular communications will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment. Due to high vehicular speed, sporadic connection, limited communication range, and large volume of data that need to be transmitted, vehicular communications have the crucial requirements of fast authentication and encryption/decryption. This paper addresses the new and special challenges related to vehicular communications, such as large overhead and latency, presents our Hybrid scheme HASVC to address the authentication issue, and evaluates its performance so as to meet the stringent requirement of real time vehicular communications. The experimental results show that our authentication protocol can securely protect the exchanged information with less overhead and less authentication latency.

The emerging vehicular communications will enable a variety of applications for safety, traffic efficiency, driver assistance and infotainment. Due to high vehicular speed, sporadic connection, limited communication range, and large volume of data that need to be transmitted, vehicular communications have the stringent requirement of real-time vehicular communications. This paper presents our tests with 802.11p device and presents its performance evaluation in terms of throughput, loss rate, communication range and resending rate. The experimental results show that the 802.11p device can achieve real-time vehicular communications and is suitable for the applications with medium throughput, and a few hundred meters of communication range.

Vehicular Ad-hoc Network (VANET) is an emerging new technology to enable communications among vehicles and nearby roadside infrastructures to provide intelligent transportation applications. In order to provide stable connections between vehicles, a reliable routing protocol is needed. Currently, there are several routing protocols designed for MANETs could be applied to VANETs. However, due to the unique characteristics of VANETs, the results are not encouraging. In this paper, we propose a new routing protocol named AODV-VANET, which incorporates the vehicles' movement information into the route discovery process based on Ad hoc On-Demand Distance Vector (AODV). A Total Weight of the Route is introduced to choose the best route together with an expiration time estimation to minimize the link breakages. With these modifications, the proposed protocol is able to achieve better routing performances.

The emergence of personal mobile device with low cost sensors, such as accelerometer and digital compass, has made dead-reckoning (DR) an attractive choice for indoor pedestrian tracking. In this paper, we propose a robust DR pedestrian tracking system on top of such commercially accessible sensor sets capable of DR. The proposed method exploits the fact that, multiple DR systems, carried by the same pedestrian, have stable relative displacements with respect to the center of motion, and therefore to each other. We first formulate the robust tracking task as a generalized maximum a posteriori sensor fusion problem, and then we narrow it to a simple computation procedure with certain assumptions. A prototype is implemented and evaluated with a benchmark system that collects ground truth efficiently and accurately. In a practical indoor testbed, the proposed scheme has exhibited robust tracking performance, with reduction in average tracking error up to 73.7%, compared to traditional DR tracking methods.

In this paper, we propose a novel algorithm to improve the accuracy in estimating the angle-of-arrival (AoA) when the MUSIC (MUltiple SIgnal Classification) algorithm is used. An optimal precoder, with the objective to minimize the estimation errors of the AoAs, is first derived. However, to compute the optimal precoder requires the channel state information (CSI) exclusive of the receiver array, which cannot be separately estimated practically. A more feasible precoder design approach, which leverages on the feedback instantaneous CSI estimated at the receiver, is next proposed. Using the ideal optimal design to benchmark the performance, our simulation shows that the proposed practical precoder can achieve near-optimal performance, and both can achieve about 4-6 dB improvement in signal-to-noise ratio (SNR) compared to the case when only MUSIC algorithm is applied without precoding. Finally, the performance of the AoA estimator under channel estimation errors is studied through simulation, to demonstrate the robustness of the proposed approach.

Human motion capture technologies have been widely used in a wide spectrum of applications, including interactive game and learning, animation, film special effects, health care, navigation, and so on. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors-on-chip, human motion capture using wearable microsensors has become an active research topic. Because of the agility in movement, upper-limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and propose a novel ubiquitous upper-limb motion estimation algorithm, which concentrates on modeling the relationship between upper-arm movement and forearm movement. A link structure with 5 degrees of freedom (DOF) is proposed to model the human upper-limb skeleton structure. Parameters are defined according to Denavit-Hartenberg convention, forward kinematics equations are derived, and an unscented Kalman filter is deployed to estimate the defined parameters. The experimental results have shown that the proposed upper-limb motion capture and analysis algorithm outperforms other fusion methods and provides accurate results in comparison to the BTS optical motion tracker.

In this paper, we propose a three-tier framework for Location-Aware Service Provision and Discovery (LASPD), with special emphasis on mobile service provisioning. In the first tier, the world is geographically divided into autonomous areas to facilitate local service administration and management. In the second tier, service providers of an area with adequate computing capability are organized into a structured peer-to-peer network. The third tier supports mobile service providers equipped with less capable devices such as smart phones or PDAs. These providers are allowed to delegate their services to one of the designated peer nodes of the second tier, known as "proxy". In LASPD, the location-awareness is achieved during a service discovery by exploiting the locality-preserving property of the Hilbert space filling curve. Besides, we leverage on our previously proposed rewiring mechanism to make the network navigable and self-organized. To have a better understanding of the framework characteristics, we conduct simulations to study the routing behaviors of peer nodes in different tiers as well as measuring the performance of the prototype in real-world environments.

Human motion capture technologies are widely used in interactive game and learning, animation, film special effects, health-care and navigation. Because of the agility, upper limb motion estimation is the most difficult in human motion capture. Traditional methods always assume that the movements of upper arm and forearm are independent and estimate their movements separately; therefore, the estimated motion are always with serious distortion. In the paper, we proposed a novel ubiquitous upper limb motion estimation method using wearable micro-sensors, which concentrated on modeling the relationship of the movements between upper arm and forearm. Exploration of the skeleton structure of upper limb as a link structure with 5 degrees of freedom was firstly proposed to model human upper limb motion. After that, parameters were defined according to Denavit-Hartenberg convention, forward kinematic equations of upper limb were derived, and an Unscented Kalman filter was invoked to estimate the defined parameters. The experimental results have shown the feasibility and effectiveness of the proposed upper limb motion capture and analysis algorithm.

Recently there has been increasing interest in building networks with Ambient Intelligence (AmI), which incorporates the user-centricity and context awareness. However, both the Internet TCP/IP protocol stack and the seven-layer OSI reference model are not suitable for AmI networks, because they do not specifically take the end-user requirements into consideration in their architecture design. Under the client-server architecture, we propose to explicitly take the end-user into account by defining a new layer called User Layer above the traditional application layer. The User Layer empowers the end-users to influence network performance based on their interaction activities with the networks. We adopt the Model Human Processor (MHP) approach for building the User Model. After that we present an exemplary User Layer implementation to illustrate how the User Layer interacts with the underlying protocol stack and improves end-user's satisfaction with network performance.

"Vehicle to Grid" power or V2G will be a new green energy scheme that allows electricity to flow from Electric Vehicles (EVs) to power lines. The objective of this paper is to design and develop a secure wireless communication platform for V2G research, with the aim to develop a suitable wireless test bed for the V2G in Singapore. First, this paper presents our system block diagram and required methods design which include V2G architecture design, integrated OBU (on-board unit) design, base station (Aggregator), Telematics, V2G authentication protocol, and schedule optimization of EVs against Grid conditions. It then focuses on the authentication protocol for secure wireless communications between the Aggregators and EVs. It addresses the new and special challenges related to EVs, such as large overhead and latency, which are crucial for secure wireless communications with dynamic and fast moving EVs. Finally, the paper presents the initial test results and shows that our authentication protocol can securely protect the exchanged information with less overhead and less authentication latency.

In this paper we present the design of an application framework for context-aware Sharing Of Living Experience (SOLE) in mobile environments. With SOLE, users can share their experience of daily activities. They can specify where to store their information - on their mobile devices to have full access control over their data, or in the distributed SOLE application servers for ease of access by others. SOLE leverages on our service-oriented middleware (Coalition) to achieve context-awareness. To cater for resource constraints of mobile devices, the concept of "proxy" is introduced. As a proof of concept, we develop a prototype of SOLE for mobile Android phone users. Preliminary experiments have been carried out to test the performance of the framework.

This paper addresses building networks that emphasizes on user-centric human computer interaction and context awareness. To achieve this user-centric intelligent network goal, we propose to explicitly take the end-user into account by defining a new layer called the User Layer above the traditional application layer. By exposing some lower layer information to the end-user, the new User Layer establishes a feedback loop between the end-user and the underlying network infrastructure, empowering the end-user to control and influence network performance based on his own behavior and preferences. A cross-layer design approach using a shared database between the different lower layers is adopted. To illustrate the User Layer in action, we present an exemplary implementation of the User Layer, which can dynamically allocate network resources by leveraging on the TCP flow control mechanism. We evaluated network performance via simulation and show that such a design improves the user perceived quality of service (QoS).

The use of wearable micro-sensors provides an easy-to-use and cost-efficient way for human motion capture. It is essential but difficult to estimate the global displacements of human walking in micro-sensor motion capture system. This paper proposes a novel method to estimate the global displacements of walking based on the pedestrian dead reckoning technique. The global displacements are obtained from the estimated walking distance and the heading direction. Walking distance is calculated using the extension and flexion angles of the lower limbs. Heading is estimated by the fusion of magnetometer and gyroscope readings accomplished by a complementary Kalman filter. The displacements are used to drive a skeleton model to animate the walking movements of the subject. Walking experiments have demonstrated that the proposed method can estimate the displacements accurately and animate the human walking motions smoothly and vividly.

In this paper, we propose a fingerprint-based localization scheme that exploits the location dependency of the channel impulse response (CIR). We approximate the CIR by applying inverse Fourier transform to the receiver's channel estimation. The amplitudes of the approximated CIR (ACIR) vector are further transformed into the logarithmic scale to ensure that elements in the ACIR vector contribute fairly to the location estimation, which is accomplished through nonparametric kernel regression. As shown in our simulations, when both the number of access points and density of training locations are the same, our proposed scheme displays significant advantages in localization accuracy, compared to other fingerprint-based methods found in the literature. Moreover, absolute localization accuracy of the proposed scheme is shown to be resilient to the real time environmental changes caused by human bodies with random positions and orientations.

Application-level multicast suffers some disadvantages in terms of high multicast delay, overloading at rendezvous point (RP) and single-point of failure. In this paper, we propose DINCast to optimize application-level shared-tree multicast. The general idea of DINCast is to form a special logical data loop and use this loop instead of the RP as multicast sources. To demonstrate its effectiveness, we first analyze multicast delay with the inclusion of transmission delay, queuing delay and propagation delay of a typical symmetrical shared-tree multicast. We evaluate three placement schemes of DINCast. Our simulation results show that DINCast is effective in optimizing the multicast delay over the shared-tree multicast and the reduced rate of multicast delay may be up to 50%. Next, for any multicast tree, we carry out further simulations to optimize multicast delay. The experimental results show that DINCast can achieve better performance than original shared-tree multicast.

The problem of jointly optimizing the transmitter and receiver beamforming weights in the downlink of a cyclic prefix-free MC-CDMA system over multipath fading channels is addressed in this paper. Both the base station and user terminals are equipped with antenna arrays to leverage on the spatial information provided by the antenna arrays. The preceding and equalization matrices are designed with the aim of minimizing the overall mean-squared-error (MSE) of the system subject to a power constraint for each transmitter antenna, which has the additional benefit of reducing the probability of clipping of the transmitted signals from the transmitter. This is highly favorable for a MC-CDMA system due to the multi- carrier modulation involved. The performance of the optimization process is supported by simulation results.

In this paper, we consider the medium access control (MAC) protocol design problem for random access cognitive radio networks. A MAC protocol using a two-level opportunistic spectrum access strategy, called CR-CSMA, is proposed to efficiently schedule secondary users' packets, and yet to protect primary users' operations. We employ normalized throughput and average packet delay as network metrics, and derive closed-form expressions to evaluate our protocol. For various offered traffic rates of secondary users and frame lengths, the optimal performances of secondary users can be achieved at the same spectrum sensing time, and also there exists a tradeoff between performance and agility.

This paper presents the generalized butterfly graph (GBG) and its application to video stream authentication. Compared with the original butterfly graph, the proposed GBG provides significantly increased flexibility, which is necessary for streaming applications, including supporting arbitrary bit-rate budget for authentication and arbitrary number of video packets. Within the GBG design, the problem of constructing an authentication graph is defined as follows: given the total number of packets to protect, the expected packet loss rate for the network, and the available overhead budget, how should one design the authentication graph to maximize the probability that the received packets are verifiable? Furthermore, given the fact that media packets are typically of unequal importance, we explore two variants of the GBG authentication, packet sorting and unequal authentication protection, which apply unequal treatment to different packets based on their importance. Lastly, we examine how the proposed GBG authentication can be applied within the context of rate-distortion-authentication (R-D-A) optimized streaming: given a media stream protected by GBG authentication, the R-D-A optimized streaming technique computes an optimized transmission schedule by recognizing and accounting for the authentication dependencies in the GBG authentication graph.

This paper shows how Rate-Distortion-Authentication (R-D-A) optimized streaming may be performed with the Generalized Butterfly Graph (GBG) stream authentication method. R-D-A streaming is designed to compute an optimized transmission policy by accounting for both coding and authentication dependencies, and GBG is designed to protect the authenticity of a media stream. The GBG graph is better suited for streaming than the original Butterfly graph since it is highly flexible and can support an arbitrary number of packets and arbitrary overhead. However, the dependency chains within the GBG graph are much longer and are tangled with each other — making it very difficult to quantify the dependencies necessary to perform R-D-A streaming. We propose a method to estimate the authentication dependencies, which is then used by the R-D-A technique to compute the optimized transmission policy.

Video streaming with authentication is practically important, where a packet is decoded only when it is both received and authenticated. Recent work examined the problem of rate-distortion-authentication (R-D-A) optimized streaming of authenticated video. The original R-D-A technique assumes that each packet has only one deadline, its display deadline, and that a packet is not considered for transmission after its deadline. However, for video protected with an inter-packet graph-based authentication technique, a video packet can still be useful for verification of other packets even if it misses its own display deadline. We formulate the problem of multiple-deadline R-D-A optimized streaming and also propose ways to reduce the complexity. Simulation results using H.264 and NS-2 demonstrate that multiple-deadline R-D-A optimization achieves performance improvements of up to 4 dB over single-deadline R-D-A optimization.

This paper proposes a stream authentication method based on the generalized butterfly graph (GBG) framework. Compared with the original Butterfly graph, the proposed GBG graph supports an arbitrary overhead budget and number of packets. Within the GBG framework, the problem of constructing an authentication graph is considered as a design problem: Given total number of packets, packet loss rate, and overhead budget, we show how to design the graph (number of rows and columns and edge allocation among nodes) to maximize the expected number of verified packets. In addition, we also propose a new evaluation metric called loss-amplification-factor (LAF), which measures the extent to which the authentication method exacerbates the effective packet loss rate. Experimental results demonstrate significant performance improvements over existing authentication methods like EMSS, augmented chain, and the original Butterfly.

Traffic patterns for mobile communication networks are well known and accurately modelled. However, traffic generation behaviors for sensor networks are often lacking in research and not well understood. We investigate one class of sensor networks for a target tracking application using realistic sensing models and a general target motion model. Results show that the probability of arrivals for a tracking sensor network fits a quadratic curve.

Sensor networks deployed for the purpose of target tracking and intrusion detection have very specific requirements in terms of sensing coverage, timeliness of data and network lifetime. Many existing work focus on wakeup schemes that only attempt to prolong network usage but failed to address issues such as coverage, timeliness of data delivery, target tracking continuity and deployment cost all at the same time. Given any maximum target speed, our solution based on a combinatorics approach, guarantees delay bounds and node lifetime bounds, critical for any practical deployment of a target tracking application. We support our solution further with simulation results.

We define authenticated video as decoded video that results from those received packets whose authenticities have been verified. Generic data stream authentication methods usually impose overhead and dependency among packets for verification. Therefore, the conventional rate-distortion (R-D) optimized video streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the overhead and additional dependencies for authentication. In this paper, we study this practical problem and propose an Rate-Distortion-Authentication (R-D-A) optimized streaming technique for authenticated video. Based on packets' importance in terms of both video quality and authentication dependencies, the proposed technique computes a packet transmission schedule that minimizes the expected end-to-end distortion of the authenticated video at the receiver subject to a constraint on the average transmission rate. Simulation results based on H.264 JM 10.2 and NS-2 demonstrate that our proposed R-D-A optimized streaming technique substantially outperforms both prior (authentication-unaware) R-D optimized streaming techniques and data stream authentication techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed R-D-A Optimization technique still maintains optimized video quality. Furthermore, we examine a low-complexity version of the proposed algorithm, and also an enhanced version which accounts for the multiple deadlines associated with each packet, which is introduced by stream authentication

This paper proposes an optimized content-aware authentication scheme for JPEG-2000 streams over lossy networks, where a received packet is consumed only when it is both decodable and authenticated. In a JPEG-2000 codestream, some packets are more important than others in terms of coding dependency and image quality. This naturally motivates allocating more redundant authentication information for the more important packets in order to maximize their probability of authentication and thereby minimize the distortion at the receiver. Towards this goal, with the awareness of its corresponding image content, we formulate an optimization framework to compute an authentication graph to maximize the expected media quality at the receiver, given specific authentication overhead and knowledge of network loss rate. System analysis and experimental results demonstrate that the proposed scheme achieves our design goal in that the rate-distortion (R-D) curve of the authenticated image is very close to the R-D curve when no authentication is required

In this paper, we propose an optimized content-aware authentication scheme for JPEG-2000 streams over lossy networks, where a received packet is consumed only when it is both decodable and authentic. In a JPEG-2000 codestream some packets are more important than others in terms of coding dependency and visual quality. This inspires us to allocate more redundant authentication information for the more important packets to minimize the distortion of the authenticated image at the receiver. In other words, with the awareness of image content, we formulate an optimization framework, which is able to build an authentication graph yielding the best visual quality at the receiver, given a specific authentication overhead and network condition. Experimental results demonstrate that the proposed scheme achieved our design goals in that the R-D curve of an authenticated image is very close to its original one where no authentication is applied.

Stream authentication methods usually impose overhead and dependency among packets. The straightforward application of state-of-the-art rate-distortion (R-D) optimized streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the additional dependencies. This paper proposes an R-D optimized streaming technique for authenticated video, by accounting for authentication dependencies and overhead. It schedules packet transmission based on packets' importance in terms of both video quality and authentication dependencies. The proposed technique works with any stream authentication method as long as the verification probability can be quantitatively computed from packet loss probability. Simulation results based on H.264 JM 10.1 and NS-2 demonstrate that the proposed authentication-aware R-D optimized streaming technique substantially outperforms authentication-unaware R-D optimized streaming techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed technique still maintains R-D optimized video quality.

This paper proposes a content-aware authentication scheme optimized to account for distortion and overhead for media streaming. When authenticated media is streamed over a lossy network, a received packet is consumed only when it is both decodable and authenticated. In most media formats, some packets are more important than others. This naturally motivates allocating more redundant authentication information for the more important packets in order to maximize their probability of authentication and thereby minimize distortion at the receiver. Toward this goal, with awareness of the media content, we formulate an optimization framework to compute an authentication graph to maximize the expected media quality at the receiver, given specific authentication overhead and knowledge of network loss rates. Experimental results with JPEG-2000 coded images demonstrate that the proposed method achieves our design goal in that the R-D curve of the authenticated image is very close to the R-D curve when no authentication is required

In this paper, we propose DINPeer, an optimized peer-to-peer (P2P) overlay network for service discovery by overcoming limitations in current multicast discovery approaches and P2P overlay systems. DINPeer exploits a spiral-ring method to discover an inner ring with most powerful nodes (DIN Nodes) to form a logical DINloop. With the facilitation of the DINloop, multiple DIN Nodes easily form Steiner trees using Steiner tree-based heuristic routing algorithm. DINPeer further integrates the DINloop and Steiner trees with the P2P overlay network. The key features of DINPeer include that multiple DIN Nodes function as the Rendezvous Points (RPs) for theirs associated logical spaces respectively, and Steiner trees facilitate the communication among multiple DIN Nodes. Multiple powerful DIN Nodes release the burden on the centralized server and the self-recovered DINloop avoids the single point of failure. Simulations show that DINPeer is able to reduce multicast delay for the fast service discovery.

In this paper, we present a hybrid receiver scheme to overcome the shortcoming of the conventional interference cancellation (IC) receiver, for multiuser multicode CDMA (M-CDMA) systems. In conventional IC receivers, RAKE receiver is used in the first stage to obtain initial data estimation but substantial interference at the output of the RAKE receiver renders the initial data estimation highly inaccurate. Our proposed scheme consists of an equalizer for obtaining initial data estimation, followed by a parallel interference cancellation (PIC) section for enhancing the accuracy of data estimation. Simulation results are presented, which show that our proposed scheme achieves much better BER performance than the RAKE-PIC scheme

Classical tracking methods are not concerned with energy efficiency and require precise localisation. We addressed these in our previous work through HMTT (hierarchical Markov decision process for target tracking) that tracks single targets at location granularity. HMTT conserves energy by reducing the rate of sensing but preserves acceptable tracking accuracy through trajectory prediction. In this paper, HMTT is extended for the multiple targets case where the state of clusters could be affected by multiple incoming targets and where multiple updates are required at the lower level. The theoretical performance of HMTT in the multiple targets case is derived and simulations demonstrate its effectiveness against 2 other predictive tracking algorithms with up to 200% improvement.

In this paper, software tools which simulate the error correcting capability of forward error correction (FEC) codes, for the transmission of MPEG advanced audio coding (AAC) bitstreams over a wireless channel, are described. The tools consist of two applications. The first application is a channel transmission simulator with the features to set the choice of FEC codes, wireless channel characteristics and observe the simulation results. The second application is a graph plotting software that facilitates analysis of simulation results. Such a tool makes it convenient to investigate the transmission of audio data over wireless links.

In this paper, we present a new entropy coder, context-based bit plane Golomb coder (CB-BPGC) for scalable image coding, which achieves better coding performance with lower complexity compared to the state-of-the-art JPEG 2000 entropy coder EBCOT. Because of the direct output lazy bit planes, applying the partial decoding on the corrupted bit planes, and the better compression ratio which may lead to corruption to the less important codestream, CB-BPGC appears more resilient to errors when simulated on the wireless channel based on Rayleigh fading model.

In this paper, we propose a butterfly-graph based stream authentication scheme for lossy networks where the streaming packets could be lost in both random and burst ways. Due to the nice properties of butterfly graph, the proposed scheme is quite robust and efficient. Theoretical analysis and simulation results show that the proposed scheme outperforms existing schemes in terms of overhead and authentication probability while maintaining the same levels of sender/receiver delay and robustness.

Compression ratio and computational complexity are two major factors for a successful image coder. By exploring the Laplacian distribution of the wavelet coefficients, a new bit plane entropy coder is proposed in this paper. Compared with the state-of-the-art JPEG2000 entropy coder (EBCOT), the proposed coder achieves a 0.75% better loss less performance for 5 level 5/3 wavelet decomposition at block size 64 £ 64 and 2.56% at block size 16 £ 16. Experimental results also show PSNR improvements of about 0.13dB at 1bpp and 0.25dB at 2bpp on average for lossy compression. However, the gain in coding performance is not based on increasing computational complexity but in stead a reduction by using a static arithmetic coder which avoids complicated adaptive procedure.

To overcome the existing scalability problems, DINloop (Data-in-Network loop) based multicast with MPLS (multiprotocol label switching) is proposed to optimize inter-domain multicast. In our approach, multiple DIN Nodes in the core network form the DINloop using MPLS so that multiple multicast sessions share a single DINloop. We adopts a label stack method to use fewer labels and label switch paths (LSPs) are established between DIN Nodes to create the DINloop. Packet processing module analyzes the coming multicast packet and MPLS Manager assigns a label stack to it. Simulations show that DINloop based multicast uses the least message load needed to form the multicast architecture. In addition, the routing table size in the core router does not increase as the number of multicast group increases, and therefore DINloop based multicast increases the routing scalability for inter-domain multicast. Finally, the multicast delay in DINloop based multicast is moderate lager than other protocols.

To overcome scalability and control overhead problems existing in inter-domain multicast, DINloop (data-in-network loop) based multicast with MPLS is proposed. DINloop is a special logical path formed using MPLS label switched paths and it consists of multiple DIN nodes which are core routers that connect to each intra-domain. We use DINloop to manage inter-domain multicast group membership. Facilitated by the DINloop, multiple DIN nodes in the core network easily form a Steiner tree for multicast traffic. Furthermore, traffic for different multicast groups sharing the same path are aggregated through a label stack. Simulations demonstrate that DINloop-based multicast results in less message load needed to form the multicast structure. In addition, the routing table size in other core routers does not increase as the number of multicast group increases, and therefore routing scalability is improved. Finally, the inter-domain multicast delay in DINloop-based multicast is lower than that of a unidirectional tree.

For power-constrained wireless ad hoc networks, a crucial issue in routing and multicasting is to conserve as much power as possible while still achieving good throughput performance. In this paper, we propose a class of power-efficient multicasting algorithms using a new cost function that jointly optimizes power conservation and path efficiency. Simulation results demonstrate significant performance improvement in terms of power efficiency and interference control, while achieving better path efficiency compared with the algorithms that use pure power as the cost function.

In this paper, we propose a lossy-to-lossless watermarking scheme for JPEG2000/J2K images. The watermarking is incorporated into the procedure of J2K coding in a way that the final watermark coded J2K bitstream can still maintain lossless-to-lossy scalability. To recover the original image, the J2K decoder does not need to extract the embedded watermark. This is achieved by dividing the magnitude bits of the wavelet coefficients into two portions, controlled by the watermarking survival rate (WSR). The upper portion is used to embed watermarks, while the lower portion is modified to compensate for the distortion introduced by the watermarks in the upper portion. Watermark detection can be done either from the upper portion or from the lower portion, based on a presetting threshold. Experimental results show that the J2K coded watermarked images still nearly follow the rate-distortion curve optimized for their original J2K images.

The multi-modality nature of sensor networks and their potentially large-scale deployment have generated highly dimensional network data. This paper proposes a hierarchical collaborative data fusion scheme based on particle filters for cross-sensor fusion and cross-modality fusion for target tracking applications.

In this paper, a system using soft decision decoding of convolutional codes is investigated for MPEG-4 advanced audio coding. Under severe channel random bit error rates of 4.00x10/sup -2/ and above, the proposed scheme is able to achieve above 90% improvement in residual bit error rate performance over the current scheme. The average percentage overhead incurred using the proposed scheme is about 21%, which is the same as that of the original UEP scheme. An analysis with the theoretical bounds shows that within experimental error, the proposed scheme can perform to near optimum as compared to the current scheme.

Emerging technologies, like RPR, POS and GE, are being pushed to replace the traditional SONET/SDH and ATM in WAN. This paper evaluates performance issues of RPR, POS and GE on a testbed, including fault recovery, network stress, QoS and multicast behavior. Unlike other papers, which may have done performance investigations on a theoretical or simulation basis, all results presented in this paper are measured from a physical testbed network.

This paper proposes joint spatial-frequency quantization for a zerotree wavelet video encoder. Following temporal redundancy reduction via block motion estimation, the displaced frame difference signal is discrete wavelet transformed, zerotree encoded and optimally quantized. Improved performance is obtained compared to H.263 and systems nor employing joint quantization, while at the same time preserving spatial and frequency scalability.

Previously, we (see Wong, Sundberg and Seshadri, GLOBECOM '93, p.1649-1653, 1993) proposed a low-delay multiple access scheme, called shared time-division duplexing (STDD). This scheme allows both the up-link and downlink traffic to share a common channel, thereby achieving high statistical multiplexing gain even with a low population of simultaneous conversations. Here, we focus on transmitting speech using this multiple access scheme by a suitable choice of speech coder, channel coder and modulation. In particular, we consider low delay, high quality speech coding and digital modulation systems based on adaptive DPCM, with QDPSK or pseudo-analog transmission (skewed DPSK). The choice of the alternative systems depends on required end-to-end delay, recovered speech quality and bandwidth efficiency. Typically, with a total capacity of 1 MBaud, 2 ms frame and 8 kBaud speech coding rate, low delay STDD is able to support 48 pairs of users compared to 38, 35 and 16 for TDMA with speech activity detection, basic TDMA and PRMA respectively. This corresponds to respective gains of 26%, 37% and 200%.>

The author examines the performance of a packet reservation multiple access (PRMA) protocol in a metropolitan microcellular radio environment using computer simulations. Call characteristics, mobility, and channel conditions have an impact on PRMA performance which is measured in terms of the average packet dropping rate per call. In order to maintain a reasonable level of service quality for calls in progress, teletraffic loading can be controlled by introducing a call setup queue and limiting the number of active subscribers. Call mobility is found to have a minor impact on PRMA performance. PRMA is also fairly robust against adverse channel conditions with a drop in performance of about 15% when the channel packet loss rate is less than 0.01. Performance comparisons to other protocols are also carried out.>

The teletraffic performance of a highway microcellular digital mobile radio system having an oversailing macrocell that spans many microcells is presented. The microcellular cluster is composed of concatenated segments of the highway where each segment is a microcell, typically 500-2000 m in length, with the base stations located at lamp-post elevations. A narrowband time-division-multiple-access arrangement supporting ten channels per carrier and one carrier per base station is used. The teletraffic analysis assumes there are n-up and n-down lanes, and that the vehicular speeds conform to a truncated Gaussian distribution whose mean speed is 100 or 50 km/h when the vehicular traffic is free-flowing or in traffic-congested conditions, respectively.>

Packet communication systems cannot, in general, guarantee accurate and prompt delivery of every packet. The effect of network congestion and transmission impairments on data packets is extended delay; in voice communications these problems lead to lost packets. This paper describes techniques for replacing missing speech with waveform segments from correctly received packets in order to increase the maximum tolerable missing packet rate. After presenting a simple formula for predicting the probability of waveform substitution failure as a function of packet duration and packet loss rate, we introduce two techniques for selecting substitution waveforms. One method is based on pattern matching and the other technique explicitly estimates voicing and pitch. Both approaches achieve substantial improvements in speech quality relative to silence substitution.

In the past few years, a great deal has been learned about the modeling and equalization of multipath fading in microwave digital radio systems. This paper adds a number of new results to this body of knowledge. We begin with a description of two published multipath fading models that were derived from the same collection of data, i.e., the threepath and polynomial models. We then derive outage probability characteristics for a digital radio link usingM-level quadrature amplitude modulation, cosine rolloff spectral shaping, and some form of equalization. We obtain results for both models, several kinds of equalization, and various combinations of system parameters. We find that the theoretically optimal equalizer, which is nonlinear, is not measurably better in performance than the best of the linear equalizers, which is simpler. Also, the performance of a properly equalized receiver can be very accurately estimated by knowing just the probability distribution, over many fades, of the gain amplitude at a single frequency. Finally, we show the influences on performance of such parameters as cosine rolloff factor, symbol rate, and unfaded carrier-to-noise ratio.

A new diversity technique is proposed to combat Rayleigh fading in digital mobile radio systems transmitting speech signals. The speech signals are μ-law PCM encoded (\mu = 255, 8 kHz sampling, 8 bits/code word, 64 kbit/s data rate), and alternate data words are used to form two streams called "odd" and "even." The even stream is delayed by τ seconds and the streams are interleaved prior to radio transmission using two-level PSK modulation. At the receiver the odd data stream is delayed by τ and interleaved with the even stream. Consequently, if an error burst occurs, the effect of the reshuffling of the data stream is, in general, to place words with bit errors in juxtaposition to those correctly received. After μ-law PCM decoding of the words, a statistical error detection strategy is evoked to identify the erroneous samples. These samples are replaced by adjacent sample interpolation to give the recovered speech sequence. No recourse to channel protection coding is made. In our experiments a Rayleigh fading envelope was generated from a hardware simulator and stored in a computer, along with four sentences of speech. The system was then simulated and the recovered speech perceived. The objective performance measures were segmental SNR for the audio signal, and BER. Different error detection strategies were examined and restrictions on τ investigated. For a mobile speed of 30 mph, SNR values of 32, 21, and 16 dB were obtained for BER values of 0.1, 1, and 2 percent, corresponding to SNR gains over an uncorrected system of 3, 9, and 11 dB, respectively.

In pulse code modulation (PCM) of speech, transmission errors cause changes in the statistical properties of the received samples. Recently published work describes computer simulations of a difference detection and correction (DDC) system which detect errors on the basis of these changes. The system examines the differences between adjacent received samples. If a particular difference exceeds a threshold, which is dependent on the rms value of a block of 64 differences, the quantized sample responsible for this large difference is deemed to be erroneous. A nonlinear filter of the median type is then introduced as a corrector. We now present a comprehensive description of the hardware realization of this system, and present measured results of signal-to-noise ratio (snr) as a function of bit error rate, input power and the error criterion for narrow-band white noise input signals. An improvement of 6 dB in snr is achieved for error rates between 0.2 and 2.0%. The snr results are consistent with subjective impressions of the quality of speech processed by the DDC system.