Telerehabilitation is a cost-effective alternative to in-clinic rehabilitation. Although convenient, it lacks immersive and free-viewpoint patient visualization. Current research explores two solutions to this issue. Mesh-based methods use 3D models and motion capture for AR visualization. However, they are labor-intensive and less photorealistic than 2D images. Microsoft's Holoportation generates photorealistic 3D models with eight RGBD cameras in real time. However, it requires complex setups, high GPU power, and high-speed communication infrastructure, making deployment challenging. This article presents a Real-Time Free-Viewpoint Holographic Patient Rendering (RT-FVHP) system for telerehabilitation. Unlike traditional methods that require manually crafted assets such as 3D meshes, texture maps, and skeletal rigging, our data-driven approach eliminates the need for explicit asset definitions. Inspired by the HumanNeRF framework, we retarget dynamic human poses to a canonical pose and leverage 3D Gaussian Splatting to train a neural network in canonical space for patient representation. The trained model generates 2D RGB$\sigma$σ outputs via Gaussian Splatting rasterization, guided by camera parameters and human pose inputs. Compatible with HoloLens 2 and web-based platforms, RT-FVHP operates effectively under real-world conditions, including handling occlusions caused by treadmills. Occlusion handling is accomplished using our Shape-Enforced Gaussian Density Control (SGDC), which initializes and densifies 3D Gaussians in occluded regions using estimated SMPL human body priors. This approach minimizes manual intervention while ensuring complete body reconstruction. With efficient Gaussian rasterization, the model delivers real-time performance of up to 400 FPS at 1080p resolution on a dedicated RTX6000 GPU.

Adaptive compressive spectrum sensing (ACSS) plays a vital role in cognitive radio networks due to its reduced sampling rate and power consumption. Most of the existing ACSS schemes focus on the improvement of spectrum sensing performance, but do not provide the deterministic assurance of sensing results. Hence, we propose an ACSS based on deterministic estimation model (ACSS-DEM) to provide the confidence level of the reconstructed signals. This is realized by deriving the closed-form expression of the cumulative distribution function (CDF) of reconstructed errors. Firstly, in each sensing interval of ACSS, we propose a novel signal reconstruction algorithm that incorporates the prior knowledge into ℓ2,1-norm minimization of block-sparse signals. Secondly, a prior knowledge refining strategy is designed through convex geometry theory to further improve the reconstruction accuracy. Finally, the CDF of reconstruction error is derived and regarded as a stopping criteria for observation sample collection. The experiment results demonstrate that the proposed ACSS-DEM delivers optimal spectrum-sensing performance, offering over a 12.2% improvement in detection performance at a false alarm probability of 0.1, compared with other typical ACSS methods, e.g., ACSS-JL, ACSS-SOC, and ACSS-CV.

Since the waveforms of the signals received at adjacent times are different due to varying modulated signals, it is still a key challenge on how to describe temporal correlations precisely in compressive spectrum sensing (CSS). In this paper, we propose a higher-order cumulant-assisted CSS (HOC-CSS) algorithm, where the semantic correlation of adjacent received signals in higher-order domain is exploited. Firstly, the fourth-order cumulants of the reconstructed signals in the previous time are calculated. Secondly, the fourth-order cumulants are integrated with$\ell _{p}$-norm minimization to estimate the current spectrum signals. Furthermore, a two-branch CSS scheme is proposed to eliminate the effect causing by adjacent signals under different modulations. The HOC-CSS is performed in one branch and a noninformative CSS algorithm is performed in another branch, thereby two different reconstructed signals are obtained. The Euclidean distances between the measurements and the projection of reconstructed signals are calculated. The final output is the estimated signal with smaller Euclidean distance. The experimental results demonstrate that the proposed algorithm has more than 8% and 10% detection performance improvement at false alarm probability 0.1, under compressive ratio of 0.4 and 0.5 respectively, compared with the state-of-the-art CSS schemes, e.g., HMCPI-CSS, Weighted$\ell _{1}$-CSS, AR-IRLS, and IP-IRLS.

Recently, with the development of automatic driving, high-precision localization has become indispensable for safety. The inertial measurement unit (IMU) integrated with ultra-wideband (UWB) technology has been thought as a promising scheme by the company Humatics for trains. Generally, the spaces of transportation are extended along a long path with flexural tunnels and sideway obstacles around, where non-line-of-sight (NLoS) propagation happens frequently. However, a large interval deployment of UWB anchors along such narrow path with NLoS interference will dramatically decrease localization performance. Hence, we propose a robust algorithm to mitigate the NLoS interference. Firstly, a joint LoS/NLoS detection and mitigation algorithm is proposed to improve the ranging accuracy of mobile target with UWB tags under mixed LoS/NLoS interference. Secondly, we improve the conventional Kalman Filter (KF) algorithm with forward-backward propagation to integrate temporal correlations further. Finally, a comprehensive fusion localization scheme with ranging error mitigation and improved KF is proposed. The simulation results show that, regarding the static localization, the proposed joint LoS/NLoS detection and mitigation algorithm outperforms five other typical NLoS elimination algorithms, and achieves 48.2%, 62.4%, 45.5%, 69.9% and 50.2% gains in terms of root mean square error (RMSE), under NLoS scenarios with a mean ranging error of 0.5 m. Regarding the dynamic localization, compared with two other typical KF localization algorithms, the proposed localization scheme achieves 20.9% and 14.5% localization accuracy improvements in LoS scenarios, 45.7% and 47.2% improvements in mixed LoS/NLoS scenarios, respectively. Furthermore, the effectiveness of the proposed scheme is also verified in our testing platform.

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routes meet at the same relay node. Therefore, those busy nodes should be avoided when a new path is established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses a spatio-temporal deep learning algorithm to predict the next-time snapshot of a directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. The DHM is then used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It splits a single path into multiple paths converge later on into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than existing routing schemes.

In this article, we propose a distributed medium access control (MAC) protocol for the unmanned aerial vehicle (UAV) network with directional antennas. It uses a distributed learning algorithm to control each node's communication parameters (such as sending rate) according to the observations of only 1-hop neighbors. Because of the asynchronous and decentralized nature of multi-agent deep distributed reinforcement learning (MADDRL), we adopt Multi Agent Deep Deterministic Policy Gradient (MADDPG) [3] to generate discrete actions for the MAC layer protocol. Under the individual and distributed observations of the environment, each node/agent can update their own states and rewards locally. Moreover, two major communication quality evaluation metrics - throughput (THR) and delay (DEL) are used to show the effectiveness and scalability of the distributed algorithm. From the results, the protocol using MADDPG shows more advantages than the general MAC schemes, including higher end-to-end THR, lower queuing DEL, and lower communication message exchange.

In dense traffic cases, the channels among vehicles and infrastructures in cellular vehicle-to-everything (C-V2X) network are likely to be congested. Vehicles can select spectrum resources autonomously by adopting sensing-based semipersistent scheduling scheme, which may result in frequent packet collisions due to the random selection process, especially when spectrum resources are limited. So far, there still lacks a definite stochastic expression to characterize the channel selection process for vehicles in C-V2X. In this article, we propose a novel deep reinforcement learning (DRL)-based dynamic spectrum access (DSA) algorithm for C-V2X via imitating Indian buffet process (IBP), aiming to meet the vehicles’ strict communication requirements on high-transmission rate and low-collision probability. First, we explore the correlations among historical spectrum access data to acquire the channel availability list. Then, we exploit DRL to achieve distributive DSA among vehicles. Specifically, the channel state prediction for the distributive DSA is facilitated via imitating the classic IBP, and the spectrum access decisions are made by leveraging the deep$Q$-learning network combined with long short-term memory technique. Finally, comprehensive simulation results are presented to show that the proposed algorithm can improve the transmission rate by 15% and reduce the collision probability by 12% with a false alarm probability of 0.1 compared with other spectrum access methods.

Stroke is the leading long-term disability and causes a significant financial burden associated with rehabilitation. In poststroke rehabilitation, individuals with hemiparesis have a specialized demand for coordinated movement between the paretic and the nonparetic legs. The split-belt treadmill can effectively facilitate the paretic leg by slowing down the belt speed for that leg while the patient is walking on a split-belt treadmill. Although studies have found that split-belt treadmills can produce better gait recovery outcomes than traditional single-belt treadmills, the high cost of split-belt treadmills is a significant barrier to stroke rehabilitation in clinics. In this article, we design an AI-based system for the single-belt treadmill to make it act like a split-belt by adjusting the belt speed instantaneously according to the patient's microgait phases. This system only requires a low-cost RGB camera to capture human gait patterns. A novel microgait classification pipeline model is used to detect gait phases in real time. The pipeline is based on self-supervised learning that can calibrate the anchor video with the real-time video. We then use a ResNet-LSTM module to handle temporal information and increase accuracy. A real-time filtering algorithm is used to smoothen the treadmill control. We have tested the developed system with 34 healthy individuals and four stroke patients. The results show that our system is able to detect the gait microphase accurately and requires less human annotation in training, compared to the ResNet50 classifier. Our system "Splicer" is boosted by AI modules and performs comparably as a split-belt system, in terms of timely varying left/right foot speed, creating a hemiparetic gait in healthy individuals, and promoting paretic side symmetry in force exertion for stroke patients. This innovative design can potentially provide cost-effective rehabilitation treatment for hemiparetic patients.

Human pose estimation (HPE) has attracted a significant amount of attention from the computer vision community in the past decades. Moreover, HPE has been applied to various domains, such as human–computer interaction, sports analysis, and human tracking via images and videos. Recently, deep learning-based approaches have shown state-of-the-art performance in HPE-based applications. Although deep learning-based approaches have achieved remarkable performance in HPE, a comprehensive review of deep learning-based HPE methods remains lacking in literature. In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE. We summarize these methods of 2-D and 3-D HPE, and their applications, discuss the challenges and the research trends through bibliometrics, and provide insightful recommendations for future research. This article provides a meaningful overview as introductory material for beginners to deep learning-based HPE, as well as supplementary material for advanced researchers.

5G-based millimeter Waves (mmWave) systems have the prospective of enabling > 1Gbps communications in the Intelligent Transportation Systems (ITS). ITS relies on vehicle-to-everything (V2X) communications to share information among vehicles. However, the V2X Communications via existing technologies such as DSRC, 3G, 4G and LTE, are not able to achieve such a high data rate. Although 5G-based mmWave can support ultra-low-delay V2X transmissions, it comes with beam alignment difficulties as well as the routing stability issues due to rapid mobility of vehicles. The dynamic vehicle traffic causes frequent beam misalignment which tends to degrade the quality-of-service (QoS) performance. In this paper, we first propose a 3D-based position detection scheme for beam alignment/selection purpose. Then a group-based routing algorithm is performed to select a secure path for achieving trustworthy data transmissions. The road traffic is automatically segmented to divide the vehicles into different groups, and each group head is selected and members are added. Group members are authenticated by the group head via elliptic curve algorithms. Huffman coding is performed to compress the data and encrypt the binary files. This proposed novel intelligent beam control and secure stable routing scheme have been verified in simulations to demonstrate much better performance than existing schemes.

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

The emergence of multi-beam directional antennas (MBDAs) has paved the way for fast and high-throughput data communications by providing concurrent multi-directional transmissions. However, the existing routing protocols are not capable of utilizing the advantages of MBDAs. In this paper, we have developed a new routing scheme, called volcano routing, which can exploit the concurrent packet dispatching capability of MBDAs for high-throughput data delivery. Its topology resembles the flow of volcano lava and several routing “pipes” are used, which can detour around the network “holes” or blocked areas. The routing process consists of two phases: 1) Main path search phase: There is a main path at the core of each pipe. Multiple optimal main paths are formed that have a high potential of adding side nodes to enable multi-beam communications. A hierarchical scoring system and the performance metrics are used to evaluate the quality of the main paths. 2) Volcano establishment phase: The top-quality main paths are selected, and side paths are formed around each main path to establish the volcano pipes. A multi-beam traffic scheduling and dispatching policy is also proposed to achieve better performance. Our results show that the volcano routing scheme can exploit the advantages of MBDAs for achieving high data rates.

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

A set of novel routing protocols is proposed for directional hierarchical airborne networks. These networks use a two-level architecture, where the higher-level network is sparse with directional long-distance links that support the high data-rate communication. The lower-level network consists of high-density nodes with short-distance and low data-rate links. We assume that the higher-level nodes are equipped with multi-beam antennas, whereas the lower-level nodes have omni-directional antennas. We use the bio-inspired algorithms (based on moth and ant behaviors) to design the routing schemes for both levels. Specifically, we use the male moth's light source pursuing pattern for routing the data from an event node to the highly mobile sink in lower-level network, and the ant's chemical trail maintenance principle to trace the trajectory of commander node to deliver the data from lower-level node to the commander node in the higher-level network, with minimum delay. In addition, we construct a weighted fence routing topology among higher-level nodes with multi-beam antennas, in order to achieve high throughput. Our simulation results demonstrate that significant performance improvement is achieved by the bio-inspired routing schemes, compared with conventional ad hoc routing schemes.

This paper presents an intelligent spectrum mobility management scheme for cognitive radio networks. The spectrum mobility could involve spectrum handoff (i.e., the user switches to a new channel) or stay-and-wait (i.e., the user pauses the transmission for a while until the channel quality improves again). An optimal spectrum mobility management scheme needs to consider its long-term impact on the network performance, such as throughput and delay, instead of optimizing only the short-term performance. We use a machine learning scheme, called the Transfer Actor-Critic Learning (TACT), for the spectrum mobility management. The proposed scheme uses a comprehensive reward function that considers the channel utilization factor (CUF), packet error rate (PER), packet dropping rate (PDR), and flow throughput. Here, the CUF is determined by the spectrum sensing accuracy and channel holding time. The PDR is calculated from the non-preemptive M/G/1 queueing model, and the flow throughput is estimated from a link-adaptive transmission scheme, which utilizes the rateless (Raptor) codes. The proposed scheme achieves a higher reward, in terms of the mean opinion score, compared to the myopic and Q-learning based spectrum management schemes.

This paper aims to develop a home-oriented cyber-physical system to help patients improve their motion coordination capability via physical training. The measures evaluated by the system include the motion style of the legs, the periodicity of the foot trajectory, and the foot balance level, which are recommended by physical therapists. The motions of the legs and feet are recorded by thermal camera, and the plantar pressure is measured by the insole pressure sensors. We have developed innovative algorithms to extract the leg skeletons from the thermal images, and to implement motion signal auto-segmentation, recognition, and analysis for the above-mentioned measures. The experimental results have verified that the proposed system could efficiently acquire and analyze the lower-limb motion information.

Current key management protocols involve high computational complexity and large-scale broadcast, which are inapplicable for Unmanned Aerial Vehicle (UAV) networks. This paper proposes a novel group key management protocol, which avoids high computation cost by employing a secret sharing algorithm based on Chinese Remainder Theory. Furthermore, the proposed scheme dynamically chooses the information source as the key distribution center and piggybacks the key management messages onto routing communications, which are suitable for UAV networks and build secure sessions in an efficient manner.

The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed. The KMV-Cast is built on a linear video transmission instead of a traditional digital video system, and exploits the hierarchical Bayesian model to integrate the correlated information into the video reconstruction at the receiver. The correlated information is distilled to obtain its intrinsic features, and the Bayesian estimation algorithm is used to maximize the video quality. The KMV-Cast system consists of both likelihood broadcasting and prior knowledge broadcasting. The simulation results show that the proposed KMV-Cast scheme outperforms the typical linear video transmission scheme called Softcast, and achieves 8 dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., -10 dB), and 5 dB more of PSNR gain at high-SNR channels (i.e., 25 dB). Compared with the traditional digital video system, the proposed scheme has 7 dB more of PSNR gain than the JPEG2000 + 802.11a scheme at a 10-dB channel SNR.

We propose a novel spectrum decision scheme (i.e., channel selection and handoff) for wireless mesh networks (WMN) which use multiple channels and nodes equipped with multi-beam directional antennas. Our scheme has the following features: (i) It performs spectrum decision by considering various WMN parameters, including the channel quality, beam orientation, antenna-caused deafness and capture effects, and application priority level. (ii) It uses the reinforcement learning (RL)-based spectrum decision process to achieve the optimal quality of multimedia transmission in the long term. However, a newly-joined WMN node could take a long time to make a correct spectrum decision due to the difficult choice of initial RL parameters. Therefore, our scheme uses the apprenticeship learning in conjunction with the RL model, to speed up the spectrum decision process by choosing a suitable neighboring node (called “expert”) to teach a newly-joined node (called “apprentice”). Our experiments demonstrate that the proposed spectrum decision scheme improves the network performance and multimedia transmission quality.

In this paper, we propose to build a comprehensive cyberphysical system (CPS) with virtual reality (VR) and intelligent sensors for motion recognition and training. We use both wearable wireless sensors (such as electrocardiogram, motion sensors) and nonintrusive wireless sensors (such as gait sensors) to monitor the motion training status. We first provide our CPS architecture. Then we focus on motion training from three perspectives: 1) VR-first we introduce how we can use motion capture camera to trace the motions; 2) gait recognition-we have invented low-cost small wireless pyroelectric sensor, which can recognize different gaits through Bayesian pattern learning. It can automatically measure gait training effects; and 3) gesture recognition-to quickly tell what motions the subject is doing, we propose a low-cost, low-complexity motion recognition system with 3-axis accelerometers. We will provide hardware and software design. Our experimental results validate the efficiency and accuracy of our CPS design.

This paper presents an information-gain-based sensor selection approach as well as a sensor sensing probability model-based calibration process for multihuman tracking in distributed binary pyroelectric infrared sensor networks. This research includes three contributions: 1) choose the subset of sensors that can maximize the mutual information between sensors and targets; 2) find the sensor sensing probability model to represent the sensing space for sensor calibration; and 3) provide a factor graph-based message passing scheme for distributed tracking. Our approach can find the solution for sensor selection to optimize the performance of tracking. The sensing probability model is efficiently optimized through the calibration process in order to update the parameters of sensor positions and rotations. An application for mobile calibration and tracking is developed. Simulation and experimental results are provided to validate the proposed framework.

Conventional pyroelectric infrared (PIR) motion sensors use paired elements for the detection of moving targets. This method makes them incapable of measuring thermal signals from static targets. We need an active sensor that can detect static thermal subjects. This paper presents our design of active PIR sensors. The proposed PIR sensing systems can actively detect static thermal targets by using three methods that are suitable to different applications: 1) a sensor that can be rotated by a self-controlled servo motor for the detection of moving or static thermal subjects nearby; 2) a sensor that is equipped with a mask for low-complexity posture recognition; and 3) a sensor that can be worn on the wrist for the recognition of surrounding subjects (this sensor is especially useful for blind users). Compressive sensing (CS) theory indicates that random down-sampling method can capture more accurate information of the original signal than the evenly spaced sampling. Based on CS theory, we have developed the random sampling structures for the active PIR systems, and have built a statistical feature space for human scenario recognition. The experimental results demonstrate that the active sensing system can efficiently measure the static thermal targets, and the random sampling scheme has a better recognition performance than the even sampling scheme.

In this paper, we propose the concept of "channel+ beam" handoff (CBH) in cognitive radio networks (CRNs) with multi-beam smart antennas (MBSAs). We address the multi-class spectrum handoff issue, based on the user priority, by using the mixed Preemptive/Non-Preemptive M/G/1 queueing model with discretion rule. The discretion rule sets the threshold for the preemption of the low-priority secondary users (SUs) based on the remaining service time. Since each beam of the MBSA can use a different CRN channel, we design a delay-constrained CBH scheme in which the packets in the interrupted beam can be detoured through neighboring beams. The probability of beam handoff is determined based on their channel capacity and buffer fullness of the queue associated with the neighboring beams. By using the CBH model with discretion rule, a low priority SU can escape from multiple interruptions during the completion of its service. Our simulation results show that the mixed queueing model with discretion rule reduces the queueing delay (i.e., handoff delay) of the high priority SUs, and the packet detouring scheme gives fair spectrum access opportunities to the low priority SUs.

Conventional wireless mesh network (WMN)routing protocols are designed for the nodes that use the omni-directional or single-beam directional antennas. This research presents a throughput-efficient routing scheme for WMN, by taking advantage of the nodes equipped with the multi-beam directional antennas (MBDAs). Our routing design has the following two novel features: First, it is a cross-layer design by integrating the routing scheme with multi-beam oriented medium access control (MAC) scheme. Second, the routing topology has a diamond-like shape and uses the multi-path routes (i.e., one main path and a few side paths). The diamond shape makes the traffic converge and diverge periodically in the routing paths, which exploits the simultaneous data delivery capability of multi-beam antennas, and enhances the network throughput. Our simulation results demonstrate the high throughput efficiency of the proposed multi-beam routing scheme.

Multiple-input multiple-output (MIMO) is a promising way to increase link capacity and energy efficiency in the next generation communication systems. However, the benefits of such an approach depend on proper channel state information (CSI) availability at the transmitter. The CSI is usually estimated at the receiver and fed back to the transmitter through a band-limited channel. Thus, an efficient feedback scheme is needed. In this paper, a comprehensive Bayesian compressive sensing (BCS) based feedback mechanism is proposed for time-varying spatially and temporally correlated vector autoregression (VAR) wireless channel, and the feedback rate distortion function is derived in closed form in statistics. The proposed BCS feedback scheme utilizes the sparse CSI features and prior knowledge to significantly compress the dimensionality of the feedback CSI. Furthermore, the relationship between the feedback rate and downlink capacity is derived in closed form in statistics to guide rate-adaptive feedback in MIMO system. We find out that the ergodic downlink capacity of a user is determined only by its own feedback rate in the proposed feedback scheme. Theoretical and simulation results all show that the proposed feedback scheme can realize efficient, rate-adaptive feedback based on downlink capacity requirement, and the proposed feedback performance is superior to other related works.

Cognitive radio (CR) is a critical technology for improving spectrum utilization and solving the radio spectrum scarcity problem. In CR devices, spectrum sensing is important to implement opportunistic spectrum access. Many spectrum sensing schemes have been proposed, including uncooperative, cooperative, centralized, and distributed algorithms. However, they aimed to obtain a global consensus sensing result, which may not always be possible in large-scale cognitive radio networks (CRNs) due to heterogeneous spectrum availability in different areas. Hence, some new spectrum sensing schemes should be designed to discover idle heterogeneous spectrum in CRNs. In this paper, we propose an intelligent cooperative spectrum sensing algorithm based on a non-parametric Bayesian learning model, namely the hierarchical Dirichlet process, which groups spectrum sensing data without the need to know the number of hidden spectrum states, and discovers a common sparse spectrum within each group. Furthermore, a concisely distributed information exchange scheme is designed, where intra-cluster and inter-cluster spectrum information is shared for global spectrum cognition. Experimental results show that the proposed algorithm can exploit the spatial relationship among sensed data to achieve a better spectrum sensing performance in terms of detection probability and false alarm probability.

In this paper, we present a framework for complex scenarios recognition with crowded walkers. This study aims to develop an alternative surveillance system to traditional video camera and visual sensor based systems. Instead of utilizing visual devices in traditional surveillance systems, our crowded scene analysis is based on PIR (Pyroelectric Infrared) sensor networks with intelligent algorithms for context pattern extraction and analysis. Specifically, we will propose two new ideas to handle the crowded scenes: (1) Use hierarchical Bayesian NMF (Non-negative Matrix Factorization) algorithm to automatically identify the basic pattern basis, which will be used for accurate scenario recognition, (2) Use a tree-based structure to organize all basic features for fast object recognition. The experimental results valid the efficiency of the proposed two schemes on crowded scenario recognition with low-cost, non-visual system. The results also demonstrate that our framework is appropriate to be implemented in a wireless sensor based monitoring system under severe circumstances.

Enabling the spectrum handoff for multimedia applications in cognitive radio networks (CRNs) is challenging, due to multiple interruptions from primary users (PUs), contentions among secondary users (SUs), and heterogenous Quality-of-Experience (QoE) requirements. In this paper, we propose a learning-based and QoE-driven spectrum handoff scheme to maximize the multimedia users' satisfaction. We develop a mixed preemptive and non-preemptive resume priority (PRP/NPRP) M/G/1 queueing model for modeling the spectrum usage behavior for prioritized multimedia applications. Then, a mathematical framework is formulated to analyze the performance of SUs. We apply the reinforcement learning to our QoE-driven spectrum handoff scheme to maximize the quality of video transmissions in the long term. The proposed learning scheme is asymptotically optimal, model-free, and can adaptively perform spectrum handoff for the changing channel conditions and traffic load. Experimental results demonstrate the effectiveness of the proposed queueing model for prioritized traffic in CRNs, and show that the proposed learning-based QoE-driven spectrum handoff scheme improves quality of video transmissions.

The unequal error protection (UEP) has shown promising results for transmitting video over error-prone wireless channels. In this paper, we investigate the cross-layer design of forward error correction (FEC) schemes by using the UEP Raptor codes at the application layer (AL) and UEP rate compatible punctured convolutional (RCPC) codes at physical layer (PHY) for prioritized video packets. The video packets are prioritized based on their contribution to the received video quality. A genetic algorithm (GA)-based optimization algorithm is proposed to find the optimal parameters for both Raptor and RCPC codes, to minimize the video distortion and maximize the peak signal-to-noise-ratio for the given video bit rates and channel constraints (i.e., SNR and available bandwidth). We evaluate the performance of four combinations of the UEP schemes for H.264/AVC encoded video sequences over the AWGN and Rayleigh fading channels and show the superiority of the optimized cross-layer UEP FEC scheme. For Rayleigh fading channel, the proposed cross-layer optimization uses two different time-scales at AL and PHY which allows PHY to adapt faster to the changing channel quality.

This paper presents a flexible fiber-optic sensor-based pressure sensing system for human activity analysis and situation perception in indoor environments. In this system, a binary sensing technology is applied to reduce the data workload, and a bipedal movement-based space encoding scheme is designed to capture people's geometric information. We also develop a nonrepetitive encoding scheme to eliminate the ambiguity caused by the two-foot structure of bipedal movements. Furthermore, we propose an invariant activity representation model based on trajectory segments and their statistical distributions. In addition, a mixture model is applied to represent scenarios. The number of subjects is finally determined by Bayesian information criterion. The Bayesian network and region of interests are employed to facilitate the perception of interactions and situations. The results are obtained using distribution divergence estimation, expectation-maximization, and Bayesian network inference methods. In the experiments, we simulated an office environment and tested walk, work, rest, and talk activities for both one and two person cases. The experiment results have demonstrated that the average individual activity recognition is higher than 90%, and the situation perception rate can achieve 80%.

Distributed binary pyroelectric sensor network (PSN) is a low-cost alternative to video systems for human monitoring applications. This paper presents a PSN-based mobile target recognition system, which aims to achieve multitarget, complex scenario recognition. In this system, a novel pseudorandom visibility mode is designed for the sensor arrays to help capture statistical information of scenarios, and a sensor array fusion scheme is adopted to facilitate discriminative feature extraction. Moreover, we propose a statistical subspace representation model called probabilistic nonnegative matrix factorization (PNMF) to seek the scenario patterns rather than the object characteristics. We also further prove that our PNMF model is a generic model for NMF based algorithms. Original NMF, sparse NMF, and smooth NMF are special cases of the PNMF model. The simulation and experimental results demonstrate the advantages of our proposed method. Our system can be further developed to function as an independent facility for intelligent monitoring applications, especially under poor illumination circumstances.

Cognitive Radio (CR) can efficiently utilize the licensed wideband spectrum whenever the primary users (PUs) are absent. Spectrum sensing is the first step and an important function to fulfill the CR system. A cyclostationary feature detector can robustly detect the PU's modulated signals even under strong interferences. However this detector needs high signal sampling rate and also puts heavy computation burden on the system. Compressive sensing (CS) can compress the data at the front sampling end but has high overload and delay from the reconstruction side. In this work we generate the compressive CR spectrum measurement by utilizing both the cyclostationary feature and sparsity prior knowledge at the spectrum sensing front end, and we apply the compressive signal processing (CSP) without the need of signal or feature reconstruction. This can significantly shorten the CR spectrum sensing time. Our experimental results have shown the pattern analysis accuracy and efficiency of our CSP scheme.

Recently, multimedia transmission over cognitive radio networks (CRNs) becomes an important topic due to the CR's capability of using unoccupied spectrum for data transmission. Conventional work has focused on typical quality-of-service (QoS) factors such as radio link reliability, maximum tolerable communication delay, and spectral efficiency. However, there is no work considering the impact of CR spectrum sensing frequency and packet-loading scheme on multimedia QoS. Here the spectrum sensing frequency means how frequently a CR user detects the free spectrum. Continuous, frequent spectrum sensing could increase the medium access control (MAC) layer processing overhead and delay, and cause some multimedia packets to miss the receiving deadline, and thus decrease the multimedia quality at the receiver side. In this research, we will derive the math model between the spectrum sensing frequency and the number of remaining packets that need to be sent, as well as the relationship between spectrum sensing frequency and the new channel availability time during which the CRN user is allowed to use a new channel (after the current channel is re-occupied by primary users) to continue packet transmission. A smaller number of remaining packets and a larger value of new channel availability time will help to transmit multimedia packets within a delay deadline. Based on the above relationship model, we select appropriate spectrum sensing frequency under single-channel case, and study the trade-offs among the number of selected channels, optimal spectrum sensing frequency, and packet-loading scheme under multi-channel case. The optimal spectrum sensing frequency and packet-loading solutions for multi-channel case are obtained by using the combination of Hughes-Hartogs and discrete particle swarm optimization (DPSO) algorithms. Our experiments of JPEG2000 packet-stream and H.264 video packet-stream transmission over CRN demonstrate the validity of our spectrum sensing frequency selection and packet-loading scheme.

Abstract In this paper, we use a multiple recursive generator (MRG) to generate sequences of numbers with very long periods, i.e., pseudo‐random sequences. The MRG effectively constructs a block cipher which satisfies important quality requirements such as security, long period, randomness, and efficiency. We compare our approach with another lightweight block cipher based on a linear congruential generator (LCG) and analyze the efficiency in terms of the number of basic operations that are being performed. We also study the effects of using special classes of MRG which hold certain portability and efficiency properties, and analyze their advantages in this context. The proposed cipher is a lightweight cipher, which is very useful for resource limited resources such as sensor nodes in sensor networks, radio frequency identification (RFID) tags, etc. Copyright © 2010 John Wiley & Sons, Ltd.

Implantable medical devices (IMDs) have played an important role in many medical fields. Any failure in IMDs operations could cause serious consequences and it is important to protect the IMDs access from unauthenticated access. This study investigates secure IMD data collection within a telehealthcare [mobile health (m-health)] network. We use medical sensors carried by patients to securely access IMD data and perform secure sensor-to-sensor communications between patients to relay the IMD data to a remote doctor's server. To meet the requirements on low computational complexity, we choose N-th degree truncated polynomial ring (NTRU)-based encryption/decryption to secure IMD-sensor and sensor-sensor communications. An extended matryoshkas model is developed to estimate direct/indirect trust relationship among sensors. An NTRU hardware implementation in very large integrated circuit hardware description language is studied based on industry Standard IEEE 1363 to increase the speed of key generation. The performance analysis results demonstrate the security robustness of the proposed IMD data access trust model.

The speed gap between processor and memory is the major bottleneck for modern computing systems. Many modern processors, such as the CELL processor, employ multi-core, multimodule architecture to hide memory access latency. However, making effective use of multiple memory modules remains difficult, considering the combined effect of performance and energy requirements. This paper studies the scheduling and assignment problem that optimize both energy and performance. An efficient algorithm, EALSPP (Energy Aware Loop Scheduling with Prefetching and Partition), is proposed. The algorithm attempts to maximize energy saving while hiding memory latency with the combination of loop scheduling, data prefetching, memory partition, and heterogeneous memory module type assignment. Experimental results demonstrate the effectiveness of our approach.

Energy saving is critical to real-time embedded systems. In many embedded systems, some tasks contain conditional instructions or operations that could have different execution times for different inputs. Due to the uncertainties in execution time of these tasks, this paper models each varied execution time as a probabilistic random variable. We propose a practical algorithm to minimize the expected value of total energy consumption while satisfying the timing constraint with a guaranteed confidence probability for uniprocessor embedded systems with continuous probability distributions. The experimental results show that our approach achieves significant energy saving than previous work.

Many elder patients have multiple health conditions such as heart attacks (of various kinds), brain problems (such as seizure, mental disorder, etc.), high blood pressure, etc. Monitoring those conditions needs different types of sensors for analog signal data acquisition, such as electrocardiogram (ECG) for heart beats, electroencephalogram (EEG) for brain signals, and electromyogram (EMG) for muscles motions. To reduce mobile-health (m-health) cost, the above sensors should be made in tiny size, low memory, and long-term battery operations. We have designed a series of medical sensors with wireless networking capabilities. In this paper, we report our work in three aspects: (1) networked embedded system design, (2) network congestion reduction, and (3) network loss compensation. First, for networked embedded system design, we have designed an integrated wireless sensor network hardware / software platform for multi-condition patient monitoring. Such a system integrates ECG/EEG/other sensors with Radio Frequency Identification (RFID) into a Radio Frequency (RF) board through a programmable interface chip, called PSoc. Second, for network congestion reduction, the interface chip can use compressive signal processing to extract bio-signal feature parameters and only transmit those parameters. This provides an alternative approach to sensor network congestion reduction that aims to alleviate ?hot spot? issues. Third, for network loss compensation, we have designed wireless loss recovery schemes for different situations as follows. (1) If original sensor data streams are transmitted, network congestion will be a big concern due to the heavy traffic. A receiver-only loss prediction will be a good solution. (2) If the signal parameters are transmitted, the transmission loss mandates a 100% recovery rate. We have comprehensively compared the performance of those schemes. The proposed mechanisms for m-health system have potentially significant impacts on today's elder nursing home management and other mobile patient monitoring applications.

Abstract Anonymity is an important security aspect of wireless communications and has continuously attracted significant attention. Implementing anonymity of mobile users not only protects their privacy but also reduces the chances of attacks based on impersonation; therefore security can be improved. Untraceability is a related issue to anonymity. If a user is traceable, its hidden identity can be revealed through profiling the activities associated to a user. In this paper, we conduct a survey on anonymity issues of wireless communication systems. We first discuss general issues of anonymity in wireless communication systems. Then we survey some protocols in the literature, which are designed for wireless mobile systems as well as wirelessad hocnetworks. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Wireless sensor network security requires the cryptography software extremely low complex and energy efficient due to the limited memory and CPU capacity in a sensor. The NTRU (Nth degree truncated polynomial ring) encrypt algorithm has been shown to provide certain advantages when designing low power and resource constrained systems, while still providing comparable security levels to higher complexity algorithms. Unlike the current works that build NTRU software in a chip, this research focuses on the hardware implementation of NTRU algorithms because hardware implementation has much higher execution speed than software implementation. In contrast to previous research, the focus is shifted away from specific optimizations but rather provides a study of many of the recommended practices and suggested optimizations with particular emphasis on polynomial arithmetic and parameter selection. Recommendations for algorithm and parameter selection are made regarding implementation in hardware with respect to the resources available. Copyright © 2008 John Wiley & Sons, Ltd.

This work proposes an interface design of a low-power programmable system on chip for intelligent wireless sensor nodes to reduce the overall power consumption of the heart disease monitoring system, by lending them the capability of processing complex functions and performing rapid computations on a large amount of data at the node. This facilitates the node to intelligently monitor a medical signal for impending events instead of transmitting the signal to the base station constantly. Lowering the transmission data rate decreases the transmission power consumption in a node, thereby lengthening the node life and in turn increasing the reliability of the network. This work also implements a thresholding technique, which controls the data transmission rate depending on the value of the monitored signal, and a cardiac monitoring system that performs computations at the node for the detection of either a skipped heart beat or a reduced heart rate variability, in which event the signal is transmitted to the base station for monitoring/recording or alerting the crew. The performance analysis of the system shows that there are reductions in the system power consumption and data transmission rate, which in turn reduces the network traffic and averts congestion.

Abstract Wireless transmission of a patient's electrocardiogram (ECG) signals can be used to reduce cardiac healthcare cost. However, wireless transmissions have high error rates due to radio interference. The ECG signal, where every second of data could mean abnormal patterns, cannot tolerate such losses. Due to this healthcare crisis, the ability for a device to remotely monitor a patient's medication intake and transmit accurate ECG readings, while being cost efficient, is a major innovation. In this research, we integrate a multi‐hop wireless sensor network (WSN) with radio frequency identification (RFID) readers. Our system has two distinct features: (1) remotely supervise patient medication intakeviaRFID technology, and (2) accurately and remotely transmitting a patient's ECG by adopting extended Kalman filter (EKF) for wireless error recovery. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract An event‐to‐sink reliable transport (ESRT) control scheme was recently proposed to address the event‐to‐sink reliability issues in wireless sensor network (WSN). In this paper, we study the performance of ESRT in the presence of ‘over‐demanding’ event reliability, using both the analytical and simulation approaches. We show that the ESRT protocol does not achieve optimum reliability and begins to fluctuate between two inefficient network states. With insights from update mechanism in ESRT, we propose a new algorithm, called enhanced ESRT (E2SRT), to solve the ‘over‐demanding’ event reliability problem and to stabilize the network. Simulation results show that E2SRT outperforms ESRT in terms of both reliability and energy consumption in the presence of ‘over‐demanding’ event reliability. Besides, it ensures robust convergence in the presence of dynamic network environments. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we propose to use a Multiple Recursive Generator (MRG) to generate sequences of numbers with very long periods, i.e., pseudo-random sequences. The MRG effectively constructs a block cipher which satisfies important quality requirements such as security, long period, randomness, and efficiency. The proposed cipher is a light weight cipher, which is very useful for resource limited resources such as sensor nodes in sensor networks, RFID tags, etc.

In this paper, we propose and study two different bandwidth partition schemes for integrated voice/video/data traffic in the IEEE 802.11e wireless LANs: a Static/fixed bandwidth Partition scheme, and a Dynamic budget Partition scheme. The proposed schemes are compared and evaluated via extensive simulations.

It is very important to monitor the water quality of lakes since any abnormal chemical components/pollutants can possibly cause health problems. Chemical Water Sensors can be used for such long-term monitoring purpose. In this paper, we propose a scalable, low-energy, delay-tolerant Water-quAlity moniToring sEnsor netwoRk (WATER) model, which has essential differences from terrestrial radio sensor networks due to its highly variable, long propagation delay and mobility nature. In the vertical direction, we propose a light-weight time synchronization mechanism that can achieve satisfactory timestamp accuracy. On the other hand, malicious people can use many network attacks (such as Sybil attacks, wormhole attacks, replay attacks, Byzantine attacks, etc.) to mislead water quality monitoring in WATER platforms. To make our time synchronization protocol dependable, we propose a correlation-based security model to detect outlier timestamp data and identify nodes generating insider attacks, which is different from external attacks due to the complete keying material disclosure. Our correlation-based security scheme can also countermeasure many insider attacks (i.e. assuming the enemies already captured the water sensors and got to know the keying materials). Detail experiments have validated the efficiency of our security approaches. The proposed secure time synchronization mechanism (we call it WATERSync) is especially important to navy/military underwater sensor systems.

In this paper, we study MPEG-4 transmissions over the IEEE 802.11e wireless local area networks (WLANs). In (Y. Xiao et al., 2007), we provided a simulation of MPEG-4 using OPNET over WLANs, and simulation results show that a higher throughput does not always mean a better quality of MPEG-4 video. Therefore, in this paper, we propose two schemes to enhance MPEG4 transmissions over WLAN: 1) we propose a prioritized frame cross-layer transmission scheme between the medium access control (MAC) layer and the application layer, and 2) we adopt a measurement admission control scheme for IEEE 802.11e. Simulation results show advantages of the proposed schemes.

In mobile cellular networks, bandwidth is deterministic in terms of number of channels by frequency division, time division, or code division. On the other hand, bandwidth partition schemes in the contention-based medium access control (MAC) in distributed wireless LANs are extremely challenging due to the contention-based nature. In this paper, we propose and study a dynamic bandwidth partition with finer-tune (DP-FT) scheme for integrated voice/video/data traffic in the IEEE 802.11e wireless LANs.

Abstract One of today's most pressing matters in medical care is response time to patients in need. Scope of this research is to suggest a solution that would help reduce response time in emergency situations utilizing technologies of wireless sensor networks. The enhanced power efficiency, minimized production cost, condensed physical layout, and reduced wired connections present a much more proficient and simplified approach to the continuous monitoring of patients' physiological status. The proposed sensor network system is composed of wearable vital sign sensors and a workstation monitor. The wearable platforms are to be distributed to patients of concern. The wearable platforms can provide continuous electrocardiogram (ECG) monitoring by measuring electrical potentials between various points of the body using a galvanometer. They will then relay the ECG signals wirelessly to the workstation monitor. In addition to displaying the data, the workstation will also perform signal wavelet transformation for ECG characteristic extractions. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract Underwater Acoustic Networks (UANs) have important applications in ocean exploration and lake pollution monitoring. UANs are however different from terrestrial sensor networks due to their highly variable, long propagation delay, and mobility. Clock synchronization is an important protocol to achieve timing‐based sensor communications. In this paper, we propose a three dimensional, scalable UAN time synchronization scheme that can achieve both horizontal (i.e., in the same water depth) and vertical (i.e., from bottom up to the surface) clock synchronization to overcome the effects of long acoustic delay. To secure UAN clock synchronization services, we also propose a two‐step security UAN synchronization model: (1) correlation test and (2) statistical reputation and trust model. The proposed model can detect outlier timestamp data and identify nodes generating insider attacks. Copyright © 2007 John Wiley & Sons, Ltd.

Cardiovascular diseases are the single largest cause of morbidity and mortality in the U.S. and Western world. Tele- cardiology through RFID-based wireless sensor networks can provide anytime cardiac patient monitoring in large nursing homes. Wireless medical sensors and PDA devices can provide continuous transmission of patients' cardiac data (such as ECG, blood pressure, SpO2, etc.). However, the radio broadcasting nature has the risk of losing confidentiality (i.e. privacy) of patients' data. This paper discusses our research on a secure RFID/sensor based tele-cardiology system. It is based on the light-weight encryption and key management algorithms. The RFID helps trace the mobility of patients and manage medical facilities in nursing homes.

In this paper, we propose Least Total-Route- Temperature (LTRT), a thermal aware routing algorithm, to reduce temperature caused by biomedical sensors implanted in human bodies. In the proposed scheme, nodes' temperatures are converted into graph weights and minimum temperature routes are obtained. Simulations are conducted to show the advantages of the proposed scheme when comparing with three other related schemes.

Telemedicine has been in use for many years and it is the use of telecommunications technologies to consult with remote physician. In this paper, we shed light on telemedicine in terms of the common usage and the future potentials of the technology with some examples.

Recently, a remote-sensing platform based on wireless interconnection of tiny ECG sensors called Telecardiology Sensor Networks (TSN) provided a promising approach to perform low-cost real-time cardiac patient monitoring at any time in community areas (such as elder nursing homes or hospitals). The contribution of this research is the design of a practical TSN hardware/software platform for a typical U.S. healthcare community scenario (such as large nursing homes with many elder patients) to perform real-time healthcare data collections. On the other hand, due to the radio broadcasting nature of MANET, a TSN has the risk of losing the privacy of patients' data. Medical privacy has been highly emphasized by U.S. Department of Health and Human Services. This research also designs a medical security scheme with low communication overhead to achieve confidential electrocardiogram data transmission in wireless medium.

Our research aims to address the challenging trustworthiness issues in wireless sensor and actuator networks (WSANs). As trustworthiness requires data to be transmitted among actuators and sensors with desired 'reliability' and 'security', we propose a low-complexity transmission reliability scheme that is based on local wireless path repair and hop-to-hop retransmission. Since WSANs have specific network constraints and data transmission requirements compared to general ad hoc networks and other wireless/wired networks, the security issues need to be tackled accordingly. We propose to seamlessly integrate WASN security with a promising routing architecture that is scalable and energy-efficient. In this paper, we also develop two-level re-keying/re-routing schemes that can not only adapt to a dynamic network topology but also securely update keys for each data transmission session.