We present iDT-diet, an intelligent digital twin prototype designed to model the long-term influence of diet quality on health biomarkers and chronic conditions. The system integrates three novel components: (i) a random forest learning model enhanced with Choquet LASSO feature selection for capturing complex, nonlinear interactions in temporal health data; (ii) a translation module that converts predictive outputs into natural language narratives of physical and biomarker states; and (iii) a generative 3D visualization engine that produces dynamic, personalized digital twins reflecting evolving health trajectories. This integration uniquely links advanced machine learning, interpretable communication, and immersive visualization within a single framework. While the current implementation focuses on retrospective digital twin generation, the system architecture supports real-time data integration, enabling continuous monitoring, predictive simulation, and personalized recommendation delivery for diet and lifestyle management.

Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including OLS regression and Choquet OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. MSE was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework.

Dietary behaviors play a pivotal role in promoting overall health and preventing chronic diseases (e.g., hypertension and diabetes). The widespread adoption of smartwatches offers a promising platform for continuous dietary monitoring. However, existing smartwatch-based dietary monitoring approaches struggle with challenges in real-world scenarios, including dynamic interference, gesture generalization, and user diversity. To address these limitations, we proposeDietWatch, a real-world dietary monitoring system that utilizes a commercial smartwatch to capture and analyze fine-grained dietary behaviors.DietWatchincorporates a dynamic interference mitigation module to suppress acoustic and inertial noise. It further employs a contrastive learning-based framework to distinguish eating gestures from diverse daily activities, without constraining users’ eating styles and activity types. To enhance generalizability across users,DietWatchadopts a cross-user adaptation mechanism to extract user-independent features. Furthermore, a clustering algorithm is designed to estimate dietary time, while an attention-based multimodal fusion method is employed to analyze biting and chewing frequencies and identify food categories. Experimental results demonstrate thatDietWatchachieves 79.95% temporal Intersection over Union for eating time detection, 85.68% accuracy in food classification, and mean absolute errors of 1.26 bites/min for biting frequency and 7.71 chews/min for chewing frequency estimation.

6G networks require a sustainable and dependable power supply to ubiquitous space–air–ground connectivity infrastructures. Wireless power transfer (WPT) offers a promising path to sustainable energy delivery; however, concurrent transmitters can experience destructive interference when operating asynchronously. Most existing studies focus on centralized or synchronized WPT systems, leaving the asynchronous regime largely unexplored. In contrast to 5G’s tightly coordinated and slowly varying links, 6G WPT must function under non-stationary mobility, higher carrier frequencies, and dense power transmitter deployments. To bridge this gap, we translate key 6G stressors into design laws and probability guarantees. Specifically, we present a new systematic framework for asynchronous concurrent WPT, featuring a unified kernel that captures frequency, timing, and phase dispersions as a single retention term across instantaneous, short-time, and long-time scales. Further, we provide closed-form ppm/time budgets for a target retention, a retention cumulative distribution function that tightens asO(N−2), and a Doppler time-to-null scheduler coupled to the harvester. Finally, we perform deterministic and Monte Carlo analyses, together with experimental studies.

Beamforming techniques are utilized in millimeter wave (mmWave) communication to address the inherent path loss limitation, thereby establishing and maintaining reliable connections. However, adopting standard defined beamforming approach in highly dynamic vehicular environments often incurs high beam training overheads and reduces the available airtime for communications, which is mainly due to exchanging pilot signals and exhaustive beam measurements. To this end, we present a multi-modal sensing and fusion learning framework as a potential alternative solution to reduce such overheads. In this framework, we first extract the features individually from the visual and GPS coordinates sensing modalities by modality specific encoders, and subsequently fuse the multimodal features to obtain predicted top-k beams so that the best line-of-sight links can be proactively established. To show the generalizability of the proposed framework, we perform a comprehensive experiment in four different vehicle-to-vehicle (V2V) scenarios from real-world multi-modal sensing and communication dataset. From the experiment, we observe that the proposed framework achieves up to 77.58% accuracy on predicting top-15 beams correctly, outperforms single modalities, incurs roughly as low as 2.32 dB average power loss, and considerably reduces the beam searching space overheads by 76.56% for top-15 beams with respect to standard defined approach.

The demand for surface electromyography (sEMG)-based exoskeletons is rapidly increasing due to their noninvasive nature and ease of use. With increase in use of Internet of Things (IoT)-based devices in daily life, there is a greater acceptance of exoskeleton-based rehab. As a result, there is a need for highly accurate and generalizable gesture classification mechanisms based on sEMG data. In this work, we present a framework which preprocesses raw sEMG signals with empirical Fourier decomposition (EFD)-based approach followed by dimension reduction. This resulted in improved performance of the hand gesture classification. EFD decomposition’s efficacy of handling mode mixing problem on nonstationary signals, resulted in less number of decomposed components. In the next step, a thorough analysis of decomposed components as well as interchannel analysis is performed to identify the key components and channels that contribute toward the improved gesture classification accuracy. As a third step, we conducted ablation studies on time-domain features to observe the variations in accuracy on different models. Finally, we present a case study of comparison of automated feature extraction-based gesture classification versus manual feature extraction-based methods. Experimental results show that manual feature-based gesture classification method thoroughly outperformed automated feature extraction-based methods, thus emphasizing a need for rigorous fine tuning of automated models.

To enhance the security of Internet of Medical Things (IoMT) systems against cyber threats, we introduce a Multi-Level Blockchain Infrastructure combined with a Differentiated Communication Strategy for real-time medical data transmission, utilizing an embedded hardware setup to provide cost-effective solution for resource-limited environments. The workflow of Multi-Level Blockchain Architecture is proposed, incorporating an embedded hardware-driven K-Nearest Neighbours (KNN) algorithm for diagnosing real-time Electrocardiogram (ECG) signals, with Rivest-Shamir-Adleman (RSA) encryption protocol to secure medical records. The simulations are performed using MIT-BIH arrhythmia and normal sinus rhythm datasets. The feature extraction algorithm, incorporated with optimized KNN model, demonstrated promising results for each emergency classification class. The evaluation metrics for Healthy, E1, and E2 classes showed an accuracy of 91.1%, 88.9%, 84.4%; precision of 78.9%, 100%, 75%; and specificity of 86.7%, 100%, 86.7%, respectively. The feasibility of executing optimized algorithms on Arduino platform is confirmed through computational complexity, execution time, and memory requirement. The proposed system architecture provides a robust solution for enhancing IoMT security, strengthening the confidentiality, integrity, and authenticity of medical data, safeguarding against cyber threats. Additionally, the simple embedded hardware setup offers a user-friendly solution, making it well-suited for resource-constrained surroundings.

LoRa, designed for Low-Power, Wide-Area Networks (LPWANs), is widely used in the Internet of Things (IoT). In contrast, Wireless Personal Area Network (WPAN) technologies like ZigBee struggle to connect directly to LPWANs due to their limited communication range and differing modulation schemes. ZigBee uses Offset Quadrature Phase-Shift Keying (OQPSK) modulation, while LoRa employs Chirp Spread Spectrum (CSS) modulation, complicating cross-technology communication. To address this challenge, we propose a novel approach for seamless physical-layer cross-technology communication between ZigBee and LoRa networks, bridging the gap between short-range and long-range communication technologies. We introduce ZigRa, a communication method that leverages neural networks for efficient modulation translation between ZigBee's IEEE 802.15.4 standard and LoRa's CSS modulation. The core of ZigRa is a deep learning model that adapts and optimizes the transformation of ZigBee signals into ultra-narrowband single-tone sinusoidal signals, which can be reliably detected by LoRaWAN base stations. Our solution enables ZigBee devices to seamlessly connect to LoRa-based LPWANs, overcoming modulation mismatches and providing long-range connectivity. Extensive evaluations with both USRP hardware and commercial devices demonstrate that ZigRa achieves a frame reception rate exceeding 85% at distances up to 500 meters, significantly enhancing the interoperability and coverage of heterogeneous IoT networks.

Low-Power Wide-Area Networks (LPWANs) have emerged as a promising communication technology for the Internet of Things (IoT). However, frequency overlap among wireless networks using different radio technologies creates significant interference, compromising communication reliability. This challenge is particularly urgent in LoRa networks, which coexist in the 2.4 GHz ISM band with other IoT transmitters capable of transmitting at much higher power levels. In our study, we begin by providing a comprehensive understanding of the LoRa physical layer (PHY), including insights into modulation and demodulation mechanisms. Leveraging this knowledge, we successfully implemented a real-time LoRa PHY on the GNU Radio Software-Defined Radio platform. To address cross-technology interference during peak detection, we introduce a spectrum merging technique that maintains phase coherence between superimposed peaks, minimizing spectral leakage artifacts. Beyond that, our analysis actively enhances the performance of commercial LoRa devices. Furthermore, we systematically explore the interference dynamics between LoRa and IEEE 802.15.4g networks. Our rigorous investigation reveals LoRa’s ability to achieve high packet reception rates, even in the presence of strong IEEE 802.15.4g interference.

Learning from massive amounts of domain-specific information requires new algorithms and models for parsing the ever-expanding field of big data. Such algorithms for exploring and identifying key features in vast databases require analysis of complex interactions to uncover critical features under a variety of circumstances. We study a comprehensive collection of health-related data, showing that our novel Choquet Integral activation function for deep neural networks transforms high-dimensional data into simpler sub-feature sets that better model complex interactions. While standard methods account for unitary feature tracking, they do not extend to multiple feature subsets, an impactful and necessary knowledge base. To this end, our novel activation function creates a sub-additive tool that better considers the weighted compilation of features within a robust set of standard benchmarks, advancing the synergistic and antagonistic relationships among features, capturing non-linear dependencies. We present the theoretical underpinnings, highlighting balanced fuzzy measures and sub-additivity for an optimized model based on real-world health data targeting weight loss. We further test different model settings, akin to hyper-parameter optimization. Despite computational time consumption, which could be improved via nowadays more powerful computing units, this novel method can be implemented as a pre-trained model using big data to identify heretofore unknown sub-additive feature interactions in a variety of fields such as biomedicine, fraud detection, cyber-security, and finance.

Beamforming techniques are essential to compensate for severe path loss in millimeter-wave (mmWave) communications. These techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information and exhaustive search, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. This paper proposes a solution that utilizes deep learning to predict the optimal beams for vehicular communication at 60 GHz. By analyzing vehicular position information, the solution can identify the beams that provide sufficient mmWave received powers, ensuring the best line-of-sight links for vehicle-to-vehicle (V2V) communications. The proposed solution was tested on real-world measured mmWave sensing and communication datasets, and the results show that it can achieve an average of 84.58% of received power of link status, making it a promising solution for beamforming in mmWave enabled V2V communications.

Vehicle-to-Vehicle (V2V) cooperative perception has become increasingly popular in the field of autonomous driving, effectively overcoming the inherent limitations of single-vehicle perception systems, such as limited range and susceptibility to occlusions. In a V2V system, vehicles in close proximity can share perception data. To fuse this data, which is collected from different viewpoints by each vehicle, accurate pose information (including position and heading direction) is essential to transform the received data to the receiving vehicle's viewpoint. However, pose errors, often caused by measurement noise or sensor failures, can lead to severe misalignment during data fusion, resulting in incorrect object detections and potentially hazardous decisions in autonomous driving systems. To address this challenge, we present BB-Align, a lightweight pose recovery framework that utilizes Lidar Bird's-eye View (BV) images and object bounding Boxes for relative pose estimation. Designed as a plug-and-play solution, the proposed method requires no additional model training, enabling effortless integration into existing V2V systems. Our approach uses Lidar-derived BV images with a Log-Gabor filter-based feature map for effective image matching despite image sparsity. To reduce errors from self-motion distortion, we also integrate object bounding boxes for finer alignment. The proposed method is rigorously evaluated on the V2V 4Real dataset-currently the only real-world V2V dataset. Our approach demonstrates high pose estimation accuracy, outperforming an existing graph-matching method. It achieves translation and rotation errors of less than 1 m and 1°, respectively, in 80 % of cases within a 70 m range between vehicles. Furthermore, by integrating the proposed framework into cooperative object detection models under serious pose error, the result shows up to a 2x increase in Average Precision (AP) compared to those without pose recovery, with more pronounced improvements in the short range.

Internet of Things (IoT)-based solutions are gaining momentum in delivering efficient solutions in health care domain, reducing financial and physical burden on patients and improving ease of treatment for physicians. One such smart health care solution are Serious games. Serious games aid rehabilitation in various fields. For physical rehabilitation, personalization is important for improving training results. A scientific approach for difficulty level design can facilitate players to get effective rehabilitation. The automation of personalized difficulty level design helps the self-guided game-based rehabilitation approach, become simplified and efficient. AI is advancing the design of personalized serious game for rehabilitation through data-driven and individual-oriented methods. In this work, we present Generative AI-based design of gamified training plan, especially difficulty level plan which could go beyond rule-based solutions. We apply generative adversarial networks (GANs) to address the problem arising from large sequential data and variable requirement. This helps to overcome the limitation of unrealistic long term practice session for a rehabilitation patient by simplifying the training time. When compared with the results from long short-term memory (LSTM)-based approach, our GANs-based approach gave a 4.5X less variation in difficulty level and 6.5X less loss which proved the efficacy of our proposed approach in generating accurate difficulty levels. When compared with existing literature our proposed work simultaneously performs better on various parameters, namely, faster convergence, minimum emphasis on past performance of players, and low-data requirement for training and demographic flexibility.

The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Medical data is sensitive and highly regulated and anonymization is often insufficient to protect a patient's identity. Traditional clustering algorithms are also unsuitable for longitudinal behavioral health trials, which often have missing data and observe individual behaviors over varying time periods. In this work, we develop a new decentralized federated multiple imputation-based fuzzy clustering algorithm for complex longitudinal behavioral trial data collected from multisite randomized controlled trials over different time periods. Federated learning (FL) preserves privacy by aggregating model parameters instead of data. Unlike previous FL methods, this proposed algorithm requires only two rounds of communication and handles clients with varying numbers of time points for incomplete longitudinal data. The model is evaluated on both empirical longitudinal dietary health data and simulated clusters with different numbers of clients, effect sizes, correlations, and sample sizes. The proposed algorithm converges rapidly and achieves desirable performance on multiple clustering metrics. This new method allows for targeted treatments for various patient groups while preserving their data privacy and enables the potential for broader applications in the Internet of Medical Things.

Crowdsensed data trading (CDT) solves the problem of data resource scarcity and diversity, faced in conventional data trading by dispatching workers to perform data collection tasks and sharing data through trading. In CDT, both worker and data requesters need to provide geographic location or task location information for spatiotemporal data collection tasks. Existing research has insufficiently addressed the simultaneous consideration of both location privacy information and overlooked the variability in data quality resulting from variations in worker task accessibility and location. To address this problem, we propose a privacy-preserving task allocation scheme with regional coverage based on homomorphic encryption, which allows workers to perform tasks within the qualified region, the degree of regional coverage is associated with data quality to provide diversified data. To solve the sensing data trading and allocation problem for many-to-many users, we further introduce double auction. And thus propose a privacy-preserving data trading scheme to protect bidding information privacy, this scheme ensures the truthfulness of the auction process and mitigates participant manipulation. Besides, we employ a secure multiparty computing strategy to implement truth discovery in CDT, which enables third-party platforms to perform accurate task allocation and winner decisions based on encrypted location and bidding information. Extensive theoretical and simulation analyses show that the proposed scheme satisfies the expected economic properties (truthfulness, individual rationality, etc.), privacy, and effectiveness.

The metaverse is a human-centric beyond-reality virtual world, in which people use virtual identities to live, work, and socialize. Due to the openness and sharing of metaverse applications, the virtual-real identity link (VRIL) may cause uncertainties and unpredictable risks. At present, the research on VRIL risks is still in its infancy and VRIL risk predictions lack a comprehensive theoretical system and methodological tool. In this paper, we first construct a VRIL attack model, according to which an attacker can link a user’s real and virtual identities together using the information observed in the real and virtual worlds. Then we propose the tuple frequency-based VRIL prediction (TupPre) model and discover the population distribution, recursive hypergeometric (RH) distribution, and approximate binomial distribution of the tuple frequency (i.e., the occurrence times of attribute value combinations) given incomplete information. Focusing on the tuple frequency estimation error in biased samples, we introduce attribute value correlation knowledge to improve the prediction performance. The experimental results on generated and real-world datasets show that the TupPre model has excellent performance, with a mean area under the curves (AUCs) of 0.86 to 0.98 on these datasets, and it performs even more superior with certain background knowledge (mean AUC 0.95~0.98). The discovered basic distribution rules of the tuple frequency and the proposed quantitative analysis method for metaverse VRIL risk predictions construct the foundation of the identity privacy framework for the metaverse.

Cross-technology communication is essential for the Internet of Multimedia Things (IoMT) applications, enabling seamless integration of diverse media formats, optimized data transmission, and improved user experiences across devices and platforms. This integration drives innovative and efficient IoMT solutions in areas like smart homes, smart cities, and healthcare monitoring. However, this integration of diverse wireless standards within cross-technology multimedia communication increases the susceptibility of wireless networks to attacks. Current methods lack robust authentication mechanisms, leaving them vulnerable to spoofing attacks. To mitigate this concern, we introduce DeepSpoof, a spoofing system that utilizes deep learning to analyze historical wireless traffic and anticipate future patterns in the IoMT context. This innovative approach significantly boosts an attacker's impersonation capabilities and offers a higher degree of covertness compared to traditional spoofing methods. Rigorous evaluations, leveraging both simulated and real-world data, confirm that DeepSpoof significantly elevates the average success rate of attacks.

To improve the compression performance of screen content coding, extension coding standards (HEVC-SCC, VVC-SCC) have been developed. However, considering the compression ratio alone may lead to packet losses in bitstreams which may cause plenty of images decoded incorrectly, degrading the video quality at the receiver side. Thus, it urgently needs to study source-channel jointly coding scheme of screen content video. The most significant challenge lies in the complex spatial-temporal characteristics of screen content video, which complicate the creation of an accurate end-to-end distortion model. In this article, we delve into the traits of screen content video and construct an end-to-end distortion model. Building upon this, we introduce an error resilient coding scheme specifically for screen content video. More specifically, we first consider the characteristic of non-stationary temporal domain variation and classify the screen content images into three types of frames using a fast block-searching method. We then propose an adaptive error concealment method, taking into account the spatial-temporal prediction characteristics. Following this, we derive a pixel-level end-to-end distortion model and incorporate it into the rate distortion optimization process. Our experimental results reveal that, compared to state-of-the-art methods, our proposed method significantly enhances both objective and subjective quality across a variety of channel conditions.

Millimeter wave (mmWave) based human activity recognition is important in smart health in terms of studying user lifestyle. In practical health IoT scenarios, fast and accurate human activity recognition is critically important. In this work, we design and implement a lightweight deep learning solution for human activity recognition based on discrete Fourier transformation. The model has a fairly small number of model parameters while offering high accuracy in activity recognition. The core of the solution is a discrete Fourier transform module inside a neural network, which converts the temporal features of mmWave radar activity data into frequency features before activity recognition is performed by a simple classifier. We have extensively evaluated this solution against other traditional deep learning models in mmWave human activity recognition. The evaluation demonstrates that the DFT-based network can achieve the same accuracy as other traditional neural network models, but with a very small computational load.

As my term as the Editor-in-Chief (EiC) of IEEE Internet of Things Journal (IoT-J) has ended, I would like to share my appreciation for all of you, including authors and IoT-J team. I have been serving as the EiC since 1 January 2020, just before the pandemic. I am thankful that the pandemic is mostly behind us, and I am happy that our editorial board members have been able to work on making the Journal successful during that difficult time. It has been a challenging time for all the authors and the entire editorial team. The coronavirus pandemic ushered in mask mandates, quarantines, school and business closures, and so on, devastatingly impacting everyone’s lives. When I started to serve as the EiC, succeeding former EICs Prof. Sherman Shen and Dr. Chongang Wang, it was predictable that the pandemic impact would hurt the submissions and interfere with the editors’ engagement in the review process. At that time, I felt that there were many and varied challenges in front of us.

Automatic modulation identification (AMI) is a technique to detect the modulation type and order of a received signal, which has the potential to enhance cognitive radio capabilities for future generations of communication devices. However, AMI classifiers traditionally have exhibited low efficiency in low signal-to-noise ratio (SNR) environments. Hence, to address this problem we present our novel Gated Graph Convolutional Neural Network (GGCNN) classifier for feature-based AMI. This architecture includes a robust feature extraction stage to extract deep correlative patterns about the received symbols. Not only does this feature extraction stage use the temporal characteristics of the received symbols, but it also takes advantage of embedded signaling features from the received signal. In the proposed classifier, the received constellations are treated as a graph, allowing it to outperform state-of-the-art classifiers due to its strong performance in graph classification. This is observed clearly in the visualization of the extracted features, even for high-order modulation schemes. In this paper, we present our systematic research conducted for maximizing the efficiency obtainable by our classifier. Extensive simulation results demonstrate a significant accuracy improvement of 18.44 percentage points, and an efficiency increase by 60.78% for our GGCNN-AMI classifier compared to state-of-the-art classifiers in low-SNR environments.

Human daily activity monitoring has its particular significance in smart health. Human activity recognition based on mmWave has drawn enormous research efforts and achieved significant progress. Most of these solutions, however, work on data that has been manually segmented for each piece to contain only a single activity, which is impractical in reality where the sensor continuously generates data containing a series of activities. To address this challenge, this paper proposes a multi-head attention model that can detect the transition from one activity to another in a stream of mmWave sensor data of various human activities by analyzing the inner correlation of mmWave radar data fragments with a sliding window mechanism. Furthermore, the model then recognizes the new activity type in the data once it detects an activity transition. The solution has been extensively evaluated with a sparse point cloud dataset generated by a mmWave radar, which contains five types of activities. The experiment results show that the solution can achieve an accuracy of 98% in detecting activity transition at its best.

The value of trajectory data lies mainly in the spatio-temporal correlation. However, the existing privacy protection methods ignore the spatio-temporal correlation of trajectory data, resulting in a large error in trajectory proportion estimation and Top-K classification. For the privacy of truck trajectory in intelligent logistics, the location and trajectory data perturbation method based on quadtree indexing is proposed, which leverages location generalization and local differential privacy techniques. Our proposed algorithms are suitable for datasets with a large sample space and can protect the trajectory privacy of truck drivers while preserving the strong correlation between adjacent spatio-temporal nodes in the trajectory. The results of simulation on a real trajectory dataset show that the proposed methods not only meet the trajectory privacy requirements of users but also have a good performance in trajectory proportion estimation and Top-K classification.

To guarantee heterogeneous performance requirements of diverse vehicular services, it is necessary to design a full cooperative policy for both vehicle to infrastructure (V2I) links and vehicle to vehicle (V2V) links. This paper investigates how to improve the quality of experience (QoE) of the V2I users for video services while satisfying the delay requirements of both V2I and V2V links. In specific, a QoE maximization problem is formulated with consideration of vehicular collaboration where task offloading decision, channel reuse decision and power allocation of V2V users are all included. A multi-agent reinforcement learning (MARL) framework is then designed, where a new reward function is proposed to evaluate the utility of the considered network. Thereafter, a proximal policy optimization approach is proposed to enable each V2V user to learn policy individually with the shared global network reward. The effectiveness of the proposed approach is finally validated with comparison of other baseline approaches through extensive simulation experiments.

The Intelligent Transportation System has become one of the most globally researched topics, with Connected and Autonomous Vehicles(CAV) at its core. The CAV applications can be improved by the study of vehicle platooning immune to real-time traffic and vehicular network losses. In this work, we explore the need to integrate the Network model and Platooning system model for highway environments. The proposed platoon model is designed to be adaptive in length, providing the node vehicles to merge and exit. This overcomes the assumption that all the platoon nodes should have a common source and destination. The challenges of the existing platoon model, such as relay selection, acceleration threshold, are addressed for highly modular platoon design. The presented algorithm for merge and exit events optimizes the trade-off between network parameters such as communication range and vehicle dynamic parameters such as velocity and acceleration threshold. It considers the network bounds like SINR and link stability and vehicle trajectory parameters like the duration of the vehicle in the platoon. This optimizes the traffic throughput while maintaining stability using the PID controller. The work tries to increase the vehicle inclusion time in the platoon while preserving the overall traffic throuahput.

How to improve the content caching efficiency and user coverage rate at the same time is a fundamental challenge in edge caching networks. This paper studies an edge caching scheme based on user interest to address this issue. Specifically, a group interest aware caching framework is first developed. An individual interest prediction scheme is then proposed by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, the group interest is represented by a weighted average approach, based on which a caching scheme is further proposed. Moreover, the effectiveness of the proposed method is validated by extensive experiments with a real-world dataset.

Internet of Things has been one of the catalysts in revolutionizing conventional healthcare services. With the growing society, traditional healthcare systems reach their capacity in providing sufficient and high-quality services. The world is facing the aging population and the inherent need for assisted-living environments for senior citizens. There is also a commitment by national healthcare organizations to increase support for personalized, integrated care to prevent and manage chronic conditions. Many applications related to In-Home Health Monitoring have been introduced over the last few decades, thanks to the advances in mobile and Internet of Things technologies and services. Such advances include improvements in optimized network architecture, indoor networks coverage, increased device reliability and performance, ultra-low device cost, low device power consumption, and improved device and network security and privacy. Current studies of in-home health monitoring systems presented many benefits including improved safety, quality of life and reduction in hospitalization and cost. However, many challenges of such a paradigm shift still exist, that need to be addressed to support scale-up and wide uptake of such systems, including technology acceptance and adoption by patients, healthcare providers and policymakers. The aim of this paper is three folds: First, review of key factors that drove the adoption and growth of the IoT-based in-home remote monitoring; Second, present the latest advances of IoT based in-home remote monitoring system architecture and key building blocks; Third, discuss future outlook and our recommendations of the in-home remote monitoring applications going forward.

Under the pressure of the growing millennial population and senior citizens are aging, which is one of the top societal priorities in many countries, the provision of healthcare needs to evolve and improve. A roadmap paved by World Health Organization (WHO) in March 2019 called Global Strategy on Digital Health 2020-2024, specified a grand vision of promoting healthy lives and well-beings for everyone, everywhere, at all ages[1]. WHO urges all nations to work hand in hand in developing and delivering Digital Health initiatives supported by robust government strategies that amalgamate financial, organizational, human and technological resources[2]. In particular, there are some niches areas in the strategies such as the adoption of distributed sensors and assisted living emerging in recent years. To this end, a lot of efforts both from the research community and industrial providers are anticipated to put forth in the coming decade, in implementing the concept of assisted living using hardware devices into meaningful solutions for fulfilling the growing needs of assisted living.

K-nearest neighbor (kNN) search is an important problem in data mining and knowledge discovery. Inspired by the huge success of tree-based methodology and ensemble methods over the last decades, we propose a new method for kNN search, random projection forests (rpForests). rpForests finds nearest neighbors by combining multiple kNN-sensitive trees with each constructed recursively through a series of random projections. As demonstrated by experiments on a wide collection of real datasets, our method achieves a remarkable accuracy in terms of fast decaying missing rate of kNNs and that of discrepancy in the k-th nearest neighbor distances. rpForests has a very low computational complexity as a tree-based methodology. The ensemble nature of rpForests makes it easily parallelized to run on clustered or multicore computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights on rpForests by showing the exponential decay of neighboring points being separated by ensemble random projection trees when the ensemble size increases. Our theory can also be used to refine the choice of random projections in the growth of rpForests; experiments show that the effect is remarkable.

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume all the data are already in one place, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with “minimal” communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while a 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing.

Unmanned aerial vehicles (UAVs) can be flexibly deployed to offload cellular traffic or to provide video services for emergency scenarios without infrastructure. However, the inherent resource allocation and three-dimensional (3D) aerial trajectory design have not been formally studied. In this paper, we study the joint resource allocation and 3D aerial trajectory design for dynamic adaptive streaming over HTTP (DASH)-enabled services in a UAV communication system, where a UAV is employed as a base station for multiuser video streaming. Various factors are taken into account, including video data rate, quality variation, communication outage, play interruption, etc. By adopting a video streaming utility model, two fundamental problems are formulated with different practical aims: the first problem maximizes the minimum utility for all users within a given time horizon such that max-min fairness can be provided, and the second problem minimizes the UAV operation time subject to the individual utility requirement for all users to prolong UAV endurance. To tackle the first non-convex problem, we decouple it into three sub-problems, and a three-stage iterative algorithm is proposed to obtain a suboptimal solution by solving the three sub-problems with successive convex approximation and alternating optimization techniques. An exponential search based algorithm is proposed for the second problem by utilizing the structure of the considered problem and a similar three-stage iterative algorithm. Extensive simulations are carried out to evaluate the performance, and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of UAV movement for video streaming and unveil the tradeoff between utility and quality variance.

Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. We validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP.

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made at the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hybrid human-artificial intelligence approach is developed to improve the user hit rate for video caching. Specifically, individual user interest is first characterized by merging factorization machine (FM) model and multi-layer perceptron (MLP) model, where both low-order and high-order features can be well learned simultaneously. Thereafter, a social aware similarity model is constructed to transfer individual user interest to group interest, based on which, videos can be selected to cache at the network edge. Furthermore, a dual bisection exploration scheme is proposed to optimize wireless resource allocation and video coding rate. The effectiveness of the proposed video caching and delivery scheme is finally validated by extensive experiments with a real-world dataset.

To fulfill the diversified requirements of vehicular network services, we design an intelligent slice coordinator in this paper, which consists of two parts, service clustering and slice scheduling. In the first part, service clustering captures the Service Level Agreement (SLA) of services and clusters them based on K-means++ clustering algorithm according to the similarity of service requirement. Meanwhile, the services will be mapped into different slices. In slice scheduling module, we design the shared proportional fairness scheme (SPFS) to deal with the imbalance of radio resource utilization, and then further design the resource allocation algorithm based on linear programming obstacle method to solve the optimal slice weight distribution and maximize the slice load variation tolerance. Simulation results show that the SPFS has smaller average bit transmission delay (BTD) than the static slicing scheme, and the optimal slice weight distribution can be obtained under different user load distribution scenarios. The BTD gain achieves 1.5632 in the uniform user load scenario with 20 users per slice.

Wireless Body Area Network (WBANs) would benefit reasonable after the introduction of mmWaves in the communication. The high frequency spectrum can enhance channel capacity and reduce the package area. It however makes the communication link susceptible to noise interference. As the noise level increases, it restricts the use of mmWave spectrum for high SNR level applications. The paper proposes a method of spectrum switching to tap into advantages of mmWave only when necessary thus minimizing the drawbacks on the overall performance. The hybrid approach of using multiple spectrum exploits the characteristics of high channel capacity and high noise immunity which are found at the extreme ends of the spectrum. This paper compares different threshold parameters to conclude SNR as a more reliable factor. We develop an algorithm based on this threshold and simulate spectrum switching from 5 GHz to 60 GHz where the User Equipment is in constant motion over a range of 100 m. We present the graphical results of variance of SNR, Friis model for path loss and numerically prove a significant increase in the average channel capacity using Shannon's theorem while maintaining SNR above the threshold limit. The paper also presents the challenges reflected by this spectrum switching on the supporting parameters like directivity, energy consumption and beamforming array structures.

Artificial intelligence (AI) is becoming a mainstream for telecommunication industry. With the utilization of millimeter-wave in 5G network, it becomes feasible to use beamforming techniques for on-body sensors in Wireless Body Area Network (WBAN) applications. Thus, there is a need for developing beamforming algorithms that can optimize WBAN network performance and a realistic dataset that can be used for training, testing, and benchmarking of the algorithms. Thus, we propose a dataset generation method for mmWave WBAN that utilizes computer vision and an adaptive deep learning-based algorithm for performance optimization of mmWave WBAN beamforming. Two major ideas are proposed: First, collecting human poses from estimation of 3D human poses in videos and generating more realistic poses using generative adversarial nets (GAN) are adopted; second, a GAN aims to predict the next beamforming directions using the previous set of directions as inputs. With available labeled human pose videos, the WBAN dataset we generate provides a sufficient amount of samples for training, testing, and benchmarking of beamforming algorithms. Additionally, the proposed adaptive beamforming algorithm does not require any intrusive data gathering methods. Our numerical studies show the advantages of our proposed approaches.

To reconcile the conflict between ceaselessly growing mobile data demands and the network capacity bottleneck, we exploit the terminal-edge-cloud collaboration to design a streaming distribution framework, SD-TEC, with the major objective to avoid streaming interruptions caused by inter-cluster handovers and corresponding user defections. First, the merge-and-split rule in the coalition game is employed for virtualized passive optical network clustering to structurally reduce the inter-cluster handover frequency. Second, the terminal-edge collaboration leverages device-to-device communications to sustain streaming services when inter-cluster handovers inevitably occur, reducing the time of possible streaming interruptions and improving the quality of experience of multimedia services. Lastly, the edge-cloud collaboration proactively caches streaming contents to alleviate the traffic congestion of peak hours and considers user priorities and buffer queue underflow/overflow to manage both fronthaul and backhaul resources. Simulation results validate the efficiency of our proposed SD-TEC in reducing the traffic congestion and streaming interruptions caused by inter-cluster handovers.

To alleviate pilot contamination for multi‐cell large‐scale multiple‐input multiple‐output (MIMO) systems, here the authors propose a constellation coordination scheme according to a constellation coordination constraint (CC constraint) on the large‐scale fading factors. In fact, a detailed analysis of the uplink process introduces the CC constraint, which reveals that if the CC constraint cannot be satisfied, the error probability will be larger than a threshold. Otherwise, the error probability goes to zero if both the number of antennas at the base station and signal‐to‐noise ratio go to infinity. Furthermore, by modelling the location of users as a Poisson point process, the authors derive a safe area threshold according to the CC constraint, through which an adaptive pilot reuse is proposed. In this scheme, users outside the dynamic safe area threshold are allowed to reuse the pilot, while other users are not allowed. Simulation results show that the CC scheme alleviates the pilot contamination effectively and the proposed pilot reuse scheme based on the safe area threshold improves the uplink achievable rate of the system significantly.

The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a near-optimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

With the popularization of intelligent terminals, especially current trends, such as “Industrie 4.0” and the Internet of Things, mobile crowdsensing is becoming one of the promising applications built on smart devices in mobile networks. However, the existing mobile crowdsensing models are mostly based on a centralized platform, which is not fully trusted in reality and results in the existence of fraud and other security problems. Furthermore, the data quality collected through crowdsensing is varied, and the location privacy is difficult to guarantee, especially at the worker selection stage. To solve these two problems, an effective blockchain-based location-privacy-preserving crowdsensing model, CrowdBLPS, is proposed in this article. First, the idea of a blockchain is introduced into this model. The decentralized structure and the consensus approach are applied to realize the nonrepudiation and nontampering of information. Second, to improve the data sensing quality and protect worker privacy, a two-stage approach, including the preregistration stage and the final selection stage, is proposed. Finally, we further implement a prototype on the Ethereum public testing network, and the experimental results show the feasibility, availability, and reliability of CrowdBLPS.

In-band full duplex (IBFD) wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, how- ever, is throttled by its key challenge, namely self-interference. Therefore, effective self- interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non- linear self-interference cancellation solution: Deep learning based Self-Interference Cancellation (DSIC) to enable IBFD wireless. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self- interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self- interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.

In this paper, a video collaborative distribution mechanism is studied in Cloud Radio Access Networks (C-RANs) with object to guarantee the quality of experience (QoE) for different users. Specifically, a framework which enables the remote radio head (RRH)-to-device (R2D) technology to cooperate with the device-to-device (D2D) technology is constructed to transmit video traffic efficiently. Besides, a new QoE evaluation model is built in terms of the transition characteristics of video quality version and the interruption characteristics of video transmissions. Then, the optimal choice of video quality version is studied to achieve a good tradeoff among the video quality, interruption and smoothness for a target user. Moreover, a resource allocation policy is proposed to reduce the mean latency caused by video interruption of the whole network. Simulation results verify that the proposed mechanism performs better than other existing ones when the latency jointly caused by the quality version transition and the transmission interruption is sensitive to the users.

Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy. This work proposes a deep learning radio imaging solution to visualize real-time user indoor activities. The proposed solution uses a low-power, MIMO Frequency Modulated Continuous Wave (FMCW) radar array to capture the reflected signals from human objects, and then constructs 3D human visualization through a serials of data analytics including: 1) a data preprocessing mechanism to remove background static reflection, 2) a signal processing mechanism to transfer received complex radar signals to a matrix containing spatial information, and 3) a deep learning scheme to filter abnormal frames resulted from rough surface of human body. This solution has been extensively evaluated in an indoor research lab. The constructed real-time human images are compared to the camera images captured at the same time. The results show that the proposed radio imaging solution can result in significantly high accuracy.

In fibre‐wireless (FiWi) access networks, the achievement of survivability becomes critical because any failure of FiWi components may result in severe traffic interruptions. In this study, the authors proposed a new backup fibre deployment mechanism with the consideration of time‐varying daily traffic demand. Firstly, a new remote node structure for the protection of switching is established to help the backup fibre deployment. Secondly, they formed a minimum deployment cost problem in terms of the daily traffic demand and interrupted traffic protection. Furthermore, a two‐stage algorithm is proposed to solve this problem and obtain an optimal deployment pattern. Simulation results demonstrate that the proposed mechanism can reduce the cost of the backup fibre deployment and the delay of the interrupted traffic recovery significantly.

The rapid growth of Internet of Vehicles (IoV) has brought huge challenges for large data storage, intelligent management, and information security for the entire system. The traditional centralized management approach for IoV faces the difficulty in dealing with real-time response. The blockchain, as an effective technology for decentralized distributed storage and security management, has already showed great advantages in its application of Bitcoin. In this paper, we investigate how the blockchain technology could be extended to the application of vehicle networking, especially with the consideration of the distributed and secure storage of big data. We define several types of nodes such as vehicle and roadside for vehicle networks and form several sub-blockchain networks. In this paper, we present a model of the outward transmission of vehicle blockchain data, and then give detail theoretical analysis and numerical results. This paper has shown the potential to guide the application of blockchain for future vehicle networking.

Advancements in miniaturized electronics and smart sensors combined with a broad platform of smart phones, big data, cloud service, and wireless communication have not only empowered wearable technology, they have also increased users life expectancy. For example, a wearable system provides unobtrusive ambulatory, continuous, ubiquitous health measurement, and real-time solution for patients physical without hampering the natural movement of the wearer. However, the ability to measure one's Autonomic Nervous System (ANS) using wearable biosensors in healthcare applications has been limited due to several challenges related to a lack in wearability, accuracy, reliability, and low-power consumption. In this paper, we presented a novel wearable ring sensor for the continuous measurement of four ANS activities: 1) electrodermal activity; 2) heart rate; 3) skin temperature; and 4) locomotion. Detailed information is given regarding the development of the proposed ring sensor followed by a discussion of the evaluation that was done utilizing the wearable sensor on volunteers to gather data. Specifically, volunteers wore the ring sensor while being simultaneously monitored with real-time telemetry, the sensor values are processed and analyzed. This paper is a continuation and extension of earlier work by the authors. New validation, experimental results, and development of the mobile application have been added to improve the previous system. The experiment demonstrated accurate results, and data were collected from 43 participants of diverse age, body mass, height, and race. Additionally, to evaluate the performance of the developed ring sensor, we compared the results with a state-of-the-art open source device. This paper aims to improve the worn biomedical sensor market, specifically when it comes to size and accuracy of worn sensors.

In many applications of Internet of Things (IoT), the huge amount of data are generated by sensor nodes and processing them are complex. Offloading data classification and anomaly event detection tasks to sink nodes in sensor networks can reduce the computing complexity, lower remote communication loads, and improve the response time for the delay-sensitive IoT applications. Many existing classification and anomaly detection methods cannot be directly applied to these IoT applications, because the computing and energy resources of sensors are limited. In this paper, a new feature-based learning system for IoT applications is proposed to effectively classify data and detect anomaly event. Especially, based on the theory of distributed compression, the sparsity and relativity of the data are exploited to obtain the classification features, which can reduce the computation overhead and energy consumption. Further, an RBF-BP hybrid neural network is employed to detect the anomaly event based on the classification results, by which the training time of neural network can be significantly reduced and the accuracy can be improved for users' decisions.

The ever-increasing demand for videos on mobile devices poses a significant challenge to existing cellular network infrastructures. To cope with the challenge, we propose a user-centric video transmission mechanism based on device-to-device communications that allows mobile users to cache and share videos between each other, in a cooperative manner. The proposed solution jointly considers users' similarity in accessing videos, users' sharing willingness, users' location distribution, and users' quality of experience (QoE) requirements, in order to achieve a QoE-guaranteed video streaming service in a cellular network. Specifically, a service set consisting of several service providers and mobile users, is dynamically configured to provide timely service according to the probability of successful service. Numerical results show that when the number of providers and demanded videos is 40 and 2, respectively, the improved users experience rate in the proposed solution is approximately 85%, and the data offload rate on base station(s) is about 78%.

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests, rpForests, for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.

Smart health calls for novel approaches to detect vital signs in non- contact, non-invasive and non-intrusive matters. In this work, we design a solution that monitors the rates of heartbeats and respiration simultaneously by using a Frequency Modulated Continuous Wave (FMCW) radar with multiple antennas. This solution measures the reflections from heartbeats and respiration at a high frequency of 4 K H z to capture fine dynamics of motions with big data. It employs multiple antennas and superposition to reduce the interference noises from unwanted motions in the background and any detection defects. The heart and respiration rates are detected in the frequency domains after a chain of preprocessing techniques on the sensed big data. With extensive experiments in a lab office, this system demonstrates high accuracies in various cases: 98% in the still case, 95% with finger motions and 96% with body motions. The tests also confirm that multiple antennas and signal superposition improve the detection accuracy and reliability.

With a tremendous increase of traffic demands in fiber- wireless (FiWi) access network, achieving good survivability is facing significant challenges because vast traffic could be interrupted due to the failure of any FiWi components. Especially, when a segment failure occurs, all the components in the segment could be disconnected with optical line terminal (OLT). Several existing research works focus on handling segment failures by deploying backup fibers. However, they ignore the fact that the traffic demand is frequently varied with different human daily needs. Therefore, a new protection scheme based on daily traffic demand (PS-DTD) is proposed to deal with the segment failure instead in this paper. In the scheme, we optimize the deployment of backup fibers by solving minimum cost maximum flow and minimum cost maximum matching problems. Simulation results show that the proposed PS- DTD mechanism outperforms the existing ones in terms of cost and efficiency especially under high traffic demands.

Advancements in miniaturized electronics and smart sensors combined with a broad platform of smart phones, big data, cloud service and wireless communication have not only empowered wearable technology, they have also increased users life expectancy. This is done through a range of applications including; tracking physical activity, personalized health care, and recommendations for enhancing user experience. However, the ability to measure one's emotional state using wearable biosensors in healthcare applications has been limited due to several challenges related to a lack in comfort, accuracy, reliability and low-power consumption. In this paper, we presented a novel wearable ring sensor for the continuous measurement of four SNS activities: Electrodermal activity (EDA), heart rate, skin temperature and locomotion. Detailed information is given regarding the development of the proposed ring sensor followed by a discussion of the evaluation that was done utilizing the biosensor on volunteers to gather data. Specifically, volunteers wore the ring sensor while being simultaneously monitored with real- time telemetry and while going through different emotional states. The experiment demonstrated accurate results, and data were collected from 43 participants of diverse age, body mass, height, and race. Additionally, to evaluate the performance of the developed ring sensor, we compared the results with a state-of-the-art open source device. This research aims to improve the worn biomedical sensor market, specifically when it comes to size and accuracy of worn sensors.

Indoor human activity recognition has been extensively investigated. However, most of the solutions require sensors e.g. 9-axis IMU be equipped on human body or use image processing that presents privacy issues. This work proposes an ambient radar sensor based a solution to recognize the activities that humans normally perform in indoor environments. This solution uses a 7.8 GHz radar to emit 16 pulse signals every second and samples the reflected signals at 128 KHz to capture the fine dynamics of human activities. This solution designs a set of data preprocessing algorithms, including a data refining algorithm to filter outlier data, a contrastive divergence algorithm to remove background static reflection, and a transformation algorithm to convert the signal data into feature- rich spatial location changes. This solution also develops schemes to separate a collection of various activities into individuals. A lowpass frequency filter is designed to remove unwanted noisy data and the motion intensity is used to classify the activities into two high-level groups. It uses a slope-based approach and a k- means clustering to further finely recognize each activity. This solution has been extensively evaluated in a spacious research lab room and shows outstanding accuracy.

Device-to-device (D2D) communication can realize the direct communication between mobile users with short distance. It is an enabling technology for realizing Internet of Things in the long-term evolution-advanced system to the future fifth-generation mobile communication system. D2D communication multiplexes the licensed spectrum of cellular users (CUs) to D2D users (DUs) to improve resource utilization of cellular networks. In this paper, a cross-cell fractional frequency reuse-based frequency resource multiplexing (CFRM) scheme is proposed for the multicell D2D communication. In the proposed CFRM, each cell is first divided into two regions, and each region is allocated different spectrum resources to reduce the interference between neighboring cells. Then, the uplink resources of CUs are partially multiplexed by DUs, which can decrease the interference of the DUs to the CUs. The simulation results show that CFRM can reduce the interference, guarantee the quality of service of CUs, and increase the throughput of cellular networks.

The papers in this special section focuses on Big Data infrastructure. These papers address Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand.

The papers in this special section focus on Big Data infrastructure. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand. In this special issue, we invite articles on innovative research to address challenges of Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures, Hadoop or Spark with emphasis on addressing real-time requirements imposed by emerging Big Data applications such as sensing data, e-commerce data, business transactions and web logs, and etc.

A combinatorial system reliability modeling method is proposed to consider the effects of correlated probabilistic competing failures caused by the probabilistic-functional-dependence (PFD) behavior. PFD exists in many real-world systems, such as sensor networks and computer systems, where functions of some system components (referred to as dependent components) rely on functions of other components (referred to as triggers) with certain probabilities. Competitions exist in the time domain between a trigger failure and propagated failures of corresponding dependent components, causing a twofold effect. On one hand, if the trigger failure happens first, an isolation effect can take place preventing the system function from being compromised by further dependent component failures. On the other hand, if any propagated failure of the dependent components happens before the trigger failure, the propagation effect takes place and can cause the entire system to fail. In addition, correlations may exist due to the shared trigger or dependent components, which make system reliability modeling more challenging. This paper models effects of correlated, probabilistic competing failures in reliability analysis of nonrepairable binary-state systems through a combinatorial procedure. The proposed method is demonstrated using a case study of a relay-assisted wireless body area network system in healthcare. Correctness of the method is verified using Monte-Carlo simulations.

Wireless Body Area Networks (WBAN) have broad prospects for the use in mobile health, sports training support, etc. One critical research problem on WBAN is the cross-interference among multiple WBANs when they are close to each other, because they work on unlicensed open wireless frequency bands. This paper proposes a social group interaction power control game model to mitigate inter- WBAN interference, which consists of new utility and cost functions designed to accommodate both convergence speed and quality. This work proves that only one Nash equilibrium (NE) point exists for this game model, which guarantees its convergence. Extensive simulation has been performed to evaluate the performance and the results demonstrate that the proposed algorithm is highly effective and the convergence is rapid.

Wireless Body Area Network (WBAN) applications are becoming popular today. To protect patients' private data during transportation, secure wireless communications are mandatory in WBANs. Encryptions and secret keys are the base of secure communications over insecure wireless environments. Given most wireless nodes in WBANs are resource-constrained, efficiency is an implicit requirement of the key generation methods for WBAN wireless communications. Finding secure and efficient key generation method for WBANs is the goal of this article. We propose a practical, pure software method in this article. The new method has been proved to be highly secure and efficient.

Millimeter wave (mmWave) communication has been a promising technology of future fifth generation (5G) cellular networks. Due to the tremendous propagation loss of mmWave communication, device-to-device (D2D) communications are widely used over directional mmWave networks to improve the network throughput. In this paper, a new time resource sharing scheme is proposed based on Stackelberg game for interference D2D links to further enhance the network throughput. The D2D links causing interference can access to the time resource by paying higher price, while the D2D links causing no interference can also be scheduled in the scheme. Concurrent transmission scheduling between D2D links causing interference is formulated as a non-cooperative game, which achieves a distributed transmission power control solution among the interference D2D links. Moreover, the price strategy can be adjusted by setting the interference threshold such that the transmission quality can be guaranteed. The simulation results show that the proposed scheme can achieve significant network throughput gain compared with traditional concurrent transmission scheme.

Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods typically use random initialization, and could incur substantial computation costs in processing big data, although PSO facilitates the global optimization, based on our previous work [1]. This paper further developed and evaluated our data density-pattern based algorithm to guide initialization and to achieve better computational efficiency of PSO-based FCM. Data density patterns vary over the entire data space and the data points in high density areas are more likely around the cluster centroids. Based on this fact, our new algorithm attempts to improve the computational efficiency by auto-fusing data characteristics around the cluster centroids to initialize our algorithm. We evaluated our method using real and simulated imbalanced big data, and found this new method achieved comparable clustering performance as PSO-based FCM in terms of clustering cost, consistency and accuracy, but not consistently better than simple FCM. In terms of computational efficiency for imbalanced big data, our method seems to be comparable with PSO-based methods in terms of iterations and computational time, but both seem not comparable to simple FCM for imbalanced big data processing. Our simulation indicates that the classical PSO based FCM is slightly better than FCM on computational efficiency, although the clustering performance seems comparable. These findings seem to further support the robustness of FCM in big data processing.

Massive trust assessment (MTA) in an Online Social Network (OSN), i.e., computing the trustworthiness of all users in the network, is crucial in various OSN-related applications. Existing solutions are either too slow or inaccurate in addressing the MTA problem. We propose the OpinionWalk algorithm that accurately and efficiently conducts MTA in an OSN. OpinionWalk models trust by the Dirichlet distribution and uses a matrix to represent the direct trust relations among users. From the perspective of a user, other users' trustworthiness are stored in a column vector that is iteratively updated when the algorithm “walks” through the network, in a breadth-first search manner. We identify the overlapping subproblems property in MTA and prove OpinionWalk is a more efficient solution. The accuracy and execution time of OpinionWalk are evaluated and compared to benchmark algorithms including EigenTrust, TrustRank, MoleTrust, TidalTrust and AssessTrust, using two real-world datasets (Advogato and Pretty Good Privacy). Experimental results indicate that OpinionWalk is an efficient and accurate solution to MTA, compared to previous algorithms.

This paper develops the traffic models of smart grid electronic data (E data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of E data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data's sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

Wireless body area networks (WBANs) require lightweight and resource efficient security approaches. In the literature, biometric-based security approaches have been well studied for WBANs when each individual person may have unique biometric features. However, additional sensing hardware (e.g., ECG sensors) are usually required for every sensor in these approaches, which make them unpractical in real application settings. Unlike them, in this paper, we propose a physical layer-based security approach utilizing physical channel information and remove the extra hardware requirements. Especially, a group-based cooperation on symmetric secret key generation via physical or link layer received signal strength indicator (RSSI) data accumulating is investigated. We propose a practical cooperative group solution to increase the similarity, fluctuation and density of RSSI data for high efficient key generation. The major innovation is to make full use of multiple channels between a participant node and a group or between two groups to randomly synthesize RSSI data with multifold data density and improved data similarity and fluctuation. In addition, several group models are described with the details of their specific protocol design. Furthermore, a prototype is implemented and verified in an experimental environment to demonstrate the high practicality and efficiency of our solution.

This paper presents the design and prototype of a wireless health monitoring system using mobile phone accessories. We focus on measuring real-time electrocardiogram (ECG) and heart rate monitoring using a smartphone case. With the increasing number of cardiac patients worldwide, this design can be used for early detection of heart diseases. Unlike most of the existing methods that use an optical sensor to monitor heart rate, our approach is to measure real-time ECG with dry electrodes placed on smartphone case. The collected ECG signal can be stored and analyzed in real time through a smartphone application for prognosis and diagnosis. The proposed hardware system consists of a single chip microcontroller (RFduino) embedded with Bluetooth low energy, hence miniaturizing the size and prolonging battery life. The system called “smart case” has been tested in a laboratory environment. We also designed a 3-D printed smartphone case to validate the feasibly of the system. The results demonstrated that the proposed system could be comparable to medical grade devices.

The world has entered into a “smart” era. One area becoming smart is the place where we live-homes. Smart homes are expected to be equipped with numerous sensors to continually monitor, sense, and actuate the space. The data from these sensors can be used to provide various types of services by automating common tasks while causing minimal disruption to daily life. In order to provide these services, a system must have sufficient intelligence to predict future events based on its observations. This paper first examines the requirements for smart home predictions. It then comprehensively reviews prediction algorithms and variations that have been proposed and investigated in smart environments, such as smart homes. It is these prediction algorithms that provide the intelligence required by a smart home. Comparisons are also made upon these prediction algorithms on their features and models.

With advances in Internet of Things (IoT), intelligent data sensors are being added to more and more devices that interact with human's daily life in areas, such as medical services, smart grids, and financial services. IoT has made big contributions to data growth, requiring highly reliable data storage solutions. Storage area networks (SANs) are one of such solutions. To meet high reliability and availability requirements, SANs have to provide fault tolerance through redundancy to minimize or eliminate system downtime, thus preventing business discontinuity due to catastrophic events. Mesh is one of the common SAN topologies that have been applied to implement a fault tolerant SAN in practice. In this paper, failure behavior of a mesh SAN is modeled using a dynamic fault tree (DFT) in the case of perfect links, or a network graph in the case of imperfect links. Based on the constructed DFT or network graph model, reliability of the mesh SAN is evaluated using a binary decision diagram-based method. Results obtained from the case study can provide insights into the behavior of general mesh SAN systems, providing guidelines in the reliable design and operation of SANs.

Multimedia communication in the Internet of Things (IoT) can potentially reach into a vast array of areas and touch people’s lives in profound and different ways. For example, real-time multimedia communication could be applied in the current U.S. 911 system to provide responders with detailed information about the nature and severity of an incident before they arrive on the scene, if the callers can transmit image and/or video of the incident site. City governments can also allow citizens to report traffic and road conditions by uploading real-time multimedia data via a specific smartphone app.

Combining tiny sensors and wireless communication technology, wireless body area network (WBAN) is one of the most promising fields. Wearable and implantable sensors are utilized for collecting the physiological data to achieve continuously monitoring of people’s physical conditions. However, due to the openness of wireless environment and the significance and privacy of people’s physiological data, WBAN is vulnerable to various attacks; thus, strict security mechanisms are required to enable a secure WBAN. In this article, we mainly focus on a survey on the security issues in WBAN, including securing internal communication in WBAN and securing communication between WBAN and external users. For each part, we discuss and identify the security goals to be achieved. Meanwhile, relevant security solutions in existing research on WBAN are presented and their applicability is analyzed.

Cognitive radio (CR) is an emerging technique to improve the efficiency of spectrum resource utilization. In CR networks, the selfish behavior of secondary users (SUs) can considerably affect the performance of primary users (PUs). Accordingly, game theory, which considers the game players' selfish behavior, has been applied to the design of CR networks. Most of the existing studies focus on the network design only from the network perspective to improve system performance, such as utility and throughput. However, the users' experience to the service, which cannot simply be reflected by quality of service, has been largely ignored. The user-perceived multimedia quality and service can be different from the actual received multimedia quality, and thus is very important to consider the network design. To better serve the network users, quality of experience (QoE) is adopted to measure the network service from the users' perspective and help improve the users' satisfaction to the CR network service. As CR networks require a lot of data storage and computation for spectrum sensing, spectrum sharing, and algorithm design, cloud computation comes as a convenient solution, because it can provide massive storage and fast computation. In this paper, we propose to design a user-oriented CR cloud network for multimedia applications, where the user's satisfaction is reflected in the CR cloud network design. In the proposed framework, the PU and SU game is formulated as Stackelberg game. In particular, a refunding term is defined in the users' utility function to effectively consider and to reflect the network users' QoE requirement. Our contributions are twofold: 1) a game-based CR cloud network design for multimedia transmission is proposed, and the network user's QoE requirement is satisfied in the design and 2) the existence and the uniqueness of the Stackelberg Nash equilibrium are proved, and the design is optimal. Our simulation results demonstrate the effectiveness of the game user-oriented CR cloud network design.

To relieve the current overload of cellular networks caused by the continuously growing multimedia service, mobile edge collaboration, which exploits edge users to distribute videos for base station (BS), provides an effective way to share the heavy BS load. With the emergence of mobile edge technologies for Internet-of-Things applications, such as device to device and machine to machine, how to exploit users' social characteristics and mobility to minimize the number of transmissions of BS and how to improve the quality of experience of users have become the key challenges. In this paper, we study two aspects that are critical to these issues. One is the two-step detection mechanism, namely the establishment of virtual communities and collaborative clusters. Specifically, we take into consideration user preference for content and location. First of all, a virtual community is established, which exploits the coalition game based on the user's preference list to dynamically divide users into multiple communities. Then, to take full advantage of the temporary link established between users, a grid-based clustering method is proposed to manage the video requesting users. On the other hand, we propose a scalable video coding sharing scheme based on user's social attributes. This approach makes video distribution more flexible at the edge of mobile network through collaboration among users, and effectively reduces transmission energy consumption of transmitters. Numerical results show that the proposed mechanism can not only effectively alleviate the BS load, but also dramatically improve the reliability and adaptability of video distribution.

To offload and alleviate the heavy base station (BS) traffic load caused by the rapidly growing video services, device-to-device (D2D) communication, as one of the most indispensable technologies of the future cellular networks, can be potentially exploited by mobile users to distribute videos for a BS. In this paper, an effective pricing-based multicast video distribution system and a grid-based clustering method are proposed to support the distribution. Moreover, with the consideration of users' mobility and social characteristics, we classify them into multicast and core types by studying the user stay probability and familiarity. In particular, core users can cooperate with the BS to distribute videos to the multicast users through intracluster D2D multicast. However, core users cannot selflessly help the BS to distribute videos; instead, they will evaluate their personal benefits before distributing the videos to the multicast users. Further, a Stackelberg game-based pricing mechanism is proposed to inspire the core users to distribute videos. Simulation results demonstrate that the proposed mechanism can not only effectively alleviate the BS traffic load, but also significantly improve the effectiveness and reliability of video transmission.

Basically, a WBAN consists of few wearable sensors attached to body parts, clothes, implanted underneath the skins or inner body. A WBAN consists of a central hub (i.e., base station) that controls and communicates with sensors. WBANs may deeply overlap on each other in a crowded area such as hospital because of their rapid mobility, small network size, flexible topology, and higher network density. This overlapping may raise severe interference issues. This WBAN interference can be imposed on sensor-to-BS (i.e., base station), sensor-to-sensor or BS-to-BS, while in traditional networks the interference is imposed merely among few nodes and interference to BS is very rare. In case of deeply overlapping WBANs, interference avoidance employing power control schemes is plausibly unrealistic, because, nodes and hub from other network may stay much closer than nodes and hub of its own network. A possible way to avoid interference is co-existing on the limited channels by sharing the time-frames alternately which is referred to as self-coexistence. In this paper we formulate the WBAN self-coexistence problem as a linear integer programming optimization problem considering service priorities and user demands and propose a solution named Fair Frame Distribution for Self-coexistence (FDS). Comprehensive simulation results show that the proposed FDS algorithm preserves service priority and maximizes fairness.

In this paper, we propose a novel Media Access Control (MAC) scheme for Cognitive Radio Ad Hoc Networks (CRAHNs). The scheme includes two mechanisms. One mechanism is called Exposed Terminal Secondary users Communication (ETSC), which allows a Secondary User (SU) to continue communication attempt even when the occupied channel could be used by a new Primary User (PU). The other is called Four-Stage RTS/CTS Back protocol (FSRCB) of PUs by which the communication links between PUs can be created after at most four RTS/CTS shacking attempts. FSRCB can support a PU receiver which is in the interference area of a SU sender, and decrease transmission power to increase frequency utilization efficiency. Compared with the traditional "PU-driven SU self- termination" scheme, our scheme can achieve much better performance, including lower dropping rate, higher success rate, higher throughput, higher channel utilization efficiency, and lower blocking probability.

To further increase the system capacity in cellular networks, establishing stable D2D (Device-to-Device) links with efficient power allocation is necessary due to the communication interferences. Existing works are mainly focused on the interference control and mitigation at the physical layer. However, the information from social interactions among D2D users are also helpful to improve the system performance. In this paper, we first evaluate the level of social ties and take it as the decision metric for transmission mode selection, which can effectively offload mobile traffic. Then a utility-based maximization game is proposed to reduce interference among D2D pairs. In this game, we use the effective social distance as the penalty coefficient, and perform distributed control of the transmission power for D2D communication. Numerical results demonstrate that the proposed scheme significantly improve the delivery ratio and reduce interference by only sacrificing a small amount of total utility.

Continuously monitoring the vital signs over a long period of time is important for heart diseases. However, a traditional wearable device may be inconvenient to carry. Therefore, the size of low power ICs and wireless modules in this device need to be minimized for healthcare system. In this paper, we proposed non-contact and low power sensors with integrated kinetic sensor for multiparameter real time monitoring. The proposed system consists of a non-contact electrocardiogram (ECG) sensor with fully integrated analog front end (AFE), a temperature sensor, an accelerometer, and a Bluetooth low energy (BLE) module. The system is small with the size of 50.5 x 15.2 x 6mm. The developed wearable glass in the system can be used by inpatient, outpatient or people with disability. The device could also be used by aging people who live alone, capable of sensing fall detection, temperature and monitoring ECG. An Android application is developed to perform data processing, and it also sends alerts to authorities in case of emergency.

The demands and interests in non-contact health monitoring have increased rapidly in recent years due to its noninvasive method and easy to use in daily life. In this paper, a real time and non-contact based cardiac monitoring system including a sensor is presented. The sensor is capacitively-coupled with human skin and does not cause impediments in the natural movement of the user. Moreover, low power RF module nRF24L01+ and passive components were used for reducing power consumption. Current monitoring systems for premature infants are not convenient due to the usage of patched or adhesive tape for monitoring vital information. The front end circuit is integrated with multiple stages of amplifier, filters, analog to digital converter and the wireless module. The non-contact capacitive electrode was integrated into the leather belt as well as in the chair to monitor electrocardiography (ECG) signals. In this work, a simple and easily accessible ECG monitoring sensor node for body area networks was designed and tested. The proposed system was compared through experiments with the one with Ag/Agcl based electrodes. The results show that the proposed system could accurately be used as a medical grade equipment for long-term health monitoring.

Internet of Things (IoT) heavily relies on wireless communication to interconnect various sensors and hubs. This paper proposes a symbol-level information multiplexing mechanism (SIMPLEX) that exploits link margin in wireless networks to minimize channel underutilization. Multiplexing is achieved by carrying extra information through a type of specially designed symbols inserted. The key enabler of the inserting and detecting such specially symbols is a per-bit channel assessment scheme that hierarchically estimates the error probability of a received symbol. On the GNU SDR testbed experiments, SIMPLEX shows an accuracy of 97% in recognizing the special symbols in demultiplexing. By varying the frequency domain indices of such specially symbols and their positions on I-Q constellation map, SIMPLEX provides a series of multiplexing rates that carry different amount of extra information. We also design an adaptive multiplexing rate selection scheme to dynamically achieve optimal exploitation of link margin upon instant channel conditions, which can find the optimal multiplexing rates over 90% of the time on the testbed. Multiplexing throughput gain has been theoretically derived and empirically validated of high accuracy with only a negligible deviation to experiment results. A throughput gain can be obtained as much as up to 55%.

Cyber-physical systems (CPS) are emerging research areas that involve multiple disciplines. Two critical components in CPS are networking technologies and security. Because of the multi-disciplinary nature, the networking and security of CPS expand beyond traditional computing domains and have to consider the impact of applied physical systems. Therefore, new innovations are required such as novel transmission technologies, networking protocols, architectures, and security solutions. As a result, a significant amount of research work is expected for new models, performance analysis as well as evaluation, prototypes, and testbeds. This special issue focuses on research interests and activities related to networking and security in SmartGrid, Transportation and Medical Systems, with an emphasis on original analytical, experimental, and systems-related papers in these target domains.

The papers in this special issue focus on cloud computing-based video processing and content sharing. With the rapid growth of IPTV and mobile video applications and driven by urgent demands from industry and users, video processing and content sharing technologies have received significant research attention in recent years. Cloud-based video processing and content sharing networks are promising technologies to orchestrate large-scale and efficient video distribution between mobile clients and multimedia cloud systems. The objective of this special issue is to identify and promote advancements in media cloud-based video processing and content sharing technologies to advance current and enable future anywhere and anytime video processing and streaming applications.

To preserve the privacy of multimedia big data and achieve the efficient data aggregation in wireless multimedia sensor networks (WMSNs), a distributed compressed sensing--based privacy-preserving data aggregation (DCSPDA) approach is proposed in this article. First, in this approach, the original multimedia sensor data are compressed and measured by distributed compressed sensing (DCS) and the compressed data measurements are uploaded to the sink, by which the inherent characteristics between sensor data can be obtained. Second, the original multimedia data are jointly recovered and the common and innovation sparse components are obtained through solving the optimization problem and linear equations at the sink. Third, through least squares support vector machine (LSSVM) learning of the sparse components, the sparse position configuration can be determined and disseminated for each node to conduct the privacy-preserving data configuration. After receiving the configuration message, original multimedia sensor data are accordingly customized, compressed, and measured by the common measurement matrix, aggregated at the cluster heads, and transmitted to the sink. Finally, the aggregated multimedia sensor data are recovered by the sink according to the data configuration to achieve the privacy-preserving data aggregation and transmission. Our comparative simulation results validate the efficiency and scalability of DCSPDA and demonstrate that the proposed approach can effectively reduce the communication overheads and provide reliable privacy-preserving with low computational complexity for WMSNs.

Intermittently connected wireless networks (ICWNs) have been studied in recent years to solve the disruption problem in mobile ad hoc networks and improve the utilization of temporary links raised by node movements. In ICWNs, the packet storing-carrying-forwarding principle is adopted through the cooperation between multiple nodes. Therefore, it is critical to include the connection status of nodes in designing efficient packet forwarding mechanism. In this paper, a node service ability aware packet forwarding mechanism is proposed based on the connection status. First, the connection model is established to analyze the transition of connection status; moreover, the service ability can be evaluated according to the connection strength and connection availability. Second, packet forwarding levels are determined based on their transmitting status to exploit the limited buffer resources. Consequently, the efficient packet forwarding mechanism can guarantee the flexibility of packet transmission in both complex and dynamic network scenarios. Numerical results show that about 20% delivery ratio increase can be achieved by the proposed mechanism, while the overheads and latency are reduced.

This paper proposes a Contrarian Probabilistic Model (CPM) to evaluate the effectiveness of contrarians' investment in preferred stocks using big data from Tradeline. CPM accommodates the unique features of investment data which are often correlated, nested, heterogeneous, non-normal with missing values. The clustering and statistical inference are integrated in CPM, which enables joint investment behavior trajectory pattern recognition and risk analyses based on the entire variance-covariance structure between and within clusters. The empirical study using CPM provides a finer and comprehensive evaluation of contrarian investment in preferred stocks. Two distinctive investment behavior trajectory clusters were identified, showing a few high-risk-seeking contrarians achieved high returns over five year long-term investment, while the majority of contrarians did not outperform glamour stockholders in preferred stock investment. Although CPM was developed using historical data, it could be developed into an analytical tool for online near real time big investment data analyses.

Based on empirical studies, the feature of random initialization in Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods affects the computational performance especially in big data. As the data points in high-density areas are more likely near the cluster centroids, we design a new algorithm to guide the initialization according to the data density patterns. Our algorithm is initialized by fusing the data characteristics near the cluster centers. Our evaluation results from real data show that our approach can significantly improve the computational performance of PSO-based Fuzzy clustering methods, while preserving comparable clustering performance.

A miniaturized monopole antenna was designed and fabricated on an organic paper and LCP material for wireless body area network. Compared with previous work, the proposed design has 20% reduction of the antenna size but with enhanced performance. The effects of the compact coplanar antenna under different twisting conditions is described in this paper. The proposed antennas are simulated and designed on an organic paper and a Liquid Crystal Polymer (LCP) substrate with dielectric constant Dr= 3.4 and thickness 15μm and 5μm respectively, occupying the area of 22×30mm2. A detailed discussion about radiation pattern, Gain, antenna efficiency and power pattern is given with the help of experimental and numerical results.

In traditional wireless network infrastructure, multiple wireless networks with various access points (APs) would be deployed in the same area. Although this deployment can easily provide service for mobile user equipment (MUE), any AP only allows the authorized MUEs to access, and thus some wireless networks might be overloaded and others might be lightly loaded. As a result, resource allocation would be inefficient. Using wireless network virtualization, an infrastructure provider (InP) can deploy only a single physical AP in the same area. This AP, which is controlled by a network operator (NO), is shared by multiple service providers (SPs) coexisting in the same AP. In the framework of wireless network virtualization, NO is in charge of resource allocation for the whole system and SP focuses on the access, connection and resource requirement of MUEs (such as the desired transmission power in downlink). In this paper, a Game theory based Two Steps Power Allocation scheme for wireless network virtualization, called G2SPA, is proposed, which designs a Stacklberg Equilibrium price strategy based on the interactions between SP and MUE, and then performs McAfee based auction to reallocate resource. The numerous experimental simulation results show that the rightness and effectiveness of G2SPA.

Cognitive radio (CR) networks have been developed to fully utilize spectrum resources. In the CR networks, imperfect sensing and selfish behavior of secondary users (SU) can significantly degrade the performance of primary users (PU). Game theory, which considers the game players' (PU/SU) selfish behavior, has been extensively investigated in the design of CR network. However, most related works focus on the network protocol design from the network perspective to improve video transmission system (i.e., PU) performance such as utility and throughput. However, a user's satisfaction such as user's quality of experience (QoE) has been largely neglected in existing works. In this paper, we propose to design a game user-oriented CR network for video applications, aiming to improve the QoE of users. In the proposed framework, the primary user games and the secondary user games are formulated as Stackelberg game. Specifically, a refunding term is defined in the user's utility function to effectively consider and to reflect the QoE requirement of video streaming users. Our contributions include two folds: (1) A game based CR network design for video transmission is proposed, and the network user's QoE requirement is satisfied in the design; (2) The existence and uniqueness of the Stackelberg game's Nash equilibrium is proved, and the design is optimal. Our theoretical analysis and simulation results demonstrate the effectiveness of the game-based user-oriented CR network design.

Increasing interests in remote monitoring of vital signs through telecommunication, especially with wireless and mobile communication have enabled a new generation of information system for healthcare applications. The system may include miniature sensor nodes embedded with wireless communications and a mobile computer delivering information to remote locations. In this paper, we introduced an inexpensive and ultra-low power system for measuring ECG and heart rate in contact and contact-less manners. Existing monitoring system uses gel or electrodes for measuring ECG signals. However, in this study, we used unique electric potential EPIC sensors from Plessey's and infrared sensors. The prototype is capable of monitoring both heart rate and ECG signals with a hibernation mode, which would require less power to transmit the data. Our developed prototype can be used to monitor premature infants as their skins are sensitive and current system uses patches or gel to collect biomedical signals. The proposed prototype for monitoring vital signs has been tested in our lab. The results show that it can achieve low power consumption though a hibernation mode.

This paper proposes a visualization aided pattern validation to identify optimal number of clusters for big longitudinal web behavior intervention data. The proposed validation consists of two parts: The weighted validation index including overlap and separation measures, and visualization integrating a between-stress mapping and trajectory characterization. The proposed method is applied to a longitudinal web behavior intervention dataset and a set of simulated zero-inflated data using parameters from this web trial. Four engagement patterns for this web behavioral intervention are identified and validated using our proposed method.

It is important to identify the epidemic sources during epidemic outbreaks to optimize the control strategies. However, the identification process is difficult due to the dynamics and complexity of epidemic networks. In this paper, we propose an identification algorithm to more accurately localize the epidemic source based on social contact networks (SCNs) only with a limited number of observers. We give an approximate solution for the SCNs. The proposed algorithm is validated on both real and artificial SCNs. The obtained results demonstrate that the proposed algorithm achieves better performance than existing methods.

It is a challenge to support multimedia services with high Quality of Service (QoS) requirements for upcoming 5G radio access techniques that has the characteristics of Heterogeneous network Architecture, Heterogeneous Terminals and Heterogeneous Spectrum (H3ATS). Multi-view Video (MVV) consisting of multiple video streams captured by close spaced cameras is increasingly popular, permitting changeable viewpoints by playing different streams. As those close spaced cameras will capture overlapping frames (OFs) and transmit OFs in multiple streams, when switching video streams from one to another, it is redundant to transmit and receive OFs in the latter stream for base stations and users, respectively. Moreover, since the data rate requirement of a MVV with multiple streams is much higher than that of the traditional video with single-stream, base stations will consume tremendous bandwidth if the number of playing MVVs increases. To eliminate OFs and offload traffic from base stations, we propose a new MVV stream architecture called Overlapping Reduced Multi-view Video Transmission (ORMVVT) and a new network architecture named Multicast Multi-Traffic Source (MMTS) for multicast small cell networks (MSCNs). Then, an optimization problem is formulated to minimize the data rate of small cells under QoS constraint. An Offloading Based Traffic Placement (OBTP) scheme is introduced to solve the optimization problem. Simulation results show that the proposed low-complexity OBTP is able to get a higher performance than the traditional schemes in terms of bandwidth saving.

Cloud radio access networks (C-RANs) have attracted considerable attention because of the capability of meeting the exponential increasing traffic demand in the future communication systems. In this paper, we consider the segment training based channel estimation in C-RANs. As the classical minimum mean-square-error estimator has cubic complexity in the dimension of the covariance matrices, due to the inversion operation, we propose a low-complexity channel estimator by means of the \emph{L}-degree matrix polynomial expansion, which can significantly reduce the computational complexity without degrading much performance. The numerical results are presented to verify the proposed channel estimators, and the simulation results show there are significant performance gains from our proposal.

The articles in this special section are focused on two major aspects of Internet of things (IoT) technologies for smart and connected health services (SCH): 1) monitoring and assisting individuals by means of smart systems including sensors, devices, and robotics; and 2) creating interoperable digital health information infrastructures to increase medical/health information availability and use. The papers published in this SI provide evidence that SCH tools that rely upon IoT technologiescould significantly improve clinical outcomes and thequality of life of individuals undergoing monitoring.

Abstract Security and video quality are progressively significant attributes for wireless multimedia sensor networks. Most of existing research considers security and video quality separately. However, it is crucial to integrate security and video quality together for video transmission because delivering video data across a secure path does not often meet video quality requirements in many traditional approaches. Applying the general concept of secret sharing algorithm on a data packet and delivering it through disjoint multipaths can be considered to deliver the data securely. However, using the general concept of secret sharing is not efficient when large‐size video data are routed. To tackle these issues, we propose a novel security and quality aware routing (SQAR) protocol to address these two issues concurrently. We jointly consider security and video quality in wireless multimedia networks by proposing a video distortion model based on a new secret image sharing scheme. In SQAR, a secret image sharing is only applied on the intra‐frames of the video codec H.264 and can significantly reduce the transmission overheads. Simulation results show that SQAR scheme can achieve better trade‐off between the security and quality over the traditional routing protocols. Copyright © 2015 John Wiley & Sons, Ltd.

Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.

Wireless sensor networks WSNs have found a wide variety of applications recently. However, the challenges in WSNs still remain in improving the sensor energy efficiency and information quality distortion reduction of the sensing data transmissions. In this paper, we propose a novel cross-layer design of resource allocation and channel coding to protect distributed source coding DSC-based data transmission. Resource allocation strategies include rate adaptation and automatic repeat-request retransmissions. Our proposed joint design of resource allocation, channel coding, and DSC can improve the network energy efficiency and information quality while meeting the data transmission latency requirements. Further, we investigate how the resource allocation enables the network to achieve unequal error protection among correlated DSC streams. Our simulation studies demonstrate that the proposed joint design significantly improves the DSC-based data transmission quality and the network energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

Hadoop is the de facto engine that drives current cloud computing practice. Current Hadoop architecture suffers from single point of failure problems: its job management lacks of fault tolerance. If a job management fails, even if its tasks remains still active on cloud nodes, this job loses all state information and has to restart from scratch. In this work, we propose a distributed MapReduce engine for Hadoop with the Distributed Hash Table (DHT) algorithm that drives the scalable peer-to-peer networks today. The distributed Hadoop engine provides the fault-tolerance capability necessary to support efficient job computation required in the cloud computing with numerous jobs running at a moment. We have implemented the proposed distributed solution into Hadoop and evaluated its performance in job failures under various network deployments.

Assessing multi-hop interpersonal trust in online social networks (OSNs) is critical for many social network applications such as online marketing but challenging due to the difficulties of handling complex OSN topology, in existing models such as subjective logic, and the lack of effective validation methods. To address these challenges, we for the first time properly define trust propagation and combination in arbitrary OSN topologies by proposing 3VSL (Three-Valued Subjective Logic). The 3VSL distinguishes the posteriori and priori uncertainties existing in trust, and the difference between distorting and original opinions, thus be able to compute multi-hop trusts in arbitrary graphs. We theoretically proved the capability based on the Dirichlet distribution. Furthermore, an online survey system is implemented to collect interpersonal trust data and validate the correctness and accuracy of 3VSL in real world. Both experimental and numerical results show that 3VSL is accurate in computing interpersonal trust in OSNs.

Wireless communication is indispensable to Internet of Things (IoT). Carrier sensing multiple access/collision avoidance (CSMA/CA) is a well-proven wireless random access protocol and allows each node of equal probability in accessing wireless channel, which incurs equal throughput in long term regardless of the channel conditions. To exploit node diversity that refers to the difference of channel condition among nodes, this paper proposes two opportunistic random access mechanisms: overlapped contention and segmented contention, to favor the node of the best channel condition. In the overlapped contention, the contention windows of all nodes share the same ground of zero, but have different upper bounds upon channel condition. In the segmented contention, the contention window upper bound of a better channel condition is smaller than the lower bound of a worse channel condition; namely, their contention windows are segmented without any overlapping. These algorithms are also polished to provide temporal fairness and avoid starving the nodes of poor channel conditions. The proposed mechanisms are analyzed, implemented, and evaluated on a Linux-based testbed and in the NS3 simulator. Extensive comparative experiments show that both opportunistic solutions can significantly improve the network performance in throughput, delay, and jitter over the current CSMA/CA protocol. In particular, the overlapped contention scheme can offer 73.3% and 37.5% throughput improvements in the infrastructure-based and ad hoc networks, respectively.

The nine papers in this special issue cover multiple aspects of emerging wireless body area networks (WBANs) for ubiquitous healthcare.

In multimedia communication, various data rates, security strategies, and data qualities are required for different content forms such as text, audio, images, video, and interactivity content forms. In this paper, we propose a secure and robust approach to achieve the multimedia multiple access (MA) communication using self-encoded spread spectrum (SESS). In this proposed system, SESS multiple access (SESS-MA) is a novel approach to multimedia system due to its unique secure and flexible spreading nature. Iterative detection is applied for an improved multimedia quality of service (QoS) at the receiver. The number of iterations needed is evaluated separately according to different multimedia contents. Simulation studies demonstrate that the proposed scheme ensures satisfactory data quality, security, and robustness.

Wireless Body Area Network (WBAN) includes a set of body sensor nodes which are placed around human body, collecting data while sending them to medical center. In order to deliver the body signal to remote terminals in timely fashion, an extended communication architecture dubbed “beyond-BAN communication” was proposed. However, existing architectures are not suitable for the scenarios with high mobility of both patients and physicians due to the fluctuation of wireless links. Furthermore, when the amount of healthcare content is large, the quality of delivery is hard to be guaranteed. To address these challenging issues, we propose a novel network architecture, which integrates WBAN with the Long Term Evolution (LTE) networking and Named Data Networking (NDN). The integration with LTE is to enlarge the radio coverage and guarantee the quality of wireless transmissions, while the integration with NDN is to leverage edge router caching technique to enhance the capacity of the WBAN coordinator, and to avoid the packets loss by adapting to dynamic wireless link conditions with the adaptive streaming technique. The experimental results conducted by OPNET Modeler prove that our solution improves the Quality of Service (QoS) performance of WBAN transmission significantly.

Accurate and real-time tracing of epidemic sources is critical for epidemic origin analyses and control when outbreaks of epidemic diseases occur. Such tracing requires the simultaneous availability of information about social interactions among people as well as their body vital signs. Existing epidemic control methods are limited due to their inability to collect the above two types of information at the same time. In this paper, for the first time, we propose integrating wireless body area networks (WBANs) for body vital signs collection with mobile phones for social interaction sensing to achieve the desired epidemic source tracing. In particular, we design a mobile phone capability driven hierarchical social interaction detection framework integrated with WBANs. With this framework, we further propose a set of epidemic source tracing and control algorithms including genetic algorithm based search and dominating set identification algorithms to effectively identify epidemic sources and inhibit epidemic spread. We have also conducted extensive simulations, analyses, and case studies based on real data sets, which demonstrate the accuracy and effectiveness of our proposed solutions.

Cloud multimedia services provide an efficient, flexible, and scalable data processing method and offer a solution for the user demands of high quality and diversified multimedia. As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering the limited bandwidth available for mobile streaming and different device requirements, this study presented a network and device-aware Quality of Service (QoS) approach that provides multimedia data suitable for a terminal unit environment via interactive mobile streaming services, further considering the overall network environment and adjusting the interactive transmission frequency and the dynamic multimedia transcoding, to avoid the waste of bandwidth and terminal power. Finally, this study realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environments.

3D video applications such as 3D-TV and 3D games have become more and more popular in recent years. These applications raised significant challenges in the media security, processing and transmissions. Especially, when 3D videos are delivered over wireless networks, the video streaming suffers the potential malicious attacks. One of the most important security challenging issues is how to guarantee the integrity of media content over error-prone wireless networks. To address this challenge, in the paper, we for the first time propose an authentication approach for 3D video transmission over wireless networks, which can improve the reconstructed media quality under error-prone wireless environment with lower authentication overheads and energy consumption. The proposed method is based on color-depth 3D video coding approach, which can save bandwidth, be tolerable to packet losses and thus satisfy the users' Quality of Experience (QoE) requirements. Our major contribution in this paper includes: (1) designing a joint source-channel-authentication coding framework for color-depth-based 3D video transmission; (2) proposing a media quality prediction model for color-depth-based 3D video transmission; (3) developing optimization for graph-based authentication on 3D video transmission to improve reconstructed media quality, reduce authentication overheads and energy consumption. Experimental results demonstrated the effectiveness of our proposed solutions.

ABSTRACT In this paper, a new channel allocation and re‐location scheme is proposed for cognitive radio users to efficiently utilize available spectrums. We also present a multiple‐dimension Markov analytical chain to evaluate the performance of this scheme. Both analytical results and simulation results demonstrate that the new scheme can enhance the radio system performance significantly in terms of blocking probability, dropping probability, and throughput of second users. The proposed scheme can work as a non‐server‐based channel allocation, which has practical values in real engineering design. Copyright ©2011 John Wiley & Sons, Ltd.

In this paper, an Alamouti space time based cooperative wireless cellular network for multimedia transmission is proposed. According to various requirements of text, voice, image, video or medical signal transmissions, this multiplexing-diversity balanced structure is provided for such comprehensive multimedia transmission in wireless cellular networks. Simulation results of this Alamouti based cooperative network have been compared with Maximum Ratio Combining (MRC) method. Simulation results show the BER performance of proposed scheme outperforms several other spatial modulation methods.

Database-generated dynamic web pages (db-pages, in short), whose contents are created on the fly by web applications and databases, are now prominent in the web. However, many of them cannot be searched by existing search engines. Accordingly, we develop a novel search engine named Dash, which stands for Db-pAge Search, to support db-page search. Dash determines db-pages possibly generated by a target web application and its database through exploring the application code and the related database content and supports keyword search on those db-pages. In this paper, we present its system design and focus on the efficiency issue. To minimize costs incurred for collecting, maintaining, indexing and searching a massive number of db-pages that possibly have overlapped contents, Dash derives and indexes db-page fragments in place of db-pages. Each db-page fragment carries a disjointed part of a db-page. To efficiently compute and index db-page fragments from huge datasets, Dash is equipped with MapReduce based algorithms for database crawling and db-page fragment indexing. Besides, Dash has a top-k search algorithm that can efficiently assemble db-page fragments into db-pages relevant to search keywords and return the k most relevant ones. The performance of Dash is evaluated via extensive experimentation.

In this paper, an Alamouti based cooperative wireless networks for multimedia transmission is proposed. According to various requirements of video, image, voice or medical signal transmissions, the multiplexing-diversity balanced structure is provided for such comprehensive multimedia network. Simulation results of this Alamouti based cooperative network have been compared with Maximum Ratio Combining (MRC) method. Simulation results show the BER performance of proposed schedule outperforms several other spatial modulation methods.

With the fast growing of multimedia communication applications, cognitive radio networks have gained the popularity as they can provide high wireless bandwidth and support quality-driven wireless multimedia services. In multimedia applications such as video conferences over the cognitive radio, the Quality of Experience (QoE) that directly measures the satisfaction of the end users cannot be easily realized due to the limited spectrum resources. The opportunistic spectrum access cognitive radio (CR) is an efficient technology to address this issue. However, the unstable channels allocated to the multimedia secondary users (SUs) can be re-occupied by the primary users (PUs) at any time, which makes the CR difficult to meet the QoE requirements. Therefore, it is important to study how to allocate frequency or spectrum resources to SUs according to their QoE requirements. This paper proposes a novel QoE-driven channel allocation scheme for SUs and cognitive radio networks (CRN) base station (BS). The historical QoE data under different primary channels (PCs) are collected by the SUs and delivered to a Cognitive Radio Base Station (CRBS). The CRBS will allocate available channel resources to the SUs based on their QoE expectations and maintain a priority service queue. The modified ON/OFF models of PCs and service queue models of SUs are jointly investigated for this channel allocation scheme. The performance of multimedia transmission of images and H.264 videos under our CR channel allocation scheme is studied, the results show that the proposed channel allocation approach can significantly improve the QoE of the priority-based SUs over the cognitive radio networks.

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data communications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

There is a strong need to explore green and harvestable energy in computer communications. However, adapting wireless network performance to harvested energy has largely been ignored in literature. In this paper, we propose a new resource allocation scheme to improve data delivery quality in energy harvesting enabled wireless networks. In the proposed approach, packet Automatic Repeat reQuest (ARQ) limit of each wireless node is adaptively adjusted according to harvested energy. To achieve such optimal retry adaptation, energy neutrality constraint is considered in the overall optimization process. Simulation results show that the proposed retry adaptation approach significantly improves packet delivery ratio by exploring the harvested energy.

In this paper, we present a cooperative transmission approach to reduce the end-to-end delay in the context of AODV based multi hop wireless networks. The underneath idea is to effectively increase the average reach of each hop so that data packets can arrive at the destination in less number of hops with lower end-to-end delay. The existing approaches of increasing transmitted power are not effective while they increase the network interferences. Unlike them, we exploit the concept of cooperative beamforming to reduce the end-to-end delay by increasing the effective communication distances and reducing the communication interferences in wireless ad-hoc networks.

In this paper, we proposed an integrated biometric-based security framework for wireless body area networks, which takes advantage of biometric features shared by body sensors deployed at different positions of a person's body. The data communications among these sensors are secured via the proposed authentication and selective encryption schemes that only require low computational power and less resources (e.g., battery and bandwidth). Specifically, a wavelet-domain Hidden Markov Model (HMM) classification is utilized by considering the non-Gaussian statistics of ECG signals for accurate authentication. In addition, the biometric information such as ECG parameters is selected as the biometric key for the encryption in the framework. Our experimental results demonstrated that the proposed approach can achieve more accurate authentication performance without extra requirements of key distribution and strict time synchronization.

Multimedia networking has been expanding its definition beyond communications of text, image, audio, and video as the Next Generation Internet evolves from social networks to cyber-physical networks. One application related to transportation infrastructure includes health monitoring of existing paved and un-surfaced roads. Embedding remote sensing and spatial information technology into roadways facilitates constant observation of road conditions and automatic detection of frost and thaw fronts during the spring thaw and recovery period. A system is being developed which provides critical quantitative data to eliminate or supplement components of current visual inspection procedures, and thus greatly assists transportation agencies in making spring load restriction (SLR) placement and removal decisions. This paper presents the data visualization module of a Decision Support System for Spring Load Restriction (DSS-SLR). After temperature data are collected by underground sensors and transferred by wireless/wired networks to a central database, a user can view the spatial and temporal temperature patterns via a Web browser. An embedded interpolation routine and a graphical user interface (GUI) enable the user to view a cross section showing frost and thaw depths over time. Our work pioneers this new frontier of multimedia networking, which will have significant applications with regard to health monitoring of existing paved roadway systems, as well as un-surfaced road evaluation and maintenance.

Intelligent Transportation System (ITS) is to utilize the sensing, networking and information technologies for transport applications such as road and vehicles. In ITS, various sensors and networks play a vital role in the capturing and transmitting of transportation information. Especially, wireless networks' spectrum resource and reliability are important factors in this system. In this paper, we firstly give a brief description of the techniques adopted by the Intelligent Transportation System, and we then study an heterogeneous sensing and networking platform for the ITS traffic monitoring applications. A case study on video camera based traffic monitoring is given and the adoption of Low-Density Parity-Check (LDPC) coding is proved to support reliable communication.

Many key predistribution (KP) protocols have been proposed and are well accepted in randomly deployed wireless sensor networks (WSNs). Being distributed and localized, they are perceived to be scalable as node density and network dimension increase. While it is true in terms of communication/computation overhead, their scalability in terms of security performance is unclear. In this paper, we conduct a detailed study on this issue. In particular, we define a new metric called Resilient Connectivity (RC) to quantify security performance in WSNs. We then conduct a detailed analytical investigation on how KP protocols scale with respect to node density and network dimension in terms of RC in randomly deployed WSNs. Based on our theoretical analysis, we state two scaling laws of KP protocols. Our first scaling law states that KP protocols are not scalable in terms of RC with respect to node density. Our second scaling law states that KP protocols are not scalable in terms of RC with respect to network dimension. In order to deal with the unscalability of the above two scaling laws, we further propose logical and physical group deployment, respectively. We validate our findings further using extensive theoretical analysis and simulations.

Joint exploration of intra/inter-video coding versatility in signal processing domain and signature/hash diversity in the information security domain has not been well investigated in the literature. In this paper, we show multimedia stream authentication quality can be improved significantly by exploring these diversities. Specifically, we propose a new rate distortion framework by considering intra/inter-signature/hash diversity to simultaneously provide video service quality, communication rate efficiency and video content integrity for wireless video streaming. This research aims to provide a unique fusion of inter/intra video coding versatility and signature/hash authentication diversity to provide video authentication and traffic control. The results based on simulations demonstrate the effectiveness of the proposed scheme in achieving video robust authentication quality with limited rate constraint.

Understanding channel dynamics is essential and critical to a variety of research orientations on wireless networks. This paper presents observations and analysis from extensive measurements on IEEE 802.11g channels in an indoor environment with a customized testbed and measurement tools. We obtained the following major observations: 1) delivery ratio varies smoothly and large time scale delivery ratios change largely; 2) however, Signal-to-Noise Ratio (SNR) varies largely in micro time scale, but stable in large time scale; 3) conformable to what is observed by other researchers, frame delivery ratio is not strongly correlated with SNR; 4) large time scale loss rate information is not informative.

Wireless multimedia sensor networks (WMSNs) support many acoustic applications for audio surveillance, animal tracking/vocalization, human health monitoring, etc. However, resource constraints in sensor networks (such as limited battery power, bandwidth/computation capability, etc.) pose challenges for the quality and security of audio data transmission and processing. The security is a critical issue since audio information can be accessed or even manipulated in WMSNs. In order to ensure security, audio quality and energy efficiency, we propose an index-based selective audio encryption scheme for WMSNs. The scheme protects data transmissions by incorporating both resource allocation and selective encryption based on modified discrete cosine transform (MDCT). In this proposed scheme, the audio data importance is leveraged using the MDCT audio index, and wireless audio data transmission proceeds with energy efficient selective encryption. The simulation results show that the proposed approach offers a significant gain in terms of energy efficiency, encryption performance and audio transmission quality.

Selective encryption for video streaming was proposed for efficient multimedia content protection. However, the issues on joint optimization of video quality, content protection, and communication energy efficiency in a wireless sensor network (WSN) have not been fully addressed in the literature. In this paper, we propose a scheme to optimize the energy, distortion, and encryption performance of video streaming in WSNs. The contribution of this work is twofold. First, a channel-aware selective encryption approach is proposed to minimize the extra encryption dependency overhead at the application layer. Second, an unequal error protection (UEP)-based network resource allocation scheme is proposed to improve the communication efficiency at the lower layers. Simulation experiments demonstrate that the proposed joint selective encryption and resource allocation scheme can improve the video transmission quality significantly with guaranteed content protection and energy efficiency.

Media integrity, transmission quality, and energy efficiency are critical for secure wireless image streaming in a wireless multimedia sensor network (WMSN). However, conventional data authentication and resource allocation schemes cannot be applied directly to WMSN due to the constraints on limited energy and computing resources. In this paper, we propose a quality-driven scheme to optimize stream authentication and unequal error protection (UEP) jointly. This scheme can provide digital image authentication, image transmission quality optimization, and high energy efficiency for WMSN. The contribution of this research is two-fold as summarized below. First, a new resource allocation-aware greedy stream authentication approach is proposed to simplify the authentication process. Second, an authentication-aware wireless network resource allocation scheme is developed to reduce image distortion and energy consumption in transmission. The scheme is studied by unequally protected image packets with the consideration of coding and authentication dependency. Simulation results demonstrate that the proposed scheme achieves a performance gain of 3 ~ 5 dB in terms of authenticated image distortion.

A cross layer Unequal Error Protection (UEP) scheme based upon the multimedia position-value (P-V) partitioning has been recently proposed to only improve energy-distortion performance in Wireless Multimedia Sensor Networks (WMSN). There is also a great potential to adapt this UEP scheme into multimedia stream integrity protection. However, performance gains in multimedia transmissions with considerations of energy, distortion, and authentication in WMSN is a challenge. This difficulty results from the media codec dependency on both the content of multimedia stream authentication and protection on the integrity process itself. In this article, we propose a novel scheme which optimally matches the application layer multimedia codec dependency to both lower layer resource allocation requirements and the upper layer stream authentication. The goal of this scheme is to optimize the integrity and quality performance gain within energy constraint. The contribution of the proposed approach is two folds. Firstly, a stream authentication scheme matched with P-V codec dependency is proposed which significantly reduces additional authentication dependency overhead. Secondly, a new resource allocation scheme is proposed to improve energy-distortion-authentication performance with regard to codec dependency. Simulation studies demonstrate that the proposed scheme significantly impacts achieving multimedia transmission quality with energy efficiency and authentication assurance.

Transmissions of large sized images can be a bottleneck for a wireless sensor network (WSN) due to its limited resources. Security can be another concern. This paper proposes a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide the transmission and security sharing patterns based on the path diversities. Our proposed approach for secret image sharing on multiple node-disjoint paths for image delivery is to achieve high security without any key distribution and management, and thus the key management related problems do not exist. The energy efficiency is another major contribution made in this paper. This scheme does not only allow each image sensor to transmit optimal fractions of overlapped images through appropriate transmission paths in an energy-efficient way, but also provides unequal protection to overlapped image regions by path selections and adaptive bit error rate (BER) requirement. The simulation results show that the proposed scheme can achieve considerable gains in terms of network lifetime extension, image transmission security enhancement, image quality improvement, and energy efficiency for wireless sensor networks.

Distributed Source Coding (DSC) is an effective means in reducing data redundancy in Wireless Sensor Networks (WSNs). However, the issues on designing multirate DSC with multirate transmission in WSNs have not been well addressed in the literature. In this paper, we propose a cross-layer approach to achieve optimal DSC data quality while assuring energy efficiency and latency requirement through adjusting multirate DSC data dependencies and network multirate transmission parameters jointly. Specifically, the DSC coding dependency among multirate source coding sensors are fine-tuned to balance compression efficiency and transmission robustness. Transmission rate and retransmission limit on each wireless link are optimized accordingly to achieve Unequal Error Protection (UEP) among inter-dependent DSC streams. Simulation studies demonstrate that the proposed scheme ensures satisfactory DSC data quality and energy efficiency.

Abstract In this paper, we propose an innovative adaptive secure image delivery approach, to achieve effective and efficient image encryption based on an optimal transmission rate in terms of energy efficiency in wireless sensor networks (WSN). Our contribution is three folds—First, the proposed component based selective encryption scheme achieves high image security, while incurring little encryption overhead; furthermore, the proposed adaptive encryption scheme achieve the best security effort within delay bound; finally, the proposed transmission rate optimization improves energy efficiency, and feeds back effective path capacity parameters for adaptive encryption. Simulation results show a significant gain in both energy efficiency and security based on the proposed approach for image secure transmissions in WSN. Copyright © 2007 John Wiley & Sons, Ltd.

Multimedia delivery over Wireless Sensor Networks (WSNs) is energy critical. In this paper, we study an energy constrained cross layer optimization scheme to improve digital image quality in cross polarization discrimination (XPD) based Multiple Input Multiple Output (MIMO) WSNs. The contribution of this research can be summarized in three parts. First, link layer energy consumption and packet loss ratio in multirate XPD based MIMO WSN are modeled. Secondly, XPD based modulation optimization cross link and physical layer is proposed, achieving considerable energy efficiency. Finally, a new Position-Value (P- V) based Unequal Error Protection (UEP) scheme is studied and discussed in XPD MIMO WSNs, with more efficient resource allocation compared to traditional approaches. Simulation results show the proposed modulation optimization scheme improves energy efficiency considerably, and position based UEP achieves significant distortion-energy gain compared with traditional layer based resource allocation schemes in XPD MIMO WSNs.

A secure image transmission mechanism is necessary when malicious intruders intend to access and modify content delivery over wireless networks. To assure data integrity, authentication is required for multimedia data delivered over wireless image sensor networks. Watermarking technique is an effective vehicle to assert and assure the image data authentications. There have been recent works reported on watermarking, but few with the consideration of energy cost in terms of the data communication and processing, which is a key constraint to many embedded systems and wireless sensor networks. Most watermarking systems only target at minimizing watermarked image distortion and increasing robustness at the source coding site for lossy image processing. The watermarked image distortion caused by error-prone wireless environments during transmission has not been fully considered. In this paper, an innovative energy-aware adaptive watermarking scheme for realtime image delivery is proposed in wireless multimedia sensor networks. This new scheme allocates network resource to protect the watermarked image transmission while embedding watermark coding redundancies into the images. Dynamic watermark thresholds are applied to be adaptive to the network condition (packet loss ratio) and the inter-frame correlation is exploited to reduce processing delay. The simulation results show that the proposed adaptive watermark system can achieve considerable energy efficiency and assure the data integrity.

Joint consideration of energy efficiency, multimedia quality and media authenticity has largely been overlooked in the studies of secure wireless multimedia streaming. In this paper, we propose an authentication - resource allocation framework for secure transmission of multimedia streaming over energy constrained wireless networks. The contributions of this research can be summarized in three aspects. Firstly, a hash chain based stream level authentication scheme compatible for JPEG2000 compression standard is proposed. Secondly, link layer energy- distortion is modeled, and the energy-distortion convex hull is efficiently identified, which significantly reduces optimization complexity. Finally, an authentication oriented resource allocation scheme is proposed based on the proposed authentication scheme and link layer energy-distortion convex hull, where the authenticated image quality is optimized according to energy budget constraints.

Image transmissions in Wireless Multimedia Sensor Networks (WMSNs) are often energy constrained. They also have requirement on distortion minimization, which may be achieved through Unequal Error Protection (UEP) based communication approaches. In related literature with regard to wireless multimedia transmissions, significantly different importance levels between image-pixel-position information and image-pixel-value information have not been fully exploited by existing UEP schemes. In this paper, we propose an innovative image-pixel-position information based resource allocation scheme to optimize image transmission quality with strict energy budget constraint for image applications in WMSNs, and it works by exploring these uniquely different importance levels among image data streams. Network resources are optimally allocated cross PHY, MAC and APP layers regarding inter-segment dependency, and energy efficiency is assured while the image transmission quality is optimized. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency. The performance gain in terms of distortion reduction is especially prominent with strict energy budget constraints and lower image compression ratios.

The advancement in Distributed Source Coding (DSC) for mission-driven wireless sensor networks (WSNs) has opened a new vista for its wide applications in multiple correlated sensor networks such as real time target tracking and environment monitoring. The features of the DSC applications offer many potential opportunities for sensor networks to utilize multirate transmissions for network performance enhancement. In this paper, we propose a methodology for cross-layer optimization between routing and DSC in WSNs, and we introduce a multirate based routing scheme for mission-driven DSC applications that can considerably extend network lifetime. The proposed scheme adopts the rate assignment based on residual energy and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate demand, information precision requirement, and energy constraints in the networks adopting DSC. This approach is different from the traditional multirate research in link adaptation, where the focus was to increase channel throughput based on rate adaptation from variable channel conditions. The objective of this work is not to utilize multirate for increasing channel throughput, but to exploit multirate capability for routing optimization in DSC based applications with consideration for energy consumption. We also introduce the concept of "Energy Usage Scheduling (EUS)" to optimize the energy usage based on the rate constraints and DSC application needs. Simulation results show that the proposed scheme can achieve a significantly longer network lifetime than that reported in the literature.

Many Sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless Sensor Network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a Multi-Rate Least Cost Path (MRLCP) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate ratebased routing paths. A specific genetic algorithm is designed to solve such discrete optimization problem. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.

In this paper, we propose a new nonlinear classifier based on a generalized Choquet integral with signed fuzzy measures to enhance the classification power by capturing all possible interactions among two or more attributes. A special genetic algorithm is designed to implement this classification optimization with fast convergence. Instead of using a discrete misclassification rate, the objective function to be optimized in this research is a continuous Choquet distance with a penalty coefficient for misclassified points. The numerical experiment shows that the special genetic algorithm effectively solves the nonlinear classification problem and this nonlinear classifier accurately identifies classes.

Many sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in wireless sensor networks (WSN). The large size image transmissions cause bottlenecks in WSN due to the limited energy resources and network capacity. In this paper, we propose a collaborative transmission scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on transmission path diversities, which achieves minimal energy consumption, balanced sensor lifetime and required image quality. This optimization scheme not only allows each image sensor to transmit optimal fractions of the overlapped images through appropriate transmission paths in energy-efficient way, but also provides unequal protection on the overlap image regions through path selections and resource allocations to achieve good transmission image quality. The simulation results show that the proposed image transmission scheme can achieve considerable gains in terms of the network lifetime extension, image distortion reduction, and energy efficiency.

In image transmission applications over wireless sensor networks (WSN), energy efficiency and image quality are both important factors for joint optimization. In this paper, we propose a new position-value (P-V) oriented cross layer optimization approach to minimize energy consumption with distortion bounds for image transmission applications in WSN, by exploring importance level diversity for position-magnitude information. To put more effort on transmitting important position information and relatively less effort on unimportant magnitude information, desirable BER, ARQ retry limit, transmission rate and distortion reduction of image are jointly optimized cross PHY, MAC and APP layers, and image quality is assured while energy consumption is minimized. Simulation results demonstrate the effectiveness of the proposed approach in achieving energy efficiency while maintaining image quality.

Recent advances in distributed source coding (DSC) for mission-driven wireless sensor networks (WSN) are related to the coding for multiple correlated sensors in applications such as real time target tracking and environment monitoring. The characteristic of these DSC applications provides significant potential opportunities in the associated sensor network to utilize multirate transmissions for enhancing network performance. In this paper, we study a methodology for interplay optimization between routing and DSC in WSN, and propose a novel multirate based routing scheme for mission-driven DSC applications that considerably extends network lifetime. The proposed scheme adopts the rate assignment based on the residual energy, and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate constraint, information precision requirement, and the energy constraints in the network for DSC. Simulation results show that this multirate based routing scheme achieves significantly longer network lifetime compared to other existing research in WSN for DSC applications.

Many Sensor applications such as monitoring and surveillance may require image sensor array to conduct collaborative image transmissions in Wireless Sensor Network (WSN). The large size image transmission in WSN is a bottleneck due to the limited energy resources. In this paper, we propose an optimal scheme for image sensors to utilize inter-sensor correlations to decide transmission patterns based on a Multi-Level Rate-Oriented routing (MLRR) routing scheme, which achieves high energy efficiencies and longer network lifetime. This optimization scheme allows each image sensor to transmit optimal fractions of the overlapped images through appropriate multiple Rate-Oriented routing paths. The simulation results by using GA algorithm show that the proposed image transmission scheme can achieve considerable gains in terms of the WSN energy efficiency and network lifetime extension.

Selective encryption for secure image transmission is an important ongoing research area which aims to scramble the critical information correlation and inter-dependency within final image compression bit streams. However, how to take Unequal Error Protection (UEP) method for transmitting partially encrypted image data in error-prone communication channels is still an open research problem in Wireless Sensor Networks (WSN). In this paper, we propose a new cross layer optimization approach for energy efficient delivery of selectively encrypted images in WSN while assuring image quality. In the proposed approach, identified importance levels of diversity for encrypted procession blocks as well as position-value (P-V) information are extensively explored within image compression and encryption bit streams. Desirable BER, ARQ retry limit, transmission rate are jointly optimized across PHY, MAC and APP layers regarding the image distortion reduction bound and communication energy efficiency. Both image quality and security are assured while energy consumption is minimized. Simulations results have shown significant gain in image security assurance and transmission quality achievement with energy efficient communications in WSN.

Image sensors' digital transmissions are significant challenge to the Wireless Sensor Networks (WSN) due to resource constraints. In this paper, we propose a novel position-value (P-V) oriented multirate image transmissions approach called Image Component Multirate Transmissions (ICMT) over WSN. This method adapts different reliable levels of transmissions to various P-V segmented components within the compressed image bit stream. Simulation results show that the proposed scheme achieves high energy efficiency in WSN while enhancing the image transmission quality.

Signal processing applications such as Distributed Source Coding (DSC) in Wireless Sensor Network (WSN) are often multirate in nature, which provide additional significant opportunities in WSN design for achieving energy efficiency. In this paper, we analyze the DSC traffic for signal processing applications in multirate WSN and propose a novel rate oriented approach with MAC and PHY cross layer utilization to achieve high energy efficiency. Simulation results show that the proposed approach achieves significant energy efficiency by a factor of three while maintaining the transmission quality in WSN. This approach can be easily extended to other WSN MAC and PHY designs, providing multirate service to application layer to allow achieving higher energy efficiency

Distributed source coding (DSC) based signal processing applications involve data transmissions at multirates in wireless sensor network (WSN). In this paper, we propose a novel cross layer approach in WSN to achieve energy efficiency using multirates desirable for the DSC based signal processing applications and current network conditions. Our approach can be summarized into two parts: first, energy efficient multirate MAC-PHY supporting DSC applications; and second, rate oriented interaction of MAC-PHY and DSC applications. Multirate MAC-PHY utilizes desirable bit error rate (BER), channel attenuation, corresponding modulation schemes, and multirate requirements, to determine the minimum desirable transmission power. Rate oriented interaction provides optimal transmission rates feedback to interact with DSC applications, making DSC applications adaptively adjust information coding rate according to network conditions. This interactive approach achieves further energy saving significantly by making the DSC application aware of WSN conditions. Besides DSC, this work can be easily extended to other multirate signal processing applications in WSN.