Millimeter-wave (mmWave) radar has emerged as a promising sensing modality for 3D human pose estimation in ubiquitous Internet of Things (IoT) applications, owing to its privacy-preserving nature and robustness under challenging illumination conditions. However, accurate skeletal reconstruction remains difficult due to the inherent sparsity, non-uniform distribution, and instability of radar point clouds. To address these challenges, this paper proposes MS-STPoseNet, a unified multi-scale spatio-temporal learning framework for robust mmWavebased human pose estimation. For spatial representation, MS-STPoseNet employs a hierarchical multi-scale spatial encoder built upon PointNet++, which leverages multi-scale grouping to effectively capture body structures across different spatial resolutions, from fine-grained joint regions to limb- and torso-level configurations, under irregular radar observations. For temporal modeling, a multi-branch Temporal Convolutional Network (TCN) with different dilation rates is introduced to model multi-rate motion dynamics, enabling effective representation of both rapid limb movements and smoother torso motions. An attention mechanism is further incorporated to enhance informative temporal features while suppressing noise. The entire framework is trained end-to-end to estimate per-frame 3D poses by exploiting short-term temporal context, thereby improving robustness under noisy sensing conditions. Extensive experiments conducted on a self-collected dataset and two public benchmarks demonstrate that MS-STPoseNet consistently outperforms state-of-the-art methods in terms of pose estimation accuracy and cross-subject generalization, achieving an MPJPE of 3.08 cm on the self-collected dataset. In addition, the proposed framework exhibits favorable computational efficiency and a compact model size, highlighting its potential applicability to practical IoT sensing systems.

In mobile edge computing (MEC), computation offloading serves as an effective solution to bridge the gap between the stringent latency requirements of computational tasks and the limited processing capabilities of terminal devices (TDs). However, complex inter-task dependencies, dynamic network conditions, and the decentralized architecture of MEC systems pose significant challenges to efficient and adaptive task offloading. To address these challenges, this paper investigates dependency-aware task offloading and resource optimization in MEC environments. First, we propose a task feature extraction method based on dependency-aware graph neural networks (FEDG), which captures the hierarchical structure and varying importance of subtask dependencies by adaptively learning the aggregation weights of predecessor nodes and edges. Then, to address the joint dependency-aware task offloading and resource allocation problem under partial observability in MEC networks, we design a Dependency-aware Graph-based Multi-Agent deep reinforcement learning (DGMA) algorithm. DGMA integrates adaptive prioritized experience replay and correlation-based selective parameter sharing to improve learning efficiency and accelerate convergence in multi-agent environments. Extensive simulations demonstrate that DGMA achieves superior performance in terms of delay, energy consumption, offloading utility, and deadline violation rate.

Accurate Channel State Information is prerequisite for intelligent sensing and ubiquitous connectivity. However, the diversity of channel conditions—from sparse to dense and static to fast-varying—fundamentally challenges traditional single and fixed estimation algorithms. To address this issue, this paper proposes a Physics-Prioritized Mixture of Experts (PP-MoE) scheme, leveraging the MoE paradigm’s ability to allocate resources to specialized experts tailored for distinct physical environments. The proposed scheme features an innovative heterogeneous expert library, where the architecture of each expert is customized with embedded physical priors to match its specific propagation environment. To enable intelligent scheduling, we design a hybrid decision gating network that collaboratively leverages physical formula computation and data-driven deep learning to achieve accurate channel environment identification and expert routing. Furthermore, to overcome the expert collapse problem, we propose a three-stage training strategy—pretraining, freezing, and fine-tuning to ensure training stability specialization. Extensive simulations demonstrate that PP-MoE significantly outperforms traditional and deep learning baselines. Notably, in the low-SNR region (0–15 dB), it achieves an NMSE nearly an order of magnitude lower than LMMSE. Additionally, PP-MoE maintains high efficiency with only 0.0256 GFLOPs. This work provides an effective paradigm for designing adaptive and physically reliable wireless physical layers.

The unpredictability of network environments, limited edge resources, and the high complexity of collaborative policies are significantly hindering the development of the Industrial Internet of Things (IIoT). These challenges are particularly pronounced in healthcare, where high-priority, delay-sensitive medical tasks and large-scale personalized services face substantial obstacles. To address these challenges, this paper proposes the Self-Attention Enhanced QMIX with Multi-Pass Multi-Task Execution (SAE-MT-QMIX) algorithm, aimed at optimizing communication and computing resource allocation as well as task offloading strategies. By leveraging Digital Twin (DT) support, the algorithm achieves collaborative optimization of communication, computing, and control within the Internet of Medical Things (IoMT), significantly enhancing the quality of service for massive personalized applications. The algorithm adopts a distributed execution and centralized training framework: the distributed execution component uses the Multi-Pass Multi-Task Deep Q-Network (MPMT-DQN) algorithm to handle the complexity of parameterized action spaces in multi-task scenarios, while the centralized training component employs the Self-Attention Enhanced QMIX (SAE-QMIX) algorithm to dynamically optimize credit assignment across multiple users. Simulation results demonstrate that SAE-MT-QMIX significantly reduces delay and energy consumption compared to baseline methods. It ensures effective optimization of communication, computing, and control in dynamic IoMT, efficiently addressing diverse demands and tasks while enhancing service quality and system adaptability.

Accurate future traffic flow prediction is essential for decision-making in travel recommendations and route planning, aiming to reduce congestion and enhance traffic safety. Traditional traffic flow prediction models often face limitations in quality and structure, leading to increased training costs and inefficiencies, due to data scarcity and centralized training modes that compromise data privacy. To address these issues, we propose a model called 2MGTCN, which combines Multi-modal Graph Convolutional Networks (GCN) and Temporal Convolutional Networks (TCN) for Cross-city Traffic Flow Prediction (TFP). Our 2MGTCN model utilizes federated transfer learning (FTL) to transfer the model from the source to the target domain, mitigating data scarcity. It also incorporates GCN and TCN to capture both spatial and temporal information, enhancing cross-city adaptability. Additionally, Grey Relation Analysis (GRA) and Dynamic Time Warping (DTW) methods are applied to capture road relationships, and a Federated Parameter Aggregation based on Spatial Similarity (FPASS) algorithm is proposed for ensuring effective parameter aggregation by considering spatial similarity. Simulation results show that our 2MGTCN algorithm outperforms traditional TFP models in both centralized and distributed training modes, ensuring higher accuracy and better privacy protection.

Predicting the causal flow by fusing multimodal perception is fundamental for constructing the bodily awareness of soft robots. However, forming such a predictive model while fusing the multimodal sensory data of soft robots remains challenging and less explored. In this study, we leverage the free energy principle within a Bayesian probabilistic deep learning framework to merge visual, pressure, and flex sensing signals. Our proposed multimodal association mechanism enhances the fusion process, establishing a robust computational methodology. We train the model using a newly collected dataset that captures the grasping dynamics of a soft gripper equipped with multimodal perception capabilities. By incorporating the current state and image differences, the forward model can predict the soft gripper's physical interaction and movement in the image flow, which amounts to imagining future motion events. Moreover, we showcase effective predictions across modalities as well as for grasping outcomes. Notably, our enhanced variational autoencoder approach can pave the way for unprecedented possibilities of bodily awareness in soft robotics.

The integration of edge computing with digital twins (DTs) has been instrumental in driving substantial advancements in the Internet of Vehicles (IoV) domain in recent times, particularly within the 6G wireless networks where DTs enable real-time simulation, monitoring, analysis, and high-speed transmissions for connected vehicles. Despite these benefits, several challenges arise, including dynamic network topologies resulting from the high-speed vehicle mobility, frequent edge server switches causing instability and increased latency, and the limited computing resources struggling to cope with the demanding computational tasks. This article addresses these issues by proposing a framework where the vehicles serve as the auxiliary mobile edge computing (MEC) servers. It introduces an enhanced density-based spatial clustering of applications with the noise (DBSCAN) algorithm designed to improve the clustering of vehicles under high-speed movement scenarios. Moreover, a multi-to-multi matching algorithm is devised to effectively associate vehicles with the auxiliary MEC servers. To alleviate the problem of insufficient computing resources due to intense computational loads during DT updates, a deep reinforcement learning (DRL)-based approach is utilized to make the optimal computation offloading decisions. This work further refines the offloading strategy by adopting the improved double deep Q-network (DDQN) and the dueling deep Q-network algorithms. Simulation experiments validate that the proposed clustering improvement and the DRL-based offloading decision-making scheme outperform the existing baseline methods across multiple performance metrics, such as clustering effectiveness, processing latency reduction, algorithmic efficiency, and convergence rate.

The prediction of user trajectories is a fundamental component to support urban traffic management and various advanced transportation applications, such as traffic optimization and location-based services. Trajectory data typically contains multiple behavioral patterns and contexts, including different travel purposes, modes of transportation, time intervals, and geographic regions. These complex factors collectively influence the prediction of user trajectories. However, trajectory prediction models face challenges in effectively distinguishing between these various patterns. In this paper, we propose a novel stack Transformer-based multimodal long-term trajectory prediction (SMTTP) scheme for heterogeneous user behavior patterns. First, a learnable trajectory similarity measure method is proposed to estimate the relative distance between multi-attribute variable-length trajectories. Then, to address the instability of trajectory clustering caused by random initialization, a cluster head initialization algorithm based on high confidence nodes is developed to improve clustering stability and reduce convergence time. In addition, a Transformer-based trajectory prediction model with multi-dimensional feature fusion is proposed to achieve accurate and efficient long-term trajectory prediction. Experimental results on the real telecom dataset in Shanghai, China show that the proposed SMTTP scheme can achieve improved performance in trajectory prediction in terms of prediction error, and also has high accuracy and stability in unsupervised trajectory clustering.

High mobility of Internet of Vehicles (IoV) brings rapidly changing network topology, and massive data produced by vehicles aggravate heavy burden to the network. These may lead unreliable and high latency of data transmission and processing, which is not facilitate the application and popularization of automatic driving. Evolutions of intelligent vehicles and edge intelligence promising technologies enable vehicles as agents. Vehicles have abilities to act as aided Mobile Edge Computing (MEC) servers to support ultra-low communication and computing latency and super-high reliability data transmission and processing. In this paper, we elect some vehicles as aided MEC servers and design an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to involve more scattered vehicles for clustering. We adopt a Multi- Multi matching algorithm to pair vehicles and the aided MEC server, and designed a multi -agent- based task offloading mechanism to reduce latency and improve resource utilization efficiency. Furthermore, a reward mechanism is proposed to stimulate vehicles to be aided MEC servers instead of refusing to provide services. Evaluation results verify the proposed offloading method could effectively reduce the average delay, improve computing resources utilization and increase the benefit of aided MEC servers and service providers.

With the dispersed and privacy-preserving features, federated learning (FL) enables connected and autonomous vehicles (CAVs) to achieve cooperative perception, decision-making, and planning by utilizing the learning capabilities and sharing model parameters. However, the discrepancies in local training cost and model upload durations between various CAVs make the energy and time costs caused by traditional FL algorithms unfair. In this paper, a fair and efficient FL algorithm is proposed with to address the challenges arising from imbalanced data distribution and fluctuating channel conditions. Specifically, to achieve uniformity in total time and energy cost among CAVs, a personalized approach is employed for the local training rounds of each CAV. This approach ensures fairness and training effectiveness while reducing the local training time in each round of global iteration. Furthermore, it enhances the convergence speed of the global model. Extensive simulations demonstrate that the proposed algorithm achieves fairness in energy cost while reducing the duration of each round of global iteration.

Integrated sensing, communication, and computation (ISCC) has become one of the research hotspots focused on the sixth generation (6G) communication systems. However, due to the demand for the coexistence of sensing, communication, and computing functions, there is uneven scheduling of resources among all parties, resulting in difficulties in accurately perceiving the environment, processing massive data efficiently in real-time, and experiencing large latency in task processing in the Internet of Vehicles (IoV). In this work, we propose a novel wireless scheduling architecture to enhance the coordination gains of sensing, communication, and computation from a perspective of joint optimization, enabling true on-demand services. Specifically, we first adopt the modified Cobb-Douglas utility function, the communication rate, perception of mutual information (MI), and calculation delay as coordinated gains. Next, we formulate a joint user association and subchannel assignment problem to capture the network externalities induced by resource competition among vehicle user devices (VUEs) with multi-functional requirements. To achieve a mutually satisfactory solution, we propose the dung beetle optimizer (DBO) algorithm to maximize the average utility of all VUEs in the network. Simulation results show that, compared with the baseline algorithm, the average utility gain of the proposed algorithm can reach up to 7.21%.

Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm.

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge Computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption.

As one of the enabling technologies for E-Health, Internet of Medical Things (IoMT) interconnects various medical devices to collect and exchange healthcare information. To enable low-delay healthcare information processing, Mobile edge computing (MEC) can be incorporated in IoMT which can process various data in proximity of the medical devices. In this paper, we propose a Combinatorial Auction and Improved Particle Swarm Optimization based Computation Offloading Approach (CA-PSO) for e-healthcare to meet the Quality of Service (QoS) requirements of low delay and low energy consumption in healthcare monitoring. Firstly, we formulate a joint optimization problem to minimize the system cost consisting of delay and energy consumption, and transform this problem into a potential game. Secondly, we use combinatorial auction algorithm to analyze the offloading situation for different channels and servers, and combine channels and servers to simplify the original problem. Then we combine the offloading combination with improved Particle Swarm Optimization (PSO) to solve the optimal offloading strategy and server resource allocation. The simulation results show that compared with the comparison algorithm, the CA-PSO algorithm has achieved better performance in terms of average processing cost, delay, and energy consumption.

In-depth analysis of medical data through machine learning to achieve disease prediction is beneficial to the early detection and treatment of diseases. However, medical data involves mass patient privacy, and datasets of different medical institutions cannot be directly shared due to privacy protection. So medical data often exists in the form of data islands, which makes it difficult for most existing prediction models to complete disease prediction. In this paper, a digital twin assisted efficient clustering Federated Learning (FL) algorithm for disease prediction is proposed. It can break data islands to predict diseases on the premise of privacy security. Firstly, we design an efficient clustering Federated Learning with Client Selection (FLCS) protocol based on heterogeneity and contribution to improve the training efficiency and prediction accuracy. Secondly, we use digital twin to assist the FLCS protocol to carry out large-scale prediction. In addition, the shapley value introduced in the calculation of client contribution makes the model interpretable and enhances the reliability of prediction results. Finally, the evaluation results show that compared with the common prediction models and FedAvg algorithm, the FLCS protocol assisted by digital twin has better efficiency and accuracy in binary classification prediction.

Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding various neural architectures that fit diverse Artificial Intelligence of Things (AIoT) scenarios. Recently, to prevent the leakage of private information while enable automated machine intelligence, there is an emerging trend to integrate federated learning and neural architecture search (NAS). Although promising as it may seem, the coupling of difficulties from both tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive nonindependent and identically distributed (non-IID) data among AIoT devices in a federated manner is a hard nut to crack. In this article, to tackle this challenge, by leveraging the advances in ProxylessNAS, we propose a federated direct neural architecture search (FDNAS) framework that allows for hardware-friendly NAS from non-IID data across devices. To further adapt to both various data distributions and different type of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a cluster federated direct neural architecture search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint. Extensive experiments on non-IID data sets have shown the state-of-the-art accuracy–efficiency tradeoffs achieved by the proposed solution in the presence of both data and device heterogeneity.

Heart disease seriously threatens human life due to high morbidity and mortality. Accurate prediction and diagnosis become more critical for early prevention, detection, and treatment. The Internet of Medical Things and artificial intelligence support healthcare services in heart disease monitoring, prediction, and diagnosis. However, most prediction models only predict whether people are sick, and rarely further determine the severity of the disease. In this article, we propose a machine learning based prediction model to achieve binary and multiple classification heart disease prediction simultaneously. We first design a Fuzzy-GBDT algorithm combining fuzzy logic and gradient boosting decision tree (GBDT) to reduce data complexity and increase the generalization of binary classification prediction. Then, we integrate Fuzzy-GBDT with bagging to avoid overfitting. The Bagging-Fuzzy-GBDT for multiclassification prediction further classify the severity of heart disease. Evaluation results demonstrate the Bagging-Fuzzy-GBDT has excellent accuracy and stability in both binary and multiple classification predictions.

Digital mymargin Twin (DT) and Intelligent Reflective Surface (IRS), the most two promising technologies of 6G make the Internet of Vehicles (IoV) more adaptive. However, future autonomous driving needs powerful networking resources and high-quality wireless communications to guarantee the Quality of Service (QoS). Especially considering the time-varying physical operating environments of IoV, it is extremely urgent to improve resource utilization and wireless channel quality. In this work, we propose a Digital Twin-Driven Vehicular Task Offloading and IRS Configuration Framework (DTVIF) to efficiently monitor, learn, and manage the IoV. Specifically, we adopt Mobile Edge Computing (MEC) and IRS to provide augmented computing capacities for vehicles and improve transmission performance when vehicles communicate to MEC servers. DT is employed to achieve real-time data collection and digital representation of physical operating environments of IoV to better support decisions making. In order to reduce the overall delay and energy consumption of DTVIF, we propose a Two-Stage Optimization for Jointly Optimizing Task Offloading and IRS Configuration (TSJTI) algorithm based on Deep Reinforcement Learning (DRL) and Transfer Learning (TFL). In the first stage, we introduce Double Deep$Q$-learning Networks (DDQN) to find the optimal offloading decision. In the second stage, based on the parameters learned from the first stage, we migrate the parameters from the first stage to find the optimal IRS configuration based on the Deep Deterministic Policy Gradient (DDPG) method. The simulations demonstrate that the proposed algorithm can effectively reduce the processing latency of task offloading and reduce the average energy consumption in DTVIF.

While deploying on edge devices, deep learning mod-els often encounter various strict resource constraints. Automated machine learning becomes popular in finding various neural architectures that fit diverse Internet of Things (IoT) scenarios to handle these problems with less human efforts. Recently, there is an emerging trend to integrate federated learning and Neural Architecture Search (NAS) to prevent private data leakage while enabling automated machine learning. The algorithm development is quite challenging because of the coupling of difficulties from both tenets, although promising as it may seem. Especially, it is a hard nut to efficiently search the optimal neural architecture directly from massive non-Independent and Identically Distributed (non-IID) data among IoT devices in a federated manner. In this paper, by leveraging the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows hardware-friendly NAS from non-IID data across devices to tackle the challenge. Extensive experiments on non-IID datasets demonstrate the state-of-the-art accuracy-efficiency trade-offs achieved by proposed methods.

Wireless Body Area Networks (WBANs) have been developed rapidly with the increasing popularity of wireless network and wearable technologies. The inherent characteristics of convenience and efficiency for health monitoring facilitate the depth and width of WBAN applications. However, the inter-WBAN interference problem affects the network performance in intensive WBAN scenarios, degrading reliability and increasing latency of health data. In this paper, we propose a Two-Tier Multi-channel Medium Access Control (2TM-MAC) protocol with interference mitigation for reliable health monitoring. Specially, the 2TM-MAC establishes an inter-WBAN interference matrix for every WBAN to show the mutual interference among coexisting WBANs. We design a multi-channel selection algorithm at the first tier to select different numbers of channels for each WBAN to avoid inter-WBAN interference and collisions. At the second tier, the hub of each WBAN schedules the available channels assigned from the first tier to sensor nodes according to their traffic requirements, mitigating the intra-WBAN interference as well. 2TM-MAC protocol enhances the reliability of emergency data and service experience in healthcare applications. Simulation results show the 2TM-MAC protocol significantly improves the network throughput and decreases the average packet delay compared with IEEE 802.15.6 for densely deployed coexisting WBANs scenarios.

Wireless Body Area Network (WBAN) technology jumps into popularity owing to its real-time ability and high reliability in health monitoring. The accompanying interference problem must be highly concerned in coexisting densely deployed WBANs since the inter-WBAN interference results in high delay and low reliability data transmissions, especially with the movement of human body. In the paper, we analyze the dynamic interference with human mobility in multiple coexisting WBANs with the consideration of different distances between inter-WBANs and varying number of coexisting WBANs. Moreover, we investigate the influence of inter- WBAN interference on the performance of normalized throughput and average access delay of different traffic types. The results show that the interference generated by mobile neighbour WBANs extremely decreases the throughput of the target WBAN and increases the average packet delay 1.76 times of emergency data compared with the target WBAN without interference. The dynamic interference analysis provides insights on the practical WBAN management and interference mitigation protocol design, especially for the deeply deployed coexisting WBAN scenarios.

Wireless body area networks (WBANs) can be widely used in wireless medical, motion detection etc. However, the existence of interference results in the increase in energy consumption and delay. To mitigate interference, nodes are prevented from using the same or similar spectrum resources by adopting multi‐channel media access control (MAC) protocols. Here, the authors propose a multi‐channel MAC protocol towards energy efficiency and low interference (EIMAC). Firstly, the states of each channel are clarified by the channel mapping mechanism. A novel channel selection strategy, considering the unfairness between high or low priorities, then is carried out. After that, considering the characteristics of node including residual energy, user priority, and data volume, the authors propose a low energy consumption enabled transmission mechanism. Lastly, the authors utilise a novel collision avoidance mechanism to reduce the collision probability of packets. Numerical results show that EIMAC significantly enhance the performance of WBANs in terms of delay, throughput, and energy consumption.

Intelligent wireless body area networks (WBANs) have entered into an incredible explosive popularization stage. WBAN technologies facilitate real-time and reliable health monitoring in e-healthcare and creative applications in other fields. However, due to the limited space and medical resources, deeply deployed WBANs are suffering severe interference problems. The interference affects the reliability and timeliness of data transmissions, and the impacts of interference become more serious in mobile WBANs because of the uncertainty of human movement. In this paper, we analyze the dynamic interference taking human mobility into consideration. The dynamic interference is investigated in different situations for WBANs coexistence. To guarantee the performance of different traffic types, a health critical index is proposed to ensure the transmission privilege of emergency data for intra- and inter-WBANs. Furthermore, the performance of the target WBAN, i.e., normalized throughput and average access delay, under different interference intensity are evaluated using a developed three-dimensional Markov chain model. Extensive numerical results show that the interference generated by mobile neighbor WBANs results in 70% throughput decrease for general medical data and doubles the packet delay experienced by the target WBAN for emergency data compared with single WBAN. The evaluation results greatly benefit the network design and management as well as the interference mitigation protocols design.

Wireless Body Area Networks (WBANs) have attracted significant attentions because of their important role in medical applications with the development of requirements in health monitoring and diagnosis. IEEE 802.15.6, as the international standard for WBAN, supports network in operating on, in or around human body. Owing to the special propagation characteristics as affected by human body, WBAN needs reliable access mechanism to guarantee the stability of nodes access and information transmission. To achieve the high slot utilization rate and low average packet delay, we propose a gated scheduled access mechanism based on IEEE 802.15.6 and study the impact of allocation slot length on network performance. To examine the performance of our proposal, we conduct hardware experiment through a novel prototype system based on IEEE 802.15.6 standard. The experiment results show that the obtained optimal allocation slot length under the gated scheduled access mechanism can well satisfy the Quality of Service (QoS) requirements in different data rates, which is consistent with the results of theoretical analysis. The comparison results also show that our prototype system can be a practical reference in the future study of wireless body area network.

The precise spatio-temporal position data of vehicles is useful for most studies, such as wireless link lifetime and node degree in vehicular ad hoc networks. However, due to the system errors and random errors, the existing Global Positioning System (GPS) only provides the positional accuracy about 10m or even worse. In this paper, to address the issue of positional accuracy, a Clustering and Approximating (C-A) algorithm is proposed. We first divide each road into several small parts which are described by linear functions. Then a linear regression algorithm is utilized to approximate traces under system errors, which is reliable for reducing GPS errors. Particularly, when two roads are very close, GPS points may be mapped on adjacent roads. A clustering algorithm is taken to separate GPS points and their positions are revised by the iterative utilization of the linear regression algorithm. In the end, the method mentioned above smoothes raw GPS data of buses in Taiwan to make it available for further researches. Compared with existing methods, the method described in this paper characterized with low cost and high reliability in different situations. Besides, its simple model will make the process of revising data more convenient.

Wireless Body Area Networks (WBAN) are becoming increasingly important for health care with the development of health consciousness and health detection requirements. A lot of researches have been devoted to the progress of WBAN, for example, the improvement of protocols designed for WBAN, the optimization of parameters and the system performance evaluation. However, the main premise of all the directions above is to provide an efficient and reliable simulation platform, since the existing platforms cannot respond to accord with the reality with high performance. To work this issue out, we construct the WBAN simulation platform on Network Simulation 3 (NS-3) which specifies in good expandability and resources saving and agrees with real networks in many aspects comparing with the other simulators. In this paper, we propose our implementation of WBAN module based on IEEE 802.15.6 standard. Our simulation platform consists of Medium Access Control (MAC) and Physical (PHY) layer of IEEE 802.15.6. Then in order to make the simulation more objective, we design a proper simulation scenario according to the practical application and analyze the simulation results. Finally, we compare the throughput saturation threshold on NS-2 and NS-3 with the analysis results respectively to indicate the superiorities of NS-3 and verify the validity and effectiveness of our WBAN module.