Identifying influential spreaders in a temporal social network, which has potential applications including network immunization, epidemic control, and viral marketing, is a fundamental class of problems. In this context, various centrality algorithms have been introduced to quantify influential spreaders, focusing on three categories: topology-based methods, dynamics-based methods, and machine learning-based methods. However, topology-based methods tend to consider single temporal features, while the consideration of multi-temporal features is subject to the same challenges of high temporal complexity as dynamics-based methods, and machine learning-based methods face challenges related to dependency on the training dataset. In this paper, we propose a novel centrality algorithm based on multiple diffusion information (RPT: Multi-diffusion information centrality based on R-path trees) to identify the influential node in a temporal social network. This algorithm considers three different temporal features and has lower temporal complexity using a newly proposed representation structure known as an R-path tree (a distinctive inverted tree that encompasses the earliest arrival paths from other nodes to the root node). Through experiments carried out on 12 empirical social networks, the results show that the effectiveness of RPT in identifying influential spreaders generally exceeds that of other baseline measures.

In the context of vehicle-road-cloud integration, multi-vehicle platooning systems have become an important approach for improving road traffic efficiency, driver comfort, driving safety, energy consumption, and mitigating traffic congestion. However, under high-speed mobility, Vehicle-to-Vehicle (V2V) communication within multi-vehicle platoons is susceptible to delays caused by interference and the inherent uncertainties of wireless communication channels. These delays present considerable challenges to achieving effective multi-vehicle cooperative control. To overcome the limitations of existing research, this paper proposes a joint communication and control optimization strategy for multi-vehicle platooning systems. A novel spacing error metric is introduced, which uses the real-time velocity of each vehicle to improve the platooning system responsiveness. Furthermore, we derive the Signal-to-Interference-plus-Noise Ratio (SINR) threshold to ensure the stability and reliability of the platoon. This ensures safe distances and synchronized speeds among all vehicles, even when communication delays occur. Finally, the proposed joint optimization strategy is validated through performance comparisons, demonstrating its effectiveness and superior performance.

In distributed systems, data may partially overlap in sample and feature spaces, that is, horizontal and vertical data partitioning. By combining horizontal and vertical federated learning (FL), hybrid FL emerges as a promising solution to simultaneously deal with data overlapping in both sample and feature spaces. Due to its decentralized nature, hybrid FL is vulnerable to model poisoning attacks, where malicious devices corrupt the global model by sending crafted model updates to the server. Existing work usually analyzes the statistical characteristics of all updates to resist model poisoning attacks. However, training local models in hybrid FL requires additional communication and computation steps, increasing the detection cost. In addition, due to data diversity in hybrid FL, solutions based on the assumption that malicious models are distinct from honest models may incorrectly classify honest ones as malicious, resulting in low accuracy. To this end, we propose a secure and efficient hybrid FL against model poisoning attacks. Specifically, we first identify two attacks to define how attackers manipulate local models in a harmful yet covert way. Then, we analyze the execution time and energy consumption in hybrid FL. Based on the analysis, we formulate an optimization problem to minimize training costs while guaranteeing accuracy considering the effect of attacks. To solve the formulated problem, we transform it into a Markov decision process and model it as a multiagent reinforcement learning (MARL) problem. Then, we propose a malicious device detection (MDD) method based on MARL to select honest devices to participate in training and improve efficiency. In addition, we propose an alternative poisoned model detection (PMD) method considering model change consistency. This method aims to prevent poisoned models from being used in the model aggregation. Experimental results validate that under the random local model poisoning attack, the proposed MDD method can save over 50% training costs while guaranteeing accuracy. When facing the advanced adaptive local model poisoning (ALMP) attack, utilizing both the proposed MDD and PMD methods achieves the desired accuracy while reducing execution time and energy consumption.

Electronic healthcare (e-health) allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by artificial intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. First, medical data are both horizontally and vertically partitioned. Since single horizontal federated learning (HFL) or vertical federated learning (VFL) techniques cannot deal with both types of data partitioning, directly applying them may consume excessive communication cost due to transmitting a part of raw data when requiring high modeling accuracy. Second, a naive combination of HFL and VFL has limitations including low training efficiency, unsound convergence analysis, and lack of parameter tuning strategies. In this article, we provide a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data are both horizontally and vertically partitioned. Specifically, we propose a hybrid federated learning framework with one intermediate result exchange and two aggregation phases. Based on this framework, we develop a hybrid stochastic gradient descent (HSGD) algorithm to train models. Then, we theoretically analyze the convergence upper bound of the proposed algorithm. Using the convergence results, we design adaptive strategies to adjust the training parameters and shrink the size of transmitted data. The experimental results validate that the proposed HSGD algorithm can achieve the desired accuracy while reducing communication cost, and they also verify the effectiveness of the adaptive strategies.

Mobile edge computing (MEC) decentralizes the computational and storage capabilities of the network to edge nodes, providing support for the dynamic deployment and rapid response of mobile services. However, the large-scale distributed deployment of edge nodes, their widespread geographic distribution, multi-dimensional and complexly dependent service characteristics, and the dynamically changing network environment pose challenges to energy-efficient service deployment. In this paper, we consider multi-dimensional features for service deployment, including dynamic traffic demand, geography information, service semantics, and service popularity, with the aim of improving the availability of edge services and reducing network energy consumption. Firstly, to handle the large volume of edge service data, we design a multi-dimensional feature extraction approach based on the Transformer model, which does not rely on the sequential order of data and can enhance computational efficiency of edge models through parallel processing. Then, to adapt to the dynamically changing edge network environment, we propose an Energy-Efficient Service Deployment algorithm (EESD) based on the improved Dueling Deep Q-Network, which makes service deployment and base station switching decisions in a learning-based manner. Finally, simulation results demonstrate that EESD outperforms comparison algorithms in terms of model convergence, system total cost, and energy consumption.

Transfer learning (TL) has shown potential for body sensor network (BSN) applications. However, TL performance is influenced by various factors, such as the BSN environment, user-specific physiological signs, or sensor modalities, leading to negative transfer (NT) effects. Limited research has defined NT from perspectives, such as data quality, domain transferability, and transfer components, but the cognition of NT in multimodal BSNs remains insufficient. In this article, we explore the correlation between transfer modes and NT, proposing a knowledge transfer framework composed of common domain adaptation constraints to reveal the NT effects in overconstrained environments. This effect can explain why strongly constrained transfer algorithms are effective but not necessarily optimal in terms of performance. In the three typical BSN scenarios of emotion recognition, fall detection, and daily activity recognition, we demonstrate the NT in overconstrained environments and its potential influencing factors using different constraint combinations and based on different multimodal body sensor fusion architectures. Extensive experiments focus on discussing the impact of category-condition constraints, domain similarity, and fusion architectures on the NT effect in BSNs. This work can provide a new perspective for the design of multimodal BSN knowledge transfer scheme.

The swift expansion of the Internet of Things (IoT) has brought about convenient services, but it has also increased cyber threats. An intrusion detection system (IDS) is an effective tool of mitigating security concerns by identifying suspicious network activities. Many decentralized deep learning methods, such as federated learning (FL), have been applied for intrusion detection. However, developing an effective and reliable collaborative IDS is still challenging due to data privacy leakage during model updates and high communication overhead by local model parameters. Moreover, existing FL methods are limited in practicality since they only perform well under data independent identically distribution (IID), which is not commonly found in real scenarios. To solve these issues, we propose a novel collaborative intrusion detection framework with strong privacy preservation in IoT networks (CIDIoT). Specifically, the CIDIoT extends the improved generative adversarial network model without exchanging individual network data to enable efficient intrusion detection. To enhance the privacy preserving of the framework, differential privacy noise and dynamic threshold secret sharing are added to the uploaded model information and downloaded data while keeping communication efficiency. A novel robust aggregation method is also developed to increase the robustness of the CIDIoT against imbalanced and Non-IID data case. Extensive experimental results on two real-world heterogeneous data sets validate that CIDIoT significantly outperforms other state-of-the-art methods in terms of detection accuracy, communication overhead, and cooperative privacy preservation.

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, and industrial dust), continuous object tracking has a pivotal role in protecting the safety of the people, especially in hazardous industries. To improve production safety, the Industrial Internet of Things (IIoT) has become a promising technology for continuous object tracking. However, IIoT can hardly satisfy the requirements of both energy efficiency and tracking accuracy due to diffusion characteristics, redundant packets, unnecessary awakened nodes, etc. To address these challenges, we propose a two-stage continuous object predictive tracking scheme based on a state transition model (TCOT-STM). First, the predictive tracking process of TCOT-STM is partitioned into two stages to determine wake-up regions where the future continuous objects are located. Considering the high diffusion speed in the tracking process, stage I tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage II tracking is designed by intercluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model (STM) based on finite state machines is designed to awaken nodes selectively. Finally, with the STM and the wake-up regions determined by two-stage tracking, the potential boundary nodes are proactively awakened for predictive tracking. Simulation results demonstrate that the proposed TCOT-STM can reduce energy consumption and communication cost while improving tracking accuracy.

In Mobile Edge Computing (MEC) networks, dynamic service migration can support service continuity and reduce user-perceived delay. However, service migration in MEC networks faces significant challenges due to the uncertainty in future traffic demands, the distributed architecture of MEC networks, high operating costs and the dynamism of network resources. Digital Twins (DT), which achieve the mapping of physical entities to virtual digital models in cyberspace, provide new perspectives for intelligent and efficient service provisioning in MEC networks. In this paper, we propose a traffic prediction-assisted federated deep reinforcement learning scheme to efficiently migrate services and improve the cost efficiency of DT-enabled MEC networks. Specifically, to address the coupled spatio-temporal dependencies of mobile traffic and the imbalance in traffic data, a Multi-order Spatio-temporal information integration-based distributed Traffic Prediction (MSTP) scheme is proposed, which achieves high-accuracy mobile traffic prediction at a low cost. Then, we propose a Federated Cooperative cost-efficient Service Migration (FCSM) algorithm that adaptively adjusts service migration strategies in a distributed manner to respond to future traffic demands. Moreover, a theoretical model is developed to analyze the convergence of FCSM and derive the upper bound of the time-average squared gradient norm. Finally, extensive simulations demonstrate that the proposed schemes achieve excellent traffic prediction performance, enhance users’ Quality of Service (QoS), and significantly reduce the system cost of MEC networks.

E-health systems consist of intelligent devices, medical institutions, edge nodes, and cloud servers to improve healthcare service quality and efficiency. In e-health systems, patients’ data are cooperatively collected by their wearable devices and the hospital they have visited, i.e., vertically distributed data. The data on wearable devices share the same feature set but are different in sample spaces, i.e., horizontally partitioned data. Meanwhile, hospitals target various user groups resulting in high data diversity, i.e., non-identically distributed data. These three characteristics cause that existing federated learning frameworks cannot efficiently train models on medical data. Furthermore, model training in e-health is time-sensitive because some diseases mutate very quickly and spread easily, which requires fast convergence of machine learning algorithms. In this paper, we address the problem of how to efficiently and rapidly train global models on e-health data. Specifically, we propose a multilayer federated learning framework to cope with data that are vertically, horizontally, and non-identically distributed. Moreover, we develop a Multi-Layer Stochastic Gradient Descent (MLSGD) algorithm towards the proposed framework to learn the optimal global model. To improve training efficiency, partial models learned by devices are aggregated on edge nodes before exchanging intermediate results with hospitals. The weight of local models is proportional to local data size when performing global aggregation to balance the impact of local models on the global model. We also prove the convergence of the MLSGD algorithm from a theoretical perspective. The experimental results from the real-world dataset MIMIC-III validate that the proposed algorithm converges fast and achieves desired accuracy.

Edge-assisted Internet of Agriculture Things (Edge-IoAT) connects massive smart devices managed by edge nodes to collect crop data for distributed computing, such as federated learning, to guide agricultural production. In Edge-IoAT, data are cooperatively collected by edge nodes and the server, i.e., vertically partitioned. In addition, sample size and distribution are different for edge nodes, i.e., horizontally partitioned. Existing federated learning frameworks are not applicable for Edge-IoAT because they do not consider both types of data partitioning simultaneously. Moreover, the excessive energy consumption may cause premature interruption of model training, and spectrum scarcity prevents a portion of edge nodes from communicating with the server. Given limited energy and communication resources, training accuracy relies on how to schedule devices. In this paper, we first propose a joint federated learning framework for Edge-IoAT to cope with both vertically and horizontally partitioned data. After that, we formulate an energy-aware device scheduling problem to assign communication resources to the optimal edge node subset for minimizing the global loss function. Then, we develop a greedy algorithm to find the optimal solution. Experiments in a Nebraska farm show that the proposed framework with energy-aware device scheduling achieves a fast convergence rate, low communication cost, and high modeling accuracy under resource constraints.

It is important to locate the fault distance and identify the fault types quickly, take effective measures to maintain line stability, and minimize the losses timely when there are short-circuit faults in transmission lines. For this purpose, a method based on deep learning is proposed for short-circuit faults identification in the transmission line. According to the similarity of samples in the reconstruction phase, a minimum neighborhood sample set is selected from the massive samples firstly, and then, the samples are trained using the back propagation algorithm along time in a recurrent neural network (RNN) with long-short term memory (LSTM) units. Compared with existing algorithms, the experimental results show that this algorithm meets the requirements of rapid fault diagnosis in the case of variable parameters, and higher fault type recognition accuracy and lower fault distance error can be obtained.

Mobile-edge computing (MEC) sinks computation and storage capacities to network edge, where it is close to users to support delay-sensitive services. However, due to the dynamic and stochastic properties of MEC networks, the deployed services may be frequently migrated among edge servers to follow the mobility of users, which greatly increases the network operational cost. In this article, considering the service migration cost brought by user mobility, we study the joint optimization problem of service deployment and request routing decisions to maximize the long-term network utility of MEC networks. First, we propose a Lyapunov optimization-based online service migration algorithm to decompose the continuous optimization problem into a number of one-slot online optimization problems. Then, to address the NP-hard issue of one-slot optimization, we use a randomized rounding technique to implement service migration and request routing. Furthermore, through a closed-form theoretical analysis, we prove that the proposed algorithm not only greatly meets the local user requests and enables approximate performance guarantees but also adaptively balances the service migration cost and system performance online. Finally, extensive simulations are conducted, which demonstrate that our algorithm can efficiently utilize the storage and computation resources of edge servers, and maximize the long-term network utility while ensuring the stability of service migration cost.

The Internet of Things (IoT) technology has expanded network space by interconnected devices, which has been widely used in various fields, such as environmental monitoring, object tracking, risk warning, etc. Due to insufficient computing capacity, limited battery life, and unreliable communication environment in IoT, unmanned aerial vehicle (UAV)-enabled edge computing has been recently utilized to provide enhanced coverage and efficient computational support in the scenarios with sparse or unreliable ground infrastructure, such as disaster rescue, emergency response, military fields, etc. However, UAV-enabled edge computing faces many challenges, such as low offloading efficiency, high energy consumption, high complexity, etc. In this article, a distributed computation offloading scheme is proposed to provide computational support to large-scale IoT nodes and optimize the energy efficiency of multiple UAVs. First, to provide accurate and efficient computational support, a real-time intelligent positioning algorithm is designed to obtain the precise location information of IoT nodes. Then, a distributed computation offloading and path planning algorithm is presented, which jointly optimizes the computation offloading of large-scale IoT nodes and trajectory planning of multiple UAVs to reduce the energy consumption of UAVs. Furthermore, we develop a closed-form theoretical analysis model to demonstrate that the algorithm enables a performance guarantee related to energy efficiency. Finally, extensive simulations have been conducted and show that the proposed scheme can greatly improve the system utility and energy efficiency.

Internet of Battlefield Things (IoBT) connects massive tactical devices to collect battlefield situations and share perceived information. The IoBT can enhance the intelligent battlefield command, collaborative attack, and other applications, such as landmine trigger and post-war clearance. Existing routing path selection methods designed for wireless sensor networks (WSNs) are effective but still face challenges in IoBT scenarios. First, tactical devices follow nonuniform distributions with high density on boundaries in IoBT to prevent the location of devices from being speculated and protect strategic positions, which results in unbalanced energy consumption. Second, increasing latency in IoBT is caused by various data generation probabilities of tactical devices. Third, the military task features, such as landmine explosion, disconnection, and failure of tactical devices, may put forward special requirements on network robustness. To this end, we propose a routing path selection method with joint optimization in IoBT based on nonuniform node distributions and location-related data generation probabilities. Specifically, we first investigate and formulate the distribution and data generation probability of tactical devices. Based on the special features, energy consumption, latency, and network robustness are analyzed during multihop communications in IoBT. Then, a joint optimization problem is formulated to minimize energy consumption and latency, while maximizing the network robustness simultaneously. Furthermore, two path assignment algorithms are developed to solve this optimization problem. Finally, our simulation results show that the proposed routing path selection method can reduce energy consumption and latency with the guaranteed robustness of IoBT.

With the thriving of wearable devices and the widespread use of smartphones, the e-healthcare system emerges to cope with the high demand of health services. However, this integrated smart health system is vulnerable to various attacks, including intrusion attacks. Traditional detection schemes generally lack the classifier diversity to identify attacks in complex scenarios that contain a small amount of training data. Moreover, the use of cloud-based attack detection may result in higher detection latency. In this paper, we propose an Edge-assisted Anomaly Detection (EAD) scheme to detect malicious attacks. Specifically, we first identify four types of attackers according to their attacking capabilities. To distinguish attacks from normal behaviors, we then propose a wrapper feature selection method. This selection method eliminates the impact of irrelevant and redundant features so that the detection accuracy can be improved. Moreover, we investigate the diversity of classifiers and exploit ensemble learning to improve the detection rate. To reduce high detection latency in the cloud, edge nodes are used to concurrently implement the proposed lightweight scheme. We evaluate the EAD performance based on two real-world datasets, i.e., NSL-KDD and UNSW-NB15 datasets. The simulation results show that the EAD outperforms other state-of-the-art methods in terms of accuracy, detection rate, and computational complexity. The analysis of detection time validates the fast detection of the proposed EAD compared with cloud-assisted schemes.

Multimodal multiobjective problems (MMOPs) exist in scientific research and practical projects, and their Pareto solution sets correspond to the same Pareto front. Existing evolutionary algorithms often fall into local optima when solving such problems, which usually leads to insufficient search solutions and their uneven distribution in the Pareto front. In this work, an improved membrane algorithm is proposed for solving MMOPs, which is based on the framework of P system. More specifically, the proposed algorithm employs three elements from P system: object, reaction rule, and membrane structure. The object is implemented by real number coding and represents a candidate solution to the optimization problem to be solved. The function of the reaction rule of the proposed algorithm is similar to the evolution operation of the evolutionary algorithm. It can evolve the object to obtain a better candidate solution set. The membrane structure is the evolutionary logic of the proposed algorithm. It consists of several membranes, each of which is an independent evolutionary unit. This structure is used to maintain the diversity of objects, so that it provides multiple Pareto sets as output. The effectiveness verification study was carried out in simulation experiments. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving all 22 multimodal benchmark test problems in CEC2019.

Depression and anxiety are a couple of pernicious mental states, which may affect lifestyle and quality and even become the primary causes of disability worldwide. Hence, daily monitoring of the mental states is significant for avoiding possible injury. Dynamics of the human blood vascular system convey significant information on recording the emotion and the mental state, which can be monitored via photoplethysmography (PPG). It is one of the best schemes with the advantages of nonintrusiveness and low cost. Conventional approaches for PPG signal analysis usually depend on handcrafted feature extraction and classification, thus resulting in a lack of feature discrimination and difficulties in generalization. In this article, we propose an attentive deep recurrent architecture called Arousal-valence Networks (ArvaNets), which benefits from graph convolutional networks and recurrent neural networks. Our approach overcomes the limitations of previous methods by automatically extracting the learnable spatial representations from a rigorous custom data set as semantic motifs to infer immediate emotions, which are mapped to a 2-D arousal-valence coordinate system. Finally, we exploit long short-term memory (LSTM) units to output the mental states by incorporating the temporal factor. During the entire inference, we propose a spatiotemporal attention mechanism based on correlation fractal dimensions (CFDs) and time-averaged wall shear stress (TAWSS) to capture and stress the key subtle motifs for performance optimization. Experimental results demonstrate the proposed architecture has enough competitiveness in the tasks of emotion and mental-state recognition for daily monitoring.

With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has been greatly improved. However, the high computational and storage costs of the large-scale network have been a potential concern for the future widespread clinical application. In this paper, an alternative Multi-ringed (MR)-Forest framework, against the resource-consuming neural networks (NN)-based architectures, has been proposed for false positive reduction in pulmonary nodule detection, which consists of three steps. First, a novel multi-ringed scanning method is used to extract the order ring facets (ORFs) from the surface voxels of the volumetric nodule models; Second, Mesh-LBP and mapping deformation are employed to estimate the texture and shape features. By sliding and resampling the multi-ringed ORFs, feature volumes with different lengths are generated. Finally, the outputs of multi-level are cascaded to predict the candidate class. On 1034 scans merging the dataset from the Affiliated Hospital of Liaoning University of Traditional Chinese Medicine (AH-LUTCM) and the LUNA16 Challenge dataset, our framework performs enough competitiveness than state-of-the-art in false positive reduction task (CPM score of 0.865). Experimental results demonstrate that MR-Forest is a successful solution to satisfy both resource-consuming and effectiveness for automated pulmonary nodule detection. The proposed MR-forest is a general architecture for 3D target detection, it can be easily extended in many other medical imaging analysis tasks, where the growth trend of the targeting object is approximated as a spheroidal expansion.

Dynamic resource control and routing are important for realising the intelligent control of data transmission in wireless multi‐hop networks. It is well known that back‐pressure routing based on a max‐weight policy maximises network throughput and optimises resource allocation in multi‐hop wireless networks with time‐varying channels. Due to the slow routing convergence and complex control of data queues, however, back‐pressure routing also results in large end‐to‐end delays and a waste of network resources, particularly when the network loads are light or moderate. In this study, a differential back‐pressure routing scheme with single‐queue management is proposed to improve the packet delay performance and simplify the management of data queues. Unlike in traditional back‐pressure routing, the authors use the differences in the rates of change in data queue length to calculate data pressure. Compared with the method of data backlog calculation based on queue length differences, this method achieves faster routing convergence. They also consider the ceiling problem for a single queue and enhance the effect of the queue cap on the routing metric by means of dynamic weighting. Simulation results show that the proposed routing algorithm achieves a 20% decrease in end‐to‐end delay in grid and random networks.

With ever-rapid development of information and communication technologies, e.g., smart city, industrial Internet, Internet of Things, etc., enormous amounts of data are explosively generated and delivered to the computing center for further processing, which brings additional burden to both transfer components (e.g., the Internet) and data processing units. To efficiently schedule data transfer especially for delay-sensitive traffic, a scalable network architecture with intelligent traffic engineering (TE) policy is indispensable. In this paper, we propose a TE engine DTE-SDN by utilizing the software defined networking (SDN) technology, aiming to schedule the transfer of delay-sensitive traffic. Particularly, with OpenFlow, DTE-SDN captures an overall view of the network in real-time and can monitor the quality of service (QoS) metrics (e.g., throughput and delay) of each network link. In order to schedule the delay-sensitive transfer, a dynamic-scheduling scheme capable of multipath routing is proposed, which allows DTE-SDN to compute the near-optimal scheduling (e.g., path selection and flow distribution), based on the instantaneous QoS metrics. Especially, in the scheduling scheme, we propose a probabilistic-matching approach that aims to distribute the traffic among multiple end-to-end paths according to the computed flow distribution policy and can be deployed in SDN-enabled switches (e.g., Open vSwitch). The simulation results demonstrate that DTE-SDN is able to measure the throughput and delay with acceptable error range and can dramatically enhance the transfer efficiency than the traditional scheduling algorithms.

Continuous body temperature measurement (CBTM) is of great significance for human health state monitoring. To avoid interfering with users' daily activities, CBTM is usually achieved using wearable noninvasive thermometers. Current wearable noninvasive thermometers employ steady-state models used in nonwearable thermometers; as a result, the reaction time is long and the measurement can be disturbed by users' activities. However, there is no work to solve these issues. In this paper, first, differences between wearable and nonwearable temperature measurement are analyzed. Second, the relationship among the human body temperature, the skin temperature, and the device temperature is modeled based on artificial neural networks (ANNs). Third, this paper proposes a novel multiple ANNs-based wearable CBTM method. Experiments show that the reaction time of the proposed method is about one-tenth of that of other popular wearable noninvasive CBTM methods, while the accuracy and the robustness are improved.

Platooning has been identified as a promising vehicular traffic management strategy to improve road capacity, energy efficiency, and on-road safety in intelligent transportation systems (ITS). Inter-vehicle communications within a platoon and among multiple platoons can assist platoon control by maintaining a constant inter-vehicle distance, which in turn enhances road safety. An efficient method of sharing inter-vehicle information successfully and timely is critical to many platooning applications. In this paper, a resource allocation (RA) approach is proposed to support inter-vehicle communications underlaying cellular network for a multiplatooning (a chain of platoons) scenario. By applying the evolved multimedia broadcast multicast services (eMBMS) in the Evolved Node B (eNB), the transmission delay for intra-platoon and inter-platoon communications can be reduced. Then, using the proposed subchannel allocation and power control schemes, the number of required subchannels and the transmission powers of each vehicle and the eNB can be minimized. Numerical results show that the proposed approach outperforms the candidate RA scheme in terms of transmission delay, especially in a multiplatooning scenario with a large number of vehicles.

Due to the limited battery power of sensor nodes and harsh deployment environment, it is of fundamental importance and a great challenge to achieve high energy efficiency and strong robustness in large-scale wireless sensor networks (LS-WSNs). To this end, we propose two self-organizing schemes for LS-WSNs. The first scheme is the energy-aware common neighbor scheme, which considers the neighborhood overlap in link establishment. The second scheme is energy-aware low potential-degree common neighbor (ELDCN) scheme, which considers both neighborhood overlap in topology formation and the potential degrees of common neighbors. Both schemes generate clustering-based and scale-free-inspired LS-WSNs, which are energy-efficient and robust. However, the ELDCN scheme shows higher energy efficiency and stronger robustness to node failures, because it avoids establishing links to hub-nodes with high potential connectivity. Analytical and simulation results demonstrate that our proposed schemes outperform the existing scale-free evolution models in terms of energy efficiency and robustness.

In vehicular ad hoc networks (VANETs), multi-hop wireless broadcast has been considered a promising technology to support safety-related applications that have strict quality-of-service (QoS) requirements such as low latency, high reliability, scalability, etc. However, in the urban transportation environment, the efficiency of multi-hop broadcast is critically challenged by complex road structure, severe channel contention, message redundancy, etc. In this paper, we propose an urban multi-hop broadcast protocol (UMBP) to disseminate emergency messages. To lower emergency message transmission delay and reduce message redundancy, UMBP includes a novel forwarding node selection scheme that utilizes iterative partition, mini-slot, and black-burst to quickly select remote neighboring nodes, and a single forwarding node is successfully chosen by the asynchronous contention among them. Then, bidirectional broadcast, multi-directional broadcast, and directional broadcast are designed according to the positions of the emergency message senders. Specifically, at the first hop, bidirectional broadcast or multi-directional broadcast conducts the forwarding node selection scheme in different directions simultaneously, and a single forwarding node is successfully chosen in each direction. Then, directional broadcast is adopted at each hop in the message propagation direction until the emergency message reaches an intersection area where multi-directional broadcast is performed again, which finally enables the emergency message to cover the target area seamlessly. Analysis and simulation results show that the proposed UMBP significantly improves the performance of multi-hop broadcast in terms of one-hop delay, message propagation speed, and message reception rate.

In urban vehicular networks, traveling users can enjoy Internet multimedia services through various mobile devices, such as smart phones and laptops. To maintain seamless and ubiquitous Internet connectivity, an efficient handoff scheme has to be employed when mobile users travel across different access networks. However, in the urban vehicular environment, the high velocity of vehicles and the random mobility of users impose great challenges to the design of an effective handoff scheme. In this paper, we propose an Efficient Proxy Mobile IPv6 (E-PMIPv6)-based handoff scheme that guarantees session continuity for urban mobile users. In the registration process, E-PMIPv6 enables mobile users to obtain seamless Internet connectivity either from fixed roadside units or mobile routers and improves cache utilization at the local mobility anchor by merging the binding cache entries of the mobile users. In the handoff process, E-PMIPv6 comprehensively considers various handoff scenarios in the urban vehicular environment and provides transparent network-based mobility support to individual mobile users or a group of users in the same mobile network without disrupting ongoing sessions. In addition, E-PMIPv6 eliminates packet loss by either packet buffering or packet tunneling to improve handoff performance in each handoff scenario. Finally, a detailed analytical model is developed to study the performance of E-PMIPv6 in terms of handoff latency, signaling overhead, buffering cost, and tunneling cost. Analysis and simulation results demonstrate that the proposed E-PMIPv6 successfully extends the scalability of user mobility and greatly improves handoff efficiency in urban vehicular networks.

Enabling vehicular communications is expected to revolutionize the transport infrastructure and support many traffic management applications such as platooning. Sharing vehicle information such as speed and acceleration wirelessly among platoons plays an effective role in platoon control by maintaining a constant inter-vehicle and inter-platoon distances. However, the performance of (inter and intra-) platoon communications in terms of throughput, transmission delays and packet transmission collisions can undermine the effectiveness of information sharing on platoon control. In this paper, we present probabilistic performance analysis of IEEE 802.11p Distributed Coordination Function (DCF) for inter-platoon communications in a multiplatooning scenario (i.e, a chain of platoons). The expressions for the transmission attempt probability, packet collision probability, network throughput and packet delay are derived accordingly. Numerical results show that the performance of inter-platoon communications depends on the vehicle's role in one platoon and its platoon position within the multiplatoon and that the end-to- end delay of platoons can be reduced by adjusting the contention window size.

In this paper, we present three network evolution models for generating fault-tolerant and energy- efficient large-scale peer-to-peer wireless sensor networks (WSNs) based on complex networks theory. Being scale-free is one of the intrinsic features of complex networks-based evolution models that generates fault- tolerant topologies. In this work, we argue that fault- tolerant topologies are not necessarily energy efficient. The three proposed energy-aware evolution models are energy-aware common neighbors (ECN), energy- aware large degree promoted (ELDP) and energy-aware large degree demoted (ELDD). ECN considers neighborhood overlap, whereas ELDP and ELDD consider topological overlap for node attachment. The ELDP model promotes the establishment of links to nodes with a large degree, whereas the ELDD model demotes this strategy. Performance evaluations demonstrate that the proposed models outperform a candidate clustering-based model, thereby providing greater energy savings and fault- tolerance. Among the proposed models, ECN is the winner in-terms of energy efficiency, ELDD performs best in- terms of fault-tolerance, and ELDP conveniently provides balance between the two.

Many complex systems, such as software systems, are full of complexity arising from interactions among basic units (such as classes, interfaces and struts in object-oriented software systems). One of the most successful approaches to capture the underlying structural features of large-scale software systems is the investigation of hierarchical organization. However, the hierarchy of software networks has not been thoroughly investigated. In this paper, the crucial fraction (CF) in software networks has been extracted and analyzed in a set of real-world software systems. First, the classes and the relationships between them have been extracted into software networks. Then software networks have been divided into different layers, and CF of software networks has been extracted by k-core. The empirical studies in this paper reveal that software networks represent flat hierarchical structure. Finally, CF has been measured by the relevant complex network parameters respectively, and the relations between CF and overall network have been analyzed by the case studies of software networks. The results show that CF represents characteristics of scale-free, small-world, strong connectivity, and the units in CF are frequently reused and dominate the overall system.

Particle filters (PF) have been widely used in the estimation of the state transition and observation of non-linear/non-Gaussian systems, and samples degeneracy is the main issue of particle filters. In this paper, a novel PF - selectively iterative particle filter (SIPF) is proposed for target tracking in wireless sensor networks (WSNs). There are two novel strategies in SIPF, the statistics based threshold and particles refining. The key insight of SIPF is from an experimental observation that, the more suitable divergence of particles can yield the better estimation. The performance of the proposed SIPF is tested on two theoretical models, and then it is used in a target tracking issue in WSN in the distributed model. Experimental results show that the proposed SIPF can greatly improve the accuracy of object tracking, and in theoretical models the estimation error is only about 10%, while in practical models is only about 25% compared to other existing 9 filtering methods.

With the pervasive use of smart phones in the daily life, location privacy has become one of cruxes for the success of mobile social networks (MSNs). In this paper, we propose a Voronoi-social-spot-aided Location Privacy-preserving (VSLP) packet forwarding protocol to improve the packet forwarding efficiency and at the same time protect receiver's location privacy. In VSLP, we first identify the social spot locations according to the user mobility information, and then build a Voronoi diagram based on the defined social spots. On the edge of Delaunay triangulation over the Voronoi diagram, we deploy multiple storage devices to help receivers to temporarily store the packets. With the security analysis, we show that the location privacy can be achieved. Using extensive simulations, we show that VSLP can enhance the packet forwarding efficiency with improved packet delivery ratio and reduced average packet delay.

This paper addresses the issue of non-uniform motion deblurring due to hand shake for a single photograph. The main difficulty of spatially variant motion deblurring is that the deconvolution algorithm can not directly be used to estimate the blur kernel as the kernel of different pixels are different to each other. In this paper, the blurred image is considered as a weighed summation of all possible poses, and we proposed to use a PSO (particle swarm optimization) to optimize the weighed parameters of the corresponding poses after building the motion model of the camera. The main issue of using a PSO for deblurring is that it is generally impossible to obtain the ground true of the observed blurred image, which must be used as the input of the PSO algorithm. To solve this problem, firstly a novel image prediction method is proposed which combines a shock filter and a non-linear structure tensor with anisotropic diffusion. The main advantage of the proposed prediction method is that the deblurring process is not misled by rich texture in the image. Secondly an alternatively optimizing procedure is used to gradually refine the motion kernel and the latent image. Experimental results show that our approach makes it possible to model and remove non-uniform motion blur without hardware support.

In order to achieve cooperative driving in vehicular ad hoc networks (VANET), broadcast transmission is usually used for disseminating safety-related information among vehicles. Nevertheless, broadcast over multihop wireless networks poses many challenges due to link unreliability, hidden terminal, message redundancy, and broadcast storm, etc., which greatly degrade the network performance. In this paper, we propose a cross layer broadcast protocol (CLBP) for multihop emergency message dissemination in inter-vehicle communication systems. We first design a novel composite relaying metric for relaying node selection, by jointly considering the geographical locations, physical layer channel conditions, moving velocities of vehicles. Based on the designed metric, we then propose a distributed relay selection scheme to guarantee that a unique relay is selected to reliably forward the emergency message in the desired propagation direction.We further apply IEEE802.11e EDCA to guarantee QoS performance of safety related services. Finally, simulation results are given to demonstrate that CLBP can not only minimize the broadcast message redundancy, but also quickly and reliably disseminate emergency messages in a VANET.

Broadcast is an effective approach for safety-related information exchange to achieve cooperative driving in vehicular ad hoc network (VANET). However, it suffers from several fundamental challenges such as message redundancy, link unreliability, hidden terminal and broadcast storm, etc., which degrade the efficiency of the network greatly. To address these issues, this paper proposes a position based multi-hop broadcast protocol (PMBP) for emergency message dissemination in inter-vehicle communications. By adopting a cross-layer approach considering both the MAC and Network layers in the proposed scheme, the candidate vehicle for forwarding an emergency message is selected according to its distance from the source vehicle in the message propagation direction. Analysis and simulation results show that PMBP can not only quickly deliver emergency messages, but also reduce broadcast message redundancy significantly.

Particle filtering (PF) has been widely used in solving nonlinear/non Gaussian filtering problems. Inferring to the target tracking in a wireless sensor network (WSN), distributed PF (DPF) was used due to the limitation of nodes' computing capacity. In this paper, a novel filtering method -- asynchronous DPF (ADPF) for target tracking in WSN is proposed. There are two keys in the proposed algorithm. Firstly, instead of transferring value and weight of particles, Gaussian mixture model (GMM) is used to approximate the posteriori distribution, and only GMM parameters need to be transferred which can reduce the bandwidth and power consumption. Secondly, in order to use sampling information effectively, when target moving to the next cluster head region, the GMM parameters are transfer to the next cluster head, and combine with the new local GMM parameters to compose the new GMM parameters incrementally. The ADPF can also deal with the situation of different number of nodes in different cluster when using the dynamic cluster structure. The proposed ADPF is compared to some other DPF for WSN target tracking, and the experimental results show that not only the precision is improved, but also the bandwidth and power is reduced.

This paper proposes a multi-channel token ring media access control (MAC) protocol (MCTRP) for inter-vehicle communications (IVC). Through adaptive ring coordination and channel scheduling, vehicles are autonomously organized into multiple rings operating on different service channels. Based on the multi-channel ring structure, emergency messages can be disseminated with a low delay. With the token based data exchange protocol, the network throughput is further improved for non-safety multimedia applications. An analytical model is developed to evaluate the performance of MCTRP in terms of the average full ring delay, emergency message delay, and ring throughput. Extensive simulations with ns-2 are conducted to validate the analytical model and demonstrate the efficiency and effectiveness of the proposed MCTRP.

This paper proposes a multi-channel token ring protocol (MCTRP) for inter-vehicle communications (IVC). With MCTRP, the emergency messages can be quickly disseminated with bounded delay, and the desired quality-of-service (QoS) for multimedia traffic can be provided with limited hardware requirement and signaling cost. In addition, MCTRP ensures that all nodes have an equal opportunity to transmit their data to achieve fairness. Through extensive simulations, it is demonstrated that MCTRP can effectively meet the requirements of IVC.

The greatest benefit of Webit is without a doubt that it enables a standard connection to non-PC devices using the Internet. Since the use of Webit opens a new method for maintaining and supervising non-PC devices, it has become a helpful tool for users to control and manage devices remotely. Webit actualizes the connection between non-PC devices and Internet, thus all kinds of devices around us may be controlled and accessed over the Internet through a standard Web browser. In this paper, we introduce the reasons that why provide Internet connectivity for non-PC devices. We present the architecture of Webit that can support Internet connectivity for non-PC devices. We also present how to design and implement it and finally compare the results of Webit's performance and other similar Internet servers.

Network resource is opened for every user in LAN. Considering administration and security, we must restrict them to some degree. Whatever the rank of LAN member is, most of them should be free of route when access to network. Virtual local area network, being a new technique to settle broadcast containment and network security, has already become an important strategy in LAN solution. In this paper, we introduced some kinds of VLAN implementation methods and their main drawbacks in Ethernet environment. On the basis of them, we proposed a new flexible VLAN administration strategy based on service and we set up VASS architecture, a fair integrated VLAN architecture based on service.