Cross-view geo-localization is a crucial task with diverse applications, yet it remains challenging due to the significant variations in viewpoints and visual appearances between images from different perspectives. While recent advancements have been made, existing methods often suffer from high model complexity, excessive resource consumption, and the impact of sample learning difficulty on optimization. To overcome these limitations, we optimize the Vision Mamba (Vim) model, built on a State Space Model (SSM) architecture, by replacing the traditional classification head with Channel Group Pooling (CGP) for efficient feature integration. This optimization reduces model parameters by 1.5% and computational complexity by 0.4%. Additionally, we propose a novel Dynamic Weighted Batch-tuple Loss (DWBL) to dynamically adjust the weighting of negative samples, improving model performance. By combining CGP and DWBL, we develop an efficient end-to-end network, VimGeo, which achieves state-of-the-art performance with enhanced computational efficiency. Specifically, VimGeo achieves a Recall@1 of 81.67% on the CVACT_test dataset, outperforming prior approaches. Extensive experiments on CVUSA, CVACT, and VIGOR datasets validate VimGeo's effectiveness and competitiveness in cross-view geo-localization tasks, achieving the leading results among sequence modeling-based methods. The implementation is available at: https://github.com/VimGeoTeam/VimGeo.

Advanced driver assistance systems (ADASs) enhance driving safety and convenience by providing auxiliary functions. However, traditional rule-based or learning-based ADAS lack the capability for commonsense-based environmental understanding and multisensor data fusion, which leads to limitations in complex dynamic environments. Multimodal large language models (MLLMs) can effectively integrate data from different modalities and possess strong environmental perception and commonsense reasoning abilities, offering more intelligent driver assistance services within Internet of Things (IoT) networks. In this article, we propose a cloud-edge collaborative ADAS based on MLLMs, utilizing IoT networks by deploying a smaller model, CogVLM2, at the edge and a larger model, ChatGPT-4o, in the cloud to achieve collaborative driver assistance services. Specifically, we first reannotate the BDD-X dataset and use it to fine-tune CogVLM2 with LoRA, while applying few-shot learning to ChatGPT-4o to enhance their understanding and decision-making capabilities in traffic scenarios. We then formulate service latency, energy consumption, and Quality-of-Service (QoS) models for the cloud-edge collaborative ADAS in IoT networks, optimizing the combination of these models. Finally, we design an improved DDPG-based task offloading algorithm by introducing a multistep reward mechanism and using a diffusion model to generate noise, aiming to determine the optimal execution location (i.e., cloud, edge, or local) for each task. Experimental results show that both CogVLM2 and ChatGPT-4o can achieve basic ADAS functionality. After fine-tuning and few-shot learning, their task success rates were significantly improved. Moreover, compared to other mainstream deep reinforcement learning-based task offloading algorithms, the improved DDPG task offloading algorithm demonstrates better performance in latency, energy consumption, and QoS within IoT networks.

Federated learning (FL) over mobile devices is an emerging distributed learning paradigm for numerous delay sensitive applications. In FL, the training delay is composed of the computing and communication delay. Some of the participating mobile devices may have slow local computing or wireless communications, which results in high FL training delay. Intuitively, if fast devices help slow ones, the FL training delay can potentially be reduced. However, helping each other among devices requires frequent transmissions and may cause additional delay. Fortunately, we observe that device-to-device (D2D) transmission, a fast and direct transmission, may be applied between device pairs to mitigate the additional delay from frequent transmissions. Inspired by those observations, we develop the D2D transmission assisted FL (DAFL), a novel FL scheme to improve the training delay over mobile devices. Briefly, we first put the eligible mobile devices into pairs, assigning each pair to one of the four types of relation: 1) similar computing, large communication gap; 2) similar communication, large computing gap; 3) one with faster computing and the other with faster communication; and 4) one with both faster computing and communication. We design the process for each type of device pair to: 1) improve the transmission delay of each pair, by letting the fast device help with the model parameters transmission to the server and 2) improve the computing delay by splitting learning task between paired devices. The emulation results demonstrate that DAFL surpasses existing peer designs in terms of reducing training delay by more than 20%.

With the development of intelligent transportation systems (ITSs), digital twin (DT) technology is becoming increasingly widespread in the application of connected automated vehicles (CAVs) to enhance driving safety. However, when DT systems are used for driving safety decisions through virtual control of reality and virtual reflection of reality, decision errors may occur, which can be fatal for the driving safety of CAVs. The main reasons are attributed to three aspects: 1) the accuracy; 2) the communication delay; and 3) the safety control of the DT system. In this article, we study to improve the accuracy and safety of the DT system decisions with communication delay. First, we considered powertrain factors to construct a high-precision and high-fidelity DT system. We use the Goodness-of-Fit Functions (GoFs) and Measure-of-Performances (MoPs) to fit the vehicle’s model and carry out error measurements in the DT system. Second, we analyze the stability of the DT system using plant stability and string stability under time delay. The effective range of time delay ensures the accuracy and stability of the DT system, and provides a safety constraint for the design of the CAV’s controller. Finally, we propose a DT-assisted robust safety-critical traffic control (RSTC) strategy based on the control barrier functions (CBFs). This strategy ensures the driving safety of CAVs with preceding and following vehicles while maintaining traffic stability. The theoretical analysis and experimental results present that the proposed scheme can effectively avoid conflicts and crash risks to ensure driving safety.

Group vehicle trajectory prediction (GVTP) is important for analyzing the traffic states and optimizing the traffic management. However, existing studies have performance bottlenecks in the prediction accuracy and inference latency. To tackle the problems, we propose a linear UNet-enhanced fully connected spatial–temporal GNN (LUFC-STGNN) for GVTP. First, a spatial graph is constructed by integrating prior-based and data-driven methods to capture both explicit and implicit spatial interactions between the vehicles. After that, a comprehensive temporal graph is created to capture the varying strengths of temporal interactions between all vehicles throughout historical timestamps. Furthermore, a fully connected spatial–temporal graph combining the spatial and temporal graphs is introduced to extract the effective spatial–temporal interaction features of the vehicles through the graph convolution operation. Finally, a linear UNet-based temporal dependency encoder (LU-TDE) is designed to further enhance the model’s ability of capturing the potential temporal patterns in the vehicle interactions. The encoder with linear complexity explores the multiscale temporal dependencies from the spatial–temporal interaction features but also reducing the inference latency. Experiments results based on real-world datasets show that compared to state-of-the-art models, our model reduces the average root mean square error over the 5-s prediction horizon by 31% and 10% on the NGSIM and HighD datasets, while reducing the inference latency by at least 1.26 times.

Abstract Scene risk identification is essential for the traffic safety of Internet of Vehicles. However, the performance of existing risk identification approaches is heavily limited by the imbalanced historical data and the poor model interpretability. Meanwhile, the large processing delay and the potential privacy leakage threat also restrict their application. In this paper, a novel risk identification model is proposed that leverages the synthetic minority over‐sampling technique nearest neighbor (SMOTEENN) method to balance between high‐risk and low‐risk data. The risk identification model has fine interpretability by using recursive feature elimination cross validation (RFECV) with the Shapley additive explanation (SHAP) to analyze the importance of different features, and further elaborately design the Focal Loss function to tackle the disparity between the difficult and easy sample learning. The proposed interpretability scene risk identification framework, named iScene, is built on the infrastructure of 6G space‐air‐ground integrated networks (SAGINs) with blockchain assistance. The model updata efficiency and privacy preservation are effectively enhanced. An elastic computing offloading algorithm is applied to minimize the system overhead under the hierarchical edge service architecture. The experimental evaluation is carried out to verify the effectiveness of the proposed risk identification framework. The results indicate that the G‐Mean value is increased by 23.4%, while the task average response delay is reduced by 21.2%, compared to that in the traditional risk identification approaches with local computing services.

Vehicle trajectory prediction (VTP) is important for ensuring safe decision-making and planning in Internet of Vehicles (IoV). In complex traffic scenarios, accurate and reliable trajectory prediction requires comprehensive understanding of the interaction behaviors among vehicles. However, existing methods fail to effectively capture vehicle interaction features and fully explore their potential dependencies, limiting improvements in prediction accuracy. To this end, we propose a social attention network fused multipatch temporal–variable dependency (SAN-FTVD) model to tackle the above problems. In specific, we first design a variable token embedding module (VTEM) to extract the motion state information of vehicles, which independently embeds each variable of vehicle historical data into a variable token. After that, we propose a physical informed vehicle interaction encoder (PI-VIE) to capture vehicle interaction features over continuous time. The encoder is combined with physical priors to encode vehicle interaction features based on the correlations between the variable tokens. Following that, a temporal–variable dependency fusion module (TVDFM) is proposed to extract and fuse the multipatch temporal and variable dependencies, fully exploring potential dependencies in vehicle interaction features. Numerical results demonstrate that compared with the state-of-the-art model, the proposed model reduces the average prediction root mean square error over 5-s time range by 8% and 7% on two public data sets with 75% less inference cost. Furthermore, extensive ablation experiments validate the effectiveness of the above modules in the model.

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthetic data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.

Federated learning (FL) over mobile devices is an emerging distributed learning paradigm for numerous delay sensitive applications. In FL, the training delay is composed of the computing and communication delay. Some of the participating mobile devices may have slow local computing or wireless communications, which results in high FL training delay. Intuitively, if fast devices help slow ones, the FL training delay can potentially be reduced. However, helping each other among devices requires frequent transmissions and may cause additional delay. Fortunately, we observe that Device-to-Device (D2D) transmission, a fast and direct transmission, may be applied between device pairs to mitigate the additional delay from frequent transmissions. Inspired by those observations, we develop the D2D transmission assisted FL (DAFL), a novel FL scheme to improve the training delay over mobile devices. Briefly, we first put the eligible mobile devices into pairs, each pair consisting of a fast and a slow device. Then, we apply D2D transmission between each device pair to: (1) improve the transmission delay of each pair, by letting the fast device help with the model parameters transmission to the server, and (2) improve the computing delay by splitting learning task between paired devices. The emulation results demonstrate that DAFL surpasses existing peer designs in terms of reducing training delay by more than 20%.

In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.

Most existing multi-unmanned aerial vehicle (multi-UAV) systems focus on fly path or energy consumption for task assignment, while little attention has been paid to the dynamic feature of the task, resulting in poor task completion ratio. The machine learning (ML) paradigm provides new methodologies for task assignment. However, ML methods are usually of heavy resource-consumption that cannot be directly applied in the UAV. In this paper, a digital twin (DT) assisted task assignment approach is proposed to improve the resource-intensive utilization and the efficiency of deep reinforcement learning (DRL) in multi-UAV system. The approach has a three-layer network structure which can dynamically assign tasks based on the task time constraints. Moreover, the approach is divided into two stages of initial task-assignment and task-reassignment. In the first stage, airship divides a task into multiple subtasks according to the shortest distance based on genetic algorithm and assigns them to UAVs. In the second stage, the DT can be leveraged to enable the airships to learn from the features of tasks and to generate the Q-value of the estimated value network of DRL for UAVs via pre-train of DT. The Q-value can be directly applied for deep Q-learning network (DQN) in the UAVs to reduce the training episode. Furthermore, the DQN is adopted to train task-reassignment strategy. Simulation results indicate that the DQN with DT can significantly reduce the training episode, improving 30% of the task completion ratio and 19% of the system energy efficiency compared with that of the baseline methods.

Model update compression is a widely used technique to alleviate the communication cost in federated learning (FL). However, there is evidence indicating that the compression-based FL system often suffers the following two issues, i) the implicit learning performance deterioration of the global model due to the inaccurate update, ii) the limitation of sharing the same compression rate over heterogeneous edge devices. In this paper, we propose an energy-efficient learning framework, named Snowball, that enables edge devices to incrementally upload their model updates in a coarse-to-fine compression manner. To this end, we first design a fine-grained compression scheme that enables a nearly continuous compression rate. After that, we investigate the Snowball optimization problem to minimize the energy consumption of parameter transmission with learning performance constraints. By leveraging the theoretical insights of the convergence analysis, the optimization problem is transformed into a tractable form. Following that, a water-filling algorithm is designed to solve the problem, where each device is assigned a personalized compression rate according to the status of the locally available resource. Experiments indicate that, compared to state-of-the-art FL algorithms, our learning framework can save five times the required energy of uplink communication to achieve a good global accuracy.

To facilitate the implementation of deep learning-based vehicular applications, vehicular federated learning is introduced by integrating vehicular edge computing with the newly emerged federated learning technology. In vehicular federated learning, it is widely considered that the raw data collected by vehicles have complete ground-truth labels. This, however, is not realistic and inconsistent with the current applications. To deal with the above dilemma, a semisupervised vehicular federated learning (Semi-VFL) framework is proposed. In the framework, each vehicular client uses labeled data shared by an application provider, and its own unlabeled data to cooperatively update a global deep neural network model. Furthermore, the application provider combines the multidimensional contract theory with prospect theory (PT) to design an incentive mechanism to stimulate appropriate vehicular clients to participate in Semi-VFL. Multidimensional contract theory is used to deal with the information asymmetry scenario where the application provider is not aware of vehicular clients’ 3-D cost information, while PT is used to model the application provider’s risk-aware behavior and make the incentive mechanism more acceptable in practice. After that, a closed-form solution for the optimal contract items under PT is derived. We present the real-world experimental results to demonstrate that Semi-VFL achieves the advantages in both the test accuracy and convergence speed, in comparison with existing baseline schemes. Based on the experimental results, we further perform the simulations to verify that our incentive mechanism is efficient.

Dynamic off-chain routing in payment channel network (PCN)-based Internet of Things (IoT) is attracting increasing research attention. However, there are two major issues in dynamic routing in PCN-based IoT with resource-limited devices. The first issue is how to achieve high long-term transaction efficiency in PCN with dynamic channel capacities. The second issue is how to achieve a lightweight routing algorithm deployed on IoT devices while achieving high transaction efficiency, i.e., successful payment amount and success ratio. Therefore, in this paper, we propose a compact deep reinforcement learning (DRL) algorithm to learn the joint dynamic and lightweight routing policy for maximizing long-term transaction efficiency. To obtain optimal performance in dynamic routing problems for off-chain systems, a proximal policy optimization algorithm is employed to create an actor–critic learning structure for training the teacher DRL model. To obtain a compact and efficient student DRL model, an adaptive pruning technique is utilized for pruning unnecessary parameters of networks in the teacher model adaptively without affecting its learning ability. Furthermore, knowledge distillation is leveraged to improve the performance of the student network. Thus, a compact and efficient student DRL model can be developed and implemented to maximize the long-term transaction efficiency in off-chain systems on resource-limited IoT devices. The simulation results demonstrate that the proposed DRL algorithm outperforms the other baseline algorithms in PCN transaction efficiency while requiring only 10% of the computation and storage resources compared with that of the original teacher model.

Large-scale Internet applications running on data centers are typically instantiated as a set of containers. Assigning a container to its affinity machine can reduce communication and transport costs while assigning it to the anti-affinity machine may affect the proper operation of the container. Existing container scheduling methods cannot accommodate these two types of requirements. In order to reduce the operation and maintenance cost of data centers, this paper focuses on the container instance allocation problem in heterogeneous server cluster, and proposes a global cost-aware scheduling algorithm (GCCS) to solve it. The purpose is to minimize the total power consumption of the cluster from a global perspective, while trying to meet the affinity/anti-affinity requirements of applications. We study the number of containers per server selected by the application, model it as an integer linear program (ILP), and then propose a heuristic search algorithm to repair the relaxation solution of the ILP into a suboptimal feasible solution. In particular, we use Bayesian optimizer to perform a number of automated development and exploration processes for the selection of the cost coefficient. The experiments are carried out with the best cost coefficient recommended by Bayesian optimizer. Finally, the results demonstrate that GCCS can significantly reduce the total power consumption of the cluster, while maintaining a high affinity satisfaction ratio.

The coupling of federated learning (FL) and multi-access edge computing (MEC) has the potential to foster numerous applications. However, it poses great challenges to train FL fast enough with limited communication and computing resources of mobile edge devices. Motivated by recent development in ultra fast wireless transmissions and promising advances in artificial intelligence (AI) computing hardware of mobile devices, in this paper, we propose a time efficient FL over future mobile edge devices, called dynamic batch sizes assisted federated learning (DBFL) with convergence guarantee. The DBFL allows batch sizes to increase dynamically during training, which can unleash the computing potential of GPU’s parallelism for on- device training and effectively leverage the fast wireless transmissions (WiFi-6, 5G, 6G, etc.) of mobile edge devices. Furthermore, based on the derived DBFL’s convergence bound, we develop a batch size control scheme to minimize the total time consumption of FL over mobile edge devices, which trade-offs the “talking”, i.e., communication time, and “working”, i.e., computing time, by adjusting the incremental factor appropriately. Extensive simulations are conducted to validate the effectiveness of our proposed DBFL algorithm and demonstrate that our scheme outperforms existing time efficient FL approaches in terms of the total time consumption in various settings.

Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without revealing the local data. Gradient compression could be applied to FL to alleviate the communication overheads but the existing schemes still face challenges. To deploy green MEC, we propose FedGreen, which enhances the original FL with fine-grained gradient compression to control the total energy consumption of the devices. Specifically, we introduce the relevant operations including device-side gradient reduction and server-side element-wise aggregation to facilitate the gradient compression in FL. According to a public dataset, we evaluate the contributions of the compressed local gradients with respect to different compression ratios. Furthermore, we investigate a learning accuracy-energy efficiency tradeoff problem and the optimal compression ratio and computing frequency are derived for each device. Experimental results show that given the 80% test accuracy requirement, compared with the baseline schemes, FedGreen reduces at least 32% of the total energy consumption of the devices.

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.

The 5th generation (5G) mobile network technology is accelerating the development of autonomous vehicles by significantly shortening the communication latency and improving the reliability of network connection and transmission. However, as the number of vehicles increases, neither cloud servers nor multi-access edge computing (MEC) servers alone could sufficiently meet the Quality-of-Service (QoS) requirements for computing-intensive vehicle tasks. In this paper, we consider a hierarchical offloading scenario, where vehicle tasks are allowed to execute in MEC servers, convergence servers or cloud servers. To reduce the cost of latency and energy, we optimize the communication and computation resource allocation problem. The optimization problem is converted to a Markov decision process, and deep reinforcement learning is used to tackle the resource slicing and scheduling problem. Simulation results show that the proposed scheme is more resilient and efficient than that of single cloud server offloading or single MEC server offloading.

Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a machine learning model in a decentralized manner. Specifically, the data owners (e.g., IoT device consumers) keep their raw data and only share their local computation results to train the global model of the model owner (e.g., an IoT service provider). When executing the federated learning task, the data owners contribute their computation and communication resources. In this situation, the data owners have to face privacy issues where attackers may infer data property or recover the raw data based on the shared information. Considering these disadvantages, the data owners will be reluctant to use their data to participate in federated learning without a well-designed incentive mechanism. In this article, we deliberately design an incentive mechanism jointly considering the task expenditure and privacy issue of federated learning. Based on a differentially private federated learning (DPFL) framework that can prevent the privacy leakage of the data owners, we model the contribution as well as the computation, communication, and privacy costs of each data owner. The three types of costs are data owners' private information unknown to the model owner, which thus forms an information asymmetry. To maximize the utility of the model owner under such information asymmetry, we leverage a 3-D contract approach to design the incentive mechanism. The simulation results validate the effectiveness of the proposed incentive mechanism with the DPFL framework compared to other baseline mechanisms.

Wireless body area network (WBAN) has become a promising technology, which can be widely applied in health monitoring, and so on. However, the performance of a practical WBAN may severely suffer from the degradation caused by dynamic nature of wireless channels with the movements of human body. Traditional communication frameworks cannot catch up with the channel variation of dynamic WBANs, which may severely degrade the performance, so an accurate channel prediction model is necessary for developing an efficient transmission strategy. In this paper, we propose a DeepBAN communication framework for dynamic WBANs. In our proposed framework, a temporal convolution network (TCN) based deep learning approach is adopted for channel prediction, the computationally intensive task of which is processed by mobile edge computing (MEC), to reduce the response time. Given the predicted channel conditions, we propose a joint power control, time-slot allocation, and relay selection algorithm to maximize the energy efficiency of the system, taking into account the transmission reliability and end-to-end latency requirements. We evaluate the performance of DeepBAN, and the results show that it can achieve energy-efficient, reliable, and low-latency data transmission in dynamic WBANs, which can improve the system energy efficiency by 15% compared with the stochastic scheduling scheme.

Parked Vehicle (PV) assistance in vehicular edge computing and networks is proposed to exploit underutilized computing resources from PVs for enhancing the resource capacity at the edge vehicular network. Containerization is used to improve task execution of PVs with fast start-up time, less hardware overheads and safe resource isolation. To this end, we introduce a task-container matching market to provide on-demand offloading services. For network implementation, the related entities including requesters, PVs with containers as performers and a service provider are described. Considering parking behaviors and resource availability, we measure the serviceabilities of PVs to select appropriate PVs for reliable and efficient task processing. According to utility functions, preference profiles of requesters and performers in the task-container matching market are modeled through the best response analysis. Finally, we apply matching game approach to cope with associations between tasks and containers deployed inside PVs. Numerical results demonstrate that compared with baseline schemes, our scheme accomplishes more tasks and acquires a higher overall utility in computation offloading.

Machine learning is increasingly becoming a powerful tool to make decisions in a wide variety of applications, such as medical diagnosis and autonomous driving. Privacy concerns related to the training data and unfair behaviors of some decisions with regard to certain attributes (e.g., sex, race) are becoming more critical. Thus, constructing a fair machine learning model while simultaneously providing privacy protection becomes a challenging problem. In this paper, we focus on the design of classification model with fairness and differential privacy guarantees by jointly combining functional mechanism and decision boundary fairness. In order to enforce ϵ-differential privacy and fairness, we leverage the functional mechanism to add different amounts of Laplace noise regarding different attributes to the polynomial coefficients of the objective function in consideration of fairness constraint. We further propose an utility-enhancement scheme, called relaxed functional mechanism by adding Gaussian noise instead of Laplace noise, hence achieving (ϵ, δ)-differential privacy. Based on the relaxed functional mechanism, we can design (ϵ, δ)-differentially private and fair classification model. Moreover, our theoretical analysis and empirical results demonstrate that our two approaches achieve both fairness and differential privacy while preserving good utility and outperform the state-of-the-art algorithms.

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

Data centers are key participants in emergency demand response (EDR), where the grid coordinates large electricity consumers for reducing their consumption during emergency situations to prevent major economic losses. While existing literature concentrates on owner-operated data centers (e.g., Google), this work studies EDR in multi-tenant colocation data centers (e.g., Equinix) where servers are owned and managed by individual tenants and which are better targets of EDR. Existing EDR mechanisms incentivize tenants energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants' participation in EDR. This paper addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using the descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation through the clock auction, where operator can only know the aggregate of the tenants' values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulating descending clock auction, in which the amount of energy/price the tenants are willing to reduce for a given price/energy to meet EDR is protected.

The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehicular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such integrated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation-based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level of security for data sharing in VECONs.

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by parked vehicular computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized rewards for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.

Internet of Things (IoT) provides ubiquitous intelligence and pervasive interconnections to diverse physical objects. The overall network performance of existing IoT is restricted by limited network lifetime. Hence, energy harvesting technology with energy replenishment from mobile charger is proposed to prolong the network lifetime. Energy harvesting IoT is emerged. Nodes can not only request energy replenishment from the mobile charger, but also transfer surplus energy to the mobile charger for improving energy utilization. This gives rise to bidirectional energy flows in the network. A new paradigm that energy flows coexist with data flows is further resulted in. But there exist great challenges on controlling these flows. Toward centralized flow control, we exploit software defined networking to simplify and optimize network management, thus introduce software defined energy harvesting IoT (SEANET). In our proposed architecture, the data plane, energy plane, and control plane are decoupled to support enhanced communications and flexible energy scheduling. We consider reliable communications for SEANET, and propose to relay data packets among the nodes with high reputation values and sufficient energy. In particular, reputation values of nodes are computed by the multiweighted subjective logic for higher accuracy. Besides, a Nash bargaining game is formulated to solve the benefit allocation problem for energy trading in SEANET. Numerical results indicate that SEANET improves data traffic by reducing packet loss, optimizes energy utilization, and saves energy.

Generalized approximate message passing (GAMP) is an efficient algorithm for the estimation of independent identically distributed random signals under generalized linear model. The sum-product GAMP has long been recognized as an approximate implementation of the sum-product loopy belief propagation. In this letter, we propose to view the message passing in a new perspective of expectation propagation (EP). Comparing with the previous methods that were based on Taylor expansions, the proposed EP method could unify the derivations for the real and the complex GAMP, with a difference only in the setup of Gaussian densities.

In industrial Internet of things (IIoT), peer-to-peer (P2P) energy trading ubiquitously takes place in various scenarios, e.g., microgrids, energy harvesting networks, and vehicle-to-grid networks. However, there are common security and privacy challenges caused by untrusted and nontransparent energy markets in these scenarios. To address the security challenges, we exploit the consortium blockchain technology to propose a secure energy trading system named energy blockchain. This energy blockchain can be widely used in general scenarios of P2P energy trading getting rid of a trusted intermediary. Besides, to reduce the transaction limitation resulted from transaction confirmation delays on the energy blockchain, we propose a credit-based payment scheme to support fast and frequent energy trading. An optimal pricing strategy using Stackelberg game for credit-based loans is also proposed. Security analysis and numerical results based on a real dataset illustrate that the proposed energy blockchain and credit-based payment scheme are secure and efficient in IIoT.

As a promising branch of Internet of Things, Internet of Vehicles (IoV) is envisioned to serve as an essential data sensing and processing platform for intelligent transportation systems. In this paper, we aim to address location privacy issues in IoV. In traditional pseudonym systems, the pseudonym management is carried out by a centralized way resulting in big latency and high cost. Therefore, we present a new paradigm named Fog computing supported IoV (F-IoV) to exploit resources at the network edge for effective pseudonym management. By utilizing abundant edge resources, a privacy-preserved pseudonym (P3) scheme is proposed in F-IoV. The pseudonym management in this scheme is shifted to specialized fogs at the network edge named pseudonym fogs, which are composed of roadside infrastructures and deployed in close proximity of vehicles. P3scheme has following advantages: 1) context-aware pseudonym changing; 2) timely pseudonym distribution; and 3) reduced pseudonym management overhead. Moreover, a hierarchical architecture for P3scheme is introduced in F-IoV. Enabled by the architecture, a context-aware pseudonym changing game and secure pseudonym management communication protocols are proposed. The security analysis shows that P3scheme provides secure communication and privacy preservation for vehicles. Numerical results indicate that P3scheme effectively enhances location privacy and reduces communication overhead for the vehicles.

Emergency Demand Response (EDR) is crucial for improving the grid reliability and for meeting the power demand during crisis. Power-hungry data centers have been facing an urge to reduce their consumption to meet the EDR. The energy reduction in colocation data centers which house servers for multiple tenants (e.g., Equinix) that are better targets for EDR is less explored than the energy reduction in owner-operated data centers (e.g., Google). Existing EDR mechanisms incentivize tenants energy reduction. Such designs can either be gamed by strategic tenants or untrustworthy colocation operators for illegal gains. These serious privacy concerns stand as barrier preventing the tenants' participation in EDR. This paper addresses such concerns by proposing a privacy-preserving and strategy-proof mechanism using descending clock auction. Privacy is protected by implementing homomorphic encryption for aggregation of energy through clock auction, where operator can only know the aggregate of the tenants' values or bids but not their individual private values or confidential information submitted to meet the EDR. We evaluate the privacy and performance of this scheme by formulation through descending clock auction, in which the amount of energy the tenants are willing to reduce for a given price to meet EDR is protected.

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

Geo-distributed mobile cloud computing (GMCC) integrates location information into mobile cloud computing, that has high potential for a large variety of applications. In a vehicular environment, a GMCC provides a large number of resources to vehicles that are geographically close to them. However, there are few studies that focus on security and privacy issues in a GMCC scenario. Vehicles need sufficient pseudonyms to periodically change for privacy preservation. In this paper, we focus on pseudonym management in GMCC system for vehicular environment. We design a three-layer on-demand pseudonym system to manage the pseudonyms. Moreover, we propose a secure pseudonym distribution scheme for secure communication among vehicles. As the number of demanded pseudonyms varies with traffic loads in different clouds, we use a newsvendor model to address the optimal on-demand pseudonym distribution problem. Numerical results indicate our proposed schemes not only improve utility of the clouds, but also maximize utilization of the pseudonyms.

With the rapid increase of mobile devices, the computing load of roadside cloudlets is fast growing. When the computation tasks of the roadside cloudlet reach the limit, the overload may generate heat radiation problem and unacceptable delay to mobile users. In this paper, we leverage the characteristics of buses and propose a scalable fog computing paradigm with servicing offloading in bus networks. The bus fog servers not only provide fog computing services for the mobile users on bus, but also are motivated to accomplish the computation tasks offloaded by roadside cloudlets. By this way, the computing capability of roadside cloudlets is significantly extended. We consider an allocation strategy using genetic algorithm (GA). With this strategy, the roadside cloudlets spend the least cost to offload their computation tasks. Meanwhile, the user experience of mobile users are maintained. The simulations validate the advantage of the propose scheme.

Cloud-enabled vehicular network is an emerging paradigm which utilizes cloud computing to enhance the performance of vehicular network. But some issues still need to be addressed and we focus on the pseudonym resources management, which is crucial for vehicles to guarantee location privacy. A new three-plane hierarchical architecture with software defined network technology is proposed to manage the pseudonym resources. We use two-sided matching theory to solve the pseudonym resources allocation problem among pseudonym pools in different roadside unit clouds. Numerical results show that our proposed approach optimizes the pseudonym resources utilization and also improves the privacy entropy of vehicles.

The limited lifetime of wireless nodes has become the essential bottleneck of system performance and wide-scale deployment of wireless networks. In this paper, we consider a practical mobile chargeable network in which each single mobile charger is able to charge multiple target nodes simultaneously. To tackle the problem, the cooperative grouping of the wireless nodes is proposed to reduce the number of traversing spots of the mobile chargers. Meanwhile, the cooperative charging among the mobile chargers is studied to conserve their energy consumption. The numerical results show that the proposed scheme outperforms existing strategies in both even-density and uneven-density wireless networks.

Spectrum access strategy plays a critical role in multichannel cognitive radio networks (CRNs). However, the CRNs cannot obtain the maximal throughput, when the existing access strategies, including overlay, underlay, and hybrid access strategies, are applied to multichannel CRNs. In this paper, we present a generalized access strategy in a multichannel CRN smart home environment, in which a secondary user (SU) system selects part of channels for sequential spectrum sensing, and accesses these channels based on the sensing results. Moreover, it accesses the remaining channels directly. We then formulate a two-phase optimization framework, which takes the sensing channel selection, sensing time allocation, and the power allocation into consideration, to maximize the gross average throughput of the multichannel CRN. In the sensing phase, a generalized access strategy algorithm (GAS) is first proposed, where we prove that only part of channels needs to be selected for spectrum sensing to achieve the maximum throughput. An optimal stopping rule is proposed to determine the optimal number of selected sensing channels. In addition, a completed hybrid access strategy algorithm is further investigated where the SU system senses all channels. An approximation algorithm is also presented to achieve suboptimal results with low computational complexity. In the transmission phase, the transmission powers of all channels are optimized via convex algorithms. Numerical experiments show that, compared with the existing schemes, the proposed schemes are able to achieve considerable throughput improvement.

Vehicular social network (VSN) is envisioned to serve as an essential data sensing, exchanging and processing platform for the future Intelligent Transportation Systems. In this paper, we aim to address the location privacy issue in VSNs. In traditional pseudonym-based solutions, the privacy-preserving strength is mainly dependent on the number of vehicles meeting at the same occasion. We notice that an individual vehicle actually has many chances to meet several other vehicles. In most meeting occasions, there are only few vehicles appearing concurrently. Motivated by these observations, we propose a new privacy-preserving scheme, called MixGroup, which is capable of efficiently exploiting the sparse meeting opportunities for pseudonym changing. By integrating the group signature mechanism, MixGroup constructs extended pseudonym-changing regions, in which vehicles are allowed to successively exchange their pseudonyms. As a consequence, for the tracking adversary, the uncertainty of pseudonym mixture is accumulatively enlarged, and therefore location privacy preservation is considerably improved. We carry out simulations to verify the performance of MixGroup. Results indicate that MixGroup significantly outperforms the existing schemes. In addition, MixGroup is able to achieve favorable performance even in low traffic conditions.

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and power grid, which provides flexible demand response management (DRM) for the reliability of smart grid. EV mobility is a unique and inherent feature of the V2G system. However, the inter-relationship between EV mobility and DRM is not obvious. In this paper, we focus on the exploration of EV mobility to impact DRM in V2G systems in smart grid. We first present a dynamic complex network model of V2G mobile energy networks, considering the fact that EVs travel across multiple districts, and hence EVs can be acting as energy transporters among different districts. We formulate the districts' DRM dynamics, which is coupled with each other through EV fleets. In addition, a complex network synchronization method is proposed to analyze the dynamic behavior in V2G mobile energy networks. Numerical results show that EVs mobility of symmetrical EV fleet is able to achieve synchronous stability of network and balance the power demand among different districts. This observation is also validated by simulation with real world data.

Research on the smart grid is being given enormous supports worldwide due to its great significance in solving environmental and energy crises. Electric vehicles (EVs), which are powered by clean energy, are adopted increasingly year by year. It is predictable that the huge charge load caused by high EV penetration will have a considerable impact on the reliability of the smart grid. Therefore, fair energy scheduling for EV charge and discharge is proposed in this paper. By using the vehicle-to-grid technology, the scheduler controls the electricity loads of EVs considering fairness in the residential distribution network. We propose contribution-based fairness, in which EVs with high contributions have high priorities to obtain charge energy. The contribution value is defined by both the charge/discharge energy and the timing of the action. EVs can achieve higher contribution values when discharging during the load peak hours. However, charging during this time will decrease the contribution values seriously. We formulate the fair energy scheduling problem as an infinite-horizon Markov decision process. The methodology of adaptive dynamic programming is employed to maximize the long-term fairness by processing online network training. The numerical results illustrate that the proposed EV energy scheduling is able to mitigate and flatten the peak load in the distribution network. Furthermore, contribution-based fairness achieves a fast recovery of EV batteries that have deeply discharged and guarantee fairness in the full charge time of all EVs.

As the next-generation power grid, smart grid will be integrated with a variety of novel communication technologies to support the explosive data traffic and the diverse requirements of quality of service (QoS). Cognitive radio (CR), which has the favorable ability to improve the spectrum utilization, provides an efficient and reliable solution for smart grid communications networks. In this paper, we study the QoS differential scheduling problem in the CR-based smart grid communications networks. The scheduler is responsible for managing the spectrum resources and arranging the data transmissions of smart grid users (SGUs). To guarantee the differential QoS, the SGUs are assigned to have different priorities according to their roles and their current situations in the smart grid. Based on the QoS-aware priority policy, the scheduler adjusts the channels allocation to minimize the transmission delay of SGUs. The entire transmission scheduling problem is formulated as a semi-Markov decision process and solved by the methodology of adaptive dynamic programming. A heuristic dynamic programming (HDP) architecture is established for the scheduling problem. By the online network training, the HDP can learn from the activities of primary users and SGUs, and adjust the scheduling decision to achieve the purpose of transmission delay minimization. Simulation results illustrate that the proposed priority policy ensures the low transmission delay of high priority SGUs. In addition, the emergency data transmission delay is also reduced to a significantly low level, guaranteeing the differential QoS in smart grid.

Mobile cloud computing is a promising technology for pervasive healthcare, which guarantees real-time health monitoring and electronic medical records sharing in different environments. In this paper, we present a hierarchical mobile cloud computing framework with three layers for pervasive healthcare. The scalable and hierarchical mobile cloud framework can be used to disperse the global storage and management load. We also study the social characteristics among patients and divide the patients into different social groups for privacy protection. A secure electronic medical records sharing scheme and a real-time health information transmission scheme are proposed. The security analysis shows that our schemes not only provide secure communication but also protect privacy of the patients.

False data injection attacks have serious threat to the smart grid, e.g., may incur power outage or blackout. Normally, an intruder should have priori knowledge of the linear structure matrix and then control all smart meters to perform attacks. State-of-the-art studies have proven in theory that false data injection attacks can be unobservable when an intruder coordinately controls a small number of smart meters. However, there are no practical or implementable unobservable false data injection attacks with low-sparsity yet in the literature. In this paper, we propose a two-stage attacking scheme to demonstrate the practical feasibility of unobservable false data injection attacks in the smart grid. In the first stage, we explore the parallel factor analysis to derive the linear structure matrix of the smart grid using the intercepted data. In the second stage, we construct the sparse attack vector via a linear-based relaxation approach, which is used as the false data. Results indicate that we can realize highly successful attacking performance with a low detection probability.

Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles (EVs) and grid, which provides powerful demand response, balancing the electricity demand and supply in smart grid. Mobility is the key feature of EVs, which is also a significant challenge for V2G systems. In order to model the EV mobility in V2G systems, we propose a complex networking modeling for V2G mobile energy network. Each district has a V2G system. EVs travel among different districts. The EV fleets transport energy and impact the V2G systems of districts. The theory of complex network synchronization is employed to analyze the dynamics of the mobile energy network. Numerical results show the energy transportation of EV fleets may achieve synchronous stability of demand level of different districts, balancing the demand response in the mobile energy network.

Abstract In cognitive vehicular networks (CVNs), spectrum sensing and access are introduced as the promising technologies to fully exploit the underutilized licensed spectrum. Because the sensing ability of a single secondary vehicular user (SVU) is affected by high mobility, dynamic topology, and unreliable wireless environment, collaborative sensing is developed to increase the sensing accuracy and efficiency. Generally, the synchronization is required in the collaborative sensing in CVN. However, it is difficult to keep all SVUs synchronized with others for sensing under the high dynamic network topology, and the sensing overhead of the synchronous cooperative action may be significant. In this paper, we first propose an asynchronous cooperative sensing scheme in which each SVU provides an energy information (EI) that is tagged with location and time information. The sensing decision will be made on account of the EI. Considering the temporal and spatial diversities of each SVU, we assign different weights to each EI and formulate the probabilities of detection and false alarm as the optimization problems to find the optimal weight of each EI. Then, based on the asynchronous sensing, the specifications of the opportunistic spectrum access mechanism are elaborated in both centralized and decentralized CVNs for the sake of practical implementation. We analyze the system performance in terms of achievable throughput and transmission delay. Numerical results show that the proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Energy is the impediment to various applications of battery-powered wireless sensor networks (WSNs). Beyond the battery constraint of sensors/aggregation and forwarding nodes (AFNs), the major energy consumption of WSNs is from the longdistance multi-hop transmissions from the sensors/AFNs to the sink. To address these issues, in this paper, we employ a wireless charging vehicle (WCV) to travel inside WSNs to replenish the energy of sensors/AFNs, and cut long-distance transmissions into short-distance ones. Different from prior works, we let the WCV not only recharge the AFNs selectively, but also collect data from chosen AFNs and bring collected data back to the sink. The chosen AFNs play as virtual sinks, and nearby AFNs can use short-distance transmissions to deliver their traffic to the chosen AFNs. We formulate this problem into an energy replenishment optimization with joint consideration of sensed data delivery, flow routing, wireless power transfer, etc. Since the formulated problem is mixed integer nonlinear programming which is NP-hard to solve, we also develop a heuristic algorithm for feasible solutions. Through simulations, we show that the solution of the proposed algorithm is close to the optimal one and the energy replenishment is optimized while data delivery guaranteed.

Providing adequate and reliable spectrum resources for unlicensed users in spectrum database-based cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities and radio environment. In this paper, we propose an adaptive spectrum access method based on spectrum database for cognitive radio (CR) networks. While making decision to access the licensed spectrum, the secondary users (SUs) not only use the spectrum information informed by the spectrum database but also use the local sensing to confirm the specific condition of the spectrum. The adaptive sensing and access process is modeled as an optimal decision process by maximizing the achievable throughput of CR networks. The dynamic programming algorithm is developed to find the optimal sensing and access policy for each SU. Simulation results show that the proposed sensing and access policies can provide reliability guarantees for finding spectrum opportunities in terms of dynamic radio environment.

In this paper, we study the routing problem in multi-hop cognitive radio networks (CRNs). We observe that Primary Users (PUs) in CRNs exhibit unevenly geographic distribution due to their social behaviors. In the area with densely-distributed PUs, the low spectrum availability will severely decrease the Secondary Users (SUs) communication reliability. We are motivated to exploit Primary User (PU) social features to set up a multi-hop routing path providing reliable end-to-end communications. There are two main challenges: 1) how to quantitatively represent and characterize PU social features in CRNs; 2) given the knowledge of the PU social features, how to design an efficient route to improve the packet-forwarding reliability. We first propose the concept of PU community in CRNs. Maximum likelihood estimation method is then employed to predict the number of potential PUs. The estimated results effectively reflect the spectrum utilization, and hence, provide important information for PU community formulation. After that, considering the existence of PU community, we propose a hybrid routing scheme to alleviate the impact of PU community on packet-forwarding reliability. Simulation results demonstrate that the proposed scheme significantly improves the reliability of end-to-end communications.

This paper studies the discriminatory channel estimation (DCE) performance between a legitimate receiver (LR) and an unauthorized receiver (UR) in the multiple-input multiple-output (MIMO) wireless systems. DCE is a recently developed concept that intentionally degrades the channel estimation at the UR so as to minimize the probability of confidential information being eavesdropped by the UR. Usually, the existing DCE scheme is based on the linear minimum mean square error (LMMSE) method with two-way training. In this paper, we propose a new two-way training for DCE based on semiblind approach, e.g., the whitening-rotation (WR)-based channel estimator. To characterize the DCE performance, we derive the closed-form of the normalized mean squared error (NMSE) to the channel estimation at both the LR and the UR. Simulation results show that the proposed two-way training achieves higher performance compared to the two-way training designs in the literature.

Plug-in hybrid electric vehicles (PHEVs) are receiving growing attention to achieve a sustainable transport system and society. Due to the limited vehicle battery capacity, PHEVs perform charging and re-charging from time to time. It is visioned that the charging load with high PHEVs penetration will pose a considerable impact on the residential distribution network. Therefore, implementation of coordinated PHEVs charging becomes necessary for smart grid. For maintaining the household load, the limited energy supply may not fulfill all PHEVs charging load at any time. Thus, the fairness of energy scheduling among PHEVs should be considered. In this paper, charging fairness (CF) and discouraging-charging fairness (DCF) are proposed to guarantee the charging opportunity of each PHEV and fast recovery of PHEV driving ability. We formulate the problem of the fair energy scheduling in residential distribution network as a Semi Markov Decision Process (SMDP). The technique Neuro-Dynamic Programming (NDP) is exploited to solve the corresponding problem in SMDP. In the scheduling process, Entropy Weight Method (EWM) is proposed to consider three key metrics: CF, DCF and cable power loss. Simulation results illustrate that the proposed scheduling scheme is able to avoid a number of peak load caused by PHEVs charging and at the same time reduce power loss without affecting traveling plan.

Recently, plug-in hybrid electric vehicles (PHEVs) have attracted considerable attention as a sustainable transport system and also an essential component of the smart grid. With the rapid growth of PHEVs penetration, the charging and discharging of PHEVs will pose a significant impact on the residential electricity distribution network. For this reason, the management of PHEV charging and discharging has become one of the key issues in the research of PHEVs. In most existing work, PHEVs are supposed to operate individually for charging and discharging in the grid. However, we argue that, by leveraging the cooperation among PHEVs, the grid will efficiently stimulate PHEV users to charge in load valley and discharge in load peak. As a consequence, the electricity load is well balanced. Meanwhile, the PHEV users also achieve higher profit. The PHEV charging and discharging cooperation is a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of coalition game. The simulation results indicate that the peak-valley difference in electricity load of the grid is significantly reduced. Besides, the PHEV users have better satisfaction in the vehicle battery status and the economic profit.

Discriminatory channel estimation (DCE) is a recently developed strategy to enlarge the performance difference between a legitimate receiver (LR) and an unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless system. Specifically, it makes use of properly designed training signals to degrade channel estimation at the UR, which in turn limits the UR's eavesdropping capability during data transmission. In this paper, we propose a new two-way training scheme for DCE through exploiting a whitening-rotation (WR) based semiblind method. To characterize the performance of DCE, a closed-form expression of the normalized mean squared error (NMSE) of the channel estimation is derived for both the LR and the UR. Furthermore, the developed analytical results on NMSE are utilized to perform optimal power allocation between the training signal and artificial noise (AN). The advantages of our proposed DCE scheme are twofold: Compared with the existing DCE scheme based on the linear minimum mean square error (LMMSE) channel estimator, the proposed scheme adopts a semiblind approach and achieves better DCE performance; and the proposed scheme is robust against active eavesdropping with the pilot contamination attack, whereas the existing scheme fails under such an attack.

Previous cooperative sensing schemes require the cooperative Secondary Users (SUs) to behave in a synchronous way. This requires each SU to start cooperations at the same time by stopping their own transmissions. In multi-hop cognitive radio networks, it is very difficult to keep all SUs synchronized with others for sensing. In this paper, we propose an asynchronous cooperative sensing scheme for multi-hop cognitive radio networks in which each SU only provides its energy information in stead of ceasing its own transmission to perform the cooperative sensing. Each energy information is assigned an appropriate weight by considering the temporal and spatial diversities of each SU. We formulate the probabilities of detection and false alarm as optimization problems to find the optimal weight for every energy information. The achievable throughput has been derived. Numerical results show that the proposed scheme is able to achieve substantially higher throughput compared with the existing schemes.

Cognitive Radio (CR) is a promising technology to efficiently utilize the limited spectrum resources for the rapidly increasing demands on wireless applications and services. Security is very challenging in a CR network due to its unique characteristics, e.g. systems co-existence and interference guarantee. In this paper, we will study the performance of CR networks under Primary User Emulation Attacks (PUEA) that is a typical security issue. A multi-dimensional Markov model is built and the system performance is evaluated under generic system models, including the common control channel (CCC) adoption and a joint PUEA detection and call admission control scheme. We introduce a new performance metric CCC recovery time and redefine outage probability to identify and evaluate the important impact of CCC on the CR network. In addition, we calculate blocking rate, dropping rate, and outage probability of the CR networks. Numerical results show that the system performance may deteriorate severely when attacked. The joint PUEA detection mechanism and guard channel strategy are able to significantly alleviate the performance degradation.

In cognitive radio networks, spectrum sensing and access scheme affects the system performance. In this paper, a new wideband mixed access scheme is proposed, in which the Secondary Users (SUs) sense the channels via wideband spectrum sensing, and access them with a mixed access strategy. In order to maximize the ergodic throughput of SUs, we find optimal sensing time and transmission power of each channel, while protecting the Primary Users (PUs) from interference. It is shown that the optimization problem can be formulated as a convex problem. Moreover, we present a QoS-aware low complexity scheme, in which the SUs select several specific channels to sense. An effective sensing channels selection criterion is proposed. Numerical results show that the proposed schemes can effectively improve the system performance.

In the smart grid, an efficient and reliable communication infrastructure is crucial for improving system efficiency and stability. In this paper, we leverage cognitive radio technology to construct the smart grid communication infrastructure. The proposed network architecture consists of three sub-areas, cognitive Home Area Networks (HAN), cognitive Neighborhood Area Networks (NAN) and cognitive Wide Area Networks (WAN). We focus on the spectrum resource management in cognitive NANs for efficient smart grid services. A new spectrum access paradigm called hybrid spectrum access is proposed, in which both licensed and unlicensed spectrum bands are intelligently scheduled for the transmission of smart grid services. The admission control problem under hybrid spectrum access is deliberately investigated. The optimal number of leased and reserved channels are derived through two-dimension Markov chain analysis. Numeric results indicate that the hybrid spectrum access strategy significantly improves the network capacity in supporting the smart grid services, compared to the traditional fixed spectrum access strategy.

In this paper, we study the reliable packet forwarding in Wireless Sensor Networks (WSNs) with the multiple-input multiple-output (MIMO) and orthogonal space time block codes (OSTBC) techniques. The objective is to propose a cross-layer optimized forwarding scheme to maximize the Successful Transmission Rate (STR) while satisfying the given end-to-end power consumption constraint. The channel coding, power allocation, and route planning are jointly considered to significantly improve the transmission quality in terms of STR. The joint optimization design is formulated as a global deterministic optimization and also a local stochastic optimization issues. It is found that the stochastic optimization approach can effectively model, analyze, and solve the routing problem. In order to substantially reduce the implementation complication of the global optimization, we propose a low-complexity distributed scheme. The determination of relaying nodes and power budgets are decoupled, i.e. performing route planning and power allocation separately. We have shown that the result in the distributed scheme is able to provide sufficiently accurate predication of the global optimization. In addition, the proposed scheme can clearly reduce the Symbol Error Rate (SER) and achieve higher STR compared with two existing energy-efficient routing protocols, in which no joint design is considered.

Abstract Wireless sensor network (WSN) is an important instrument to realize wireless monitoring and control in various application fields. Energy conservation are crucial for WSNs to prolong the network lifetime. In this paper, we study sleeping management schemes which can efficiently control the network topology and significantly reduce the energy consumption by selectively turning off the radios of abundant sensor nodes. Based on the observation that a Matérn Hard‐core Process (MHP) could thin out evenly distributed nodes from a dense graph, we propose Backbone Energy Efficient Sleeping (BEES) management scheme, whose central idea is to generate and maintain the backbone by simulating MHPs. There are three attractive features of BEES: (i) the backbone size could be conveniently scaled according to the practical requirements; (ii) the backbone is energy efficient in the point of view of packet forwarding (or routing); (iii) the construction of backbone is robust to possible ranging errors, which ensures the feasibility and reliability in practice. Theoretical analysis and simulation experiments are carried out to demonstrate the correctness and effectiveness of BEES. Numerical results indicate that, compared with two existing sleeping management schemes, BEES provides about 30% wider range of scalability, consumes about 13% less routing energy, and achieves 11% ∼ 20% longer network lifetime under different traffic loads. Copyright © 2009 John Wiley & Sons, Ltd.

Sensor nodes in Cognitive Sensor Networks (CSNs) can work on different frequency bands (or channels) according to dynamically available wireless resources. This paper proposes a spectrum-aware routing scheme for CSNs, which jointly considers traffic balance, route configuration and power control for reliable end-to-end communications. Bayesian learning method is used to estimate the number of neighboring Primary Users (PUs) and Secondary Users (SUs). The estimated results effectively reflect the spectrum utilization and provide important information for route configuration. Multiple Attribute Decision Making (MADM) method is employed to combine the routing objectives of reliability, energy efficiency and path delay into a single target function. Randomized route selection strategy is adopted for traffic balance. The simulation results demonstrate that the proposed spectrum-aware routing scheme significantly improves the communication reliability, and simultaneously has satisfying performances in energy efficiency and end-to-end delay.

In this paper, we review some results on the multiplexing gain of of the sum rate of the Gaussian multi-antenna compound broadcast channel in the high SNR regime. The transmitter transmits to each user one private message. The channel realization for each user is arbitrarily chosen from a finite set known to the transmitter. To achieve the multiplexing gain region of the channel, we discuss the methods of interference alignment and subspace coding. We interpret the interference alignment scheme for compound MIMO broadcast channel as a special case of subspace coding. We also point out that the subspace coding method could be applied in wireless multihop network to improve the efficiency of intra-cluster broadcasting.

In Cognitive Radio (CR) networks, spectrum sensing is a crucial technique to discover spectrum opportunities for the Secondary Users (SUs). The Quality-of-Service (QoS) of spectrum sensing is characterized by both sensing accuracy and sensing efficiency. Here, sensing accuracy is represented by the false alarm probability and the detection probability while sensing efficiency is represented by the metrics sensing overhead and throughput. The literature has mainly focused on improving sensing accuracy while sensing efficiency has been largely ignored. In this paper, we propose a group-based cooperative Medium Access Control (MAC) protocol, which concentrates on improving sensing efficiency without degrading spectrum sensing accuracy. The MAC protocol is specified and implemented in three phases: reservation, sensing and transmission. The protocol incorporates a group-based cooperative spectrum sensing scheme. In particular, the cooperative SUs are grouped into several teams. During a sensing period, each team senses a different channel. As a consequence, multiple distinct channels can be simultaneously detected within one sensing period. Then, we formulate throughput maximization problems in both time-invariant and time-varying channel scenarios to determine the key design parameters. In addition, an SU-selecting algorithm is presented to selectively choose the cooperative SUs based on the channel dynamics and usage patterns in order to substantially reduce sensing overhead. Numerical results indicate that the proposed strategy is able to significantly decrease sensing overhead and increase throughput with guaranteed sensing accuracy.

Abstract Multiple‐input multiple‐output (MIMO) enabled wireless sensor networks (WSNs) are becoming increasingly important since significant performance enhancement can be realized. In this paper, we propose a packet forward strategy for MIMO sensor networks by jointly considering channel coding, rate adaptation, and power allocation. Each sensor node has multiple antennas and uses orthogonal space time block codes (OSTBC) to exploit both spatial and temporal diversities. The objective is to determine the optimal routing path that achieves the minimum symbol error rate (SER) subject to the source‐to‐destination (S‐D) energy consumption constraint. This SER‐based quality‐of‐service (QoS) aware packet forwarding problem is formulated into the framework of dynamic programming (DP). We then propose a low‐complexity and near‐optimal approach to considerably reduce the computation complexity, which includes state space partition and state aggregation techniques. Simulations indicate that the proposed protocol significantly outperforms traditional algorithms. Further still, the performance gain increases with tighter S‐D energy constraint. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we study the joint optimization problem on channel coding, power allocation, and route planning in wireless sensor networks (WSN) using dynamic programming (DP). Each sensor node has multiple antennas and applies orthogonal space time block codes (OSTBC) in order to improve the transmission reliability. A decode-and-forward protocol is adopted to relay the signals. The objective function is to determine the packet forwarding route that has the maximum successful transmission rate (STR) subject to the source-to-destination (S-D) energy consumption constraint. Specifically, we cast this energy and quality-of-service (QoS) aware packet forwarding problem into the framework of DP, such that adaptive power allocation can be jointly realized at each sensor node. State space partition techniques and state aggregation approximation architecture are introduced to derive the value function. Simulation results show that the proposed protocols significantly outperform classical routing algorithms, especially when the energy constraint becomes stringent.

In Cognitive Radio (CR) networks, spectrum handoff is an important functionality to guarantee the reliability and continuity of the communications of Secondary Users (SUs). In this paper, we integrate soft-QoS based spectrum handoff mechanism into the framework of Call-Admission-Control (CAC) for CR networks. By doing so, the proposed CAC strategy has in-built capability to deal with resilient services of wireless applications, and hence obviously improves the QoS and spectrum utilization of CR networks. We adopt Markov chain model to describe the procedures of the proposed CAC and spectrum handoff strategy. After that, the problem is formulated as a nonlinear optimization where the dropping probability is minimized under the constraint of blocking probability. The method of branch-and-bound is employed to make the optimization problem tractable. The proposed CAC strategy is evaluated through simulation experiments. The numerical results indicate that the proposed CAC strategy outperforms two traditional CAC strategies. It achieves a much better tradeoff in dropping and blocking probabilities, and meanwhile significantly enhances the spectrum utilization of CR networks.

We consider energy efficient packet forwarding with quality-of-service (QoS) guarantee for wireless sensor networks (WSNs). In most existing wireless network protocols, route planning and transmission rate setting are considered independently, however, we observe that energy consumption can be significantly reduced by combining these two aspects together when making forwarding decision. This is because this combination potentially increases one more degree of freedom (i.e., the temporal dimension) for the original routing problem. We are motivated to exploit this additional degree of freedom and devise forwarding protocol that conserves more energy than traditional algorithms. In particular, we cast the packet forwarding problem into the framework of dynamic programming. State space partition technique and state aggregation approximating architecture are introduced to produce an approximating optimal value function. Simulation experiments are carried out to evaluate the performance of the proposed forwarding protocol. The results indicate that our protocol outperforms classical routing algorithms, especially when the QoS constraint becomes stringent.