The emergence of deploying Deep neural network (DNN) services on edge servers has spurred research into efficiently provisioning inference services. However, previous studies have neglected to consider the implications of different types of DNN and varying quality of service (QoS) requirements on QoS violation rates. In this article, we propose a novel framework, named Coinf, for scheduling heterogeneous DNN inference tasks on edge servers. Coinf has the following four advantages to effectively handle attribute analysis, performance balancing, parallel execution, and model accuracy: (1) It enables efficient profiling of domain-specific attributes of various DNN tasks during the offline stage, achieved by constructing a regression model to predict the end-to-end latency of each task. (2) By utilizing the predicted execution time, Coinf achieves a commendable balance among inference latency, system throughput, and QoS violation rate. (3) It employs emerging deep reinforcement learning (DRL) to aggregate individual DNN tasks into batches, enabling concurrent parallel execution. (4) Coinf preserves the accuracies of the provided DNN models by not modifying them. Numerical experiments are constructed to validate the reliability and efficiency of Coinf in handling heterogeneous inference tasks.

Federated Learning (FL) enables collaborative model training while preserving data privacy, but the presence of noisy labels in local datasets remains a significant challenge, particularly under heterogeneous noise conditions and class imbalance. In this work, we introduce a novel Multi-Stage Robust Federated Learning (MRFL) framework to address these issues. In the warm-up noise detection stage, MRFL computes per-class average losses on each client and employs a Gaussian mixture model to accurately identify clients with substantial label noise. In the subsequent noise-robust training stage, a robust loss function and noise solver are designed to distinguish clean from noisy samples, while semi-supervised learning is used to recover valuable information from tail classes. Moreover, a robust weighted aggregation strategy is adopted to mitigate the adverse effects of noisy clients. Extensive experiments on CIFAR-10/100-LT and ICH datasets demonstrate that MRFL outperforms state-of-the-art methods in federated noisy label learning scenarios characterized by data heterogeneity and imbalance.

Recent years have seen extensive demonstration of the validity and reliability of emotional information contained within electroencephalography (EEG) data. Nonetheless, challenges persist in the realm of cross-subject emotion recognition utilizing EEG data. Most existing research methods focus on intra-subject emotion recognition, while their application effectiveness in cross-subject emotion recognition is relatively inferior. Therefore, we propose a network based on a bidirectional selective state space model (SSM) with multi-scale convolution and additive gated attention. Specifically, the model initially captures global emotion-related information from differential entropy data using a bidirectional selective SSM, while simultaneously extracting local information at various scales through the multi-scale convolutional module. Subsequently, the model extracts deep emotion-related information from the data through the additive gated attention and ultimately inputs the processed data into a multilayer perceptron (MLP) to obtain emotion classification results. Experimental results validate the model's efficacy in cross-subject emotion recognition tasks, achieving accuracy rates of 86.22% and 74.98% on the SEED and SEED-IV datasets, respectively. By leveraging the attention mechanism, the study explored the differential contributions of various cortical areas to emotional processing, providing insights into the neural mechanisms underlying emotional responses.

Recent years have seen extensive demonstration of the validity and reliability of emotional information contained within electroencephalography (EEG) data. Nonetheless, challenges persist in the realm of cross-subject emotion recognition utilizing EEG data. Most existing research methods focus on intra-subject emotion recognition, while their application effectiveness in cross-subject emotion recognition is relatively inferior. Therefore, we propose a network based on a bidirectional selective state space model (SSM) with multi-scale convolution and additive gated attention. Specifically, the model initially captures global emotion-related information from differential entropy data using a bidirectional selective SSM, while simultaneously extracting local information at various scales through the multi-scale convolutional module. Subsequently, the model extracts deep emotion-related information from the data through the additive gated attention and ultimately inputs the processed data into a multilayer perceptron (MLP) to obtain emotion classification results. Experimental results validate the model’s efficacy in cross-subject emotion recognition tasks, achieving accuracy rates of 86.22% and 74.98% on the SEED and SEED-IV datasets, respectively. By leveraging the attention mechanism, the study explored the differential contributions of various cortical areas to emotional processing, providing insights into the neural mechanisms underlying emotional responses.

In vehicular networks enhanced by uncrewed aerial vehicles (UAVs), vehicle state information is efficiently collected, and traffic safety is assured. UAVs, serving as aerial base stations, enable vehicle network access and provide edge computing services in the absence of roadside units (RSUs). This study explores a multi-UAV-assisted vehicular network, where multiple UAVs collaboratively offer services to vehicles. The goal is to minimize task completion time by optimizing trajectory planning, spectrum resource allocation, and dynamic data offloading. An enhanced multiagent deep deterministic policy gradient (MADDPG) algorithm is introduced to address the optimization challenge in cooperative multi-UAV scenarios. Within this framework, each UAV, acting as an agent, devises strategies for movement, data offloading, and resource allocation based on the current states of vehicles and fellow UAVs. The simulation results reveal that the proposed algorithm improves task completion efficiency and ensures vehicle Quality of Service (QoS) over existing benchmarks.

The rapid development of generative audio models has raised concerns about copyright protection and traceability. To tackle these challenges, we first propose a robust and imperceptible watermarking framework embedded directly into the generative process. Our method embeds watermarks into the convolutional layers of the generative model, allowing the synthesis of watermarked audio without compromising quality. A trainable binary mask selectively modulates kernel weights, ensuring precise and efficient embedding. The framework incorporates a dedicated encoder-decoder architecture for accurate watermark embedding and extraction. A normalization step aligns the modified kernel weights with the original statistical properties to preserve the model's performance. Experimental results across multiple datasets demonstrate the method's robustness against common audio attacks. Additionally, the approach achieves high audio fidelity and near-perfect watermark recovery, offering a practical solution for traceable and secure audio synthesis.

Integrating data security with resource management enhances security, efficiency, and reliability of blockchain-enabled mobile edge computing (MEC) systems. However, challenges such as secure data storage, timely task execution, and limited parallelism introduce complexities in task offloading decisions and resource allocation strategies. To address these challenges, the task latency minimization problem in blockchain-enabled MEC networks is formulated as an NP-hard optimization problem. The model incorporates constraints on parallelism, partial task offloading, bandwidth and computation resource allocation among mobile users (MUs) and edge servers (ESs). To enhance the reliability and transparency of data storage, a dual-blockchain framework is proposed, consisting of multiple MU blockchains and a dedicated ES blockchain. To tackle the NP-hard problem, the original optimization problem is decomposed into multiple sub-problems, facilitating parameter decoupling. An alternating optimization algorithm is employed to refine task offloading decisions and resource allocation of MUs and ESs with limited parallelism. The ESs update their strategies iteratively based on feedback mechanisms. Additionally, a task prioritization formulation is developed to enhance scalability, considering sub-level task importance, urgency, and first-level task classification. Extensive simulation experiments demonstrate that the proposed algorithm achieves lower task latency compared to existing methods across varying network sizes, offloading schemes, and parallelism constraints. By optimizing the parallel processing of tasks, the waiting latency of this algorithm is reduced on average by 35. 35%, 57. 16% and 35. 35% compared to other methods, respectively.

For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because the content popularity is usually unknown in advance and may vary over time. In this paper, we propose an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks. We aim to design an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP). The formulated optimization problem is non-convex and NP-hard. To tackle this intractable problem, we propose a DT-assisted Online Caching Algorithm (DT-OCA). In specific, we first decompose our formulated problem into a series of subproblems, each handling a cache period. For each cache period, we use a DT-based prediction method to effectively capture future content popularity, and develop an online caching strategy. Competitive ratio analysis and extensive experimental results demonstrate that our algorithm has promising performance, and outperforms other benchmark algorithms. Insightful observations are also found and discussed.

Edge service caching can significantly mitigate latency and reduce communication and computing overhead by downloading and caching service (application) data from clouds. The freshness of cached service data is critical when providing satisfactory services to users, but has been overlooked in existing research efforts. In this paper, we study the online low-latency and fresh service provisioning in mobile edge computing (MEC) networks. Specifically, we jointly optimize the service caching, task offloading, and resource allocation. To solve the formulated joint online long-term optimization problem, we design a Lyapunov-based online framework that decouples the problem at temporal level into a series of per-time-slot subproblems. For each subproblem, we propose an online integrated optimization-deep reinforcement learning (OIODRL) method, which consists of an optimization stage and a learning stage. In the optimization stage of OIODRL, a quadratically constrained quadratic program (QCQP) transformation and a semidefinite relaxation (SDR) method are utilized; in the learning stage of OIODRL, a deep reinforcement learning (DRL) algorithm is applied. Extensive simulations show that the proposed OIODRL method achieves a near-optimal solution and outperforms benchmark methods.

Personalized decentralized federated learning (PDFL) has emerged recently to address the problem of single point of failure and data heterogeneity in traditional centralized federated learning. However, existing works on PDFL still have two challenges that urgently need to be solved. First, model updates exposed by point-to-point communication during collaborative training in PDFL may disclose sensitive information about clients. Second, the distributed structure makes PDFL vulnerable to Byzantine attacks, which can disrupt the network by introducing poisoned data or faulty behaviors. In this article, we propose a privacy-enhanced and Byzantine-resilient approach to effectively address the dual challenges of privacy and security in PDFL. In particular, each client is required to build a unique critical parameter index set by evaluating the importance of its model parameters and broadcasting it to neighbors. To improve Byzantine resilience, we propose a novel weight allocation scheme based on the critical parameter index set for clients to alleviate the negative impact of Byzantine neighbors in the model aggregation. To enhance privacy protection while boosting personalization, we combine with model decoupling and execute a clipping-robust personalized local training for each client to achieve user-level differential privacy. We finally conduct exhaustive experiments on FEMNIST, SVHN, and CIFAR10 datasets and various settings. Experimental results demonstrate that compared to five state-of-the-art baselines, our proposed method achieves excellent performance with user-level differential privacy guarantee in PDFL and implements additionally superior Byzantine robustness in adversarial settings.

Vehicular edge computing (VEC) has emerged as a promising paradigm for efficient processing of computation-intensive and delay-sensitive tasks by coordinating service placement and task offloading. Existing research mainly focused on edge-edge and edge-cloud collaborations to enhance system performance and resource utilization. However, the potential of vehicle-vehicle collaboration remains under-explored. To bridge this gap, we propose a novel three-layer VEC architecture integrating vertical collaboration across different layers with horizontal collaboration within the same layer (vehicle-edge-cloud collaboration). Recognizing the dynamic nature of the Internet of Vehicles, we introduce link duration constraints to quantify the impact of vehicles’ mobility on wireless communications. We formulate a mixed-integer nonlinear programming problem for joint service placement, task offloading, and computing resource allocation to minimize the total task completion delay of vehicles. To solve it, a two-stage heuristic algorithm is designed, including a semidefinite relaxation-based approximation method for the task offloading and computing resource allocation problem without storage capacity constraints and a heuristic approach for the service placement problem. Extensive simulations conducted on synthetic and realistic road topologies demonstrate that the proposed algorithm can obtain feasible solutions and achieve significantly lower delay than five benchmark methods.

With the development of wireless communication technologies, mobile users can locate themselves and thereby query the untrusted server for Location Based Services (LBS). However, the private information implied by these locations is disclosed to the untrusted server. Limited by computation, communication and storage resources of mobile devices, existing works focused on protecting fine-grained information, namely the snapshot location, or the coarse-grained statistics, i.e., the location histogram, using differential privacy. Nevertheless, preserving the snapshot location cannot prevent the privacy disclosure based on the location histogram, and vice versa. To this end, we propose road network-aware and differentially private framework that can protect both the snapshot location and the location histogram simultaneously. Specifically, we first design Road Network-based Obfuscated Locations Sampling algorithm to sample road networks into discrete locations. Then, we propose Semantic-based Histogram Privacy Protection to elaborately choose discrete locations that satisfy the location histogram differential privacy. Thereafter, we design Road Network-based Differential Privacy Mechanism to perturb these selected discrete locations to protect the user’s snapshot location. Then, we theoretically prove that the proposed framework provides snapshot location ϵ-differential privacy and location histogramc-differential privacy. Finally, the extensive results on four real-world datasets validate the superiority of our work. Specifically, the adversary’s Estimation Error in our work is reduced by 10-12 times compared to the latest work focusing on location histograms, while the adversary’s User Recognition Rate is decreased by 2-5 times compared to the latest work focusing on snapshot locations. Furthermore, our work has excellent performance in terms of Implausible Location Rate, Precision, and Recall.

In the vehicular edge networks (VEN), task migration is complicated by issues like vehicle movement, diverse resource allocation, and integrating sensing with communication technologies. This paper presents a task migration strategy to optimize task flow under limited resources in PMN-assisted VEN. Vehicles can send public and private tasks to roadside units (RSUs), constrained by bandwidth, computational power, and storage space. Public tasks aim at data collection for road transportation management, while private tasks cover a spectrum of services from work to entertainment. To address the limitations imposed by resource scarcity and meet the demands of task migration, we have developed a dual-layer graph neural network (GNN) that leverages vehicle mobility patterns. In particular, the first layer of GNN acquires vehicle information and the latest surrounding information, and sends it to the nearby RSU. Considering the variety of tasks and multi-dimensional resource constraints, the second GNN layer forecasts RSU resource availability and vehicular trajectories. Subsequently, a task-based maximum flow algorithm (T-MFA) is proposed to refine task migration paths and resource allocation strategies to maximize task flow. Simulation experiments validate the efficacy of the proposed algorithm, demonstrating its capability to achieve optimal task migration by accommodating differences in tasks, resources, and capacities.

Conventional audio steganography methods typically require embedding secret information into the carrier, making them vulnerable to steganalysis. To address this issue, we propose a novel coverless audio steganography method that hides information by generating carriers and establishing mapping rules rather than embedding data directly. Our approach leverages a differential privacy clustering algorithm to cluster audio data and select representative audio files, thereby enhancing the security of the steganography. Additionally, we introduce an improved audio feature extraction method that combines traditional Mel-frequency cepstral coefficients (MFCC) with global statistical information, significantly boosting the robustness of the secret information against common audio attacks, particularly time-stretching attacks. Experimental results show that our method achieves a robustness rate of up to 95% against time-stretching and maintains an average security accuracy rate exceeding 97% across various attack scenarios. The proposed method ensures that the audio carrier remains unaltered, thus effectively resisting detection by steganalysis tools. This innovative approach provides a practical and efficient solution for the secure transmission of information in the digital era.

Decentralized federated learning emerged to eliminate the reliance on the central server and address the single point of failure and the network bottleneck in centralized federated learning. However, existing works on decentralized federated learning suffer from the following three challenges. First, the transmission of model parameters between devices results in significant bandwidth consumption and network congestion. Second, the decentralized architecture involves numerous devices, which increases the risk of poisoned behavior. Third, the data heterogeneity of devices seriously affects the model accuracy. Unfortunately, there is a lack of research that can effectively address all the challenges above. In this article, we propose a novel scheme of D ecentralized federated learning toward C ommunication E fficiency, R obustness, and P ersonalization (i.e., D-CERP). We aim at customizing personalized models for each client with lower communication and computation overhead, which can also defend against Byzantine attacks in the decentralized scenario. Specifically, we employ local sparse training with a personalized mask to better fit the heterogeneous data for each client and reduce both on-device computation overhead and cross-device communication overhead. Besides, we apply a trusted neighbor selection scheme based on multi-armed bandit by assigning rewards to high-quality submissions of each communication round, thereby improving the Byzantine robustness. In our experiments, we utilize two data partitioning methods to simulate the heterogeneity of clients in the decentralized setting and conduct exhaustive experiments on CIFAR10, CIFAR100, and Tiny-ImageNet. Experimental results demonstrate that compared to several state-of-the-art baselines, D-CERP achieves comparable personalization with a lower overhead in non-adversarial settings and provides additionally superior Byzantine robustness in adversarial settings.

The energy harvesting D2D-enabled cellular network (EH-DCN) has emerged as a promising approach to address the issues of energy supply and spectrum utilization. Most of existing works mainly focus on the throughput, while the information freshness, which is critical to the time-sensitive applications, has been rarely explored. Considering above facts, we aim to develop an optimal mode selection and resource allocation (MSRA) policy that maximizes the long-term overall throughput of a time-varying dynamic EH-DCN, subject to an age of information (AoI) constraint. As the MSRA policy involves both continuous variables (i.e., bandwidth, power, and time allocations) and discrete variables (i.e., mode selection and channel allocation), the optimization problem is proved to be nonconvex and NP-hard. To solve the nonconvex NP-hard problem, we exploit a deep reinforcement learning (DRL) approach, called MSRA twin delayed deep deterministic policy gradient (MSRA-TD3). The MSRA-TD3 employs a double critic network structure to better fit the reward function, and could effectively mitigate the overestimation of Q-value in deep deterministic policy gradient (DDPG), which is a classical DRL algorithm. It is worth noting that in the design of the MSRA-TD3, we use the throughput of user equipments (UEs) at the previous time slot as a state to bypass the channel state information estimation resulting from the time-varying dynamic environment, and take the weights of throughput and AoI penalty into the reward function to evaluate two performance. Simulations demonstrate that the established MSRA-TD3 algorithm achieves better performance in terms of throughput and AoI than comparison DRL algorithms.

The emergence of new applications has led to a high demand for mobile-edge computing (MEC), which is a promising paradigm with a cloud-like architecture deployed at the network edge to provide computation and storage services to mobile users (MUs). Since MEC servers have limited resources compared to the remote cloud, it is crucial to optimize resource allocation in MEC systems and balance the load among cooperating MEC servers. Caching application data for different types of computing services (CSs) at MEC servers can also be highly beneficial. In this article, we investigate the problem of hierarchical joint caching and resource allocation in a cooperative MEC system, which is formulated as an infinite-horizon cost minimization Markov decision process (MDP). To deal with the large state and action spaces, we decompose the problem into two coupled subproblems and develop a hierarchical reinforcement learning (HRL)-based solution. The lower layer uses the deep$Q$network (DQN) to obtain service caching and workload offloading decisions, while the upper layer leverages DQN to obtain load balancing decisions among cooperative MEC servers. The feasibility and effectiveness of our proposed schemes are validated by our evaluation results.

In Mobile Edge Computing (MEC), the collaboration between end devices and servers guarantees the low-latency and high-accuracy video stream analysis. However, such paradigm of video stream offloading poses a serious threat to the location privacy and the usage pattern privacy of end devices. The existing works offer strict privacy guarantee for users, but they do not take the features of video stream into consideration, thus leading to the relatively higher computation cost. To tackle this issue, we propose a personalized and differential privacy-aware video stream offloading scheme that supports users personalized and time-varying privacy requirements, provides corresponding differential privacy preservation, and generates minimal latency and energy cost. Specifically, we formulate an NP-hard optimization that jointly optimizes the video frame rate, frame resolution and offloading ratio to maximize the analysis accuracy of video stream and minimize the energy cost and the latency subject to the channel bandwidth, computing resources, and personalized and time-varying privacy requirements. Then, we design a online learning-based and personalized privacy-aware video stream offloading algorithm for the optimization problem and thereby obtain the optimal video stream offloading scheme. Last, the extensive experimental results validate the superior performance of the proposed scheme, compared to the three latest existing works.

Digital communities are dynamically developed with users admitted in as digital identities, and process their affairs via egalitarian decision processes, namely one person one vote. However, the digital democracy in these digital communities is threatened by Byzantines therein. Most existing works focused on Byzantine detection, but we are interested in growing Byzantine-resilient community rather than whitelisting. Several works concerning developing a Byzantine-resilient digital community are vulnerable to the collapse of these selected digital identities or impractical binarized trust relations among digital identities. To this end, we propose two practical schemes based on edge links and attributes that can achieve temporal dynamic and Byzantine-resilient digital communities, providing digital democracy. Specifically, we first propose the mixed sampling of links and attributes in digital community to output node-edge sequences. Then, we further design the skip gram-based quantification of trust relationships using the node-edge sequences. Thereafter, based on the quantified trust relationships, we propose vertex-based and edge-based strategies that prove the constraints when dynamically developing a Byzantine-resilient digital community. The key advantage is that our work can be applied to any graph containing both digital identity nodes and attribute nodes, rather than the graphs with one kind node and the fully connected graphs. Last, we conduct experiments on four real-world datasets, and the extensive results indicate the superior performance of our work, compared to four existing works. This work can be applied to social networks, online shopping platforms, etc., and keep digital democracy therein.

Vehicular edge computing (VEC) enables vehicles to perform computation-intensive and delay-sensitive tasks through task offloading. Previous works either focused on task offloading based on vehicle-to-infrastructure (V2I) mode or assumed the existence of sufficient spectrum resources. However, given the exponential increase in the number of vehicles, it is essential to explore the influence of both spectrum scarcity and inter-vehicle cooperation on VEC network performance. In this paper, we investigate a joint spectrum sharing and task offloading scheme to minimize the total completion delay of tasks. Differing from previous works, our scheme incorporates task offloading based on vehicle-to-vehicle (V2V) and V2I modes while enabling V2V links to share the uplink spectrum of V2I links. To solve the formulated non-convex mixed-integer nonlinear programming problem, we propose a distributed and iterative algorithm based on the coalition formation game (CFG). Specifically, we formulate spectrum sharing and task offloading problems as many-to-one matching games with externalities and obtain the sub-channel allocation and server selection policies using the CFG approach, whose stability and convergence are analyzed. On this basis, power control and offloading ratio policies are derived using dual decomposition and quadratically constrained quadratic programming, respectively. Numerical results show that the proposed scheme reduces the total completion delay by an average of$65.31\%$,$51.22\%$, and$29.97\%$, respectively, compared to three baseline schemes under varying numbers of task vehicles.

The resource allocation problem in Multi-access Edge Computing (MEC) has been widely studied to maximize its operation efficiency under limited resource constraint. However, the existing literatures overlooked the setup cost and the associated dynamic operations. In this work, we consider server configuration and overload in the multi-server scenario where servers are switched on/off depending on the network environment. A novel pricing mechanism maximizing the utility of base station (BS) monitoring multiple servers is proposed, which jointly optimizes the setup cost and server load. We aim to maximize the BS utility under one-day task requests, and divide the time into off-peak and peak periods based on task requests. In the off-peak period, we flexibly switch on/off servers for BS to reduce setup costs. In the peak period, to avoid overloading, we introduce crowdsourcing where servers as agents purchase idle resources from private users (PUs) for mobile users (MUs) and minimize MUs’ cost by a contract-based knapsack algorithm. Lastly, a pricing mechanism is proposed to solve the BS utility maximization problem with an exploratory Upper Confidence Bound (UCB)-based algorithm adjusting server prices dynamically. Simulation results show that the proposed algorithm is superior to others in minimizing MUs cost and maximizing BS utility.

In this work, we study the problem of dispatching multiple unmanned aerial vehicles (UAVs) for data collection in internet of things (IoT), where each UAV departs from its start point, visits some IoT devices for data collection and returns to its destination point. Considering the UAV’s limited onboard energy and the time required to collect data from all IoT devices, it is essential to appropriately assign the data collection task for each UAV, such that none of the dispatched UAVs consumes excessive energy and the maximum task completion time among all UAVs is minimized. To optimize those two conflicting objectives, we focus on minimizing the maximum task completion time and the maximum energy consumption among all UAVs, by jointly designing the flight trajectory, hovering positions for data collection and flight speed of each UAV. We formulate this problem as a multi-objective optimization problem with the aim of obtaining a set of Pareto-optimal solutions in terms of time or energy dominance. Due to the NP-hardness and complexity of the formulated problem, we propose a multi-strategy multi-objective ant colony optimization algorithm (MSMOACO), which is developed based on a constrained ant colony optimization algorithm with a fitnessguided mutation strategy and an adaptive hovering strategy being delicately incorporated, to solve the problem. To accommodate the practical scenario, we also design a novel geometry-based collision avoidance strategy to reduce the possibility of collisions among UAVs. Extensive evaluations validate the effectiveness and superiority of the proposed MSMOACO, compared with previous approaches.

Federated Learning (FL) is a machine learning setting where multiple worker devices collaboratively train a model under the orchestration of a central server, while keeping the training data local. However, owing to the lack of supervision on worker devices, FL is vulnerable to Byzantine attacks where the worker devices controlled by an adversary arbitrarily generate poisoned local models and send to FL server, ultimately degrading the utility (e.g., model accuracy) of the global model. Most of existing Byzantine-robust algorithms, however, cannot well react to the threatening Byzantine attacks when the ratio of compromised worker devices (i.e., Byzantine ratio) is over 0.5 and worker devices’ local training datasets are not independent and identically distributed (non-IID). We propose a novel Byzantine-robust Fed erated Learning under Super vision (FedSuper), which can maintain robustness against Byzantine attacks even in the threatening scenario with a very high Byzantine ratio (0.9 in our experiments) and the largest level of non-IID data (1.0 in our experiments) when the state-of-the-art Byzantine attacks are conducted. The main idea of FedSuper is that the FL server supervises worker devices via injecting a shadow dataset into their local training processes. Moreover, according to the local models’ accuracies or losses on the shadow dataset, we design a Local Model Filter to remove poisoned local models and output an optimal global model. Extensive experimental results on three real-world datasets demonstrate the effectiveness and the superior performance of FedSuper, compared to five latest Byzantine-robust FL algorithms and two baselines, in defending against two state-of-the-art Byzantine attacks with high Byzantine ratios and high levels of non-IID data.

With the development of electronic information technology, trading between different homes has been possible. In this paper, we investigate the energy optimization problem among multiple homes in a intelligent community, and introduce a double auction method for guiding them to trade energy. Each home is equipped with an energy storage system (ESS), an electric vehicle (EV) and load, and connected to the renewable energy and the power grid. The EV operates as an energy producer and consumer to help home energy management system (HEMS) flexibly regulate the power balance. To facilitate trading between homes and reduce the total cost of the system, we introduce a double auction (DA) market among homes, which can also protect their privacy. We formulate this problem as a partially observable markov decision process (POMDP) and propose a DA-based multi-agent proximal policy optimization (DA-MAPPO) algorithm, which solves the problem of sequential decisions and dimensional disasters. The simulations show that the model proposed can reduce the total system cost by 14.8% compared with the model without DA market. The proposed algorithm can improve the motivation of homes to participate in the DA market and protect their privacy compared with other algorithms.

Mobile-edge computing (MEC) raises the problem of selfish user devices that utilize less computing resources than expected to execute offloading tasks or maliciously discard computation tasks. However, most of the existing work either focused on the task offloading or concentrated on the trust mechanism in MEC systems. By jointly considering the two challenges, in this article, we propose a selfish-aware and learning-aided computation offloading scheme for edge–cloud collaboration network. Specifically, we first design a selfishness evaluation mechanism to evaluate the selfishness of the user devices based on the historical interaction records of the edge–cloud collaboration network. Then, we construct the task offloading model which introduces the selfishness evaluation mechanism to suppress the selfish user devices. On this basis, we further formalize the selfish-aware task offloading as an optimization problem of the weighted sum of time latency and energy consumption. Thereafter, we take one step further formalizing the optimization problem as a Markov decision process (MDP) and design a task offloading algorithm based on deep reinforcement learning (DRL) to find the optimized task offloading decision. The simulation results demonstrate that our work can decrease the time latency and energy consumption as well as suppress the selfish user devices.

Multi-access Edge Computing (MEC) enables mobile users to run various delay-sensitive applications via offloading computation tasks to MEC servers. However, the location privacy and the usage pattern privacy are disclosed to the untrusted MEC servers. The most related work concerning privacy-preserving offloading schemes in MEC either consider an impractical MEC scenario consisting of a single user or take a large amount of computation and communication cost. In this article, we propose a deep reinforcement learning based joint optimization of delay and privacy preservation during offloading for multiple-user wireless powered MEC systems, preserving users’ both location privacy and usage pattern privacy. The main idea is that, to protect both the two kinds of privacy, we propose to disguise users’ offloading decisions and deliberately offloading redundant tasks along with the actual tasks to the MEC servers. On this basis, we further formalize the task offloading as an optimization problem of computation rate and privacy preservation. Then, we design a deep reinforcement learning based offloading algorithm to solve such an non-convex problem, aiming to obtain the better tradeoff between the computation rate and the privacy preservation. Finally, extensive simulation results demonstrate that our algorithm can maintain a high level of computation rate while protecting users’ usage pattern privacy and location privacy, compared with two learning-based methods and two Baselines.

With the advances in the Internet-of-Things technology, electric vehicles (EVs) have become easier to schedule in daily life, which is reshaping the electric load curve. It is important to design efficient charging algorithms to mitigate the negative impact of EV charging on the power grid. This article investigates an EV charging scheduling problem to reduce the charging cost while shaving the peak charging load, under unknown future information about EVs, such as arrival time, departure time, and charging demand. First, we formulate an EV charging problem to minimize the electricity bill of the EV fleet and study the EV charging problem in an online setting without knowing future information. We develop an actor–critic learning-based smart charging algorithm (SCA) to schedule the EV charging against the uncertainties in EV charging behaviors. The SCA learns an optimal EV charging strategy with continuous charging actions instead of discrete approximation of charging. We further develop a more computationally efficient customized actor–critic learning charging (CALC) algorithm by reducing the state dimension and thus improving the computational efficiency. Finally, simulation results show that our proposed SCA can reduce EVs’ expected cost by 24.03%, 21.49%, 13.80%, compared with the eagerly charging algorithm, online charging algorithm, reinforcement learning (RL)-based adaptive energy management algorithm, respectively. CALC is more computationally efficient, and its performance is close to that of SCA with only a gap of 5.56% in the cost.

With the rapid developments of electric vehicles (EVs) in recent years, it is desirable to improve the energy efficiency to prolong the limited life of battery and extend the cruising range of EVs. In real EVs, the battery thermal management system is installed to cool the battery and maintain the expected high power output. In this article, we propose a novel energy management strategy based on deep reinforcement learning (DRL) considering battery thermal effects on energy efficiency. The main idea is to formulate energy management as an optimization problem, further extract the state sequence features of the vehicle via gated recurrent unit (GRU), and finally, propose a double deep$Q$network (double DQN)-based algorithm to obtain the optimal strategy. Comparisons of our double DQN algorithm and existent fuzzy control, as well as two other conventional reinforcement learning (RL) algorithms, are conducted under New European Driving Cycle, FTP-75, HWFET, and US06 cycles, and the results demonstrate that the proposed algorithm achieves an energy reduction of more than 6.7% during aggressive driving.

The emergence of mobile edge computing (MEC) enables resource-limited user devices to run computation-intensive applications with the aid of edge server, but the untrustworthiness of the third-party edge server raises the leakage risk of users’ privacy. In this article, we consider an edge-cloud collaboration (ECC) scenario, consisting of multiple user devices with energy harvesting component, one edge server and one cloud server, and we concern about the issues of time latency, energy consumption, and privacy level of user devices in the process of task offloading. Specifically, we formulate the tradeoff between offloading cost and privacy level as a jointly optimization problem, and further model it as a Markov decision process (MDP). We then propose a privacy-preserving task offloading building upon the deep$Q$-network (DQN), which enables user devices to make the optimal offloading decision to decrease delay, reduce energy cost, and enhance privacy level. Extensive simulation results prove that our method can reduce the offloading cost whilst boosting the privacy level of user devices compared to conventional reinforcement learning (RL) algorithm and two baselines.

For Internet and cloud computing service providers, running massive geo-distributed data centers incurs prodigious electricity cost and water consumption as well as carbon emission rooted in electricity generation. Thus, it is critical significant for providers to lower down the operation cost of data centers. In this article, we investigate the problem of energy management for geo-distributed data centers with renewable resources and energy storages. We aim to minimize the long-term operation cost including electricity cost, water consumption, and carbon emission by leveraging the spatiotemporal diversity of these system states. To this end, we first formulate the cost minimization problem as a stochastic optimization problem, then we adopt the Lyapunov optimization technique to design a close-to-optimal online algorithm which only needs the current system information and achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. To reduce the computational complexity and unnecessary communication, we further propose a distributed algorithm based on the distributed computing framework alternating direction method of multipliers (ADMM), which enables each data center to make their own control decisions. Based on the real-world traces and extensive simulations, we demonstrate the effectiveness of our proposed algorithms.

In this paper, we consider an online decision problem, where a decision maker has an option to buy a discount plan for his/her regular expenses. The discount plan costs an immediate upfront charge plus a commitment charge per time slot. Upon expiration, the discount period can be extended if the decision maker continues paying the commitment charge, or be canceled if he or she decides not to pay the commitment charge anymore. We investigate online algorithms for the decision maker to decide when to buy the discount plan and when to cancel it without the knowledge of his/her future expenses, aiming at minimizing the overall cost. The problem is an extension of the classic Bahncard Problem, which is applicable for a wide range of online decision scenarios. We propose a novel deterministic online algorithm which can achieve a closed-form competitive ratio upper bounded by 4. We further propose a randomized online algorithm with a smaller competitive ratio and two variants tailored for average-case inputs and time-varying parameters, respectively. Lastly, we evaluate our algorithms against state-of-the-art online benchmark algorithms in two real-world scenarios.

Mobile Edge Computing (MEC) brings abundant cloud resources to the edge of the network and provides great opportunities to improve user's quality of experience. While many recent studies have investigated the problem of computation offloading, service caching is also an important design topic of MEC. Service caching stores application-related databases or libraries in advance and enables corresponding user tasks to be offloaded. Due to the limited resources in the edge server, service caching decisions have to be made judiciously to maximize the system performance. In this paper, we study the problem of joint service caching, computation offloading, transmission and computing resource allocation in a general scenario of multiple users with multiple tasks. We aim to minimize the overall computation and delay costs for all users and formulate the optimization problem as a quadratically constrained quadratic program (QCQP) which is non-convex and NP-hard. To solve this challenging problem, we propose an efficiently approximate algorithm based on semidefinite relaxation (SDR) approach and alternating optimization which always computes a locally optimal solution. Moreover, we extend the study to the scenario where each user has a computation cost constraint. Simulation results show that our algorithm can minimize the system cost effectively by utilizing the available system resources.

Content‐centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content‐centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache‐enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell‐partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

In speech data publishing, users' data privacy is disclosed and thereby more privacy of users is breached since speech data contains a large amount of information about speakers. Existing work focused on sanitization in speech content, speakers' voice, and data descriptions, without considering the correlation of speech content and speaker's voice. Therefore, these existing work cannot protect speakers' data privacy when attackers utilize such correlation to identify speakers' speech data. To tackle this problem, in this article, we propose a protocol to decrease such potential risks in speech data publishing while keeping the balance of privacy preservation and data utility. Specifically, we define both the risks of privacy disclosure and the data utility loss in speech content, speaker's voice, and data set description. Moreover, we do the first attempt to formalize the correlation between speech content and speaker's voice and regard it as a new kind of privacy leakage risk. Thereafter, we utilize the classifier in machine learning and optimize speech data sanitization considering the defined risks of privacy disclosure and data utility loss. Finally, simulation results validate the effectiveness of the proposed protocol.

An increasing amount of users' locations are aggregated, and the statistical results about the collected data are further released to support mobile applications, such as point-of-interest recommendation and smart transportation. However, such statistical results cause users' membership privacy leakage. Unfortunately, most studies concerning data aggregation focused on privacy preservation and various attacks rather than the membership inference attacks. Moreover, literature about membership inference attacks mainly aimed at machine learning models and gene sequences rather than the locations in data aggregation. More importantly, these work concerning membership inference attacks assumed that adversaries know the exact data of victims, which is always impossible in practical scenarios. To this end, we propose LocMIA, a more invasive attack system that allows adversaries to launch membership inference attacks against aggregated location data without reliance on any prior knowledge of the locations of victims. The main idea of LocMIA is to train a binary classifier to infer whether a specific victim's location data is involved in the aggregation group, based solely on the data aggregation's output (i.e., the statistical results). Finally, experimental results on a real-world check-in data set prove the devastating privacy leaks caused by the proposed LocMIA.

Data aggregation based on machine learning (ML), in mobile edge computing, allows participants to send ephemeral parameter updates of local ML on their private data instead of the exact data to the untrusted aggregator. However, it still enables the untrusted aggregator to reconstruct participants’ private data, although parameter updates contain significantly less information than the private data. Existing work either incurs extremely high overhead or ignores malicious participants dropping out. The latest research deals with the dropouts with desirable cost, but it is vulnerable to malformed message attacks. To this end, we focus on the data aggregation based on ML in a practical setting where malicious participants may send malformed parameter updates to perturb the total parameter updates learned by the aggregator. Moreover, malicious participants may drop out and collude with other participants or the untrusted aggregator. In such a scenario, we propose a scheme named DAML , which to the best of our knowledge is the first attempt toward verifying participants’ submissions in data aggregation based on ML. The main idea is to validate participants’ submissions via SSVP, a novel secret-shared verification protocol, and then aggregate participants’ parameter updates using SDA, a secure data aggregation protocol. Simulation results demonstrate that DAML can protect participants’ data privacy with preferable overhead.

With the rapid development of cloud computing, data centers are increasing in quantity and scale for large Internet service providers to provide global Internet services. The enormous operation cost of data centers attracts lots of researchers' attention and their huge energy consumption have brought great challenges to the sustainable development of green data centers. In this paper, we investigate the workload scheduling and energy management issues of geo- distributed data centers with renewable resources and energy storages. We formulate the minimization problem of operation cost including electricity cost, water consumption and carbon emission as a stochastic optimization problem. For minimizing the long-term operation cost, we adopt the Lyapunov optimization technique to design an online algorithm which achieves a delicate tradeoff between system cost and performance of delay tolerant workloads. Moreover, extensive simulation results show the correctness and effectiveness of proposed online algorithm based on the real-world traces.

This work studies an electric vehicles (EVs) loaded microgrid with renewable energy resources, energy storage system (ESS) and external power grid. The microgrid's energy management problem is formulated to maximize its daily average operation revenue and balance the supply and demand based on the system statistical information, i.e., electricity market price, renewable energy arrivals and EVs' charging characteristics. For online optimization, we develop a reinforcement learning (RL) based approach to smartly control the microgrid's ESS in real-time by considering future reward of an charging/discharging action. Moreover, to speed up the RL training stage, a prediction model using long short term memory (LSTM) networks is adopted to explore the system input traces for more accurate future reward counting in current learning process. The simulation results validate the superior performance of the proposed algorithm with comparison to the conventional online optimization version.

Mobile-edge computing (MEC) has evolved as a promising technology to alleviate the computing pressure of mobile devices by offloading computation tasks to MEC server. Energy management is challenging since the unpredictability of the energy harvesting (EH) and the quality of service (QoS). In this paper, we investigate the problem of power consumption in a multiuser MEC system with EH devices. The system power consumption, which includes the local execution power and the offloading transmission power, is designated as the main system performance index. First, we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming, which is difficult to solve due to the time-coupling constraints. Then, we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints. We design an online algorithm based on the Lyapunov optimization method, which only uses current states of the mobile users and does not depend on the system statistic information. Furthermore, we propose a distributed algorithm based on the alternating direction method of multipliers to reduce the system computational complexity. We prove the optimality of the online algorithm and the distributed algorithm using rigorous theoretical analysis. Finally, we perform extensive trace-simulations to verify the theoretical results and evaluate the effectiveness of the proposed algorithms.

This paper proposes an online algorithm for energy storage management in networked microgrids (MGs) with co-generation based on the concept of quality-of-service in electricity (QoSE). The concept of networked MG with distributed renewable energy supply and co-generation makes power supply smarter for electricity/heat using, which has advantages of increasing power supply efficiency and reliability by coordinately scheduling the power supply in a networked way. The demands include quality usage of electricity load and heat. The networked MG central controller aims to minimize the operation cost and guarantee the outage probability of quality usage, i.e., QoSE, by scheduling electricity among renewable energy sources, energy storage systems, co-generation, and external utility market. We formulate the problem as a stochastic programming problem with QoSE and battery capacity constraints. By introducing the QoSE virtual queues and energy storage virtual queues, we transform the original problem into a problem that is applicable to employ the Lyapunov optimization technique. The proposed algorithm is an online algorithm with low complexity for practical implementation, and also provides several deterministic performance bounds. We perform extensive simulations to demonstrate the effectiveness of the proposed algorithm, which exhibits significant efficiency on operation cost reduction compared with an alternative benchmark solution.

Due to the popularity of mobile computing and mobile sensing, users' traces can now be readily collected to enhance applications' performance. However, users' location privacy may be disclosed to the untrusted data aggregator that collects users' traces. Cloaking users' traces with synthetic traces is a prevalent technique to protect location privacy. But the existing work that synthesizes traces suffers from the social relationship based de-anonymization attacks. To this end, we propose W3-tess that synthesizes privacy-preserving traces via enhancing the plausibility of synthetic traces with social networks. The main idea of W3-tess is to credibly imitate the temporal, spatial, and social behavior of users' mobility, sample the traces that exhibit similar three-dimension mobility behavior, and synthesize traces using the sampled locations. By doing so, W3-tess can provide “differential privacy” on location privacy preservation. In addition, compared to the existing work, W3-tess offers several salient features. First, both location privacy preservation and data utility guarantees are theoretically provable. Second, it is applicable to most geo-data analysis tasks performed by the data aggregator. Experiments on two real-world datasets, loc-Gwalla and loc-Brightkite, have demonstrated the effectiveness and efficiency of W3-tess.

With the development of renewable energy technology and communication technology in recent years, many residents utilize renewable energy devices in their residences with energy storage systems. However, it is a great challenge to share residents' energy with others in the smart community for minimizing the total cost of all residents. In this paper, we investigate the problem of energy management and task scheduling for a smart community with residential combined heat and power system (resCHP) and renewable energy to pay the least bill. We take heterogeneous task arrival into consideration, which widely exists in the community. We formulate the minimum cost problem of a non-cooperative community as a random non-convex optimization problem with physical constraints. Our objective is to minimize the community time-average cost, including the cost of the external grid and natural gas. We adopt the Lyapunov optimization theory to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on Sarsa Algorithm. Finally, we present extensive simulations to validate the proposed algorithms by using real trace data.

Energy consumption in the base stations (BSs) recently has aroused significant concerns especially when renewable power has been widely applied. In this paper, we jointly integrate power from the power grid and renewable energy to investigate energy management in the BSs with sleep- awake capability for cellular networks. In our system model, the BSs are equipped with two charging batteries operating at double timescales, exhibiting a more practical performance and heterogeneous energy storage capability. We formulate the energy management problem as a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. We adopt Lyapunov optimization approach to tackle the problem by relaxing the battery constraints and reformulating the problem with virtual queues of the state of charge and the quality of service (QoS). We design an online algorithm with quick convergence speed and low complexity which avoids depending on statistics of system. We perform extensive simulations to verify the theoretical analysis.

In this paper, we study an energy cost minimization problem in cellular networks, where base stations (BSs) are supplied with hybrid energy sources including harvested recyclable energy (RE), external power grids (PGs), distributed local generators (LGs) and power storages, and operate with sleep mode techniques. We formulate the problem into an optimization programming to achieve optimal decisions for energy scheduling and sleep control. To avoid frequent switching, we implement BS sleep mode techniques on a larger timescale by adopting a two-timescale approach. Based on the Lyapunov technique, we further propose a close-to-optimal algorithm which only requires mean price of PG energy in each time frame instead of future information about stochastic inputs (e.g., the amount of RE harvesting and user demand for data traffic). The proposed algorithm can achieve approximately minimal energy cost and ensure the stability of workload and battery virtual queues. We present theoretical analysis as well as numerical simulations to demonstrate the performance of the proposed algorithm. The results present the stability of queues and the reduction in system cost.

Mobile-edge computing (MEC) has evolved as a promising technology to alleviate the computing pressure of mobile devices by offloading computation tasks to MEC server. Energy management is challenging since the unpredictability of the energy harvesting and the quality of service (QoS). In this paper, we investigate the problem of power consumption in a multi-user MEC system with energy harvesting (EH) devices. The system power consumption, which includes the local execution power and the offloading transmission power, is designated as the main system performance index. First, we formulate the power consumption minimization problem with the battery queue stability and QoS constraints as a stochastic optimization programming, which is difficult to solve due to the time-coupling constraints. Then, we adopt the Lyapunov optimization approach to tackle the problem by reformulating it into a problem with relaxed queue stability constraints.We design an online algorithm based on the Lyapunov optimization method, which only uses current states of the mobile users (MUs) and does not depend on the system statistic information. Moreover, we prove the optimality of the online algorithm using rigorous theoretical analysis. Finally, we perform extensive trace-simulations to verify the theoretical results and evaluate the effectiveness of the proposed algorithms.

In 2008, the Federal Communications Commission issued a ruling permitting the unlicensed usage of TV white spaces (TVWS), i.e. locally vacant TV channels. Due to its low‐frequency range (50–698 MHz), the TV spectrum has much better propagation characteristic and higher‐spectral efficiency, resulting in a wide range of potentially important applications. However, unlike typical cellular and industrial scientific medical bands, TVWS are subjected to high‐spatial variation, temporal variation, and fragmentation, resulting in new challenges in TVWS identification and in implementing a wireless network in this band. Identification and network design are the two key issues required to be addressed while investigating TVWS. These two problems have been widely discussed in several existing literature. Applications in TVWS are also an important topic, which has not been adequately explored. This study provides an up‐to‐date survey of TVWS and its applications in future wireless networks and communication. Various problems and challenges associated with each use case as well as the possible enabling methods to address these challenges are also presented.

Mobile-edge computing (MEC) has aroused significant attention for its performance to accelerate application's operation and enrich user's experience. With the increasing development of green computing, energy harvesting (EH) is considered as an available technology to capture energy from circumambient environment to supply extra energy for mobile devices. In this paper, we propose an online dynamic tasks assignment scheduling to investigate the tradeoff between energy consumption and execution delay for an MEC system with EH capability. We formulate it into an average weighted sum of energy consumption and execution delay minimization problem of mobile device with the stability of buffer queues and battery level as constraints. Based on the Lyapunov optimization method, we obtain the optimal scheduling about the CPU-cycle frequencies of mobile device and transmit power for data transmission. Besides, the dynamic online tasks offloading strategy is developed to modify the data backlogs of queues. The performance analysis shows the stability of the battery energy level and the tradeoff between energy consumption and execution delay. Moreover, the MEC system with EH devices and task buffers implements the high energy efficient and low latency communications. The performance of the proposed online algorithm is validated with extensive trace-driven simulations.

It is a promising vision to exploit white spaces , that is, vacant VHF and UHF TV channels, to meet the rapidly growing demand for wireless data services in both outdoor and indoor scenarios. While most prior works have focused on outdoor white space, the indoor story is largely open for investigation. Motivated by this observation and discovering that 70% of the spectrum demand comes from indoor environment, we carry out a comprehensive study to explore indoor white spaces. We first conduct a large-scale measurement study and compare outdoor and indoor TV spectrum occupancy at 30+ diverse locations in a typical metropolis—Hong Kong. Our results show that abundant white spaces are available in different areas in Hong Kong, which account for more than 50% and 70% of the entire TV spectrum in outdoor and indoor scenarios, respectively. Although there are substantially more white spaces indoors than outdoors, there have been very few solutions for identifying indoor white space. To fill in this gap, we develop the first data-driven, low-cost indoor white space identification system for White-space Indoor Spectrum EnhanceR (WISER), to allow secondary users to identify white spaces for communication without sensing the spectrum themselves. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%--40% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

In this paper, we consider a typical microgrid scenario that consists of centralized power grid, renewable energy generation, and combined heat and power (CHP) local (co-)generation, as well as power and heat energy storage devices. We aim to minimize the microgrid's operating cost by formulating it as a stochastic non-convex optimization programming, which is challenging to solve optimally. We design an online algorithm by developing a modified Lyapunov optimization approach based on the random system inputs (e.g., the acquired electricity from power grid, the charging/discharging of the energy storage devices, obtained power from the local generator, and the renewable energy generation etc.), which does not require any statistic information of the system. Considering that the nonconvexity of the problem is caused by the dependence of power in battery pack and heat energy in thermal tank, we further explore the relation between them and convert the problem into a convex stochastic optimization programming. We show that the proposed algorithm is efficient with very low computational complexity and is proved to achieve near optimal performance. Moreover, extensive empirical evaluations using real-world traces are provided to study the effectiveness of the proposed algorithm.

It is a promising vision to utilize white spaces, i.e., vacant VHF and UHF TV channels, to satisfy skyrocketing wireless data demand in both outdoor and indoor scenarios. While most prior works have focused on exploring outdoor white spaces, the indoor story is largely open for investigation. Motivated by this observation and that 70% of the spectrum demand comes from indoor environments, we carry out a comprehensive study of exploring indoor white spaces. We first present a large-scale measurement of outdoor and indoor TV spectrum occupancy in 30+ diverse locations in a typical metropolis Hong Kong. Our measurement results confirm abundant white spaces available for exploration in a wide range of areas in metropolises. In particular, more than 50% and 70% of the TV spectrum are white spaces in outdoor and indoor scenarios, respectively. While there are substantially more white spaces in indoor scenarios than in outdoor scenarios, there is no effective solution for identifying indoor white spaces. To fill in this gap, we propose the first system WISER (for White-space Indoor Spectrum EnhanceR), to identify and track indoor white spaces in a building, without requiring user devices to sense the spectrum. We discuss the design space of such system and justify our design choices using intensive real-world measurements. We design the architecture and algorithms to address the inherent challenges. We build a WISER prototype and carry out real-world experiments to evaluate its performance. Our results show that WISER can identify 30%-50% more indoor white spaces with negligible false alarms, as compared to alternative baseline approaches.

A cognitive network refers to the one where two overlaid structures, called primary and secondary networks coexist. The primary network consists of primary nodes who are licensed spectrum users while the secondary network comprises unauthorized users that have to access the licensed spectrum opportunistically. In this paper, we study the percolation degree of the secondary network to achieve k-percolation in large scale cognitive radio networks. The percolation degree is defined as the number of nearest neighbors for each secondary user when there are at least k vertex-disjoint paths existing between any two secondary relays in the percolated cluster. The percolated cluster is formed when there are an infinite number of mutually connected secondary users spanning the whole network. Each user in the cluster is possibly connected to several neighbors, inducing more communication links between any two of them. Since nodes located near the boundary have fewer neighbors, the boundary effect becomes a bottleneck in determining the percolation degree. For cognitive networks, when the primary node density becomes considerably large, the boundary effect spreads inside the network. The transmission area of most secondary users who are located near the primary nodes decreases due to the restriction of the primary network. Therefore, to ensure k-connectivity in the percolated cluster, each secondary user must be connected to more neighbors, and the percolation degree of the secondary network yields a function of the primary node density. We specify the relationship into three regimes regarding the topology variation of the cognitive network. A closed-form expression of the percolation degree under different primary node densities is presented. The expression characterizes the connectivity strength in the secondary percolated cluster, therefore providing analytical insight on fault tolerance improvement in cognitive networks.

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

We study the multicast throughput capacity for hybrid wireless mobile ad hoc networks (MANETs) with a directional antenna and delay constraint. The hybrid wireless network consists of a mobile ad hoc network with $n$ nodes and $m$ regularly placed base stations connected by high-bandwidth wired links. For the MANET, there are $n_s$ multicast sessions and each multicast session has one source which transmits identical informations to its associated $p$ destinations. Assuming that the mobile nodes adopt 2D-i.i.d. mobility model, we investigate the ad hoc mode multicast throughput capacity when each node is equipped with a directional antenna along with a tolerant delay $D$. That is, a source node transmits to its $p$ destinations only with the help of normal nodes within $D$consecutive time slots. Otherwise, the transmission will be switched to the infrastructure mode, where the base stations serve as relays. We find that the multicast throughput capacity of a hybrid wireless MANETs greatly depends on the delay constraints $D$, the number of base stations $m$, and the beamwidth of directional antenna $\theta$. We show that the multicast throughput capacity of the hybrid directional wireless network is $\Theta(\sqrt{\theta})+\Theta(\frac{m}{n_sp}W_2)$ bits/sec, if $D=\Omega(\frac{n_s}{(\log p)^2(\log(\theta n_sp))^2})$; $\Theta(\frac{m}{n_sp}W_2)$ bits/sec, if $D=O(\sqrt[3]{\frac{n_s}{(\log p)^2(\log(\theta n_sp))^2}})$; and $O((\log p)(\log(\theta n_sp))\sqrt{\frac{\theta D}{n_s}})+\Theta(\frac{m}{n_sp}W_2)$ bits/sec, otherwise. We analyze the impact of $D$, $m$ and $\theta$ on the multicast throughput capacity of the hybrid MANET. Finally, we derive lower bound of the muticast throughput using a similar raptor coding approach as in\cite{ZY10}.

We study the throughput capacity of hybrid wireless networks with a directional antenna. The hybrid wireless network consists of n randomly distributed nodes equipped with a directional antenna, and m regularly placed base stations connected by optical links. We investigate the ad hoc mode throughput capacity when each node is equipped with a directional antenna under an L-maximum-hop resource allocation. That is, a source node transmits to its destination only with the help of normal nodes within L hops. Otherwise, the transmission will be carried out in the infrastructure mode, i.e., with the help of base stations. We find that the throughput capacity of a hybrid wireless network greatly depends on the maximum hop L, the number of base stations m, and the beamwidth of directional antenna \theta. Assuming the total bandwidth W bits/sec of the network is split into three parts, i.e., W_1 for ad hoc mode, W_2 for uplink in the infrastructure mode, and W_3 for downlink in the infrastructure mode. We show that the throughput capacity of the hybrid directional wireless network is \Theta(\frac{nW_1}{\theta^2L\log n})+\Theta(mW_2), if L=\Omega(\frac{n^{1/3}}{\theta^{4/3}\log^{2/3} n}); and \Theta((\theta^2L^2\log n)W_1)+\Theta(m W_2), if L=o(\frac{n^{1/3}}{\theta^{4/3}\log^{2/3} n}), respectively. Finally, we analyze the impact of L, m and \theta on the throughput capacity of the hybrid networks.