Transformers are increasingly becoming the dominant model in the field of computer vision, thereby catalyzing research efforts aimed at unraveling the interpretability of transformers. Existing explanation techniques, whether attention-based or gradient-based, furnish a dependable approach to quantifying the impact of input features on model predictions from the perspective of dissecting self-attention mechanisms. However, current research overlooks the block-to-block constraints, which result in misdirection in attribution. In this work, we propose a block-wise constraints-free interpretation method, Independent Block Level Attribution (IBA), which maintains the relative independence of each block in the model. The IBA reconfigures the model into mutually unaffected class-semantic blocks via class-semantic relevance, each of which performs the attribution computation independently, thus minimizing the influence of inter-block constraints on the model interpretation performance. Extensive perturbation and segmentation experiments unequivocally demonstrate the superiority of our method, showcasing its significant outperformance compared to current interpretation methods. Additionally, we also apply IBA to the text transformer to demonstrate the generalization of our method. Our code is available athttps://github.com/qinanin/TMM-IBA.git.

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports$3.12\times $more demand when compared to related existing approaches.

The advent of 5G wireless technology marks a significant milestone in telecommunications, enhancing consumer and industrial applications through Industry 4.0 technologies. The radio access network (RAN) and its downlink throughput are vital for Internet service providers. However, ensuring 5G RAN downlink throughput reliability requires robust failure diagnosis strategies for quick identification and resolution, posing challenges in cost efficiency, expert trustworthiness, and adaptability. We present 5G RAN Downlink Throughput Abnormality Diagnosis (5GR-DTAD), a diagnostic framework that fusing domain expertise with machine learning techniques, incorporating both feed-forward and long short-term memory (LSTM) neural networks. Experimental results on real-world data from multiple base stations show that 5GR-DTAD outperforms existing methods in precision, recall, and F1 scores by up to 29.85%, 42.28%, and 35.74%, respectively. 5GR-DTAD improves diagnostic accuracy while minimizing reliance on labeled data, offering an adaptable and cost-effective solution for various 5G RAN conditions.

Congestion control (CC) plays a pivotal role in low-latency interactive video streaming such as cloud gaming. However, existing end-to-end CC methods often cause self-induced network queuing. As a result, they may largely delay video frame transmission and undermine the user’s quality of experience. In this paper, we present a new, practical CC algorithm namedPudicathat strives to achieve near-zero queuing delay and high link utilization while respecting cross-flow fairness. Pudica introduces several judicious approaches to utilize the paced frame to probe the bandwidth utilization ratio (BUR) instead of bandwidth itself. By leveraging BUR estimations, Pudica designs a holistic bitrate adjustment policy to balance low queuing, efficiency, and fairness. We conducted thorough and comprehensive evaluations in real production networks. In comparison to the state-of-the-art methods, Pudica reduces the average and tailed frame delay by 3.1$\times$and 5.1$\times$, respectively. Meanwhile, it increases the frame bitrate by 12.1%. Pudica has been deployed in a large-scale cloud gaming platform, currently serving millions of players.

Knowledge distillation (KD), a technique widely employed in computer vision, has emerged as a de facto standard for improving the performance of small neural networks. However, prevailing KD-based approaches in video tasks primarily focus on designing loss functions and fusing cross-modal information. This overlooks the spatial-temporal feature semantics, resulting in limited advancements in model compression. Addressing this gap, our paper introduces an innovative knowledge distillation framework, with the generative model for training a lightweight student model. In particular, the framework is organized into two steps: the initial phase is Feature Representation, wherein a generative model-based attention module is trained to represent feature semantics; Subsequently, the Generative-based Feature Distillation phase encompasses both Generative Distillation and Attention Distillation, with the objective of transferring attention-based feature semantics with the generative model. The efficacy of our approach is demonstrated through comprehensive experiments on diverse popular datasets, proving considerable enhancements in video action recognition task. Moreover, the effectiveness of our proposed framework is validated in the context of more intricate video action detection task. Our code is available at https://github.com/aaai-24/Generative-based-KD.

Safety protective gear (SPG) detection based on the machine learning model plays an important role in improving outdoor personnel safety in the electric power industry. However, the detection method of transmitting video to the cloud faces a series of challenges, such as privacy disclosure and high latency. To solve this problem, we present FtlSPG, a federated transfer learning framework for SPG detection. In particular, under the three-layer pyramid architecture of “site-companyCloud-globalServer,” we propose a federated personalized model based on local batch normalization and dynamical weighting for the source domain with labeled video. Moreover, a federated domain adaptation model based on a federated deep adversarial network and model self-training is presented for the target domain with unlabeled video. Finally, we verify the effectiveness of FtlSPG in real-world power companies. Extensive experiments demonstrate that FtlSPG can significantly outperform existing schemes, in terms of privacy protection, detection precision, and response latency.

Knowledge and data dual-driven (KDDD) represents a novel paradigm that leverages the strengths of data-driven methods in feature representation and knowledge transfer, while also incorporating expertise accumulated by domain experts. This integration allows KDDD methods to enhance the interpretability, reliability, and robustness of fault diagnosis (FD) approaches, making them widely studied in the field of industrial equipment (IE) FD. Despite the existence of systematic and valuable reviews on IE FD, there remains a gap in the literature regarding the review of KDDD IE FD methods. Therefore, conducting a comprehensive investigation into KDDD IE FD methods is of utmost importance and necessity. Such an investigation will facilitate readers’ understanding of advanced technologies and enable the rapid design of effective solutions for real-world IE FD problems. In this survey, we first outline the limitations of data-driven and knowledge-based FD methods, highlighting the need for KDDD methods. Subsequently, we delve into the details of how domain knowledge can be effectively integrated with deep learning models. Additionally, we analyze challenges of KDDD methods in real-world IE FD applications, while also discussing novel solutions for prospective research directions. Finally, we conclude this survey, emphasizing the inspiration it offers to researchers interested in advancing IE FD, and its potential to stimulate practical IE FD research.

The fusion of federated learning and differential privacy can provide more comprehensive and rigorous privacy protection, thus attracting extensive interests from both academia and industry. However, facing the system-level challenge of device heterogeneity, most current synchronous FL paradigms exhibit low efficiency due to the straggler effect, which can be significantly reduced by Asynchronous FL (AFL). However, AFL has never been comprehensively studied, which imposes a major challenge in the utility optimization of DP-enhanced AFL. Here, theoretically motivated multi-stage adaptive private algorithms are proposed to improve the trade-off between model utility and privacy for DP-enhanced AFL. In particular, we first build two DP-enhanced AFL frameworks with consideration of universal factors for different adversary models. Then, we give a solid analysis on the model convergence of AFL, based on which, DP can be adaptively achieved with high utility. Through extensive experiments on different training models and benchmark datasets, we demonstrate that the proposed algorithms achieve the overall best performances and improve up to 24% test accuracy with the same privacy loss and have faster convergence compared with the state-of-the-art algorithms. Our frameworks provide an analytical way for private AFL and adapt to more complex FL application scenarios.

Mobile 360-degree video streaming has grown significantly in popularity but the quality of experience (QoE) suffers from insufficient and variable wireless network bandwidth. Recently, saliency-driven 360-degree streaming overcomes the buffer size limitation of head movement trajectory (HMT)-driven solutions and thus strikes a better balance between video quality and rebuffering. However, inaccurate network estimations and intrinsic saliency bias still challenge saliency-based streaming approaches, limiting further QoE improvement. To address these challenges, we design a robust saliency-driven quality adaptation algorithm for 360-degree video streaming, RoSal360. Specifically, we present a practical, tile-size-aware deep neural network (DNN) model with a decoupled self-attention architecture to accurately and efficiently predict the transmission time of video tiles. Moreover, we design a reinforcement learning (RL)-driven online correction algorithm to robustly compensate the improper quality allocations due to saliency bias. Through extensive prototype evaluations over real wireless network environments including commodity WiFi, 4G/LTE, and 5G links in the wild, RoSal360 significantly enhances the video quality and reduces the rebuffering ratio, thereby improving the viewer QoE, compared to the state-of-the-art algorithms.

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.

The development of cloud and edge computing has enabled the easy access of artificial intelligence (AI) services for massive heterogeneous and resource-constrained devices. Particularly, computation-intensive AI services can be orchestrated and deployed in the cloud or edge according to varying performance and cost requirements. Nonetheless, the improved accessibility of deep learning (DL) model variants and the evolving of computational intelligence paradigms pose great challenges for orchestrating large-scale DL inference services in the cloud-edge continuum. Focusing on cloud or edge-based deployment, existing work on multi-variant service orchestration often has a limited solution space of deployment plans. To address this limitation, we first propose a novel multi-paradigm deployment model (MPDM) for service orchestration, which not only considers the model variants but also allows the co-existence of multiple paradigms for large-scale inference service deployment. The service deployment in the MPDM model is then formulated as a multiobjective optimization problem of seeking a better tradeoff among the system accuracy, service scale, and deployment cost. To solve the multiobjective optimization, we further propose a weighted metric-based constructive heuristic algorithm (WCH), which can efficiently obtain an approximately optimal Pareto frontier. Extensive experimental results have validated the effectiveness and efficiency of WCH, and revealed the impacts of both multi-paradigm deployment and edge-cloud collaborative intelligence (ECCI) paradigm on large-scale DL serving systems.

Emerging intelligent applications based on accurate and timely stream analytics require real-time CNN inference of massive data continuously generated at the pervasive end devices. Due to the resource constraints, neither computing locally at end devices nor transmitting to remote servers is competent for computation-intensive CNN inference on large-volume images in real-time. Therefore, Collaborative Inference (CI), which conducts inference sequentially from the local device to the remote server with compressed intermediate inference data, is rapidly promoted. Due to the essential communication in collaboration, the CI efficiency is sensitive to network conditions, and will degrade under the unpredictable network fluctuations in practice, which may cause a severe delay in CI and degrade the responsiveness of stream analytics. For accurate and timely stream analytics in practical fluctuating networks, we present RTCoInfer, the real-time CI framework with run-time transmission adaption considering the network conditions. Specifically, we propose a novel Switchable CNN integrating CNNs with different compression rates on the partition layer for the run-time transmission adjustment, and construct a real-time controller determining the compression rate to maintain the real-time CI for stream analytics. Extensive experiments show that, compared with state-of-the-art methods, RTCoInfer achieves better efficiency and unprecedented resilience in real-time stream analytics.

Image classification using convolutional neural networks (CNNs) is critical for broader industrial applications like defect detection. To protect sensitive data during the industrial process, increasing institutions are highly interested in training CNN classifiers collaboratively with federated learning (FL). However, the existing FL solutions cannot address the sample deficiency and heterogeneous learning resource issues at different practical institutions. In this article, we present a federated lightweight relation network (FLRN), a lightweight industrial image classifier based on our federated few-shot learning (FFSL) architecture. Results of extensive experiments considering different real-world FFSL scenarios indicate that, unlike the state-of-the-art few-shot learning method relation network (RN), the FLRN performs well on not only FL participants with mutually isolated classes of samples but also external institutions with limited samples from unseen classes. Compared to the RN with the predominating FedAvg-based FL deployment, the FLRN manages to achieve as low as$\text{29.6}\times$less client–cloud communication,$\text{5.2}\times$less computation, and$\text{22.0}\times$less storage costs of clients.

5G wireless networks are expected to satisfy different delay requirements of various traffics by network resource scheduling. Existing scheduling methods perform poorly in practice due to their unrealistic assumption on the access to the full channel state information (CSI) or the explicit mathematical expression of network delay. In this paper, we consider the delay-oriented packet scheduling problem in multi-cell 5G downlink networks with multiple users and traffic types (e.g., FTP, VoIP and video streaming), and formulate it as a partially observable Markov decision process (POMDP). We design a delay-oriented downlink scheduling framework based on deep reinforcement learning (DRL) to autonomously schedule the active traffic flows without the full channel information. Furthermore, a recurrent proximal policy optimization (RPPO) algorithm is proposed to perceive the underlying state and accelerate learning under different time granularities, with the policy gradient theorem under POMDP strictly proved. By incorporating the future traffic information provided by a proposed spatial-temporal prediction algorithm, RPPO can balance the load and achieve lower delay in real-time multi-cell multi-user scenarios. Results of extensive experiments on a realistic 5G simulator demonstrate that our framework significantly outperforms existing approaches in terms of both tail delay and average delay for up to 48% and 41.7%, respectively.

Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel w-event LDP paradigm to provide practical privacy guarantee for infinite streams. By constructing a unified error analysis, we adapt the existing budget division framework in centralized differential privacy (CDP) for LDP-IDS, which however incurs prohibitive noise and expensive communication cost. To this end, we propose a novel and extensible framework of population division and recycling, as well as online adaptive population division algorithms for LDP-IDS. We provide theoretical guarantees and demonstrate, through extensive discussions, that our proposed framework not only achieves significant reduction in utility loss and communication overhead, but also enjoys great compatibility for varied analytic tasks and flexibility of incorporating ideas of many existing stream algorithms. Extensive experiments on synthetic and real-world datasets validate the high effectiveness, efficiency, and flexibility of our proposed framework and methods.

LoRaWAN deployments follow anad hocdeployment model that has organically led to overlapping communication networks, sharing the wireless spectrum, and completely unaware of each other. LoRaWAN uses ALOHA-style communication where it is almost impossible to schedule transmission between networks belonging to different owners properly. The inability to schedule overlapping networks will cause internetwork interference, which will increase node-to-gateway message losses and gateway-to-node acknowledgment failures. This problem is likely to get worse as the number of LoRaWAN networks increases. In response to this problem, we propose IRONWAN, a wireless overlay network that shares communication resources without modifications to underlying protocols. It utilizes the broadcast nature of radio communication and enables gateway-to-gateway communication to facilitate the search for failed messages and transmit failed acknowledgments already received and cached in overlapping network’s gateways. IRONWAN uses two novel algorithms: 1) a real-time message interarrival predictor, to highlight when a server has not received an expected uplink message and 2) the interference predictor, to ensure that extra gateway-to-gateway communication does not negatively impact the communication bandwidth. We evaluate IRONWAN on a 1000-node simulator with up to ten gateways and a 10-node testbed with 2-gateways. The results show that IRONWAN can achieve up to 12% higher packet delivery ratio (PDR) and total messages received per node while increasing the minimum PDR by up to 28%. These improvements save up to 50% node’s energy. Finally, we demonstrate that IRONWAN has comparable performance to an optimal solution (wired and centralized) but with 2–32 times lower communication costs. IRONWAN also has up to 14% better PDR when compared to FLIP, a wired-distributed gateway-to-gateway protocol in certain scenarios.

Video object detection is a fundamental technology of intelligent video analytics for Internet of Things (IoT) applications. However, even with extraordinary detection accuracy, predominating solutions based on deep convolutional neural networks (DCNNs) cannot achieve real-time online object detection on video streams with a low end-to-end (E2E) response latency and therefore cannot be applied to proliferating latency-sensitive IoT applications like autonomous driving requiring large-scale intelligent video analytics. To address this issue, we present EC2Detect, an edge-cloud collaborative real-time online video object detection method. Specifically, we propose a tracking-assisted object detection architecture based on edge-cloud collaboration with keyframe selection, where the accurate but heavy object detection is conducted by the Cloud on sparse keyframes adaptively selected according to their semantic variation, and the lightweight object tracking is used to localize and identify objects in other frames at edge devices. Extensive experiments of our real-world prototype demonstrate that, EC2Detect significantly outperforms state-of-the-art methods in terms of processing speed (up to$4.77\times $faster), E2E latency (up to$8.12 \times $lower), and edge-cloud bandwidth occupation ($17 \times $lower) with an acceptable mAP, which can effectively support large-scale intelligent video analytics in practice. Source code of EC2Detect is available athttps://github.com/ECCDetect/ECCDetect.

Federated learning (FL) is capable of analyzing tremendous data from smart edge devices in Industrial Internet of Things (IIoTs), empowering numerous industrial applications. However, the increasing privacy concerns and deployment costs of IIoT environment have been posing new challenges for FL. This article proposes PCFed, a novel privacy-enhanced and communication-efficient FL framework to provide higher model accuracy with rigorous privacy guarantees and great communication efficiency. In particular, we develop a sampling-based intermittent communication strategy via a PID (proportional, integral, and derivative) controller on the cloud server to adaptively reduce the communication frequency. In addition, we design a budget allocation mechanism to balance the tradeoff between model accuracy and privacy loss. Then, we develop PCFed+, an enhanced variant for PCFed, with further consideration of infinite data streams on edge servers. Extensive experiments demonstrate that both PCFed and PCFed+ can significantly outperform existing schemes, in terms of communication efficiency, privacy protection, and model accuracy.

An edge-cloud collaborative intelligent (ECCI) platform is of great significance for the agile development and rapid deployment of ECCI applications, which are essential for realizing smart industry in the era of Industry 4.0. However, the existing platforms lack considering the high real-time latency demand of industrial operations, which severely hinders the development of smart industry and may even lead to severe industrial accidents. To effectively reduce the response latency of industrial applications, in this article, we propose an ECCI platform IndustEdge. It takes time-sensitive networking as the deterministic transport for the link layer, and provides an extensible ECCI orchestration component to reduce the system level latency. Furthermore, IndustEdge has an ECCI algorithm library for different collaborative modes and provides the complete life cycle management for ECCI applications. We implement platforms for both the real-world prototype and emulated-world emulation, and conduct two case studies to evaluate the effectiveness of IndustEdge.

Edge Intelligence (EI) aims at addressing concerns like response latency risen by the conflict between predominating Cloud-based deployments of computationally intensive AI applications and the expensive uploading of explosive end data. Convolutional Neural Networks (CNNs) leading the latest flourish of AI inevitably suffer from the aforementioned conflict. There emerge increasing EI-driven attempts on fast CNN inference with high accuracy in the End-Edge-Cloud (EEC) collaborative computing paradigm, where, however, neither model compression approaches for on-device inference nor collaborative inference methods across devices can effectively achieve the trade-off between latency and accuracy of End-to-End (E2E) inference. In this article, we present CNNPC that jointly partitions and compresses CNNs for fast inference with high accuracy in collaborative EEC systems. We implemented CNNPC (source code available athttps://github.com/IoTDATALab/CNNPC) and evaluated its performance within extensive real-world EEC scenarios. Experimental results demonstrate that, compared with state-of-the-art single-end and collaborative approaches, without obvious accuracy loss, collaborative inference based on CNNPC is up to$1.6\times$and$5.6\times$faster, and requires as low as$4.30\%$and$6.48\%$communications, respectively. Besides, when determines the optimal strategy, CNNPC requires as low as$0.1\%$actual compression operations that the traversal method (the only viable method providing the theoretically optimal strategy) requires.

Latency-aware service placement is promising in reducing the overall service response latency of proliferating edge-cloud collaborative smart manufacturing systems. However, intuitive latency estimators used by existing service placement approaches cannot accurately depict the nonlinear end-to-end (E2E) latency of multihop microservices with complex dependencies, which is severely hindering the effectiveness of latency-aware service placement. To address this issue, in this article, we present a microservice placement mechanism for edge-cloud collaborative smart manufacturing (MPCSM), where a microservice placement algorithm latency-aware edge-cloud collaborative placement supported by an accurate data-driven E2E latency estimation method is proposed. We build a real-world collaborative prototype, and conduct a case study on semiconductor manufacturing to elaborate the construction of our latency estimator. Results of extensive experiments demonstrate that the error of our E2E latency estimator is up to 10× less than that of existing ones, and the overall service latency with MPCSM is up to 10× less than that with existing service placement approaches.

The proliferation of mobile smart devices with ever improving sensing capacities means that human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large scale and flexible sensing solution. The use of personal mobile devices is a sensitive issue, therefore it is mandatory for practical MCSs to preserve private information (the user's true identity, precise location, etc.) while collecting the required sensing data. However, well intentioned privacy protection techniques also conceal autonomous, or even malicious, behaviors of device owners (termed as self-interested), where the objectivity and accuracy of crowdsensing data can therefore be severely threatened. The issue of data quality due to untruthful reporting in privacy-preserving MCSs has been yet to produce solutions. Bringing together game theory, algorithmic mechanism design, and truth discovery, we develop a mechanism to guarantee and enhance the quality of crowdsensing data without jeopardizing the privacy of MCS participants. Together with solid theoretical justifications, we evaluate the performance of our proposal with extensive real-world MCS trace-driven simulations. Experimental results demonstrate the effectiveness of our mechanism on both enhancing the quality of the crowdsensing data and eliminating the motivation of MCS participants, even when their privacy is well protected, to report untruthfully.

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9× less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss.

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype1with multiple edge devices and a public Cloud, and conduct extensive experiments using real-world surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7× less bandwidth cost and 5.4× faster query response time than the cloud-only solution; and can improve query accuracy by up to 43.9% and achieve 15.8× speedup respectively, in comparison with edge-only approaches.

Mobile Crowdsensing is a promising paradigm for ubiquitous sensing, which explores the tremendous data collected by mobile smart devices with prominent spatial-temporal coverage. As a fundamental property of Mobile Crowdsensing Systems, temporally recruited mobile users can provide agile, fine-grained, and economical sensing labors, however their self-interest cannot guarantee the quality of the sensing data, even when there is a fair return. Therefore, a mechanism is required for the system server to recruit well-behaving users for credible sensing, and to stimulate and reward more contributive users based on sensing truth discovery to further increase credible reporting. In this paper, we develop a novel Cheating-Resilient Incentive (CRI) scheme for Mobile Crowdsensing Systems, which achieves credibility-driven user recruitment and payback maximization for honest users with quality data. Via theoretical analysis, we demonstrate the correctness of our design. The performance of our scheme is evaluated based on extensive real-world trace-driven simulations. Our evaluation results show that our scheme is proven to be effective in terms of both guaranteeing sensing accuracy and resisting potential cheating behaviors, as demonstrated in practical scenarios, as well as those that are intentionally harsher.

Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate large-scale crowdsourcing tasks (e.g., urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.

Security plays a vital role in promoting the practicality of Wireless Body Sensor Networks (BSNs), which provides a promising solution to precise human physiological status monitoring. A fundamental security issue in BSN is key management, including establishment and maintenance of the key system. However, current BSN key management solutions are either designed for specific phases of a BSN's life-time or restricted to strong assumptions such as homogeneous BSN composition, pre-deployed key materials, and existing secure path, which limits their applications in real-world BSNs. In this paper, we develop the Systematic Key Management (SKM) for practical BSNs, where basic human interactions are conducted for non-predeployed secure BSN initialization, and authenticated key agreement is achieved using lightweight non-pairing certificateless public key cryptography. We construct a BSN prototype consisting of self-designed motes and Android phones to evaluate the real-world performance of SKM. Through extensive simulations and test-bed experiments, we demonstrate that our lightweight SKM scheme manages to provide high security guarantee while outperforming state-of-the-art approaches in terms of both computation and storage efficiency.

The smart grid shall not only integrate the intermittent resources (IRs) to meet the diverse demands of users and reduce the greenhouse gas emission, but also integrate Electricity Vehicles (EVs) as the energy storage facility to smooth the bulk power generation over time. In this paper, we model and analyze the impact of integrating IRs and EVs on the bulk power generation in the smart grid. In particular, we introduce the reliability ratio to quantify the power generation capacity of intermittent resources and model the process of charging and discharging of EVs as a queuing system. We extend the Security-Constrained Economic Dispatch (SCED) and include the reliability limit of IRs and the number of EVs in the power generation dispatch process and formally analyze the effect of IRs and EVs on the bulk power generation. We conduct extensive simulation and our data shows that increasing IRs can decrease the bulk generation and the curve of bulk generation over time becomes smooth as the number of EVs increases.