The rapid growth of UAV-enabled low-altitude networks (LANs) increases the demand for secure data sharing to support mission-critical applications, yet existing solutions struggle with LANs dynamics, quantum vulnerabilities of traditional cryptography, and unbalanced accountability-privacy. To address these gaps, a blockchain-based privacy-preserving (BCPP) model has been introduced for UAV data sharing in LANs, with three key components: 1) a consortium blockchain optimized for UAV mobility, which replaces centralized trust with distributed consensus to ensure data integrity without single-point dependencies; 2) a novel lattice-based certificateless undeniable signature (CL-US) scheme built on the Ring-Learning With Errors (Ring- LWE) problem—this scheme binds UAV senders/receivers to data transmission for accountability while using pseudonymous identifiers to protect identity privacy. Formal security analysis under the random oracle model (ROM) proves that the proposed CL-US scheme achieves soundness and unforgeability against adaptive chosen-message attacks under the hardness assumption of the Ring-LWE problem. The performance evaluations of the key size and time consumption show that this CL-US scheme is efficient and storage-saving to similar schemes. This work provides a quantum-resilient solution balancing non-repudiation, privacy, and efficiency for dynamic LANs, laying a foundation for secure post-quantum LANs deployment in mission-critical scenarios.

Urban Smart Sensing Devices (SSDs) require timely and secure code updates to maintain reliable operation. However, trust verification in vehicle-assisted code dissemination remains insufficiently explored, making systems vulnerable to malicious vehicles that distribute corrupted or outdated code. This paper proposes a Trust-driven Reliable Code Dissemination (TRCD) scheme, where mobile vehicles opportunistically act as “code mules” for efficient and secure dissemination. TRCD employs a two-layer trust evaluation mechanism: (i) selected anchor SSDs evaluate local vehicle trust in real time, and (ii) the code center constructs global trust by aggregating UAV-verified and Local High-Trust Vehicle (LHTV) reported reputation information to identify trustworthy vehicles and exclude malicious ones. To improve dissemination efficiency, we further design (i) an anchor SSD selection algorithm for cost-efficient trust collection, (ii) a token-based mechanism for prioritizing high-value tokens under storage constraints, and (iii) a genetic algorithm-based UAV trajectory optimization method for trust-aware dissemination. We also provide theoretical analysis on convergence behavior, computational complexity, and performance bounds. Extensive simulations using real-world taxi trajectory datasets show that TRCD achieves 98.2% trusted-vehicle identification accuracy, 99.4% malicious-vehicle detection accuracy, and reduces UAV flight distance by 64.5% compared with baseline methods, while significantly improving dissemination reliability. These results demonstrate that TRCD provides an efficient, scalable, and secure solution for urban SSD code dissemination.

Mobile Crowdsensing (MCS) has emerged as an effective paradigm for large-scale data collection, yet it faces two inherently conflicting challenges: minimizing platform cost and reducing the Age of Information (AoI). Existing approaches primarily focus on task assignment optimization or assume fully rational worker behavior, which often limits their effectiveness in achieving timely data updates without incurring substantial costs. To address these challenges, a Herd Effect-based AoI-aware (HEA) data collection scheme is proposed to minimize AoI under budget constraints. Specifically, the HEA scheme incorporates insights from behavioral economics to influence workers’ mobility decisions by exploiting the herd effect. By incentivizing a subset of pioneer workers to adopt a high-effort mobility mode (i.e., the Boosted Mode), the observable behaviors of these pioneers induce subsequent workers to adjust their decisions accordingly, thereby forming a self-reinforcing herd behavior. This mechanism enables timely data collection while effectively controlling the overall incentive expenditure. The effectiveness of the proposed scheme is validated through rigorous theoretical analysis and extensive experiments conducted on multiple real-world datasets. Experimental results demonstrate that HEA consistently outperforms representative baseline schemes, achieving significant AoI reductions with marginal additional cost and substantially improving the trade-off between data timeliness and budget efficiency.

Mobile crowd sensing (MCS) has gained much attention as an important component of next-generation Internet in recruiting workers to collect data to build and deliver services to users for profit. However, recruiting reliable workers with low cost remains a critical challenge under the Information Elicitation Without Verification (IEWV) problem, where worker data quality cannot be directly observed. A large number of studies have been conducted assuming known data quality of workers and using incentives to motivate them to actively participate in data collection. Such assumptions limit their applicability in real-world MCS systems, as platforms cannot directly observe data quality after collection. Therefore, recruiting credible workers to maximize platform profit remains a challenging issue that has not been adequately addressed. In this paper, an LSTM-assisted Trust Worker Recruitment (LTWR) scheme is proposed to maximize platform profit in MCS. First, we propose a trust evaluation mechanism that utilizes the credible data to estimate the quality of workers, effectively addressing the IEWV problem. Second, an LSTM-based data completion method is employed to reduce sensing cost while preserving data quality. Third, we model platform revenue as a function of data quality and design a worker recruitment strategy to maximize profit. Finally, extensive experiments validate the effectiveness of the proposed LTWR scheme in terms of data quality, cost, and profit.

Quantum key distribution (QKD) offers unconditional security for mobile aerial networks, but existing protocols face challenges in low-altitude, continuously moving aerial networks because of hardware complexity and sensitivity to channel noise. This paper proposes mobile aerial semi-quantum communication (MASQC), a novel lightweight protocol that integrates and adapts decoy state analysis with time-batched polarization encoding over free-space optics to enable secure key distribution among aerial vehicles (AVs) and base stations. MASQC introduces six key innovations tailored for aerial networks: (1) a decoy state semi-quantum protocol implementation requiring only passive single-photon detection on AVs while providing robust security against photon-number-splitting attacks, (2) time-batched polarization encoding with 1-5 ms windows and real-time trajectory prediction to compensate for Doppler effects, (3) comprehensive post-quantum authentication infrastructure using CRYSTALS-Dilithium signatures and CRYSTALS-Kyber key encapsulation, (4) adaptive security thresholding (0.08-0.12) with dual-layer error analysis combining conventional QBER and decoy state bounds, (5) hybrid quantum-classical resilience with emergency key pools and perfect forward secrecy mechanisms, and (6) encrypted relay capabilities through neighboring AVs during line-of-sight disruptions. Unlike prior semi-quantum or decoy-state protocols designed for static environments, MASQC provides a holistic system solution addressing the unique constraints of low-altitude aerial networks. Comprehensive simulation results using the Qiskit framework demonstrate the effectiveness of MASQC with an 85.3% network success rate, high bits/second key generation rate per AV, and robust security validation including a high attack detection probability against sophisticated eavesdropping attempts.

Unmanned aerial vehicles (UAVs) have become indispensable tools in logistics, mapping, and disaster response, etc. However, effective cooperation among multiple UAVs remains challenging due to limited direct communication and energy constraints in aerial environments. Conventional device-to-device (D2D) frameworks on the ground are insufficient to cope with the complexity in UAV-assisted networks, such as frequent topology variation, interference fluctuation, and battery power. To overcome these limitations, this paper proposes a semantic-aware cellular D2D communication architecture, namedAirtalking, which integrates semantic encoding and decoding to enable efficient UAV-to-UAV (U2U) data exchange. Unlike traditional bit-level transmission, semantic communication focuses on delivering essential information, thereby reducing payload size and improving time and energy efficiency. We design algorithms to balance latency and energy consumption and simulate with different scheduling policies under varying flight conditions. The results show that the proposed aerial U2U framework can improve communication stability and efficiency, demonstrating the feasibility of deploying semantic communication in practical multi-UAV systems.

Owing to the harsh underwater environment and limited energy replenishment, extending network lifetimes and achieving energy efficiency are critical challenges in underwater wireless sensor networks (UWSNs). In this paper, a novel multiobjective Arctic puffin optimization (MOAPO) method that is specifically tailored for clustering and routing in UWSNs is proposed. Within the proposed MOAPO framework, K-means++ clustering is first used to optimize the initial cluster head (CH) positions, thereby improving clustering performance and ensuring more effective coverage and resource utilization. A feedback-based mechanism is used to adaptively adjust the behavior conversion factor to balance global exploration and local exploitation, thereby avoiding premature convergence and improving the quality of the selected CHs. A fitness function that incorporates the node energy, communication distance, and CH selection frequency is constructed, with the weights dynamically tuned according to the current energy state of the network, which results in energy-efficient and energy-balanced CH selection. Based on the selected CHs, a multiobjective routing strategy in which the energy levels, delays, and packet loss rate are considered is applied to construct reliable data transmission paths. The simulation results confirm that compared with existing methods, the MOAPO method achieves lower energy consumption and a longer network lifetime, thus demonstrating superior robustness and adaptability.

Cold chain logistics systems (CCLSs) require the secure, efficient, and fresh management of cold products. Blockchain technology facilitates cross - institutional data - sharing for CCLSs, yet heterogeneous blockchains across different institutions give rise to ’data islands’ and privacy issues. Facing these problems,we propose a cross-chain privacy-preserving (CCPP) framework based on a notary mechanism. This CCPP model aggregates heterogeneous institutional chains through a notary network, enabling inter-blockchain operability while eliminating the data islands. Meanwhile, to ensure transaction security and address the quantum-vulnerability of traditional signature schemes, we design an identity-based ring signature (ID-RS) scheme. The ID - RS combines the identity mechanism (for traceability) with the ring mechanism (to guarantee the signer’s unconditional anonymity), and combats quantum threats by relying on the lattice assumption (a foundation for post-quantum security). Under the random oracle model, we prove the correctness, unforgeability, and anonymity of the ID-RS scheme. Additionally, experimental results under 80-bit security settings show that our ID-RS scheme outperforms other signature schemes in signature size and verification latency. These findings validate that the CCPP framework and the ID-RS scheme together offer an efficient and practical foundation for privacy-preserving, quantum-resistant data sharing among heterogeneous blockchain-based CCLSs.

Parameter-efficient fine-tuning (PEFT) has become the mainstream paradigm for adapting large language models (LLMs) to downstream tasks. However, existing PEFT approaches often suffer performance degradation when applied to complex tasks. To address this limitation, we propose a hierarchical Pyramid-Structured Low-Rank Adaptation (PRLoRA) method. PRLoRA constructs a pyramid network architecture characterized by multi-scale representations that effectively balance local precision with global contextual awareness. In addition, it incorporates a local optimization module that evaluates the importance of LLM weights and adaptively selects locally optimal positions within the pyramid, thereby accelerating convergence. The pyramid structure fusion LoRA (PSFLoRA) module within PRLoRA fuses multi-scale features of the pyramid to enhance the method’s effective rank. We conduct comprehensive experiments on 14 datasets; PRLoRA achieves state-of-the-art performance on both the GLUE benchmark and arithmetic reasoning tasks. In particular, it outperforms LoRA by 6.33% on AddSub and surpasses the previous state-of-the-art, DoRA, by 1.89% on GSM8K. Extensive ablation studies validate the contribution of each component and demonstrate PRLoRA’s robustness across diverse architectures (LLaMA3, OPT, BLOOM, Gemma) and multiple model sizes (1B, 3B, 8B).

Multimodal Large Language Models (MLLMs) integrate multimodal encoders with Large Language Models (LLMs) to overcome the limitations of text-only models. Traditional LLMs are deployed on high-performance cloud servers, but MLLMs, which process multimodal data, face high transmission latency and privacy risks when tasks are offloaded to the cloud. Intelligent edge computing is a promising solution for supporting such latency-sensitive and privacy-sensitive tasks. However, the heterogeneity of edge environments makes efficient MLLM inference challenging. In this work, we enhance MLLM inference efficiency in heterogeneous edge environments by decoupling MLLM into LLM and multimodal encoders, deploying the LLM on high-performance devices and the multimodal encoders on lower-capability devices. Additionally, we observe that processing MLLM tasks in edge environments involves numerous configuration parameters that impact inference speed and energy consumption in an unknown and possibly time-varying fashion. To address this challenge, we present an adaptive scheduling algorithm that assigns parameters to tasks or minimizing energy consumption while meeting maximum latency constraints. The results of extensive experimental trials demonstrate that the proposed approach consistently outperforms existing state-of-the-art methods, achieving significant improvements in both latency reduction and energy efficiency.

With powerful cloud computing capacities, machine learning as a service (MLaaS) framework provides intelligent services and well-trained artificial intelligence (AI) models by clouds for resource-constrained end devices. However, there are AI model leakage and illegal abuse issues during model transmission and deployment. Existing mainstream protection methods face the following problems: (i) The watermark-based methods only provide passive verification afterward rather than active protection. (ii) Encryption-based methods are low efficiency in computation and low security in key storage. (iii) Existing methods are not device-bind without the ability to avoid illegal abuse issues. To deal with these problems, we propose a device-bind and key-storageless cloud-AI model intellectual property (IP) protection mechanism. First, a physical unclonable function (PUF) and permute-diffusion encryption empowered cloud-AI model protection framework is proposed, including the PUF-based secret key generation and the geometric-value transformation based weights encryption. Second, we design an Anderson PUF based key generation protocol to generate device-bind robust secret keys. Third, a permutation and diffusion-based intelligent model weights encryption/decryption method is proposed for effective cloud-AI model protection, where chaos theory is utilized to convert PUF-based secret keys to encryption/decryption keys. Finally, experimental evaluation demonstrates the effectiveness and reliability of the proposed intelligent model IP protection mechanism.

The General Data Protection Regulation (GDPR) aims to enable the free flow of personal data while enhancing individual control. Integrating privacy-preserving data retrieval methods can provide stronger protection for personal privacy. However, existing approaches lack compliance mechanisms aligned with the GDPR, making it difficult in practice to simultaneously satisfy the principles of lawfulness and data minimization, while also exhibiting clear limitations in both security and efficiency. To address these problems, we propose VOLE-PDRAA, an efficient privacy-preserving data retrieval protocol with anonymous authorization based on the Vector-OLE (VOLE). Specifically, VOLE-PDRAA constructs a VOLE-blinded identifier by integrating pseudorandom linear encoding with VOLE-derived correlation vectors, enabling rigorous anonymity guarantees during authorization. Building on this, the protocol incorporates a non-interactive zero-knowledge proof (NIZK) to achieve anonymous authorization for the data subject and to generate verifiable informed consent proofs, thereby meeting the principle of lawfulness. Meanwhile, the data controller can verify whether each retrieval request falls within the scope authorized by the data subject without learning any identifiable information, thus maintaining adherence to the data-minimization principle in a post-quantum environment. Furthermore, VOLE-PDRAA utilizes labeled private set intersection (labeled-PSI) to safeguard the confidentiality of identifiers and their associated records under post-quantum security conditions, while enabling large-scale batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability (UC) framework. Experimental evaluation validates its superiority through comparison with state-of-the-art work.

Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.

In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.

As the in-network computing (INC) paradigm evolves, efficient scheduling of dependent tasks within complex network systems becomes increasingly crucial. The network needs to handle high-level resource demands while adhering to strict latency requirements. Deterministic delay constraints are particularly critical in applications that rely on directed acyclic graphs (DAGs). To address this challenge, we first propose a deterministic delay-aware task scheduling optimization problem over INC to maximize resource utilization and ensure task acceptance. We accurately establish the complex deterministic delay constraint through traffic arrival and service curves and utilize network calculus for conversion to facilitate solving. Then, we further transform the task optimization problem into MDP and develop a deep reinforcement learning (DRL) algorithm that combines graph neural network (GNN) and delay-aware proximal policy optimization (DPPO) to solve it, called the Deterministic Delay-aware Task Scheduling (DDTS) scheme. It utilizes multilayer GNN to handle task dependencies and applies the DPPO algorithm to introduce deterministic delay penalty factors to evaluate policy operations, achieving optimal task scheduling. The simulation results demonstrate the significant advantages of the DDTS scheme over existing algorithms and task scheduling schemes in terms of task acceptance rate and resource utilization.

In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.

The diverse applications of internet of things (IoT) have significantly increased the demand for efficient and reliable wireless networks, making power consumption a critical concern. Reconfigurable intelligent surface (RIS) have been proposed as a solution to mitigate power consumption in wireless communication systems by dynamically adjusting the signal propagation direction between transmitters and receivers. Due to the operational status of IoT devices and the complex association relationships between RISs and devices, a dynamic and highly variable communication environment is typically resulted, which renders power consumption optimization more challenging, as compared to conventional methods that do not incorporate RISs. This paper addresses the optimization of power consumption and IoT device coverage rate in an indoor communication scenario to improve system performance. We design an Adaptive Hybrid Optimization Strategy based on the association between RISs and devices to maximize the device coverage rate. Additionally, we optimize the phase shifts of multiple RISs to minimize system power consumption using the relaxation transformative method while satisfying the coverage rate constraint. Extensive simulation results demonstrate that, in an indoor environment with several obstacles, the proposed algorithm achieves a higher device-centric coverage rate compared to a solution without RIS and exhibits lower power consumption compared to strategies that rely more on base stations.

Generating sketches that accurately reflect the content of reference images presents numerous challenges. Current methods either require paired training data or fail to accommodate a wider range and diversity of sketch styles. While pre-trained diffusion models have shown strong text-based control capabilities for reference-based content sketch generation, state-of-the-art methods still struggle with reference-based sketch generation for given content. The main difficulties lie in (1) balancing content preservation with style enhancement, and (2) representing content image textures at varying levels of abstraction to approximate the reference sketch style. In this paper, we propose a method (Ref2Sketch-SA) that transforms a given content image into a sketch based on a reference sketch. The core strategies include (1) using DDIM Inversion to enhance structural consistency in the sketch generation of content images; (2) injecting noise into the input image during the denoising process to produce a sketch that retains content attributes while aligning with, yet differing in texture from, the reference. Our model demonstrates superior performance across multiple evaluation metrics, including user style preference.

Music Information Retrieval (MIR) is an important field in music data mining while Automatic Music Transcription (AMT) is a fundamental task in MIR, playing a crucial role in enabling various downstream applications. A key challenge in AMT is the lack of pairing between music data and symbolic labels, as these two types of information often exist independently, rendering them unusable for model training. Current AMT models often struggle with poor generalization, limiting their effectiveness in transcribing music beyond the datasets they were trained on. Furthermore, existing AMT datasets suffer from data imbalance, with most research efforts focused on highresource instruments, neglecting the transcription challenges associated with low-resource instruments. To address these issues, we propose a novel approach based on single note audio synthesis, which enables AMT model training using only MIDI files. Our method not only overcomes the reliance on paired music data and symbolic labels but also introduces timbre expansion, increasing the diversity and volume of training data. This significantly enhances the zero-shot capabilities of AMT models. Additionally, our approach demonstrates strong potential for instrument transfer, breaking the data barriers between different instruments and providing critical support for low-resource instrument training. Experimental results show that our method significantly improves zero-shot performance and enables effective instrument transfer, leading to enhanced transcription accuracy for low-resource instruments.

Large language model (LLM) have shown their potential to improve the efficiency and effectiveness of medical works. On the other hand, developments in federated learning (FL) have made it possible to utilize medical textual datasets in different data silos to fulfilling the need to improve medical insight while addressing stringent privacy and security concerns. However, fine-tuning LLMs in FL results in high communication costs due to frequent transmissions between the FL server and the client and a large number of trainable parameters. To solve this problem, we propose GFLLM: Group Aggregation for Federated Learning in Large Language Models. Our method first assigns the clients into groups and performs partial model aggregation within the group, and then the clients with larger bandwidth with the cloud server upload the weights. The results of simulation experiments demonstrate that our proposed method saves 27.5 % of the time on average over traditional FL when facing LLMs with 7 billion parameters.

Blockchain-enabled Internet of Medical Things (BIoMT) has received extensive attention and in-depth research to solve the centralized, data island problems with the rapid developments of blockchain-related technologies. However, many different chains with different data structures, consensus protocols, and cryptographic algorithms are constructed, which brings a new “data island” problem. Meanwhile, the cryptographic algorithms used in most current BIoMT systems are weak against quantum attacks. In this article, a cross-chain privacy-preserving (CCPP) model and a designated verifier proxy signature (DVPS) scheme have been proposed. This CCPP model is equipped with the relay chain technology and DVPS to achieve secure cross-chain medical data-sharing among different BIoMT systems. The DVPS scheme is constructed with lattice theory, which can achieve signer proxy, designated user verification, and anti-quantum attack. Then, the security proof shows that the proposed DVPS can capture the security properties of correctness, unforgeability, the signer’s anonymity, and nontransferability. The performance evaluations show that the cross-chain transactions are efficient and stable with the transaction number increasing, and the proposed DVPS is efficient about the key size, time consumption, and energy consumption. This work can also improve the privacy security of system users and medical data in BIoMT systems and promote the value play of medical data.

Backscatter communication (BC) is a promising wireless communication technology due to its low cost, ultra-low power consumption, and ease of maintenance. However, the broadcasting and openness nature of BC by backscattering incident radio signals for message transformation introduces severe security threats, creating a bottleneck that hinders its further development. Mutual and handover authentication across multiple access points (APs) is essential to secure large-scale BC systems containing mobile backscatter devices (BDs). However, an effective scheme is still absent in the current literature. In this paper, we propose HABC, a mutual and handover authentication scheme designed to secure BC systems, which can resist various attacks. HABC leverages the physical layer feature channel impulse response (CIR) to authenticate BD. Using secret keys, the BD can verify the source of a received signal. When a BD transits from the coverage of a source AP to a target AP, HABC supports handover authentication through the control of a server based on BD location prediction to maintain continuous communications. Theoretical analysis and numerical experimental evaluation validate the satisfactory performance of HABC in terms of accuracy and robustness, as well as its superiority through comparison with cutting-edge related work.

Traffic signal control plans significantly impact transportation system efficiency by regulating traffic conditions at intersections. Adaptive traffic plans that can adjust to real-time road conditions are more effective as a result. Reinforcement learning succeeds at adapting strategies based on feedback derived from the environment, and is thus proficient in dealing with complex traffic scenarios that change dynamically. However, current RL methods rely on significant computational periods to obtain precise functioning mechanisms within the scenarios, posing barriers to their adoption for new scenarios. In addition to directly optimizing the RL model itself to enable fast learning from scratch, another idea is to make the model transferable or reusable with the learned experience. Given the diversity of migration scenarios, the underlying control algorithm should guarantee convergence and endeavor to be parameter-insensitive. From the above concern, we proposed MetaSignal, an efficient meta-reinforcement learning method for traffic signal control. Specifically, our approach utilizes the Fourier basis as the value function approximation in reinforcement learning, distinguishing it from methods like neural network approximation. This linear approximation offers advantages such as convergence facilitation, error bound achievement, and reduced parameter dependence. The meta-learning framework adopts a model-agnostic approach, enabling effective adaptation of the base model to the target scenario with limited training cost. Empirically, the proposed method shows promising and stable performance for traffic signal control through comprehensive comparison experiments in both synthetic and real-world traffic networks.

Vehicle GPS devices provide abundant trajectory data that can be exploited to generate helpful pick-up service recommendations for taxi drivers. However, existing trajectory clustering approaches struggle to perform well on trajectory data with different distribution characteristics (e.g., dense in downtown and discrete in suburbs) simultaneously. Additionally, current prediction models mainly focus on subsection prediction but fail to produce accurate multisection predictions. To this end, we propose a recommendation framework, namely TaxiGuider, that can generate accurate pick-up cluster recommendations for taxi drivers. First, historical pick-up points are extracted from the entire vehicle trajectory data after preprocessing. Then, a graph Laplacian-based multiple spatial-temporal clustering approach is presented to generate clusters that can effectively match the distribution of trajectory data. Furthermore, a pick-up frequency prediction model that employs a multi-head attention mechanism is proposed to produce accurate multisection predictions that can help taxi drivers make comprehensive considerations for their next destination. Finally, top$N$clusters with the highest predicted pick-up frequency are recommended to the target taxis according to their request. Experimental results on real-world datasets suggest that TaxiGuider outperforms state-of-the-art approaches in terms of both subsection and multisection predictions. Moreover, it produces pick-up cluster recommendations with superior prediction and classification accuracy simultaneously.

To support the escalating traffic demands in the 6G era, the novel computing paradigm of in-network computing (INC), where tasks can be processed on the forwarding path, is emerging with enhanced network performance and improved service quality. Considering the large network scale with high dynamics, effective task scheduling in INC paradigm becomes imperative but challenging. In this work, we investigate the task scheduling in INC paradigm to minimize both the task delay and network energy consumption, while considering constraints on task latency, traffic dynamics, and available communication and computing resources. We first construct a novel computing and communication model considering the traffic variation in network nodes on the transmission path. To solve the task scheduling problem, we propose an algorithm, named as NBFNDRL, which is a deep reinforcement learning (DRL) algorithm based on Neural Bellman Ford networks (NBFNet). NBFNet can learn high-dimensional correlated graph structural information, utilize message-passing mechanisms to represent changes in traffic between adjacent nodes, predict scheduling paths, and provide a basis for DRL decision-making. The DRL agent trains and updates NBFNet through interaction with the environment. Finally, we present simulation results to demonstrate the effectiveness of our proposed approach in comparison to benchmark algorithms and various computing paradigms.

Large language models (LLMs) bring great potential for next-generation malware family recognition with their capacity to understand complex code semantics by integrating multi-dimensional data features. However, existing fine-tuning methods still rely on well-labelled datasets and powerful computation resources, which is particularly challenging when the variety and amount of malware grow in real-time. To more effectively recognize unknown malware varieties based on LLMs, a novel multi-dimensional knowledge-embedded prompt learning (MKPL) framework is proposed, in which prompts are generated through two main steps: 1) cross-linguistic prompt paraphrasing (CPP) for embedding multi-dimensional knowledge into templates, and 2) prompt scoring for selecting the most effective prompt templates. Moreover, to reduce feature loss during prompt tuning, a sampling-infer-concatenation pipeline is designated to process these long API malware sequences. Specifically, a single-sentence template can be upgraded to a multi-sentence template by integrating statistic features into CPP, which is essential to improve the robustness of recognition results. Comprehensive experiments across eight malware families in few-shot scenarios demonstrate the proposed method’s superior performance in all metrics.

In this paper, we address the significant challenge of data scarcity in the field of crack segmentation, a key aspect of structural health monitoring. To tackle this, we introduce the CrackGrowDiff framework, an innovative approach for expanding crack datasets. Utilizing a two-stage controllable generation process that combines a random walk algorithm and semantic diffusion models, our framework minimizes discrepancy of misalignment between synthetic data and original data while enhancing data informativeness. We further ensure the quality and informativeness of synthetic data through feature space diversity, employing a pre-trained Variational Autoencoder (VAE) for selection based on Kullback-Leibler (KL) divergence. Comparative experiments demonstrate CrackGrowDiff’s superiority over traditional data augmentation and GANs-based methods, making it a substantial advancement in addressing the data scarcity in crack segmentation tasks. A DEMO and related code will be made public: https://huggingface.co/spaces/QinLei086/Two-stage-SDM-for-crack-dataset-expending

Recently, Peer-to-Peer Semantic Network (P2PSN) has significantly boosted transmission efficiency among humans, machine agents, and smart devices. Despite these enhancements, the intelligent components within P2PSN pose vulnerabilities to backdoor attacks, where adversaries introduce specific pattern triggers to poison the training set, which prompts the well-trained P2PSN system to generate targeted malicious predictions when inputted with trigger-embedded data. Current backdoor methodologies exhibit several deficiencies: 1) pattern-based trigger lacking physical meaning and explainability; 2) visible trigger design that can be easily detected by defenders; 3) unstable attack performance resulting from communication interference. To overcome these shortcomings, we propose a novel invisible instruction backdoor attack scheme on Peer-to-Peer Semantic Networks: InviINS. The proposed method embeds text instructions on partial training samples as invisible triggers instead of pattern triggers, thereby poisoning the training set of P2PSN model before learning without visually discernible changes in data, and subsequently backdooring the model via training. In InviINS, adversaries can directly set instructions based on practical scenarios to launch attacks. Meanwhile, to accelerate backdoor convergence, a contrastive backdoor training methodology is presented to enhance the model’s sensitivity to instruction triggers and bolster its prediction performance on normal samples. Experiments with different poisoning-rates, signal-to-noise ratios, channel usages, and trigger types demonstrate that the InviINS can achieve a high attack success rate (~ 100%) while preserving the model performance on main tasks (accuracy drop < 3%).

Edge-Level Graph Learning System (EGLS) exhibits diverse applicability in management of distributed vehicular networks, e.g., flow prediction, route planning, and accident forecasting. For the EGLS training, expensive hardware resource consumption, traffic data collection, and dedicated training procedures make the learning algorithms become valuable intellectual property (IP) for the EGLS owner (e.g., Uber and Lyft), and they cannot tolerate the infringement act of their models’ intellectual property. To enhance its IP protection, we present ActIPP, the first active IP protection methodology for EGLS, which incorporates a built-in access control function in the model to safeguard against unauthorized queries. Specifically, it is achieved via a creative edge backdoor mechanism, wherein the edge training samples are poisoned via user-specific access tokens to induce legal outputs from a well-trained EGLS model for authorized users. Moreover, related token regulating strategies were proposed to dynamically realize the addition and revocation of user tokens by model retraining to guarantee access control in EGLS. Additionally, a Graph Mutual Information-based adaptive token generation method is presented to augment the access control embedding. Based on experiments with various real-world datasets, ActIPP demonstrates high success rates of IP protection (accuracy drop < 4%) under various scenarios and efficiently prevents unauthorized access (unauthorized access accuracy < 6%).

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text and producing fluent, succinct, and precise linguistic expressions. Limited battery life and computing power make it challenging to process LLM inference tasks in mobile devices. Intelligent edge computing brings the opportunity to help users process LLM inference tasks in real-time by offloading computations to nearby edge devices. However, due to the undetermined relationship between various task requirements and offloading configurations, inefficient offloading leads to unaffordable additional energy consumption, especially for intelligent tasks. This paper first investigates the energy consumption issue with different offloading configurations and task requirements in an intelligent edge testbed. According to the preliminary experiment results, we formulate the LLM offloading problem as a multi-armed bandit (MAB) problem and then use an upper confidence bound (UCB) bandit algorithm to find the energy-efficient offloading configurations. Extensive simulation results show that our approach enhanced the energy efficiency for offloading LLM inference tasks with different requirements in the intelligent edge environment.

Summary Deep learning has shown its great potential in real‐world applications. However, users(clients) who want to use deep learning applications need to send their data to the deep learning service provider (server), which can make the client's data leak to the server, resulting in serious privacy concerns. To address this issue, we propose a protocol named EPIDL to perform efficient and secure inference tasks on neural networks. This protocol enables the client and server to complete inference tasks by performing secure multi‐party computation (MPC) and the client's private data is kept secret from the server. The work in EPIDL can be summarized as follows: First, we optimized the convolution operation and matrix multiplication, such that the total communication can be reduced; Second, we proposed a new method for truncation following secure multiplication based on oblivious transfer and garbled circuits, which will not fail and can be executed together with the ReLU activation function; Finally, we replace complex activation function with MPC‐friendly approximation function. We implement our work in C++ and accelerate the local matrix computation with CUDA support. We evaluate the efficiency of EPIDL in privacy‐preserving deep learning inference tasks, such as the time to execute a secure inference on the MNIST dataset in the LeNet model is about 0.14 s. Compared with the state‐ofthe‐art work, our work is 1.8–98 faster over LAN and WAN, respectively. The experimental results show that our EPIDL is efficient and privacy‐preserving.

The integration of vehicle edge computing (VEC) and air-ground integrated network is considered as a key technology to achieve autonomous driving. It exploits the ubiquitous service coverage and enables tasks to be offloaded to various components, such as high-altitude platform (HAP), unmanned aerial vehicle (UAV), and roadside unit (RSU). In this article, we address the challenge of minimizing the overall task offloading delay in the air-ground integrated VEC network through a joint multicomputation equipment selection and multidimensional resource allocation (JCESRA) problem. Considering the nonconvexity inherent in the problem, we employ the fundamental idea of the block coordinate descent (BCD) method to tackle it. Initially, we exclude the HAP and decompose the primal problem into three subproblems: 1) low-altitude computation equipment selection; 2) joint bandwidth and computation resource allocation; and 3) UAV trajectory design. The first subproblem, which involves integer programming, is solved by using the many-to-one matching method. Meanwhile, we utilize the CVX and successive convex approximation (SCA) method to solve the last two subproblems, respectively. Considering the matching externality, we utilize the coalition game method to deal with it. Based on the solutions of the three subproblems, the JCESRA algorithm without considering the HAP has been proposed. Subsequently, we consider the HAP into the problem. Because the task offloading decision and computation resource allocation of the HAP problem can be viewed as a knapsack problem, we utilize the dynamic programming method to solve it. Because some tasks are offloaded to the HAP, there are some redundant computation resources in UAVs and RSU. We reallocate the computation resources of UAVs and RSU to further reduce the task offloading delay. At last, we present the complete JCESRA algorithm. The simulation results unequivocally indicate that the proposed JCESRA algorithm outperforms other algorithms by significantly reducing the task offloading delay.

Blockchain technology brings a method for cross-institution health data sharing through the systems of the Internet of Medical Things (IoMT). As different medical institutions compete to establish their own blockchain ledgers, it leads to new problems of “data island”. In this paper, a relay chain-based multi-chain fusion (MCF) model has been designed for blockchain-enabled IoMT (BIoMT), which can achieve cross-institution health data sharing by composing different blockchains together. In this MCF model, the existing patient private health chain, medical institution chain, and government supervision chain compose a cross-chain health data-sharing platform, which extends the storage capacity of health data, and the capacity of data sharing among different departments, institutions, and fields. Meanwhile, a cross-chain transaction model has been established which helps to achieve secure cross-chain transactions among different medical institutions. Then, to guarantee user privacy in the cross-chain transaction process, a designated verifier signature (DVS) scheme is proposed. Only the designated verifier can verify this DVS and other users cannot identify the real signer. This DVS also can achieve the anonymity of the signer as the third party cannot distinguish the signature generated by the signer or the verifier. Moreover, the proposed DVS scheme can be proved to capture the unforgeability, non-transferability, and signer anonymity with the random oracle model. The theoretical analyses and efficiency comparisons are given which show the efficiency of the proposed DVS scheme compared with similar schemes. The performance simulation of the cross-chain transaction shows that the MCF model is secure and practical for cross-chain health data sharing among different BIoMT systems.

Compared with terrestrial network, the air-ground integrated network consisting of unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection. Therefore, the air-ground integrated network can provide effective communication services for the Internet of remote things (IoRT). In order to reduce the end-to-end (e2e) packet delay and avoid network congestion of the two-hop network, we investigate a joint packet scheduling, resource allocation, and UAV trajectory design problem, with the objective of minimizing the average packet queue delay from HAP to IoRT devices in the air-ground integrated network. This problem is non-convex and difficult to solve by the traditional methods. In order to solve this problem, we reformulate it into a Markov decision process (MDP) firstly. And then, considering there are continuous and discrete hybrid action spaces in the MDP, we separate the primal action spaces into two sub-action spaces, and utilize the basic idea of multi-agent deep deterministic policy gradient (MADDPG) and multi-agent double deep Q network (MADDQN) methods to solve them, respectively. After that, in order to improve the stability, convergence rate and learning efficiency, we introduce the basic idea of adaptive prioritized experience replay, and propose a hybrid MADDPG-adaptive prioritized experience replay (MADDPG-APER) algorithm. Simulation results show that the proposed algorithm can reduce the average packet queue delay compared with other benchmark algorithms.

In the Internet of Autonomous Vehicles (IoAV), task offloading is a method to address computationally intensive tasks and ensure the safe operation of vehicles. However, under extreme weather conditions, the number of these tasks significantly increases, posing higher risks and challenges. Therefore, to mitigate risks and ensure the safe operation of vehicles, it is crucial to make quick and effective decisions during the task offloading process. Currently, most methods in this domain utilize Deep Reinforcement Learning (DRL). However, the large number of parameters in deep networks results in the characteristics of long decision time and large consumption of computational resources. In order to solve this problem, this paper proposes a task offloading scheme named Dynamic Pricing Driven Double Broad Reinforcement Learning (DP-DBRL), which utilizes Double Broad Reinforcement Learning (DBRL) to reduce model memory consumption and decision time. Additionally, it considers the high-speed mobility and resource variability to devise a more efficient dynamic pricing scheme that minimizes the overall delay in task processing for vehicles. To validate the proposed scheme, we conduct simulations using the VISSIM platform, meanwhile, we simulate task offloading scenarios under extreme weather conditions by randomly reducing factors such as the transmission rate and task execution efficiency of infrastructure and vehicles on the road. Finally, we deployed the proposed scheme both locally and on the Huawei Atlas 500 device to demonstrate its effectiveness and lightweight nature.

The Internet of Robotic Things (IoRT) thrives in extreme environments where human operation is often unfeasible, positioning it a pivotal innovation for future enhancements in quality of life. Nonetheless, IoRT faces substantial challenges, particularly in timely environmental sensing and decision making. This article introduces a novel reconfigurable intelligent surface (RIS)-enabled integrated sensing, computing, and communication (ISCC) system, specifically tailored for IoRT applications. The proposed system leverages RIS technology to enhance the transmission rates during critical task offloading processes from robots to mobile edge computing (MEC) platforms, addressing a significant bottleneck in current IoRT frameworks. Within our system, we address a complex optimization problem that aims to simultaneously boost computational speed, communication rate, and sensing accuracy, thereby maximizing the overall system performance. Given the nonconvex nature of this challenge, a block coordinate descent (BCD) algorithm is employed to decompose the problem into two manageable subproblems effectively. The first subproblem focuses on minimizing computing latency through strategic allocation of edge computing resources, while the second targets maximizing communication speed and improving sensing precision. To tackle these objectives, our approach integrates alternating optimization (AO) with objective function conversion techniques, crafting a robust methodology that adapts to the dynamic needs of IoRT environments. Our extensive simulations validate the proposed algorithm’s effectiveness, showcasing significant enhancements in Quality of Service (QoS) and notable reductions in system latency.

Due to hardware limitations, Internet of Things (IoT) devices without integrated security become easy targets for network attacks. IoT device identification is significant for network security management. Despite many efforts, previous studies either require excessive features raising concerns about efficiency and privacy, or underutilize the data resources to fulfill the potential of simple features. Moreover, the severe data imbalance problem is unaddressed. In this article, we present IoTProfile, an efficient IoT device identification framework via time series dictionary. It only considers simple packet-level attributes and maps them into different time windows. On this basis, it further follows a shuffle&split organization scheme to structure the imbalanced data as multichannel time series. By performing random convolutional kernel transformations in two ways and aggregations, IoTProfile captures discriminative patterns and forms the frequency count of recurring patterns to profile the network behaviors of IoT devices over a period of time. The experimental results show that IoTProfile is superior to the other state-of-the-art methods in terms of both identification effectiveness and time overhead, achieving 99.81% and 97.65% Macro-F1 scores on the University of New South Wales and University of New Brunswick data sets in under 4 min.

The immersive experience and 360-degree visual stimulation offered by virtual reality (VR) have contributed to its widespread adoption in games, education, and healthcare. The quality of experience (QoE), as a significant performance indicator, is used to measure user experience from subjective and objective perspectives and is required to satisfy high data rate, low delay, and high reliability in the wireless VR system. To achieve a higher data rate for VR users, a terahertz (THz) network is deployed. However, THz frequency experiences severe signal attenuation due to complex indoor obstacles, which can be alleviated by utilizing a reconfigurable intelligent surface (RIS) equipped with programmable metamaterial reflective elements. Taking inspiration from these considerations, this paper investigates a new framework for indoor multi-user multi-RIS-assisted THz wireless VR systems. Based on the scenario, an optimization problem is formulated to maximize the QoE by jointly optimizing the passive beamforming at RIS, the transmit power allocation among VR users, and the rendering capacity allocation among virtual objects. To achieve an optimal solution, we decompose the optimization problem into two stages: stage-1 aims to minimize BER and maximize data transmission rate, while stage-2 aims to maximize rendering capacity among virtual objects. Objective function conversion and alternative optimization (AO) methods are employed to address the two problems. Extensive simulations are conducted to validate the feasibility of the proposed system model and to showcase the superior performance of the proposed method in terms of QoE compared to other baseline methods.

This letter proposes a new atmospheric phase screen (APS) compensation method for ground-based synthetic aperture radar interferometry (InSAR). Conventionally, the APS trend is retrieved employing the assumption that the displacement component is a high spatial frequency component, thus making it separable from the APS trend and easy to be identified as outliers during the trend estimation whether using least squares or spectral analysis. However, landslides generally occur over a wide area forming a low spatial frequency component similar to the atmospheric component, which could give significant bias during the parameter estimation. This led to the suppression of the observed displacement from its true since it was partly misidentified as the APS. This specific APS misidentification issue is rarely mentioned in previous literature regarding APS compensation. A robust method to address this issue is proposed in this letter, dubbed best subsample consensus (BeSSaC). This method employs a robust estimation technique using the least median absolute residual criteria, it is shown that a wide-displacement component can be effectively separated from the APS using this technique.

Thyroid cancer is the most pervasive disease in the endocrine system and is getting extensive attention. The most prevalent method for an early check is ultrasound examination. Traditional research mainly concentrates on promoting the performance of processing a single ultrasound image using deep learning. However, the complex situation of patients and nodules often makes the model dissatisfactory in terms of accuracy and generalization. Imitating the diagnosis process in reality, a practical diagnosis-oriented computer-aided diagnosis (CAD) framework towards thyroid nodules is proposed, using collaborative deep learning and reinforcement learning. Under the framework, the deep learning model is trained collaboratively with multiparty data; afterward classification results are fused by a reinforcement learning agent to decide the final diagnosis result. Within the architecture, multiparty collaborative learning with privacy-preserving on large-scale medical data brings robustness and generalization, and diagnostic information is modeled as a Markov decision process (MDP) to get final precise diagnosis results. Moreover, the framework is scalable and capable of containing more diagnostic information and multiple sources to pursue a precise diagnosis. A practical dataset of two thousand thyroid ultrasound images is collected and labeled for collaborative training on classification tasks. The simulated experiments have shown the advancement of the framework in promising performance.

Social virtual reality (VR) applications provide a diverse and evolving ecosystem for different groups to socialize in VR. Understanding how people explore social VR applications is crucial for VR developers, such as designing social VR content. Previous work has focused on interviewing participants to study the user experience (UX) of social VR. However, the potential value of user reviews of social VR platforms is largely unexplored. In this article, we collect 105 757 user reviews of nine social VR applications from two digital distribution platforms (Steam and Oculus) to study the impact of social VR on people by in-depth analysis of reviews related to avatars, harassment, and physical reactions of different groups. We observe that players prefer avatar customization, and social VR applications are suitable places for some groups, such as lesbian, gay, bisexual, transgender, queer (LGBTQ). However, there are also many complaints from players about harassment and bullying in these social VR applications. Our findings highlight potential design implications of social VR applications for creating more friendly and fulfilling social VR experiences for users.

Along with emerging mobile Internet applications embedded in tremendous growth of computing demand, mobile edge computing (MEC) could effectively address the issue of compute-intensive and latency-sensitive computation imposed on mobile terminals through performing computation offloading strategies. However, how to find optimal decisions of transmission power, computing capacity demand, and offloading demand at the end-user and how to determine the resource pricing and allocation at the MEC server with the limited computing capacity still remain challenging issues in operating the MEC system in an optimal fashion. For multiuser in signal cell network with MEC, a dynamic pricing-based computation offloading solution is investigated in this article. Through the use of Q-learning algorithm comprehensively considering those sensitive factors, e.g., time cost, energy consumption and dynamic pricing, the offloading decision at the end-user is achieved with the consideration of time-varying wireless channel conditions. According to the resources supply and demand relationship, a dynamic pricing algorithm for the MEC server is designed to adjust the pricing strategy to achieve the win–win situation. Simulation results have been shown to demonstrate the efficiency in making offloading decision while the wireless channel is fast fading and the resource pricing is adjusted dynamically, and in enhancing utilities for both end-users and the MEC server.

Time delay has always been one of the main factors affecting the application performance of neural network (NN) systems, and dynamic performance research of NNs with time delays has been the focus of many scholars in recent years. This article enquires into the exponentially synchronous problem of switched delayed NNs with time delay in the leakage term. Adopting an unusual form from a common switched system, the switching modes of the switched delayed NNs system in this article are dependent on time delays. In the first place, the master, slave, and error NNs models are reconstructed into the switched form by introducing the switched delay idea. Then with the help of the admissible edge-dependent average dwell time (AED-ADT) method and delay-dependent switching adjustment indicators, a novel set of generalized delay-mode-dependent multiple Lyapunov-Krasovskii functionals (MLKFs) is built for analyzing the cases where a state-feedback controller exists and does not exist in the model, and where parts of LKFs may increase during the period when the corresponding subsystems are activated. For these cases, several effective exponential synchronization criteria and switching laws are presented accordingly. At last, the verification of the theoretical results is shown through a few examples.

Utilizing the stable underlying and cloud-native functions of vehicle platoons allows for flexible resource provisioning in environments with limited infrastructure, particularly for dynamic and compute-intensive applications. To maximize this potential, we propose the creation of a trading market to encourage interactions between service supporters (vehicle platoons) and requesters (task vehicles). Current trading decisions based on game and negotiations can lead to unpredicted handover costs and increased communication overhead in dynamic environments. Moreover, existing research tends to overlook a mutually beneficial trading philosophy by focusing on either the service supporters’ profitability or the user experience of resource-restrained requesters. Addressing these issues, we introduce a multi-objective optimization problem to model environmental dynamics and uncertainty, aiming to maximize both platoons’ and task vehicles’ long-term utilities while maintaining a satisfactory service access ratio. To tackle the problem within acceptable time frames, we develop a global-local training architecture, incorporating a hybrid action space and prioritized sampling into a multi-agent reinforcement learning algorithm that utilizes a twin delayed deep deterministic gradient (GL-HPMATD3). This approach facilitates consensus in the trading market on key issues, including service request selection, resource allocation, and trading pricing. Through extensive experimentation and comparison, we demonstrate our mechanism’s superior performance in convergence, service access ratio, player utility, execution latency, and trading pricing relative to several state-of-the-art and baseline methods.

Modern local persistent memory (PM) file systems often rely on CPU-based memory copying for data transfer between DRAM and PM, resulting in significant CPU resource consumption. While some nascent systems explore DMA (direct memory access) as an alternative for improved efficiency, the intricacies and trade-offs remain obscure. This paper investigates the feasibility of DMA for PM I/O and argues that it is not a straightforward replacement for CPU-based methods. Two key limitations hinder the direct adoption: poor performance for small data and limited bandwidth. To relieve these issues, we propose PM-DMA, a novel I/O mechanism that leverages the strengths of both CPU and DMA. It incorporates three key components: (1) L-Switch, seamlessly switches between CPU and DMA modes based on workload characteristics, maximizing performance; (2) D-Pool, reduces DMA setup overhead, improving responsiveness; (3) P-Mode, allows servicing requests through multiple channels, even hybrid CPU-DMA ones, for enhanced throughput. We implemented PM-DMA on two well-known PM file systems, NOVA and WineFS, utilizing Intel I/OAT technology. Our experimental results demonstrate substantial CPU consumption reductions across diverse workloads. Notably, under heavy load, PM-DMA delivers up to a$10.4\times$performance improvement.

Recently, unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has emerged as a practical paradigm to enable low latency computing offloading for dispersed users in the fifth generation (5G) wireless networks. However, severe security and privacy concerns are associated with the open environment between the UAVs and edge computing nodes. In this paper, we address these challenges by integrating blockchain technology into UAV-enabled MEC networks. We present an innovative Delegated Proof of Stake (DPoS) consensus mechanism where the UAV is a primary node and verification nodes are edge computing nodes selected by the reputation mechanism. To enhance mobile users’ Quality of Service (QoS), edge computing resources need to be allocated among UAV and verification nodes. Based on this, we propose the trading mechanism for resource pricing and allocation based on the two-stage Stackelberg differential game. Meanwhile, dynamic states of user demands and verification node reputations are modeled using differential equations as constraints of the objective function at various stages to simulate adaptive service requests for users and incentivize active participation for verification nodes. Simulation results prove the effectiveness of the proposed resource trading scheme and demonstrate the equilibrium and convergence status of resource pricing and allocation for edge computing.

Long-tail service recommendation provides an unexpected but reasonable experience for potential developers when they construct mashups. However, the lack of available information makes it difficult to recommend highly relevant long-tail services for target mashups. Collaborative tagging systems employ extensive tag records to replenish the available information of long-tail services, whereas existing tag-aware approaches are unable to learn multi-aspect embeddings from graphs with different structures and relationships for long-tail services. To this end, we present a novel approach, namely collaborative tag-aware graph neural network, to recommend satisfactory long-tail services by extracting multi-aspect embeddings. Firstly, a tensor decomposition is executed to parameterize mashups, tags, and services as low-dimensional vector representations, respectively. Then, an interaction-aware heterogeneous neighbor aggregation is presented to aggregate both neighboring node features and interaction strength to enhance the embedding quality of long-tail services. Next, a diffusion-aware homogeneous neighbor aggregation is proposed to assign higher weights for long-tail neighboring nodes so as to reduce the influence of popular neighboring nodes during the aggregation process. Furthermore, a type-aware attention network is employed to update the final node embedding by aggregating multi-aspect embeddings. Experimental results on two real-world Web service datasets indicate that the proposed approach generates superior accuracy and diversity than state-of-the-art approaches in the aspect of long-tail service recommendation.

Autonomous driving has brought about a growing interest in enhancing traffic efficiency and ensuring road safety. One of the fundamental functions of autonomous driving technology is lane-keeping, which has become a popular research topic in autonomous driving. However, current deep reinforcement learning (DRL)-based algorithms used for solving lane-keeping problems have limitations, such as low sample utilization and high time cost in complex scenarios. To address this, we propose a lane-keeping transfer Double Deep Q Network (LK-TDDQN) framework that leverages transfer learning (TL). Our framework enables autonomous vehicles to perform lane-keeping tasks in similar scenarios, transferring knowledge from a single-lane rural road scenario to a two-lane racing scenario. The effectiveness of the proposed LK-TDDQN was demonstrated in several simulation experiments in OpenAI Gym. These simulations demonstrate that our approach can enhance decision-making efficiency by 22% and reduce the time cost of autonomous vehicles by 11%, ensuring the safety of autonomous driving while alleviating the burden on drivers.

With the advent of the Internet of Things (IoT) era, the multiunmanned aerial vehicle (UAV) networks have attracted great attention in the fields of source detection and localization. However, as the real-time signal processing performance of the UAV is limited by the computing speed and accuracy of the embedded hardware, the effectiveness of source localization is greatly reduced. Aiming at improving the accuracy and computational efficiency of source localization, a Cramer–Rao bound (CRB) weighted multi-UAV network source localization method is proposed based on the deep neural networks (DNNs) and spatial-spectrum fitting (SSF). The proposed source localization system is composed of UAVs equipped with a radar array. The source location can be achieved using the direction of arrival (DOA) of the source signals of UAVs, but the accuracy and real-time performance of the conventional DOA estimation algorithms are not satisfactory, and the data fusion strategy of the conventional cross-location framework needs further improvement. In the proposed method, a DNN-based SSF, denoted as the deep SSF (DeepSSF), is designed to achieve accurate DOA estimation. In the DeepSSF, the DOA estimation performance is guaranteed by the DNN’s strong nonlinear fitting ability and highly parallel structure. In addition, based on the obtained DOA information, the source is located once by every two UAVs. Finally, the source localization is realized based on the weighted CRB according to the principle that the more the DOA distribution deviates from zero, the lower the estimation accuracy. The simulation results verify the efficiency of the proposed method.

Internet of Medical Things (IoMT) aggregates a series of smart medical devices and fully uses the collected health data to improve user experience, medical resource utilization, and full life cycle protection. However, privacy leakage, data loss, and inefficient sharing problems are still serious in the data-sharing process between different smart medical devices. This article first introduces an efficient privacy-preserving model with blockchain to construct a secure data-sharing mechanism between different device nodes. This model utilizes distributed storage form to solve the centralized management problem and provides a fundamental secret reconstruction and retrieval framework. Then, a lightweight$(t,n)$-threshold secret sharing$(t/n$-SS) scheme is designed to strengthen the medical data-sharing security and efficiency. It utilizes the interleaving encode technology to decrease the length of original message into$n$small shares. These small shares are also suitable for data transmission and processing with a more energy-efficient way. It can protect privacy by destroying the data’s semantic meaning. Meanwhile, it only needs less than$t (t\leq n)$shares to recover the original secrets, making the sharing process more efficient. Moreover, the performance evaluations of transaction processing in IoMT show that the proposed model is very stable. The simulation and performance evaluation results show that this$t/n$-SS scheme is energy efficient, storage saving, and strong fault tolerance than similar literature.

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless-powered communication networks (FD-WPCNs). This article investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of the formulated nonconvex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

With the fast development of modern networking technologies, the transmission rate and reliability of wireless networks have been greatly improved. Meanwhile, the fast-developing Internet of Things (IoT) devices provide continually increasing computation capability. Federated learning (FL) was proposed to leverage communication and computation resources to perform machine learning (ML) tasks on IoT devices. In wireless FL systems, devices train ML models with local data sets, and a base station (BS) aggregates these trained models so that data privacy is guaranteed by the isolation of data sets. However, in the existing works, the same global model is transmitted to devices individually over the wireless channel multiple times, while the updated local models are received only by the BS, which ignores the wireless broadcast channel, incurs large communication overhead, and slows down the overall training process. In this article, we propose the FedBroadcast protocol to efficiently exploit the shared wireless broadcast channel for the two-way model transmission in FL. In the downloading step, we let BS broadcast the global model once for all scheduled devices, and design a dynamic programming-based algorithm to schedule devices optimally. In the uploading step, we also leverage the wireless broadcast channel and select some devices to receive all the updated local models without waiting for model downloading in the next round. Finally, to solve the potential block-cyclic sampling problem brought by device scheduling, we adopt pluralistic averaging modification, which improves the convergence performance under extreme data distributions. Extensive experiments demonstrate that FedBroadcast outperforms the existing wireless FL under different system settings.

As one of the challenging cross-modal tasks, video question answering (VideoQA) aims to fully understand video content and answer relevant questions. The mainstream approach in current work involves extracting appearance and motion features to characterize videos separately, ignoring the interactions between them and with the question. Furthermore, some crucial semantic interaction details between visual objects are overlooked. In this paper, we propose a novel Relation-aware Graph Reasoning (ReGR) framework for video question answering, which first combines appearance–motion and location–semantic multiple interaction relations between visual objects. For the interaction between appearance and motion, we design the Appearance–Motion Block, which is question-guided to capture the interdependence between appearance and motion. For the interaction between location and semantics, we design the Location–Semantic Block, which utilizes the constructed Multi-Relation Graph Attention Network to capture the geometric position and semantic interaction between objects. Finally, the question-driven Multi-Visual Fusion captures more accurate multimodal representations. Extensive experiments on three benchmark datasets, TGIF-QA, MSVD-QA, and MSRVTT-QA, demonstrate the superiority of our proposed ReGR compared to the state-of-the-art methods.

Quantum sealed-bid auction (QSA) is expected to provide an alternative to classical sealed-bid auction to defend against attacks from quantum adversaries. However, most previous QSA protocols required bidders to have quantum operation capabilities, resulting in excessive quantum resource requirements. To relieve the burden of protocol quantum resources, a measurement-based quantum sealed-bid auction protocol with the one-way transmission is proposed. Compared to previous protocols, our protocol only requires bidders to perform measurements, and no other operations, such as Pauli or Phase operations, are required. In order to verify the correctness and feasibility of the proposed QSA protocol, the corresponding quantum circuits are designed and simulated by IBM Qiskit. The proposed protocol satisfies security properties, including anonymity, fairness, verifiability, privacy, and non-repudiation. In addition, we further extend the protocol to scenarios that do not require an auctioneer and achieve bid privacy protection for unsuccessful bidders. Therefore, our protocol may have broad applications in auction scenarios.

As a new and effective technology, network function virtualization (NFV) manages network nodes to improve service performance for the next-generation network. Despite its promised effectiveness, efficiently deploying the functions to different consumers is the major difficulty presented in NFV. In this article, the multiple-Walrasian auction graphic model combined with virtual network functions of different bundled tree nodes based on Vickrey–Clarke–Grove (VCG) payment is proposed to maximize the social effectiveness of NFV. It is the first work to define the virtualized service as a tree valuation in the NFV market. To solve this problem, novel algorithms, including the valuation structure algorithm and auction strategy, are implemented along with the NFV to schedule network resources. With all theories considered, we conducted a comprehensive simulation to verify the tree valuation mechanism. The results confirmed that the tree valuation mechanism outperforms the backpack auction model and reserve auction model with respect to social welfare.

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

The mobile edge computing (MEC) technology can simultaneously provide high-speed computing services for multiple vehicular users (VUs) in vehicular edge computing (VEC) networks. Nevertheless, due to the open feature of the wireless offloading channels and the high mobility of the vehicles, the security and stability of the offloading process would be seriously degraded. In this paper, by utilizing the physical layer security (PLS) technique and spectrum sharing architecture, we propose a deep reinforcement learning based joint secure offloading and resource allocation (SORA) scheme to improve the secrecy performance and resource efficiency of the multi-user VEC networks, where the VU offloading links share the frequency spectrum preoccupied with the vehicle-to-vehicle (V2V) communication links. We use Wyner’s wiretap coding scheme to obtain the achievable secrecy rate and guarantee that confidential information cannot be decoded by multiple mobile eavesdroppers. We aim at minimizing the system processing delay while securing the wireless offloading process, by jointly optimizing the transmit power, the frequency spectrum selection and the computation resource allocation. We formulate the optimization problem as a multi-agent collaborative optimal decision problem and solve it with a double deep Q-learning algorithm. Besides, we set a punishment mechanism for the rate degradation to guarantee the communication quality of each V2V link. Simulation results demonstrate that multiple VU agents adopting the SORA scheme can rapidly adapt to the highly dynamic VEC networks and cooperate to improve the system delay performance while increasing the secrecy probability.

With the continuous innovation in manufacturing, Unmanned Aerial Vehicles (UAVs) have gradually become commodities from just professional equipment. As a universal type, quadrotor UAV allows us to see its potential for applications in multiple fields. Moreover, in the brand new field of aerial computing, UAVs have started to play a leading role in providing computing services to mobile users. However, limited by the performance of onboard equipment, we often cannot rely on one UAV to complete complex computing tasks. This paper first carries out a real-world case study and discovers the importance of flying attitude in applying quadcopter UAVs to achieve aerial edge computing. Then in designing the collaboration algorithms, we apply Monte Carlo Tree Search (MCTS) to realize the independent operations of UAVs while assisting each other in accomplishing the common goals. In performance evaluation, we compare the performance of our proposed solution with the existing methods. Finally, the results show that our MCTS-based algorithm can implement efficient collaboration among UAVs while reducing energy consumption and time cost in providing AEC services.

Dynamically scaling Virtual Network Function (VNF) is essential for network operators to make their networks accommodate time-varying network traffic. However, current research only applies homogeneous VNF scaling or focuses on mathematical optimizations without implementations in a real-world environment. To build this gap, this paper presents HyScaler, a dynamic, hybrid VNF scaling system implemented in an open-source NFV platform. With HyScaler, we can build elastic Service Function Chains (SFCs) by scaling overloaded VNF Instances (VNFIs) across multiple CPU cores or physical servers. HyScaler implements this by (1) monitoring traffic loads for each deployed VNFI; (2) scaling overloaded VNFIs through all kinds of VNF scaling methods; and (3) placing and chaining the new VNFI using the Global VNF Placement and Chaining (GVPC) algorithm. Extensive experiments are conducted to validate the scalability of our HyScaler and the effectiveness of the GVPC algorithm. Compared with the original NFV platform, experimental results show that HyScaler can improve the performance of VNFIs by about 1.02 times.

Video Frame Interpolation (VFI) is a fascinating and challenging problem in the computer vision (CV) field, aiming to generate non-existing frames between two consecutive video frames. In recent years, many algorithms based on optical flow, kernel, or phase information have been proposed. In this article, we provide a comprehensive review of recent developments in the VFI technique. We first introduce the history of VFI algorithms’ development, the evaluation metrics, and publicly available datasets. We then compare each algorithm in detail, point out their advantages and disadvantages, and compare their interpolation performance and speed on different remarkable datasets. VFI technology has drawn continuous attention in the CV community, some video processing applications based on VFI are also mentioned in this survey, such as slow-motion generation, video compression, video restoration. Finally, we outline the bottleneck faced by the current video frame interpolation technology and discuss future research work.

Age of Information (AoI) is a newly emergent performance metric to quantify the freshness of data from destinations' perspectives. In this paper, we investigate and analyze AoI in the context of a multiple source updating system. In such a system, multiple loT devices continuously monitors physical environment and sends data to a remote destination for status updates through a Network Function Virtualization (NFV)-enabled network. Considering that the Virtual Network Function (VNF) placement can unnecessarily influence the AoI of the updates, we study the VNF placement problem in such a system. The problem is hence formulated as a mathematical optimization problem aiming to minimize the long-term average AoI of all updates received at the destination. To solve this prob-lem, we propose a Deep Reinforcement Learning (DRL)-based VNF placement approach called VNF-AoI, where a learning agent or decision-maker interacts with a system environment and consequently provides an optimal VNF placement policy according to the experience it has learned. Finally, we conduct extensive simulations to validate the effectiveness of our proposed approach. Numerical results clearly demonstrate that our VNF-AoI surpasses other two baseline algorithms by averagely 13.8 % higher acceptance ratio and 20.3 % lower average AoI at the destination.

Recently, technological development in the creation of programmable metamaterial has aided progress in the development of the Reconfigurable Intelligent Surface (RIS), which has been considered one of the fundamental technologies for future wireless communication systems. In this paper, we investigate the problem of maximizing the average achievable rate for multiple users indoor wireless communication environment assisted by multiple RISs. Unlike most existing works considering single-RIS single-user scenarios or single-RIS multi-user scenarios, the multi-RIS can reflect the signals from several transmission links in all destinations. The average achievable rate maximization problem for indoor communication systems is solved by optimizing the phase shifts of reflective elements. Thus, we propose a Multi-verse Optimizer approach to solve the problem. Our simulation results demonstrate that a communication system with multiple RISs provides considerable achievable rate gains relative to baseline schemes.

Underwater acoustic sensor networks (UASNs) currently provide an important technical means of underwater communication, but there are difficulties in power updating or power replenishment because the sensor nodes work in an underwater environment. Therefore, energy consumption optimization has become the focus of research on UASNs. Node clustering is widely considered to be able to optimize network energy consumption. Although the current clustering-based routing method prolongs the network life cycle to a certain extent, some nodes may fail due to excessive energy consumption caused by excessive data transmission tasks, and the problems of high and uneven energy consumption still exist. To extend the life cycle of UASNs, this article proposes an unequal clustering method based on particle swarm optimization. Our method uses iterative updates to dynamically adjust the cluster size based on the remaining energy of the cluster head, the distance from the cluster head to the Sink node, and the number of times the cluster head forwards data between clusters to balance the cluster head load. The energy consumption of the whole path from the cluster head to the Sink node and the number of hops required are considered in the intercluster transmission phase. Simulation results show that this method can effectively reduce the network energy consumption.

With the rapid development of wireless communication technologies, the emerging multimedia applications make mobile Internet traffic grow explosively while putting forward higher service requirements for the next-generation wireless networks. Therefore, how to achieve low-latency content transmission by effectively allocating heterogeneous network resources to improve the network quality of service and end-user quality of experience is a key issue to be solved urgently in the current Internet. In this article, we propose a deep reinforcement learning (DRL)-based resource allocation scheme to improve content distribution in a layered fog radio access network (FRAN). We formulate the optimal resource allocation problem as a minimal delay model, where in-network caching is deployed and the same content requests from mobile users can be aggregated in the queue of each base station. To cope with the increasing user requests and overcome capacity constraints of the FRAN, moreover, a cloud–edge cooperation offloading scheme is utilized in our model, where the integrated allocation of caching, computing, and communication resources and joint optimization between in-network caching and routing are considered to promote resource utilization and content delivery. In our solution, a new DRL policy is designed to make cross-layer cooperative caching and routing decisions for the arriving content requests according to request history information and available network resources in the system. Simulation results demonstrate that our proposed model can performs much better than the existing cloud–edge cooperation schemes in the FRAN.

Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this article proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on the blockchain technology, the data sensed by fully trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Second, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the malicious data reporting in the following task through punishment. Third, note that the sensing of data standard generates additional costs, therefore, a drone flight route scheme based on a simper deep reinforcement learning with multihead attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.

Contemporarily, the fast development of computing, communication, and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such a process also raises two challenging but critical issues: 1) trustworthy and 2) privacy issue for data collectors. Therefore, this article proposes a novel system, which is designed over the distributed mobile-edge network to sufficiently exploit advantages of blockchain and differential privacy (DP) to collect trustworthy data and protect privacy for data collectors. First, to improve trustworthiness of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Second, with the assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthiness for data collectors. Third, we enforce DP on the data stored in a global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experimental results prove that the proposed system comprehensively improves performance of data collections in distributed network without adding any additional cost for the cloud server, compared to other schemes.

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization.

Artificial Intelligence of Things (AIoT) is an emerging area of future Internet of Things (IoT) to support intelligent IoT applications. In AIoT, intelligent edge computing technologies accelerate intelligent services’ processing speed with much lower cost than simple cloud-aided IoT architecture. However, there is still a lack of resource strategy to optimize the energy efficiency of AIoT with intelligent edge computing. Therefore, in this article, we focus on the energy consumption of edge devices and cloud services in processing AIoT tasks and formulate the optimization problem in scheduling tasks in the edge and the cloud. Meanwhile, a novel online method is proposed to solve the optimization problem. We investigate the energy consumption of several typical intelligent edge devices and the cloud service in an intelligent edge computing testbed. Extensive simulation-based performance evaluation shows that the proposed method outperforms other strategies with lower energy consumption.

The Inter-datacenter transfer is a fundamental service for global cloud applications. Geo-distributed data centers become an essential resource for their application performance which may be destroyed by network congestion. Recent years, most inter-datacenter transfer methods focus on allocating transfers by bandwidth allocation to achieve low cost or high utilization. However, the congestion condition is rarely considered in these works. In this article, we introduce a congestion-aware traffic allocation method named CONA (CONgestion-Aware), whose target is to maximize the profit of allocation transfer among multiple data centers. On this purpose, a maximizing optimization model is proposed, and an efficient link grading strategy is presented. A matrix transformation method is also introduced to simplify the optimization problem. Furthermore, the link congestion condition is considered by the controller, as well as the prediction on link congestion. To verify our proposed method, simulation model is established and comprehensive experiments are conducted. The experimental results show that our method brings higher profit than fair share and greedy traffic allocation method.

Neural networks (NNs) have been deeply studied due to their wide applicability. Since time delays are unavoidable in reality, it is basic and crucial for all applications based on NNs to guarantee system stability under the influence of mixed time delays. To better exploit the variation information of time delay, we introduce the switching idea and approaches into mixed time-delay NNs to solve the stability problem. First, the considered mixed time-delay NNs are modeled as the switched NNs by dividing the two classes of time delays, discrete and distributed time delays, into some variable intervals and combining these intervals as new switching modes. With the help of mode-dependent average dwell-time switching, Lyapunov theory, and mathematical techniques, several exponential stability criteria on the modeled switched systems containing different modes are obtained. Moreover, via introducing the mathematical condition of the unstable subsystem in the switching system, a less conservativeness condition on the exponential stability of the modeled NNs is proposed. We perform three examples for testifying the validity of the proposed methods over existing ones.

This article analyzes the exponentially stable problem of neural networks (NNs) with two additive time-varying delay components. Disparate from the previous solutions on this similar model, switching ideas, that divide the time-varying delay intervals and treat the small intervals as switching signals, are introduced to transfer the studied problem into a switching problem. Besides, delay-dependent switching adjustment indicators are proposed to construct a novel set of augmented multiple Lyapunov-Krasovskii functionals (LKFs) that not only satisfy the switching condition but also make the suitable delay-dependent integral items be in the each corresponding LKF based on each switching mode. Combined with some switching techniques, some less conservativeness stability criteria with different numbers of switching modes are obtained. In the end, two simulation examples are performed to demonstrate the effectiveness and efficiency of the presented methods comparing other available ones.

As the inevitable part of intelligent service in the new era, the services for AI tasks themselves have received significant attention, which due to the urgency of energy and computing resources, is difficult to implement in a stable and widely distributed system and coordinately utilize remote edge devices and cloud. In this article, we introduce an AI-based holistic network optimization solution to schedule AI services. Our proposed deep Q-learning algorithm optimizes the overall throughput of AI co-inference tasks themselves by balancing the uneven computation resources and traffic conditions. We use a multi-hop DAG (Directed Acyclic Graph) to describe a deep neural network (DNN) based co-inference network structure and introduce the virtual queue to analyze the Lyapunov stability for the system. Then, a priority-based data forwarding strategy is proposed to maximize the bandwidth efficiency, and we develop a Real-time Deep Q-learning based Edge Forwarding Scheme Optimization Algorithm (RDFO) to maximize the overall task processing rate. Finally, we conduct the platform simulation for the distributed co-inference system. Through the comparison with other benchmarks, we testify to the optimality of our proposal.

Point-of-interest (POI) recommendation has a wide range of application values in smart city services computing. However, extreme sparsity of user-POI matrix seriously affects the recommendation accuracy. Rich contextual information is often utilized to solve data sparsity, whereas how to efficiently integrate them becomes another challenge. To this end, we merge the contextual information into probabilistic diffusion process to propose a novel approach, namely context-enhanced probabilistic diffusion, to generate satisfying POI recommendations under sparse data environment. First, the check-in data is preprocessed to construct the relevant scores that can reflect the relevant degrees between users and POIs expressly. Then, we extract social explicit and implicit trusts from user relationships, and integrate them with time influence to present a time-enhanced social diffusion process to obtain time-social probabilistic score. Next, by merging time factor into geographical distance, a time-enhanced geographical diffusion process is executed to generate time-geographical probabilistic score. Furthermore, we present a context-aware probabilistic matrix factorization to predict the relevant score for a target user on each POI. Finally, unchecked-in POIs with highest predicted relevant scores are recommended for the target user. Experiments executed on real-world datasets suggest that, the proposed approach outperforms the state-of-the-art approaches in terms of the recommendation accuracy.

Fog computing provides new ideas for solving big data problems in the Internet of Things (IoT) era. With fog, we can save much time on long-distance transmission, thereby increasing the efficiency of network service. However, to transform fog computing from a technology to a service, we first need to properly handle the trading relationship between users and service provider in fog. In this paper, we design an efficiency-aware dynamic service pricing strategy to optimize the payoffs of both users and provider in fog computing. In the modeling, we design a Stackelberg competition-based model while regarding provider as the market leader, and each individual user as a follower. Users in this model care about how to obtain the most cost-effective services. And provider pays attention to taking full advantage of the geo-distributed characteristic in setting prices with multiple customers. In the performance evaluation part, we carry out experiments using real-world datasets to simulate this continuous negotiation process between supply and demand in fog pricing. The results show that our strategy can solve the dual-objective optimization problem while establishing a stable trading relationship between the two sides.

An independent microgrid plays a significant role in supporting disaster management, especially during a power outage. However, the limited power supply needs sophisticated scheduling to manage distributed power consumption and supply for a long service period. This paper proposes a distributed power scheduling strategy in the smart microgrid to extend the power supply after a disaster happens. We first investigate the power supply and consuming devices in microgrid and model the distributed scheduling as a multi-armed bandit (MAB) problem. To solve the MAB problem, we design an exploration algorithm to formulate the optimal scheduling strategy. Extensive simulation results show that the proposed method outperforms other strategies in extending the power supply period during the power outage after a disaster happens,

Electronic medical records (EMRs) are the most critical data in human health management. As in traditional centralized healthcare service systems (HSSs), user privacy security, EMRs data leakage, tampering, and island are some significant problems. However, blockchain is a promising technology to protect the privacy and realize cross-institutional data sharing for solving these problems. In this article, a novel peer-to-peer EMRs data management and trading system called healthchain has been proposed based on consortium blockchain technology. Through this distributed system, the patient can access their EMRs in different institutions freely, and the EMRs can be traded among different users conveniently. Then, to balance EMRs data supply and demand, we establish a Stackelberg pricing model to evaluate EMRs data providers and consumers' interactions. The optimal unit price and data amounts can be found by applying the backward induction method, and the maximizing benefits of the participants can be obtained by achieving the Nash equilibrium in the proposed game. Moreover, security analysis shows the healthchain can provide secure EMRs management and trading, and the simulation results show that the proposed pricing model can help the healthchain achieve social welfare maximization.

Cloud computing is a scalable and efficient technology for providing different services. For better reconfigurability and other purposes, users build virtual networks in cloud environments. Since some applications bring heavy pressure to cloud datacenter networks, it is necessary to recognize and optimize virtual networks with different applications. In some cloud environments, cloud providers are not allowed to monitor user private information in cloud instances. Therefore, in this paper, we present a virtual network recognition and optimization method to improve quality-of-service (QoS) of cloud services. We first introduce a community detection method to recognize virtual networks from the cloud datacenter network. Then, we design a scheduling strategy by combining SDN-based network management and instance placement to improve the service-level agreements (SLA) fulfillment. Our experimental result shows that we can achieve a recognition accuracy as high as 80 percent to find out the virtual networks, and the scheduling strategy increases the number of SLA fulfilled virtual networks.

Recommender system (RS) suggests relevant objects to generate personalized service and minimize information overload issue. User-based collaborative filtering (UBCF) plays a dominant role in practical RSs. However, traditional UBCF suffers from a recommendation overfitting problem, i.e., recommendations generated by UBCF usually concentrate on popular items, resulting in lower diversity. In addition, UBCF cannot maintain a reasonable tradeoff between the accuracy and diversity of recommendations because raising the diversity is often accompanied by a decrease in accuracy. In this article, we propose a novel approach, namely link-based collaborative filtering, to enhance the recommendation accuracy and diversity simultaneously without employing additional complex information. First, a user–item bipartite network is constructed based on the user–item rating matrix of RSs. Then, a global–local weighted bipartite modularity is presented to conduct link partition so that links with the same community can not only be relatively denser but also own the same characteristic. Furthermore, redundant links are removed from each community by utilizing a link reduction algorithm so that neighborhood of a target user can be selected according to the more efficient nonredundant links. Finally, rating prediction is executed based on the rating information of neighborhood. Also, items owning the highest predicted rating scores will be recommended to the target user. Experimental results from three real datasets of RSs suggest that, without taking advantage of special additional data, our proposed approach outperforms the state-of-the-art studies and is able to generate personalized recommendations with satisfying accuracy and diversity simultaneously.

Nowadays, wilderness monitoring provides massive data output for supporting agricultural production, environmental protection, and disaster monitoring. However, smart upgrading alone for these wireless nodes cannot meet the softwarized network needs today, relating to the explosion of multi-dimensional data and multi-species equipment. In this article, we conduct a comprehensive solution for the UAV based data collection strategy in an “air-to-ground” intelligent softwarized collection system. The innovation in this article is that after using the IoT nodes to complete the data collection process through the proposed bandwidth-weighted traffic pushing optimization (BWPTO) algorithm, the system infers the future changes according to the current network state using a deep Q-learning (DQL) network. Then, by developing the proposed AIIPO (Air-to-Ground Intelligent Information Pushing Optimization) algorithm, the entire network can “forward-looking” the uploaded information to potentially idle nodes in the future, thus achieve the optimized system performance. Through the final mathematical experiments, we prove the optimality of our proposed routing algorithm and forwarding strategy, which are more applicable in the dynamic “air-to-ground” distributed data collection system than other benchmark solutions.

The relational database service in cloud usually achieves energy efficiency by using virtualization technology, in which it consolidates multiple independent database systems into a single physical machine while enforcing the hardware-level isolation among them. However, the disk I/O performance is inevitably hurt due to the resource contention on the shared device. We propose VMSQL, a novel disk I/O model for the virtualized relational database management system (RDBMS). VMSQL has two innovations over the original disk model of virtualized database systems. First, VMSQL enforces the synchronous operation in guest operating system to handle with the transaction commitment. Due to its simplicity, a portion of CPU cycles is decoupled from I/O buffer management and then used to serve the upcoming requests, thereby improving their response times. Second, in host system, VMSQL asynchronizes the storage path of transactions which are committed from the different co-located guest databases. An obvious advantage of this procedure is that systems can apply host-level improvements into the disk I/O performance of virtualized RDBMS, relieving the random I/O and enhancing the throughput of whole system. We implement a prototype of this Sync-Async model in QEMU-KVM hypervisor, in which the InnoDB engine is deployed in the guest operating system. Extensive experiments are conducted to verify its advantages and the results are positive without any loss of ACID-compliance. In the meanwhile, VMSQL incurs moderate overhead at the hypervisor layer.

Web usage prediction and recommendation have validated its importance through the enormous economic benefits it brings in areas such as e-commerce. It also plays an essential role in the low latency services needed by the new generation network services. As a usage record of network user interaction with the network, the network session logs contains the user's preferences and behaviour rules. In this paper, we propose a new web usage prediction and recommendation model by graph learning the potential laws of web session logs and achieving recommendations by anticipating the future behaviour exploiting prediction cluster. The model values multiple sets of information recorded in the session logs to master its consistency and complementarity and enhance the accuracy of the task. Experimental results on real session dataset show that the proposed model is reliable and efficient.

Network slicing, as an effective way of using heterogeneous network resources, is widely used in today's radio access network (RAN). However, because of the greater randomness of equipment capacity and mobility, the existing allocation schemes of network slicing do not make use of existing resources effectively. In this regard, this paper studies how to improve the efficiency of network slicing utilization in one base station (BS) area through deep Q-learning allocation strategy. First, we propose an allocation strategy that uses the preference matrix to prioritize all network slices. Then, with low coupling, a realtime updating Q-learning model is developed to calculate the preference matrix. Finally, we demonstrate through simulation that our proposal can improve the efficiency of service delivery in a heterogeneous wireless network region.

As the core technology of the artificial intelligence in the new era, AI technology applied in health care devices has received significant attention. However, due to the limitation of the power supply and computation resource, it is difficult to implement a stable and large AI based human interaction task processing system from the remote edge devices to the centered clouds. In this paper, we propose a holistic network solution that focuses on solving the potential problems of network congestion with the explosive growth of IoT health care devices supported AI inference tasks. First, we propose a multi-hop maximum weight network to describe a DNN inference network based on edge computing. Then, we propose a Maximum Weight Wave propulsion Algorithm (MWWP) algorithm to reduce the overall network latency. Finally, we build up a prototype of a distributed AI inference system and test the computation and transmission performance. Besides, through large-scale experiments, we prove the optimality of our holistic solution.

Smart home is an indispensable part of Internet of Things(IoT) owing to the prompt development and application of smart devices. However, the data collected from smart homes usually need to be processed by a cloud server, which means there is a risk of leaking the privacy of users during the transmission. In this situation, edge computing is considered to be an ideal platform for smart home, which enable data to be processed at edge nodes. Unfortunately, because of unsecured Wi-Fi connection and smart devices, edge nodes also have the possibility to encounter malicious attacks. Hence, in this paper, we designed an intrusion detection system (IDS) to be deployed on edge nodes. We convert network traffic to images which are applied to train a convolutional neural network (CNN) to classify the categories of network traffic. Furthermore, Auxiliary Classifier Generative Adversarial Network (AC-GAN) is adopted to generate synthesized samples to expand the intrusion detection dataset. We experiment on the UNSW-NB15 dataset which contains substantial network traffic about the normal and anomalies. The proposed scheme is effective to minor categories of which precision could be improved 12%. Besides, the precision can reach 96% in binary classification about normal and anomaly.

Standardization facilitates the management of Internet of Things (IoT) and expedites the generation of IoT big data. However, there is not yet a big data management architecture matching such IoT. Current methodologies, which mainly adopts Simple Network Management Protocol (SNMP), is defective in the following two aspects. First, facing ubiquitous sensor and actuator nodes, timeliness and scalability can hardly be assured by the centralized paradigm. Second, existing management infrastructure cannot perform data analysis and is thus not smart enough, which wastes the value of big data. To address these issues, we propose a big data management architecture for standardized IoT. First, we design a scalable and smart SNMP, which has a hierarchical and decentralized paradigm, and is embedded with edge MapReduce to perform distributed big data analysis. Second, we put forward an Edge MapReduce-based Random Matrix Model (RMM) algorithm for anomaly detection in IoT, which is parallelized and particularly suitable for high-dimensional big data. Third, we conduct a case study of smart grids, where the architecture is implemented using virtual machines and deployed to detect malfunctions in electrical grids. Experiment results demonstrate that the architecture has good performance in terms of timeliness and scalability.

Unmanned Aerial Vehicles (UAVs) uses evolved significantly due to its high durability, lower costs, easy implementation, and flexibility. After a natural disaster occurs, UAVs can quickly search the affected area to save more survivors. Dataset is crucial in developing a round-the-clock rescue system applying deep learning methods. In this paper, we collected a new thermal image dataset captured by UAV for post-disaster search and rescue (SAR) activities. After that, we employed several different deep convolutional neural networks to train the pedestrian detection models on our datasets, including YOLOV3, YOLOV3-MobileNetV1 and YOLOV3-MobileNetV3. Because the onboard microcomputer has limited computing capacity and memory, for balancing the inference time and accuracy, we find optimal points to prune and fine-tune the network based on the sensitivity of convolutional layers. We validate on NVIDIA's Jetson TX2 and achieve 26.60 FPS (Frames per second) real-time performance.

Vehicular fog computing (VFC) could provide fast task processing services for vehicles. To make vehicles/fog nodes willing to buy/sell resources, a double auction mechanism considering the interests of all parties is needed. However, few works study the auction issue in VFC. Different from the existing edge-related auction which only considers the price, some nonprice attributes (location, reputation, and computing power) are also important for providing fair resource allocation in VFC. In this article, we propose a multiattribute-based double auction mechanism in VFC, which considers both the price and nonprice attributes for constructing reasonable matching. To the best of our knowledge, this is the first work to consider multiattribute-based auction in VFC. Our auction mechanism could satisfy computational efficiency, individual rationality, budget balance, and truthfulness. To verify the proposed mechanism, we simulate VFC using VISSIM and extract the driving data. The experimental results show the effectiveness and efficiency of this mechanism.

Graphics processing unit (GPU) accelerated processing performs significant efficiency in many multimedia applications. With the development of GPU cloud computing, more and more cloud providers focus on GPU-accelerated services. Since the high maintenance cost and different speedups for various applications, GPU-accelerated services still need a different pricing strategy. Thus, in this paper, we propose an optimal pricing strategy of GPU-accelerated multimedia processing services for maximizing the profits of both the cloud provider and users. We first analyze the revenues and costs of the cloud provider and users when users adopt GPU-accelerated multimedia processing services then state the profit functions of both the cloud provider and users. With a game theory based method, we find the optimal solutions of both the cloud provider's and users' profit functions. Finally, through large scale simulations, our pricing strategy brings higher profit to the cloud provider and users compared to the original pricing strategy of GPU cloud services.

As an efficient development for industrial and scientific applications, workflow technologies have received substantial attention in recent decades. To address the issue of workflow scheduling in a state-of-the-art cloud environment, based on analysis of a decentralized architecture for workflow scheduling, a dependable scheduling strategy with active replica placement (DS-ARP) is proposed in this paper. In this proposal, by analyzing control/data dependencies in a workflow, a game-theory-based active replica placement model is first developed to achieve reasonable replica placement; then, a dependable scheduling algorithm is proposed to enhance the system reliability and security. With five well-known workflow applications, CloudSim-based simulations are performed, and the analytical results are shown to demonstrate the performance of DS-ARP on an average number of initiated replicas, costs resulting from canceled replicas, makespans, deadline violation rates and resource utilization rates.

Open social network (OSN) plays a more significant role in information propagation through the rapid developing of information technology. Since information diffusion is an essential process happens in OSN, it has been studied in many studies. Several models have been proposed to infer the diffusion process and reproduce diffusion network. However, these methods have two critical problems: 1) ignoring the effects of user social characteristics and 2) inaccuracy resulted from calculating the influence of different features independently. To address these limitations, a diffusion inferring method based on a recommender system (DIM-SPTF) was proposed. The DIM-SPTF method considers the propagation process between the users as the recommendation process of information and employs a recommender system to infer the propagation relationship. Through determining the propagation relations among all users in the observed topic data set, an information diffusion network can be finally obtained. Experimental results show that DIM-SPTF leads to improvements in performance compared with the state-of-the-art methods.

The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs due to its outstanding performance. However, the traditional UBCF is subject to the new user cold-start issue because a new user is often extreme lack of available rating information. In this article, we develop a novel approach that incorporates a bipartite network into UBCF for enhancing the recommendation quality of new users. First, through the statistic and analysis of new users' rating characteristics, we collect niche items and map the corresponding rating matrix to a weighted bipartite network. Furthermore, a new weighted bipartite modularity index merging normalized rating information is present to conduct the community partition that realizes coclustering of users and items. Finally, for each individual clustering that is much smaller than the original rating matrix, a localized low-rank matrix factorization is executed to predict rating scores for unrated items. Items with the highest predicted rating scores are recommended to a new user. Experimental results from two real-world data sets suggest that without requiring additional complex information, the proposed approach is superior in terms of both recommendation accuracy and diversity and can alleviate the new user cold-start issue of UBCF effectively.

Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

Modern cloud computing platforms based on virtual machine monitors (VMMs) host a variety of complex businesses which present many network security vulnerabilities. In order to protect network security for these businesses in cloud computing, nowadays, a number of middleboxes are deployed at front-end of cloud computing or parts of middleboxes are deployed in cloud computing. However, the former is leading to high cost and management complexity, and also lacking of network security protection between virtual machines while the latter does not effectively prevent network attacks from external traffic. To address the above-mentioned challenges, we introduce a novel customized network security for cloud service (CNS), which not only prevents attacks from external and internal traffic to ensure network security of services in cloud computing, but also affords customized network security service for cloud users. CNS is implemented by modifying the Xen hypervisor and proved by various experiments which showing the proposed solution can be directly applied to the extensive practical promotion in cloud computing.

Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand-supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex-concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader-follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis.

Intelligent embedded-enable smart manufacturing is an important infrastructure for future industries. Increasing security threats are disturbing the normal operations of smart manufacturing. As a novel type of threat, an advanced persistent threat (APT) has the novel features of strong concealment, latency, and long-term entanglement, which can penetrate the core systems of smart manufacturing, especially for intelligent embedded systems, and cause great destruction from the cyber side to physical side. However, the existing security schemes cannot provide sustainable resource management, which causes the core system in smart manufacturing not to perform sustainable secure detection and defense against APTs. To address this challenge, this paper proposes a sustainable secure management mechanism for smart manufacturing against APTs. The proposed mechanism includes two parts: sustainable threat intelligence analysis and sustainable secure resource management. Sustainable threat intelligence analysis provides sustainable discovery of the indications of potential APTs, which has features of a weak signal, low correlation, and slow time variation. The sustainable secure resource management provides deep and continuous protection for intelligent embedded systems in smart manufacturing. The evaluations show the defense capabilities and the feasibility of the proposed mechanism.

Network edge equipment has generated a large amount of fast- growing data, which has placed a heavy burden on the collaboration of heterogeneous networks. Due to the diversity of edge computing application scenarios, many new requirements are advocated for unified data storage management, such as latency and processing efficiency. Traditional centralized cloud storage can no longer meet the on- demand of edge computing in the case of a surge in data volume. Therefore, a unified storage architecture is required for the current improvements in computational offloading schemes and storage optimization algorithms. To solve these challenges and make data intelligent collaborative storable, this paper proposes a novel unified storage architecture for big data in the edge-cloud, which supports edge services in order to extend Hadoop at the edge. The functions of the edge nodes are proposed to synchronize the edge nodes of the same neighborhood and store data dynamically via Q- learning based on popularity, in order to mitigate network load pressure and improve the efficiency of edge services. An intelligent scheme that impacts the quality of service (QoS) through data marginal storage is proposed to improve the resource scheduling and to the distribution of storage space. Simulation results demonstrate the merits and efficiency of the proposed intelligent architecture is superior to the comparison schemes.

Due to the shortage and unbalance of medical resources, it is difficult for patients in the countryside to get high-quality and timely medical services from the central medical facility. Existing researches of fog e-health has the potential of providing real-time medical services for the countryside with body sensor networks (BSN), but there are two limitations. On one hand, because of the medical services requiring not only low-latency but also high-quality, constructing an AI e-health service on resource-constrained fog with edge AI is necessary but unsolved. On the other hand, because of the regional differences in disease risk, there is a lack of an effective mechanism to provide a customized fog AI e-health service for patients in different regions. To address these issues, a smart customized e-health (SCEH) framework is proposed in this paper to provide edge-intelligent and customized medical services for the countryside. Firstly, semantics-based lightweight and meticulous load management mechanism is designed to reduce data load and involve medical semantic. Secondly, model-ensemble based fog AI collaborative analysis mechanism is proposed for load balance and knowledge integration. Thirdly, an attention-weight based customized fog AI e-health generation mechanism is devised for regional medical model reconstruction. The simulation results demonstrate the effectiveness of SCEH which ensures both the accuracy and low latency of fog e-health with limited resource.

Deep learning combining with cloud computing is a surging technology recently which is a new paradigm called DLAS (deep learning as a service). To supply good services, resource utilization and user performance must be considered and satisfied. In this paper, we formulate a competitive market between a provider and users in cloud computing. Then, we schedule multi-types computational virtualized resources including CPU, GPU and TPU to maximize the revenue of users and the provider. To find the optimal solution, we propose two efficient decision and pricing strategies called Dealer strategies for users and the provider, respectively. Finally, We evaluate our method compared with Elastic pricing strategy and Amazon EC2, and Dealer strategies can bring better revenue than others.

Internet of Things(IoT) is an important part of the new generation of information technology and an important stage of development in the era of informatization. As a next generation network, Information Centric Network (ICN) has been introduced into the IoT, leading to the content independence of IC-IoT. To manage the changing network conditions and diagnose the cause of anomalies within it, network operators must obtain and analyze network status information from monitoring tools. However, traditional network supervision method will not be applicable to IC-IoT centered on content rather than IP. Moreover, the surge in information volume will also bring about insufficient information distribution, and the data location in the traditional management information base is fixed and cannot be added or deleted. To overcome these problems, we propose a name-based smart tracking system to store network state information in the IC-IoT. Firstly, we design a new structure of management information base that records various network state information and changes its naming format. Secondly, we use a tracking method to obtain the required network status information. When the manager issues a status request, each data block has a defined data tracking table to record past requests, the location of the status data required can be located according to it. Thirdly, we put forward an adaptive network data location replacement strategy based on the importance of stored data blocks, so that the information with higher importance will be closer to the management center for more efficient acquisition. Simulation results indicate the feasibility of the proposed scheme.

Software-defined networking (SDN) allows the smart grid to be centrally controlled and managed by decoupling the control plane from the data plane, but it also expands attack surface for attackers. Existing studies about the security of SDN-enabled smart grid (SDSG) mainly focused on static methods such as access control and identity authentication, which is vulnerable to attackers that carefully probe the system. As the attacks become more variable and complex, there is an urgent need for dynamic defense methods. In this paper, we propose a security function virtualization (SFV) based moving target defense of SDSG which makes the attack surface constantly changing. First, we design a dynamic defense mechanism by migrating virtual security function (VSF) instances as the traffic state changes. The centralized SDN controller is re-designed for global status monitoring and migration management. Moreover, we formalize the VSF instances migration problem as an integer nonlinear programming problem with multiple constraints and design a pre-migration algorithm to prevent VSF instances' resources from being exhausted. Simulation results indicate the feasibility of the proposed scheme.

Information-centric networking (ICN) is a novel networking architecture with subscription-based naming mechanism and efficient caching, which has abundant semantic features. However, existing defense studies in ICN fails to isolate or block efficiently novel content threats including malicious penetration and semantic obfuscation for the lack of researches considering ICN semantic features. More importantly, to detect potential threats, existing security works in ICN fail to use semantic reasoning to construct security knowledge-based defense mechanism. Thus ICN needs a smart and content-based defense mechanism. Current works are not able to block content threats implicated in semantics. Additionally, based on traditional computing resources, they are incompatible with ICN protocols. In this paper, we propose smart reasoning based content threat defense for semantics knowledge enhanced ICN. A fog computing based defense mechanism with content semantic awareness is designed to build ICN edge defense system. In addition, smart reasoning algorithms is proposed to detect implicit knowledge and semantic relations in packet names and contents with context communication content and knowledge graph. On top of inference knowledge, the mechanism can perceive threats from ICN interests. Simulations demonstrate the validity and efficiency of the proposed mechanism.

Maintenance of communications during disasters is still a serious challenge due to power loss and equipment damage. An additional emergency communication usually needs a lot of investment and becomes a burden in normal. Since IoT devices become more and more important for human life, billions of connected devices become a great opportunity for building communications to resilient emergencies. In this paper, we present AWCT, a novel communication framework to support consistent communications with IoT devices in normal and emergency. In the AWCT framework, we design an orchestration of the low-power wide-area network (LPWAN) and ad hoc networks to build standby communication during emergencies. Meanwhile, we also optimize the cost of the AWCT framework by leveraging the standby time and battery capability. We test the AWCT framework with small testbed and extensive simulations. Evaluation results show that our work has great potential for building future disaster resilience IoT.

The Information-Centric Network possessing the content-centric features, is the innovative architecture of the next generation of network. Collaborating with fog computing characterized by its strong edge power, ICN will become the development trend of the future network. The emergence of Information-Centric Multimedia Network (ICMN) can meet the increasing demand for transmission of multimedia streams in the current Internet environment. The data transmission has become more delay-constrained and convenient because of the distributed storage, the separation between the location of information and terminals, and the strong cacheability of each node in ICN. However, at the same time, the security of the multimedia streams in the delivery process still requires further protection against wiretapping, interception or attacking. In this paper, we propose the delay-constrained green security communications for ICMN based on chaotic encryption and fog computing so as to transmit multimedia streams in a more secure and time-saving way. We adapt a chaotic cryptographic method to ICMN, implementing the encryption and decryption of multimedia streams. Meanwhile, the network edge capability to process the encryption and decryption is enhanced. Thanks to the fog computing, the strengthened transmission speed of the multimedia streams can fulfill the need for short latency. The work in the paper is of great significance to improve the green security communications of multimedia streams in ICMN.

The consumer Internet of Things (IoT), which exploits wireless personal area network (WPAN) technology, is undergoing rapid growth. Although the consumer IoT enables users to control many devices and offers conveniences and benefits for daily life, its long-term operation capabilities are subject to a bottleneck related to power management. To save energy and prolong the lifetime of an IoT system, the basic idea is to allow idle devices to go to sleep. Because excessively frequent switching between the awake and asleep phases will consume a significant amount of power, it is essential to properly schedule the order of multiple communication streams among multiple devices such that the total number of wake-up events is as small as possible. Based on the typical communication protocols deployed in IoT systems, this problem can be divided into two cases: 1) the inter-superframe case and the 2) intrasuperframe case. The former case has been well studied in existing works, whereas research on the latter case is currently immature. In this paper, we propose an efficient scheme for addressing the stream order scheduling (SOS) problem in the intrasuperframe case. Mobile edge computing technology is utilized in the proposed scheme to reduce the network load, and three heuristic algorithms are proposed to improve the scheme's performance. We report various tests conducted on 4800 random original IoT topologies and 19000 random Hamiltonian edge-dual topologies, and the experimental results demonstrate that our scheme achieves optimal solutions with a very high success probability.

As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the global positioning systems (GPSs). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on direction-of-arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three base stations (BSs) equipped with a multiple input multiple output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a sparse Bayesian learning (SBL)-based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of nonuniform noise, where the covariance matrix of nonuniform noise is estimated by a least squares (LSs) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.

Internet of Things (IoT) becomes a very important area for providing various services on connected smart devices. For the isolation of different services in IoT, software-defined networking (SDN)-based virtual networks will be a scalable and flexible solution. However, in a large-scale IoT, as smart devices will move long distance between different positions, virtual network management becomes very difficult in providing network services. In this paper, we propose the LS-SDV, an efficient virtual network management framework in large-scale software-defined IoT (SDIoT). In this framework, we design a two-layer distributed control plane to manage devices and virtual networks in a large-scale environment. To the best of our knowledge, the LS-SDV is the first work to apply distributed control plane for virtual network management in softwarized networks. Moreover, based on the novel structure of the LS-SDV, we also provide a solution for network flow scheduling through network analysis. We evaluate the performance of our framework and virtual network management by extensive simulation and experiment in an open SDN framework.

Smart meters have already been widely used for electric utilities to provide reliable power service. Since those meters keep reporting customer's energy consumption data in minute-level or even second-level, Terabyte-level big data has to be stored and analyzed for the companies. To relieve the storage and computation pressure, some companies attempt to outsource their data on the cloud. However, this exposes customer's privacy at risk, because customer's activities can be inferred from analyzing the meter readings. In this paper, we propose a privacy-friendly cloud storage (PCS) scheme and three secure cloud statistic (SCS) schemes for smart meter data on outsourced clouds. Putting these schemes together achieves three queries from the electric companies. Next, we provably analyze the privacy and the security for these schemes. Finally, we design MapReduce algorithms to show the performance for the cloud statistic.

By modeling an action as the evolution of postures, Posture-based recognition algorithms possess interpretative patterns. However, there are two limitations resulted from individual diversity. First, the same action can be performed by different organs. Such actions are denoted as ambiguous actions. Second, the postures of the same action can be badly influenced by personal characteristics (e.g., personal habits and the height). In order to tackle the problems above, we propose a hierarchical posture representation (HPR). A posture is composed of several skeletal points. Each skeletal point can perform a unique operation and provide a certain amount of information. Thus, an action can be recognized based on the relationships and the information contribution levels of all skeletal points. In HPR, each skeletal point is first represented by its interaction features with other skeletal points. The interaction features are independent of the type of posture. Then, the information contribution level of each skeletal point is estimated based on its interaction features. An end-to-end adaptive action recognition network (AARN) is proposed to accomplish the three processes (estimating the information contribution level, mining the relationships among skeletal points, and recognizing actions). The proposed algorithm shows promising accuracy on two databases. Our work does not only solve the problem above but also introduce a novel action modeling method.

Multi-view data provide more comprehensive information than single views by providing different feature sets of the same object. Learning its data structure through spectral clustering has always been the mainstream of research. However, due to the limitation of its core graph theory, traditional spectral graph-based multi-view clustering algorithms are inapplicable for analyzing large-scale data sets. In this paper, we propose MultiSpectralNet (MvSN), a deep learning approach to spectral multi-view clustering, provides mapping multi-view data points to their fusion eigenvectors and can obtain a more accurate data structure by correcting the misleading information in the single views to a certain extent by feedback in the network training process. In addition, our model can cluster large multi-view data sets and provide cluster prediction for out-of-sample extension. We test ACC and normalized mutual information (NMI) of our method in clustering several artificial and real-world data sets, and the experimental results show that our method outperforms conventional compared state-of-the-art works.

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users' location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle's trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers' control on location privacy without affecting the utility of UBI. Our defense framework can also detect users' dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework.

Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in this article, we introduce a framework enabling mobile crowdsensing in fog environments with a hierarchical scheduling strategy. We first introduce the crowdsensing framework that has a hierarchical structure to organize different resources. Since different positions and performance of fog nodes influence the quality of service (QoS) of IoT applications, we formulate a scheduling problem in the hierarchical fog structure and solve it by using a deep reinforcement learning–based strategy. From extensive simulation results, our solution outperforms other scheduling solutions for mobile crowdsensing in the given fog computing environment.

Three-dimensional (3D) scene understanding is of great significance to many robotic applications. With the huge development of the deep learning methods, especially the convolutional neural network (CNN), 3D robotic vision has achieved a satisfactory performance. However, in most scenarios, sustainability becomes a severe problem, and few existing approaches pay enough attention to energy consumption. In this paper, we propose an energy-aware system for sustainable robotic 3D vision. Our contributions mainly include: 1) an effective CNN model for the 3D scene understanding; and 2) an offloading strategy to make the deep model more sustainable. First, we design a deep CNN model to analyze the 3D point cloud data. The proposed model contains 92 layers for a state-of-the-art recognition accuracy, which, however, bring a big burden to the computing hardware. Then, we formulate this deep learning computation problem as a non-cooperative game, and adopt a heuristic algorithm to balance the local computing and cloud offloading, in order to obtain an optimal solution, in which both the efficiency and energy-saving are taken into account. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.

Despite all the benefits 60 GHz networks bring about, such as high network bandwidth, effective data rates, etc., one of its main application scenarios, Line-of- Sight (LOS) communications, still has troubles in actual indoor environments due to its high directionality. Traditional beam training methods are inaccurate and time-wasting, leading to unstable and inefficient wireless networks. Therefore, in this paper, we attempt to address this problem from a new aspect, i.e., assisting the signal adaptation with human mobility prediction. A state-of-the-art long short-term memory (LSTM) model is adopted to analyze the past trajectories and predict the future position, which can serve as an important reference for the transmitters to proactively adjust their beams and provide seamless coverage. In addition, we also design an algorithm to optimize the beam selection problem and improve the network quality. To the best of our knowledge, this is the first work in the field to use deep learning models for the beam selection problem. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.

The diverse applications and high-quality services in satellite-enabled smart cities have led to geographical unbalance of computation requirements. Traditional centralized cloud services and massive migration of computing tasks result in the increase of network delay and the aggravation of network congestion. Deploying fog nodes at the network edge has become a way to improve the quality of service (QoS). However, the dynamic requirements and application in various scenarios still challenge the network, resulting in geographical unbalance of computing resource demands. Nowadays, computing resources of on-board computers and devices in the Internet of Vehicles (IoV) are abundant enough to mitigate the geographical unbalances in computing power demand. Efficient usage of the natural mobility of constantly moving vehicles to solve the problems above remains an urgent need. In this paper, vehicle mobility-based geographical migration model of vehicular computing resource is established for satellite-enabled smart cities. By using the road- status-awareness of fog nodes, the status of roads is precisely quantified as the basis for vehicle mobility- based resource migration. An incentive scheme that affects the vehicle path selection through resource pricing is proposed to balance the resource requirements and to geographically allocate computing resources. Simulation results indicate that the advantages and efficiency of the proposed scheme are significant.

Recently, an accelerating number of studies are dedicated to deploying various IoT applications in the information centric network paradigm which has lower system complexity than traditional network architectures. However, such a paradigm poses a number of security challenges especially when it is applied in real-time e-health applications. Firstly, it is difficult to ensure security of sensitive data in such a distributed data caching environment because after the data is published in the form of a packet to the information centric network (ICN), it is no longer controlled by the data publisher. Secondly, in some real-time e-health applications, terminal medical sensors are usually resource-constrained, limiting the direct adoption of expensive cryptographic primitives. In order to address these challenges, a resource-efficient secure data sharing scheme in information centric e-health system is proposed, one that utilizes ciphertext-policy attribute based encryption (CP-ABE) and adapts it to the above-mentioned system with respect to necessary security requirements. It also exploits computation resources of fog nodes and employs outsourcing cryptography to improve system efficiency.The evaluation demonstrates that the scheme can significantly reduce the computation overheads of the resource-constrained terminal medical devices, and can better support the real-time e-health applications.

Information Centric Network (ICN) is one of the promising architectures in the next generation networks. The content-based routing in ICN can satisfy the content distribution of large-scale data. For prompt content obtainment, it is important to realize the content analysis before the content reaches application layer. The novel characteristics of data naming in ICN make it possible to search and analyse content during the transmission of content, which can directly get the critical content without the process of the application layer. In this paper, we propose a MapReduce enabling content analysis architecture for ICN. MapReduce framework can realize the parallelization of content collection and analysis during the routing process. For more efficient content collection, we put forward an optimal selection for mapper nodes. Moreover, Convolutional Neural Network (CNN) is deployed in the MapReduce architecture providing further analysis for ICN content. The simulation result shows the advantages of the proposed architecture.

Data stream learning in non-stationary environments and skewed class distributions has been receiving more attention in machine learning communities. This paper proposes a novel ensemble classification method (ECSDS) for classifying data streams with skewed class distributions. In the proposed ensemble method, back-propagation neural network is selected as the base classifier. In order to demonstrate the effectiveness of our proposed method, we choose three baseline methods based on ECSDS and evaluate their overall performance on ten datasets from UCI machine learning repository. Moreover, the performance of incremental learning is also evaluated by these datasets. The experimental results show our proposed method can effectively deal with classification problems on non-stationary data streams with class imbalance.

Recently, demands for low-power wide-area (LPWA) machine-type communications have increased dramatically. It is expected that LPWA connections will reach 2 billion in 2020, exceeding the number of traditional cellular users. Narrowband Internet of Things (NB-IoT), a new radio access technology, has been released by the Third Generation Partnership Project for such demands. NB-IoT supports super coverage extension, massive number of connections and long user lifetime with low power cost and low device complexity. With such prominent features, NB-IoT has become one of the dominating technologies in LPWA networks, applicable to a large range of IoT application scenarios such as smart meter, smart parking, smart home, smart tracking, e-health, etc. However, NB-loT is still in its infancy, needing deep theoretical investigation of modeling and optimizing system performance. Also, emerging applications that can be enabled by NB-loT and implementation challenges therein need further exploration.

Memory object caching systems, such as Memcached and Redis, have been proved to be a simple and high-efficient middleware for improving the performance of Internet of Things (IoT) devices querying the database in cloud. However, its performance guarantee is built on the fact that the target data, queried by the IoT device, will be accessed many times and hit in the caching system. Therefore, when database system is handling the unrepeated IoT queries, it usually presents the suboptimal performance, which greatly impairs the efficiency of real-time data processing on IoT devices. To improve this issue, we propose Seer-MCache, the memory object caching system with a smart prefetching (read-ahead) function, to fill up the caching system with the desired data before the intensive IoT queries arriving. Seer-MCache includes a set of rules to launch the specific behaviors of read-head. These rules are able to be customized according to the workload characteristics and system load. We implement a prototype system in Redis (caching layer) and MySQL server (database system). Extensive experiments are conducted to verify the effectiveness of Seer-MCache, the results show that Seer-MCache can improve the performance of read-intensive workload up to 61% (39.5% in average). Meanwhile, the cost of the read-ahead behavior is moderate and controllable.

With the proliferation of mobile devices, crowdsensing has become an appealing technique to collect and process big data. Meanwhile, the rise of fifth generation wireless systems, especially the new cellular base stations with computing ability, brings about the revolutionary edge computing. Although many approaches regarding the mobile crowdsensing have emerged in the last few years, very few of them are focused on the combination of edge computing and crowdsensing. In this paper, we adopt the state-of-the-art edge computing method to solve the crowdsensing problem with the real-time sensing data, and more importantly, make human be in the loop again, in order to respect the users’ willing and privacy. A distributed deep learning model is adopted to extract features from the captured data, which is not only a compression process to reduce the communication cost, but an encryption procedure for safety protection. The proposed model enables the crowdsensing system to fully harness the computing capacity of edge nodes and devices, and obtain a strong data analysis ability to process the captured data. Simulations demonstrate that our approach is robust and efficient, and outperforms other strategies in several related tasks.

As one of the most striking research hotspots in both academia and industry, Internet of Things (IoT) has been constantly changing our daily life by joining together nearly all we can imagine. From home furnishings and vehicles to urban facilities, all these smart things need powerful managing and processing capabilities to deal with mass multimedia data in different content forms such as images, audios, and videos. Nowadays, since Moore's Law is no longer applicable, conventional thinking may not be adequate in facing the explosive growing amount of data. Hence, in this paper, we adopt the idea of in-memory processing to solve the problem of real-time multimedia big data computing in IoT. We apply closed-loop feedback in the scheduling method design to integrate in-memory storages of all devices within a 3-tier network structure. In addition, we consider the respective conditions of different real-time required levels and content forms. The analysis results show that our scheduling method can achieve better workload allocation with less latency in comparison of existing methods.

The current boom in the Internet of Things (IoT) is changing daily life in many ways, from wearable devices to connected vehicles and smart cities. We used to regard fog computing as an extension of cloud computing, but it is now becoming an ideal solution to transmit and process large-scale geo-distributed big data. We propose a Byzantine fault-tolerant networking method and two resource allocation strategies for IoT fog computing. We aim to build a secure fog network, called “SIoTFog,” to tolerate the Byzantine faults and improve the efficiency of transmitting and processing IoT big data. We consider two cases, with a single Byzantine fault and with multiple faults, to compare the performances when facing different degrees of risk. We choose latency, number of forwarding hops in the transmission, and device use rates as the metrics. The simulation results show that our methods help achieve an efficient and reliable fog network.

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cyber-security threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.

Radio frequency (RF) energy harvesting techniques are becoming a potential method to power battery-free wireless networks. In RF energy harvesting communications, energy cooperation enables shaping and optimization of the energy arrivals at the energy-receiving node to improve the overall system performance. In this article, we propose an energy cooperation scheme that enables energy cooperation in battery-free wireless networks with RF harvesting. We first study the battery-free wireless network with RF energy harvesting and then state the problem that optimizing the system performance with limited harvesting energy through new energy cooperation protocol. Finally, from the extensive simulation results, our energy cooperation protocol performs better than the original battery-free wireless network solution.

With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

Sensing devices acting as interconnected data sources are becoming increasingly ubiquitous in concepts of Internet of Things (IoT)-enabled smart cities, but they typically lack physical protection and are susceptible to being compromised. To address this issue, a great-alternative-region (GAR)-based approach for deploying network monitors to locate compromised data sources is proposed. The GAR concept is introduced according to the network topology and connectivity characteristics, and the GARs with the most complete connectivity are identified as the candidate monitor locations, thereby transforming the problem of monitor deployment into a traditional K-center problem. Based on the demonstrated relationship between the monitor locations and the locating accuracy, the optimization objective for reasonably deploying monitors is designed to minimize the maximum number of hops between the data sources and their nearest monitors, and the optimal deployment pattern is achieved using an improved genetic algorithm. Finally, simulation-based results are presented to illustrate the performance of this approach.

Human activity recognition (HAR) is widely applied to many industrial applications. In the context of Industry 4.0, driven by the same demand of machines' self-organizing ability, HAR can also be adopted in elderly healthcare. However, HAR should be adaptive to the application scenarios in elderly healthcare. In this paper, we propose a nonintrusive activity recognition method that can be applied to long-term and unobtrusive monitoring for elderlies. The method is robust to obstruction and nontarget object interference. Skeleton sequence is estimated from RGB images. Based on two activity continuity metrics, an interframe matching algorithm is proposed to filter nontarget objects. In order to make full use of spatial-temporal information, we propose a novel activity encoding method based on the interframe joints distances. A convolutional neural network is used to learn the distinguishing features automatically. A specific data augmentation method is designed to avoid the overfitting problem on small-scale datasets. The experiments are performed on two public activity datasets and a newly released noisy activity dataset (NAD). The NAD contains obstruction, nontarget object interference. The experimental results show that the proposed method achieves the state-of-the-art performance while only using one ordinary camera. The proposed method is robust to a realistic environment.

Network function virtualization (NFV) is an emerging scheme to provide virtualized network function services for next-generation networks. However, finding an efficient way to distribute different resources to customers is difficult. In this paper, we develop a new double-auction approach named DARA that is used for both service function chain routing and NFV price adjustment to maximize the profits of all participants. To the best of our knowledge, this is the first work to adopt a double-auction strategy in this area. The objective of the proposed approach is to maximize the profits of three types of participants: 1) NFV broker; 2) customers; and 3) service providers. Moreover, we prove that the approach is a weakly dominant strategy in a given NFV market by finding the Bayesian Nash equilibrium in the double-auction game. Finally, according to the results of the performance evaluation, our approach outperforms the single-auction mechanism with higher profits for the three types of participants in the given NFV market.

In software-defined networks (SDNs), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane.

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near‐sink nodes is reduced, the energy consumption of the near‐sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.

Information-centric networking (ICN) and software defined networking (SDN) has been perceived as a promising paradigm for integrating distributed generation (DG) into smart grid networks for flexibility and dynamic features. However, flexible, controllable and reliable time synchronization still remains an open issue for supervisory control and data sensing in smart grid. Firstly, for information-centric smart grid, smart grid entities may obtain available data from caching routers, and data delivery between caching routers with edge devices results in time synchronization requirements of on-path caching routers. Without on-path time synchronization of caching routers, it may lead to maliciously fluctuations if energy data for monitoring the energy supplying and consumption are collected and traversed at an inappropriate time. Secondly, as scale expanding and network environment of smart grid becomes complex and changeable, on-path time synchronization needs a unified and dynamic management and control. To address these issues, in this paper we propose a novel scheme of software- defined on-path time synchronization (SD-OPTS) scheme for information-centric smart grid. In proposed scheme, all on-path caching routers share the time stamps from master clock and synchronize the time of local clock during one-time synchronization process. Besides, SDN controller estimates on-path caching routers' sync error, before choosing the nearest nodes to implement accurate time synchronization. The simulation results demonstrate the efficiency of software-defined on-path time synchronization scheme. The SD-OPTS scheme supports the flexibility, controllability and reliability of time synchronization.

Fog computing, shifting intelligence and resources from remote cloud to edge networks, has the potential of providing low-latency for the end-to-end communication from data sources to users. However, it's hard to enhance resource-efficiency in the existing relatively static and proprietary framework of fog nodes due to the diversity of service requirements. With the growing deployment of fog computing, the overall resource consumption in fog will be huge without considering the efficient service provisioning in each fog node. On one hand, different fog users require diverse local services policies which are carried out within the fog nodes. Moreover, for one user, the requirements on services are time-varying. On the other hand, the processing strategies on different types of content (e.g. video, audio, etc.) are also distinct. These dynamic features impose the need for user-driven and content-based service reconstruction in order to achieve the high recycling utilization of resources of fog system. To this end, we propose a software-defined efficient service reconstruction (SDSR) scheme in fog using content awareness and weighted graph. Service reconstruction mechanism is devised to dynamically recycle modularized resources after mapping different contents to relevant operations. Weighted graph is introduced to schedule and optimize the services reconstruction in terms of resource saving during content-driven controlling. User-defined interfaces are designed to enable fog users to reconfigure the recyclable resource modules. Simulation results demonstrate that the service cost of each fog nodes is reduced significantly, thus promote efficient service provisioning for the whole fog system.

To enforce surrounding surveillance efficiently and reduce the heavy cost to deploy various infrastructures, mobile sinks are perceived to have potentials for utilization by wireless multimedia sensor networks (WMSNs). However, since high-mobility usually causes communication disconnections and the high re-transmission rate will consume more network resources, quality of experience (QoE) monitoring and control is a must that WMSNs with mobile sinks (MS-WMSNs) should provide satisfactory services with constrained resources. In this paper, we propose a novel QoE-named content-centric network paradigm for MS-WMSNs, which supports location independent networking and low redundancy data aggregation. Each network node constructs a hierarchical content naming tree (HCNT) negotiated by QoE parameters. The MS prioritizes the sensing data and caches them differentially by identifying these QoE parameters based content names. Simultaneously, to verify the feasibility, we design a stochastic network calculus model to analyse the performances of our proposed network paradigm at worst-case situation. Simulation results show that the proposed paradigm reduces end-to-end communication delay.

Coflow is a collection of parallel flows, while a job consists of a set of coflows. A job is completed if all of the flows completes in the coflows. Therefore, the completion time of a job is affected by the latest flows in the coflows. To guarantee the job completion time and service performance, the job deadline and the dependency of coflows needs to be considered in the scheduling process. However, most existing methods ignore the dependency of coflows which is important to guarantee the job completion. In this paper, we take the dependency of coflows into consideration. To guarantee job completion for performance, we formulate a deadline and dependency-based model called MTF scheduler model. The purpose of MTF model is to minimize the overall completion time with the constraints of deadline and network capacity. Accordingly, we propose our method to schedule dependent coflows. Especially, we consider the dependent coflows as an entirety and propose a valuable coflow scheduling first MTF algorithm. We conduct extensive simulations to evaluate MTF method which outperforms the conventional short job first method as well as guarantees the job deadline.

As an extension for Internet of Things (IoT), Internet of Vehicles (IoV) achieves unified management in smart transportation area. With the development of IoV, an increasing number of vehicles are connected to the network. Large scale IoV collects data from different places and various attributes, which conform with heterogeneous nature of big data in size, volume, and dimensionality. Big data collection between vehicle and application platform becomes more and more frequent through various communication technologies, which causes evolving security attack. However, the existing protocols in IoT cannot be directly applied in big data collection in large scale IoV. The dynamic network structure and growing amount of vehicle nodes increases the complexity and necessary of the secure mechanism. In this paper, a secure mechanism for big data collection in large scale IoV is proposed for improved security performance and efficiency. To begin with, vehicles need to register in the big data center to connect into the network. Afterward, vehicles associate with big data center via mutual authentication and single sign-on algorithm. Two different secure protocols are proposed for business data and confidential data collection. The collected big data is stored securely using distributed storage. The discussion and performance evaluation result shows the security and efficiency of the proposed secure mechanism.

Currently, over nine billion things are connected in the Internet of Things (IoT). This number is expected to exceed 20 billion in the near future, and the number of things is quickly increasing, indicating that numerous data will be generated. It is necessary to build an infrastructure to manage the connected things. Cloud computing (CC) has become important in terms of analysis and data storage for IoT. In this paper, we consider a cloud broker, which is an intermediary in the infrastructure that manages the connected things in CC. We study an optimization problem for maximizing the profit of the broker while minimizing the response time of the request and the energy consumption. A multiobjective particle swarm optimization (MOPSO) is proposed to solve the problem. The performance of the proposed MOPSO is compared with that of a genetic algorithm and a random search algorithm. The results show that the MOPSO outperforms a well-known genetic algorithm for multiobjective optimization.

No abstract available.

Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments.

Robotic is a great substitute for human to explore the dangerous areas, and will also be a great help for disaster management. Although the rise of depth sensor technologies gives a huge boost to robotic vision research, traditional approaches cannot be applied to disaster-handling robots directly due to some limitations. In this paper, we focus on the 3D robotic perception, and propose a view-invariant Convolutional Neural Network (CNN) Model for scene understanding in disaster scenarios. The proposed system is highly distributed and parallel, which is of great help to improve the efficiency of network training. In our system, two individual CNNs are used to, respectively, propose objects from input data and classify their categories. We attempt to overcome the difficulties and restrictions caused by disasters using several specially-designed multi-task loss functions. The most significant advantage in our work is that the proposed method can learn a view-invariant feature with no requirement on RGB data, which is essential for harsh, disordered and changeable environments. Additionally, an effective optimization algorithm to accelerate the learning process is also included in our work. Simulations demonstrate that our approach is robust and efficient, and outperforms the state-of-the-art in several related tasks.

Transportation carbon emission is a significant contributor to the increase of greenhouse gases, which directly threatens the change of climate and human health. Under the pressure of the environment, it is very important to master the information of transportation carbon emission in real time. In the traditional way, we get the information of the transportation carbon emission by calculating the combustion of fossil fuel in the transportation sector. However, it is very difficult to obtain the real-time and accurate fossil fuel combustion in the transportation field. In this paper, we predict the real-time and fine-grained transportation carbon emission information in the whole city, based on the spatio-temporal datasets we observed in the city, that is taxi GPS data, transportation carbon emission data, road networks, points of interests (POIs), and meteorological data. We propose a three-layer perceptron neural network (3-layerPNN) to learn the characteristics of collected data and infer the transportation carbon emission. We evaluate our method with extensive experiments based on five real data sources obtained in Zhuhai, China. The results show that our method has advantages over the well-known three machine learning methods (Gaussian Naive Bayes, Linear Regression, and Logistic Regression) and two deep learning methods (Stacked Denoising Autoencoder and Deep Belief Networks).

With more and more trajectory traces available, conducting analysis and mining on those trajectories can obtain valuable information. Although the published traces are often made anonymous by substituting the true identities of mobile nodes with random identifiers, the privacy concern remains. In this paper, we propose a new de-anonymization attack based on the movement pattern of moving objects. Since moving objects are open to observe in public spaces, an attacker can easily learn information about a victim's movement either through direct observations or from third parties. After collecting a few trajectory segments of a mobile object, the movement pattern of the victim can be extracted, using an improved TF-IDF method. By comparing the movement pattern of the victim with those extracted from historical anonymous traces, it is possible to identify the victim from the anonymous traces. We conduct extensive trace-driven simulations and the results demonstrate that the attacker is able to de-anonymize anonymous trajectories with high probability.

With the rapid growth of the Internet, the current TCP/IP based network cannot well satisfy the requirements such as scalable content distribution, mobility, security and so on. The new networking architectures which aren't based on TCP/IP have been a trade of next generation networking such as Information-Centric Networking (ICN). In smart grid, parts of communication protocol in IEC 61850 are also not based on TCP/IP architecture such as Sampled Value (SV) and Generic Object-Oriented Substation Event (GOOSE). IEC 61850 based smart substation employing wireless network has significantly improved the interoperability and interconnection of substation devices. However, with the amount and openness growth of Intelligent Electronic Devices (IED) nodes, these changes introduce new efficiency, reliability and security challenges especially in wireless network. The strict time requirement has limited the use of heavyweight security protocols to fight against cyber- attacks. To address these issues, a name-based security mechanism using ICN is proposed for the non TCP/IP based SV and GOOSE communication. Publish/ subscribe (pub/sub) based access control and lightweight encryption algorithm are utilized to secure the decentralized large-scale smart grid data sharing. The results show the proposed mechanism is secure and efficient for IEC 61850 communication employing wireless network.

Software defined networks (SDN) is perceived to have specific capabilities for utilization by network infrastructures automatically. The success of OpenFlow protocol is to decouple control plane from data plane completely. However, current SDN still regards the network as a group of devices rather than a holistic resource, and traffic monitoring and control only relies on network states but not including traffic behaviours. Although speed of packet forwarding is improved significantly, QoS demands can not be satisfied when network congested, unavailability of SDN in some resource constrained scenes does not present well. To address this, we propose an application-aware traffic control scheme, in which both network states and traffic behaviours are exploited cooperatively. Deep Packet Inspection (DPI) is introduced into SDN controller. Meanwhile, a mechanism for packet classification and behaviour matching is designed. To perform information exchange between components, a publish/subscribe based middle ware is designed. Besides, mathematical models for analysing network throughput and latency are established. Simulation results show that proposed scheme can facilitate the improvement of throughput and reduce latency time of end-to-end communication.

Delay/disruption tolerant network (DTN) is a typical high durable mobile transmission network. The mobile nodes in DTN move around and share information with each other without an end-to-end path in the network. The previous studies reveal that a main characteristic of such network is that mobile nodes in the networks currently move along with several same paths (namely, traces) frequently. Therefore, in such network condition, we look into the recorded traces as part of priorities, while using t-distribution for the future prediction of nodes trends. We propose a novel localized multi-copy mobile network routing algorithm, which utilizes traces to optimize overall benefit of the network. In extensive simulations, the results show that the algorithm gain a better performance than existing routing algorithms.

Vehicular ad-hoc networks (VANETs) will play an important role in next generation transportation systems, which is expected to be, cost-effective, and adaptable, making it ideal for providing network connection service to drivers and passengers. As a new paradigm of cloud computing, vehicular cloud computing (VCC) will improve scalability and flexibility of VANET services. However, traditional VANETs are hard to support VCC in the absence of network virtualization. In this paper, we propose a combined network virtualization scheme by introducing software defined networking (SDN) to VANETs. We combine two solutions of network isolation to enable network virtualization in software defined VANETs. To optimize the quality of service (QoS) of each virtual VANET, we also model the solution assignment as a non-cooperative game and find the Pareto efficient solution for fair assignment. From the experimental results, the new network virtualization scheme provides a better QoS than original solutions.

Mobile positioning technology is one of location-based services (LBS) which locates mobile devices and cooperative positioning is promising to increase its energy efficiency. The cooperative positioning, however, shares location information between the devices and users may be unwilling to provide their location information to others with additional energy consumption. In this paper, we propose a new positioning scheme using social networks, that motivates the users to participate in the cooperative positioning. Firstly, we define the social utility to indicate the strength of the social network relationship between two nodes, based on the number of mutual friends. Then, we formulate an optimization problem to maximize an integrated payoff taking both social utility and limited battery life into account. Next, we propose an efficient algorithm to find the optimal solution. Through extensive simulations, our algorithm is verified in terms of higher integrated payoff. Next, we propose an efficient algorithm to find the optimal solution. Through extensive simulations, our algorithm is verified in terms of higher integrated payoff.

We propose a novel event data collection approach named reliability and multipath encounter routing (RMER) for meeting reliability and energy efficiency requirements. The contributions of the RMER approach are as follows. 1) Fewer monitor nodes are selected in hotspot areas that are close to the Sink, and more monitor nodes are selected in nonhotspot areas, which can lead to increased network lifetime and event detection reliability. 2) The RMER approach sends data to the Sink by converging multipath routes of event monitoring nodes into a one-path route to aggregate data. Thus, energy consumption can be greatly reduced, thereby enabling further increased network lifetime. Both theoretical and experimental simulation results show that RMER applied to event detection outperforms other solutions. Our results clearly indicate that RMER increases energy efficiency by 51% and network lifetime by 23% over other solutions while guaranteeing event detection reliability.

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.

Since the technology of mobile cloud computing has brought a lot of benefits to information world, many applications in mobile devices based on cloud have emerged and boomed in the last years. According to the storage limitation, data owners would like to upload and further share the data through the cloud. Due to the safety requirements, mobile data owners are requested to provide credentials such as authentication tags along with the data. However, it is impossible to require mobile data owners to provide every authenticated computational results. The solution that signers’ privilege is outsourced to the cloud would be a promising way. To solve this problem, we propose three secure multi-entities delegated authentication protocols (MEDAPs) in mobile cloud computing, which enables the multiple mobile data owners to authorize a group designated cloud servers with the signing rights. The security of MEDAPs is constructed on three cryptographic primitive identity-based multi-proxy signature (IBMPS), identity-based proxy multi-signature (IBPMS), and identity-based multi-proxy multi-signature (IBMPMS), relied on the cubic residues, equaling to the integer factorization assumption. We also give the formal security proof under adaptively chosen message attacks and chosen identity/warrant attacks. Furthermore,compared with the pairing based protocol, MEDAPs are quite efficient and the communication overhead is nearly not a linear growth with the number of cloud servers. Copyright⃝c 2015 John Wiley & Sons, Ltd.

Cyber-physical systems (CPSs) have recently become an important research field not only because of their important and varied application scenarios, including transportation systems, smart homes, surveillance systems, and wearable devices but also because the fundamental infrastructure has yet to be well addressed. Wireless sensor networks (WSNs), as a type of supporting infrastructure, play an irreplaceable role in CPS design. Specifically, secure communication in WSNs is vital because information transferred in the networks can be easily stolen or replaced. Therefore, this paper presents a novel distributed low-storage clone detection protocol (LSCD) for WSNs. We first design a detection route along the perpendicular direction of a witness path with witness nodes deployed in a ring path. This ensures that the detection route must encounter the witness path because the distance between any two detection routes must be smaller than the witness path length. In the LSCD protocol, clone detection is processed in a nonhotspot region where a large amount of energy remains, which can improve energy efficiency as well as network lifetime. Extensive simulations demonstrate that the lifetime, storage requirements, and detection probability of our protocol are substantially improved over competing solutions from the literature.

Wireless sensor networks (WSNs) are increasingly being deployed in security-critical applications. Because of their inherent resource-constrained characteristics, they are prone to various security attacks, and a black hole attack is a type of attack that seriously affects data collection. To conquer that challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. More importantly, the generation and the distribution of detection routes are given in the ActiveTrust scheme, which can fully use the energy in non-hotspots to create as many detection routes as needed to achieve the desired security and energy efficiency. Both comprehensive theoretical analysis and experimental results indicate that the performance of the ActiveTrust scheme is better than that of the previous studies. ActiveTrust can significantly improve the data route success probability and ability against black hole attacks and can optimize network lifetime.

This paper first presents an analysis strategy to meet requirements of a sensing application through trade-offs between the energy consumption (lifetime) and source-to-sink transport delay under reliability constraint wireless sensor networks. A novel data gathering protocol named Broadcasting Combined with Multi-NACK/ACK (BCMN/A) protocol is proposed based on the analysis strategy. The BCMN/A protocol achieves energy and delay efficiency during the data gathering process both in intra-cluster and inter-cluster. In intra-cluster, after each round of TDMA collection, a cluster head broadcasts NACK to indicate nodes which fail to send data in order to prevent nodes that successfully send data from retransmission. The energy for data gathering in intra-cluster is conserved and transport delay is decreased with multi-NACK mechanism. Meanwhile in inter-clusters, multi-ACK is returned whenever a sensor node sends any data packet. Although the number of ACKs to be sent is increased, the number of data packets to be retransmitted is significantly decreased so that consequently it reduces the node energy consumption. The BCMN/A protocol is evaluated by theoretical analysis as well as extensive simulations and these results demonstrate that our proposed protocol jointly optimizes the network lifetime and transport delay under network reliability constraint.

In cyber-physical systems (CPS), service organizers (SOs) aim to collect service from service entities at lower price and provide better combined services to users. However, each entity receives payoffs when providing services, which leads to competition between SOs and service entities or within internal service entities. In this paper, we first formulate the price competition model of SOs where the SOs dynamically increase and decrease their service prices periodically according to the number of collected services from entities. A game based services price decision (GSPD) model which depicts the process of price decisions is proposed in this paper. In the GSPD model, entities game with other entities under the rule of “survival of the fittest” and calculate payoffs according to their own payoff-matrix, which leads to a Pareto-optimal equilibrium point. Numerous experiments demonstrate that the GSPD model can explain the price dynamics in the real world, and also can help decision makers a lot under various scenarios.

Social Peer-to-Peer (P2P) is a novel model to organize sensor networks, which can establish social relationships in an autonomous way with the benefits of extending the network boundaries and enhancing the network scalability. However, the complexity and time dependence characteristics introduced by social P2P model raise difficulties for assessing and selecting security services accurately and effectively in sensor networks. To address this, we propose a chance discovery based security service selection scheme for social P2P based sensor networks. We firstly establish the security assessment model for services in social P2P based sensor networks, regarding the security factors of exploitability, credibility, severity, confidentiality, integrality, availability and importance weight. More importantly, time dependence characteristics introduced by social P2P are considered during security assessment. Next, a security service selection scheme is proposed based on KeyGraph construction as well as the computation of its connection and tightness values. Finally, the service request forwarding model is established. The simulation results show the effectiveness and accuracy of the proposed security service selection scheme, which improves the feasibility and security of integrated sensor network and social networks.

Summary In delay tolerant networks, most of the existing message forwarding algorithms prefer to deliver messages to the nodes with a higher popularity or centrality in the hope of maximizing the delivery ratio or minimizing the end‐to‐end delay. This forwarding scheme is prone to cause unfair load distribution and further lead to network congestion, overlooked in the previous work. In this paper, we discuss the network congestion from a community perspective and take it into account the design of message forwarding algorithms. We first put forward a novel distributed community detection approach, which could track the evolution of communities. Based on the identified communities, we develop a congestion avoidance mechanism to divert the load away from the congested areas to the alternative custodians and further present a congestion‐aware message forwarding algorithm where messages can avoid being transmitted to the congested nodes. We finally evaluate the effectiveness of distributed community detection and congestion‐aware message forwarding through the extensive real‐trace driven simulations. Copyright © 2015 John Wiley & Sons, Ltd.

Despite the numerous benefits brought by device‐to‐device (D2D) communications, the introduction of D2D into cellular networks poses many new challenges in the resource allocation design because of the co‐channel interference caused by spectrum reuse and limited battery life of user equipment's (UEs). Most of the previous studies mainly focus on how to maximise the spectral efficiency and ignore the energy consumption of UEs. In this study, the authors study how to maximise each UE's Energy Efficiency (EE) in an interference‐limited environment subject to its specific quality of service and maximum transmission power constraints. The authors model the resource allocation problem as a non‐cooperative game, in which each player is self‐interested and wants to maximise its own EE. A distributed interference‐aware energy‐efficient resource allocation algorithm is proposed by exploiting the properties of the nonlinear fractional programming. The authors prove that the optimal solution obtained by the proposed algorithm is the Nash equilibrium of the non‐cooperative game. The authors also analyse the tradeoff between EE and SE and derive closed‐form expressions for EE and SE gaps.

With the increasing sensing and communication in cyber physical social system (CPSS), the data volume is growing much rapidly in recent years. Secure deduplication has attracted considerable interests of storage provider for data management efficiency and data privacy preserving. One of the most challenging issues in secure deduplication is how to manage data and the convergent key when users frequently update it. To solve this problem, D. Koo et al. use bilinear paring as the key method. However, bilinear paring requires high computation cost for implementations. In this paper, we propose a session-key-based convergent key management scheme, named SKC, to secure the dynamic update in the data deduplication. Specifically, each data owner in SKC can verify the correctness of the session key and dynamically change it with the data update. Furthermore, to enable group combination and remove the aid of gateway (GW), a convergent key sharing scheme, named CKS, is presented. Security analysis demonstrates that both SKC and CKS can protect the confidentiality of the data and the convergent key in the case of dynamic updates. The simulation results show that our SKC and CKS can significantly reduce computation complexity and communication during the data uploading phase.

In mobile social networks (MSN), with the aim of conserving limited resources, egotistic nodes might refuse to forward messages for other nodes. Different from previous work which mainly focuses on promoting cooperation between selfish nodes, we consider it from a more pragmatic perspective in this paper. Be specific, we regard selfishness as a native attribute of a system and allow nodes to exhibit selfish behavior in the process of message forwarding. Apparently, selfishness has a profound influence on routing efficiency, and thus novel mechanisms are necessary to improve routing performance when self-centered nodes are considered. We first put forward a stateless approach to measure encounter opportunities between nodes, and represent forwarding capabilities of nodes by combining the acquired encounter opportunities with node selfishness. We then quantify receiving capabilities of nodes based on their available buffer size and energy. Taking both forwarding and receiving capabilities into account, we finally present a forwarding set mechanism, which could be deduced to a multiple knapsack problem to maximize the forwarding profit. Consequently, we take all the above studies into the design of a context-aware message forwarding algorithm (CAMF). Extensive trace-driven simulations show that CAMF outperforms other existing algorithms greatly. In fact, it achieves a surprisingly high routing performance while consumes low transmission cost and resource in MSN.

Participatory sensing enables to broadly collect information from mobile devices with embedded sensors. However, users have to use limited energy on the mobile devices as well as have to pay more communication fee when reporting sensory data to service providers. It is important to give them incentives to send information in participatory sensing systems. In this paper, we propose a novel incentive mechanism for participatory sensing based on the evolutionary algorithm. Our proposed algorithm is evaluated by extensive simulations and its performance is compared with state-of-the-art algorithms. The simulation results demonstrate that our algorithm successfully increases the number of participating users while it can also uniformly expand the coverage of a sensing area.

The introduction of device-to-device (D2D) into cellular networks poses many new challenges in the resource allocation design due to the co-channel interference caused by spectrum reuse and limited battery life of user equipments (UEs). In this paper, we propose a distributed interference-aware energy-efficient resource allocation algorithm to maximize each UE's energy efficiency (EE) subject to its specific quality of service (QoS) and maximum transmission power constraints. We model the resource allocation problem as a noncooperative game, in which each player is self-interested and wants to maximize its own EE. The formulated EE maximization problem is a non-convex problem and is transformed into a convex optimization problem by exploiting the properties of the nonlinear fractional programming. An iterative optimization algorithm is proposed and verified through computer simulations.

Advances in low-power wireless communications and micro-electronics make a great impact on a transportation system and pervasive deployment of road-side units (RSU) is promising to provide drive-thru Internet to vehicular users anytime and anywhere. Downloading data packets from the RSU, however, is not always reliable because of high mobility of vehicles and high contention among vehicular users. Using inter-vehicle communication, cooperative downloading can maximize the amount of data packets downloaded per user request. In this paper, we focus on effective data downloading for realtime applications (e.g., video streaming, online game) where each user request is prioritized by the delivery deadline. We propose a cooperative downloading algorithm, namely MMCD, which maximizes the amount of data packets downloaded from the RSU while minimizing delivery delay of each user request. The performance of MMCD is evaluated by extensive simulations and results demonstrate that our algorithm can reduce mean delivery delay while gaining downloading throughput as high as that of a state-of-the-art method.

In this paper, we consider the application scenario where multiple Base Stations (BSs) cooperate to transmit signals to a mobile terminal in the same frequency and any of the cooperative BSs is allowed to enter into sleeping mode to save energy. The mobile terminal employs the Joint Maximum Likelihood Sequence Estimation (JMLSE) based Joint Signal Detection (JSD) to simultaneously detect multiple co-channel signals and judge whether a cooperative BS is active or not. The detection error probability of JSD is analyzed in this paper. For the case of BS sleeping, the error probability is computed based on a tentative modulation scheme which incorporates the M constellation points of conventional M-QAM and the additional constellation point 0. For the case of BS cooperation, the error probability bounds are derived based on a genie-aided receiver, and a new Tighter Lower Bound (TLB) is derived by replacing the genie with a less generous one. Simulation results have verified that the computed error probability can provide a rapid and accurate estimation of the Symbol Error Rate (SER) performance.

With the popularity of intelligent mobile devices, enormous urban information has been generated and required by the public. In response, ShanghaiGrid (SG) aims to providing abundant information services to the public. With fixed schedule and urban-wide coverage, an appealing service in SG is to provide free message delivery service to the public using buses, which allows mobile device users to send messages to locations of interest via buses. The main challenge in realizing this service is to provide efficient routing scheme with privacy preservation under highly dynamic urban traffic condition. In this paper, we present an innovative scheme BusCast to tackle this problem. In BusCast, buses can pick up and forward personal messages to their destination locations in a store-carry-forward fashion. For each message, BusCast conservatively associates a routing graph rather than a fixed routing path with the message in order to adapt the dynamic of urban traffic. Meanwhile, the privacy information about the user and the message destination is concealed from both intermediate relay buses and outside adversaries. Both rigorous privacy analysis and extensive trace-driven simulations demonstrate the efficacy of BusCast scheme.

ABSTRACT Two novel public key encryption schemes based on the non‐Abelian factorization problems were proposed. Both of them are proved to be indistinguishable against adaptively chosen ciphertext attack (IND‐CCA2) in the random oracle models. These constructions have the potential to resist Shor's quantum algorithm attack proposed in 1994 and give affirmative answers for the open question announced by Myasnikov, Shpilrain and Ushakov in 2011. Copyright © 2013 John Wiley & Sons, Ltd.

Mobile social networks are expected to substantially enrich interaction with ubiquitous computing environments by integrating social context information into local interactions. However, in mobile social networks, the mobile users may face the risk of leaking their personal information and their location privacy. In this study, we first model the secure friend discovery process as a generalized privacy-preserving interest/profile matching problem. Then, we identify a new security threat arising from existing secure friend discovery protocols, coined as runaway attack, which is expected to introduce serious fairness issue. To address this new threat, we introduce a novel blind vector transformation technique, which could hide the correlation between the original vector and the transformed result. Based on it, we propose our fairness-aware privacy preserving interest/profile matching protocol, which enables one party to match its interest with the profile of another, without revealing its real interest and profile and vice versa. The detailed security analysis as well as real-world implementations demonstrate the effectiveness and the efficiency of the proposed protocol.

Providing real-time traffic information in metropolises is desired since it can not only facilitate the traffic management but also save the time of travelers on road as well as the vehicle fuel consumption which is crucial in low-carbon society. However, to obtain the traffic information is extremely difficult due to the high cost of deploying a tremendously large number of sensors on every road segments or intersections. Recently, the ShanghaiGrid (SG) project presents an innovative cost-efficient way to address this issue by deploying traffic sensors on several thousands mobile taxies. Traffic condition information perception from these sensory data is very challenging because individual taxi reports are error-prone and sparse in terms of temporal and spatial distribution. In this paper, we use a data aggregation approach to overcome the aforementioned challenge, i.e., the ”error-prone” problem and ”sparse” problem. We first extensively study the characteristics of the measurement data from over 3000 operational taxies in Shanghai City. Utilizing the spatial correlation of traffic conditions, we propose a correlation based traffic estimation algorithm to successfully expand the coverage of taxi sensors. Our experimental result demonstrates the significance of the proposed algorithm by providing the traffic information at any time and any location in Shanghai City.

Although the reliability of the composition of web services has attracted much research works about it, but an important facet of it - MTTF (Meantime to Failure) has not been given enough considerations. The research presented in this paper intends to fill this gap by illustrating on an example of composite web service how the redundant system works and what kinds of practical results can be derived. The main contributions of this work includes: First, provide the concept of MTTF of composite web service, which is little concerned in previous works. Second, describing the calculation method of MTTF of composite web based on the workflow composition pattern. Third, presents the quantitative analysis of MTTF of composite web service for non-redundant services, part-redundant services, and all-redundant services based system. And we show that by an experiment, to achieve the higher reliability of a system, it is necessary to decrease the failure rate and increase the repair rate in addition to providing redundant system.

We study data fusion in sensor networks using mobile agents (MAs),which are capable of saving energy of sensor nodes and performing advanced computation functions based on the requests of various applications. Research on MAs still remains unfledged in development of application-oriented data fusion, which is highly desired in wireless sensor networks (WSNs) deployed in recent days for environmental and disaster monitoring. In this paper, we propose a dynamic itinerary planning for MAs (DIPMA) to collect data from sensor networks with an application-oriented approach. In particular, the DIPMA algorithm is applied to the data collection for frost prediction which is a real-world application in agriculture using next- generation sensor networks. The performance of the DIPMA is evaluated by simulations and the experimental results show that the total execution time of MA can be reduced significantly with our approach while sound prediction accuracy is maintained.

This paper proposes a mobile-agent-based data gathering system (called TinyBee) in wireless sensor networks. Most existing mobile-agent-based systems consider only static sinks/servers. In this paper, we consider both mobile servers and lightweight mobile agents. We aim to design a data gathering system using a special kind of mobile agent called TinyBee to collect data all over a network. TinyBee migrates from node to node after being dispatched from a mobile server in order to collect data so that physical movement of mobile servers is greatly reduced. Mobile-agent-based approaches outperform traditional client/server paradigms in terms of execution time and power consumption. Extensive simulation results demonstrate that our proposed schemes achieve significant performance gains.