The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.

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.

Mobile Crowdsensing (MCS) has emerged as a promising paradigm for large-scale, real-time data collection by leveraging the sensing capabilities of widely distributed mobile workers. However, its practical adoption is challenged by privacy risks and unsustainable incentive structures that inadequately compensate for workers' inherent participation costs, leading to diminished motivation, sparse task coverage, and reduced data availability. Existing approaches either provide limited and utility-degrading privacy protection or design incentive mechanisms that incur substantial costs, even under the Nash equilibrium. To bridge this gap, we propose C-PRISM (Compre hensive Privacy-preserving and Behavioral-Incentive Sustainable crowdsensing Mechanism), an integrated framework that seamlessly combines privacy-preserving techniques with behavioral economic incentive design. Specifically, C-PRISM employs ran domized matrix perturbation for fine-grained location protection and a two-phase proxy re-encryption protocol to secure task details and sensing data across evaluation, recruitment, and transmission. Building upon this secure foundation, behavioral economic incentives grounded in prospect theory, the Aronson effect, and a dual reference-point model are introduced to promote sustained worker participation at sub-Nash-equilibrium costs. Rigorous theoretical analysis validates C-PRISM's security and individual rationality. Extensive experiments on real-world datasets demonstrate that C-PRISM increases data collection efficiency by 7.42%-193.75%, improves worker retention by 2.53%-69.78%, and effectively maintains overall system utility.

Federated learning (FL) is a promising paradigm that can enable collaborative model training between vehicles while protecting data privacy, thereby significantly improving the performance of intelligent transportation systems (ITSs). In vehicular networks, due to mobility, resource constraints, and the concurrent execution of multiple training tasks, how to allocate limited resources effectively to achieve optimal model training of multiple tasks is an extremely challenging issue. In this paper, we propose a mobility-aware multi-task decentralized federated learning (MMFL) framework for vehicular networks. By this framework, we address task scheduling, subcarrier allocation, and leader selection, as a joint optimization problem, termed TSLP. For the case with a single FL task, we derive the convergence bound of model training. For general cases, we first model TSLP as a resource allocation game, and prove the existence of a Nash equilibrium (NE). Then, based on this proof, we reformulate the game as a decentralized partially observable Markov decision process (DEC-POMDP), and develop an algorithm based on heterogeneous-agent proximal policy optimization (HAPPO) to solve DEC-POMDP. Finally, numerical results are used to demonstrate the effectiveness of the proposed algorithm.

Spatial Crowdsourcing (SC) has become a popular paradigm for executing spatiotemporal tasks, such as urban sensing, ride-hailing, and last-mile delivery. In many of these service applications, it is common for workers to undertake multiple tasks simultaneously, as this allows them to efficiently complete tasks with a convenient route. In multi-task SC, location privacy, movement cost, and service quality constitute the three most critical factors. However, the inherent contradictions among these factors pose significant challenges in simultaneously ensuring them all, and to date, no existing work has addressed all three factors concurrently. To fill the research gap, in this paper, we propose a Privacy-Preserving, Cost-Aware, and Quality-Enabled Service Planning (PCQ-SP) scheme for multi-task SC. First, we introduce a secure distance acquisition method with additive homomorphic encryption and garbled circuits, which enables the calculation of distances between workers and tasks while preserving both their location privacy. Second, we design a Thompson Sampling-based quality evaluation method to assess each worker's service quality by balancing exploration and exploitation. Third, we propose a minimum-cost flow-driven service planning method to derive the optimized service plans, which aims for high quality and low cost while preserving location privacy. To our knowledge, our PCQ-SP scheme is the first to simultaneously consider location privacy, movement cost, and service quality in SC systems. Extensive evaluations demonstrate the effectiveness of our PCQ-SP scheme in enhancing service quality, reducing movement costs, and ensuring location privacy.

In Sparse Mobile Crowdsensing, many data inference schemes have been proposed without considering the sensing dataset with different trust levels which contains unrecognizable false data, resulting in high inference error and poor practical utility. To address this challenge, we propose a Transformer based Truthful Data Inference (TTDI) scheme that can tolerate false data interference to enhance data inference quality. Firstly, the trust-weighted loss function (TLF) is proposed to reduce the false interference for data inference model training. The TLF implements a differential model parameter updating strategy for data with different trust levels, which can enhance and weaken the effect of high-trust and low-trust data on the trained data inference model, respectively. Secondly, the transformer based truthful spatiotemporal feature extraction (T-TSFE) algorithm is proposed to mitigate the mutual interference between biased spatiotemporal features. The T-TSFE extracts data features through temporal and spatial multi-head attention respectively, which reduces the propagation of false information across the spatiotemporal dimensions. Finally, we conduct extensive experiments with four real-world air quality datasets to verify the effectiveness of the TTDI in data inference quality.

With the emergence of fluid antenna (FA) in wireless communications, the capability to dynamically adjust port positions offers substantial benefits in spatial diversity and spectrum efficiency, which are particularly valuable for mobile edge computing (MEC) systems. Therefore, we propose an FA-assisted MEC offloading framework to minimize system delay. This framework faces two severe challenges, which are the complexity of channel estimation due to dynamic port configuration and the inherent non-convexity of the joint optimization problem. Firstly, we propose Information Bottleneck Metric-enhanced Channel Compressed Sensing (IBM-CCS), which advances FA channel estimation by integrating information relevance into the sensing process and capturing key features of FA channels effectively. Secondly, to address the non-convex and high-dimensional optimization problem in FA-assisted MEC systems, which includes FA port selection, beamforming, power control, and resource allocation, we propose a game theory-assisted Hierarchical Twin-Dueling Multi-agent Algorithm (HiTDMA) based offloading scheme, where the hierarchical structure effectively decouples and coordinates the optimization tasks between the user side and the base station side. Crucially, the game theory effectively reduces the dimensionality of power control variables, allowing deep reinforcement learning (DRL) agents to achieve improved optimization efficiency. Numerical results confirm that the proposed scheme significantly reduces system delay and enhances offloading performance, outperforming benchmarks. Additionally, the IBM-CCS channel estimation demonstrates superior accuracy and robustness under varying port densities, contributing to efficient communication under imperfect CSI.

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.

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes aQuality-awareDual-BilateralPrivacyPreserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

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.

Mobile Crowd Sensing (MCS) has emerged a promising paradigm for large-scale data collection, which recruits truthful workers to enhance data quality, thereby maximizing the platform's profit. However, most existing work has the assumption that workers' trust remains stable once verified and truthful workers can consistently provide high-quality data. However, in real-world scenarios, there exist workers whose trust may change dynamically over time, which makes it difficult to ensure high-quality data collection due to the current lack of sustainable trust verification research, thereby damaging the platform's profit. To tackle the above challenge, we propose a novel Truthful Workers Recruitment with Sustainable Trust Verification (TWR-STV) scheme. The proposed TWR-STV scheme identifies the dynamic trust of workers and assesses the data value based on the quality of collected data, thereby selecting high-value workers to maximize the profit. First, we establish a dynamic trust model to capture the change in workers' trust. Second, we propose a stable and trustworthy worker identification method based on a two-layer sustainable data verification to identify workers' trust and the stability of workers' trust. Third, a multi-armed bandit-based stable and trustworthy worker recruitment scheme is proposed, in which high-value workers are recruited to increase platform profit and trustworthy workers who can be effectively verified are prioritized for recruitment to maintain sustainable trust verification. Furthermore, the proposed TWR-STV scheme takes into account the decay effect of time and trust on data value, which is more practical. Finally, we theoretically analyze the regret bound of TWR-STV and conduct experiments on a real dataset. Experimental results show that our proposed solution is superior to existing solutions in terms of total profit.

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.

Secure data aggregation is a powerful strategy for ensuring both data availability and privacy protection in smart grids. However, existing methods face two significant challenges: first, the substantial increase in communication and computation costs caused by malfunctioning smart meters; second, the risk of identity privacy leakage. To address these issues, we propose an efficient, fault-tolerant, and dual privacy-preserving data aggregation scheme. Our scheme effectively eliminates reliance on a trusted authority (TA) by leveraging an enhanced Paillier cryptosystem and a dual-secret sharing mechanism while ensuring robust fault tolerance. Additionally, it incorporates a pseudonym mechanism to safeguard user identity privacy. To meet the statistical requirements of modern smart grids, the scheme extends support for multidimensional data aggregation. Security analysis confirms that the proposed scheme provides dual privacy protection, ensures semantic security, and resists collusion attacks among participants. Furthermore, performance evaluations demonstrate that the proposed scheme maintains low communication and computation costs. Specifically, in fault-tolerant aggregation scenarios, its computation costs remain significantly lower than that of existing schemes, highlighting its efficiency. These results affirm the scheme’s practicality for smart grid applications.

Quantum computing increases the security risks of data in intelligent Internet of Things (IoT) based on traditional cryptography. Multivariate public key cryptography has the security advantage of resisting quantum computing and Rainbow is an important research focus in it. However, the Rainbow algorithm’s secret keys are too large to suitable for resource-constrained IoT system. We propose an efficient Rainbow signature scheme based on circulant and Toeplitz matrices for intelligent IoT. In our scheme, the variable matrices of polynomials have special forms constructed from circulant and Toeplitz matrices. Every variable matrix of central maps and public key polynomials is divided into four submatrices. Three submatrices of variable matrices are generated using seeds randomly chosen. The fourth submatrix is generated by an affine map and three submatrices. Therefore, the public key consists of seeds and variables in the fourth submatrices, rather than all variables of polynomials. Then, the correctness of our scheme has been proved and we provide a security analysis. More specifically, it is proved that the scheme can resist five attacks against Rainbow, such as Direct attacks, Unbalanced oil vinegar attacks, MinRank attacks, HighRank attacks, and Rainbow-band-separation attacks. At last, according to the experimental results, our system’s public key sizes are 97.96% smaller and private key size are 92.12% than the key sizes of the standard Rainbow scheme. Additionally, compare with other Rainbow-like schemes, the comparison analysis and experimental results show that our scheme has less communication costs and small key size than those in the similar literatures for IoT.

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.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

Smart cities have entered into a new era with the widespread commercialization of 5G. As a key supporting technique for smart cities, Internet of everything (IoE) puts forward higher requirements for real-time data and massive access. In this article, we propose a novel embedded cognitive radio (CR) enabled rate splitting multiple access (ECR_RSMA) solution, in which the secondary user (SU) can access more than one idle spectrum holes of the primary users (PUs) by spitting the data rate without causing additional interference to the PUs. To ensure the quality of service (QoS) of each user, a composite successive interference cancellation (SIC) decoding scheme is specially designed, and the channel prediction algorithm is employed to combat the channel aging effect. The effectiveness of the proposed ECR_RSMA is verified through computer simulations. Compared with the state-of-the-art MA schemes, such as CR_NOMA, the proposed solution can significantly improve the performance in terms of the spectrum efficiency as well as the outage probability.

Deep Learning methodologies have demonstrated exceptional efficacy in sentiment classification tasks. However, their extended inference times often impede practical deployment, particularly in resource-constrained environments. This paper addresses the challenge of reducing inference time by introducing a novel in-GPU caching approach, termed SentireCache, specifically designed for sentiment classification tasks. While traditional caching methods with the cosine similarity measurement have shown some reduction in inference time, they suffer from low hit rates and accuracy. To overcome this limitation, we incorporate a token filtering mechanism based on saliency into the caching system, along with simplified similarity calculation methods. The effectiveness of our proposed approach is theoretically analyzed. Moreover, extensive experimentation is conducted to compare SentireCache with other state-of-the-art caching methods. The results demonstrate a significant 37.7% reduction in inference time with an average performance degradation of 4.69%.

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.

The evolution of intelligent transportation systems necessitate the integration of advanced technologies to address challenges in data processing, real-time decision-making, and network coverage. In this paper, we present a novel intelligent transportation system architecture that synergizes Unmanned Aerial Vehicles (UAVs), Digital Twins (DTs), and an Integrated Sensing, Communication, and Computation (ISCC) framework. In this system, UAVs equipped with edge computing capabilities collaborate with the ground-based cloud center to process data collected by perception devices (PDs). Each UAV and PD is mirrored by a Digital Twin (DT) at the base station, enabling real-time monitoring and predictive analytics. We intend to maximize the network lifetime and minimize the economic overhead, which is achieved through the joint optimization of the association policies of UEs, input data caching decisions, UAVs’ flight trajectory and speed, task processing mode selection and data unload proportion allocation under joint processing. To tackle the inherent challenges of nonlinearity, dynamic network conditions, and heterogeneous data sources, the problem is modeled as a Markov Decision Process (MDP) and solved using an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, adapted to handle both continuous and discrete action spaces. The experimental results show that compared to the baseline algorithm, this approach not only exhibits faster convergence but also achieves outstanding performance in maximizing system utility for intelligent transportation systems.

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.

Multi-task assignment is widely used in mobile crowdsensing (MCS) to efficiently utilize limited resources such as shared user pool, user capability constraints and so on. In MCS, users need to submit data streams to perform sensing tasks, which involve a large amount of private information. However, the privacy leakage when users perform tasks across different types and submit multimodal data streams in multi-task assignment has not been fully addressed in current works. Privacy requirements vary for users with different activity levels in multi-task assignment. Specifically, users with higher activity levels tend to handle more task types and submit more data types, which poses more serious consequences of privacy leakage. Meanwhile, the privacy requirements of users are dynamic due to the user’s changing activity. In this work, we propose a user-driven local differential privacy framework for multi-task assignment called UD-LDP. First, we design a flexible privacy model called$w$-adjacent-event privacy to provide accurate privacy protection for users with different activity levels. Then, we introduce information entropy to quantify privacy requirements of user’s activity in real-time. After that, we propose a privacy-aware budget allocation method to dynamically allocate personalized privacy budgets for each user. At last, we design a variance-optimized selection method that chooses rational privacy budgets and users for release to improve data utility. The effectiveness of our framework is supported by experiments conducted on both real-world and synthetic datasets.

Real-time video detection is essential for many mobile visual applications, which brings the heavy computational burden of deep neural networks. Mobile edge computing offers a promising solution by deploying computational resources near mobile devices. However, achieving efficient video detection on mobile devices requires addressing challenges such as different performance requirements, diverse computing and network conditions, and system dynamics. We propose a realtime video detection framework in mobile edge computing, where multiple video streams from mobile devices are processed while balancing key performance metrics with consideration of grouping. A joint optimization problem of task scheduling, model selection, and resource provisioning is formulated for the system, where decisions are made on two timescales. To this end, we propose a window controller to unify decision-making at the time-slot level. We design an online scheduling algorithm based on multi-agent deep reinforcement learning to enable adaptive and distributed scheduling, while a masking-enhanced attention mechanism enables efficient explicit information exchange between mobile devices. Experimental evaluations across different numbers of mobile devices demonstrate that, in terms of average reward, the proposed algorithm outperforms local processing by 14.600%, fixed offloading by 10.007%, and four learning-based scheduling baselines by an average of 2.267%.

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

Handwriting recognition systems have greatly enhanced AIoT applications, especially in human-computer interaction. Wireless-based methods, favored for their non-invasive nature and ease of deployment, are becoming more common. However, existing works, which typically depend on the user’s position, often perform poorly in varied writing positions. Additionally, they do not incorporate user identity information, which could lead to security vulnerabilities by failing to reject unauthorized users. To address these issues, this article introduces RF-Eye , a system that enables contactless, position-independent handwriting recognition and user identification without prior training. Its innovative approach uses each Radio-frequency identification (RFID) tag as a unique viewpoint for observing hand movements and employs pairs of tags to track directional changes. Specifically, building upon the signal transmission model and the Fresnel Zone, we propose a novel feature, DCG , to capture changes in gesture direction and confirm its consistency across different positions. Based on DCG , we develop unique patterns for common handwriting symbols that enhance our recognition algorithm. Moreover, to strengthen the system security, we link these patterns with distinct handwriting styles through the extraction of finer-grained features, thus, preventing the misuse of the system by unauthorized users. Extensive experiments demonstrate RF-Eye ’s efficacy, which achieves recognition accuracies of 93.5%, 95.2%, and 95.8% for 26 lowercase letters, 10 digits, and 10 graphic symbols, respectively, and identifying unauthorized users with 98.6% accuracy.

Mobile Crowd Sensing is a prevalent and efficient paradigm for multi-source data collection, where Multi-source Data Fusion (MDF) plays a crucial role in constructing quality data collection services. Current MDF methods often require the majority of participating sensing sources to be credible, or assume that the workers’ credibility is either prior known or easily calculable. However, due to the presence of uncredible environments and the problem of Information Elicitation Without Verification (IEWV), these methods are impractical. It may lead to a vicious cycle where the recruitment of uncredible workers affects the quality of the estimated truth, which can further lead to misjudgments of worker credibility, thereby exacerbating the quality of subsequent recruitment. In this article, a Reliable Multi-source Data Fusion scheme with Cross Validation (RMDF-CV) is proposed to obtain reliable truth for service construction. Specifically, we first introduce the Combinatorial Multi-Armed Bandit (CMAB) model to recruit high-credibility workers by balancing exploration and exploitation. Then, we establish three-stage truth data through three different data sources: Unmanned Aerial Vehicles, credible workers, and Deep Matrix Factorization. Theoretical analyses and extensive simulations confirm the excellent performance of our RMDF-CV scheme.

In the service of Mobile Crowd Sensing (MCS), High-quality Data Collection (HDC), Bilateral Location Privacy Preservation (BLPP), and sensing cost are three pivotal issues. It is widely believed that HDC necessitates the recruitment of workers with high Quality of Service (QoS), which is related to the sensing data capabilities of the recruited workers and the worker-task distances. However, submitting high-quality data demands more resources from the workers, incurring higher costs. Meanwhile, BLPP techniques, aiming to conceal the locations of the workers and tasks, may impede the evaluation of the workers' QoS. Therefore, there is still a lack of a low-cost and BLPP high QoS data collection research. Motivated by this, we propose a Quality-Aware, Low-Cost, and Privacy-Preserving Data Collection Service (QLP-DCS) for MCS. First, we propose a matrix perturbation-based approach to achieve BLPP while preserving the partial order relationship of distances. Subsequently, we employ the Upper Confidence Bound indexes-based reverse auction recruiting workers to balance exploration and exploitation with the low sensing cost. Then, we propose a multi-level truth discovery approach and establish an effective trust verification mechanism. Theoretical analysis and extensive experiments validate the superior performance of our QLP-DCS.

Electrocardiogram(ECG) signal analysis is crucial in healthcare service computing. Ensuring accurate assessment of ECG signal quality is vital to prevent wastage of transmission bandwidth and ineffective analysis caused by noise. This enables the efficient utilization of service resources. However, existing ECG signal quality assessment(SQA) methods primarily focus on single-modal learning, overlooking the interrelation of ECG in a multimodal feature space and failing to effectively exploit available information for pattern mining. In this paper, we model the SQA for ECG as an anomaly detection problem and propose a multimodal unsupervised SQA method. It jointly explores the boundaries between high-quality ECG and noise in both the time and frequency domains by introducing time-frequency pseudo anomalies. Specifically, we first simulate real ECG noise from the time-domain using a combination of a series of noises and convert it to the frequency-domain to form time-frequency pseudo-anomalies. Next, we map the time-frequency pseudo anomalies onto hyperspheres and jointly refine the hyperspheres learned only from high-quality ECG samples in both feature spaces. Finally, the noise score is defined as the distance from the joint time-frequency features to the center of the hypersphere. Multiple experiments on various real-world ECG datasets validate the superior performance of our proposed method.

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 recent years, federated learning has emerged as an effective approach for the collaborative learning of decentralized data. Vertical federated learning (VFL) is a scenario of federated learning for cross-silo cooperation, where multiple parties with different features about the same set of data jointly train machine learning models without exposing their raw data. Most existing works of VFL focus on a single-job training of one machine learning model. In this paper, we propose a new framework for multi-job VFL, where multiple independent models are trained simultaneously in a cross-silo environment. We formulate the multi-job VFL scheduling problem and propose an efficient solution based on the rolling horizon method. We conduct extensive experiment to evaluate the performance of the solution. The experimental results show that our algorithm outperforms the other baseline algorithms.

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

The rapid development of artificial intelligence has led to a continuous expansion in the scale of the neural network models being trained, resulting in the gradual insufficiency of model training resources to support the increasingly larger models. Electric vehicles have become more intelligent thanks to their increasingly powerful computing resources. When these vehicles are not in operation, a significant amount of computing resources are left idle. This paper proposes a new distributed machine-learning system, i.e., virtual data center with parking electric vehicles (PDC), which is composed of central servers, hybrid local area networks, and electric vehicles in parking lots. The PDC is aimed at utilizing the powerful computational capabilities of electric vehicles in parking lots to provide robust computational resources for model training in favor of organizations such as universities and companies. This is accomplished by collecting jobs that need to be trained from the server, gathering information about the computation and communication resources of parking lots, and distributing the jobs that need to be trained to various electric vehicles. We further propose a reinforcement learning-based scheduling algorithm to minimize the job completion time for the jobs. The simulation results justify the feasibility of the PDC system and the efficiency of our proposed scheduling algorithm.

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) devices has given rise to IoAT, promising transformative applications across various domains. Federated Learning (FL) and Split Learning (SL) are pivotal in harnessing the potential of IoAT, enabling decentralized model training while preserving data privacy. However, the heterogeneity and scalability challenges in IoAT environments necessitate advanced frameworks. In this paper, we introduce an integration of block-wise Neural Architecture Search (NAS) with a multi-partition SFL framework, called NASFLY. This approach uses only devices for actual model training and offers flexible scaling of model fragments to accommodate a wide range of device capabilities. In particular, NASFLY allows devices to utilize idle periods during the lengthy SFL forward and backward propagation phases. This is achieved by employing auxiliary model components dispatched from the server to conduct local supernet elastification using the device’s local dataset. Our method alternates between SFL for backbone network optimization and the local supernet elastification within NAS, where knowledge from the backbone network is transferred to the local supernet branches using distillation techniques. We also propose a device clustering algorithm to further improve training efficiency. Our experimental results demonstrate that this methodology significantly enhances device utilization and improves training efficiency compared with the conventional SFL.

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 While data is distributed in multiple edge devices, federated learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transferring raw data. FL generally exploits a parameter server and a large number of edge devices during the whole process of the model training, while several devices are selected in each round. However, straggler devices may slow down the training process or even make the system crash during training. Meanwhile, other idle edge devices remain unused. As the bandwidth between the devices and the server is relatively low, the communication of intermediate data becomes a bottleneck. In this article, we propose time‐efficient asynchronous federated learning with sparsification and quantization, that is, TEASQ‐Fed. TEASQ‐Fed can fully exploit edge devices to asynchronously participate in the training process by actively applying for tasks. We utilize control parameters to choose an appropriate number of parallel edge devices, which simultaneously execute the training tasks. In addition, we introduce a caching mechanism and weighted averaging with respect to model staleness to further improve the accuracy. Furthermore, we propose a sparsification and quantitation approach to compress the intermediate data to accelerate the training. The experimental results reveal that TEASQ‐Fed improves the accuracy (up to 16.67% higher) while accelerating the convergence of model training (up to twice faster).

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 blockchain-based Internet of Things (IoT) has served widely across various industries for authentication, cooperation, and data sharing but faced the severe challenge of storage scalability. The storage burden gets worse for IoT devices with limited resources. The state data is essential for efficient transaction issuance and validation. This article proposes the Verkle accumulator-based stateless transaction validation (VA-STV) scheme for permissionless blockchains to decrease the storage burden of the state data on each node with the acceptable overhead of computation and communication. In the scheme, the current state is summarized as the commitment maintained in the latest block header, and one witness is generated for each token to guarantee its validity. State transitions are realized by updating the commitment and witnesses so that no state is stored on nodes acting as validators and miners. Only the nodes acting as traders should maintain the tokens controlled by themselves and the witnesses locally. The VA-STV is based on the Verkle accumulator (VA), which is a combination of the Verkle tree (VT) and the KZG polynomial commitment scheme. Simulation results show that the VA-STV provides a smaller witness size ($0.6\times $–$0.74\times $) and faster commitment generation ($6\times $–$14\times$) than the existing stateless schemes in the same settings, which indicates the advantages of VA-STV in succinctness and efficiency. Besides, a tradeoff between the communication and computation requirements can be achieved by adjusting the branching factor, which improves the adaptability of the proposed scheme for different IoT scenarios.

With the advancement of mobile communication technology, there has been a marked increase in the demand for personalized and ubiquitous Internet of Things (IoT) services, raising the expectations for network Quality of Service (QoS) and Quality of Experience (QoE). Existing popularity-prediction-based content caching policies improve QoS and QoE by precaching contents at the network edge, but jointly optimizing multiple network metrics remains a challenge. To address this challenge, we propose a many-objective optimization-based popularity prediction for cooperative caching (MaOPPC-Caching) framework for cloud–edge–end collaborative IoT networks. This framework simultaneously optimizes prediction accuracy, delay, offloaded traffic, and load balance. We integrate three prediction algorithms to forecast content popularity and present a horizontal and vertical collaborative caching decision strategy to generate caching forms based on the predicted results. Then, the many-objective evolutionary algorithm (MaOEA) is employed to optimize the combined proportions to take full advantage of hidden preferences and popularity characteristics of both users and items. To promote the convergence of the framework, we present a knowledge mining-based MaOEA (KMaOEA) to incorporate knowledge mining into the optimization process. Simulation results show that the proposed MaOPPC-Caching framework outperforms existing prediction algorithms in terms of four evaluation indicators. Furthermore, KMaOEA shows a significant advantage over NSGA-III in load balance, as indicated by a Mann–Whitney rank sum test with a$p$-value of 0.040.

Mobile crowdsensing (MCS) is a crucial component in the Industrial Internet of Things (IIoT), mainly due to its role in collecting data and enhancing applications. Nonetheless, it faces challenges in maintaining data quality and cost efficiency. Low-quality workers and their deceptive data bids undermine the trustworthiness of MCS data collection. Despite this, prior studies have not sufficiently scrutinized the validity of data and bids. These issues could render MCS services ineffective and unaddressed, hindering IIoT development. In response, we propose an Intelligent Data and Bid dual truth discovery (DTD) scheme. Initially, the scheme applies a detection algorithm to identify the features of ground truth data sensed by unmanned aerial vehicles. The approach uses features to evaluate the data trust from unknown workers and filter out low-quality workers. Subsequently, the scheme evaluates the bid trust from reliable workers by calculating their bid confidence intervals. Upon completing this assessment, the scheme identifies high-quality workers. This process hinges on a contribution value incorporating both data and bid trust. Ultimately, the scheme assigns these high-quality workers to sense the subsequent tasks. This approach significantly improves the data quality and reduces costs for MCS in IIoT. The experimental results demonstrated that the DTD scheme outperforms the existing main schemes in terms of sensitivity (improvement 44%), specificity (improvement 5%), accuracy (improvement 18%), and F1-score (improvement 47%). It also reduced data bias by 24 percentage points and reduced costs by 38 percentage points.

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.

For multiple unmanned-aerial-vehicles (UAVs)-assisted mobile-edge computing (MEC) networks, we study the problem of combined computation and communication for user equipments deployed with multitype tasks. Specifically, we consider that the MEC network encompasses both communication and computation uncertainties, where the partial channel state information and the inaccurate estimation of task complexity are only available. We introduce a robust design accounting for these uncertainties and minimize the total weighted energy consumption by jointly optimizing UAV trajectory, task partition, as well as the computation and communication resource allocation in the multi-UAV scenario. The formulated problem is challenging to solve with the coupled optimization variables and the high uncertainties. To overcome this issue, we reformulate a multiagent Markov decision process and propose a multiagent proximal policy optimization with Beta distribution framework to achieve a flexible learning policy. Numerical results demonstrate the effectiveness and robustness of the proposed algorithm for the multi-UAV-assisted MEC network, which outperforms the representative benchmarks of the deep reinforcement learning and heuristic 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.

As Industrial Internet of Things (IIoT) systems have superior distributed characteristics, federated learning (FL) has gradually become a mainstream distributed computing paradigm in recent years, promoting its intelligent development. A key factor of FL is the model accuracy, which relies on the truth of the participating industrial edge device (IED)’s parameter. However, some untrusted IEDs can contaminate models by fabricating false FL parameters, thereby deceiving rewards, known as model poison attack (MPA). In this article, a truth-discovery-based multidimensional bidding (TD-MDB) strategy is proposed to obtain truthful parameters at a low cost for FL. In the TD-MDB strategy, parameter server (PS) will randomly assign the lightweight trust evaluation model to test the trust of each IED according to the accuracy of IED’s calculation. Then, in order to resist MPA and inference attack, we use an improved clustering algorithm to divide the trusted IEDs into different clusters based on their different trust and stability. Finally, a device selection strategy is proposed to select cost-effective IEDs for model training based on Asynchronous Advantage Actor–Critic (A3C) algorithm. In the TD-MDB strategy, the IEDs that provide more resources, high trust, and low bid will be selected to train the model and maximize the real revenue of the system with high accuracy. We finally carry out extensive evaluations, where results demonstrate that the TD-MDB strategy can effectively provide an efficient offloading strategy, resist MPA and ensure that truth computing for FL, which is better than the state-of-the-art strategies in model accuracy, system revenue, and other performance indicators.

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.

Recurrent neural network (RNN), a branch of deep learning, is a powerful model for sequential data that has outstanding performance on a wide range of important Internet of Things (IoT) tasks. This unprecedented growth of RNN model has however encountered both heterogeneous IoT data and privacy issues. Existing RNN model can not deal with heterogeneous sequential data; often the larger datasets used in training of RNN model contain sensitive information. To tackle these challenges and for the first time, this research proposes a novel differentially private tensor-based RNN (DPTRNN) that can be applied in many challenging deep learning sequence tasks for IoT systems. Specifically, to process heterogeneous sequential data, we propose a tensor-based RNN model. To guarantee privacy, we develop a tensor-based back-propagation through time algorithm with perturbation to avoid exposing the sensitive information for training the tensor-based RNN model within the framework of differential privacy. Thorough security analysis shows that the differential private tensor-based RNN efficiently protects the confidentiality of sensitive user information for IoT. Our results from extensive experiments on two challenging large video datasets suggest that our proposed scheme is practical with guarantee of data privacy preservation and acceptable accuracy loss.

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.

This paper considers an active reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system. We aim to maximize radar signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the beamforming matrix at a dual-function radar-communication (DFRC) base station (BS) and the reflecting coefficients at an active RIS subject to the quality of service (QoS) constraints of communication user equipments (UEs) and the transmit power constraints of active RIS and DFRC BS. To tackle the optimization problem, the majorization-minimization (MM) algorithm is applied to address the nonconvex radar SINR objective function, and the resulting quartic problem is solved by developing an semidefinite relaxation (SDR)-based approach. Moreover, we derive the scaling order of the radar SINR with a large number of reflecting elements. Next, the transmit power allocation problem and the deployment strategy of the active RIS are studied with a moderate number of reflecting elements. Finally, we validate the potential of the active RIS in ISAC systems compared to passive RIS. Additionally, we deliberate on several open problems that remain for future research.

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.

With mobile phones becoming increasingly prevalent and embedding high-quality microphones, attackers have the ability to employ these microphones to eavesdrop user’s keyboard input. However, existing work usually assumes that keystroke eavesdropping is performed against known environments and victims, which inevitably makes attack systems lack generalization. To reveal the real threat of the acoustic signal-based attack strategy, this paper proposes a keystroke eavesdropping algorithm called KeystrokeSniffer, which is robust to unknown input environments and unknown victims. In particular, to mimic the real input environment of victims, an environment estimation algorithm is first designed by extracting the timbre-related characteristics to predict the keyboard type and identifying large-size key data from collected unlabeled samples to estimate the 3D microphone coordinates. Then, by imitating unknown environments and victim data, this algorithm achieves effective keystroke eavesdropping with a small training set. By further considering the commonalities of different keystroke habits, a robust feature extraction method that reflects the keystroke location is adopted to reduce the impact of individual input habits. Extensive experimental results using various commodity smartphones indicate that the scheme is capable of predicting keyboard input accurately under different unknown scenarios. Specifically, even when both the victims and keyboards are unknown, KeystrokeSniffer can still achieve high Top-5 accuracy, reaching 79.5% in predicting keystrokes and 96.7% in predicting meaningful words, which demonstrates KeystrokeSniffer has excellent generalization capabilities. By setting different parameter values of various impact factors, e.g., noise and hand length factors, the strong robustness of the system is demonstrated, which proves that KeystrokeSniffer can violate privacy in real situations.

The emergency of intelligent transportation system provides new methods to monitor and alleviate traffic problems by employing many Internet of Things (IoT) devices, namely workers, to collect data. However, there are many malicious workers (or spammers) negatively impacting the system, so it is urgent to propose an effective true data evaluation method. In this paper, a UAV-assisted Intelligent True Data Evaluation (UITDE) method has been proposed to solve this problem effectively. UITDE has two main contributions. First, we abandon traditional algorithms that only use workers’ data to obtain estimated truths and compute their trusts directly. In contrast, we employ Unmanned Aerial Vehicles (UAVs) to collect data as ground truths to test and calculate workers’ trusts. Second, there are some unmalicious workers who collect inaccurate data due to their aged sensing equipment, extreme weather, or other objective reasons, so we propose a data rectification algorithm to rectify their data. The results show that our proposed method outperforms the compared methods, in terms of workers’ trust evaluation, utility, and accuracy.

Mobile edge computing (MEC) offers promising solutions for various delay-sensitive vehicular applications by providing high-speed computing services for a large number of user vehicles simultaneously. In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure offloading for vehicular edge computing (VEC) networks in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading from the user vehicles to the MEC server at the base station, the physical layer security (PLS) technology is leveraged, where a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate offloading of the user vehicle. We formulate a joint optimization of the transmit power, the computation resource allocation and the selection of jammer vehicles in each NOMA cluster, with the objective of minimizing the system energy consumption while subjecting to the computation delay constraint. Due to the dynamic characteristics of the wireless fading channel and the high mobility of the vehicles, the joint optimization is formulated as a Markov decision process (MDP). Therefore, we propose an asynchronous advantage actor-critic (A3C) learning algorithm-based energy-efficiency secure offloading (EESO) scheme to solve the MDP problem. Simulation results demonstrate that the agent adopting the A3C-based EESO scheme can rapidly adapt to the highly dynamic VEC networks and improve the system energy efficiency on the premise of ensuring offloading information security and low computation delay.

Millimeter wave (mmWave) with abundant spectrum resources can realize high-rate communications in vehicular networks. However, the mobility of vehicles and the blocking effect of mmWave propagation bring new challenges to communication security. Cooperative communication is envisioned as a promising physical layer security (PLS) approach to enhance the secrecy performance, but it will induce extra energy consumption of vehicles. This paper proposes a deep recurrent reinforcement learning (DRRL)-based energy-efficient cooperative secure transmission scheme in mmWave vehicular networks, where eavesdropping vehicles attempt to intercept the multi-user downlink communications. We jointly design the mmWave beam allocation, the cooperative nodes selection, and the transmit power of vehicles. Specifically, the mmWave base station selects idle vehicles as relays to overcome the severe blocking attenuation of legitimate transmissions and controls the transmit power to reduce energy consumption. Moreover, to ensure secure transmission, a cooperative vehicle is selected to transmit jamming signals to the eavesdropping vehicles while the legitimate users are not disturbed. We conduct comprehensive interference analysis for both direct transmission and relay-aided transmission, and derive the theoretical expressions for the secrecy capacity. We then design the Dueling Double Deep Recurrent Q-Network (D3RQN) learning algorithm to maximize the total secrecy capacity subject to the energy consumption constraint. We set the energy consumption punishment mechanism to avoid relay vehicles consuming too much power for forwarding signals. We demonstrate that the proposed scheme can rapidly adapt to the highly dynamic vehicular networks and effectively improve secrecy performance while reducing the energy consumption of vehicles.

Deep neural networks are the cornerstone of many mobile intelligent systems, and their inference processes bring about computation-intensive tasks. Device-edge cooperative inference in mobile edge computing provides a fine-grained processing method to migrate the burden of inference computation. However, the geographical dispersion of resources and the mobility pattern of devices pose scheduling issues to be considered. In this paper, we propose a task scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider the resource provisioning strategy with a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Then, we leverage game theory to analyze the property of the decision-making process and propose an offline algorithm under complete information. Next, we propose an algorithm based on proximal policy optimization to enable mobile devices to make decisions in a distributed online manner. Further, we adopt a memory mechanism into the online algorithm to improve the decision-makers' understanding of the system environment. Experiments demonstrate the effectiveness of the two algorithms. The average pipeline time of the proposed online algorithm is only 61.44% of that of local processing, which is 1.196 times that of the proposed offline algorithm.

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

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.

The Maritime Internet of Things (MIoTs) provides intelligent information services for marine scientific research, emergency response and environmental monitoring by leveraging its wide coverage and ubiquitous connectivity. However, challenging maritime communication conditions and limited sea-based network resources hinder MIoT from meeting the evolving network quality of service requirements of growing maritime activities. This poses a significant challenge to ensuring real-time and reliable transmission of mixed traffic flows. To address issues such as link contention and transmission delays in software-defined MIoT systems, a deterministic scheduling and highly reliable routing mechanism based on cross-layer design is proposed. First, a deterministic scheduling mechanism for mixed traffic flows is introduced, which effectively reduces transmission delays and improves the schedulability of data flows. Second, a high-reliability, low-latency routing mechanism based on Double Deep Q Network (DDQN) is proposed, which is capable of dynamically screening neighbouring nodes based on real-time link and node states, thus facilitating fast and high-quality path selection. Extensive simulation results show that DSMTF improves flow schedulability by 28% compared to traditional algorithms, while HRLDQ increases the network packet delivery rate by 25.8% and reduces the average end-to-end delay by 23.6%.

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.

Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

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 widespread utilization of intelligent devices, massive mobile users' needs for rich multimedia services bring serious challenges in the aspects of network traffic, energy consumption and carbon emission. How to realize green content distribution by optimizing resource allocation is an urgent problem to solve in complex and dynamic networks. In this paper, we design a cross-layer cooperative scheme to promote energy efficiency in non-orthogonal multiple access (NOMA)-assisted cloud-edge-side environments. To be specific, we formulate the joint optimization issue of computation, caching and communication resources as an energy minimization model while considering request aggregation. Next, we propose a new deep reinforcement learning (DRL)-based task offloading strategy to minimize energy consumption by making optimal resource allocation decisions according to content request history and resource availability. Simulation results show that the proposed solution has better performance than current typical strategies in cloud-edge-end collaboration environments.

Deep neural networks are deemed to be the cornerstone of a series of mobile intelligent systems, and their inference processes bring about a mass of computation-intensive tasks. To migrate the burden of inference computation from resource-constrained mobile devices, device-edge cooperative inference in mobile edge computing provides a fine-grained processing method. However, the geographical dispersion of resources and the mobility pattern of devices pose technical issues in the scheduling of co-inference systems, which have not been fully considered. In this paper, we propose a learning-based scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider a resource provisioning strategy based on the number of devices and a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Next, we propose an algorithm based on proximal policy optimization for each device to make the decision independently. Further, we adopt long short-term memory in the algorithm to capture the temporal characteristics of the system state. Experiments using a real-world network and computing trace demonstrate that the proposed algorithm can efficiently sense the mobile system to make decisions at various system scales and two mobility scenes. The average pipeline time of the proposed algorithm is only 67.63% of that of local processing, which is 97.50% of that of the omniscient algorithm.

Federated learning is a distributed machine learning framework that enables distributed edge devices to jointly train machine learning models without exchanging private data. Because federated learning can leverage edge devices’ data and computing resources, the research on federated learning has set off an upsurge. However, one of major challenges faced by federated learning is the limited computation and communication resources of edge devices, which leads to low efficiency of federated learning. In this paper, we propose a federated learning framework FedPipeline, which focuses on the local training and uploading process to improve the convergence efficiency. Different from the existing federated learning algorithms with disjoint sequential steps of training and uploading for devices, FedPipeline exploits the parallelism of training and uploading that devices upload part of the model updates in advance while they are conducting local training simultaneously. In this way, the computation and communication resources of devices can be better utilized. We further design strategies for the scheduling of early uploading and the selection of uploaded updates to maximize the benefit of FedPipeline. Experimental results show that FedPipeline can effectively accelerate the convergence speed of federated learning and preserve the accuracy of the final model.

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.

Nowadays, wireless charging has become one of the most popular technologies in Internet of Things (IoT), which makes electric devices battery free and flexible. The electromagnetic coupling in antenna design is important to push the range limits beyond the near field. Previous studies did not consider the coupling among coils and they do not work for the far-field scenarios. In this article, we design an antenna for wireless charging that can expand the charging distance from 5 to 55 cm, a ten-fold improvement compared to the near-field commercial communication distance according to the simulation results. Specifically, it is practical to take into account the coupling of every two coils when we analyze the transmit power. Therefore, we first model the near-field signal propagation with higher-order factors, and establish the relationship between the geometry parameters and the transmit power. Then, since our problem is extremely nonlinear, we design the greedy search algorithm and narrow down the feasible region to obtain the optimal parameters of the coils that can maximize the charging distance. An Ansoft-High Frequency Structure Simulator is adopted to simulate the performance of the coil models, and the results show that square coils achieve the best performance. We also discuss the best arrangement of multiple coil antennas. The impact of the signal phase is also introduced and the best combination of signal phases from different transmit coils is analyzed.

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.

With the prosperous development of Internet of Things (IoT), IoT devices have been deployed in various applications, which generates large volume of image data to trace and record the users’ behaviors, resulting in better IoT services. To accurately analyze these huge data to further improve users’ experience on IoT services, deep neural networks (DNNs) are gaining more attention and have become increasingly popular. However, recent studies have shown that DNN models are vulnerable to adversarial attacks, which leads to the risk of applications in practice. Previous works are devoted to extract invariant features from the content circled by edges in images, while such features cannot efficiently deal with the adversarial effect. In this work, we first study this problem from a new angle by exploring the edge feature information, which is intractable to be influenced by adversarial attacks demonstrated by our empirical analysis. Based on this, we propose a novel edge feature-enhanced defense approach called Defed which incorporates edge feature information into denoised network to defend against various adversarial attacks in image area. For the training phase, we only add benign images as the input and exert Gaussian noise to substitute the adversarial attacks to mitigate the dependency of models on specific adversarial attacks. For inference, we design a combination of multiple Defeds trained by different Gaussian noise levels and deploy confidence intervals to judge whether an image is adversarial or not. Experiments over real-world data sets on image classification demonstrate the efficacy and superiority compared to the state-of-the-art defense approaches.

Secure ultrareliable low-latency communication (URLLC) has become a crucial requirement of mission-critical Internet of Things (IoT) applications and use cases, including automotive driving, remote surgery, and many others. However, it is still challenging to protect initial access of massive IoT devices over wireless channels, especially when malicious quantum adversaries paralyze the initial access by overhearing and tampering critical wireless messages, i.e., preambles. We propose a nested hash access system with post-quantum encryption to solve this issue. The system performs random repetition coding and nested hash coding on multidomain physical-layer resources to encode and decode preambles precisely and resiliently. Particularly, a subtle compression and encryption mechanism based on quasi-cyclic (QC)-moderate-density parity-check (MDPC) code is proposed between repetition and hashing operations to avoid passive eavesdropping during the preamble encoding process. We show that the code information can be maintained at 128-bit or higher privacy level, depending on the length of repetition code. Besides, the preamble decoding process can be proved secure agaisnt active attacks with a tolerable loss of decoding errors. Then, we formulate two nonconvex integer programming problems, each problem corresponding to the minimization of upper bound of preamble decoding error in an example application scenario. Finally, we can derive the expressions of system failure probability to evaluate the reliability of URLLC system under mission-critical IoT scenarios. Simulation results show the effectiveness of our proposed scheme despite attack.

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.

Resource allocation is conceived for cell-free (CF) massive multi-input multi-output (MIMO)-aided ultra-reliable and low latency communication (URLLC) systems. Specifically, to support multiple devices with limited pilot overhead, pilot reuse among the users is considered, where we formulate a joint pilot length and pilot allocation strategy for maximizing the number of devices admitted. Then, the pilot power and transmit power are jointly optimized while simultaneously satisfying the devices’ decoding error probability, latency, and data rate requirements. Firstly, we derive the lower bounds (LBs) of ergodic data rate under finite channel blocklength (FCBL). Then, we propose a novel pilot assignment algorithm for maximizing the number of devices admitted. Based on the pilot allocation pattern advocated, the weighted sum rate (WSR) is maximized by jointly optimizing the pilot power and payload power. To tackle the resultant NP-hard problem, the original optimization problem is first simplified by sophisticated mathematical transformations, and then approximations are found for transforming the original problems into a series of subproblems in geometric programming (GP) forms that can be readily solved. Simulation results demonstrate that the proposed pilot allocation strategy is capable of significantly increasing the number of admitted devices and the proposed power allocation achieves substantial WSR performance gain.

Emotion plays a critical role in making the computer more human-like. As the first and most essential step, emotion recognition emerges recently as a hot but relatively nascent topic, i.e., current research mainly focuses on single modality (e.g., facial expression) while human emotion expressions are multi-modal in nature. To this end, we propose an unobtrusive emotion recognition system leveraging two emotion-rich and tightly-coupled modalities, i.e., gesture and facial expression. The system design faces two major challenges, namely, how to capture the emotional expression in both modalities without disturbing the subject and how to leverage the relationship between modalities for recognizing the emotion. For the former, we explore WiFi and vision for unobtrusive and contactless gesture and facial expression sensing, respectively. For the latter, we propose a novel deep learning framework named Multi-Source Learning (MSL) to efficiently exploit both self-correlation in the modality and cross-correlation between modalities for fine-grained emotion recognition. To evaluate the proposed method, we prototype the system on low-cost commodity WiFi and vision devices, build a first-of-its-kind WiFi-Vision emotion dataset, and conduct extensive experiments. Empirical results not only verify the effectiveness of WiFE in emotion recognition, but also confirm the superiority of multi-modality over single-modality.

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.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

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.

In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.

A fundamental issue of the vehicular digital twin (DT) is efficiently synchronizing the data between the DT and the vehicular user (VUE). In this paper, we consider the heterogeneous vehicular networks (HetVNets) in which a VUE can connect to the network through different networks. The HetVNets can improve the efficiency of communication by providing seamless connections. However, the uneven distribution of VUEs and the dynamics of HetVNets make the environment more complex. Therefore, we propose the network selection algorithm for data synchronization between VUEs and DTs in the HetVNets, where the behaviour between the VUEs is considered as a competition for wireless resources. A learning-based prediction model residing in the DT is developed where the DT can predict the waiting time of each relay and transmit the predicted results to the VUE for decision-making. We model the network selection problem as a potential game considering both the transmission time and the waiting time obtained from the prediction model and prove the existence of Nash equilibrium (NE). We analyze the performance of the proposed algorithm, and simulation results show that our approach can effectively find the optimal strategy while achieving a fast convergence speed and high-level performance compared to the baselines.

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.

This paper considers the downlink data synchronization from the digital twin (DT) to the vehicle, in which a vehicle drives through consecutive roadside units (RSUs) along its trip, and the DT on the cloud transmits the data to the vehicle through the relay of RSUs. To this goal, the DT first chops the data into blocks and cache them in the RSUs along the driving path of the vehicle. The vehicle can then retrieve the blocks when driving into the RSU's coverage to recover the data. Since RSUs have different cache capacities and communication costs, the DT needs to determine how to optimally distribute the data blocks at RSUs so that vehicles can finish downloading all the data before the deadline yet with the minimal cost. To determine the optimal delivery strategy of DT, we model the problem as an optimization framework subject to the time-varying wireless channel of RSUs, their service load and the communication cost. We then resort to the Lyapunov optimization to derive a distributed solution. Using extensive simulation results, we demonstrate that our scheme can effectively reduce the cost of data synchronization and improve the network load performance.

The rapid development of communication technology improves the feasibility of modern communication networks, such as wireless networks and satellite communication networks, etc. As a result, the complexity of networks increases significantly in terms of topology, increasing the difficulty of network resource allocation (i.e., network flow scheduling) and causing network congestion. It is worth noting that congestion is extremely destructive to the network, causing the network's overall performance to decline and even crashes and paralysis in severe cases. Therefore, timely identification and dealing with the network congestion are hot and difficult points of current research. Currently, most works (i.e., the active and passive measurement) identify the congestion on the node level, which may cause impractical issues with the increasing number of nodes (i.e., increasing detection probes will exacerbate the network congestion). To this end, this paper innovatively develops an active detection method based on the improved ant colony algorithm (IAC) for congestion identification on the path level. The developed method uses the IAC algorithm to find the optimal path and identifies the congested path by comparing the optimal path before and after congestion. Besides, the queuing theory model is utilized to obtain the performance metrics (i.e., queuing waiting time) to evaluate the optimal path. Experimental results show that the developed method can accurately identify the congested path in the network. This method makes identifying congestion less expensive and has higher operational feasibility, which is meaningful for the practical reference.

With the rapid growth of service requirements, the rising concern of resource allocation fairness (e.g., the actual gained Quality of Service(QoS)) leads to the popularity of studies for fair scheduling mechanisms in service systems. The Generalized Processor Sharing (GPS) mechanism has been widely utilized as the fair reference base in the resource allocation due to its fairness and flexible configuration (i.e. scheduling based on weights). The urgent objective of GPS is to accurately evaluate the QoS of each service. Due to the inherent interconnected feature of the GPS mechanism, it is challenging to accurately evaluate individual service’ obtained QoS metrics by analytical-based methods with the growing number of services. Besides, considering the burstiness of the service requests, self-similar process is utilized to delineate the arrival of the service. Therefore, we propose a deep learning based approach, terms as DLPE_GPS, to accurately compute the QoS metrics of individual services in multi-queue GPS under self-similar request traffic. Specifically, DLPE_GPS firstly leverages knowledge-driven features to characterize each service (i.e., arrival rates, weight assigned, etc), and then the representation of each service is computed by a well designed multi-head attention mechanism considering mutual effect be-tween different service subsystems. After that, the knowledge-driven features and intermediate capacities are fused for QoS evaluation. Finally, we conduct complex experiments to show the effectiveness of the proposed method in terms of various aspects.

We have witnessed the rise of cross-data against multimodal data problems recently. The cross-modal retrieval system uses a textual query to look for images; the air quality index can be predicted using lifelogging images; the congestion can be predicted using weather and tweets data; daily exercises and meals can help to predict the sleeping quality are some examples of this research direction. Although vast investigations focusing on multimodal data analytics have been developed, few cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been carried on. In order to promote intelligent cross-data analytics and retrieval research and to bring a smart, sustainable society to human beings, the specific article collection on "Intelligent Cross-Data Analysis and Retrieval" is introduced. This Research Topic welcomes those who come from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing

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.

With the transmission and sharing of data in the Internet of Things (IoT), while bringing development to life and the economy, it also inevitably threatens the data copyright protection and authentication. Digital watermarking technology can provide an effective solution for copyright protection by embedding the watermark in the data to prove the copyright attribution. The existing methods of digital watermarking in IoT mainly target multimedia, without considering the copyright authentication in database data. Unlike multimedia information, the database does not focus on the subjective visual perception when using the data, but rather on the potential values unlocked from the data through algorithms such as data mining. Therefore, we propose a new database watermarking algorithm based on decision tree shift correction (DTSC), considering the data copyright authentication and usability when applying for data mining algorithm. The algorithm adjusts the watermarked data by the DTSC method and makes the watermarked decision tree identical to the original in the iteration process. It solves the problem of database data copyright authentication in IoT and ensures the usability of the data when used for decision tree model construction. From the simulation results, it can be seen that the proposed method ensures the usability of the data for the classification and regression tree decision tree algorithm while embedding the watermark in the database data, and the data distortion of the proposed method does not differ from that of the traditional watermarking algorithm.

Cloud storage and edge computing provide the possibility to address the tremendous storage and computing pressure caused by the explosive growth of traffic at the edge of the networks. In this scene, as data is outsourced to the cloud or edge servers, data privacy can be leaked. For enhancing security and privacy, attribute-based searchable encryption (ABSE), as an effective technical approach, achieves controllable search of ciphertext. Aiming at addressing the issues of the low search efficiency in a single-keyword ABSE scheme and the large computing overhead of the existing multikeyword ABSE schemes, we propose a novel multikeyword ABSE scheme (EMK-ABSE) through cloud-edge coordination. The huge amounts of encrypted data is stored to cloud server (CS), while the corresponding encrypted index is uploaded to the nearest edge node (EN) to perform multikeyword search and assisted decryption. To further release the computational burden of clients, a hybrid online/offline mechanism is adopted in encryption. Security analysis indicates that the multikeyword index in EMK-ABSE has secure indistinguishability under chosen keyword attack (IND-CKA). The comprehensive evaluation proves that EMK-ABSE achieves not only encrypted multikeyword retrieval but also fine-grained access control, with lower computation complexity in the three stages of encryption, trapdoor generation, and decryption. We show that the proposed scheme has higher efficiency and practicability than the selected relative works.

Millimeter wave (Mmwave) communication can realize high rate service for the upcoming Internet of Things (IoT) networks. Although directional multiantenna gains can help enhance security, randomly distributed eavesdroppers can still intercept confidential messages by residing in both the main-lobe and side-lobe areas of the beam signal. Considering the unique propagation features of mmWave, this article explores the potential of physical layer security in mmWave multiple-input–multiple-output (MIMO) systems. We propose an artificial noise (AN)-aided capacity threshold on–off secure transmission scheme to resist the eavesdropping threat. Taking into account the influence of mmWave channel characteristics, random blockage, and multiantenna gains, we first derive the closed-form expressions of transmission probability (TP) and secrecy outage probability (SOP) in a noncolluding eavesdropping scenario. Then, the lower bound of SOP with AN and closed-form expression of SOP without AN is derived in a colluding eavesdropping scenario. Theoretical analysis evaluates the impacts of various system parameters on secrecy performance and verifies the effects of AN interference on inhibiting side-lobe eavesdropping. Simulation results validate the theoretical results and indicate that the combination of capacity threshold on–off transmission scheme, AN interference, and multiantenna directional gains can effectively reduce the security threats of mmWave MIMO systems. Besides, the optimal power allocation ratio of AN in noncolluding scenarios is demonstrated and its rule is summarized, which depends on whether legitimate communication links are in blockage.

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.

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing-enabled gesture recognition due to its inherent merits like remote sensing, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “where to look” and “when to look.” To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi channel state information via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a deep residual network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

User authentication plays a critical role in access control of a man-machine system, where the knowledge factor, such as a personal identification number, constitutes the most widely used authentication element. However, knowledge factors are usually vulnerable to the spoofing attack. Recently, the inheritance factor, such as fingerprints, emerges as an efficient alternative resilient to malicious users, but it normally requires special equipment. To this end, in this article, we propose WiPass, a device-free authentication system only leveraging the pervasive Wi-Fi infrastructure to explore keystroke dynamics (manner and rhythm of keystrokes) captured by the channel state information to recognize legitimate users while rejecting spoofers. However, it remains an open challenge to characterize the behavioral features hidden in the human subtle motions, such as keystrokes. Therefore, we build a signal enhancement model using Ricean distribution to amplify user keystroke dynamics and a hybrid learning model for user authentication, which consists of two parts, i.e., convolutional neural network based feature extraction and support vector machine based classification. The former relies on visualizing the channel responses into time-series images to learn the behavioral features of keystrokes in energy and spectrum domains, whereas the latter exploits such behavioral features for user authentication. We prototype WiPass on the low-cost off-the-shelf Wi-Fi devices and verify its performance. Empirical results show that WiPass achieves on average 92.1% authentication accuracy, 5.9% false accept rate, and 6.3% false reject rate in three real environments.

Vehicular Ad hoc Networks (VANETs), as the integration of mobile vehicle and intelligent technology, has raised plenty of attentions. In VANETs, mobile smart vehicles can timely get information from surrounding environment, which brings great convenience for society. However, this highlights the need to improve data quality while protecting privacy for vehicular data provider. Thus, in this article, we propose an AI-based Trust-aware and Privacy-preserving System (ATPS) to preserve privacy for vehicular data providers while improving quality of data collections in VANETs. Our proposed ATPS system mainly consists of two schemes: 1) a Partial Ordering based Trust Management (POTM) scheme and 2) a Trajectory Privacy Preserving (TPP) scheme jointly designed by Wasserstein Generative Adversarial Networks (WGAN) and differential privacy. The POTM scheme uses partial ordering relationship to accurately evaluate and manage trusts for vehicular data providers with the assistance of fully trusted drones, and selects the vehicles with top ranks to collect data. Then, TPP scheme skillfully incorporates WGAN and differential privacy to preserve trajectory privacy for vehicular data providers in VANETs, which also guarantees data availability by adding carefully designed noise to the original trajectory. Compared to existing scheme, extensive experiments conducted on the real-world datasets demonstrates efficiency of our ATPS in terms of improving the data quality by 45.76% to 52.57%, reducing the malicious vehicle participants by 15.48% to 16.95%, preserving privacy of vehicles, and guaranteeing data availability.

Rail corrugation (RC) is one of the most important indicator to evaluate the quality of rail, which is used to describe the irregularity of rail surface, also known as rail’s vertical flatness. However, the definition of RC is still an empirical description for low-speed measurement devices. In this paper, the process of RC measurement is divided into two steps, sampling and extraction, which helps the users to understand more clearly. Then a new mathematic model is proposed to make the process of RC’s extraction be an executable operation for machine calculation. The model adopts a new concept of flexible virtual ruler to perform sliding filtering on an overall rail and extract the instantaneous RC with a successive approximation algorithm according to user’s requirements and national standards. The proposed FVR model can not only be fully compatible with the traditional extraction method of RC, but also provide a new idea for evaluating RC’s quality. Comparing with the current popular methods, the proposed model gives a meaningful strategy for rail maintenance with a complete mathematic description having more degrees of freedom. Experiment results demonstrate its validity and reliability for both indoor simulation and actual outdoor experiments.

Vehicular Edge Computing (VEC) is a promising paradigm that leverages the vehicles to offload computation tasks to the nearby VEC server with the aim of supporting the low latency vehicular application scenarios. Incentivizing VEC servers to participate in computation offloading activities and make full use of computation resources is of great importance to the success of intelligent transportation services. In this paper, we formulate the competitive interactions between the VEC servers and vehicles as a two-stage Stackelberg game with the VEC servers as the leader players and the vehicles as the followers. After obtaining the full information of vehicles, the VEC server calculates the unit price of computation resource. Given the unit prices announced by VEC server, the vehicles determine the amount of computation resource to purchase from VEC server. In the scenario that vehicles do not want to share their computation demands, a deep reinforcement learning based resource management scheme is proposed to maximize the profits of vehicles and VEC server. The extensive experimental results have demonstrated the effectiveness of our proposed resource management scheme based on Stackelberg game and deep reinforcement learning.

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.

introduction Share on Introduction to the Special Section on Cyber Security in Internet of Vehicles Authors: Ching-Hsien Hsu Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan Department of Computer Science and Information Engineering, Asia University, Taiwan; Department of Medical Research, China Medical University Hospital, China Medical University, Wufeng, Taichung, Taiwan 0000-0002-2440-2771Search about this author , Amir H. Alavi Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA 0000-0002-7593-8509Search about this author , Mianxiong Dong Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan Department of Sciences and Informatics, Muroran Institute of Technology, Muroran, Hokkaido, Japan 0000-0002-2788-3451Search about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 4November 2022 Article No.: 81pp 1–6https://doi.org/10.1145/3584746Published:15 March 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

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.

Federated learning (FL) has attracted more and more attention recently. The integration of FL and edge computing makes the edge system more efficient and intelligent. FL usually uses the server to actively select certain edge devices to participate in the global model training. However, the selected edge devices may be stragglers, or even crash during training. Meanwhile, the unselected idle edge devices cannot be fully utilized for training. Therefore, besides the widely studied communication efficiency and data heterogeneity issues in FL, we also take the above time efficiency into consideration, and propose a time-efficient asynchronous federated learning protocol, TEA-Fed, to solve these problems. With TEA-Fed, idle edge devices actively apply for training tasks and participate in model training asynchronously once assigned tasks. Considering that there may be a huge number of edge devices in edge computing, we introduce control parameters to limit the number of devices participating in training the identical model at the same time. Meanwhile, we also introduce caching mechanism and weighted averaging with respect to model staleness in the model aggregation step to reduce the adverse effects of model staleness and further improve the accuracy of the global model. Finally, the experimental results show that the protocol can accelerate the convergence of model training, improve the accuracy, and has robustness to heterogeneous data.

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,

Positions of primary and secondary users and the distance between them in a cognitive radio network play an important role in the interference caused by the secondary network to the users in the primary network. In this work, we exploit cooperative localization to estimate the distance between the nodes and then use transmission power control for interference avoidance. The distance between the primary and secondary users is estimated using a cooperative model. Next, the transmission power of the secondary user is adjusted so that it does not cause interference to the primary user. The proposed algorithm is verified by simulations. Results show that the primary user achieves considerable gain in the throughput as a result of increase in the signal to interference and noise ratio due to reduction in the interference caused by the secondary network.

This work proposes AMIS, an edge computing-based adaptive video streaming system. AMIS explores the power of edge computing in three aspects. First, with video contents pre-cached in the local buffer, AMIS is content-aware which adapts the video playout strategy based on the scene features of video contents and quality of experience (QoE) of users. Second, AMIS is channel-aware which measures the channel conditions in real-time and estimates the wireless bandwidth. Third, by integrating the content features and channel estimation, AMIS applies the deep reinforcement learning model to optimize the playout strategy towards the best QoE. Therefore, AMIS is an intelligent content- and channel-aware scheme which fully explores the intelligence of edge computing and adapts to general environments and QoE requirements. Using trace-driven simulations, we show that AMIS can succeed in improving the average QoE by 14%-46% as compared to the state-of-the-art adaptive bitrate algorithms.

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.

Mobile-edge computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in the blockchain-enabled MEC system using an asynchronous learning approach in this article. First, a blockchain-enabled secure data sharing framework in the MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in the blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Especially, an asynchronous learning approach is employed to solve the formulated problem. The numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption, and reward.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

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.

User authentication is the first and most critical step in protecting a man-machine system from a malicious spoofer. However, security and privacy are just like the two sides of one coin, hard to see both at the same time, especially by the current mainstream credential- and biometric-based approaches. To this end, we propose WiONE, a safe and privacy-preserving user authentication system leveraging the ubiquitous Wi-Fi infrastructure by exploring “how you behave” rather than “who you are”. The key idea is to apply deep learning to user physical behavior captured by Wi-Fi channel state information (CSI) to identify legitimate users while rejecting spoofers. The design of WiONE faces two challenges, namely, how to capture the subtle behavior, such as a keystroke on CSI, and how to mitigate the heavy environment-specific training required by deep learning. For the former, we design a behavior enhancement model based on the Rician fading to highlight the behavior-induced information by suppressing the behavior-unrelated information on channel response. For the latter, we develop a behavior characterization method tailored for the prototypical networks to facilitate the extraction of the domain-independent behavioral features and enable one-shot recognition of a new user in a new environment. Numerous experiments are conducted in several real-world environments, and the results show that WiONE outperforms its state-of-the-art rivals in authentication performance with much less training effort.

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.

The papers in this special section focus on advanced signal processing and artificial intelligence (AI) technologies for industrial Big Data (IBD) powered by Industry 4.0. Modern industry has evolved from the traditional manufacturing industry to digital and intelligent industry. Huge amount of complex real-time data are generated from the thousands of industrial sensors in physical and man-made environments. Industrial big data (IBD) afford us an unprecedented opportunity to obtain an in-depth understanding of Internet of Things and facilitate data-driven approaches for industrial optimization and scheduling. The papers in this section collect the latest ideas and research on advanced signal processing and artificial intelligence (AI) technologies for IBD.

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.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogenous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the capacity performance of the three-layer heterogenous satellite network. We first construct the network model and the capacity model of the network with detailed design of the network parameters. Then, taking time structure into account, we propose a time structure based augmenting path searching method, which can significantly reduce the computing complexity. Finally, based on the model and method proposed, we analyze the capacity performance of the three-layer heterogenous satellite network with numerical results.

The First International Workshop on "Intelligence Cross-Data Analytics and Retrieval" (ICDAR'20) welcomes any theoretical and practical works on intelligence cross-data analytics and retrieval to bring the smart-sustainable society to human beings. We have witnessed the era of big data where almost any event that happens is recorded and stored either distributedly or centrally. The utmost requirement here is that data came from different sources, and various domains must be harmonically analyzed to get their insights immediately towards giving the ability to be retrieved thoroughly. These emerging requirements lead to the need for interdisciplinary and multidisciplinary contributions that address different aspects of the problem, such as data collection, storage, protection, processing, and transmission, as well as knowledge discovery, retrieval, and security and privacy. Hence, the goal of the workshop is to attract researchers and experts in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, atmospheric science, and security and privacy to tackle the issues as mentioned earlier.

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.

The analytical model of trilateration localization error expresses the localization error in terms of distance estimation errors. Using the analytical model, we investigate the trilateration localization error, and derive the extreme values of its components and other novel results. We show that these extreme values occur when the distance estimation errors have opposite signs. We further show that better localization accuracy is achieved if all the distance estimation errors are negative compared to when the same magnitudes of distance estimation errors are positive. The results are applicable to all wireless communication systems and networks where trilateration and multilateration may be used for position estimation. This includes global navigation satellite systems, such as the global positioning system, wireless local area networks, wireless sensor networks, internet of things, and other miscellaneous applications. All the results are verified using simulation.

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.

Due to the limited battery capacity in mobile devices, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve the energy efficiency (EE) in Internet-of-Things (IoT) networks, i.e., device-to-device (D2D) networks, by allowing mobile devices to harvest energy from ambient radio-frequency (RF) signals. However, the nonlinear behavior of RF energy harvesters has largely been ignored in the existing works on SWIPT. In this article, we propose to maximize the sum EE of all D2D links in a D2D underlaid cellular network by optimizing the resource and power allocation based on a nonlinear energy harvesting (EH) model. Toward this end, we first propose a prematching algorithm to divide the D2D links into a SWIPT-enabled group and a non-EH group that cannot meet the EH circuit sensitivity. We then develop a two-layer iterative algorithm to jointly optimize the D2D transmission power and the power splitting ratio to maximize the EE for each SWIPT-enabled D2D link. On this basis, we build the preference lists for both SWIPT-enabled D2D links and cellular user equipment (CUE), and propose a one-to-one constraint stable matching algorithm to maximize the sum EE of all SWIPT-enabled D2D links by optimizing the spectrum resource sharing between D2D links and CUEs. The sum EE of non-EH D2D links is maximized through an iterative power control algorithm and a one-to-one stable matching algorithm. Simulation results show that our proposed algorithms achieve a much higher sum EE than the existing matching-based energy-efficient resource allocation scheme for SWIPT-enabled D2D networks.

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.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.

Cloud computing offers various services based on outsourced data by utilizing its huge volume of resources and great computation capability. However, it also makes users lose full control over their data. To avoid the leakage of user data privacy, encrypted data are preferred to be uploaded and stored in the cloud, which unfortunately complicates data analysis and access control. In particular, few existing works consider the fine-grained access control over the computational results from ciphertexts. Though our previous work proposed a framework to support several basic computations (such as addition, multiplication and comparison) with flexible access control, privacy-preserving division calculations over encrypted data, as a crucial operation in many statistical processes and machine learning algorithms, is neglected. In this paper, we propose four privacy-preserving division computation schemes with flexible access control to fill this gap, which can adapt to various application scenarios. Furthermore, we extend a division scheme over encrypted integers to support privacy-preserving division over multiple data types including fixed-point numbers and fractional numbers. Finally, we give their security proof and show their efficiency and superiority through comprehensive simulations and comparisons with existing work.

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.

Trilateration and multilateration are important location estimation techniques used in a diverse range of networks and applications. The system of equations yielded by multilateration can be reduced to simpler linear equations which can be solved to arrive at a closed form analytic solution. Exploiting this solution technique, we develop a novel and unique analytical model for the localization error resulting from trilateration. The analytical model can be used for the analysis of the localization error in all applications wherever multilateration is used for position estimation including internet of things, wireless sensor networks and global navigation satellite system thereby increasing reliability and quality of localization. As an example, we use the analytical model to corroborate the fact that localization error is a function of topology of reference positions in addition to distance estimation errors. The analytical model is verified using simulation experiments.

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.

In recent years, the fast development of mobile computing has substantially promoted the mobile edge computing (also known as multi-access edge computing, MEC). Placing content in edges is one of the most important uses of MEC for that it can benefit a variety of service and applications such as video streaming and VR/AR. Currently, most of the existing researches are application specified, and the heterogeneities in data allocating devices and content have not been sufficiently explored. Aiming at developing a general optimal data allocating decision algorithm for MEC, in this work, we carry out in-depth study on the interaction of data allocating and fetching in heterogenous edge computing networks, showing the NP-hardness of the optimal decision problem. We then present polynomial algorithms with 1 - 1/e-approximation factor. Our algorithms has reasonable performance guarantee with low computation complexity. We verify the proposed approach with analysis and simulations.

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.

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.

Video caching is one of the most important research issues in Content-Centric Network (CCN) and greatly affects its overall performance. The computational complexity of state-of-the-art optimal caching schemes is high, due to the arbitrary network topologies. In this paper, the popularity-aware video caching in topology-known CCN is studied. The complex arbitrary network typology is mapped into a virtual cascade network topology and a caching scheme is designed in accordance with the transformed virtual network rather than the original network. This scheme is proved optimal, and is with polynomial computational complexity. Simulations are conducted and the results show that the proposed scheme outperforms the existing schemes.

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.

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.

As the rapidly growing volume of data are beyond the capabilities of many computing infrastructures, to securely process them on cloud has become a preferred solution which can both utilize the powerful capabilities provided by cloud and protect data privacy. This paper puts forward a new approach to securely decompose tensor, the mathematical model widely used in data-intensive applications, to a core tensor and some truncated orthogonal bases. The structured, semi-structured as well as unstructured data are all transformed to low-order sub-tensors which are then encrypted using the fully homomorphic encryption scheme. A unified high-order cipher tensor model is constructed by collecting all the cipher sub-tensors and embedding them to a base tensor space. The cipher tensor is decomposed through a proposed secure algorithm, in which the square root operations are eliminated during the Lanczos procedure. The paper makes an analysis of the secure algorithm in terms of time consumption, memory usage and decomposition accuracy. Experimental results reveals that this approach can securely decompose tensor models. With the advancement of fully homomorphic encryption scheme, the proposed secure tensor decomposition method is expected to be widely applied on cloud for privacy-preserving data processing.

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%.

Software-defined networking (SDN) enables network subscribers to negotiate QoS parameters in a on-demand basis. On the other hand, peak-time congestion accompanying the fast-growing traffic in recent years forces Internet service provider (ISP) to put forward a new pricing scheme by taking into account when a user uses Internet in addition to how much a user uses Internet. In this paper, we study the payoff optimization problem of ISP and network subscribers in SDN-enabled networks. A self-interested network subscriber always tries to obtain network resources as much as possible even if the network is congested; on the other hand, rational ISP tends to charge a higher price without providing subscribers guaranteed Quality of Service (QoS). A Stackelberg game is hence constructed to analyze the competitive interactions between ISP and home network subscribers. Specifically, ISP decides its pricing strategy for each time slot by solving a payoff optimization problem. Given the pricing strategy, network subscribers then decide the bandwidth to be reserved in a on-demand basis aiming to optimize their own payoff as well. We analyze the Nash equilibrium solution of the game. Simulation results confirm that the proposed pricing scheme can largely improve the payoff of network subscribers and ISP, compared to the usage-based pricing (UBP) scheme. Furthermore, the portion of surplus obtained by ISP increases with the increase of traffic load.

The ongoing evolution of personal mobile devices capabilities and wireless technologies result in a huge growth of traffic on mobile networks (3G/4G). One of the most promising approaches to handle this data crisis is to offload the fast growing traffic onto femtocell networks. Since both the 3G/4G macrocell and femtocells operate on the same licensed spectrum, the cross-tier interference should be well managed. In this paper, we propose a utility-based transmission power allocation policy for the uplink transmission in femtocell networks. Our main motivation is to design the transmission power allocation policy aiming at optimizing the joint utility of femtocell users (FUs) subject to a interference temperature constraint at the macrocell base station (MBS) side. We provide a novel floating-ceiling water-filling (FCWF) algorithm with little computational overhead to obtain the optimal solution for the joint utility optimization problem. Numerical results confirm that the joint utility and average SINR can be significant improved with the proposed method.

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.

IEEE 802.11p based Dedicated Short Range Communication (DSRC) has been considered as a promising wireless technology for enhancing road safety and efficiency. However, there is lack of deep understanding about how IEEE 802.11p performs for vehicle-to- vehicle (V2V) communications in urban environments. In this demo paper, we first introduce the statistical analysis results on V2V communication performance, based on a large volume of real-world measurement trace collected in Shanghai city. With the key insights observed in our experiments, we propose a novel scheme to synthesize V2V communication traces which can be of great value for VANET-related research, such as network protocol design and simulations.

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.

Nowadays, Internet has became an indispensable part of our daily life. The network keeps expanding in scales, and the network technologies develop rapidly. From the network architecture to the network application, new technologies emerge constantly. For example, Software Defined Networking (SDN) introduces the programmability of network to achieve more flexible network management. Device-to-device (D2D) communication provides the promising solutions for social applications and content sharing applications in mobile network. Cloud computing and fog computing make it possible to maximize the utilization of network resources. This special issue is intended to introduce some new advances in the above future network technologies, in which eight high-quality papers are presented. There are three papers about SDN including the synchronization of multidomain controllers, the placement of SDN controller, and the traffic scheduling in SDN. Three papers focus on cloud computing, whose topics include the IaaS cloud broker, the fog computing, and the architecture of virtual machine storage. Other two papers are about D2D network and multicast network, respectively. The general objective of this special issue is to show the state-of-the-art in future network technologies and to point out the directions of the research on future network. As an emerging network technology, software-defined networking (SDN) attracts more and more attentions from both academia and industry. The SDN provides solutions to decouple the network control and the data transmission, which enables the network to become programmable. According to the northbound API provided by SDN controller, people can manage the network more flexibly and implement the network innovation applications more easily. However, the research in the context of SDN is still in its infancy. There are many important issues that should be addressed. Zou et al try to address the synchronization problem of multidomain SDN controllers in their paper “Active Synchronization of Multi-domain Controllers in Software Defined Networks.”1 In massive data center networks, the single controller is the bottleneck of the SDN deployment. To improve the scalability and reliability, multidomain controllers are usually adopted. Within this context, the the synchronization of multidomain controllers should be considered. To overcome the drawbacks of existing PS-based synchronization algorithm, Zou et al propose an active synchronization algorithm, which is flexibly triggered by events without considering the effect of time on synchronization. The evaluation results show that the active synchronization algorithm can achieve better load balance with less overhead. Zhao et al focus on the problem of SDN controller placement in their paper “Scalable SDN Architecture with Distributed Placement of Controllers for WAN.”2 A large number of SDN controllers are usually required to construct a scalable SDN architecture for WAN. In this architecture, the network performance should be affected by the number and the location of controllers. To minimize the number of controllers and the link delay between switches and controllers, Zhao et al propose a novel method of controller placement. In the proposed method, they formulate an Integer Linear Program (ILP) model for controller placement in WAN. Then they propose a heuristic algorithm as the solution. The numerical simulation results show that the proposed method can achieve better results. Huang et al study the problem of traffic scheduling for SDN with Deep Packet Inspection (DPI) proxies in their paper “Traffic Scheduling for Deep Packet Inspection in Software Defined Networks.”3 With the development of SDN, more and more network function proxies are deployed for flow processing, such as DPI, Firewall, and Load balancer. To ensure acceptable performance of DPI proxies in SDN, Huang et al propose an Integrated DPI Proxy Allocation and routing Determining (IPAD) problem and then formulate this problem as an Integer Linear Programming (ILP) model with the goal of minimizing the overall latency in DPI. They design a two-phase algorithm to solve the IPAD problem. The simulation results show that the proposed algorithm outperforms existing benchmark algorithms. Nowadays, cloud computing has become a highly demanded service. More and more people accept the “pay as you go” model of computing resources. More and more companies prefer using the cloud services to avoid up-front infrastructure costs. However, there are some new challenges rising with the development of cloud computing, such as the model of cloud broker, the concept of fog computing. Chen et al focus on the reservation schemes for IaaS cloud broker in their paper “Reservation Schemes for IaaS Cloud Broker: A Time-multiplexing Way for Different Rental Time.”4 As a novel service model, the cloud broker coordinates the demand of users with IaaS providers to gain high profit by leveraging the time multiplexing of VM instances and the pricing gap between on-demand billing and reservation billing. To achieve this goal, an effective reservation scheme for cloud broker is necessary. Chen et al propose an offline and an online reservation algorithms to reduce the cost of cloud broker without partitioning users' rental time. The offline algorithm is based on the demand graph while the online algorithm is based on the history data. The experimental results show that two kinds of proposed algorithms both can reduce the cost of cloud broker. Yao et al focus on the solution of cloudlet deployment for cost-effective fog computing in their paper “Heterogeneous Cloudlet Deployment and User-Cloudlet Association towards Cost Effective Fog Computing.”5 With the development of mobile cloud computing, the concept of fog computing is proposed to bring the cloud facilities closer to mobile users, thereby achieving low communication latency between mobile devices and the cloud. In fog computing research community, it is a big challenge to deploy the cloudlet servers in a cost-effective manner. To address this issue, Yao et al propose a method of heterogeneous cloudlet deployment. They formulate the deployment problem into an Integer Linear Programming model and then design a heuristic algorithm to solve it. The evaluation results validate the high efficiency of their method. Chen et al focus on the storage architecture with nonvolatile memory (NVM) device for virtual machines in their paper “MBSA: A Lightweight and Flexible Storage Architecture for Virtual Machines.”6 In cloud computing, virtualization technology is a good solution to provide a powerful computing platform. However, virtualization technology is not suitable for data-intensive workloads due to high I/O virtualization overhead. To address this problem, Chen et al propose a memory bus-based storage architecture named MBSA, in which the performance of NVM devices is fully explored to improve the storage performance of VMs. Compared with the conventional storage architecture, the MBSA can take full advantage of NVM devices. Experimental results show that the MBSA provides good performance on balancing the write operations to NVM devices with low overhead. Device-to-device (D2D) is a local short-range communication mode, by which files can be cached and shared among mobile devices within a certain range. Because D2D can be used to reduce the traffic explosion through direct communication without traversing the base station or core network, D2D sharing is viewed as a promising application of mobile social networks. To provide better quality of experience for D2D sharing, Wang et al try to design and implement a big data processing platform to analyze the empirical trace data from D2D sharing application in their paper “A Measurement Study of Device-to-Device Sharing in Mobile Social Networks Based on Spark.”7 They get a large-scale trace dataset from Xender, which is one world's leading D2D sharing application. Then they build a big data processing platform based on Spark to explore the characteristics of application and users. Finally, they discuss the potential methods to improve Xender's quality of service. Multicast is usually used to deliver the same content from a single source to a set of destinations. By multicast, the unnecessary duplicated transmissions can be reduced to save bandwidth efficiently. Although it is a traditional group communication method, multicast faces new challenges from some emerging Internet applications. For example, the problem of uncertain multicast is usually caused by content replica design in content distribution network (CDN) and datacenter network (DCN). Ren et al try to address the packing problem of uncertain multicast to minimize the total transmission cost in their paper “The Packing Problem of Uncertain Multicasts.”8 To give effective solution of network resources sharing when a set of uncertain multicast occupy the network simultaneously, they formally present the packet problem of uncertain multicast. Then they prove this problem is NP-hard and design two greedy algorithms approximating the optimal solution. Finally, they conduct large-scale simulations to verify the effectiveness and efficiency of the proposed algorithms. In this special issue, there are eight high-quality papers about recent advances in future network technologies. From these papers, we can see that SDN, Cloud, and D2D remain promising technologies of future network. With the development of these technologies, there are many important issues that need to be addressed. Moreover, we also can see that innovative Internet applications bring new challenges to traditional network technology, such as uncertain multicast in CDN or DCN. These new challenges should be addressed to promote the development of future network. We hope that the readers can benefit from this special issue. This work is supported by the JSPS KAKENHI grant number JP16F16349, JSPS KAKENHI grant number JP16K00117, KDDI Foundation, and the Dalian High-level Talent Innovation Program (No. 2015R049).

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.

Human-machine systems required a deep understanding of human behaviors. Most existing research on action recognition has focused on discriminating between different actions, however, the quality of executing an action has received little attention thus far. In this paper, we study the quality assessment of driving behaviors and present WiQ, a system to assess the quality of actions based on radio signals. This system includes three key components, a deep neural network based learning engine to extract the quality information from the changes of signal strength, a gradient-based method to detect the signal boundary for an individual action, and an activity-based fusion policy to improve the recognition performance in a noisy environment. By using the quality information, WiQ can differentiate a triple body status with an accuracy of 97%, whereas for identification among 15 drivers, the average accuracy is 88%. Our results show that, via dedicated analysis of radio signals, a fine-grained action characterization can be achieved, which can facilitate a large variety of applications, such as smart driving assistants.

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).

Content Centric Network (CCN) has become a heated research topic recently, as it is proposed as an alternative of the future network. The routers in CCN have the caching abilities and the caching strategies affect the system performance greatly. Each content in CCN is associated with a popularity, which is determined by the corresponding requested times. Popularity-aware caching scheme caches the popular content close to users and can lead to better caching performance in terms of smaller average transmission hops traveled. Content popularity significantly affects the overall system performance, and the content size is not considered during the content level popularity (i.e. item popularity) calculation. In this paper, we study the impact of the item popularity and chunk popularity in CCN, where the chunk popularity is the normalized item popularity considering the content size. Extensive simulations are conducted and the simulation results show the advantages and disadvantages of each scheme. A new popularity calculation method is proposed to perform the tradeoff between the item popularity and chunk popularity.

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.

Broadcasting of real-time video, especially accidents relevant, plays an important role in Vehicle Ad-hoc Networks (VANETs). Multi-view video enables the perception of the targeted scene from multiple angles. By subscribing the real-time multi-view video broadcast, drivers can obtain better knowledge of the highway traffic and road conditions. However, compared with the traditional single view video transmission, the multiview video transmission is more challenging, because of the VANETs' channel variation and a much larger network data rate requirement. The inherent problem thus becomes how to utilize the limited network bandwidth to stream the real-time multi-view video. As far as the authors understand, this problem has not yet been solved in any formal way. This paper focuses on the realtime multi-view broadcast over VANETs and formulates it into an optimization problem with the objective to maximize the average video quality received by users. By exploring the VANET channel characteristic, the correlations inside the multi-view and video popularity, an algorithm named as RMV is proposed to solve the optimization problem. Extensive simulations are conducted and simulation results show that the proposed scheme can outperform the comparison schemes in typical VANET scenarios to a great extent.

Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. Device-to-device (D2D) communication as an underlay of cellular network enriches local service and offloads base station. In this paper, we target the video transmission demanded by picocell edge users (PEUEs), who suffer from low quality channel due to the inter-cell interference from macrocell and long physical distance from picocell. Moreover, the wireless channels are burst-loss prone for upper layer applications such as video on demand (VoD), which makes the traditional channel coding such as forward error correction (FEC) insufficient. In this paper, we address these issues and propose a cooperative video transmission scheme to improve PEUEs' received video quality by constructing two transmission paths from picocell to each PEUE. The two transmission paths are the direct transmission from pico-eNB (i.e., base station) to PEUE and a relay-assisted path by means of D2D communication for frame recovery, respectively. Reference frame selection and unequal error protection are adopted to further improve the overall performance. Extensive simulations are conducted and results demonstrate that the proposed scheme outperforms state-of-the-art scheme in Config.4b scenarios defined by 3GPP.

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.

IEEE 802.11p based Dedicated Short Range Communication (DSRC) has been considered as a promising wireless technology for enhancing transportation safety and traffic efficiency. However, with limited literature available, there is lack of understanding about how IEEE 802.11p performs for vehicleto-vehicle (V2V) communications in urban environments. In this paper, we conduct intensive statistical analysis on V2V communication performance, based on the empirical measurement data collected from off-the-shelf IEEE 802.11p-compatible onboard units (OBUs). We have several key insights as follows. First, both line-of- sight (LoS) and non-line-of-sight (NLoS) durations follow power law distributions, which implies that the probability of having long LoS/NLoS conditions can be relatively high. Second, the packet inter-reception (PIR) time distribution follows an exponential distribution in LoS conditions but a power law in NLoS conditions. In contrast, the packet inter-loss (PIL) time distribution in LoS condition follows a power law but an exponential in NLoS condition. Third, the overall PIR time distribution is a mix of exponential distribution and power law distribution. The presented results provide solid ground to validate models, tune VANET simulators and improve communication strategies.

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.

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably.

editorial Free Access Share on Introduction to Special Issue on Multimedia Big Data: Networking Editors: Mianxiong Dong Muroran Institute of Technology, Japan Muroran Institute of Technology, JapanView Profile , Vincenzo Piuri Università degli Studi di Milano, Italy Università degli Studi di Milano, ItalyView Profile , Shueng-Han Gary Chan The Hong Kong University of Science and Technology, Hong Kong The Hong Kong University of Science and Technology, Hong KongView Profile , Ramesh Jain University of California, Irvine, CA University of California, Irvine, CAView Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 12Issue 5sDecember 2016 Article No.: 70pp 1–3https://doi.org/10.1145/2989214Published:21 September 2016Publication History 1citation336DownloadsMetricsTotal Citations1Total Downloads336Last 12 Months17Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

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.

To improve users' experience, TCP short flows that are heavily used in interactive services should be completed as soon as possible. In current data centers, large flows and head-of-line blocking in switches hinder short flows from completion, which leads to long-tailed flow completion times (FCT). Replicating short flows with multiple equal-cost paths is a promising way to reduce FCT. However, the original flow and its replicated one are quite likely to be routed to the same path (ECMP hash collision), which increases both the mean and 99-percentile FCT significantly. What's more, inadequate replication leaves many other less-congested equal-cost paths unused and limits the performance while excess replication degrades throughput of large flows. To solve these problems, we propose SmartRep, a scheme consisting of an efficient and effective traceroute based hash collision avoidance method and an algorithm to decide the optimal number of replicated flows for different short flows. SmartRep can be easily implemented in software and readily deployed in data centers. Extensive NS2 simulations show that our approach improves previous replication-based work by 25%-50% in both mean and 99th percentile FCT, and meanwhile imposes negligible impact on large flows.

Database-driven Cognitive Radio Network (CRN) has been proposed to replace the requirement of spectrum sensing of terminal devices so that the operation of users is simplified. However, location privacy issues introduce a big challenge for securing database-driven CRN due to spectrum availability information. The existing works consider either PU or SU's location privacy while not the both. In this study, we identify a unified attack framework in which a curious user could infer a target's location based on the spectrum availability/utilization information. Further, we propose a location privacy protection mechanism, which allows both SU and PU to protect their location privacy by adopting a series of countermeasures. The location privacy and spectrum utility are the trade-off. In the countermeasures of location privacy preserving spectrum query process, both SU and database aim to maximize the location privacy with constraints of spectrum utility. Thus, they can obtain higher location privacy level with sacrifice of spectrum utility as long as the spectrum utility meets the requirements. We evaluate the unified attack and defence approaches based on simulation and demonstrate the effectiveness of the proposed location privacy preserving approaches.

Many complex networks in practice can be described by weighted network models, and the BBV model is one of the most classical ones. In this paper, by introducing the concept of correlation degree between nodes, a new weighted network model based on the BBV model is proposed. The model takes the both node strength and node correlation into consideration during the network evolution, which better reveals the evolving mechanisms behind various real-world networks. Results from theoretical analysis and numerical simulation have demonstrated the scale-free property and small-world property of the network model, which have been widely observed in many real-world networks. Compared with the BBV model, the added correlation preferential attachment rule in the model leads to a faster network propagation velocity.

Subspace skyline, as an important variant of skyline, has been widely applied for multiple-criteria decisions, business planning. With the development of mobile internet, subspace skyline query in mobile distributed environments has recently attracted considerable attention. However, efficiently obtaining the meaningful subset of skyline points in any subspace remains a challenging task in the current mobile internet. For more and more mobile applications, subspace skyline query on mobile units is usually limited by big data and wireless bandwidth. To address this issue, in this paper, we propose a system model that can support subspace skyline query in mobile distributed environment. An efficient algorithm for processing the Subspace Skyline Query using MapReduce (SSQ) is also presented which can obtain the meaningful subset of points from the full set of skyline points in any subspace. The SSQ algorithm divides a subspace skyline query into two processing phases: the preprocess phase and the query phase. The preprocess phase includes the pruning process and constructing index process which is designed to reduce network delay and response time . Additionally, the query phase provides two filtering methods, SQM-filtering and ε-filtering, to filter the skyline points according to user preference and reduce network cost. Extensive experiments on real and synthetic data are conducted and the experimental results indicate that our algorithm is much efficient, meanwhile, the pruning strategy can further improve the efficiency of the algorithm.

In cloud computing, companies usually use high-end storage systems to guarantee the I/O performance of virtual machines (VM). These storage systems cost a lot of energy for their high performance. In this paper, we propose an EEVS, a deduplication-based energy efficiency storage system for VM storage. We firstly investigate some VM image files with general operating systems. With the analysis result, we find there are many redundant data blocks that bring extra energy cost VM storage. Therefore, in the EEVS, we design an online-deduplication mechanism to reduce these redundant data without service interruption, while traditional deduplication technology is used for offline backup. Based on the system design, we implement an EEVS with the existing cloud platform. Since this mechanism needs considerable computing resources, we design a deduplication selection algorithm such that the storage energy consumption is minimized for a given set of VMs with limited resources for deduplication. Experiment results in a para-virtualization environments of the EEVS show that energy consumption is reduced by even up to 66% with negligible performance degradation.

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.

Abstract Signcryption as a cryptographic primitive can carry out signature and encryption simultaneously at a remarkably reduced cost. Identity‐based cryptography is more convenient than public key infrastructure‐based cryptography in certificate management. As a result, identity‐based signcryption has been studied extensively, and many efficient and provably secure constructions have been proposed. However, most of these schemes are based on intractability assumptions from number theory, and these assumptions have been threatened by the booming quantum computation. Therefore, a recent trend in cryptography is to construct cryptosystems that are based on lattice‐based intractability assumptions because of their plausible features of quantum attack resistance. In this paper, several identity‐based signcryption schemes from lattice hardness assumptions are proposed. In the standard model, these schemes are indistinguishable againstinneradaptively chosen ciphertext attacks (IND‐CCA2) and strongly unforgeable againstinnerchosen message attacks. In our construction, it does not matter whether the original encryption scheme used to construct signcryption is deterministic or probabilistic; the resulted signcryption schemes can reach IND‐CCA2 security. To achieve this, we carefully combine three techniques—the identity‐based encryption from lattice due to Agrawal–Boneh–Boyen (EUROCRYPT 2010), the framework of lattice‐based short signature due to Boyen (Public Key Cryptography 2010), and the Canetti–Halevi–Katz (abbr. CHK) technique, with necessary and tailored optimization—for transforming an (ℓ+ 1)‐level indistinguishable under chosen plaintext attack secure hierarchical identity‐based encryption (HIBE) into anℓlevel IND‐CCA2 secure HIBE scheme. In addition, our security proof also contains a more efficient simulation tool that might have separate interest in cryptographic applications. Copyright © 2015 John Wiley & Sons, Ltd.

The papers in this special section (Part I) are devoted to the topic of cyberphysical social systems (CPSS). These systems integrate computational physical elements (seamless integration of computational algorithms and physical components) capable of interacting with, reflecting and influencing each other as well as the complicated system and information exhibited by human’s social behavior. Rapid advances in mobile cloud computing, man–machine, machine–machine communications,smart phone networks will cause a new paradigm shift in CPSS design, applications, and operations by bringing improvements not only to the quality of service (QoS) but also to quality of experiment (QoE) and quality of protection (QoP) in terms of the cost efficiency, reliability, security, and energy efficiency from the human perspective of view. The integration of cyber–physical systems and social networks also provides a novel platform to addresses challenges in cyber–physical–social interactions and human-centric technologies development by using powerful tools such as social computing, social cooperation, and social sensing. Application examples of employing mobile computing in CPSS include traffic accidents detection in smart transportation, energy management in smart grid, health monitoring and evaluation.

The six papers in this special section (Part II) on Cyber–Physical–Social Systems (CPSS) focus on computational social systems for emerging techniques for radio access networks, data deduplication, big data computing, smart community, cloud computing, and Internet of Things.

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.

Malicious and selfish behaviors represent a serious threat against routing in delay/disruption tolerant networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN is regarded as a great challenge. In this paper, we propose iTrust, a probabilistic misbehavior detection scheme, for secure DTN routing toward efficient trust establishment. The basic idea of iTrust is introducing a periodically available Trusted Authority (TA) to judge the node's behavior based on the collected routing evidences and probabilistically checking. We model iTrust as the inspection game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a node's reputation, which allows a dynamic detection probability determined by the trust of the users. The extensive analysis and simulation results demonstrate the effectiveness and efficiency of the proposed scheme.

Vehicular networks consist of highly mobile vehicles communications, where connectivity is intermittent. Due to the distributed and highly dynamic nature of vehicular network, to minimize the end-to-end delay and the network traffic at the same time in data forwarding is very hard. Heuristic algorithms utilizing either contact-level or social-level scale of vehicular mobility have only one-sided view of the network and therefore are not optimal. In this paper, by analyzing three large sets of Global Positioning System (GPS) trace of more than ten thousand public vehicles, we find that pairwise contacts have strong temporal correlation. Furthermore, the contact graph of vehicles presents complex structure when aggregating the underlying contacts. In understanding the impact of both levels of mobility to the data forwarding, we propose an innovative scheme, named ZOOM, for fast opportunistic forwarding in vehicular networks, which automatically choose the most appropriate mobility information when deciding next data-relays in order to minimize the end-to-end delay while reducing the network traffic. Extensive trace-driven simulations demonstrate the efficacy of ZOOM design. On average, ZOOM can improve 30% performance gain comparing to the state-of-art algorithms.

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.

It is anticipated that future wireless networks will make use of more renewable energy sources, e.g., solar, wind, and hydro, etc., in order to sustain the ever-growing traffic demands, while mitigating the effects of increased energy consumption. The most critical issue of developing a sustainable communications network is how to cost-effectively deploy access points (APs) with sustainable energy supplies and allocate network resources to meet the quality of service (QoS) requirements of users. In this paper, the traditional AP placement problem is revisited with sustainable power supplies. First, a constrained AP placement optimization problem is formulated. The objective is to determine the optimal placement of APs on a set of candidate locations such that the number of APs is minimized, subject to the constraints that QoS requirements of users can be fulfilled with the harvested energy. To further improve the sustainable network performance, joint power control and rate adaptation at APs is considered, based on different user demands and charging capabilities of the APs. After that, an efficient heuristic algorithm with polynomial time complexity is proposed. Extensive simulation results show that the proposed algorithm approaches the optimal solution under a variety of network settings with significantly reduced time complexity.

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.

This paper presents a new method for categorical classification. A method called two-dimensional continuous dynamic programming (2DCDP) is adopted to optimally capture the corresponding pixels within nonlinearly matched areas in an input image and a reference image representing an object without advance segmentation procedure. Then an image can be converted into a direction pattern which is made by matching pixels between a reference image and an input image. Finally, the category of the test image is deemed to be that which has the strongest correlation with the learning images. Experimental results show that the proposed method achieves a competitive performance on the Caltech 101 image dataset.

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.

Energy consumption is a major consideration in microprocessor optimization. This paper presents a tag-reduction based approach for energy saving in L1 i-cache (instruction cache) of chip multiprocessors (CMP). To our best knowledge, this is the first work that extends the tag reduction technique to the CMP. We formulate our approach to an equivalent problem which is to find an assignment of the whole instruction pages in the physical memory to a set of cores such that the tag reduction conflicts for each core can be mostly avoided or reduced. We then propose three algorithms using different heuristics for this assignment problem. The experimental results show that our proposed algorithms can save the total power up to 45.33% in average compared to the one that the tag reduction is not used. They outperform significantly the tag reduction based algorithm on single core processor as well.

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.

We consider an optimization problem in wireless sensor networks (WSNs) that is to find a multicast tree with maximum lifetime. While a recently proposed distributed algorithm for this problem guarantees to obtain optimal solutions, its high message complexity may prevent such contribution from being practically used in resource-constrained WSNs. In this paper, we proposed a new distributed algorithm that achieves a good balance on the algorithm optimality and message complexity. We use a graph theoretical approach, by the first time, to derive the bounds of both approximation ratio and message complexity in an analytical expression for this algorithm. The theoretical analysis on the tradeoff between these two performance metrics is also validated by our simulation studies.

Recently, pervasive computing has become a very hot research field. Future Creation Lab., Olympus Corp. and the University of Aizu are working on this future technology to facilitate people¿s everyday life. The system we have proposed is named UMP-Percomp, a ubiquitous multiprocessor-based pipeline processing architecture to support high performance pervasive application development. Until now, we have implemented a prototype system to evaluate the performance of the architecture. However, the structure of the prototype system has some limitations: lack of scalability, inefficient pipeline algorithm, and do not do anything if there is lack of available PEs. So we modify the design to change these disadvantages. We add a UDP server to each component to support scalability and component substitution. We design a parallel algorithm to maximum the usage of PEs, and we also consider the situation of lacking suitable PEs. Finally, we do an experiment on the new system to compare with the old system.