This paper introduces a dynamic, mathematically grounded, and fully data-driven framework for competitor analysis, index construction, and industry benchmarking—addressing fundamental limitations in traditional strategic approaches that rely on static classifications, internal disclosures, or proprietary data. Built entirely on publicly available financial signals—including firm-level equity prices, macroeconomic indicators, and sector ETFs—the framework replaces rigid taxonomies with a flexible, behaviorally revealed peer selection mechanism governed by coupled ordinary differential equations (ODEs) and speed-weighted Runge-Kutta integration. A stakeholder-aware thresholding scheme integrates strategic priorities without sacrificing analytical rigor. The resulting industry index is constructed using Self-Financing Hierarchical Risk Parity (SF-HRP), producing stable and interpretable portfolio weights. A Gram-Schmidt orthogonality ensures parsimony among macroeconomic factors (|ρ| < 0.05), enhancing robustness. Forecasting evaluations confirm that CNN-LSTM achieves superior performance (RMSE: 0.028, R 2 : 0.95), reducing error by over 80 % relative to baseline LSTM models, while WGANs capture tail risk distributions (R 2 : 0.75). Critically, the framework demonstrates that high correlation does not imply redundancy—validated through multiscale residual analysis, information-theoretic modeling, and geometric submanifold representations. At its core, this research introduces the signal-based strategic interaction model, a new theoretical paradigm that reframes financial performance as real-time strategic communication. It establishes a generalized, industry-agnostic system for dynamic competition management—scalable, transparent, and responsive to market shifts—empowering firms of all sizes to identify peers, track competitive positioning, and inform strategy with unprecedented precision and accessibility.

Radio frequency identification (RFID) systems are an indispensable part of many critical Internet of Things (IoT) applications, including supply chain management and access control. Ensuring strong security in these systems is critical to safeguarding sensitive information and protecting user privacy. In recent years, in order to meet the diversified security needs of RFID systems, authentication and key protocols based on physical unclonable functions (PUFs) have received wide attention. Nevertheless, existing protocols typically require RFID tags to pre-store an excessive number of secret credentials and impose considerable computational and communication overheads, which prove challenging for resource-constrained RFID tag. Additionally, certain lightweight protocols fall short of achieving their intended security and functional objectives, exhibiting insufficient anonymity and untraceability, and vulnerability to desynchronization attacks. To address these critical challenges, this paper first proposes a lightweight anonymous authentication and key agreement protocol designed for an ideal PUF environment. The proposed protocol integrates the arbiter PUF with cryptographic hash functions, providing robust resistance to potential attacks while minimizing system overhead. Subsequently, an enhanced protocol specifically tailored for noisy PUF scenarios is presented. This protocol employs a fuzzy extractor to reliably derive stable keys from noisy PUF responses, thereby mitigating the instability caused by inherent noise. Through comprehensive security analysis and formal verification, as well as performance evaluations compared with existing state-of-the-art protocols, both protocols are demonstrated to overcome the limitations of prior protocols and provide efficient and practically feasible solutions well suited for resource-constrained RFID environments.

Intelligent fault diagnosis of rotating machinery is essential for predictive maintenance, yet conventional deep learning models suffer from limited generalization under noisy and cross-domain conditions. Furthermore, their lack of interpretability restricts industrial trust and deployment. To address these challenges, we propose Conformer-PhyFaultNet, a novel physics-informed spectral attention Conformer that seamlessly integrates domain knowledge with advanced sequence modeling. The method embeds characteristic fault frequencies (BPFO, BPFI, FTF) directly into the spectral attention layer, guiding the network toward physically meaningful patterns rather than spurious features. A set of physics-guided tokens is introduced into the Conformer encoder, which persist across layers and acts as stable descriptors of defect signatures. The hybrid spectral attention + physics tokens mechanism enables the model to simultaneously capture local fault harmonics and long-range dependencies across time–frequency representations. Unlike conventional CNN or Transformers, proposed approach ensures interpretability by aligning attention distributions with analytic harmonics and providing layer-wise token activations as diagnostic evidence. This dual mechanism represents the key novelty, bridging analytic fault modeling with modern deep architectures for the first time in a unified framework. Extensive experiments on CWRU, Paderborn, and HUST datasets demonstrate the superiority of the proposed method: in-domain accuracy reaches 93.75, cross-domain transfer achieves 86.75 (CWRU→Paderborn) and 84.3% (HUST→CWRU), while noise robustness remains above 87% at 10 dB and 81% at 0 dB, outperforming CNN, RNN, Transformer and physics-informed baselines by significant margins. The proposed Conformer-PhyFaultNet therefore offers a technically rigorous, interpretable and noise-robust solution that can substantially enhance the reliability and adoption of intelligent predictive maintenance in industrial environments, while also enabling real-time monitoring and edge deployment in industrial IoT systems through low-latency inference (< 5 ms per segment).

In Intent-based Vehicular Edge Computing (VEC) networking, escalating demands for computational offloading and resource management in dynamic urban environments necessitate innovative solutions. This paper proposes a novel RIS-assisted NOMA-VEC framework that empowers vehicle users (VUs) to offload arbitrary task portions to multiple edge servers via any available subcarrier. This approach overcomes limitations posed by heterogeneous local computing capabilities and stringent latency constraints. By leveraging Reconfigurable Intelligent Surfaces (RIS) to enhance channel conditions through both direct and reflected links, our framework significantly improves communication reliability and offloading efficiency. To minimize the average weighted energy consumption of VUs under time-varying channels and traffic dynamics, we formulate a joint optimization problem integrating offloading decisions, power allocation, and transmission time scheduling. Addressing the problem’s inherent complexity, characterized by multi-variable coupling and non-convex constraints, we develop a two-stage decomposition strategy: Offloading decisions are dynamically adapted to environmental fluctuations using a Proximal Policy Optimization (PPO)-based algorithm, while resource allocation is resolved through a hybrid Genetic Algorithm (GA) and Sequential Least Squares Programming(SLSQP) approach, efficiently navigating combinatorial and non-convex landscapes. Extensive simulations demonstrate that our framework reduces VU energy consumption by 11.12% compared to baseline methods, validating its superior efficiency in RIS-enhanced VEC systems.

Feature embedding has become a cornerstone technology for processing high-dimensional and complex data, which results in that Embedding as a Service (EaaS) models have been widely deployed in the cloud. To protect the intellectual property of EaaS models, existing methods apply digital watermarking to inject specific backdoor triggers into EaaS models by modifying training samples or network parameters. However, these methods inevitably produce detectable patterns through semantic analysis and exhibit susceptibility to geometric transformations including rotation, scaling, and translation (RST). To address this problem, we propose a novel fingerprinting framework called POSTER for EaaS models, rather than merely refining existing watermarking techniques. Different from watermarking, the proposed POSTER establishes EaaS model ownership through geometric analysis of embedding space's topological structure, rather than relying on the modified training samples or triggers. The key innovation lies in modeling the victim and suspicious embeddings as point clouds, allowing us to perform robust spatial alignment and similarity measurement, which inherently resists RST attacks. Experiments evaluated on visual and textual embedding tasks verify the superiority and applicability. This work reveals inherent characteristics of EaaS models and provides a promising solution for ownership verification of EaaS models under black-box scenarios.

Chain-of-Thought (CoT) enhances an LLM’s ability to perform complex reasoning tasks, but it also introduces new security issues. In this work, we present ShadowCoT, a novel backdoor attack framework that targets the internal reasoning mechanism of LLMs. Unlike prior token-level or prompt-based attacks, ShadowCoT directly manipulates the model’s cognitive reasoning path, enabling it to hijack multi-step reasoning chains and produce logically coherent but adversarial outcomes. By conditioning on internal reasoning states, ShadowCoT learns to recognize and selectively disrupt key reasoning steps, effectively mounting a self-reflective cognitive attack within the target model. Our approach introduces a lightweight yet effective multi-stage injection pipeline, which selectively rewires attention pathways and perturbs intermediate representations with minimal parameter overhead (only 0.15% updated). ShadowCoT further leverages reinforcement learning and reasoning chain pollution (RCP) to autonomously synthesize stealthy adversarial CoTs that remain undetectable to advanced defenses. Extensive experiments across diverse reasoning benchmarks and LLMs show that ShadowCoT consistently achieves a state-of-the-art average Attack Success Rate of 91.2% (peaking at 94.4%) and a Hijacking Success Rate of 84.9% while preserving benign performance. These results reveal an emergent class of cognition-level threats and highlight the urgent need for defenses beyond shallowsurface-levelconsistency.

In recent years, deep learning techniques have made significant strides in molecular generation for specific targets, driving advancements in drug discovery. However, existing molecular generation methods present significant limitations: those operating at the atomic level often lack synthetic feasibility, drug-likeness, and interpretability, while fragment-based approaches frequently overlook comprehensive factors that influence protein–molecule interactions. To address these challenges, we propose a novel fragment-based molecular generation framework tailored for specific proteins. Our method begins by constructing a protein subpocket and molecular arm concept-based neural network, which systematically integrates interaction force information and geometric complementarity to sample molecular arms for specific protein subpockets. Subsequently, we introduce a diffusion model to generate molecular backbones that connect these arms, ensuring structural integrity and chemical diversity. Our approach improves synthetic feasibility and binding affinity, with a 4% increase in drug-likeness and a 6% improvement in synthetic feasibility. Furthermore, by integrating explicit interaction data through a concept-based model, our framework enhances interpretability, offering valuable insights into the molecular design process.

Extending Intelligent Transportation Systems (ITS) toward suburban and peripheral regions is challenging because dense roadside infrastructure is expensive to deploy and underutilized outside peak hours. This paper proposes a V2X-enabledresting droneframework as a dynamic traffic flow management solution for ITS, in which drones equipped with Vehicle-to-Everything (V2X) connectivity are dispatched on demand to congested suburban corridors, provide temporary ITS services, and then land on attachment points to rest in a low-power state when not needed. The framework combines a synthetic multi-city road network, a time-slot–based traffic model, and a load-dependent V2X Quality-of-Service abstraction that maps latency and packet loss into an effective drone availability metric and explicitly captures the impact of non-ideal V2X conditions on control reliability. Within this framework, we develop and evaluate GOLD, a Greedy Overlap-Limited Drone deployment algorithm that prioritizes high-gain, low-overlap locations to maximize effective (overlap-removed) ITS expansion with a limited drone fleet. GOLD is compared against a conventional local threshold-based drone deployment rule that independently scales each road point’s coverage radius with traffic intensity, modeling existing overlap-unaware UAV/ITS extensions. Simulation results over multiple random map and traffic realizations show that GOLD achieves a large fraction of the baseline’s effective coverage with substantially fewer active drones under ideal V2X conditions and maintains its relative advantage when V2X latency and packet loss degrade drone availability, demonstrating that resting drones coordinated by GOLD provide a scalable and robust complement to fixed roadside ITS infrastructure.

Video streaming has emerged as a widely used Internet service, in which adaptive bitrate (ABR) algorithms play a critical role in delivering high quality of experience (QoE). However, existing learning-based ABR methods often suffer from limited generalization in unseen and dynamically changing network conditions. Although some meta-reinforcement learning techniques have been proposed to mitigate this issue, they generally depend on additional online training or fine-tuning. To overcome these limitations, this paper introduces EAStream, an environment-aware ABR algorithm based on meta-reinforcement learning for reliable video streaming services. The method employs a variational autoencoder to extract a latent representation of the current network environment from historical interaction data. This latent variable, along with the current system state, is fed into a policy network that perceives network conditions in real time and adapts bitrate decisions accordingly, without requiring further online training. A comprehensive evaluation is conducted using diverse real-world network traces. Experimental results show that EAStream not only achieves leading performance on in-distribution test sets compared to state-of-the-art ABR algorithms, but also demonstrates superior generalization capability on out-of-distribution test scenarios.

The convergence of blockchain with Industry 5.0 technologies presents significant opportunities for healthcare data management; however, current systems face challenges related to scalability, privacy, and energy efficiency. This article introduces an innovative framework that leverages ciphertext-policy attribute-based encryption (CP-ABE), Ethereum smart contracts, and decentralized IPFS storage to address these challenges. The framework presents three main innovations: 1) a human-centric authentication system that ensures security without sacrificing cryptographic integrity; 2) post-quantum Kyber-786 algorithms paired with CP-ABE, which minimizes computational overhead by 27% while facilitating 32 ms key generation; and 3) an energy-efficient Proof of Stake (PoS) consensus mechanism that reduces energy consumption by 98% (0.05 kW/transaction) compared to traditional blockchain systems. Thorough testing demonstrates 99.5% resistance to man-in-the-middle attacks, a throughput of 15.6 MB/min at scale, and an emergency access latency of under 120 ms, which is essential for practical healthcare applications. By consolidating decentralized pseudo-identities for patient anonymity, secure audit logs, and GDPR/HIPAA-compliant data governance, this research establishes a new standard for secure, sustainable, and patient-focused health data ecosystems in the Industry 5.0 landscape.

For rapid on-orbit forwarding of high-resolution remote sensing (RS) images, the combination of very low-Earth orbit (VLEO) and optical intersatellite links (OISLs) has recently emerged as a focus of nonterrestrial networks (NTNs). However, at the height of VLEO, not only does the attenuation factor of the optical signal-to-noise ratio grow exponentially, but the service with more than ten times bandwidth expansion, which adds a significant burden to the buffer queue. To address the above reliability and latency challenges, this article proposes a routing framework based on the characteristics of the VLEO scenario. Specifically, the OISL path in VLEO is modelled first. In order to reduce the severe impact of multihop on the routing performance, we perform dynamic group scheduling for OISL. Additionally, taking advantage of the fact that RS missions can be scheduled in advance, we adopt a “route first, then establish links” approach to specifically plan service paths. Considering the coordination of a single service and overall network performance, this problem is solved using a Q-value decomposed multiagent reinforcement learning method. Simulation results demonstrate that our scheme maintains excellent reliability and latency performance under VLEO scenarios with varying heights, network scales, and traffic loads.

Internet of things (IoT) is crucial for the hierarchical medical system, which enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. In this scenario, HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes, namely, private data collection (PDC), direct channel (DC), and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the security and performance of our proposed scheme are verified, and the results demonstrate that our scheme is secure, feasible, and efficient.

The rise of dual carbon goals and Web 3.0 has spurred the rapid development of blockchain-based microgrids (BBMGs). Due to the inherent volatility in power generation within microgrids composed of distributed renewable energy sources, frequent cross-domain interactions between multiple BBMGs are required to ensure stable system operation. As an important stage of cross-domain interaction, trusted authentication is crucial to its development. Currently, cross-domain trusted authentication is generally achieved by setting a unified reputation calculation method and cross-domain validation standard for all nodes in the multimicrogrid system. However, this method identifies malicious nodes through periodic and static reputation calculations but cannot detect or prevent nodes that suddenly become malicious in real time. Moreover, the reputation calculations typically do not consider factors such as node importance and resource conditions, which is not ideal for BBMG systems with limited resources. Additionally, cross-domain authentication in the above approach is usually performed by selecting the most trusted node through polling or random methods for single validation, or by using multiple trusted nodes for consensus-based validation. These methods are inefficient and unsuitable for microgrid systems that require frequent interactions. To address these challenges, we first propose an abnormal behavior real-time detection method based on federated learning to monitor the abnormal behavior of nodes in real time. Then, factors such as abnormal behavior detection and resource conditions are introduced to propose a reputation calculation method that is more suitable for blockchain systems. Based on this method, we design a multifactor trustworthy user access control mechanism to assign roles and permissions of nodes, identifying multiple trusted cross-domain validation nodes (CDVNs) for efficient parallel authentication. To assist the system in quickly selecting and locating the optimal CDVNs and further improve the efficiency of cross-domain trusted authentication, this article proposes an efficient search and location strategy for the optimal CDVNs based on the source node to batch search for the CDVN with the smallest delay for all nodes. Experimental results show that this scheme is feasible in practical applications.

Quantum computing offers unparalleled processing power but raises significant data privacy challenges.Quantum Differential Privacy(QDP) leverages inherent quantum noise to safeguard privacy, surpassing traditional DP. This paper develops comprehensive noise profiles, identifies noise types beneficial for QDP, and highlights the need for practical implementations beyond theoretical models. Existing QDP mechanisms, limited to single noise sources, fail to reflect the multi-source noise reality of quantum systems. We propose a resilient hybrid QDP mechanism utilizing channel and measurement noise, optimizing privacy budgets to balance privacy and utility. Additionally, we introduceLifted Quantum Differential Privacy, offering enhanced randomness for improved privacy audits and quantum algorithm evaluation.

In this paper, we address the challenging source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation, given only a pinhole image pre-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is non-trivial due to three critical challenges: 1) semantic mismatches from the distinct Field-of-View (FoV) between domains, 2) style discrepancies inherent in the UDA problem, and 3) inevitable distortion of the panoramic images. To tackle these problems, we propose 360SFUDA++ that effectively extracts knowledge from the source pinhole model with only unlabeled panoramic images and transfers the reliable knowledge to the target panoramic domain. Specifically, we first utilize Tangent Projection (TP) as it has less distortion and meanwhile slits the equirectangular projection (ERP) to patches with fixed FoV projection (FFP) to mimic the pinhole images. Both projections are shown effective in extracting knowledge from the source model. However, as the distinct projections make it less possible to directly transfer knowledge between domains, we then propose Reliable Panoramic Prototype Adaptation Module (RPAM) to transfer knowledge at both prediction and prototype levels. RPAM selects the confident knowledge and integrates panoramic prototypes for reliable knowledge adaptation. Moreover, we introduce Cross-projection Dual Attention Module (CDAM), which better aligns the spatial and channel characteristics across projections at the feature level between domains. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance. Extensive experiments on the synthetic and real-world benchmarks, including outdoor and indoor scenarios, demonstrate that our 360SFUDA++ achieves significantly better performance than prior SFUDA methods.

Over the last 20 years, numerous experts in the manufacturing companies have become interested in controllingflexible joint robots. The control design process using a networked framework has played a significant role in the industrial sector. This research addresses theself–triggeredcontrol problem forTakagi–Sugeno (T–S) fuzzy systemsbased on time delay is established in this research. A newself–triggeredmechanism determines when the plant state is transmitted. Instead of continuously tracking predefined triggering conditions, theself–triggeredsystem determines the next triggering instant based on the most recently triggered state information. Theself–triggeredmechanism relying onT–S fuzzy controlleris then created using the triggered state, allowing for flexible configuration of its fuzzy rules andmembership operations (MOs)that are not dependent on the controlled fuzzy system. Furthermore, it precisely demonstrates the interaction between the triggered condition and the plant state under the constraint of mismatched premise variables. To ensure system stability and performance, delay–dependent requirements are derived using a novelLyapunov–Krasovskii functionalbased on fuzzy concepts to develop the fuzzy control design. The presence of the required controller is then shown underLMI–based conditions. Lastly, a simulation of a flexible robotic arm example is provided along with an evaluation of other triggered methods.

In nonlinear systems, monitoring control behavior, fault occurrence, and latency factor continue to be major obstacles. Traditional control models frequently handle edge–case situations inaccurately and are unable to adjust to dynamic changes. Reinforcement learning control is a novel intelligent framework that incorporates sophisticated modules for nonlinear models in order to overcome these constraints. First, an innovative approach to solving the adaptive tracking algorithm for a type of T–S fuzzy plant is presented in this research. Furthermore, the authors examine time–varying delay with the actuator failure for complex systems that are generally represented by the T–S fuzzy plant. Second, it has been demonstrated that the chosen performance index solves the delayed algebraic Riccati formula, which can be resolved by scheme of repetition methods, when the resultant plant with the tracking signal is constructed under the decoupling delayed T–S fuzzy framework. Third, the coupled delayed algebraic Riccati formulas are then solved using a Reinforcement Learning (RL) technique that makes use of complex model dynamics knowledge. Furthermore, the fundamental RL iteration technique’s convergence is demonstrated. Lastly, the power sector and a F–16 airplane flight examples are provided to demonstrate how the online repetition method outperforms the offline one in terms of tracking accuracy and effectiveness.

Over the past 20 years, many specialists in the business have become interested in managing flexible joint robotics. Considering flexibility in joints in the control scheme of inflexible robots is challenging because it eliminates certain structural properties that facilitate control, such as full activation, distinct control for every socket, and passivity of the torque generated by the motor for coupling velocity. In this research, the authors analyze asymptotic stability for neural models of fixed stochastic process that have time-varying delays, which depend on jumping modes; their derivatives no longer need to be smaller than one. Secondly, by applying a stochastic setup, the researchers explore fixed neutral models with Markovian jump variables based upon mode-dependent time-varying delays for modeling flexible joint robots. Thirdly, this research develops various stability circumstances with delay-dependent conditions based on linear matrix inequalities (LMIs) by combining the novel Lyapunov theory with the convex polyhedron technique, concluding with two numerical examples illustrating their use. As a result, an optimization problem can be addressed to calculate a control law created for implementing the stochastically stable platform. As a final step, the proposed method is verified using a quadruple-tank model and for modeling flexible joint robots. Note to Practitioners—Neural network controllers are frequently employed in the medical field, automation industry, robotics sector, and other industries where certain jobs cannot be completed by autonomous or human robotics. By increasing flexibility, refining control technique, relaxing stability conditions, and promoting human-robot collaboration, this research seeks to create novel solutions to important problems in industrial robotic manipulators. In order to maintain the system’s stability in both motion states-coupling velocity and torque motion and meet the associated position and velocity synchronization goals, this paper suggests a NN-based control technique that can manage random process and variable time-delays signals. More straightforward structures, fewer tuning factors, and improved control behaviors are some of the benefits of this approach that make engineering applications easier. Lastly, a simulation using a realistic strategy is demonstrated to validate the theoretical assertions.

Universal low bit-rate speech steganalysis is a cutting-edge research task addressing real-world application needs and has garnered significant attention recently. However, the existing methods are still inadequate in extracting available information from various steganographic domains and fail to deliver interpretable forensic results for specific steganographic domains containing embedded information. In view of this, we present a novel universal low bit-rate speech steganalysis approach that seamlessly combines domain-specific and domain-shared information, enabling comprehensive and effective speech steganography detection. This approach comprises two vital components: the Matching Identification Network (MIN) and the Content Alignment Network (CAN). The MIN incorporates three effective separable backbones for capturing informative domain-specific embeddings, inherently unveiling local and global dependencies. In this network, we also design a cross-domain matching module to establish correlations among steganographic domains, thereby enhancing detection performance through multi-domain collaboration and facilitating effective forensics for the embedded domains. Moreover, the CAN acquires more informative domain-shared embeddings by using a metric learning-based Siamese architecture to process pairs of naive and recompressed speech samples. Experimental results demonstrate that the presented method not only significantly surpasses the existing universal steganalysis methods, but also competes with or even surpasses dedicated steganalysis methods in certain cases. In addition, our method can provide accurate forensic results regarding the existence of hidden information within each steganographic domain without relevant supervisory information, marking a significant milestone in pursuit of speech steganalysis. The source code for this work will be publicly available on GitHub.

In the context of vehicle-road-cloud integration, multi-vehicle platooning systems have become an important approach for improving road traffic efficiency, driver comfort, driving safety, energy consumption, and mitigating traffic congestion. However, under high-speed mobility, Vehicle-to-Vehicle (V2V) communication within multi-vehicle platoons is susceptible to delays caused by interference and the inherent uncertainties of wireless communication channels. These delays present considerable challenges to achieving effective multi-vehicle cooperative control. To overcome the limitations of existing research, this paper proposes a joint communication and control optimization strategy for multi-vehicle platooning systems. A novel spacing error metric is introduced, which uses the real-time velocity of each vehicle to improve the platooning system responsiveness. Furthermore, we derive the Signal-to-Interference-plus-Noise Ratio (SINR) threshold to ensure the stability and reliability of the platoon. This ensures safe distances and synchronized speeds among all vehicles, even when communication delays occur. Finally, the proposed joint optimization strategy is validated through performance comparisons, demonstrating its effectiveness and superior performance.

Detecting urban anomalies helps city managers solve traffic problems in a timely manner, prevent safety hazards, and make more scientific and rational urban development plans. However, most existing urban anomaly detection methods focus on single time slots and overlook the impact of anomalous events on traffic flow patterns. To this end, we propose a novel urban traffic flow pattern anomaly detection method in this paper, referred to as UTFP-AD. Our approach constructs traffic flow over a period of time as a temporal probability distribution and uses traffic flow dynamics to detect anomalous patterns. Specifically, we construct temporal probability distributions to represent regional traffic flow patterns and calculate the traffic flow dynamics to reflect traffic flow changes. Independent and correlated feature vectors are then input into the proposed Multi-One-Class Support Vector Machine to identify the final anomalous regions. For validation, we conducted experiments using both synthetic and real-world data, comparing our method with six existing methods. The experimental results demonstrate that UTFP-AD significantly outperforms the existing methods in terms of Recall, F1Score and hit rate.

The prerequisite for implementing incentive mechanisms and reliable participant selection schemes in federated learning is to obtain the contribution of each participant. Available evaluation methods for participant contributions require the server to possess a test dataset, often impractical. Additionally, the excessively high complexity of these works is unacceptable when training complex models in large-scale federated learning system. To address these issues, we propose a lightweight contribution evaluation method for federated learning participants, named LCEFL, based on model projection theory, which does not require the server to provide a test dataset. In addition, a model compression method is designed to be used in LCEFL to reduce the computational complexity. Furthermore, a trusted aggregation method based on LCEFL is proposed, where the weight of each participant's local model is determined by its trust level, which can be calculated using its contribution evaluation result. Experimental results show that LCEFL can achieve nearly the same accuracy as schemes based on Shapley Value, while significantly reducing computational overhead by more than 50%. Compared to available aggregation methods, the proposed trusted aggregation scheme is able to accelerate the convergence speed of the global model and improve its accuracy by 2% to 45%.

Unsupervised visible-infrared person reidentification (UVI-ReID) has recently gained great attention due to its potential for enhancing human detection in diverse environments without labeling. Previous methods utilize intramodality clustering and cross-modality feature matching to achieve UVI-ReID. However, there exist two challenges: 1) noisy pseudo-labels might be generated in the clustering process and 2) the cross-modality feature alignment via matching the marginal distribution of visible and infrared modalities may misalign the different identities from the two modalities. In this article, we first conduct a theoretical analysis where an interpretable generalization upper bound is introduced. Based on the analysis, we then propose a novel unsupervised cross-modality person reidentification framework (PRAISE). Specifically, to address the first challenge, we propose a pseudo-label correction (PLC) strategy that utilizes a beta mixture model (BMM) to predict the probability of misclustering-based network's memory effect and rectifies the correspondence by adding a perceptual term to contrastive learning. Next, we introduce a modality-level alignment (MLA) strategy that generates paired visible-infrared latent features and reduces the modality gap by aligning the labeling function of visible and infrared features to learn identity-discriminative and modality-invariant features. Experimental results on two benchmark datasets demonstrate that our method achieves a state-of-the-art (SOTA) performance than the unsupervised visible-ReID methods.

To empower the management of smart meters’ demand load within a smart grid environment, this paper presents a Feed-forward Neural Network with ADaptive Evolutionary Learning Approach (ADELA). In this model, the load forecasting information is propagated via neurons of input and multiple hidden layers and the final estimated output is achieved with the help of the sigmoid activation function. An improved evolutionary algorithm is proposed for training and adjusting the interconnecting weights among the layers of the intended neural network. This model is capable of addressing the critical challenges of high volatility, uncertainty, missing smart meters data, and sudden upsurge and plunge in electricity demand. The proposed algorithm is able to learn the best suitable evolutionary operators from a given pool of operators and the probabilities associated with them. The proposed load forecasting approach is simulated over three real-world smart meter datasets, including the Australian Smart Grid Smart City project, the Irish Commission for Energy Regulation, and UMass Smart. The performance evaluation and comparison of the proposed approach with the existing state-of-the-art approaches revealed a relative improvement of up to 46.93%, 5.05%, and 2.20% in forecast accuracy over the Smart Grid Smart City, UMass Smart and the Irish Commission for Energy Regulation datasets, respectively.

This paper addresses an interesting yet challenging problem-source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation-given only a pinhole image-trained model (i.e., source) and unlabeled panoramic images (i.e., target). Tackling this problem is nontrivial due to the semantic mismatches, style discrepancies, and inevitable distortion of panoramic images. To this end, we propose a novel method that utilizes Tangent Projection (TP) as it has less distortion and meanwhile slits the equirectangular projection (ERP) with a fixed FoV to mimic the pinhole images. Both projections are shown effective in extracting knowledge from the source model. However, the distinct projection discrepancies between source and target domains impede the direct knowledge transfer; thus, we propose a panoramic prototype adaptation module (PPAM) to integrate panoramic prototypes from the extracted knowledge for adaptation. We then impose the loss constraints on both predictions and prototypes and propose a cross-dual attention module (CDAM) at the feature level to better align the spatial and channel characteristics across the domains and projections. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance. Extensive experiments on the synthetic and real-world benchmarks, including outdoor and indoor scenarios, demonstrate that our method achieves significantly better performance than prior SFUDA methods for pinhole-to-panoramic adaptation.

Internet of Things (IoT) is crucial for the hierarchical medical system, and enables the real-time monitoring and collection of data, thereby improving patient treatment outcomes. However, achieving secure, efficient, timely, and controllable medical IoT data sharing between higher-lever hospital (HLH) and lower-level hospital (LLH) is a challenging task for the hierarchical medical system. Consortium blockchain, which is an effective way to achieve secure and trustworthy data sharing, has the potential to address these issues. In this article, we propose a novel cloud-chain sharing scheme for medical IoT data based on consortium blockchain. HLH and LLH establish a consortium blockchain, where medical IoT data is stored both on-chain and off-chain. On-chain data adopt a proxy re-encryption based on elliptic curve cryptography (ECC-PRE) strategy and attribute-based strategy to facilitate secure access and controlled sharing of data. Off-chain data sharing provides three different modes: private data collection (PDC), direct channel, and cloud storage (CS), according to the urgency of patient and the sensitivity of the data. Furthermore, a file security breakpoint resume scheme, rooted in the consortium blockchain, and a file weighting strategy are employed to enhance the efficiency and timeliness of data sharing. Finally, the performance of our proposed solution is verified by experimental results, and the results demonstrate our solution is feasible and efficient. In future work, we plan to use searchable encryption technology to make this scheme more versatile and gradually implement dynamic adjustment of permissions.

Recently, a vast number of image generation models have been proposed, which raises concerns regarding the misuse of these artificial intelligence (AI) techniques for generating fake images. To attribute the AI-generated images, existing schemes usually design and train deep neural networks (DNNs) to learn the model fingerprints, which usually requires a large amount of data for effective learning. In this paper, we aim to answer the following two questions for AI-generated image attribution, 1) is it possible to design useful handcrafted filters to facilitate the fingerprint learning? and 2) how we could reduce the amount of training data after we incorporate the handcrafted filters? We first propose a set of Multi-Directional High-Pass Filters (MHFs) which are capable to extract the subtle fingerprints from various directions. Then, we propose a Directional Enhanced Feature Learning network (DEFL) to take both the MHFs and randomly-initialized filters into consideration. The output of the DEFL is fused with the semantic features to produce a compact fingerprint. To make the compact fingerprint discriminative among different models, we propose a Dual-Margin Contrastive (DMC) loss to tune our DEFL. Finally, we propose a reference based fingerprint classification scheme for image attribution. Experimental results demonstrate that it is indeed helpful to use our MHFs for attributing the AI-generated images. The performance of our proposed method is significantly better than the state-of-the-art for both the closed-set and open-set image attribution, where only a small amount of images are required for training.

Space anomaly detection plays a critical role in safeguarding the integrity and reliability of space systems amid the rising tide of threats. This survey aims to deepen comprehension of space cyber threats through space threat modeling, and meticulously examine the unique challenges of space anomaly detection. The survey identifies scalability, real-time detection, limited labeled data availability, concept drift, and adversarial attacks as key challenges based on thorough literature analysis and synthesis. By extensively exploring state-of-the-art anomaly detection techniques, the study evaluates their applicability, strengths, and limitations within space networks. Going beyond analysis, a notable contribution of this work involves integrating stream-based and graph-based methods, tailored to capture the intricate temporal and structural relationships inherent in space networks. This innovative hybrid approach holds promise for heightened detection accuracy and sets the stage for future research endeavors. As space threats continue evolving in both number and sophistication, this survey timely provides insights, recommendations, and a clear roadmap for researchers, engineers, and practitioners to fortify space anomaly detection mechanisms.

Various sensors are utilized to gather data for the Industrial Internet of Things (IIoT) environments and stored in an adaptable and affordable cloud computing infrastructure. The cloud server performs data analytics intelligently using AI-enabled applications. IIoT data must be encrypted to protect privacy because it is kept on a cloud server, which is untrusted. Nevertheless, there is a difficulty with searching encrypted data, which may be resolved by using a Public key Encryption with Keyword Search (PEKS) method. Furthermore, most existing PEKS schemes are vulnerable to Inside/Outside Keyword Guessing Attacks (IKGAs/OKGAs). Recently, some Certificateless PEKS (CL-PEKS) schemes have been proposed to circumvent certificate management issues, key escrow problems, and IKGA/OKGA. However, they are vulnerable to a malicious Key Generation Center (KGC) attack as they do not satisfy the Girault’s level-3 security. We proposed a private blockchain-assisted CL-PEKS for Multikeyword (BCT-CL-PEMKS) search in a fog-based IIoT environment. The BCT-CL-PEMKS scheme meets the Girault’s level-3 security. It used a private blockchain to manage all the fog nodes. The BCT-CL-PEMKS provides ciphertext indistinguishability, trapdoor indistinguishability, and designated-server testability in the random oracle model. We estimated the computation and communication overheads of BCT-CL-PEMKS and compared them with the other PEKS schemes for 80, 112, 128, 192, and 256-bit security levels.

The inefficient sharing of industrial cloud resour-ces among multiple users and vulnerabilities of virtual machines (VM)s and servers prompt unauthorized access to users’ sensitive data along with excess consumption of power and resource wastage. To address these entangled issues, this paper proposes a novelEmerging VMThreatPrediction and DynamicWorkloadEstimation based Resource Allocation (ETP-WE) framework that predicts VM threats and resource usage proactively in real-time. The proposed framework contributes by introducing a Risk-Score Matrix that analyses multiple risks for each VM; utilizing knowledge of proposed security and workload analyzers for efficient VM Placement (VMP), and estimating resource utilization by developing an ensemble predictor for prior mitigation of over-/under-load on servers. ETP-WE framework collaborates machine-learning-based security and workload analysis for secure and resource-efficient VMP, thereby reducing the number of security threats, optimizing resource utilization, power-consumption, and adapting to the changes in application demands. The performance of the proposed framework is evaluated using two benchmark datasets OpenNebula and Google Cluster. The simulation-based comparison with state-of-the-arts validates the efficacy of ETP-WE in terms of reduction of security threats, power consumption, and number of active servers up to 86.9%, 66.67% and 30%-80%, respectively with an improved resource utilization up to 60%-75% over existing approachesNote to Practitioners—Industry clouds serve the precise needs and provide the service features and tools as per the industry’s needs to help organizations meet their workloads processing and storage demands. For instance, healthcare and financial organizations have to comply with extended security to meet specific service requirements. To this context, we have proposed a novel ETP-WE framework for prediction and mitigation of cyberthreats on virtual resources in real-time for secure execution of industrial applications on third-party servers. ETP-WE collaborates machine-learning based security and workload analysis for secure and resource efficient VM allocation, thereby reducing number of security threats, optimizing resource utilization, power-consumption and adaptating to the changes in application demands. During the processing of any sensitive transaction such as medical data, bank transactions, ETP-WE framework will induce improved data protection by mitigating potential data breaches. ETP-WE framework will be deployed at Resource Scheduler to boost security performance by estimating the multiple risks score status of VMs engaged in execution of industrial transactions or workloads. It will help to predict and analyze the probable security threats or breaches proactively and facilitate their mitigation. The performance evaluation and comparison with state-of-the-arts validate potency of ETP-WE in terms of reduction of cybersecurity threats and number of active servers with an improved resource utilization over existing approaches.

Existing IoT Network Anomaly Detection Systems (NADSes) typically treat IoT devices as independent entities and model them by Euclidean space features. These approaches suffer from low accuracies on new attacks (e.g., platform-based attacks and evasion attacks), since they do not fully consider the semantic information including traffic periodicity and device/environment interactions. In this paper, we propose SeIoT, a knowledge graph-based bimodal anomaly detection framework for smart homes. We propose a knowledge graph structure to represent the semantic information of a smart home. First, we propose the Action Fingerprint module, an efficient and effective traffic classification approach to extract the device actions and features required by the knowledge graph. Then, we propose a bimodal anomaly detection framework including interaction-related and time-related detectors to detect the knowledge graph. We propose a feature separation-based heterogeneous graph attention network that can accurately model the interactions among devices and environments, and a method to represent traffic periodicity for the time-related detector. For evaluation, we set up a real-world testbed and evaluate the detection performance of both device-targeted attacks and platform-based attacks. Experiment results show that SeIoT can achieve better detection capability than prior work on both of the attacks.

Digital twin (DT) technology uses Internet of Things (IoT) devices to collect real-world data and build a virtual world in the DT cloud. However, many IoT devices collect data in harsh natural environments, and these data cannot be transmitted through fixed base stations. Thus, many DT services adopt dynamic data transmission methods, such as transmission through unmanned aerial vehicle base stations (UAV-BSs). However, UAV-BS approaches have many communication constraints, such as limitations on the transmission bandwidth, data throughput, and number of channels. In addition, when integrating a large amount of data submitted by IoT devices, DT service providers need a corresponding mechanism to select the most valuable device data, which can be described by a winner decision problem with the goal of maximizing utility. In this paper, we consider the problem of maximizing the utility of a DT model under UAV-BS network transmission, transform it into a mixed integer programming model with communication and computing constraints, and adopt a reverse auction mechanism to solve it. Specifically, we design an optimal reverse auction mechanism based on optimal allocation and Vickrey–Clarke–Groves (VCG) theory. Additionally, a reverse auction mechanism with polynomial execution time is designed based on monotonic allocation, network maximum flow and critical value theory. These two mechanisms are proven to satisfy individual rationality and truthfulness. Experimental results indicate the favorable performance of the designed mechanisms.

At present, the centralized learning models, used for IoT applications generating large amount of data, face several challenges such as bandwidth scarcity, more energy consumption, increased uses of computing resources, poor connectivity, high computational complexity, reduced privacy, and large latency towards data transfer. In order to address the aforementioned challenges, Blockchain-Enabled Federated Learning Networks (BFLNs) emerged recently, which deal with trained model parameters only, rather than raw data. BFLNs provide enhanced security along with improved energy-efficiency and Quality-of-Service (QoS). However, BFLNs suffer with the challenges of exponential increased action space in deciding various parameter levels towards training and block generation. Motivated by aforementioned challenges of BFLNs, in this work, we are proposing an actor-critic Reinforcement Learning (RL) method to model the Machine Learning Model Owner (MLMO) in selecting the optimal set of parameter levels, addressing the challenges of exponential grow of action space in BFLNs. Further, due to the implicit entropy exploration, actor-critic RL method balances the exploration-exploitation trade-off and shows better performance than most off-policy methods, on large discrete action spaces. Therefore, in this work, considering the mobile scenario of the devices, MLMO decides the data and energy levels that the mobile devices use for the training and determine the block generation rate. This leads to minimized system latency and reduced overall cost, while achieving the target accuracy. Specifically, we have used Proximal Policy Optimization (PPO) as an on-policy actor-critic method with it's two variants, one based on Monte Carlo (MC) returns and another based on Generalized Advantage Estimate (GAE). We analyzed that PPO has better exploration and sample efficiency, lesser training time, and consistently higher cumulative rewards, when compared to off-policy Deep Q-Network (DQN).

This paper focuses on the problem of detection and prevention of stolen and misused secrets (such as private keys) for authentication toward centralized services. We propose a solution for this problem, based on SmartOTPs, the two-factor authentication scheme against the blockchain, which is intended for smart contract wallets and utilizes one-time passwords (OTPs). We modify SmartOTPs for our purposes and utilize them in the setting of two-and-a-half-factor authentication against a centralized service provider. Out of two and a half factors of our solution, the first factor stands for the private key, and the second and a half factor stands for OTPs and their precursors (a.k.a., pre-images), where OTPs are obtained from the precursors by cryptoaraphically secure hashing. We describe the protocol for bootstrapping our approach as well as the authentication procedure. In the case of stolen creden-tials from the client, we show that our solution enables the user to immediately detect it and proceed to re-initialization with fresh credentials. We utilize blockchain-based identity management and decentralized identities of users to simplify the overhead of the registration process and reinitialization.

Fingerprint recognition technology is widely used in various terminal devices and serves as a powerful and effective method for authentication. Existing research relies on centralized training models based on datasets, overlooking the privacy and heterogeneity of the data itself, resulting in the leakage of user information and decreased recognition accuracy. In order to solve the problem of data security and privacy protection, this paper proposes a federated learning-based architecture called FedFR(Federated Learning-Fingerprint Recognition). The parameters from each endpoint are iteratively aggregated through federated learning to improve the performance of the global model under privacy constraints. Moreover, to solve the client-side unfairness issue in traditional federated learning caused by randomly selecting aggregation weights, a client selection method based on reservoir sampling is proposed, increasing the diversity of data distribution. Using the real-world databses, the effectiveness of FedFR is compared and analyzed through simulation experiments. The results show that FedFR exhibits good performance in terms of privacy protection levels, evaluation accuracy, and scalability. Distinct from traditional fingerprint recognition algorithms, FedFR improves the security and scalability of the model from the data source, providing a reference for the application of federated learning in biometric technology.

The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This article proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.

With the rapid development of cloud computing and Internet of Things (IoT), smart health (s-health) is anticipated to enhance healthcare quality significantly. However, data integrity, user anonymity, and authentication concerns have not been adequately addressed in s-health. Remote data integrity checking (RDIC) and digital signature schemes have great potential to address these requirements. Nevertheless, the direct adoption of these schemes suffers from two flaws. Firstly, they incur prohibitively high computation and communication overhead. Secondly, they leak sensitive health information about patients and do not provide complete anonymity. To address these issues, we introduce$\mathbf {A^{3}B}$-$\mathbf {RDV}$, an aggregate anonymous attribute-based remote data verification scheme. In$\mathbf {A^{3}B}$-$\mathbf {RDV}$, the integrity of an arbitrary number of cloud data files can be verified at once without downloading the whole data, thereby saving communication and computation resources. Moreover, in$\mathbf {A^{3}B}$-$\mathbf {RDV}$, data owners can be authenticated by performing highly efficient operations. Also,$\mathbf {A^{3}B}$-$\mathbf {RDV}$provides complete anonymity and supports dishonest-user traceability. We provide security definitions for$\mathbf {A^{3}B}$-$\mathbf {RDV}$and prove its security under the hardness assumption of the bilinear Diffie-Hellman (BDH) problem. Performance comparisons and experimental results indicate that$\mathbf {A^{3}B}$-$\mathbf {RDV}$is more efficient and expressive than state-of-the-art approaches.

Cloud and edge computing are gradually used to achieve complex energy operation control and massive information processing in conventional power grid. Meanwhile, with the tremendous number of distributed energy resources and power equipment integrated into the smart grid enabled by cloud and edge computing, the centralized distribution network cannot realize the flexible and realtime energy supply due to the unpredictable and wide distribution of distributed energy resources, which will further deteriorate the stability of smart grid. To solve those problems, this article proposes energy centric smart grid to achieve power dispatching with the help of cloud-edge computing. Our solution uses energy caching and energy multiple addressing of the edge router to eliminate the intermittency of renewables and speed up energy response. For the stability of energy market and to encourage users to participate in power dispatching, a cloud-edge computing-driven energy cache orchestration mechanism is designed. The empirical results show that the response time is greatly reduced to meet the stringent quality of service requirement in smart grid integrated with distributed energy resources.

Steganography based on fixed codebook has become one of the most important branches of speech steganography due to its high imperceptibility and having the largest available carrier space. As its countermeasure technique, this paper presents a novel steganalysis method based on separable convolution network (SepSteNet) with dual-stream pyramid enhanced strategy (DPES). Specifically, to better acquire discriminative representations, we design the pulse-aware separable block to capture the pulse correspondence along independent levels of pulse positions, where the pulse-aware excitation module is plugged to avoid noisy clue accumulation by adaptively emphasizing the salient part. Moreover, the global attending block is introduced to enhance correspondence features through calculating global responses at distinct subframes. In addition, to eliminate the negative impact of sample content, DPES is leveraged to incorporate cross-domain coherence features by the inverted connected dual-stream branches. With the original and calibration speech samples, two branches enable the correspondence of two detection feature domains to interact with each other to generate coherence features independent of sample content, thereby improving the detection performance. The performance of the presented method is comprehensively evaluated and compared with the state of the arts. The experimental results demonstrate that the presented method significantly outperforms the existing ones. Furthermore, DPES is shown to be a general enhancement strategy that can effectively improve the performance of the existing deep neural network for speech steganalysis. The source code for this work will be publicly available on GitHub.

With the rapid proliferation of urbanization, massive data in social networks are collected and aggregated in real time, making it possible for criminals to use images as a cover to spread secret information on the Internet. How to determine whether these images contain secret information is a huge challenge for multimedia computing security. The steganalysis method based on deep learning can effectively judge whether the pictures transmitted on the Internet in urban scenes contain secret information, which is of great significance to safeguarding national and social security. Image steganalysis based on deep learning has powerful learning ability and classification ability, and its detection accuracy of steganography images has surpassed that of traditional steganalysis based on manual feature extraction. In recent years, it has become a hot topic of the information hiding technology. However, the detection accuracy of existing deep learning based steganalysis methods still needs to be improved, especially when detecting arbitrary-size and multi-source images, their detection efficientness is easily affected by cover mismatch. In this manuscript, we propose a steganalysis method based on Inverse Residuals structured Siamese network (abbreviated as SiaIRNet method, Sia mese- I nverted- R esiduals- Net work Based method). The SiaIRNet method uses a siamese convolutional neural network (CNN) to obtain the residual features of subgraphs, including three stages of preprocessing, feature extraction, and classification. Firstly, a preprocessing layer with high-pass filters combined with depth-wise separable convolution is designed to more accurately capture the correlation of residuals between feature channels, which can help capture rich and effective residual features. Then, a feature extraction layer based on the Inverse Residuals structure is proposed, which improves the ability of the model to obtain residual features by expanding channels and reusing features. Finally, a fully connected layer is used to classify the cover image and the stego image features. Utilizing three general datasets, BossBase-1.01, BOWS2, and ALASKA#2, as cover images, a large number of experiments are conducted comparing with the state-of-the-art steganalysis methods. The experimental results show that compared with the classical SID method and the latest SiaStegNet method, the detection accuracy of the proposed method for 15 arbitrary-size images is improved by 15.96% and 5.86% on average, respectively, which verifies the higher detection accuracy and better adaptability of the proposed method to multi-source and arbitrary-size images in urban scenes.

In[1], the affiliation for Athanasios V. Vasilakos should be as follows:

With the continuous addition of an abundant of heterogeneous devices, the limitation of task delay has become an obstacle to the development of the Industrial Internet of Things (IIoT). Task offloading based on edge computing can provide low-latency computing services for these tasks. However, in the actual IIoT scenario, in contrast to cloud computing, edge computing has limited resources and computing capabilities. Resource-constrained edge resources cannot meet the offloading requirements of massive industrial devices. In this article, we propose an optimal joint offloading scheme based on resource occupancy prediction for the problem of computing offloading with limited edge resources. The scheme is divided into two parts, including edge resource occupancy prediction and task offloading. Simultaneously, considering multitask and the limitations of edge resources, gate recurrent unit (GRU) is used to predict the occupancy of edge resources. Formulating an optimal strategy of task offloading by using a reinforcement learning algorithm according to the network state and predicted results. The simulation results show that the scheme can effectively reduce the average delay of tasks, while minimizing the task offloading failure rate.

The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.

To be able to leverage big data to achieve enhanced strategic insight, process optimization and make informed decision, we need to be an efficient access control mechanism for ensuring end-to-end security of such information asset. Signcryption is one of several promising techniques to simultaneously achieve big data confidentiality and authenticity. However, signcryption suffers from the limitation of not being able to revoke users from a large-scale system efficiently. We put forward, in this paper, thefirstidentity-based (ID-based) signcryption scheme with efficient revocation as well as the feature to outsource unsigncryption to enable secure big data communications between data collectors and data analytical system(s). Our scheme is designed to achieve end-to-end confidentiality, authentication, non-repudiation, and integrity simultaneously, while providing scalable revocation functionality such that the overhead demanded by the private key generator (PKG) in the key-update phase only increases logarithmically based on the cardiality of users. Although in our scheme the majority of the unsigncryption tasks are outsourced to an untrusted cloud server, this approach does not affect the security of the proposed scheme. We then prove the security of our scheme, as well as demonstrating its utility using simulations.

With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K -order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.

Brain signals are potential biometric markers in user authentication, complementing existing biometric authentication techniques (such as those based on fingerprint, iris and facial recognition). This article proposes a novel EEG fusion method to examine the reliability and durability of EEG biometric markers across recording sessions. Our hypothesis is that models trained using EEG signals collected during various elicitation protocols can capture generalised brain patterns that pertain personalised information which can improve the durability of biometric systems. Different protocols are likely to produce different responses across brain regions, which can result in more identifiable responses from EEG. In our approach, an end-to-end convolutional neural network (CNN) model is adopted for feature extraction and classification of raw EEG data. The proposed method is evaluated on two EEG datasets which were collected over two separate sessions on different days using multiple different EEG elicitation protocols. Within-session and across-session experiments were conducted. Results for within session experiments showed that CNN models with protocol fusion can achieve similar if not better results than models trained with single protocol. In across-session scenarios, models trained with the proposed protocol fusion approach significantly outperformed single protocol based models. The obtained results illustrate the durability and reliability capabilities of the proposed fusion approach.

As a novel network infrastructure that realizes the interconnection of humans, machines, and things, 5G enables a large number of devices with communication and sensing capabilities to securely, quickly, and reliably connect to the Internet and facilitates Industrial Internet of Things (IIoT) applications such as smart cities, smart homes, and smart grids. The 5G technique has potential applications in various military and civilian settings such as radar anomaly detection, unmanned aerial vehicles (UAV) data storage sharing [A1], [A2], and traffic data analysis.

With recent developments in communication technologies, vehicular networks have become a reality with various applications. However, the cybersecurity aspect of vehicular networks is still an open issue that needs to be addressed with novel defence mechanisms against attacks. This paper first presents the state-of-the-art communication technologies in vehicular networks (either inter-vehicle networking or in-vehicle networking) along with their applications. Then we explore novel technologies including machine learning and blockchain as cybersecurity defence mechanisms in vehicular networks. Based on the extensive survey, we highlight some insights for future research to secure vehicular networks.

Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking. With the outstanding performance of deep learning, previous works have shown that deep neural networks can effectively learn 6-DoF (Degree of Freedom) poses between frames from monocular image sequences in the unsupervised manner. However, these unsupervised deep learning-based frameworks cannot accurately generate the full trajectory of a long monocular video because of the scale-inconsistency between each pose. To address this problem, we use several geometric constraints to improve the scale-consistency of the pose network, including improving the previous loss function and proposing a novel scale-to-trajectory constraint for unsupervised training. We call the pose network trained by the proposed novel constraint as TrajNet. In addition, a new DVO architecture, called deep direct sparse odometry (DDSO), is proposed to overcome the drawbacks of the previous direct sparse odometry (DSO) framework by embedding TrajNet. Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.

Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances.

We design a new blockchain-based access control protocol in IoT-enabled smart-grid system, called DBACP-IoTSG. Through the proposed DBACP-IoTSG, the data is securely brought to the service providers from their respective smart meters (SMs). The peer-to-peer (P2P) network is formed by the participating service providers, where the peer nodes are responsible for creating the blocks from the gathered data securely from their corresponding SMs and adding them into the blockchain after validation of the blocks using the voting-based consensus algorithm. In our work, the blockchain is considered as private because the data collected from the consumers of the SMs are private and confidential. By the formal security analysis under the random oracle model, nonmathematical security analysis and software-based formal security verification, DBACP-IoTSG is shown to be resistant against various attacks. We carry out the experimental results of various cryptographic primitives that are needed for comparative analysis using the widely used multiprecision integer and rational arithmetic cryptographic library (MIRACL). A detailed comparative study reveals that DBACP-IoTSG supports more functionality features and provides better security apart from its low communication and computation costs as compared to recently proposed relevant schemes. In addition, the blockchain implementation of DBACP-IoTSG has been performed to measure computational time needed for the varied number of blocks addition and also the varied number of transactions per block in the blockchain.

Secure access of the real-time data from the Internet-of-Things (IoT) smart devices (e.g., vehicles) by a legitimate external party (user) is an important security service for big data collection in the IoT-based intelligent transportation system (ITS). To deal with this important issue, we design a new three-factor user authentication scheme, called UAP-BCIoT, which relies on elliptic-curve cryptography (ECC). The mutual authentication between the user and an IoT device happens via the semitrusted cloud-gateway (CG) node in UAP-BCIoT. UAP-BCIoT supports several functionality features needed for IoT-based ITS environment including IoT smart device credential validation and big data analytics. A detailed security analysis is conducted based on the defined threat model to show that UAP-BCIoT is resilient against many known attacks. A thorough comparative study reveals that UAP-BCIoT supports better security, offers various functionality attributes, and also provides similar costs in communication as well computation as compared to other relevant schemes Finally, the practical demonstration of the proposed UAP-BCIoT is also provided to measure its impact on the network performance parameters.

Power capping is an important solution to keep the system within a fixed power constraint. However, for the over-provisioned and power-constrained systems, especially the future exascale supercomputers, powercap needs to be reasonably allocated according to the workloads of compute nodes to achieve trade-offs among performance, energy and powercap. Thus it is necessary to model performance and energy and to predict the optimal powercap allocation strategies. Existing power allocation approaches have insufficient granularity within nodes. Modeling approaches usually model performance and energy separately, ignoring the correlation between objectives, and do not expose the Pareto-optimal powercap configurations. Therefore, this article combines the powercap with uncore frequency scaling and proposes an approach to predict the Pareto-optimal powercap configurations on the power-constrained system for input MPI and OpenMP parallel applications. Our approach first uses the elaborately designed micro-benchmarks and a small number of existing benchmarks to build the training set, and then applies a multi-objective machine learning algorithm which combines the stacked single-target method with extreme gradient boosting to build multi-objective models of performance and energy. The models can be used to predict the optimal processor and memory powercap settings, helping compute nodes perform fine-grained powercap allocation. When the optimal powercap configuration is determined, the uncore frequency scaling is used to further optimize the energy consumption. Compared with the reference powercap configuration, the predicted optimal configurations predicted by our method can achieve an average powercap reduction of 31.35 percent, an average energy reduction of 12.32 percent, and average performance degradation of only 2.43 percent.

Despite the salient feature of cloud computing, the cloud provider still suffers from electricity bill, which in part comes from 1) the power consumption of running physical machines (PMs) to guarantee the resource/time requirements of virtual machines (VMs), and 2) the dynamically varying electricity price offered by smart grids. In the literature, there exist viable solutions adaptive to electricity price variation to reduce the electricity bill. However, they are not applicable to serving time-sensitive VM requests. In serving time-sensitive VM requests, it is potential for the cloud provider to apply proper consolidation strategies to further reduce the electricity bill. Few prior works have provided theoretical solutions of VM consolidation strategies that are adaptive to electricity price variations in serving time-sensitive VM requests. In this work, to address this challenge, we develop electricity-price-aware consolidation algorithms for both the offline and online scenarios. For the offline scenario, we first develop a consolidation algorithm with constant approximation, which always approaches the optimal solution within a constant factor of 5. For the online scenario, we propose an$O(\log (\frac{L_{max}}{L_{min}}))$-competitive algorithm that is able to approach the optimal offline solution within a logarithmic factor, where$\frac{L_{max}}{L_{min}}$is the ratio of the longest length of the processing time requirement of VMs to the shortest one. Our trace-driven simulation results further demonstrate that the average performance of the proposed algorithms produce near-optimal electricity bill.

Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.

Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features: 1) energy-aware routing (EAR): by considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost and 2) adaptive delayed shutdown (ADS): the delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, the EAR-ADS can achieve dual-energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. The simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

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.

Many complex systems can be modeled as temporal networks with time-evolving connections. The influence of their characteristics on epidemic spreading is analyzed in a susceptible-infected-susceptible epidemic model illustrated by the discrete-time Markov chain approach. We develop the analytical epidemic thresholds in terms of the spectral radius of weighted adjacency matrix by averaging temporal networks, e.g., periodic, nonperiodic Markovian networks, and a special nonperiodic non-Markovian network (the link activation network) in time. We discuss the impacts of statistical characteristics, e.g., bursts and duration heterogeneity, as well as time-reversed characteristic on epidemic thresholds. We confirm the tightness of the proposed epidemic thresholds with numerical simulations on seven artificial and empirical temporal networks and show that the epidemic threshold of our theory is more precise than those of previous studies.

Secure real-time data about goods in transit in supply chains needs bandwidth having capacity that is not fulfilled with the current infrastructure. Hence, 5G-enabled Internet of Things (IoT) in mobile edge computing is intended to substantially increase this capacity. To deal with this issue, in this article, we design a new efficient lightweight blockchain-enabled radio frequency identification (RFID)-based authentication protocol for supply chains in 5G mobile edge computing environment, called lightweight blockchain-enabled RFID-based authentication protocol (LBRAPS). LBRAPS is based on bitwise exclusive-or (XOR), one-way cryptographic hash and bitwise rotation operations only. LBRAPS is shown to be secure against various attacks. Moreover, the simulation-based formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool assures that LBRAPS is secure. Finally, it is shown that LBRAPS has better trade-off among its security and functionality features, communication and computation costs as compared to those for existing protocols.

Discovering optimal data analytics solutions to extract value from data for better and faster decision making is essential for many application domains, especially in the big data era. Big data analytics typically requires a tremendous amount of computational resources to process large data volumes that can be very expensive and time consuming. Our research focuses on providing optimization solutions for Analytics-as-a-Service (AaaS) platforms that automatically and elastically provision cloud resources to execute queries guaranteeing Service Level Agreements (SLAs) across a range of Quality of Service (QoS) requirements. We propose admission control and resource scheduling algorithms for AaaS platforms to maximize profits while providing time-minimized query execution plans to meet user demands and expectations. To enable timely responses as required for many domains, the algorithms utilize data splitting-based query admission and resource scheduling offering parallel processing on the split datasets. Extensive experiments are conducted to evaluate the algorithm performance compared to state-of-the-art optimization algorithms. Experimental results show that our algorithms perform significantly better from a range of perspectives, including increasing query admission rates and creating higher profits, whilst supporting efficient resource configurations that are able to support big data processing demands under tight deadlines.

Exploring optimal big data analytics solutions to benefit various domains in decision making and problem solving becomes an ever-important research area. Big data Analytics-as-a-Service (AaaS) platforms offer online AaaS to various domains in a pay-as-you-go model. Big data analytics incurs expensive costs and takes lengthy processing times due to large-scale computing requirements. To tackle the cost and time challenges for big data analytics, we focus on proposing efficient and automatic resource management algorithms to maximize profits and minimize query times while guaranteeing Service Level Agreements (SLAs) on Quality of Service (QoS) requirements of queries. For query processing constrained by tight deadlines and limited budgets, our proposed algorithms enable data splitting and sampling based resource scheduling for parallel and approximate processing that significantly reduce data processing times and resource costs. We formulate the multi-objective resource scheduling problem to optimize profits for AaaS platforms while guaranteeing SLAs of queries with minimized response times. We design extensive experiments for algorithm performance evaluation, results show our proposed algorithms outperform state-of-the-art algorithms that maximize profits for AaaS platforms while improving admission rates and minimizing response times for queries. The scheduling algorithms support elastic and automatic large-scale resource configurations to minimize resource costs, and deliver timely, cost-effective, and reliable AaaS with SLA guarantees.

Information reception in artificially synthesized molecular communication (MC) systems ideally follows the mechanisms employed by natural nanosystems to communicate. One of such reception mechanism is the so called ligand-receptor binding. Contemporary research in MC has considerably discussed this mechanism; however, the impact of a crucial parameter associated with the ligand-receptor binding action has not been given appropriate attention in the MC literature. This parameter is termed the residence time, and has played very crucial role in defining for instance, the efficacy of drugs in therapeutic processes; hence, it is critical in the performance of MC. In this paper, we employ biophysical approach to model and discuss the influence of the ligand residence time on the performance of MC systems. The performance metrics considered here are the receiver sensitivity and the intersymbol interference. Numerical results that expose the impact of the residence time on these metrics, and the interrelationships between these metrics in MC system, are discussed.

The Internet of Drones (IoD) provides a coordinated access to unmanned aerial vehicles that are referred as drones. The on-going miniaturization of sensors, actuators, and processors with ubiquitous wireless connectivity makes drones to be used in a wide range of applications ranging from military to civilian. Since most of the applications involved in the IoD are real-time based, the users are generally interested in accessing real-time information from drones belonging to a particular fly zone. This happens if we allow users to directly access real-time data from flying drones inside IoD environment and not from the server. This is a serious security breach which may deteriorate performance of any implemented solution in this IoD environment. To address this important issue in IoD, we propose a novel lightweight user authentication scheme in which a user in the IoD environment needs to access data directly from a drone provided that the user is authorized to access the data from that drone. The formal security verification using the broadly accepted automated validation of Internet security protocols and applications tool along with informal security analysis show that our scheme is secure against several known attacks. The performance comparison demonstrates that our scheme is efficient with respect to various parameters, and it provides better security as compared to those for the related existing schemes. Finally, the practical demonstration of our scheme is done using the widely accepted NS2 simulation.

Mobile vehicular cloud has become popular with the rapid development of cloud computing and mobile computing. Nested loops are usually the most critical part in multimedia and high performance Digital Signal Processing (DSP) systems which are widely used in vehicular applications and systems. In order to further explore the parallelism in nested loops, we study how to maximize the system performance with considering the energy reduction for applications on Chip Multiprocessor (CMP) architectures. We propose an algorithm Energy-Aware Loop Parallelism Maximization (EALPM) to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops. Our experiment shows that using the EALPM algorithm significantly improves both performance and energy consumption on average in comparison to other algorithms.

Currently, searchable encryption is a hot topic in the field of cloud computing. The existing achievements are mainly focused on keyword-based search schemes, and almost all of them depend on predefined keywords extracted in the phases of index construction and query. However, keyword-based search schemes ignore the semantic representation information of users' retrieval and cannot completely match users' search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, for the first time, we define and solve the problems of semantic search based on conceptual graphs (CGs) over encrypted outsourced data in clouding computing (SSCG). We first employ the efficient measure of “sentence scoring” in text summarization and Tregex to extract the most important and simplified topic sentences from documents. We then convert these simplified sentences into CGs. To perform quantitative calculation of CGs, we design a new method that can map CGs to vectors. Next, we rank the returned results based on “text summarization score”. Furthermore, we propose a basic idea for SSCG and give a significantly improved scheme to satisfy the security guarantee of searchable symmetric encryption (SSE). Finally, we choose a real-world dataset, i.e., the CNN dataset to test our scheme. The results obtained from the experiment show the effectiveness of our proposed scheme.

Millions of dedicated sensors are deployed in smart cities to enhance quality of urban living. Communication technologies are critical for connecting these sensors and transmitting events to sink. In control systems of mobile wireless sensor networks (MWSNs), mobile nodes are constantly moving to detect events, while static nodes constitute the communication infrastructure for information transmission. Therefore, how to communicate with sink quickly and effectively is an important research issue for control systems of MWSNs. In this paper, a communication scheme named first relay node selection based on fast response and multihop relay transmission with variable duty cycle (FRAVD) is proposed. The scheme can effectively reduce the network delay by combining first relay node selection with node duty cycles setting. In FRAVD scheme, first, for the first relay node selection, we propose a strategy based on fast response, that is, select the first relay node from adjacent nodes in the communication range within the shortest response time, and guarantee that the remaining energy and the distance from sink of the node are better than the average. Then for multihop data transmission of static nodes, variable duty cycle is introduced novelty, which utilizes the residual energy to improve the duty cycle of nodes in far-sink area, because nodes adopt a sleep-wake asynchronous mode, increasing the duty cycle can significantly improve network performance in terms of delays and transmission reliability. Our comprehensive performance analysis has demonstrated that compared with the communication scheme with fixed duty cycle, the FRAVD scheme reduces the network delay by 24.17%, improves the probability of finding first relay node by 17.68%, while also ensuring the network lifetime is not less than the previous researches, and is a relatively efficient low-latency communication scheme.

Nowadays automotive industry has been working on the connectivity between automobile and smartphones, e.g., Ford’s SmartDeviceLink, MirrorLink, etc. However, as the interoperability between the sma ...

Due to the widespread popularity of Internet-enabled devices, Industrial Internet of Things (IIoT) becomes popular in recent years. However, as the smart devices share the information with each other using an open channel, i.e., Internet, so security and privacy of the shared information remains a paramount concern. There exist some solutions in the literature for preserving security and privacy in IIoT environment. However, due to their heavy computation and communication overheads, these solutions may not be applicable to wide category of applications in IIoT environment. Hence, in this paper, we propose a new biometric-based privacy preserving user authentication (BP2UA) scheme for cloud-based IIoT deployment. BP2UA consists of strong authentication between users and smart devices using preestablished key agreement between smart devices and the gateway node. The formal security analysis of BP2UA using the well-known real-or-random model is provided to prove its session key security. Moreover, an informal security analysis of BP2UA is also given to show its robustness against various types of known attacks. The computation and communication costs of BP2UA in comparison to the other existing schemes of its category demonstrate its effectiveness in the IIoT environment. Finally, the practical demonstration of BP2UA is also done using the NS2 simulation.

Cloud of Things (CoT) is an integration of Internet of Things (IoT) and cloud computing for intelligent and smart application especially in mobile environment. Model Driven Architecture (MDA) is used to develop Software as a Service (SaaS) so as to facilitate mobile application development by relieving developers from technical details. However, traditional service composition or mashup are somewhat unavailable due to complex relations and heterogeneous deployed environments. For the purpose of building cloud-enabled mobile applications in a configurable and adaptive way, Model-Driven Development Patterns based on semantic reasoning mechanism are provided towards CoT application development. Firstly, a meta-model covering both multi-view business elements and service components are provided for model transformation. Then, based on formal representation of models, three patterns from different tiers of Model-View-Controller (MVC) framework are used to transform business models into service component system so as to configure cloud services rapidly. Lastly, a related software platform is also provided for verification. The result shows that the platform is applicable for rapid system development by means of various service integration patterns.

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e., clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73 percent of optimal, 30 percent better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. We observe about 25 percent lower delays and power (under fixed price constraints) and about 35 percent decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g., random waypoint and Manhattan models.

Graph robustness-the ability of a graph to preserve its connectivity after the loss of nodes and edges-has been extensively studied to quantify how social, biological, physical, and technical systems withstand to external damages. In this paper, we prove that graph robustness can be quickly estimated through the Randic index, a parameter introduced in chemistry to study organic compounds. We prove that Erdos-Renyj (ER) graphs are a good specimen of robust graphs because they lack of a clear modular structure; we derive an analytical expression for the Randic index of ER graphs and use ER graphs as an effective term of comparison to decide about graph robustness. Experiments on real datasets from different domains (scientific collaboration networks, content-sharing systems, co-purchase networks from an e-commerce platform, and a road network) show that real-life large graphs are more robust than ER ones with the same number of nodes and edges. We also observe that if node degree distribution closely follows a power law, then few edges contribute for more than half of the Randic index, thus indicating that the selective removal of those edges has devastating impact on graph robustness. Finally, we describe sampling-based algorithms to efficiently but accurately approximate the Randic index.

Multi-server environment is the most common scenario for a large number of enterprise class applications. In this environment, user registration at each server is not recommended. Using multi-server authentication architecture, user can manage authentication to various servers using single identity and password. We introduce a new authentication scheme for multi-server environments using Chebyshev chaotic map. In our scheme, we use the Chebyshev chaotic map and biometric verification along with password verification for authorization and access to various application servers. The proposed scheme is light-weight compared to other related schemes. We only use the Chebyshev chaotic map, cryptographic hash function and symmetric key encryption-decryption in the proposed scheme. Our scheme provides strong authentication, and also supports biometrics & password change phase by a legitimate user at any time locally, and dynamic server addition phase. We perform the formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to show that the presented scheme is secure. In addition, we use the formal security analysis using the Burrows-Abadi-Needham (BAN) logic along with random oracle models and prove that our scheme is secure against different known attacks. High security and significantly low computation and communication costs make our scheme is very suitable for multi-server environments as compared to other existing related schemes.

Compromised or misconfigured routers have been a major concern in large-scale networks. Such routers sabotage packet delivery, and thus hurt network performance. Data-plane fault localization (FL) promises to solve this problem. Regrettably, the path-based FL fails to support dynamic routing, and the neighbor-based FL requires a centralized trusted administrative controller (AC) or global clock synchronization in each router and introduces storage overhead for caching packets. To address these problems, we introduce a dynamic distributed and low-cost model, D2FL. Using random two-hop neighborhood authentication, D2FL supports volatile path without the AC or global clock synchronization. Besides, D2FL requires only constant tens of KB for caching which is independent of the packet transmission rate. This is much less than the cache size of DynaFL or DFL which consumes several MB. The simulations show that D2FL achieves low false positive and false negative rate with no more than 3 percent bandwidth overhead. We also implement an open source prototype and evaluate its effect. The result shows that the performance burden in user space is less than 10 percent with the dynamic sampling algorithm.

Order scheduling is of vital importance in discrete manufacturing industries. This paper takes fashion industry as an example and discusses the robust order scheduling problem in the fashion industry. In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as preproduction events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this paper, by considering the preproduction events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the aid of a multiobjective evolutionary algorithm called nondominated sorting adaptive differential evolution (NSJADE). The experimental results illustrate that it is of paramount importance to consider preproduction events in order scheduling problems in the fashion industry. We also unveil that the existence of the uncertainties in the daily production quantity heavily affects the order scheduling.

Over the last few years, a large number of Internet of Things (IoT) solutions have come to the IoT marketplace. Typically, each of these IoT solutions are designed to perform a single or minimal number of tasks (primary usage). We believe a significant amount of knowledge and insights are hidden in these data silos that can be used to improve our lives; such data include our behaviors, habits, preferences, life patterns, and resource consumption. To discover such knowledge, we need to acquire and analyze this data together in a large scale. To discover useful information and deriving conclusions toward supporting efficient and effective decision making, industrial IoT platform needs to support variety of different data analytics processes such as inspecting, cleaning, transforming, and modeling data, especially in big data context. IoT middleware platforms have been developed in both academic and industrial settings in order to facilitate IoT data management tasks including data analytics. However, engineering these general-purpose industrial-grade big data analytics platforms need to address many challenges. We have accepted six manuscripts out of 24 submissions for this special section (25% acceptance rate) after the strict peerreview processes. Each manuscript has been blindly reviewed by at least three external reviewers before the decisions were made. The papers are briefly summarized.

This paper is concerned with the consensus of multiple Euler-Lagrange systems with time-varying sampled-data control. Different from traditional sampled-data strategies, a time-varying sampled-data strategy is developed to realize the consensus of multiple Euler-lagrange systems, in which a function that can be distinct at different sampling instants is proposed to modulate the sampling interval. In addition, a new definition of average sampling interval, which is parallel to the average dwell time in switching control or average impulsive interval in impulsive control, is proposed to characterize the number of the updating of the sampling controller during some certain interval. The proposed average sampling interval makes our sampled-data strategy more suitable for a wide range of sampling signals. By utilizing the comparison principle, a sufficient criterion is obtained to guarantee the consensus of multiple Euler-Lagrange systems. The sufficient criterion is heavily dependent on the actual control duration time and the communication graph. Finally, a simulation example is presented to verify the applicability of the proposed results.

With advancements in information and communication technology, there is a steep increase in the remote healthcare applications in which patients can get treatment from the remote places also. The data collected about the patients by remote healthcare applications constitute big data because it varies with volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges which requires a specialized approach. To address this challenge, a new fuzzy rule based classifier is presented in this paper with an aim to provide Healthcare-as-a-Service. The proposed scheme is based upon the initial cluster formation, retrieval, and processing of the big data in cloud environment. Then, a fuzzy rule based classifier is designed for efficient decision making for data classification in the proposed scheme. To perform inferencing from the collected data, membership functions are designed for fuzzification and defuzzification processes. The proposed scheme is evaluated on various evaluation metrics, such as average response time, accuracy, computation cost, classification time, and false positive ratio. The results obtained confirm the effectiveness of the proposed scheme with respect to various performance evaluation metrics in cloud computing environment.

Implantable medical devices (IMDs) are man-made devices, which can be implanted in the human body to improve the functioning of various organs. The IMDs monitor and treat physiological condition of the human being (for example, monitoring of blood glucose level by insulin pump). The advancement of information and communication technology enhances the communication capabilities of IMDs. In healthcare applications, after mutual authentication, a user (for example, doctor) can access the health data from the IMDs implanted in a patient's body. However, in this kind of communication environment, there are always security and privacy issues, such as leakage of health data and malfunctioning of IMDs by an unauthorized access. To mitigate these issues, in this paper, we propose a new secure remote user authentication scheme for IMDs communication environment to overcome security and privacy issues in existing schemes. We provide the formal security verification using the widely accepted Automated Validation of Internet Security Protocols and Applications tool. We also provide the informal security analysis of the proposed scheme. The formal security verification and informal security analysis prove that the proposed scheme is secure against known attacks. The practical demonstration of the proposed scheme is performed using the broadly accepted NS2 simulation tool. The computation and communication costs of the proposed scheme are also comparable with the existing schemes. Moreover, the scheme provides additional functionality features, such as anonymity, untraceability, and dynamic implantable medical device addition.

In recent years, there are increasing interests in using path identifiers (PIDs) as inter-domain routing objects in the future Internet. While PIDs are advertised throughout the Internet in some future Internet architectures, they are kept secret in others. In this paper, we investigate the benefits, from the perspective of preventing distributed denial-of-service (DDoS) attacks, of keeping PIDs secret in future Internet when compared with advertising them throughout the Internet. Our results from extensive simulations show that keeping PIDs secret significantly helps prevent DDoS attacks.

The medical industry is increasingly digitalized and Internet-connected (e.g., Internet of Medical Things), and when deployed in an Internet of Medical Things environment, software-defined networks (SDNs) allow the decoupling of network control from the data plane. There is no debate among security experts that the security of Internet-enabled medical devices is crucial, and an ongoing threat vector is insider attacks. In this paper, we focus on the identification of insider attacks in healthcare SDNs. Specifically, we survey stakeholders from 12 healthcare organizations (i.e., two hospitals and two clinics in Hong Kong, two hospitals and two clinics in Singapore, and two hospitals and two clinics in China). Based on the survey findings, we develop a trust-based approach based on Bayesian inference to figure out malicious devices in a healthcare environment. Experimental results in either a simulated and a real-world network environment demonstrate the feasibility and effectiveness of our proposed approach regarding the detection of malicious healthcare devices, i.e., our approach could decrease the trust values of malicious devices faster than similar approaches.

Distributed computing technologies, as popularized by Hadoop, have been proliferating in Cloud and enterprise computing over ten years, with the capability of processing data across thousands of machines. There is a wide diversity of workloads in such large scale clusters shared by multi-tenant. Hence, resource utilization and task scheduling become vital to performance and bring challenges to architecture designers. We present Daphne, a hybrid scheduling framework that strikes a tradeoff among three universal scheduling frameworks: 1) centralized scheduling; 2) loose coordination scheduling; and 3) fully distributed scheduling. Daphne defines a matching tree that forwards the application to the fittest scheduler according to the characteristic and priority of the application. Besides, Daphne utilizes resource prediction to increase task throughput significantly while it does not interfere with any running workload. We implement Daphne based on YARN and demonstrate that Daphne improves task throughput by nearly 17%.

With advancements in information and communication technology (ICT), there is an increase in the number of users availing remote healthcare applications. The data collected about the patients in these applications varies with respect to volume, velocity, variety, veracity, and value. To process such a large collection of heterogeneous data is one of the biggest challenges that needs a specialized approach. To address this issue, a new fuzzy rule-based classifier for big data handling using cloud-based infrastructure is presented in this paper, with an aim to provide Healthcare-as-a-Service (HaaS) to the users located at remote locations. The proposed scheme is based upon the cluster formation using the modified Expectation-Maximization (EM) algorithm and processing of the big data on the cloud environment. Then, a fuzzy rule-based classifier is designed for an efficient decision making about the data classification in the proposed scheme. The proposed scheme is evaluated with respect to different evaluation metrics such as classification time, response time, accuracy and false positive rate. The results obtained are compared with the standard techniques to confirm the effectiveness of the proposed scheme.

To protect confidential communications from eavesdropping attacks in wireless networks, we propose a novel anti-eavesdropping scheme named AE-Shelter. In our proposed scheme, we place a number of friendly jammers at a circular boundary to protect legitimate communications. The jammers sending artificial noise can mitigate the eavesdropping capability of wiretapping the confidential information. We also establish a theoretical model to evaluate the performance of AE-Shelter. Our results show that our proposed AE-Shelter scheme can significantly reduce the eavesdropping risk without significantly degrading the network performance.

Internet of things (IoT) facilitates billions of devices to be enabled with network connectivity to collect and exchange real-time information for providing intelligent services. Thus, IoT allows connected devices to be controlled and accessed remotely in the presence of adequate network infrastructure. Unfortunately, traditional network technologies such as enterprise networks and classic timeout-based transport protocols are not capable of handling such requirements of IoT in an efficient, scalable, seamless, and cost-effective manner. Besides, the advent of software-defined networking (SDN) introduces features that allow the network operators and users to control and access the network devices remotely, while leveraging the global view of the network. In this respect, we provide a comprehensive survey of different SDN-based technologies, which are useful to fulfill the requirements of IoT, from different networking aspects-edge, access, core, and data center networking. In these areas, the utility of SDN-based technologies is discussed, while presenting different challenges and requirements of the same in the context of IoT applications. We present a synthesized overview of the current state of IoT development. We also highlight some of the future research directions and open research issues based on the limitations of the existing SDN-based technologies.

Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.

A device-to-device (D2D) assisted cellular network is pervasive to support ubiquitous healthcare applications, since it is expected to bring the significant benefits of improving user throughput, extending the battery life of mobiles, etc. However, D2D and cellular communications in the same network may cause cross-tier interference (CTI) to each other. Also a critical issue of using D2D assisted cellular networks under a healthcare scenario is the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical equipments. In consideration of CTI and EMI, we study the problem of optimizing individual channel rates of the mobile users in different priorities (different levels of emergency) within the Internet of Vehicles for mobile health, and propose an algorithm of controlling the transmit power to solve the above-mentioned problem under a game-theoretical framework. Numerical results show that the proposed algorithm can converge linearly to the optimum, while ensuring an allowable level of EMI on medical equipments.

A counterfeit drug is a medication or pharmaceutical product which is manufactured and made available on the market to deceptively represent its origin, authenticity and effectiveness, etc., and causes serious threats to the health of a patient. Counterfeited medicines have an adverse effect on the public health and cause revenue loss to the legitimate manufacturing organizations. In this paper, we propose a new authentication scheme for medicine anticounterfeiting system in the Internet of Things environment which is used for checking the authenticity of pharmaceutical products (dosage forms). The proposed scheme utilizes the near field communication (NFC) and is suitable for mobile environment, which also provides efficient NFC update phase. The security analysis using the widely accepted real-or-random model proves that the proposed scheme provides the session key security. The proposed scheme also protects other known attacks which are analyzed informally. Furthermore, the formal security verification using the broadly accepted automated validation of Internet security protocols and applications tool shows that the proposed scheme is secure. The scheme is efficient with respect to computation and communication costs, and also it provides additional functionality features when compared to other existing schemes. Finally, for demonstration of the practicality of the scheme, we evaluate it using the broadly accepted NS2 simulation.

Millimeter-wave (mmWave) communication is the rising technology for next-generation wireless transmission. Benefited by its abundant bandwidth and short wavelength, mmWave is advanced in multi-gigabit transmittability and beamforming. In contrast, the short wavelength also makes mmWave easily blocked by obstacles. In order to bypass these obstacles, relays are widely needed in mmWave communications. Unmanned autonomous vehicles (UAVs), such as drones and self-driving robots, enable the mobile relays in real applications. Nevertheless, it is challenging for a UAV to find its optimal relay location automatically. On the one hand, it is difficult to find the location accurately due to the complex and dynamic wireless environment; on the other hand, most applications require the relay to forward data immediately, so the autonomous process should be fast. To tackle this challenge, we propose a novel method AutoRelay specialized for mmWave communications. In AutoRelay, the UAV samples the link qualities of mmWave beams while moving. Based on the real-time sampling, the UAV gradually adjusts its path to approach the optimal location by leveraging compressive sensing theory to estimate the link qualities in candidate space, which increases the accuracy and save the time. Performance results demonstrate that AutoRelay outperforms existing methods in achieving an accurate and efficient relay strategy.

Detecting communities in graphs is a fundamental tool to understand the structure of Web-based systems and predict their evolution. Many community detection algorithms are designed to processundirected graphs(i.e., graphs with bidirectional edges) but many graphs on the Web-e.g., microblogging Web sites, trust networks or the Web graph itself-are oftendirected. Few community detection algorithms deal with directed graphs but we lack their experimental comparison. In this paper we evaluated some community detection algorithms across accuracy and scalability. A first group of algorithms (Label Propagation and Infomap) are explicitly designed to manage directed graphs while a second group (e.g., WalkTrap) simply ignores edge directionality; finally, a third group of algorithms (e.g., Eigenvector) maps input graphs onto undirected ones and extracts communities from the symmetrized version of the input graph. We ran our tests on both artificial and real graphs and, on artificial graphs, WalkTrap achieved the highest accuracy, closely followed by other algorithms; Label Propagation has outstanding performance in scalability on both artificial and real graphs. The Infomap algorithm showcased the best trade-off between accuracy and computational performance and, therefore, it has to be considered as a promising tool for Web Data Analytics purposes.

Spreading across many parts of the world and presently hard striking California, extended droughts could even potentially threaten reliable electricity production and local water supplies, both of which are critical for data center operation. While numerous efforts have been dedicated to reducing data centers' energy consumption, the enormity of data centers' water footprints is largely neglected and, if still left unchecked, may handicap service availability during droughts. In this paper, we propose a water-aware workload management algorithm, called WATCH (WATer-constrained workload sCHeduling in data centers), which caps data centers' long-term water consumption by exploiting spatio-temporal diversities of water efficiency and dynamically dispatching workloads among distributed data centers. We demonstrate the effectiveness of WATCH both analytically and empirically using simulations: based on only online information, WATCH can result in a provably-low operational cost while successfully capping water consumption under a desired level. Our results also show that WATCH can cut water consumption by 20 percent while only incurring a negligible cost increase even compared to state-of-the-art cost-minimizing but water-oblivious solution. Sensitivity studies are conducted to validate WATCH under various settings.

Cloud computing offers a new way of services and has become a popular service platform. Storing user data at a cloud data center greatly releases storage burden of user devices and brings access convenience. Due to distrust in cloud service providers, users generally store their crucial data in an encrypted form. But in many cases, the data need to be accessed by other entities for fulfilling an expected service, e.g., an eHealth service. How to control personal data access at cloud is a critical issue. Various application scenarios request flexible control on cloud data access based on data owner policies and application demands. Either data owners or some trusted third parties or both should flexibly participate in this control. However, existing work hasn't yet investigated an effective and flexible solution to satisfy this demand. On the other hand, trust plays an important role in data sharing. It helps overcoming uncertainty and avoiding potential risks. But literature still lacks a practical solution to control cloud data access based on trust and reputation. In this paper, we propose a scheme to control data access in cloud computing based on trust evaluated by the data owner and/or reputations generated by a number of reputation centers in a flexible manner by applying Attribue-Based Encryption and Proxy Re-Encryption. We integrate the concept of context-aware trust and reputation evaluation into a cryptographic system in order to support various control scenarios and strategies. The security and performance of our scheme are evaluated and justified through extensive analysis, security proof, comparison and implementation. The results show the efficiency, flexibility and effectiveness of our scheme for data access control in cloud computing.

Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive (ETI) control scheme is introduced to improve the performance of DE. Impulsive control (IPC), the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing IPC and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the IPC operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of IPC, two types of impulses are presented within the framework of DE in this paper: 1) stabilizing impulses and 2) destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.

In Named-Data Networking (NDN), content is cached in network nodes and served for future requests. This property of NDN allows attackers to inject poisoned content into the network and isolate users from valid content sources. Since a digital signature is embedded in every piece of content in NDN architecture, poisoned content is discarded if routers perform signature verification; however, if every content is verified by every router, it would be overly expensive to do. In our preliminary work, we have suggested a content verification scheme that minimizes unnecessary verification and favors already verified content in the content store, which reduces the verification overhead by as much as 90% without failing to detect every piece of poisoned content. Under this scheme, however, routers are vulnerable to verification attack, in which a large amount of unverified content is accessed to exhaust system resources. In this paper, we carefully look at the possible concerns of our preliminary work, including verification attack, and present a simple but effective solution. The proposed solution mitigates the weakness of our preliminary work and allows this paper to be deployed for real-world applications.

In recent years, there are increasing interests in using path identifiers (PIDs) as inter-domain routing objects. However, the PIDs used in existing approaches are static, which makes it easy for attackers to launch the distributed denial-ofservice (DDoS) flooding attacks. To address this issue, in this paper, we present the design, implementation, and evaluation of dynamic PID (D-PID), a framework that uses PIDs negotiated between the neighboring domains as inter-domain routing objects. In D-PID, the PID of an inter-domain path connecting the two domains is kept secret and changes dynamically. We describe in detail how neighboring domains negotiate PIDs and how to maintain ongoing communications when PIDs change. We build a 42-node prototype comprised of six domains to verify D-PID's feasibility and conduct extensive simulations to evaluate its effectiveness and cost. The results from both simulations and experiments show that D-PID can effectively prevent DDoS attacks.

The increased usage of smart wearables in various applications, specifically in health-care, emphasizes the need for secure communication to transmit sensitive health-data. In a practical scenario, where multiple devices are carried by a person, a common secret key is essential for secure group communication. Group key generation and sharing among wearables have received very little attention in the literature due to the underlying challenges: 1) difficulty in obtaining a good source of randomness to generate strong cryptographic keys, and 2) finding a common feature among all the devices to share the key. In this paper, we present a novel solution to generate and distribute group secret keys by exploiting on-board accelerometer sensor and the unique walking style of the user, i.e., gait. We propose a method to identify the suitable samples of accelerometer data during all routine activities of a subject to generate the keys with high entropy. In our scheme, the smartphone placed on waist employs fuzzy vault, a cryptographic construct, and utilizes the acceleration due to gait, a common characteristic extracted on all wearable devices to share the secret key. We implement our solution on commercially available off-the-shelf smart wearables, measure the system performance, and conduct experiments with multiple subjects. Our results demonstrate that the proposed solution has a bit rate of 750 b/s, low system overhead, distributes the key securely and quickly to all legitimate devices, and is suitable for practical applications.

The accurately estimated state is of great importance for maintaining a stable running condition of power systems. To maintain the accuracy of the estimated state, bad data detection (BDD) is utilized by power systems to get rid of erroneous measurements due to meter failures or outside attacks. However, false data injection (FDI) attacks, as recently revealed, can circumvent BDD and insert any bias into the value of the estimated state. Continuous works on constructing and/or protecting power systems from such attacks have been done in recent years. This survey comprehensively overviews three major aspects: constructing FDI attacks; impacts of FDI attacks on electricity market; and defending against FDI attacks. Specifically, we first explore the problem of constructing FDI attacks, and further show their associated impacts on electricity market operations, from the adversary's point of view. Then, from the perspective of the system operator, we present countermeasures against FDI attacks. We also outline the future research directions and potential challenges based on the above overview, in the context of FDI attacks, impacts, and defense.

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.

In this paper, we study the source mobility problem that exists in the current named data networking (NDN) architecture and propose a proxy-based mobility support approach named PMNDN to overcome the problem. PMNDN proposes using a proxy to efficiently manage source mobility. Besides, functionalities of the NDN access routers are extended to track the mobility status of a source and signal Proxy about a handoff event. With this design, a mobile source does not need to participate in handoff signaling which reduces the consumption of limited wireless bandwidth. PMNDN also features an ID that is structurally similar to the content name so that routing scalability of NDN architecture is maintained and addressing efficiency of Interest packets is improved. We illustrate the performance advantages of our proposed solution by comparing the handoff performance of the mobility support approaches with that in NDN architecture and current Internet architecture via analytical and simulation investigation. We show that PMNDN offers lower handoff cost, shorter handoff latency, and less packet losses during the handoff process.

Neighbor discovery, one of the most fundamental bootstrapping networking primitives, is particularly challenging in decentralized wireless networks where devices have directional antennas. In this paper, we study the following fundamental problem, which we term oblivious neighbor discovery: How can neighbor nodes with heterogeneous antenna configurations discover each other within a bounded delay in a fully decentralised manner without any prior coordination or synchronisation? We establish a theoretical framework on the oblivious neighbor discovery and the performance bound of any neighbor discovery algorithm achieving oblivious discovery. Guided by the theoretical results, we then devise an oblivious neighbor discovery algorithm, which achieves guaranteed oblivious discovery with order-minimal worst case discovery delay in the asynchronous and heterogeneous environment. We further demonstrate how our algorithm can be configured to achieve a desired tradeoff between average and worst case performance.

Many distributed applications, such as BitTorrent, need to know the distance between each pair of network hosts in order to optimize their performance. For small-scale systems, explicit measurements can be carried out to collect the distance information. For large-scale applications, this approach does not work due to the tremendous amount of measurements that have to be completed. To tackle the scalability problem, network coordinate system (NCS) was proposed to solve the scalability problem by using partial measurements to predict the unknown distances. However, the existing NCS schemes suffer seriously from either low prediction precision or unsatisfactory convergence speed. In this paper, we present a novel distributed network coordinate system (DISCS) that utilizes a limited set of distance measurements to achieve high-precision distance prediction at a fast convergence speed. Technically, DISCS employs the innovative robust nonnegative matrix completion method to improve the prediction accuracy. Through extensive experiments based on various publicly-available data sets, we found that DISCS outperforms the state-of-the-art NCS schemes in terms of prediction precision and convergence speed, which clearly shows the high usability of DISCS in real-life Internet applications.

This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other fields, e.g., sensor networks and grid systems. Self-organization mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organization mechanisms developed for use in multiagent systems. In this paper, we provide a survey of existing literature on self-organization mechanisms in multiagent systems. We also highlight the future work on key research issues in multiagent systems. This paper can serve as a guide and a starting point for anyone who will conduct research on self-organization in multiagent systems. Also, this paper complements existing survey studies on self-organization in multiagent systems.

Next generation mobile networks aim to provide faster speed and more capacity along with energy efficiency to support video streaming and massive data sharing in social and communication networks. In these networks, user equipment has to register with IP Multimedia Subsystem (IMS) which promises quality of service to the mobile users that frequently move across different access networks. After each handover caused due to mobility, IMS provides IPSec Security Association establishment and authentication phases. The main issue is that unnecessary reregistration after every handover results in latency and communication overhead. To tackle these issues, this paper presents a lightweight Fast IMS Mobility (FIM) registration scheme that avoids unnecessary conventional registration phases such as security associations, authentication, and authorization. FIM maintains a flag to avoid deregistration and sends a subsequent message to provide necessary parameters to IMS servers after mobility. It also handles the change of IP address for user equipment and transferring the security associations from old to new servers. We have validated the performance of FIM by developing a testbed consisting of IMS servers and user equipment. The experimental results demonstrate the performance supremacy of FIM. It reduces media disruption time, number of messages, and packet loss up to 67%, 100%, and 61%, respectively, as compared to preliminaries.

We propose a genetic algorithm (GA) by taking into account the correlation between the current best candidate with the other candidates in the population. In this paper we propose a new selection operator and in addition we have designed a prediction operator which works on an archive of selected candidates. We test our proposed algorithm on the problem definitions for the CEC 2014 special session and competition on single objective real-parameter numerical optimization.

In the wireless communication, there are many cases where the transmission path is obstructed by unknown objects. With the rapid development of the drone technology in recent years, the drones are advocated to serve as mobile relays to forward data streams. However, the challenges are that data transmission may suffer severe signal attenuation due to the existence of the obstructions and it is challenging to find the best location for mobile relays due to the dynamic environment and unpredictable interference. To address the problem, this paper proposes an approach that a drone can automatically find the location with the optimal link quality. We design a novel algorithm, named Path-sampling Online Tensor Update (POTU), to estimate the link quality in the space and find the optimal location. Furthermore, the algorithm is practical to the real applications due to the simplicity of implementation. In the experiment, we construct a realistic scene and compare the performance of our algorithm with the classic and the state-of-the-art algorithms. As a result, POTU outperforms existing methods in achieving the trade-off between time cost and estimation accuracy.

To accommodate the trend toward mass customization launched by intelligent manufacturing, the paper proposes the adoption of model-integrated computing (MIC) paradigm in the motion control system development process for enhancing flexibility and robustness. Hierarchical structural and behavioral diversities in motion control system are considered during the implementation of MIC paradigm. For design-phase implementation, a motion-control-domain-specific modeling language is developed, and formal semantics are integrated. With regard to execution-phase implementation, a real-time runtime framework compliant with the IEC 61499 standard is proposed. Extensions of function block chain and priority-based event propagation are proposed. Dynamically extendable FB types library for motion control domain is constructed. A prototype three-axis motion control system is modeled using the proposed modelling language and is then deployed to the implemented framework to prove the feasibility of the adoption of the MIC paradigm in motion control domain.

Neighbor discovery, the process of discovering all neighbors in a device's communication range, is one of the bootstrapping networking primitives of paramount importance and is particularly challenging when devices have directional antennas instead of omni-directional ones. In this paper, we study the following fundamental problem which we term as oblivious neighbor discovery: How can neighbor nodes with heterogeneous antenna configurations and without clock synchronization discover each other within a bounded delay in a fully decentralised manner without any prior coordination? We first establish a theoretical framework on oblivious neighbor discovery and establish the performance bound of any neighbor discovery protocol achieving oblivious discovery. Guided by the theoretical results, we then design an oblivious neighbor discovery protocol and prove that it achieves guaranteed oblivious discovery with order-minimal worst-case discovery delay in the asynchronous and heterogeneous environment. We further demonstrate how our protocol can be configured to achieve a desired trade-off between average and worst-case performance.

The proliferation of new online Internet services has substantially increased the energy consumption in wired networks, which has become a critical issue for Internet service providers. In this paper, we target the network-wide energy-saving problem by leveraging speed scaling as the energy-saving strategy. We propose a distributed routing scheme-HDEER-to improve network energy efficiency in a distributed manner without significantly compromising traffic delay. HDEER is a two-stage routing scheme where a simple distributed multipath finding algorithm is firstly performed to guarantee loop-free routing, and then a distributed routing algorithm is executed for energy-efficient routing in each node among the multiple loop-free paths. We conduct extensive experiments on the NS3 simulator and simulations with real network topologies in different scales under different traffic scenarios. Experiment results show that HDEER can reduce network energy consumption with a fair tradeoff between network energy consumption and traffic delay.

The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.

The family of conventional half-duplex (HD) wireless systems relied on transmitting and receiving in different time slots or frequency subbands. Hence, the wireless research community aspires to conceive full-duplex (FD) operation for supporting concurrent transmission and reception in a single time/frequency channel, which would improve the attainable spectral efficiency by a factor of two. The main challenge encountered in implementing an FD wireless device is the large power difference between the self-interference (SI) imposed by the device’s own transmissions and the signal of interest received from a remote source. In this survey, we present a comprehensive list of the potential FD techniques and highlight their pros and cons. We classify the SI cancellation techniques into three categories, namely passive suppression, analog cancellation and digital cancellation, with the advantages and disadvantages of each technique compared. Specifically, we analyze the main impairments (e.g., phase noise, power amplifier nonlinearity, as well as in-phase and quadrature-phase (I/Q) imbalance, etc.) that degrading the SI cancellation. We then discuss the FD-based media access control (MAC)-layer protocol design for the sake of addressing some of the critical issues, such as the problem of hidden terminals, the resultant end-to-end delay and the high packet loss ratio (PLR) due to network congestion. After elaborating on a variety of physical/MAC-layer techniques, we discuss potential solutions conceived for meeting the challenges imposed by the aforementioned techniques. Furthermore, we also discuss a range of critical issues related to the implementation, performance enhancement and optimization of FD systems, including important topics such as hybrid FD/HD scheme, optimal relay selection and optimal power allocation, etc. Finally, a variety of new directions and open problems associated with FD technology are pointed out. Our hope is that this treatise will stimulate future research efforts in the emerging field of FD communications.

With the great success of the second-generation wireless telephone technology and the third-generation mobile telecommunications technology, and the fast development of the fourth-generation mobile telecommunications technology, the phase of fifth-generation mobile networks or fifth-generation wireless systems (5G) is coming. In this article, we indicate the open research issues of 5G security and trust in the context of virtualized networking and software-defined networking. We further propose a framework of security and trust focusing on solving 5G network security issues. The proposed framework applies adaptive trust evaluation and management technologies and sustainable trusted computing technologies to ensure computing platform trust and achieve software-defined network security. It adopts cloud computing to securely deploy various trustworthy security services over the virtualized networks. We analyze that the framework can support and satisfy all security requirements specified in standardization. We also suggest future research work according to the proposed framework and discuss the advantages of our framework in terms of practical deployment. Copyright © 2015 John Wiley & Sons, Ltd.

The papers in this paper are devoted to the topic of Big Data analytics via the Internet.

With the ongoing shift of network services to the application layer also the monitoring systems focus more on the data from the application layer. The increasing speed of the network links, together with the increased complexity of application protocol processing, require a new way of hardware acceleration. We propose a new concept of hardware acceleration for flexible flow-based application level traffic monitoring which we call Software Defined Monitoring. Application layer processing is performed by monitoring tasks implemented in the software in conjunction with a configurable hardware accelerator. The accelerator is a high-speed application-specific processor tailored to stateful flow processing. The software monitoring tasks control the level of detail retained by the hardware for each flow in such a way that the usable information is always retained, while the remaining data is processed by simpler methods. Flexibility of the concept is provided by a plugin-based design of both hardware and software, which ensures adaptability in the evolving world of network monitoring. Our high-speed implementation using FPGA acceleration board in a commodity server is able to perform a 100 Gb/s flow traffic measurement augmented by a selected application-level protocol analysis.

We consider the crucial problem of maximizing the system-wide performance which takes into account request processing throughput, smartphone user experience and system stability in a participatory sensing system with cooperative smartphones. Three important controls need to be made, i.e., 1) request admission control, 2) task allocation, and 3) task scheduling on smartphones. It is highly challenging to achieve the optimal system-wide performance, given arbitrary unknown arrivals of sensing requests, intrinsic tradeoff between request processing throughput and smartphone user experience degradation, and heterogenous requests. Little existing work has studied this crucial problem of maximizing the system-wide performance of a participatory sensing system as a whole. In response to the challenges, we propose an optimal online control approach to maximize the system-wide performance of a participatory sensing system. Exploiting the stochastic Lyapunov optimization techniques, it derives the optimal online control strategies for request admission control, task allocation and task scheduling on smartphones. The most salient feature of our approach is that the achieved system-wide performance is arbitrarily close to the optimum, despite unpredictable and arbitrary request arrivals. Rigorous theoretical analysis and comprehensive simulation evaluation jointly demonstrate the efficacy of our online control approach.

Cloud providers provision their various resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.

In the problem of routing in multi-hop wireless networks, to achieve high end-to-end throughput, it is crucial to find the “best” path from the source node to the destination node. Although a large number of routing protocols have been proposed to find the path with minimum total transmission count/time for delivering a single packet, such transmission count/time minimizing protocols cannot be guaranteed to achieve maximum end-to-end throughput. In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the median throughput gain is up to 60 percent, and for each source-destination pair, the throughput gain is as high as 5.3x; for anypath routing, the maximum per-flow throughput gain is 71.6 percent, while the median gain is up to 13.2 percent.

Auction-style pricing policies can effectively reflect the underlying trends in demand and supply for the cloud resources, and thereby attracted a research interest recently. In particular, a desirable cloud auction design should be (1) online to timely reflect the fluctuation of supply-demand relations, (2) expressive to support the heterogeneous user demands, and (3) truthful to discourage users from cheating behaviors. Meeting these requirements simultaneously is non-trivial, and most existing auction mechanism designs do not directly apply. To meet these goals, this paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous requirement on their desired allocation time, application type, and even how they value among different possible allocations can be flexibly and concisely expressed. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism. To ensure truthfulness with heterogenous and online user demand, the design of COCA is driven by a monotonic payment rule and a utility-maximizing allocation rule. Moreover, our theoretical analysis shows that the worst-case performance of COCA can be well-bounded, and our further discussion shows that COCA performs well when some other important factors in online auction design are taken into consideration. Finally, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [19].

This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc.

The performance of key authentication and the degree of privacy in large-scale RFID systems are considered by many researchers as tradeoffs. Based on how keys are managed in the system, the privacy preserving tag authentications proposed in the past can be categorized into tree-based and group-based approaches. While a tree-based approach achieves high performance in key authentication, it suffers from the issue of low privacy should a fraction of tags be compromised. On the contrary, while group-based key authentication is relatively invulnerable to compromise attacks, it is not scalable to a large number of tags. In this paper, we propose a new private tag authentication protocol based on skip lists, named randomized skip lists-based authentication (RSLA). Without sacrificing the authentication performance, RSLA provides a high privacy preserving mechanism. While RSLA provides the same level of unpredictability-based-privacy and indistinguishability-based privacy compared with other structured key management approaches, our scheme achieves the highest system anonymity with good performance in key look up and update. In addition, the simulation results match our analyses closely.

Service-oriented architecture offers a flexible paradigm for information flow among collaborating organizations. As information moves out of an organization boundary, various security concerns may arise, such as confidentiality, integrity, and authenticity that needs to be addressed. Moreover, verifying the correctness of the communication protocol is also an important factor. This paper focuses on the formal verification of the xDAuth protocol, which is one of the prominent protocols for identity management in cross domain scenarios. We have modeled the information flow of xDAuth protocol using high-level Petri nets to understand the protocol information flow in a distributed environment. We analyze the rules of information flow using Z language, while Z3 SMT solver is used for the verification of the model. Our formal analysis and verification results reveal the fact that the protocol fulfills its intended purpose and provides the security for the defined protocol specific properties, e.g., secure secret key authentication, and Chinese wall security policy and secrecy specific properties, e.g., confidentiality, integrity, and authenticity.

Wireless technologies and vehicle-mounted or wearable medical sensors are pervasive to support ubiquitous healthcare applications. However, a critical issue of using wireless communications under a healthcare scenario rests at the electromagnetic interference (EMI) caused by radio frequency transmission. A high level of EMI may lead to a critical malfunction of medical sensors, and in such a scenario, a few users who are not transmitting emergency data could be required to reduce their transmit power or even temporarily disconnect from the network in order to guarantee the normal operation of medical sensors as well as the transmission of emergency data. In this paper, we propose a joint power and admission control algorithm to schedule the users' transmission of medical data. The objective of this algorithm is to minimize the number of users who are forced to disconnect from the network while keeping the EMI on medical sensors at an acceptable level. We show that a fixed point of proposed algorithm always exists, and at the fixed point, our proposed algorithm can minimize the number of low-priority users who are required to disconnect from the network. Numerical results illustrate that the proposed algorithm can achieve robust performance against the variations of mobile hospital environments.

The rapid development of the latest distributed computing paradigm, i.e., cloud computing, generates a highly fragmented cloud market composed of numerous cloud providers and offers tremendous parallel computing ability to handle big data problems. One of the biggest challenges in multiclouds is efficient workflow scheduling. Although the workflow scheduling problem has been studied extensively, there are still very few primal works tailored for multicloud environments. Moreover, the existing research works either fail to satisfy the quality of service (QoS) requirements, or do not consider some fundamental features of cloud computing such as heterogeneity and elasticity of computing resources. In this paper, a scheduling algorithm, which is called multiclouds partial critical paths with pretreatment (MCPCPP), for big data workflows in multiclouds is presented. This algorithm incorporates the concept of partial critical paths, and aims to minimize the execution cost of workflow while satisfying the defined deadline constraint. Our approach takes into consideration the essential characteristics of multiclouds such as the charge per time interval, various instance types from different cloud providers, as well as homogeneous intrabandwidth vs. heterogeneous interbandwidth. Various types of workflows are used for evaluation purpose and our experimental results show that the MCPCPP is promising.

Recently, research on Information-Centric Networking (ICN) has flourished, which attempts to shift from the current host-oriented Internet architecture to an information-oriented one. The built-in caching capability is a typical feature of ICN. In this paper, in order to fully exploit the built-in caching capability of ICN, we propose a collaborative in-network caching scheme with Content-space Partitioning and Hash-Routing, which is named as CPHR. By intelligently partitioning the content space and assigning partitions to caches, CPHR is able to constrain the path stretch incurred by hash-routing. We formulate the problem of assigning partitions to caches into an optimization problem of maximizing the overall hit ratio and propose a heuristic algorithm to solve it. We also formulate the partitioning proportion problem into a min-max linear optimization problem to balance cache workloads. By simulations with both the characteristics of real Internet traffic and traces of peer-to-peer (P2P) traffic, we show the necessity of collaborative caching since the en-route caching mode cannot yield a considerable overall hit ratio with practical cache size. It is shown as well that CPHR can significantly increase the overall hit ratio by up to about 100% with the practical cache policy Least Recently Used (LRU) while the overhead incurred is acceptable in terms of propagation latency and load on links.

An overlay macrocell-femtocell system aims to increase the system capacity with a low-cost infrastructure. To construct such an infrastructure, we need to solve some existing problems. First, there is a tradeoff between femtocell coverage and overall system throughput, which we defined as the cell-breathing phenomenon. In light of this, we propose a femtocell downlink cell-breathing control framework to strike a balance between the coverage and data rate. Second, due to the selfish nature of mobile stations, the system information collected from them does not necessarily reflect the true status of the system. Thus, we design FEmtocell Virtual Election Rule (FEVER), a voting-based direct mechanism that only requires users to report their channel quality information to the femtocell base station. Not only is it proved to be truthful and has low implementation complexity, but it also strikes a balance between efficiency and fairness to meet the different needs. The simulation results verify the enhanced system performance under the FEVER mechanism.

Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.

Abstract Topology control is an efficient strategy for improving the performance of wireless ad hoc and sensor networks by building network topologies with desirable features. In this process, location information of nodes can be used to improve the performance of a topology control algorithm and also ease its operations. Many location‐based topology control algorithms have been proposed. In this paper, we propose two location‐assisted grid‐based topology control (GBP) algorithms. The design objective of our algorithm is to effectively reduce the number of active nodes required to keep global network connectivity. In grid‐based topology control, a network is divided into equally spaced squares (called grids). We accordingly design cross‐sectional topology control algorithm and diagonal topology control algorithm based on different network parameter settings. The key idea is to build near‐minimal connected dominating set for the network at the grid level. Analytical and simulation results demonstrate that our designed algorithms outperform existing work. Furthermore, the diagonal algorithm outperforms the cross‐sectional algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

In wireless sensor networks (WSNs), there is no predefined infrastructure. Nodes need to frequently flood messages to discover routes, which badly decreases the network performance. To overcome such drawbacks, WSNs are often grouped into several disjointed clusters, each with a representative cluster head (CH) in charge of the routing process. In order to further improve the efficiency of WSNs, it is crucial to find a cluster partition with minimum number of clusters and the distance between each node to its corresponding CH can be bounded by a constant number of hops. Finding such a partition is defined as minimum d-hop cluster head set (d-MCHS) problem, which is proved to be NP-hard. In this paper, we propose a distributed approximation algorithm, named d2-Cluster, to address d-MCHS problem and prove that the approximation ratio of d2-Cluster under unit disk graph (UDG) is a constant factor λ which is related to d. To the best of our knowledge, it is the first constant approximation ratio for d-MDS problem in UDG.

The impact of both maximal ratio combining (MRC) and relay selection on the physical layer security in wireless communication systems is studied by analyzing some important factors, including the probability characteristic of the legitimate receiver (Bob) and malicious eavesdropper (Eve)'s end-to-end signal-to-noise ratio (SNR), the secrecy outage probability, and the average secrecy channel capacity over Rayleigh fading Channel. We assume that Bob receives its data from both the relay and the source by cooperative communication, provided that MRC is employed at the receiver. Compared to the conventional MRC methods, a higher spatial diversity order can be exploited by performing relay selection in the proposed method, as validated via both theoretical analysis and numerical simulation. The theoretical closed-form expressions for some figures of merit, e.g., the secrecy outage probability and the average secrecy capacity, are all consistent with the numerical results.

With the explosive growth of mobile data demand, there has been an increasing interest in deploying small cells of higher frequency bands underlying the conventional homogeneous macrocell network, which is usually referred to as heterogeneous cellular networks, to significantly boost the overall network capacity. With vast amounts of spectrum available in the millimeter-wave (mmWave) band, small cells at mmWave frequencies are able to provide multigigabit access data rates, whereas the wireless backhaul in the mmWave band is emerging as a cost-effective solution to provide high backhaul capacity to connect access points of the small cells. In order to operate the mobile network optimally, it is necessary to jointly design the radio access and backhaul networks. Meanwhile, direct transmissions between devices should be also considered to improve system performance and enhance user experience. In this paper, we propose a joint transmission scheduling scheme for the radio access and backhaul of small cells in the mmWave band, termed D2DMAC, where a path selection criterion is designed to enable device-to-device transmissions for performance improvement. In D2DMAC, a concurrent transmission scheduling algorithm is proposed to fully exploit spatial reuse in mmWave networks. Through extensive simulations under various traffic patterns and user deployments, we demonstrate that D2DMAC achieves near-optimal performance in some cases and outperforms other protocols significantly in terms of delay and throughput. Furthermore, we also analyze the impact of path selection on the performance improvement of D2DMAC under different selected parameters.

The articles in this special section aim at presenting the latest developments, trends, and solutions of Big Data analytics on the web.

Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a “specific area” (called a “face” in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target's movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the “tracker”, which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate our proposed models and algorithm, we further apply the physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

Web-based systems have enjoyed tremendous growth in both theory and applications. They are highly visible and influential realizations of user-oriented technology supporting numerous human pursuits realized across the e-service. In this paper, we focus on web-based e-service systems for the permanent magnet synchronous motor (PMSM) remote control. These systems can provide web services for updating factors and the fuzzy law of Takagi-Sugeno fuzzy, when the PMSM devices are required. This paper designs the controller of the PMSM with uncertain inertia and friction factors working under load noise in Web-based e-service systems. This controller is based on rotor field-oriented control (RFOC) structures, internal model control (IMC), and improved direct adaptive fuzzy (IDAF). In order to enhance the transient quality for the case of uncertain inertia and friction factors, we use the IDAF algorithm for the outer loop (speed loop). The IDAF is designed based on the direct adaptive fuzzy algorithm combined with the G-Fuzzy system for adjusting online updating adaption factors. The essence of IDAF is a self-learning and self-adaption system with enhanced adaptive ability through the G-Fuzzy system. For the inner loop (current loop), an improved IMC (IIMC) structure is proposed to reduce the effect of load noise. The IIMC combines the tradition IMC and a speed feedback loop to enhance the antiload noise ability of the system. The difference between our control structure and the traditional control structure is that the system could automatically realize antiload noise in the inner loop before adjusting the speed in the outer loop. This will create really high performances for PMSM control systems. We also demonstrate the effect of this control algorithm on PMSM-RFOC system control. The extensive simulation results demonstrate that the current response satisfies the condition of ability and settling time. Especially, the antiload noise ability and transient quality of the system are controlled independently. Thus, it is a solid foundation upon which to develop a high-quality PMSM electric drive in the e-service.

It is long known attackers may use forged source IP address to conceal their real locations. To capture the spoofers, a number of IP traceback mechanisms have been proposed. However, due to the challenges of deployment, there has been not a widely adopted IP traceback solution, at least at the Internet level. As a result, the mist on the locations of spoofers has never been dissipated till now. This paper proposes passive IP traceback (PIT) that bypasses the deployment difficulties of IP traceback techniques. PIT investigates Internet Control Message Protocol error messages (named path backscatter) triggered by spoofing traffic, and tracks the spoofers based on public available information (e.g., topology). In this way, PIT can find the spoofers without any deployment requirement. This paper illustrates the causes, collection, and the statistical results on path backscatter, demonstrates the processes and effectiveness of PIT, and shows the captured locations of spoofers through applying PIT on the path backscatter data set. These results can help further reveal IP spoofing, which has been studied for long but never well understood. Though PIT cannot work in all the spoofing attacks, it may be the most useful mechanism to trace spoofers before an Internet-level traceback system has been deployed in real.

Cloud-assisted vehicular delay tolerant networks (DTNs) have been utilized in wide-ranging applications where a continuous end-to-end connection is unavailable, the message transmission is fulfilled by the cooperation among vehicular nodes and follows a store-carry-and-forward manner, and the complex computational work can be delegated to the disengaged vehicles in the parking lots which constitute the potential vehicular cloud. Nevertheless, the existing incentive schemes as well as the packet forwarding protocols cannot well model continuous vehicle collaboration, resist vehicle compromise attacks and collusion attacks, leaving the privacy preservation issues untouched. In this paper, a novel threshold credit-based incentive mechanism (TCBI) is proposed based on the modified model of population dynamics to efficiently resist the node compromise attacks, stimulate the cooperation among intermediate nodes, maximize vehicular nodes’ interest, and realize the fairness of possessing the same opportunity of transmitting packets for credits. Then, a TCBI-based privacy-preserving packet forwarding protocol is proposed to solve the open problem of resisting layer-adding attack by outsourcing the privacy-preserving aggregated transmission evidence generation for multiple resource-constrained vehicles to the cloud side from performing any one-way trapdoor function only once. The vehicle privacy is well protected from both the cloud and transportation manager. Finally, formal security proof and the extensive simulation show the effectiveness of our proposed TCBI in resisting the sophisticated attacks and the efficiency in terms of high reliability, high delivery ratio, and low average delay in cloud-assisted vehicular DTNs.

Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking of indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. In this paper, we introduce ACE: a system that uses a probabilistic energy-minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities' poses. A novel cross-calibration technique is introduced to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. We design an efficient energy-minimization function that can be mapped to a binary graph-cut problem whose solution has a linear complexity on average and a third order polynomial in the worst case. We further employ clustering on the estimated location candidates to reduce outliers and obtain more accurate tracking in the continuous space. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that ACE can achieve a multi-entity tracking accuracy of less than 1.3 m. This corresponds to at least 11.8 percent, and up to 33 percent, enhancement in median distance error over the state-of-the-art DF localization systems. In addition, ACEcan estimate the number of entities correctly to within one difference error for 100 percent of the time. This highlights that ACE achieves its goals of having an accurate and efficient multi-entity indoors localization.

As the foundation of routing, topology control should minimize the interference among nodes, and increase the network capacity. With the development of mobile ad hoc networks (MANETs), there is a growing requirement of quality of service (QoS) in terms of delay. In order to meet the delay requirement, it is important to consider topology control in delay constrained environment, which is contradictory to the objective of minimizing interference. In this paper, we focus on the delay-constrained topology control problem, and take into account delay and interference jointly. We propose a cross-layer distributed algorithm called interference-based topology control algorithm for delay-constrained (ITCD) MANETs with considering both the interference constraint and the delay constraint, which is different from the previous work. The transmission delay, contention delay and the queuing delay are taken into account in the proposed algorithm. Moreover, the impact of node mobility on the interference-based topology control algorithm is investigated and the unstable links are removed from the topology. The simulation results show that ITCD can reduce the delay and improve the performance effectively in delay-constrained mobile ad hoc networks.

This paper focuses on incentivizing cooperative behavior in community-based autonomous networking environments (like mobile social networks, etc.), in which through dynamically forming virtual and/or physical communities, users voluntarily participate in and contribute resources (or provide services) to the community while consuming. Specifically, we proposed a simple but effective EGT (Evolutionary Game Theory)-based mechanism, VPEF (Voluntary Principle and round-based Entry Fee), to drive the networking environment into cooperative. VPEF builds incentive mechanism as two simple system rules: The first is VP meaning that all behaviors are voluntarily conducted by users: Users voluntarily participate (after paying round-based entry fee), voluntarily contribute resource, and voluntarily punish other defectors (incurring extra cost to those so-called punishers); The second is EF meaning that an arbitrarily small round-based entry fee is set for each user who wants to participate in the community. We presented a generic analytical framework of evolutionary dynamics to model VPEF scheme, and theoretically proved that VPEF scheme's efficiency loss defined as the ratio of system time, in which no users will provide resource, is $4/(8+M)$. $M$ is the number of users in community-based collaborative system. Finally, the simulated results using content availability as an example verified our theoretical analysis.

Our work in this paper stems from our insight that recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. Therefore, it may be feasible that we could adapt these techniques in such a way that they will provide valuable solutions to certain tricky problems in the wireless sensor network (WSN) domain. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL leverages one result from OVR to prove that the problem formulation is inherently NP-hard. Therefore, we proposed both a centralized heuristic to reduce its computational overhead and a distributed heuristic to make the algorithm scalable for large-scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to compare its performance to related protocols in both simulations and a hardware testbed.

Energy efficiency is one of the key objectives in data gathering in wireless sensor networks (WSNs). Recent research on energy-efficient data gathering in WSNs has explored the use of Compressive Sensing (CS) to parsimoniously represent the data. However, the performance of CS-based data gathering methods has been limited since the approaches failed to take advantage of judicious network configurations and effective CS-based data aggregation procedures. In this article, a novel Hierarchical Data Aggregation method using Compressive Sensing (HDACS) is presented, which combines a hierarchical network configuration with CS. Our key idea is to set multiple compression thresholds adaptively based on cluster sizes at different levels of the data aggregation tree to optimize the amount of data transmitted. The advantages of the proposed model in terms of the total amount of data transmitted and data compression ratio are analytically verified. Moreover, we formulate a new energy model by factoring in both processor and radio energy consumption into the cost, especially the computation cost incurred in relatively complex algorithms. We also show that communication cost remains dominant in data aggregation in the practical applications of large-scale networks. We use both the real-world data and synthetic datasets to test CS-based data aggregation schemes on the SIDnet-SWANS simulation platform. The simulation results demonstrate that the proposed HDACS model guarantees accurate signal recovery performance. It also provides substantial energy savings compared with existing methods.

Data collection is a crucial operation in wireless sensor networks. The design of data collection schemes is challenging due to the limited energy supply and the hot spot problem. Leveraging empirical observations that sensory data possess strong spatiotemporal compressibility, this paper proposes a novel compressive data collection scheme for wireless sensor networks. We adopt a power-law decaying data model verified by real data sets and then propose a random projection-based estimation algorithm for this data model. Our scheme requires fewer compressed measurements, thus greatly reduces the energy consumption. It allows simple routing strategy without much computation and control overheads, which leads to strong robustness in practical applications. Analytically, we prove that it achieves the optimal estimation error bound. Evaluations on real data sets (from the GreenOrbs, IntelLab and NBDC-CTD projects) show that compared with existing approaches, this new scheme prolongs the network lifetime by 1.5X to 2X for estimation error 5-20 percent.

Unlike traditional epidemic virus spreading, behavior diffusion in social networks is conducted by rational users, who would make strategic choices instead of being randomly infected with some probability. Specifically, individuals always try to maximize their utilities through rationally selecting specific behaviors (adopting a new product, or spreading a rumor, etc.). However, utility obtained by an individual, naturally contains uncertainty, and it may stem from two sources: users' imperfect and incomplete knowledge about others, and the inherently stochastic property in human behavior. Thus, it is imperative to model and analyze the diffusion pattern under the resulting uncertainty in social networks, which, however, has not yet been deeply examined by the existing works. This paper deeply explores the pattern of gossip diffusion in social networks when uncertainty exists in users' decision making. In detail, the innovative results provided in this paper are: first, inspired by random utility theory, we formulate the diffusion model based on mixed logit model that allows for user's uncertainty in determining whether to adopt a specific strategy; second, the formal analysis framework characterizing the diffusion process is derived through the approximation method of mean field theory; finally, we explore the extensive applicability of our proposed analysis framework through modeling rumor diffusion in social networks as a coordination game. Our findings are, for various structural characteristics, small uncertainty can significantly speed up the diffusion of gossip; furthermore, social networks with scale-free property can facilitate the gossip diffusion in the easiest way, but, the range of uncertainty factor that can maximize gossip diffusion is the smallest. The obtained results perfectly comply with the philosophical saying about rumor diffusion in real social life: easy come, easy go.

Modern information and communication technologies provide digital traces of human interactive activities, which offer novel avenues to map and analyze temporal features of human interaction networks. This paper explores mesoscopic patterns of human interactive activities from six real-world interaction networks with temporal-topological isomorphic subgraphs, i.e., temporal motifs. We discover two dominant mutual motifs, “Star,” “Ordered-chain,” and one dominant directed motif, “Ping-Pong,” which characterize the interactive patterns of “Leader,” “Queue,” and “Feedback,” respectively. Moreover,temporal dynamics shows that bursts are universal in human mesoscopic patterns, and the evolution of three dominant temporal motifs indicates the existence of characteristic time. Finally, we analyze temporal robustness and generalization to verify that 3-event temporal motifs are a simple yet powerful tool to capture the mesoscopic patterns of human interactive activities.

Cloud providers provision their various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances which are then allocated to the users. We design online mechanisms for VM provisioning and allocation in clouds that consider several types of available resources. Our proposed online mechanisms make no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed mechanisms are invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanisms allocate VM instances to selected users for the period they are requested for, and ensure that the users will continue using their VM instances for the entire requested period. In addition, the mechanisms determine the payment the users have to pay for using the allocated resources. We prove that the mechanisms are incentive-compatible, that is, they give incentives to the users to reveal their true valuations for their requested bundles of VM instances. We investigate the performance of our proposed mechanisms through extensive experiments.

This paper focuses on push-pull hybrid communication in a cloud-integrated sensor networking architecture, called Sensor-Cloud Integration Platform as a Service (SC-iPaaS). SC-iPaaS consists of three layers: sensor, edge and cloud layers. The sensor layer consists of wireless body sensor networks, each of which operates several sensors for a homebound patient for a remote physiological and activity monitoring. The edge layer consists of sink nodes that collect sensor data from sensor nodes in the sensor layer. The cloud layer hosts cloud applications that obtain sensor data through sink nodes in the edge layer. This paper formulates an optimization problem for SC-iPaaS to seek the optimal data transmission rates for individual sensor and edge nodes and solves the problem with respect to multiple objectives (e.g., data yield, bandwidth consumption and energy consumption) subject to given constraints. This paper sets up a simulation environment that performs remote multi-patient monitoring with five on-body sensors including ECG, pulse oximeter and accelerometer per a patient. Simulation results demonstrate that the proposed optimizer successfully seeks Pareto-optimal data transmission rates for sensor/sink nodes against data request patterns placed by cloud applications. The results also confirm that the proposed optimizer outperforms an existing well-known optimization algorithm.

Group Counseling Optimizer (GCO) is a recently proposed population-based metaheuristics that simulates the ability of human beings to solve problems through counseling within a group. It is motivated by the fact that the human thinking ability is often predicted to be the most rational. This research article examines the performance of GCO on the benchmark test suite designed for the CEC 2014 Competition for Computational Expensive Optimization. Experimental results on 24 black-box optimization problems (8 test problems with 10, 20 and 30 dimensions) have been tabulated along with the algorithm complexity metrics. Additionally we investigate the parametric behavior of GCO based on these test instances.

We present a novel human society inspired algorithm for solving single-objective bound constrained optimization problems. The proposed Monarchy Driven Optimization (MDO) algorithm is a population-based iterative global optimization technique for multi-dimensional and multi-modal problems. At its core, this technique introduces a monarchial society where the outlook of its population is fashioned by the thoughts of individuals and the monarch. A detailed study including the tuning of MDO parameters is presented along with the theory. It is applied to standard benchmark functions comprising unimodal and multi-modal as well as rotated functions. The results section suggests that, in most instances, MDO outperforms other well-known techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Gravitational Search Algorithm (GSA), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Artificial Bee Colony (ABC) optimization in terms of final convergence value and mean convergence value, thus proves to be a robust optimization technique.

Nowadays, most popular social networking services adopt centralized architecture, in which continual Internet connectivity is prerequisite for each user to exploit those services, and centralized servers are used for storage and processing of all application/context data, even though mobile users are within proximity area (like campus, event spot, and community), and can directly exchange media through various wireless technologies (e.g., Bluetooth, Wi-Fi Direct, etc.). On one hand, the omniscient centralized server may cause serious privacy concern, due to the fact that it collects and stores all users' data (messages, profiles, location, relations, etc.), On the other hand, transmitting a large amount of media generated by users in proximity through Internet, would not only bring a lot of pressure to the network infrastructure and service provider, but incur heavy data traffic cost to users. In this paper, our contributions are twofold. First, we propose a Wi-Fi Direct based P2P social networking framework, which enables direct data exchange among users without using infrastructure network when users are located in proximity, and provide solutions to two core problems in this framework, i.e., discoverability and privacy. Second, the prototype of this framework is preliminarily implemented in Android, which is composed of the following functions: localization based on Google Geocoding, Chat, and file-sharing component supporting intermittent transmission.

Distance health monitoring is becoming an attractive option for both the patients and the medical sector staff. However, these systems face many design challenges including the large amount of processed data, users' mobility, and network coverage area. Accordingly, integrating cloud computing with Wireless Body Area Networks (WBANs) is amongst the best solutions to promote health monitoring systems. In this paper, we propose a cloud-based real-time remote health monitoring system. Several novel techniques are developed to optimize the performance of the proposed system. First, we utilize the concept of data classification and aggregation to reduce the traffic flow in the cloud. Second, a dynamic channel assignment policy is developed to distribute the users' WBANs among the available frequency channels to manage interference. Third, to optimize the delay of the delivered data a delay-aware routing metric is proposed to be used in the multi-hop routing. In addition, this metric is further utilized by the association protocols used by the WBANs to connect with the cloud. The simulation results prove the efficiency of the proposed architecture in optimizing the end-to-end delay, managing interference, maximizing the network capacity, and tracking the mobility of the users.

Small cells are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to the quick variations and burstiness of their DL/UL traffic. In this paper, and for the first time, we perform a theoretical analysis on the signal-to-interference-plus-noise ratio performance of dynamic TDD transmissions in homogeneous small cell networks. The significant accuracy of the proposed theoretical analysis is corroborated by simulations. Based on our work, a partial interference cancellation scheme is proposed to reduce the outage probabilities, and it is shown that cancelling one interfering small cell on average is good enough when the traffic load is low to medium to avoid radio link failures.

In this paper, we argue that by carefully considering spatial reusability of the wireless communication media, we can tremendously improve the end-to-end throughput in multi-hop wireless networks. To support our argument, we propose spatial reusability-aware single-path routing (SASR) and anypath routing (SAAR) protocols, and compare them with existing single-path routing and anypath routing protocols, respectively. Our evaluation results show that our protocols significantly improve the end-to-end throughput compared with existing protocols. Specifically, for single-path routing, the throughput gain is up to 2.9× for anypath routing, the throughput gain is up to 62.7%.

Future wireless communication systems feature heterogeneous networks (HetNets), with small cells underlying existing macrocells. In order to maximize the off-loading benefits of small cells and mitigate the interference from the macrocell tier to the small cell tier, cell range expansion (CRE) and almost blank subframes (ABSs) have been designed for small cells and macrocells, respectively. Besides, enhanced 4th generation (4G) networks are also envisaged to adopt dynamic time division duplexing (TDD) transmissions for small cells to adapt their communication service to the fast variation of downlink (DL) and uplink (UL) traffic demands. However, up to now, it is still unclear whether it is technically feasible to introduce dynamic TDD into HetNets. In this paper, we investigate this fundamental problem and propose a feasible scheme to enable small cell dynamic TDD transmissions in HetNets. Simulation results show that compared with the static TDD scheme with CRE and ABS operations, the proposed scheme can achieve superior performance gains in terms of DL and UL packet throughputs when the traffic load is low to medium, at the expense of introducing the DL-to-UL interference cancellation (IC) functionality in macrocell base stations (BSs) and/or small cell BSs.

The performance analysis of optimal relay decode-and-forward (DF) selection for both the full-duplex (FD) and half-duplex (HD) relaying modes is studied, with some important factors such as the distributions of the received signal-to-noise ratio (SNR), the outage probability and the average channel capacity, etc., being taken into account. Different from the conventional relay selection schemes, the trade-off between the FD and HD modes is studied, with the former suffering from the impact of residual self-interference while the latter consuming more channel resources than the former by allocating two orthogonal channels for transmission and reception. The optimal power allocation (OPA) subject to individual power constrains (IPC) and sum power constrains (SPC) are also analyzed in the proposed FD scheme. In particular, the exact closed-form expressions for outage probability of the proposed FD relay selection scheme over independent and identically distributed (i.i.d.) Rayleigh fading channels are derived in this paper, with the validity of the proposed analysis being proven by simulation. It is also shown that the proposed FD scheme outperforms the HD mode in terms of average channel capacity by about 33.1%, provided that the self-interference can be successfully suppressed below the noise power level.

The power consumption of enormous network devices in data centers has emerged as a big concern to data center operators. Despite many traffic-engineering-based solutions, very little attention has been paid on performance-guaranteed energy saving schemes. In this paper, we propose a novel energy-saving model for data center networks by scheduling and routing "deadline-constrained flows" where the transmission of every flow has to be accomplished before a rigorous deadline, being the most critical requirement in production data center networks. Based on speed scaling and power-down energy saving strategies for network devices, we aim to explore the most energy efficient way of scheduling and routing flows on the network, as well as determining the transmission speed for every flow. We consider two general versions of the problem. For the version of only flow scheduling where routes of flows are pre-given, we show that it can be solved polynomially and we develop an optimal combinatorial algorithm for it. For the version of joint flow scheduling and routing, we prove that it is strongly NP-hard and cannot have a Fully Polynomial-Time Approximation Scheme (FPTAS) unless P=NP. Based on a relaxation and randomized rounding technique, we provide an efficient approximation algorithm which can guarantee a provable performance ratio with respect to a polynomial of the total number of flows.

In this paper, we tackle the problem of stimulating smartphone users to join mobile crowdsourcing applications with smartphones. Different from existing work of mechanism design, we uniquely take into consideration the crucial dimension of location information when assigning sensing tasks to smartphones. However, the location awareness largely increases the theoretical and computational complexity. In this paper, we introduce a reverse auction framework to model the interactions between the platform and the smartphones. We rigorously prove that optimally determining the winning bids is NP hard. In this paper we design a mechanism called TRAC which consists of two main components. The first component is a near-optimal approximate algorithm for determining the winning bids with polynomial-time computation complexity, which approximates the optimal solution within a factor of 1 + ln(n), where n is the maximum number of sensing tasks that a smartphone can accommodate. The second component is a critical payment scheme which, despite the approximation of determining winning bids, guarantees that submitted bids of smartphones reflect their real costs of performing sensing tasks. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanism achieves truthfulness, individual rationality and high computation efficiency.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB2F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB2F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.

In increasingly popular participatory sensing systems, new challenges are arising to select the most appropriate participants when considering their hand-held smart device's different energy conditions, uncontrollable mobility pattern, and associated sensing capabilities to best satisfy the quality-of-information (QoI) requirements of sensing tasks. This paper proposes a QoI-aware energy-efficient participant selection strategy, where four key design elements are proposed. First is QoI satisfaction metric of a sensing task that uses the data granularity and quantity collected by participants to measure to what extend the task's QoI requirements are satisfied. Second is an “energy consumption index”, which estimates the impact of energy cost during the data collection on different participant's smart devices with different remaining energy levels. Third is the estimation of the collected amount of data by participants, where a probability-based movement model is proposed. Fourth is the proposal of a multi-objective constrained optimization problem for participant selection, where task QoI requirements and energy consumption index of all participants are taken as optimization objectives, and solved by our proposed suboptimal, easy-to-implement solution. Real and extensive trace-based experiments show that, the proposed participant selection scheme can well balance the trade-off between the task QoI and energy consumptions by selecting most efficient participants, compared with existing schemes.

In this paper, we present a communication protocol between a pair of biological nanomachines, i.e., a transmitter and a receiver, built upon molecular communications in an aqueous environment. In our proposal, the receiver, acting as a control node, sends a connection setup signal to the transmitter, which stokes molecules, to start molecule transmission. The molecules transmitted by the transmitter propagate in the environment and are absorbed by the receiver through its receptors. When the receiver absorbs the desired quantity of molecules, it releases a tear-down signal to notify the transmitter to stop the transmission. The proposed protocol implements a bidirectional communication by using a number of techniques originally designed for the TCP. In fact, the proposed protocol is connection-oriented and uses the TCP-like probing to find a suitable transmission rate between the transmitter and the receiver to avoid receiver congestion. Unlike the TCP, however, explicit acknowledgments are not used since they would degrade the communication throughput due to the large delay, which is a characteristic feature of molecular communications. Thus, the proposed protocol uses implicit acknowledgments, and feedback signals are sent by the receiver to throttle the transmission rate at the transmitter, i.e., explicit negative feedback. We also present the results of an extensive simulation campaign, used to validate the proposed protocol and to properly dimension the main protocol parameters.

With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

Infrastructure-as-a-Service (IaaS) cloud computing offers customers (tenants) a scalable and economical way to provision virtual machines (VMs) on demand while charging them only for the leased computing resources by time. However, due to the VM contention on shared computing resources in datacenters, this new computing paradigm inevitably brings noticeable performance overhead (i.e., unpredictable performance) of VMs to tenants, which has become one of the primary issues of the IaaS cloud. Consequently, increasing efforts have recently been devoted to guaranteeing VM performance for tenants. In this survey, we review the state-of-the-art research on managing the performance overhead of VMs, and summarize them under diverse scenarios of the IaaS cloud, ranging from the single-server virtualization, a single mega datacenter, to multiple geodistributed datacenters. Specifically, we unveil the causes of VM performance overhead by illustrating representative scenarios, discuss the performance modeling methods with a particular focus on their accuracy and cost, and compare the overhead mitigation techniques by identifying their effectiveness and implementation complexity. With the obtained insights into the pros and cons of each existing solution, we further bring forth future research challenges pertinent to the modeling methods and mitigation techniques of VM performance overhead in the IaaS cloud.

In this paper, we investigate a market equilibrium in multichannel sharing cognitive radio networks (CRNs): it is assumed that every subchannel is orthogonally licensed to a single primary user (PU), and can be shared with multiple secondary users (SUs). We model this sharing as a spectrum market where PUs offer SUs their subchannels with limiting the interference from SUs; the SUs purchase the right to transmit over the subchannels while observing the inference limits set by the PUs and their budget constraints. Moreover, we consider each SU limits the total interference that can be invoked from all other SUs, and assume that every transmitting SU marks the interference charges to other transmitting SUs. The utility function of SU is defined as least achievable transmission rate, and that of PU is given by the net profit. We define a market equilibrium in the context of extended Fisher model, and show that the equilibrium is yielded by solving an optimization problem, Eisenberg-Gale convex program. To make the solutions of the convex program meet the market equilibrium, we apply monotone-transformation to the utility function of each SU. Furthermore, we develop a distributed algorithm that yields the stationary solutions asymptotically equivalent to the solutions given by the convex program.

Decreasing the soaring energy cost is imperative in large data centers. Meanwhile, limited computational resources need to be fairly allocated among different organizations. Latency is another major concern for resource management. Nevertheless, energy cost, resource allocation fairness, and latency are important but often contradicting metrics on scheduling data center workloads. Moreover, with the ever-increasing power density, data center operation must be judiciously optimized to prevent server overheating. In this paper, we explore the benefit of electricity price variations across time and locations. We study the problem of scheduling batch jobs to multiple geographically-distributed data centers. We propose a provably-efficient online scheduling algorithm - GreFar - which optimizes the energy cost and fairness among different organizations subject to queueing delay constraints, while satisfying the maximum server inlet temperature constraints. GreFar does not require any statistical information of workload arrivals or electricity prices. We prove that it can minimize the cost arbitrarily close to that of the optimal offline algorithm with future information. Moreover, we compare the performance of GreFar with ones of a similar algorithm, referred to as T-unaware, that is not able to consider the server inlet temperature in the scheduling process. We prove that GreFar is able to save up to 16 percent of energy-fairness cost with respect to T-unaware.

Profit is the main participation incentive for social network providers. Its reliance on user profiles, built from a wealth of voluntarily revealed personal information, exposes users to a variety of privacy vulnerabilities. In this paper, we propose to take first steps toward addressing the conflict between profit and privacy in geosocial networks. We introduce PROFILR, a framework for constructing location centric profiles (LCPs), aggregates built over the profiles of users that have visited discrete locations (i.e., venues). PROFILR endows users with strong privacy guarantees and providers with correctness assurances. In addition to a venue centric approach, we propose a decentralized solution for computing real time LCP snapshots over the profiles of colocated users. An Android implementation shows that PROFILR is efficient; the end-to-end overhead is small even under strong privacy and correctness assurances.

IP spoofing-based flooding attacks are a serious and open security problem on the current Internet. The best current antispoofing practices have long been implemented in modern routers. However, they are not sufficiently applied due to the lack of deployment incentives, i.e., an autonomous system (AS) can hardly gain additional protection by deploying them. In this paper, we propose mutual egress filtering (MEF), a novel antispoofing method, which provides continuous deployment incentives. The MEF is implemented on the AS border routers using access control lists (ACLs). It drops an outbound packet whose source address does not belong to the local AS if the packet is related to a spoofing attack against other MEF-enabled ASes. By this means, only the deployers of the MEF can gain protection, whereas nondeployers cannot free ride. As more ASes deploy MEF, deployment incentives become higher. We present the system design of MEF, and propose an optimal prefix compression algorithm to compact the ACL into the routers' limited hardware resource. With theoretical analysis and simulations with real Internet data, our evaluation results show that MEF is the only method that achieves monotonically increasing deployment incentives for all types of spoofing attacks, and the system design is lightweight and practical. The prefix compression algorithm advances the state-of-the-art by generalizing the functionalities and reducing the overhead in both time and space.

Wireless sensor networks (WSNs) are prone to propagating malware because of special characteristics of sensor nodes. Considering the fact that sensor nodes periodically enter sleep mode to save energy, we develop traditional epidemic theory and construct a malware propagation model consisting of seven states. We formulate differential equations to represent the dynamics between states. We view the decision-making problem between system and malware as an optimal control problem; therefore, we formulate a malware-defense differential game in which the system can dynamically choose its strategies to minimize the overall cost whereas the malware intelligently varies its strategies over time to maximize this cost. We prove the existence of the saddle-point in the game. Further, we attain optimal dynamic strategies for the system and malware, which are bang-bang controls that can be conveniently operated and are suitable for sensor nodes. Experiments identify factors that influence the propagation of malware. We also determine that optimal dynamic strategies can reduce the overall cost to a certain extent and can suppress the malware propagation. These results support a theoretical foundation to limit malware in WSNs.

The nine papers in this special issue cover multiple aspects of emerging wireless body area networks (WBANs) for ubiquitous healthcare.

Recently, Cloud Computing is attracting great attention due to its provision of configurable computing resources. MapReduce (MR) is a popular framework for data-intensive distributed computing of batch jobs. MapReduce suffers from the following drawbacks: 1. It is sequential in its processing of Map and Reduce Phases 2. Being cluster based, its scalability is relatively limited. 3. It does not support flexible pricing. 4. It does not support stream data processing. We describe Cloud MapReduce (CMR), which overcomes these limitations. Our results show that CMR is more efficient and runs faster than other implementations of the MR framework. In addition to this, we showcase how CMR can be further enhanced to: 1. Support stream data processing in addition to batch data by parallelizing the Map and Reduce phases through a pipelining model. 2. Support flexible pricing using Amazon Cloud's spot instances and to deal with massive machine terminations caused by spot price fluctuations. 3. Improve throughput and speed-up processing over traditional MR by more than 30% for large data sets. 4. Provide added flexibility and scalability by leveraging features of the cloud computing model. Click-stream analysis, real-time multimedia processing, time-sensitive analysis and other stream processing applications can also be supported.

The Minimal Exposure Problem (MEP), which corresponds to the quality of coverage, is a fundamental problem in wireless sensor networks. This paper exploits a biological model of physarum to design a novel biology-inspired optimization algorithm for MEP. We first formulate MEP and the related models, and then convert MEP into the Steiner problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a biological optimization solution to find the minimal exposure road-network among multiple points of interest, and present a Physarum Optimization Algorithm (POA). Furthermore, POA can be used for solving the general Steiner problem. Extensive simulations demonstrate that our proposed models and algorithm are effective for finding the road-network with minimal exposure and feasible for the Steiner problem.

Datacenter networking has gained increasing popularity in the past few years. While researchers paid considerable efforts to enhance the performance and scalability of datacenter networks, achieving reliable data delivery in these emerging networks with misbehaving routers and switches received far less attention. Unfortunately, documented incidents of router compromise underscore that the capability to identify adversarial routers and switches is an imperative and practical need rather than merely a theoretical exercise. To this end, data-plane fault localization (FL) aims to identify faulty links and is an effective means of achieving high network availability. However, existing secure FL protocols assume that the source node knows the entire outgoing path that delivers the source node's packets and that the path is static and long-lived. These assumptions are invalidated by the dynamic traffic patterns and agile load balancing commonly seen in modern datacenter networks. We propose the first secure FL protocol, DFL, with no requirements on path durability or the source node knowing the outgoing paths. Through a core technique we named delayed function disclosure, DFL incurs little communication overhead and a small, constant router state independent of the network size or the number of flows traversing a router.

Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links is affected or even jeopardized resulting in transmission failures by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with energy-efficiency, high throughput and fairness in lossy wireless networks. Building upon opportunistic routing and random linear network coding, CodePipe can not only eliminate coordination between nodes, but also improve the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer significant improvement in throughput, energy-efficiency and fairness. Moreover, we design an efficient online extension of CodePipe such that it can work in a dynamic network where nodes join and leave the network as time progresses. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols, MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.

Advancements in cloud computing enable the easy deployment of numerous services. However, the analysis of cloud service access platforms from a client perspective shows that maintaining and managing clients remain a challenge for end users. In this paper, we present the design, implementation, and evaluation of an asymmetric virtual machine monitor (AVMM), which is an asymmetric partitioning-based bare-metal approach that achieves near-native performance while supporting a new out-of-operating system mechanism for value-added services. To achieve these goals, AVMM divides underlying platforms into two asymmetric partitions: a user partition and a service partition. The user partition runs a commodity user OS, which is assigned to most of the underlying resources, maintaining end-user experience. The service partition runs a specialized OS, which consumes only the needed resources for its tasks and provides enhanced features to the user OS. AVMM considerably reduces virtualization overhead through two approaches: 1) Peripheral devices, such as graphics equipment, are assigned to be monopolized by a single user OS. 2) Efficient resource management mechanisms are leveraged to alleviate complicated resource sharing in existing virtualization technologies. We implement a prototype that supports Windows and Linux systems. Experimental results show that AVMM is a feasible and efficient approach to client virtualization.

This paper exploits the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. User Mobility introduces new complexities in enabling an optimal decomposition of tasks that can execute cooperatively on mobile clients and the tiered cloud architecture while considering multiple QoS goals such application delay, device power consumption and user cost/price. In this paper, we propose a novel framework to model mobile applications as a location-time workflows (LTW) of tasks, here user mobility patterns are translated to a mobile service usage patterns. We show that an optimal mapping of LTWs to tiered mobile cloud resources is an NP-hard problem. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (78% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing and video streaming applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint, Manhattan models and is resilient to errors/uncertainty in prediction of mobile user location-time workflows.

Differential Evolution (DE), a rather new members of the evolutionary computing paradigm, has rose to widespread popularity in a short time interval owing to its simple design and robust performance in a wide variety of problems. Inspired from the effect of the teacher on the knowledge gained by the learners in a class, we propose a teaching and learning based self-adaptive DE (TLBSaDE) for solving real parameter optimization problems. A benchmark suite consisting of 28 test problems, proposed for the CEC 2013 Special Session and Competition on Real Parameter Optimization, is used. The results are obtained on 10, 30 and 50 dimensional problems.

Estimation of Distribution algorithms (EDAs) are probabilistic-model based optimization techniques that exploit promising solution candidates by developing particles around them in accordance to a pre-specified distribution. This paper attempts to approach constrained optimization problems by an interdependent parallel functioning of a modified Gaussian distribution based EDA with differential mutation on the lines of rand/1 perturbation scheme. A modified penalty function free from scaling parameters has been proposed to deal with the constraints associated. The results have been collected from functional landscapes defined by the CEC 2010 benchmark and have been compared with existing state-of-the-art methods for constrained optimization.

Covariance Matrix Adaptation and Evolution Strategy (CMA-ES) is an efficient method of optimization that iteratively generates new individuals around an ever-adaptive recombination point. Although it ensures speed and high rate of exploitation, CMA-ES suffers a major drawback as the scheme of generating new members scattered around an influential mean may often lead to members drawn to local minima. The result is that while precision of better solutions increases, the ability to reform is lost. In this paper we incorporate a directional feature to the generation wise perturbation of individuals in standard version of CMA-ES that utilizes potentially useful information from previous generation to retain the influence of old recombination point. Coupled with a modified population size we attempt to form an algorithm that amalgamates the effectiveness of CMA-ES along with the ability to explore. The performance is tested on IEEE CEC (Congress on Evolutionary Computation) 2013 Special Session on Real-Parameter Optimization in 10, 30 and 50 dimensions. The results obtained clearly indicates that the proposed algorithm addressed as CMA-ES with Memory based Directed Individual Generation (CMA-ES-DIG) is able to perform excessively well on majority of the test cases in a statistically meaningful way.

Multi-Carrier Code Division Multiple Access (MC-CDMA) is an emerging wireless communication technology that incorporates the advantages of Orthogonal Frequency Division Multiplexing (OFDM) into the original Code Division Multiple Access (CDMA) technique. But it suffers from the inherent defect called Multiple Access Interference (MAI) due to inappropriate cross-correlation possessed by the different user codes. To reduce MAI, the multi-user detection (MUD) technique has already been proposed in which MAI is treated as noise. Due to high computational cost incorporated by the optimal MUD detector with increasing number of users, researchers are looking for sub-optimal MUD solutions. This paper proposes a binary adaptive Differential Evolution algorithm with a novel crossover strategy (MBDE_pBX) for multi-user detection in a synchronous MC-CDMA system. Since MUD detection in MC-CDMA systems is a problem in binary domain, a binary encoding rule is introduced which converts a binary domain problem of any number of dimensions into a 4-dimensional continuous domain problem. The simulation results show that this new binary Differential Evolution variant can achieve superior bit error rate (BER) performance within much lower optimum solution detection time outperforming its competitors as well as achieving 99.62% reduction in computational complexity as compared to the MUD scheme using exhaustive search.

In multimedia communication, various data rates, security strategies, and data qualities are required for different content forms such as text, audio, images, video, and interactivity content forms. In this paper, we propose a secure and robust approach to achieve the multimedia multiple access (MA) communication using self-encoded spread spectrum (SESS). In this proposed system, SESS multiple access (SESS-MA) is a novel approach to multimedia system due to its unique secure and flexible spreading nature. Iterative detection is applied for an improved multimedia quality of service (QoS) at the receiver. The number of iterations needed is evaluated separately according to different multimedia contents. Simulation studies demonstrate that the proposed scheme ensures satisfactory data quality, security, and robustness.

Cooperative communication has been proven effective in enhancing the performance of wireless networks. In this paper, we propose an adaptive multi-relay selection with power allocation mechanism to offer energy fairness at each node for a cooperative network. Unlike traditional approaches where all nodes are considered to transmit in a collaborative manner, we explicitly consider the situation where each node exhibits some degree of selfishness behavior. By introducing novel concepts of the selfishness index and utility function which denote node's benefit from cooperation, we show that the proposed cooperation scheme can well balance the energy consumption among nodes as well as maximize the network lifetime. Theoretical analysis and extensive simulation results are supplemented to show advantages of the proposed approach in power saving and power allocation fairness.

Due to various operational constraints on the unmanned autonomous vehicle (AUxV) networks operating in an adversarial environment, a fault-tolerant routing scheme is an imperative need. Looking at the risk involved in their applications such as search and rescue, threat surveillance, chemical and biohazard sampling, even a fault of minor nature in the system software/hardware may result in destructive consequences. The AUxV network member nodes vary in architecture, capability, application and power of their internal systems. In such a case it is important that the fault-tolerant scheme should take into consideration the kind of heterogeneity involved and should be able to perform in such a scenario as well. Therefore, to address these issues, in this paper we propose a cross-layer and learning automata (LA) based fault-tolerant routing algorithm for AUxVs, named as ULARC (Unmanned Vehicle Network with LA based Routing using Cross Layer Design). We use the theory of LA for the selection of optimal path for routing between source and destination. In this paper, we also focus on making our proposed strategy an energy-efficient one by using a cross-layer architecture for sleep scheduling of nodes. Further, we have devised an α-based scheduling scheme which further adds to the energy efficiency of our protocol by reducing the overhead on the network.

The paradigm of cloud computing has spontaneously prompted a wide interest in market-based resource allocation mechanisms by which a cloud provider aims at efficiently allocating cloud resources among potential users. Among these mechanisms, auction-style pricing policies, as they can effectively reflect the underlying trends in demand and supply for the computing resources, have attracted a research interest recently. This paper conducts the first work on a framework for truthful online cloud auctions where users with heterogeneous demands could come and leave on the fly. Our framework desirably supports a variety of design requirements, including (1) dynamic design for timely reflecting fluctuation of supply-demand relations, (2) joint design for supporting the heterogeneous user demands, and (3) truthful design for discouraging bidders from cheating behaviors. Concretely speaking, we first design a novel bidding language, wherein users' heterogeneous demands are generalized to regulated and consistent forms. Besides, building on top of our bidding language we propose COCA, an incentive-Compatible (truthful) Online Cloud Auction mechanism based on two proposed guidelines. Our theoretical analysis shows that the worst-case performance of COCA can be well-bounded. Further, in simulations the performance of COCA is seen to be comparable to the well-known off-line Vickrey-Clarke-Groves (VCG) mechanism [11].

Radio Signal Strength (RSS) based ranging is attractive by the low cost and easy deployment. In real environments, its accuracy is severely affected by the multipath effect and the external radio interference. The well-known fingerprint approaches can deal with the issues but introduce too much overhead in dynamic environments. In this paper, we attempt to address the issue along a completely different direction. We propose a new ranging framework called Fredi that exploits the frequency diversity to overcome the multi-path effect solely based on RSS measurements. We design a Discrete Fourier Transformation based algorithm and prove that it has the optimal solution under ideal cases. We further revise the algorithm to be robust to the measurement noises in practice. We implement Fredi on top of the USRP-2 platform and conduct extensive real environments in indoor environments. Experimental results show the superiority performance compared with the traditional methods.

In many wireless sensor network (WSN) applications, a subset of nodes (source nodes) are selected to sense the environment, generate data, and transmit them back to the sink over multiple hops. Many previous research efforts have tried to achieve trade-offs in terms of delay, energy cost, and load balancing for such data collection tasks. Our work in this paper stems from the insight that, recent research efforts on open vehicle routing (OVR) problems, an active area in operations research, are based on similar assumptions and constraints compared to sensor networks. This insight motivates us to adapt these techniques so that we can solve or prove certain challenging problems in WSN applications. To demonstrate that this approach is feasible, we develop one data collection protocol called EDAL, which stands for Energy-efficient Delay-aware Lifetime-balancing data collection. The algorithm design of EDAL borrows one research result from OVR to prove that its problem formulation is inherently NP-hard. We then proposed both a centralized heuristic to reduce its computational overhead, and a distributed heuristic to make the algorithm scalable for large scale network operations. We also develop EDAL to be closely integrated with compressive sensing, an emerging technique that promises considerable reduction in total traffic cost for collecting sensor readings under loose delay bounds. Finally, we systematically evaluate EDAL to demonstrate its performance superiority compared to related protocols.

Storing user friend lists, preferences and messages, online social networks have become a significant source of sensi-tive personal information. A recent addition to this space, geosocial networks (GSNs) such as Yelp [1] or Foursquare [2],

Cooperative communication (CC) has been proposed to achieve spatial diversity without requiring multiple antennas on a single device. Many efforts in exploiting the benefits of CC focus on improving the performance in terms of outage probability and channel capacity. However, the energy efficiency of CC, which is critical for the applications with energy constraints, has been little studied. In this paper, we study the problem of energy-efficient CC in cognitive radio networks by taking power control, relay assignment and channel allocation into consideration. We obtain the optimal power control for a single source-destination pair. After that, a heuristic algorithm is proposed to solve the the relay assignment and channel allocation problem for multiple source-destination pairs. Extensive simulations are conducted to show that the proposed algorithm can significantly improve the performance of energy efficiency over the direct transmission approach.

In this paper, we discuss issues concerned with transmission rate control in molecular communication, an emerging communication paradigm for bio-nanomachines in an aqueous environment. In molecular communication, a group of bio-nanomachines acting as senders transmit molecules, the molecules propagate in the environment, and another group of bio-nanomachines acting as receivers chemically react to the molecules propagating in the environment. In the model of molecular communication considered in this paper, senders may transmit molecules at a high rate to accelerate the receiver reactions or to increase the throughput. However, if the senders transmit molecules faster than the receivers react, the excess molecules remain in the environment and eventually degrade or diffuse away, which results in loss of molecules or degradation in efficiency. Such a potential issue associated with throughput and efficiency is in this paper discussed as an optimization problem. A mathematical expression for an upper-bound on the throughput and efficiency is first derived to provide an insight into the impact of model parameters. The optimal transmission rates that maximize the throughput and efficiency are then numerically calculated and presented, and throughput and efficiency are shown to be in trade-off relationships in a wide range of transmission rates. Further, two classes of feedback-based transmission rate control schemes are designed for autonomous bio-nanomachines to dynamically control their transmission rates, respectively based on negative and positive feedback from the receivers. The numerical evaluation of the two transmission rate control schemes is then shown to provide useful guidelines for application developers to satisfy their design goals.

In collaborative social networks (CSNs), autonomous individuals cooperate for their common reciprocity interests. The intrinsic heterogeneity of individuals' capability and willingness makes significant impact on the promotion of cooperation rate. In this paper, we propose a two-phase Heterogeneous Public Goods Game (HPGG) model to study the cooperation dynamics in CSNs. We introduce two factors to represent the heterogeneity of individual behaviors and the benefit-to-cost enhancement of population, respectively. Based on HPGG CSN model, we quantitatively investigate the relationship between cooperation rate and individuals' heterogeneous behaviors from an evolutionary game perspective. Simulations on the population structure of scale-free networks show the evolution of cooperation in CSNs has no-trivial dependence on the individuals' heterogeneous behaviors. Compared with standard PGG and single-phase heterogeneous PGG, HPGG provides a more precise mechanism to promote cooperation rate of CSNs. Finally, data traces collected from real experiments also demonstrate the preciseness of HPGG in formulating the cooperation dynamics on CSNs.

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

The wireless sensor network (WSN) technology spawns a surge of unforeseen applications. The diversity of these emerging applications represents the great success of this technology. A fundamental performance benchmark of such applications is topology control, which characterizes how well a sensing field is monitored and how well each pair of sensors is mutually connected in WSNs. This paper provides an overview of topology control techniques. We classify existing topology control techniques into two categories: network coverage and network connectivity. For each category, a surge of existing protocols and techniques are presented with the focus on blanket coverage, barrier coverage, sweep coverage, power management, and power control, five rising aspects that attract significant research attention in recent years. In this survey, we emphasize the basic principles of topology control to understand the state of the arts, while we explore future research directions in the new open areas and propose a series of design guidelines under this topic.

ABSTRACT To produce ciphertexts, two modes of encryption are applied—block ciphers, which encrypt a fixed size block of plaintext at a time, and stream ciphers, which encrypt stream data, one or more bits at a time. As one of stream ciphers, the cipher feedback (CFB) mode is implemented by a block cipher via multiple stages, and in each stage, 1 bit or a number of bits of plaintext are encrypted at a time. Throughout this paper, the study will focus upon the error performance of the stream‐based CFB under two sliding‐window protocols, go‐back‐N and selective‐reject, in an error channel in terms of throughput. We model the performance of the CFB in terms of application‐level throughput and derive the number of stages needed to achieve the optimal throughput, under a given error rate in an error channel. Copyright © 2012 John Wiley & Sons, Ltd.

Backward induction has led to some controversy in specific games, the surprise exam paradox and iterated prisoner's dilemma for example, despite its wide use in solving finitely repeated games with complete information. In this paper, a typical misuse of backward induction is revealed by analyzing the surprise exam paradox, and the reason why backward induction may fail is investigated. The surprise exam paradox represents a set of repeated games with strategy constraints and has not been fully investigated in game theory. The agents in real-world activities always face constraints in decision making, for example, a budget limitation. In a repeated game with strategy constraints, the players' choices in different stages are not independent and later choices depend on previous choices because of the strategy constraints. Backward induction cannot be applied in its normal use and it needs to be combined with Bayes' theorem in solving these kinds of problems. We also investigate how the strategy constraints influence the equilibrium and show how to solve repeated games with strategy constraints by analyzing a repeated battle of the sexes game with a budget constraint.

This paper studies a class of fast consensus algorithms for a group of identical multiagent systems each described by the linear state-space model. By using both the current and delayed state information, the proposed delay-induced consensus algorithm is shown to achieve synchronization with a faster convergence speed than the standard one when the eigenvalues of the open-loop system, control parameters, the Laplacian matrix of the network, and the delay satisfy certain conditions. In addition, some sufficient or necessary and sufficient conditions are established to guarantee the closed-loop stability of the delay-induced consensus algorithm, where an extra control parameter on the coupling strength is introduced to adjust the convergence speed of the closed-loop system flexibly. We then show that the delay-induced algorithm is robust to the small intrinsic communication or input delays, i.e., the proposed delay-induced consensus algorithm may also produce a faster convergence speed than the standard one even if there exist small intrinsic communication or input delays. Furthermore, we extend the results from the case of an undirected communication topology to those of a directed communication topology and a switching communication topology. Several simulation examples are presented to illustrate the theoretical results.

Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.

With the increase in commercial electronic equipment and its complicated control interfaces, how to design an effective and user-friendly control interface has become a topic for many researchers. This research introduces two-directional communication of an interactive graphical user interface on a universal remote control (URC). It is different from current URCs where users must often spend huge amounts of time setting the command codes and encoding each device. With the increase in the number of appliances that the controller needs to manage and the complicated and numerous control buttons, using such controllers often causes difficulties for users. This research employs a cross-platform with integration theories, so when a user wants to connect an appliance, both the appliance end and the controller end will build a two-directional connection through pairing over Radio Frequency for Consumer Electronics (RF4CE). After connection, the system will automatically set the communication protocol between the controller and the device. The appliance will automatically transmit its current state and service in the form of bundles to the controller, then the controller will project it onto an LCD screen. The controller can also show the number of appliances connected to the current position of the user, allowing the user to use one controller to control all home appliances with ease, achieving a simplified and instinctive control interface to build the integrated control environment for commercial appliances.

Fingerprint authentication for content protection in the human-machine systems, cybernetics, and computational intelligence is very popular. Because of the complex input contexts, low-quality input fingerprint images always exist with cracks and scars, dry skin, or poor ridges and valley contrast ridges. Usually, fingerprint images are enhanced by one stage in either the spatial or the frequency domain. However, the enhanced performances are not satisfactory because of the complicated ridge structures that are affected by unusual input contexts. In this paper, we propose a novel and effective two-stage enhancement scheme in both the spatial domain and the frequency domain by learning from the underlying images. To remedy the ridge areas and enhance the contrast of the local ridges, we first enhance the fingerprint image in the spatial domain with a spatial ridge-compensation filter by learning from the images. With the help of the first step, the second-stage filter, i.e., a frequency bandpass filter that is separable in the radial- and angular-frequency domains, is employed. It is noted that the parameters of the bandpass filters are learnt from both the original image and the first-stage enhanced image instead of acquiring from the original image solely. It enhances the fingerprint image significantly because of the fast and sharp attenuation of the filter in both the radial and the angular-frequency domains. Experimental results show that our proposed algorithm is able to handle various input image contexts and achieves better results compared with some state-of-the-art algorithms over public databases, and to improve the performances of fingerprint-authentication systems.

Traffic classification technique is an essential tool for network and system security in the complex environments such as cloud computing based environment. The state-of-the-art traffic classification methods aim to take the advantages of flow statistical features and machine learning techniques, however the classification performance is severely affected by limited supervised information and unknown applications. To achieve effective network traffic classification, we propose a new method to tackle the problem of unknown applications in the crucial situation of a small supervised training set. The proposed method possesses the superior capability of detecting unknown flows generated by unknown applications and utilizing the correlation information among real-world network traffic to boost the classification performance. A theoretical analysis is provided to confirm performance benefit of the proposed method. Moreover, the comprehensive performance evaluation conducted on two real-world network traffic datasets shows that the proposed scheme outperforms the existing methods in the critical network environment.

This paper proposes a semi-random backoff (SRB) method that enables resource reservation in contention-based wireless LANs. The proposed SRB is fundamentally different from traditional random backoff methods because it provides an easy migration path from random backoffs to deterministic slot assignments. The central idea of the SRB is for the wireless station to set its backoff counter to a deterministic value upon a successful packet transmission. This deterministic value will allow the station to reuse the time-slot in consecutive backoff cycles. When multiple stations with successful packet transmissions reuse their respective time-slots, the collision probability is reduced, and the channel achieves the equivalence of resource reservation. In case of a failed packet transmission, a station will revert to the standard random backoff method and probe for a new available time-slot. The proposed SRB method can be readily applied to both 802.11 DCF and 802.11e EDCA networks with minimum modification to the existing DCF/EDCA implementations. Theoretical analysis and simulation results validate the superior performance of the SRB for small-scale and heavily loaded wireless LANs. When combined with an adaptive mechanism and a persistent backoff process, SRB can also be effective for large-scale and lightly loaded wireless networks.

We propose a distributed and coordinated radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA)-based cellular networks to self-organize efficient and stable frequency reuse patterns. In the proposed radio resource allocation algorithm, each cell independently and dynamically allocates modulation and coding scheme (MCS), resource block (RB), and transmit power to its users in a way that its total downlink (DL) transmit power is minimized, while users' throughput demands are satisfied. Moreover, each cell informs neighboring cells of the RBs that have been scheduled for its cell-edge users' DL transmissions through message passing. Accordingly, the neighboring cells abstain from assigning high transmit powers to the specified RBs. Extensive simulation results attempt to demonstrate that DL power control on a per-RB basis may play a key role in future networks, and show that the distributed minimization of DL transmit power at each cell, supported by intercell interference coordination, is able to provide a 20% improvement of network throughput, considerably reduce the number of user outages, and significantly enhance spatial reuse, as compared to cutting-edge resource allocation schemes.

This article presents a novel optimization technique hybridizing the concepts of Genetic Algorithm (GA) and Lbest Particle Swarm Optimization (Lbest PSO). A new topology, namely ‘Dynamically Varying Sub-swarm’ has been incorporated in the search process and some selected crossover and mutation techniques have been used for generation updating. This novel hybridized approach simultaneously ensures a robust search process, a quick convergence and a wide variety of real life applications. Simulations performed over various benchmark functions with the proposed method have been compared with other existing strong algorithms. Experimental results support the claim of proficiency of our algorithm over other existing techniques in terms of robustness, fast convergence and, most importantly its optimal search behavior.

We present an energy efficient sensor manager for differentiated coverage of dynamic object group changing their positions with time. The information about the location of the object group is provided to the sensor manager. The manager invokes optimization algorithm whenever the obtained coverage falls below a threshold to sleep schedule the sensor network. Multi-objective Optimization (MO) algorithms help in finding a better trade-off among energy consumption, lifetime, and coverage. Here the motion of the particle is modeled to follow a polynomial variation and with a constant acceleration. We formulate the scheduling problem as a combinatorial, constrained and multi-objective optimization problem with energy and non-coverage as the two objectives to be minimized. The proposed scheme uses a recent variant of a powerful MO algorithm known as Decomposition based Multi-Objective Evolutionary Algorithm (MOEA/D). Systematic comparison with the original MOEA/D and another well-known MO algorithm, NSGA-II (Non-dominated Sorting Genetic Algorithm) quantifies the superiority of the proposed approach.

The concept of Quality of Service (QoS) offers different service levels to the network users. Through Service Level Specifications (SLSs), the users in a wireless network, which supports QoS, are able to express, at run-time, their expected service requirements through well defined parameters. Conventional QoS parameters, such as throughput, delay, jitter, packet loss rates, and so forth, are used for reliably ensuring a certain service level with respect to reliability and/or performance. However, most existing researches have ignored tunable security as a Quality of Service (QoS) parameter. The biggest challenge of integrating QoS and security parameters consists in their contrasting goals. This paper presents an idea to permit the users of an IEEE 802.11 Wireless Local Area Network (WLAN) to specify their security and QoS requirements in their Service Level Specifications (SLSs). Then, a game theoretic approach is presented so that the system can reach Service Level Agreement (SLA) with the users to ascertain a balanced set of security and QoS parameters for the users. The effectiveness of the proposed approach is verified through computer simulations.

A novel cooperative transmission protocol is proposed for multiple access scenarios, where multiple users communicate with a common destination with the assistant of multiple half-duplex relays. The proposed alternative relaying decode-and-forward (ARDF) protocol can achieve the full diversity gain by applying superposition coding at each transmitter, which also utilizes the linear zero-forcing detection at each relay to combat a serious inter-relay interference. Different to the previous uplink protocols, the proposed protocol can exploit the cooperation involving both the relays and sources, which guarantees the proposed scheme to approach the optimal multiple-input singleoutput upper bound even with general inter-relay interference. Analytical and numerical results have been provided to demonstrate the performance of the proposed protocol.

Multicast is an important mechanism in modern wireless networks and has attracted significant efforts to improve its performance with different metrics including throughput, delay, energy efficiency, etc. Traditionally, an ideal loss-free channel model is widely used to facilitate routing protocol design. However, the quality of wireless links would be affected or even jeopardized by many factors like collisions, fading or the noise of environment. In this paper, we propose a reliable multicast protocol, called CodePipe, with advanced performance in terms of energy-efficiency, throughput and fairness in lossy wireless networks. Built upon opportunistic routing and random linear network coding, CodePipe not only simplifies transmission coordination between nodes, but also improves the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities. In particular, four key techniques, namely, LP-based opportunistic routing structure, opportunistic feeding, fast batch moving and inter-batch coding, are proposed to offer substantial improvement in throughput, energy-efficiency and fairness. We evaluate CodePipe on ns2 simulator by comparing with other two state-of-art multicast protocols, MORE and Pacifier. Simulation results show that CodePipe significantly outperforms both of them.

Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.

Nature is an invaluable source of inspiration for long-standing computational problems. Genetic algorithms and ant colony optimization are the well-known successes. Drawing the inspirations from these successful experience, we exploit the computing model in a single-celled organism -- physarum polycephalum, and further design a new heuristic algorithm, Physarum Optimization, to the minimal exposure problem of wireless sensor networks. More importantly, our proposed physarum optimization algorithm can be also applied to the general Steiner tree problem.

Although wireless sensor networks (WSNs) are powerful in monitoring physical events, the data collected from a WSN are almost always incomplete if the surveyed physical event spreads over a wide area. The reason for this incompleteness is twofold: i) insufficient network coverage and ii) data aggregation for energy saving. Whereas the existing recovery schemes only tackle the second aspect, we develop Dual-lEvel Compressed Aggregation (DECA) as a novel framework to address both aspects. Specifically, DECA allows a high fidelity recovery of a widespread event, under the situations that the WSN only sparsely covers the event area and that an in-network data aggregation is applied for traffic reduction. Exploiting both the low-rank nature of real-world events and the redundancy in sensory data, DECA combines matrix completion with a fine-tuned compressed sensing technique to conduct a dual-level reconstruction process. We demonstrate that DECA can recover a widespread event with less than 5% of the data (with respect to the dimension of the event) being collected. Performance evaluation based on both synthetic and real data sets confirms the recovery fidelity and energy efficiency of our DECA framework.

Location prediction using mobile phone traces has attracted increasing attention. Owing to the irregular user mobility patterns, it still remains challenging to predict user location. Our empirical study in this paper shows that the call patterns are strongly correlated with co-locate patterns (i.e., visiting the same cell tower at the same period), and the call patterns mainly affect user short-time mobility. On top of these findings, we propose NextMe --- a novel scheme to enhance the location prediction accuracy by leveraging the social interplay revealed in the cellular calls. To identify when the social interplay will affect user mobility, we introduce the concepts of the Critical Call Pattern (CCP), and the Critical Call (CC). We validate NextMe with the MIT Reality Mining dataset, involving 350,000-hour activity logs of 106 persons, and 112,508 cellular calls. Experimental results show that the social interplay significantly improves the accuracy.

This paper focuses on throughput and efficiency of molecular communication between a pair of sender and receiver nanomachines. In the molecular communication considered in this paper, the sender transmits molecules at a fixed rate, the molecules propagate in the environment, and the receiver captures and processes the molecules following simple enzyme kinetics. We define throughput as the average number of molecules processed by the receiver per unit time, and efficiency as the throughput divided by the number of molecules transmitted by the sender per unit time. An upper bound on throughput and efficiency at steady-state are first derived. Simulation results are then provided to show that the throughput increases as the transmission rate increases and that the efficiency has an optimal transmission rate to achieve the maximum.

In cloud computing environment, various innovative multimedia (text, image, audio, video, graph, etc.) services appear. For example, users may work on one document or drawing together over Internet, users can push their complicated computing tasks to cloud servers, and users can forward their multimedia content (e.g., TV program) to cloud servers with the aim of obtaining the analyzed result (e.g., a short summarization). To support these services, various computing or management techniques are required, for example, multimedia processing, coding, communication, storage and access. Additionally, in cloud computing, security and privacy become very important issues, for example, user authentication and authorization, information exchange between clients and cloud servers, privacy protection of user-uploaded content, etc. This special issue aims to collect the best papers contributed by leading experts, and expose the readership to the latest research results on multimedia computing and management in cloud computing environment. It includes a number of related topics, demonstrates pioneer work, investigates the novel solutions and discusses the future trends in this field. This issue is composed of five papers carefully selected. Each paper is reviewed by at least two experts, with at least two rounds. The first paper, ‘Multimedia Applications and Security in MapReduce: Opportunities and Challenges’ by Z. Yu et al. 1, reviews cloud-based multimedia applications in MapReduce including texts, images, audios and videos, and investigates the arising security issues and potential countermeasures. Since MapReduce is a popular programming model for distributed storage and computation in the cloud, the paper may provide valuable information to readers. In the second paper, ‘Optimizing Queries with Expensive Video Predicates in Cloud Environment’ by L. Yu et al. 2, the methods for processing video data and executing expensive video predicates in a cloud environment are proposed to solve the real time queries of video surveillance in supermarkets. These methods prove to significantly reduce the amount of video data transmission and the number of expensive video predicates operations. It may be a good example of cloud-based media computing. The third paper, ‘Jump-start Cloud: Efficient Deployment Framework for Large-Scale Cloud’ by Y. Lin et al. 3 designs the jump-start cloud that uses an efficient cloud deployment scheme to minimize the cloud time. It may be beneficial to minimize cloud time for clouds serving multimedia applications, as they occupy much more significant cloud resources, such as I/O and networking bandwidth and computational costs in processing or transcoding multimedia contents. The tests through a Hadoop-based benchmark and MapReduce applications show the designed scheme's competences. In the fourth paper, ‘Cold chain logistics system based on cloud computing’ by X. Chen 4, a cold chain logistics system based on cloud computing is designed. In this system, the cloud computing platform can be used to connect the database between cold chain logistics and external customers, so that each database connection terminal can keep track of and update the data. It thus brings better cooperation between cold chain logistics and their customers, realizes co-control of product sales information, accelerates the speed of cold chain logistics and maximizes the interests of all parties. The fifth paper, ‘On Multi-Watermarking in Cloud Environment’ by J. Wang and S. Lian 5, focuses on the application scenarios of multi-watermarking in cloud environment by investigating the secure media distribution models suitable for cloud-based platform. In detail, the multi-watermarking based on additive rule is studied and then analyzed theoretically, and some experimental results are given to show the performances, such as security, robustness and imperceptibility. It initiates the interesting research topic of media copyright protection in cloud environment. The guest editors wish to thank Prof. Geoffrey Fox for providing the opportunity to edit this special issue on Multimedia Computing and Management in Cloud Environment, and other editors for providing latest publishing information and making this issue published. We would also like to thank the authors for submitting their works as well as the referees who have critically evaluated the papers within the short stipulated time. Finally, we hope the reader will share our joy and find this special issue very useful.

SUMMARY Cloud computing has recently attracted great attention, both commercially and academically. MapReduce is a popular programming model for distributed storage and computation in the cloud. In this paper, we survey cloud‐based multimedia applications, identifying the open issues and challenges which arise when MapReduce is used for cloud computing. Copyright © 2011 John Wiley & Sons, Ltd.

Underwater sensor networks (UWSNs) can be deployed for sensing the environment in oceanographic columns and other water bodies in which they are deployed. The peculiar characteristic of the underwater medium, coupled with the queer nature of the sound waves in water, poses an enigmatic problem for UWSN researchers. In this study, the authors focus on the problem of UWSN jamming, which is a popular type of denial-of-service attack. The existing jamming detection solutions for sensor networks are primarily targeted towards the terrestrial ones. In this work, the authors study the unique characteristics of jamming in UWSN, and propose a protocol, known as underwater jamming detection protocol (UWJDP), to detect and mitigate jamming in underwater environments. The results show that if the packet delivery ratio (PDR) is less than or equal to 0.8, the authors have the maximum probability of detecting jamming. The jamming detection ratio is around 2–11% more for the said PDR.

The Wireless Mesh Network (WMN) has already been recognized as a promising broadband access network technology from both academic and commercial perspective. In order to improve the performance of WMNs, extensive research efforts have been dedicated towards finding means to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. In case of WMNs based on IEEE 802.11 b/g standards, most recent research works have relied upon the usage of orthogonal channels for solving the Channel Assignment (CA) problem. In this paper, we explore the possibility of exploiting Partially Overlapped Channels (POCs) by introducing a novel game theoretic distributed CA algorithm. Our proposed algorithm outperforms both the conventional orthogonal channel approach and the recent heuristic CA algorithms using POC. The proposed algorithm is shown to achieve near-optimal performance in the average case. In addition, the upper bound Price of Anarchy for Multi-Radio Multi-Channel (MRMC) networks is derived to evaluate the effectiveness of the proposed approach.

With the fast growing of multimedia communication applications, cognitive radio networks have gained the popularity as they can provide high wireless bandwidth and support quality-driven wireless multimedia services. In multimedia applications such as video conferences over the cognitive radio, the Quality of Experience (QoE) that directly measures the satisfaction of the end users cannot be easily realized due to the limited spectrum resources. The opportunistic spectrum access cognitive radio (CR) is an efficient technology to address this issue. However, the unstable channels allocated to the multimedia secondary users (SUs) can be re-occupied by the primary users (PUs) at any time, which makes the CR difficult to meet the QoE requirements. Therefore, it is important to study how to allocate frequency or spectrum resources to SUs according to their QoE requirements. This paper proposes a novel QoE-driven channel allocation scheme for SUs and cognitive radio networks (CRN) base station (BS). The historical QoE data under different primary channels (PCs) are collected by the SUs and delivered to a Cognitive Radio Base Station (CRBS). The CRBS will allocate available channel resources to the SUs based on their QoE expectations and maintain a priority service queue. The modified ON/OFF models of PCs and service queue models of SUs are jointly investigated for this channel allocation scheme. The performance of multimedia transmission of images and H.264 videos under our CR channel allocation scheme is studied, the results show that the proposed channel allocation approach can significantly improve the QoE of the priority-based SUs over the cognitive radio networks.

In this paper, we study game dynamics and learning schemes for heterogeneous 4G networks. We introduce a novel learning scheme called cost-to-learn that incorporates the cost to switch, the switching delay, and the cost of changing to a new action and, captures the realistic behavior of the users that we have experimented on OPNET simulations. Considering a dynamic and uncertain environment where the users and operators have only a numerical value of their own payoffs as information, we construct various heterogeneous combined fully distributed payoff and strategy reinforcement learning (CODIPAS-RL): the users try to learn their own optimal payoff and their optimal strategy simultaneously. We establish the asymptotic pseudo-trajectories as solution of differential equations. Using evolutionary game dynamics, we prove the convergence and stability properties in specific classes of dynamic robust games. We provide various numerical examples and OPNET simulations in the context network selection in wireless local area networks (WLAN) and Long Term Evolution (LTE).

In this survey, we present a comprehensive list of major known security threats within a cognitive radio network (CRN) framework. We classify attack techniques based on the type of attacker, namely exogenous (external) attackers, intruding malicious nodes and greedy cognitive radios (CRs). We further discuss threats related to infrastructure-based CRNs as well as infrastructure-less networks. Besides the short-term effects of attacks over CRN performance, we also discuss the often ignored longer term behavioral changes that are enforced by such attacks via the learning capability of CRN. After elaborating on various attack strategies, we discuss potential solutions to combat those attacks. An overview of robust CR communications is also presented. We finally elaborate on future research directions pertinent to CRN security. We hope this survey paper can provide the insight and the roadmap for future research efforts in the emerging field of CRN security.

SUMMARY With the rapid development of cloud computing, software applications are shifting onto cloud storage rather than remaining within local networks. Software distributions within the cloud are subject to security breaches, privacy abuses, and access control violations. In this paper, we identify an insider threat to access control which is not completely eliminated by the usual techniques of encryption, cryptographic hashes, and access‐control labels. We address this threat using software watermarking. We evaluate our access‐control scheme within the context of a Collaboration‐oriented Architecture, as defined by The Jericho Forum. Copyright © 2011 John Wiley & Sons, Ltd.

Currently, on one hand, there exist much work about network formation and/or growth models, and on the other hand, cooperative strategy evolutions are extensively investigated in biological, economic, and social systems. Generally, overlay networks are heterogeneous, dynamic, and distributed environments managed by multiple administrative authorities, shared by users with different and competing interests, or even autonomously provided by independent and rational users. Thus, the structure of a whole overlay network and the peers' rational strategies are ever coevolving. However, there are very few approaches that theoretically investigate the coevolution between network structure and individual rational behaviors. The main motivation of our article lies in that: Unlike existing work which empirically illustrates the interaction between rational strategies and network structure (through simulations), based on EGT (Evolutionary Game Theory), we not only infer a condition that could favor the cooperative strategy over defect strategy, but also theoretically characterizes the structural properties of the formed network. Specifically, our contributions are twofold. First, we strictly derive the critical benefit-to-cost ratio (b/c) that would facilitate the evolution of cooperation. The critical ratio depends on the network structure (the number of peers in system and the average degree of each peer), and the evolutionary rule (the strategy and linking mutation probabilities). Then, according to the evolutionary rules, we formally derive the structural properties of the formed network in full cooperative state. Especially, the degree distribution is compatible with the power-law, and the exponent is (4-3v)/(1-3v), wherevis peer's linking mutation probability. Furthermore, we show that, without being harmful to cooperation evolution, a slight change of the evolutionary rule will evolve the network into a small-world structure (high global efficiency and average clustering coefficient), with the same power-law degree distribution as in the original evolution model.

Heterogeneities (heterogeneous characteristics) are intrinsic in dynamic and autonomous networks, and may be caused by the following factors: finite nodes, structured network graph, mutation of node's strategy and topological view, and dynamic linking, and so on. However, few works systematically investigate the effect of the intrinsic heterogeneities on the evolutionary dynamics of incentive mechanisms in autonomous networks. In this article, we thoroughly discuss this interesting problem. Specifically, this article respectively models the pairwise interaction between peers as PD (prisoner's dilemma)-like game and multiple peers' interactions as public-goods game, proposes a general analytical framework for dynamics in evolutionary game theory (EGT)-based incentive mechanisms, and draws the following conclusions. First, for explicit incentive mechanisms, due to heterogeneity, it is impossible to get the static equilibrium of absolutely-full-cooperation (or state that provides service to the networks—so-called reciprocation), but, on the other hand, heterogeneity can facilitate reciprocation evolution, and drive the whole system into almost-full-reciprocation state, that is, most of the system time would be occupied by the full reciprocation state. Second, even without any explicit incentive mechanisms, simultaneous coevolution between dynamic linking and peers' rational strategies can not only facilitate the cooperation evolution, but drive the network structure into the desirable small-world structure. The philosophical implication of our work is that simplicity and homogeneity are too idealized for incentive mechanisms in autonomous networks—diversity and heterogeneity are intrinsic for any incentive mechanism that is compatible with the essence of our real society. Diversity is everywhere.

A classic optimization problem in network routing is to minimize C + D, where C is the maximum edge congestion and D is the maximum path length (also known as dilation). The problem of computing the optimal C* + D* is NP-complete even when either C* or D* is a small constant. We study routing games in general networks where each player i selfishly selects a path that minimizes Ci+ Dithe sum of congestion and dilation of the player's path. We first show that there are instances of this game without Nash equilibria. We then turn to the related quality of routing (QoR) games which always have Nash equilibria. QoR games represent networks with a small number of service classes where paths in different classes do not interfere with each other (with frequency or time division multiplexing). QoR games have O(log4n) price of anarchy when either C* or D* is a constant. Thus, Nash equilibria of QoR games give poly-log approximations to hard optimization problems.

Wireless medical sensor networks (MSNs) enable ubiquitous health monitoring of users during their everyday lives, at health sites, without restricting their freedom. Establishing trust among distributed network entities has been recognized as a powerful tool to improve the security and performance of distributed networks such as mobile ad hoc networks and sensor networks. However, most existing trust systems are not well suited for MSNs due to the unique operational and security requirements of MSNs. Moreover, similar to most security schemes, trust management methods themselves can be vulnerable to attacks. Unfortunately, this issue is often ignored in existing trust systems. In this paper, we identify the security and performance challenges facing a sensor network for wireless medical monitoring and suggest it should follow a two-tier architecture. Based on such an architecture, we develop an attack-resistant and lightweight trust management scheme named ReTrust. This paper also reports the experimental results of the Collection Tree Protocol using our proposed system in a network of TelosB motes, which show that ReTrust not only can efficiently detect malicious/faulty behaviors, but can also significantly improve the network performance in practice.

The development of medical sensor networks (MSNs) is imperative for e-healthcare, but security remains a formidable challenge yet to be resolved. Traditional cryptographic mechanisms do not suffice given the unique characteristics of MSNs, and the fact that MSNs are susceptible to a variety of node misbehaviors. In such situations, the security and performance of MSNs depend on the cooperative and trust nature of the distributed nodes, and it is important for each node to evaluate the trustworthiness of other nodes. In this paper, we identify the unique features of MSNs and introduce relevant node behaviors, such as transmission rate and leaving time, into trust evaluation to detect malicious nodes. We then propose an applicationindependent and distributed trust evaluation model for MSNs. The trust management is carried out through the use of simple cryptographic techniques. Simulation results demonstrate that the proposed model can be used to effectively identify malicious behaviors and thereby exclude malicious nodes. This paper also reports the experimental results of the Collection Tree Protocol with the addition of our proposed model in a network of TelosB motes, which show that the network performance can be significantly improved in practice. Further, some suggestions are given on how to employ such a trust evaluation model in some application scenarios.

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data communications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.

This paper investigates the fundamental performance limits of medium access control (MAC) protocols for particular multihop, RF-based wireless sensor networks and underwater sensor networks. A key aspect of this study is the modeling of a fair-access criterion that requires sensors to have an equal rate of underwater frame delivery to the base station. Tight upper bounds on network utilization and tight lower bounds on the minimum time between samples are derived for fixed linear and grid topologies. The significance of these bounds is two-fold: First, they hold for any MAC protocol under both single-channel and half-duplex radios; second, they are provably tight. For underwater sensor networks, under certain conditions, we derive a tight upper bound on network utilization and demonstrate a significant fact that the utilization in networks with propagation delay is larger than that in networks with no propagation delay. The challenge of this work about underwater sensor networks lies in the fact that the propagation delay impact on underwater sensor networks is difficult to model. Finally, we explore bounds in networks with more complex topologies.

This paper deals with an energy saving routing solution, called Energy Saving IP Routing (ESIR), to be applied in an IP network. ESIR operation is integrated with Open Shorthest Path First (OSPF) protocol and allows the selection of the links to be switched off so that the negative effects of the IP topology reconfiguration procedures are avoided. The basic mechanisms which ESIR is based on are the concepts of SPT exportation and move. These mechanisms allow to share a Shortest Path Tree (SPT) between neighbor routers, so that the overall set of active network links can be reduced. Properties of moves are defined and the energy saving problem in an IP network is formulated as the problem of finding the Maximum Set of Compatible Moves (MSCM). The MSCM problem is investigated in two steps: firstly, a relaxed version of the problem, named basic MSCM problem, is considered in which QoS requirements are neglected; in the second step, the solution of the full problem, named QoS-aware MSCM problem, is faced. We prove that the basic MSCM problem can be formulated as the well-known Maximum Clique Problem in a graph; instead the QoS-aware MSCM introduces a condition equivalent to the Knapsack problem. ILP formulations to solve both the problems are given and heuristics to solve them in practical cases are proposed. The performance evaluation shows that in a real ISP network scenario ESIR is able to switch off up to 30% of network links by exploiting over-provisioning adopted by operators in the network resource planning phase and typical daily traffic trend.

The crucial role that networking plays in Cloud computing calls for a holistic vision that allows combined control, management, and optimization of both networking and computing resources in a Cloud environment, which leads to a convergence of networking and Cloud computing. Network virtualization is being adopted in both telecommunications and the Internet as a key attribute for the next generation networking. Virtualization, as a potential enabler of profound changes in both communications and computing domains, is expected to bridge the gap between these two fields. Service-Oriented Architecture (SOA), when applied in network virtualization, enables a Network-as-a-Service (NaaS) paradigm that may greatly facilitate the convergence of networking and Cloud computing. Recently the application of SOA in network virtualization has attracted extensive interest from both academia and industry. Although numerous relevant research works have been published, they are currently scattered across multiple fields in the literature, including telecommunications, computer networking, Web services, and Cloud computing. In this article we present a comprehensive survey on the latest developments in service-oriented network virtualization for supporting Cloud computing, particularly from a perspective of network and Cloud convergence through NaaS. Specifically, we first introduce the SOA principle and review recent research progress on applying SOA to support network virtualization in both telecommunications and the Internet. Then we present a framework of network-Cloud convergence based on service-oriented network virtualization and give a survey on key technologies for realizing NaaS, mainly focusing on state of the art of network service description, discovery, and composition. We also discuss the challenges brought in by network-Cloud convergence to these technologies and research opportunities available in these areas, with a hope to arouse the research community's interest in this emerging interdisciplinary field.

In response to Internet emergencies, Internet resiliency is investigated directly through an autonomous system (AS) level graph inferred from policy-compliant BGP paths or/and traceroute paths. Due to policy-driven inter-domain routing, the physical connectivity does not necessarily imply network reachability in the AS-level graph, i.e., many physical paths are not visible by the inter-domain routing protocol for connectivity recovery during Internet outages. We call the invisible connectivity at the routing layer, which can be quickly restored for recovering routing failures by simple configurations, as the potential routing diversities. In this paper, we evaluate two kinds of potential routing diversities, which are recognized as Internet eXchange Points (IXPs) participant reconnection and peering policy relaxation. Using the most complete dataset containing AS-level map and IXP participants that we can achieve, we successfully evaluate the ability of potential routing diversity for routing recovery during different kinds of Internet emergencies. Encouragingly, our experimental results show that 40% to 80% of the interrupted network pairs can be recovered on average beyond policy-compliant paths, with rich path diversities and a little traffic shifts. Thus, this paper implies that the potential routing diversities are promising venues to address Internet failures.

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. Since its inception in the mid 1990s, DE has been finding many successful applications in real-world optimization problems from diverse domains of science and engineering. This paper takes a first significant step toward the convergence analysis of a canonical DE (DE/rand/1/bin) algorithm. It first deduces a time-recursive relationship for the probability density function (PDF) of the trial solutions, taking into consideration the DE-type mutation, crossover, and selection mechanisms. Then, by applying the concepts of Lyapunov stability theorems, it shows that as time approaches infinity, the PDF of the trial solutions concentrates narrowly around the global optimum of the objective function, assuming the shape of a Dirac delta distribution. Asymptotic convergence behavior of the population PDF is established by constructing a Lyapunov functional based on the PDF and showing that it monotonically decreases with time. The analysis is applicable to a class of continuous and real-valued objective functions that possesses a unique global optimum (but may have multiple local optima). Theoretical results have been substantiated with relevant computer simulations.

We propose an online, multiobjective optimization (MO) algorithm to efficiently schedule the nodes of a wireless sensor network (WSN) and to achieve maximum lifetime. Instead of dealing with traditional grid or uniform coverage, we focus on the differentiated or probabilistic coverage where different regions require different levels of sensing. The MO algorithm helps to attain a better tradeoff among energy consumption, lifetime, and coverage. The algorithm can be run every time a node failure occurs due to power failure of the node battery so that it may reschedule the network. This scheduling is modeled as a combinatorial, multiobjective, and constrained optimization problem with energy and noncoverage as the two objectives. The basic evolutionary multiobjective optimizer used is known as decomposition-based multiobjective evolutionary algorithm (MOEA/D) which is modified by integrating the concept of fuzzy Pareto dominance. The performance of the resulting algorithm, which is called MOEA/DFD, is compared with the performance of the original MOEA/D, which is another very well known MO algorithm called nondominated sorting genetic algorithm (NSGA-II), and an IBM optimization software package called CPLEX. In all the tests, MOEA/DFD is observed to outperform all other algorithms.

In networked control systems (NCS), the performance degradation introduced by the heterogeneous and dynamic environment has intensified the need for reconfigurable protocol stacks (RPS). In this paper, an IEC61499-based method is proposed for the model-driven development of RPS. The method is enabled by defining a novel RPS function block (FB), which unifies the communication behavior and interface of nodes in NCS. Beyond existing communication FBs in IEC61499, the parameter reconfiguration of routing and scheduling table in RPS FB is highlighted as the core of communication layer function to adapt environment and system variations. Furthermore, the method allows for the code reconfiguration on Java algorithms in RPS FB under different application requirements. Through porting the Java virtual machine on different platforms, the code reconfiguration is implemented by reloading the .class file for a specified protocol FB. A case study on the embedded platform, such as DSP/BIOS and ARM/Linux, is conducted to demonstrate the effectiveness and feasibility of the proposed reconfiguration method for maintaining stable and predictable behavior in NCS.

Abstract In this paper we focus on the mobile GIS management system based on multi‐agents, and wider range of client devices with J2ME‐MIDP kernel are supported in it. A kit (Map Database Manager) is developed and described in detail. The kit can convert GIS data of commercial formats such as ArcGIS and Mapinfo into Geospatial data in GML, and provide Geospatial data for handheld devices freely without commercial plug‐in software. A peer to peer multi‐agents mechanism on mobile GIS services is the main subject of this paper. According to user's requirement and known buffer area radius on map, agent can define the factors to calculate the optimal path from one node to target node in a map, which considers not only the length of the road but also traffic flow, road cost, and other factors. We use the integrated cost to re‐define the road path weights, and select the optimal path among the road based on the minimum weight. The actual experimental results shown that in the quantified traffic flow and road condition, from node to other target nodes, we can get an optimal path on map, which satisfies the condition mostly. The results can greatly assist the searching of the optimal path in GIS services. Copyright © 2010 John Wiley & Sons, Ltd.

Topology control is one of the most fundamental problems in wireless sensor networks (WSNs). It is of great importance for prolonging network lifetime, reducing radio interference and energy consumption, increasing the efficiency of media access control (MAC) protocols and routing protocols, among other things. In this paper, the problem is transformed into a model of multi-criteria degree constrained minimum spanning tree (mcd-MST), and a non-dominated discrete particle swarm optimization (NDPSO) is designed to deal with this problem. To obtain a better approximation of true Pareto front, the phenotype sharing function of the objective space is applied in the definition of fitness function in NDPSO. Furthermore, a topology control scheme based on NDPSO is proposed in this paper. Simulation results show that the topology derived under the proposed scheme has low total power consumption, high robust structure and low contention among nodes, and it also can effectively prolong the lifetime of network.

In cloud computing, multiple cloud providers can cooperate to establish a resource pool to support internal users and to offer services to public cloud users. In this paper, we study the cooperative behavior of multiple cloud providers. The hierarchical cooperative game model is presented. First, given a group (i.e., coalition) of cloud providers, the resource and revenue sharing of a resource pool is presented. To obtain the solution, we develop the stochastic linear programming game model which takes the uncertainty of internal users from each provider into account. We show that the solution of the stochastic linear programming game is the core of cooperation. Second, we analyze the stability of the coalition formation among cloud providers based on coalitional game. The dynamic model of coalition formation is used to obtain stable coalitional structures. The resource and revenue sharing and coalition formation of cloud providers are intertwined in which the proposed hierarchical cooperative game model can be used to obtain the solution. An extensive performance evaluation is performed to investigate the decision making of cloud providers when cooperation can lead to the higher profit.

Sink scheduling is shown to be a promising scheme in wireless sensor networks. However, previous approaches on this topic suffer from poor performance due to lack of joint considerations. Therefore, in this paper, we aim to fill in the research blank. First, we develop a novel notation Placement Pattern (PP) to bound time-varying routes with placement of sinks. This bounding technique transforms time-related constraints into pattern-based ones and allows us to formulate this optimization in pattern domain. If there is only one sink, we develop a polynomial time algorithm to solve it optimally. If there are multiple sinks, we develop a column generation based approach to solve it efficiently. Simulations not only demonstrate the efficiency of proposed algorithms but also substantiate the importance of sink mobility for energy-constrained sensor networks.

Cross-layer design has been widely used in wireless sensor networks, especially to improve the network lifetime, as can be seen in the literature. In this paper, a cross-layer solution is combined to a transmission advertisement scheme to improve a slotted ALOHA-based wireless sensor network throughput and lifetime. This medium access method scheme has been chosen because it does not add protocol information to be transmitted with data bits, reducing transmission overhead when compared to other medium access methods. Finally, the combination of a cross-layer design and the advertisement scheme has proven to increase the network throughput by more than 10% and to double the network lifetime.

Energy efficient broadcasting is of paramount importance for many broadcast applications in wireless ad hoc networks. With respects network coding, it has been proved that the energy gain is upper bounded by 3. However, the coding opportunity is often highly dependent on the established routing paths, resulting in that a lot of coding opportunities could be lost in practice. By combining network coding with the Connected Dominating Set (CDS)-based broadcasting, we take full use of network coding. The intuition behind our algorithm is to intersect information flows at nodes in CDS to increase the coding opportunities. We propose a novel scheme named NCAB, a Network Coding Aware based Broadcast routing mechanism, integrating the network coding and the dynamic implementation of connected dominating set. Our experimental results show that NCAB provides up to 169% gains compared to flooding, and 41% gains compared to CDS-based broadcasting.

In NCS (networked control systems), the control performance depends on not only the control algorithm but also the communication protocol stack. This paper presents a framework for the protocol reconfiguration in NCSs. Task queuing, network transmission policy and communication link control are emphasized as three key protocol functionalities related to the system performance. Based on it, data and event exchange models for layers of protocol stack are developed according to the standard IEC61499, which allows for the dynamic reconfiguration management of distributed resources as well as provides a unified real time communication service for control tasks. Finally, experiments are conducted to illustrate the application and efficiency of the proposed reconfigurable protocol stack.

This paper thoroughly investigated the Evolutionary Game Theory (EGT) based modeling and analysis of reciprocation-based incentive mechanisms. Unlike existing work which adopts replicator equation to analyze the stability of incentive mechanisms (actually, replicator equation is only applicable to describe deterministic selection in infinitely large and well-mixed population), we paid special attentions to the intrinsic heterogeneity in real autonomous networks: finite users, mutation probability and structured network graph, and proposed the unified framework to characterize the evolutionary dynamics. Specifically, through modeling and analyzing Prisoner's Dilemma (PD)-like game based and Public-goods game based incentive mechanisms, we show that although it is impossible for incentive mechanisms to get the whole network into static "absolute full cooperation (or reciprocation)" state, they can still drive the whole system into "almost reciprocation" state, that is, most of the system time would be occupied by the cooperation (or reciprocation) state.

We model cognitive radio networks (CRNs) as a spectrum market where every primary user (PU) offer her subchannels with certain interference bound indicating the interference limit the PU can tolerate, and secondary users (SUs) purchase the right to access the subchannels while observing their budget constraints as well as the inference bound. In this spectrum market model, the utility of SU is defined as the achievable transmission rate in free space, and the utility of PU is given by the net profit the PU can make. Then we develop a market equilibrium in the context of Fisher model, and show that the equilibrium is obtained by solving an optimization problem called Eisenberg-Gale convex program. Furthermore, we develop a distributed algorithm with best response dynamics and price dynamics, and prove that its asymptotic solutions are equivalent to the solutions given by the convex program. Besides, we introduce adaptive step size to the price dynamics for faster convergence. With some numerical examples, we show that it helps to achieve faster convergence.

As a burgeoning technique for signal processing, compressed sensing (CS) is being increasingly applied to wireless communications. However, little work is done to apply CS to multihop networking scenarios. In this paper, we investigate the application of CS to data collection in wireless sensor networks, and we aim at minimizing the network energy consumption through joint routing and compressed aggregation. We first characterize the optimal solution to this optimization problem, then we prove its NP-completeness. We further propose a mixed-integer programming formulation along with a greedy heuristic, from which both the optimal (for small scale problems) and the near-optimal (for large scale problems) aggregation trees are obtained. Our results validate the efficacy of the greedy heuristics, as well as the great improvement in energy efficiency through our joint routing and aggregation scheme.

With the introduction of femtocells, cellular networks are moving from the conventional centralised architecture to a distributed one, where each network cell should make its own radio resource management decisions, while providing inter-cell interference mitigation. However, realising this distributed cellular network architecture is not a trivial task. In this paper, we first introduce a simple self-organisation rule under which a distributed cellular network is able to converge into an efficient resource allocation pattern, then propose a novel resource allocation model taking realistic resource allocation constraints into account, and finally evaluate the performance of the proposed self-organisation rule and resource allocation model using system-level simulations.