WiFi-based human activity recognition (HAR) has emerged as a focal point within the Internet of Things landscape, owing to its non-intrusive sensing capabilities and inherent privacy-preserving advantages. In existing WiFi-based HAR research, channel state information (CSI) is primarily utilized to capture activity-related features and enable recognition. However, CSI-based cross-domain HAR remains challenged by issues such as redundant subcarriers, limited samples in the target domain, and high sensitivity of CSI to environmental variations. To address these challenges, this paper proposes Wi-DMAR, a WiFi-based cross-domain HAR framework that integrates three key modules. First, an adaptive subcarrier selection module computes the correlation between each subcarrier and the principal components, identifies subcarriers with high contribution, preserves essential activity-related features, reduces data dimensionality, and lowers computational overhead. Second, a conditional diffusion–based data augmentation module employs a Transformer-based feature extractor to capture domain-specific representations of target-domain data, and optimizes domain consistency loss and domain-guided diffusion loss to generate pseudo samples that resemble the target-domain distribution, thereby mitigating sample scarcity. Third, an activity recognition module based on sample similarity learning reformulates the traditional label classification problem into a sample comparison task, by quantifying similarity between samples, it performs activity recognition and enhances cross-domain generalization. Experimental results demonstrate that Wi-DMAR achieves superior recognition accuracy compared with state-of-the-art cross-domain HAR methods such as DiffAR and MetaAct. Ablation studies further confirm that each core component contributes positively to performance improvements.

Intelligent connected vehicle fleet driven primarily by cloud data sharing is an efficient and energy-saving organizational model and demonstrates vast application potential in many scenarios. However, as the central hub for data sharing, the cloud platform's untrustworthiness poses severe challenges to the integrity and reliability of shared data. Existing auditing schemes fail to meet the low-latency, lightweight onboard computing, and succinct revocation demands of such fleets. Even with the application of batch auditing, the overall audit latency remains inadequately addressed. In this work, we propose an efficient auditing scheme called Drivora for cloud data sharing of intelligent connected fleet. We achieve secure delegated authenticator computation based on secret sharing and it requires only the master vehicle to perform the initial setup. Moreover, Drivora supports the aggregation of challenged blocks from multiple vehicles to produce a single integrity proof and it enables Drivora to handle multiple audit tasks from different vehicles at the same time. Furthermore, we propose a novel vehicle revocation mechanism. Different from traditional secret sharing-based approaches, Drivora requires only a single secret share to revoke multiple vehicles and achieves lower space complexity. We conduct extensive experimental evaluations of Drivora's performance and benchmark it against state-of-the-art approaches. Compared to conventional batch auditing, our scheme achieves a$2\times$-$107\times$improvement in auditing efficiency and reduces communication overhead by$2\times$-$939\times$. As the number of files increases, our overall auditing cost remains constant.

Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI) models while maintaining data privacy. To overcome the communication bottlenecks associated with centralized parameter servers, decentralized federated learning (DFL), which leverages peer-to-peer (P2P) communication, has been extensively explored in the research community. Although researchers design a variety of DFL approaches to ensure model convergence, its iterative learning process inevitably incurs considerable cost along with the growth of model complexity and the number of participants. These costs are largely influenced by the dynamic changes in topology in each training round, particularly its sparsity and connectivity conditions. Furthermore, the inherent resources heterogeneity in the edge environments affects energy efficiency of the learning process, while data heterogeneity degrades model performance. These factors pose significant challenges to the design of an effective DFL framework for EC systems. To this end, we propose Hat-DFed, a heterogeneity-aware and cost-effective decentralized federated learning framework. In Hat-DFed, the topology construction is formulated as a dual optimization problem, which is then proven to be NP-hard, with the goal of maximizing model performance while minimizing cumulative energy consumption in complex edge environments. To solve this problem, we design a two-phase algorithm that dynamically constructs optimal communication topologies while unbiasedly estimating their impact on both model performance and energy cost. Additionally, the algorithm incorporates an importance-aware model aggregation mechanism to mitigate performance degradation caused by data heterogeneity. Extensive experiments demonstrate that Hat-DFed outperforms state-of-the-art baselines, achieving an average 1.8% improvement in test accuracy while reducing total energy cost by 36.9% throughout the learning process.

The exponential growth of mobile network traffic makes accurate traffic prediction essential for network optimization. However, this remains challenging due to complex spatial-temporal dependencies and diverse traffic patterns across base stations. Existing methods often rely on single-factor modeling or fail to effectively handle this multi-pattern heterogeneity. We propose the Meta-Optimized Multi-Period Spatial-Temporal Transformer (MMSTT), a novel framework integrating dynamic graph-based spatial modeling and multi-period temporal fusion within a Transformer architecture. To address the multi-pattern nature of traffic data, we incorporate a clustering algorithm and a meta-learning optimization process. This process captures shared features across patterns during meta-training and adapts to pattern-specific characteristics during meta-testing. Experimental results on a real-world dataset demonstrate that MMSTT significantly outperforms existing baselines, reducing the mean absolute error (MAE) to 28.39, root mean square error (RMSE) to 42.83, and mean absolute percentage error (MAPE) to 0.37. Compared to the standard Transformer and GCN baselines, MMSTT achieves improvements of 38.71% in MAE, 33.57% in RMSE, and 60.22% in MAPE.

With the proliferation of data-driven services, the volume of data that needs to be processed by satellite networks has significantly increased. Federated learning (FL) is well-suited for big data processing in distributed and resource-constrained satellite environments. However, achieving robust convergence while minimizing processing time and energy consumption remains challenging. To this end, we propose a hierarchical clustered federated learning framework, FedHC. This framework employs a combined feature based dynamic clustering algorithm at the cluster aggregation stage, grouping satellites into different clusters and designating a cluster center as the parameter server (PS) to accelerate model aggregation. Several communicable cluster PS satellites are then selected through ground stations to aggregate global parameters, facilitating the FL process. Moreover, a meta-learning-driven satellite re-clustering algorithm is introduced to enhance adaptability to dynamic satellite cluster changes. Extensive experiments conducted on a satellite network testbed demonstrate that FedHC can significantly reduce processing time (up to 3x) and energy consumption (up to 2x) compared to other comparative methods while maintaining model accuracy.

In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an emerging paradigm that leverages the execution results of previous tasks to enhance the utilization of computational resources. While this paradigm has been extensively studied in terrestrial networks with abundant computing and caching resources, such as named data networking (NDN), it is essential to develop a framework appropriate for resource-constrained satellite networks, which are expected to have longer task completion time. In this paper, we propose CCRSat, a collaborative computation reuse frame-work for satellite edge computing networks. CCRSat initially implements local computation reuse on an independent satellite, utilizing a satellite reuse status (SRS) to assess the efficiency of computation reuse. Additionally, an inter-satellite computation reuse algorithm is introduced, which utilizes the collaborative sharing of similarity in previously processed data among multiple satellites. The evaluation results tested on real-world datasets demonstrate that, compared to comparative scenarios, our proposed CCRSat can significantly reduce task completion time by up to 62.1% and computational resource consumption by up to 28.8%.

Next Point-of-Interest (POI) recommendation aims to predict users’ next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address these challenges, we propose Hypergraph-enhanced Meta-learning Adaptive Network (HyperMAN), a novel framework that integrates heterogeneous hypergraph modeling with a difficulty-aware meta-learning mechanism for next POI recommendation. Specifically, three types of heterogeneous hyperedges are designed to capture high-order relationships: user visit behaviors at specific times (Temporal behavioral hyperedge), spatial correlations among POIs (spatial functional hyperedge), and user long-term preferences (user preference hyperedge). Furthermore, a diversity-aware meta-learning mechanism is introduced to dynamically adjust learning strategies, considering users behavioral diversity. Extensive experiments on real-world datasets demonstrate that HyperMAN achieves superior performance, effectively addressing cold start challenges and significantly enhancing recommendation accuracy.

Knowledge graph completion (KGC) methods aim to predict missing links by learning from existing facts in a knowledge graph. Different from KGC, Temporal Knowledge Graph Completion (TKGC) further incorporates the time validity of facts (tagged timestamps) during the learning and inference to improve the completion accuracy. Many TKGC methods achieve this by projecting the static entity representations (time-invariant) of KGC embedding methods to time-dependent representations, which vary across timestamps. However, when measuring a fact, these TKGC methods only consider its subject/object entity representations corresponding to the tagged timestamp, but ignore their historical contexts that normally carry essential supportive information. With this observation, we propose a novel context aggregation (ContxE) method to include historical contexts of subject/object entities for TKGC. To achieve that, we propose a linear-rotary time embedding to obtain time-dependent entity representations that can preserve temporal relationships, and a relation-based attention to aggregate historical context for the score measurement. Comprehensive experiments on three temporal knowledge graph datasets show that the proposed ContxE achieves improved knowledge graph completion results compared to strong counterpart methods.

With the growing deployment of robotic applications, fault diagnosis at the individual robot level (single-robot fault diagnosis) is increasingly insufficient to meet stringent safety and reliability requirements. To address these challenges, multi-robot fault diagnosis has emerged as a promising approach, which enables robots to collaboratively share sensor data from diverse tasks. This collaboration helps mitigate data scarcity and supports the development of robust global models with improved generalization capabilities. However, multi-robot fault diagnosis presents several key challenges: (1) mitigating negative transfer caused by sensor heterogeneity across different robots; (2) effectively capturing spatial-temporal dependencies within the sensor data; and (3) designing an efficient distributed learning framework that preserves data privacy while enabling collaborative model training. In this paper, we propose a novel federated spatial-temporal fault learning (FSTFL) framework based on a fused adjacency matrix. The adjacency matrix is dynamically updated and initially constructed using domain knowledge to guide the learning process. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed FSTFL framework in achieving accurate and privacy-preserving multi-robot fault diagnosis.

Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, enabling deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. This paper synthesizes recent advancements in CFTel, aiming to highlight its role in facilitating scalable, low-latency, autonomous, and AI-driven telerobotics. We analyze architectural frameworks and technologies that enable them, including 5G Ultra-Reliable Low-Latency Communication, Edge Intelligence, Embodied AI, and Digital Twins. The study demonstrates that CFTel has the potential to enhance real-time control, scalability, and autonomy while supporting service-oriented solutions. We also discuss practical challenges, including latency constraints, cybersecurity risks, interoperability issues, and standardization efforts. This work serves as a foundational reference for researchers, stakeholders, and industry practitioners in future telerobotics research.

Industrial Cyber-Physical Systems (ICPS) technologies are foundational in driving maritime autonomy, particularly for Unmanned Surface Vehicles (USVs). However, onboard computational constraints and communication latency significantly restrict real-time data processing, analysis, and predictive modeling, hence limiting the scalability and responsiveness of maritime ICPS. To overcome these challenges, we propose a distributed Cloud-Edge-IoT architecture tailored for maritime ICPS by leveraging design principles from the recently proposed Cloud-Fog Automation paradigm. Our proposed architecture comprises three hierarchical layers: a Cloud Layer for centralized and decentralized data aggregation, advanced analytics, and future model refinement; an Edge Layer that executes localized AI-driven processing and decision-making; and an IoT Layer responsible for low-latency sensor data acquisition. Our experimental results demonstrated improvements in computational efficiency, responsiveness, and scalability. When compared with our conventional approaches, we achieved a classification accuracy of 86%, with an improved latency performance. By adopting Cloud-Fog Automation, we address the low-latency processing constraints and scalability challenges in maritime ICPS applications. Our work offers a practical, modular, and scalable framework to advance robust autonomy and AI-driven decision-making and autonomy for intelligent USVs in future maritime ICPS.

Massive emissions of greenhouse gases (GHGs) have a negative impact on the development of sustainable agriculture. While techniques of imputation and forecasting facilitate the observation of GHG emissions with improved accuracy, there is a lack of an integrated model for both GHG emission data imputation and forecasting, particularly in few-shot learning scenarios. To address this issue, this paper proposes a pre-trained large language model dubbed Agri-LLM for GHG emission data imputation and forecasting in smart agriculture. Notably, this model develops an information fusion embedding layer that fuses missing patterns, temporal irregularities and incomplete time series into multi-level patched tokens. A global temporal similarity informed prompting module is further elaborated on to generate suitable prompts for target time series, based on similar temporal characteristics captured from other nodes. Finally, the model aligns the pre-trained knowledge language with multi-level integrated tokens directly without altering the large language model’s backbone. The experimental studies demonstrate that our model outperforms state-of-the-art baselines in both tasks of imputation and forecasting using full-sample training. Extensive experiments also confirm that the Agri-LLM exhibits superior performance in few-shot learning scenarios and the effectiveness of each proposed model component.

The Internet of Robotic Things (IoRT) merges the capabilities of robotics with the connectivity and computing power of Internet of Things (IoT) technologies, enabling seamless data collection, processing, and exchange. This integration enhances robotic systems with greater intelligence, mobility, and autonomy, unlocking significant potential across various applications, including sustainable agriculture. However, deploying IoRT systems in unpredictable environments poses challenges, such as network instability and hardware failures, which have not been thoroughly explored in the literature. To address these issues, this article introduces Mobility-as-a-Resilience Service (MaaRS), a model that leverages the mobility of active uncrewed aerial vehicles (UAVs), strategically relocating them to critical points of interest in response to potential data collection failures, optimizing resource allocation and enhancing system resilience, particularly in smart farm scenarios. Additionally, a robust multiagent deep deterministic policy gradient (RMADDPG) method is devised to enable efficient task allocation and system recovery in the presence of model uncertainty, observation noise, and reward uncertainty. Extensive simulations demonstrate that the proposed method achieved a significant boost in performance, efficiency, and stability over the state-of-the-art.

As LiDAR-based 3-D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3-D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this article, we investigate the robustness of 3-D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3-D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model’s discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3-D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part I

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part II

Flocking control, as an essential approach for survivable navigation of multirobot systems, has been widely applied in fields, such as logistics, service delivery, and search and rescue. However, realistic environments are typically complex, dynamic, and even aggressive, posing considerable threats to the safety of flocking robots. In this article, based on deep reinforcement learning, anAsymmetricSelf-play-empoweredFlockingControl framework is proposed to address this concern. Specifically, the flocking robots are trained concurrently with learnable adversarial interferers to stimulate the intelligence of the flocking strategy. A two-stage self-play training paradigm is developed to improve the robustness and generalization of the model. Furthermore, an auxiliary training module regarding the learning of transition dynamics is designed, dramatically enhancing the adaptability to environmental uncertainties. Feature-level and agent-level attention are implemented for action and value generation, respectively. Both extensive comparative experiments and real-world deployment demonstrate the superiority and practicality of the proposed framework.

The Internet of Robotic Things (IoRT) has experienced rapid growth and garnered increased attention in recent years. Applications (Apps) play a crucial role in IoRT, as they provide users with an intuitive interface to access and operate services. However, as user demands increase, Apps become more complex, leading to longer operation sequences and more vulnerabilities. The existing testing methods for Apps can be categorized into random, reinforcement learning, and AI-based approaches. AI-based methods offer a solution to the low coverage efficiency of random-based methods and the weak guidance of reinforcement learning-based methods. However, current AI-based methods have difficulty in capturing long-term dependencies, resulting in low coverage and less detected crashes when testing Apps with long operation sequences. To address the limitation, we propose InforTest, a novel AI-based method based on the Informer prediction model and the component tree structure. InforTest leverages Informer, which excels at extracting long-term dependencies from operation sequences, to generate human-like moves for testing Apps. To improve the efficiency of training and prediction, InforTest uses the component tree, a concise structure to represent primary data sources, i.e., screenshots. After training InforTest on the Rico dataset, our experiments with Apps in the IoRT scenario demonstrated its superiority over existing methodologies such as Monkey, Humanoid, MUBot, and Ape. Notably, InforTest achieved significant enhancements in coverage rates (increases of 67%, 34%, 19%, and 27%, respectively) and in crash detection capabilities (improvements of 175%, 81%, 71%, and 139%, respectively).

With the intelligence and automation of industrial Internet of Things, a new collaborative Cloud-Fog Automation paradigm has emerged. The emergence of federated learning (FL) has further enhanced the capabilities of Cloud-Fog Automation, making it possible to develop more secure and versatile collaborative industrial models. However, FL faces various security risks. More importantly, the security risks faced by FL when applied in Cloud-Fog Automation, along with corresponding security measures, have not yet been explored. To address this issue, we make an initial attempt to analyze the security of FL within the context of Cloud-Fog Automation, with the aim of facilitating the design of a more secure FL framework for this paradigm. Specifically, we first analyze the security risks that may be encountered at different phases, then analyze the challenges that need to be faced to resolve these risks. Subsequently, we conduct a systematic review of the state-of-the-art security solutions, and finally summarize the future research directions.

To assess the vulnerability of deep neural networks in the physical world, many studies have introduced adversarial examples and applied them to computer vision tasks such as object detection in recent years. Compared to patch-based adversarial attacks, camouflage-based attacks have received more and more attention due to their ability to attack detectors from multiple viewpoints. However, existing adversarial examples often rely on glass-box models and exhibit limited transferability to closed-box models, which remains a significant challenge. To address this issue, we propose the highly transferable camouflage attack, a novel physical adversarial attack framework designed to generate robust and efficient adversarial camouflage that can mislead object detectors in diverse scenarios. Specifically, we introduce a distraction method to distribute the features of the attention map between models, and propose enhanced transfer strategies to improve adversarial transferability through augmenting the input data and the attacked models. Extensive experiments demonstrate that our highly transferable camouflage attack can effectively mislead object detectors in both digital and physical worlds, enhancing the transferability of adversarial camouflage on multiple mainstream detectors.

Effective last-mile delivery is pivotal in smart logistics system. While existing delivery network architectures, such as Drone-as-a-Service (DaaS), are capable of enhancing order delivery effectiveness, they often fall short in provisioning diverse delivery services. Furthermore, DaaS-based last-mile delivery systems face challenges from limited payload capacity and range. In this paper, we propose a UAV-UGV integrated network architecture based on Multi-access Edge Computing (MEC), denoted as DaaS+, encompassing the diverse delivery services from both Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) for last-mile delivery. To optimize the effectiveness in the delivery process, the intricate overhead and constraints of heterogeneous delivery services are taken into the full consideration. Specifically, we present an energy-aware service model for the UAV-UGV integrated network that considers the deadline constraints of services. Additionally, we address issues of service unavailability during service provisioning. To identify optimal service provisioning plans, we design a novel Energy-Aware Holistic Service Provisioning Mechanism based on Particle Swarm Optimization (ES-PSO), which minimizes delivery energy consumption while adhering to service deadline constraints. Experimental results substantiate the effectiveness of our proposed solution, demonstrating its ability to generate superior service provisioning plans and significantly reduce total delivery energy consumption.

Robotic push-grasping in densely cluttered environments presents significant challenges due to unbalanced synergy and redundancy between both actions, leading to decreased grasp efficiency. In this paper, a novel double-critic deep reinforcement learning framework is introduced to optimize the push-grasping synergy for robotic manipulation in such environments, aiming to significantly reduce pre-grasping redundancy. This framework incorporates two distinct Deep Q-learning critics: Critic I selects the best course of actions based on the current state derived from visual interpretation, whereas Critic II evaluates the success rate of the current state-action pairing. To further refine the push-grasping synergy, an active double-step learning mechanism is introduced to optimize the training reward function for the pushing action, thereby enhancing its effectiveness through increased intentionality. Simulations show that the proposed framework outperforms contemporary counterparts, notably in grasping success rate and action efficiency. Finally, the framework’s generalization and adaptability are demonstrated by conducting real-world experiments using novel objects without the need of retraining.

By melding the capabilities of robotics with the agility of edge computing, Robotic Edge System (RES) exemplifies the next generation of Internet Intelligent Service Systems, delivering incredible efficiency and adaptability across diverse real world applications. Inevitably, RES is susceptible to mechanical disruptions of robots, particularly when some tasks are assigned to faulty ones, leading to uncertain failures and performance degradation. Due to communication and latency constraints, it is not always feasible to rely on edge/cloud computing infrastructure for system recovery. To address these issues, a UAV-as-a-Service (UAVaaS) approach is proposed that leverages the mobility of UAVs to enhance system resilience. Specifically, a Markov Decision Process (MDP) is utilized to assign tasks dynamically among active UAVs to achieve system recovery in a livestock monitoring scenario. Additionally, a Dual Noise Deep Deterministic Policy Gradient (DNDDPG)-based mechanism is proposed to minimize system recovery time and energy consumption. The proposed DNDDPG enhances exploration and decision-making during training by integrating parameter noise and behavioral noise into the classic Deep Deterministic Policy Gradient (DDPG) algorithm. The simulation results indicate that the proposed mechanism can achieve convergence within 100 episodes, thereby effectively minimizing the time and energy required for system recovery.

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization.

Presently, resource-constrained devices in manufacturing Internet of Things (MIoT), such as sensors and radio frequency identification (RFID) devices, collect a large amount of privacy-sensitive data. However, weak passwords and vulnerable encryption capabilities in MIoT have often become loopholes of security risks. To this end, this paper proposes a novel secure data-sharing scheme based on the integration of blockchain and fusion of both real and fake data to address the data security requirements of resource-constrained MIoT devices. First, a computing resource collaboration architecture is designed, thereby enabling these devices to interface with multiple devices with full resource to implement flexible resource scheduling. Then, a resource-assistance mechanism is devised through blockchain-based smart contracts by utilizing the idle resources of full nodes to complement the computational tasks launched by resource-constrained nodes. In addition, polygon semantic rules are proposed to improve the security of private data. Subsequently, real data artifacts are generated by data tampering to achieve privacy cover, avoiding the consumption of computing resources in traditional encryption algorithms. Finally, the feasibility of the proposed scheme is verified on a customized candy production line. The experimental results validate that data protection using polygon semantic rules can prevent actual data from being peeped. Moreover, the results also indicate that the proposed method can obtain resource assistance from other nodes through the resource compensation mechanism.

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, artificial intelligence, and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this article, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

Driven by digital twin (DT) technology, the industrial Internet of Things (IIoT) is expanding to open up new frontiers in industrial applications. However, traditional DT modeling approaches require synchronizing massive amounts of data, resulting in high communications overhead and privacy vulnerability. To address this problem, this article proposes a novel DT architecture for IIoT, where the DT can showcase the real-time operating status of the industrial environment. Swarm learning (SL) is an emerging decentralized federated learning (FL) technique that eliminates the need of a centralized server. We present a novel credibility-weighted SL scheme to construct the DT models, which improves data security while ensuring the fairness of participants as opposed to conventional FL. In addition, we develop a DT-assisted deep reinforcement learning algorithm for simultaneously optimizing the system reliability and energy consumption of IIoT. Simulation comparisons demonstrate that the proposed scheme outperforms some state-of-the-art benchmarks in terms of both reliability and energy consumption.

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.

Space-Air-Ground Integrated Network (SAGIN) has recently emerged as a viable solution for reliable transmission, high data rates, and seamless connectivity with extensive coverage. However, the characteristics of the computation and communication devices located at various levels of SAGIN make application placement within such environments a challenging task. Real-time service expectations and resource requirements of applications further intensify this issue, and push the domain to operate beyond its capacity, resulting in uneven delays and significant overhead. Taking these constraints into account, SAGIN’s application placement problem can be expressed as a multiobjective optimisation problem. This paper aims to solve such a problem using a Dynamic Weight-configurable Harris Hawks Optimisation (DW-HHO) algorithm, considering diverse application contexts such as deadlines, resource usage and the number of application activities. It simultaneously minimises application total service time and host resource overhead with a robust global search. The performance of the proposed solution is compared with benchmark metaheuristic solutions such as PSO, NSGA-II, Greedy and Random. Experimental results demonstrate that DW-HHO outperforms other benchmark metaheuristic solutions in optimising resource utilisation and service delivery time of applications in SAGIN environments. The proposed DW-HHO demonstrates notable improvements over existing methods. Specifically, when evaluating the total service time for PSO, NSGA-II, Greedy, and Random, DW-HHO outperforms these methods by 7.28%, 9.07%, 13.01%, and 14.97%, respectively.

With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT), intelligent information services have showcased unprecedented capabilities in acquiring and analysing information. The conventional task processing platforms rely on centralised Cloud processing, which encounters challenges in infrastructure-less environments with unstable or disrupted electrical grids and cellular networks. These challenges hinder the deployment of intelligent information services in such environments. To address these challenges, we propose a distributed task processing platform (${DTPP}$) designed to provide satisfactory performance for executing computationally intensive applications in infrastructure-less environments. This platform leverages numerous distributed homogeneous nodes to process the arriving task locally or collaboratively. Based on this platform, a distributed task allocation algorithm is developed to achieve high task processing performance with limited energy and bandwidth resources. To validate our approach,${DTPP}$has been tested in an experimental environment utilising real-world experimental data to simulate IoT network services in infrastructure-less environments. Extensive experiments demonstrate that our proposed solution surpasses comparative algorithms in key performance metrics, including task processing ratio, task processing accuracy, algorithm processing time, and energy consumption.

Incorporating edge and cloud computing with robotics provides extended options for robots to perform real-time sensing and actuation operations in various cyber–physical systems (CPSs), including smart farms. Such systems are prone to uncertain failures triggered by mechanical disruptions. Consequently, the overall system performance degrades, primarily when location-specific tasks are already assigned to a faulty robot and require immediate recovery. Using edge and cloud computing resources is not always feasible due to communication and latency constraints. Therefore, this article exclusively focuses on harnessing the mobility of robots to support the computation tasks affected by uncertain failures of previously assigned robots and ensure faster resiliency management by relocating active robots near task sources. The proposed mobility-as-a-resilience-service (MaaRS) is formulated using a Markov decision process (MDP). Later, an edge server proximal to the robots is trained using deep reinforcement learning (DRL) to assign tasks among the robots. Specifically, a multiple deep$Q$-network (MDQN)-based dynamic task allocation mechanism is proposed to converge to a solution exploring reward uncertainties with the best exploitation. Numerical evaluation using Python and TensorFlow validates the effectiveness of the proposed approach compared to other benchmarks.

The fast online classification (FOC) of network traffic plays a critical role in the network resource management and quality of service support. However, traditional network flow features result in poor performance in FOC (with fewer packets). To tackle the issue, this study proposes two new features: (1) The conditional frequency of packet size (PSize), for which the PSize is quantized into several equal bins and the PSize-level conditional frequency of two consecutive packets is calculated; (2) The statistical feature of rate sequence that is obtained by dividing the inter-arrival time into the PSize sequences. Due to the real-time requirement of online classification, we analyze the time complexity of flow feature calculation and attempt to balance the classification speed and the accuracy in feature selection by reducing the feature dimensionality. In addition, a new feature-embedded hierarchical classification structure is developed for the scenario in which the network video traffic accounts for a relatively large proportion. Fewer packets are used in the early stage of binary classification of non-video vs. video, and then, the subsequent data packets are employed for the fine-grained classification of their respective flows. The effectiveness of the proposed method is evaluated on two real-world network datasets, and our method is compared with the state-of-the-art methods in terms of time performance, resource usage, and classification accuracy . The experimental results confirm the superiority of our approach in fast online classification.

Keeping sensor data fresh is desired for Industrial Internet of Things (IIoT), especially, in real-time monitoring applications. However, this may require sensors always in active mode and, thus, incur low energy efficiency. In this article, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel. We study the problem of optimizing the sensor data freshness in terms of Age of Information (AoI) while saving energy by scheduling the sensor to sleep when needed. The problem is formulated as a Markov decision process that takes both AoI and energy consumption into account, to which we theoretically prove that the optimal scheduling policy forms a cyclic sleep–wake pattern. The optimal sleep period is also analyzed. Simulation results demonstrate that the proposed scheduling policy outperforms other existing policies.

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in detail. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. This Special Issue aims to provide a forum for the latest advances in multi-tier computing for next-generation wireless network research, innovations, and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

Multi-tier computing effectively enables flexible computation and communication resource sharing by offloading computation-intensive tasks to nearby servers along the cloud-to-thing continuum. In essence, multi-tier computing networks can distribute computing, storage, and communication functions anywhere between the cloud and the endpoint to take full advantage of the resources available along this continuum, thus extending the traditional cloud computing architecture to the edge of the network. With multi-tier computing, some application component processing, such as delay-sensitive components, can take place at the edge of the network, while other components, such as time-tolerant and computation-intensive components, can be performed in the cloud. To best meet user requirements, centralized cloud computing with extensive resources, secure environments, and powerful algorithms is still needed, but also must be complemented by distributed fog and edge computing with shared resources, accessible environments, and simple algorithms for real-time decision-making. Given heterogeneous computing resources and collaborative service architectures, future multi-tier computing networks will be capable of supporting a full range of computing and networking services for different environments and applications. Multi-tier computing enables low-latency processing by allowing data to be processed at the network edge close to end devices. It also facilitates the distribution of fog/edge nodes to collect data from end devices. Therefore, multi-tier computing effectively complements the cloud computing architecture.

At Presently, it is still a great challenge to achieve online classification of traffic flows due to the highly varying network environments, e.g., unpredictable new traffic classes, network noise, and congestion. Traditional classification methods work well in stable network environments, but may not exhibit their performance in dynamic environments. To address online classification issues, a granular computing-based classification model (GCCM) is developed, where the spatial and temporal flow granules are defined to make GCCM robust against variations and less sensitive to noise, and the correlations among flow granules are explored to establish the granular relation matrix (GRM). The inherent burst features between packets indicated by GRM prompt GCCM to achieve fine classification in unstable network environments. GCCM analyzes the burst features of packets without inspecting the payload information, and thus can be used to classify encrypted traffic as well as unencrypted traffic at a fast speed. In addition, the GCCM model, depending on difference measurement$D(\cdot)$, is a threshold-based classification, and therefore can be used to distinguish between time-varying classes. The validity of GCCM for online traffic classification is examined through theoretical results. The experimental evaluation of classification for fine and varied classes under dynamic network environments with noise and congestion also demonstrates its superiority in terms of classification accuracy and real-time performance with the state-of-the-art.

Data dissemination of vehicles is critical for vehicular networks because of the extensive impact of traffic information. The existing works for data dissemination in vehicular networks mainly use data scheduling algorithms to transmit data among vehicles. However, it is challenging to meet the ultrareliable and low-latency requirements of data transmission among vehicular networks due to the intrinsic movement characteristic of vehicles. To promote the data dissemination of vehicular networks, a data dissemination algorithm with trajectory privacy protection is proposed in this article, which leverages the cooperative distribution of key tasks and distance deviation. Specifically, the key tasks are disseminated to vehicles first, and the trajectory privacy protection scheme is further developed to guarantee the security of data transmission by the exploration of distance deviation and pseudonym entropy. Simulation results indicate that the proposed adaptive data dissemination algorithm is approximately 60%, 69%, and 50% better than the state-of-the-art scheduling algorithms in terms of connectivity degree, transmission delay, and average distance deviation for the vehicular network.

The lightweight privacy-preserving algorithm in the vehicular networks (VNs) improves the reliability of data transmission for the vehicles. However, it is challenging for vehicles to execute resource-consuming algorithms while driving. In addition, the high-speed mobility of vehicles also brings data scheduling problems for vehicles and other equipment. To tackle the problems mentioned above, this article proposes a privacy-preserving data scheduling in an incentive-driven VN, which achieves efficient and secure data transmission based on an incentive mechanism among vehicles. The algorithm first balances the benefits between the source, forwarding, and destination nodes through a multidimensional incentive mechanism to ensure positive benefits. After the vehicles participate in the task under the action of the incentive mechanism, the key task will then be identified and completed. Finally, the data interference mechanism guarantees the security of data transmission between the vehicle and the edge server. The simulation experiment results show that the proposed algorithm is superior to other algorithms in revenue, satisfaction, and reliability.

As the most important part of a smart city and long-standing challenging issue, a highly efficient smart logistic system has attracted a great deal of attention in recent years. In particular, unmanned aerial vehicles (UAVs) are ideal solutions for last-mile delivery scenarios in recent years due to their fast speed and easy deployment. However, because of the highly automatic delivery process, UAVs are still constrained by the limited payload, battery, and computing capacity for complex computational tasks. With the aid of the mobile edge computing (MEC) technology, UAVs can offload computational tasks to the MEC computational resources in various types of IoT environments. In spite of the task offloading which can enhance their task process capability, it also brings extra overhead, such as data transfer time and energy consumption. These extra overheads may significantly impact the efficiency and payload of UAV-based delivery systems. Therefore, taking the UAV last-mile delivery system with MEC as an example, this article investigates the energy-aware multi-UAV task computation management problem according to a realistic autonomous delivery network (ADNET). Specifically, we propose a computation management strategy, namely, the MEC-based task offloading and scheduling strategy (TOSS), to provide an integral approach covering both the static task offloading and scheduling algorithm, as well as the dynamic resource conflict resolution algorithm. Grounded on real-world scenarios, our experimental results show that TOSS can achieve a higher payload for UAVs by using minimum energy consumption and task makespan within the given constraints of the deadline compared to the state-of-the-art methods.

The distributed denial of service (DDoS) attack is detrimental to businesses and individuals as people are heavily relying on the Internet. Due to remarkable profits, crackers favor DDoS as cybersecurity weapons to attack a victim. Even worse, edge servers are more vulnerable. Current solutions lack adequate consideration to the expense of attackers and inter-defender collaborations. Hence, we revisit the DDoS attack and defense, clarifying the advantages and disadvantages of both parties. We further propose a joint defense framework to defeat attackers by incurring a significant increment of required bots and enlarging attack expenses. The quantitative evaluation and experimental assessment showcase that such expense can surge up to thousands of times. The skyrocket of expenses leads to heavy loss to the cracker, which prevents further attacks.

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

Remote state estimation over a wireless network is of significant importance in many industrial applications, such as condition monitoring. In these cases, sensors deliver their data to remote estimators through wireless channels, which makes communication reliability a core issue. In this article, we propose an error-aware design to carry out network scheduling and channel allocation according to estimation error covariance and channel quality, with the aim of minimizing the total estimation error covariance. We develop multidimensional conflict graphs to model the interference and conflicts, and on this basis, a two-phase heuristic algorithm is further proposed to adaptively assign slots and channels at each superframe. Theoretical analysis and extensive simulations are given to show the effectiveness of our error-aware design in preventing the estimation error covariance from diverging, and hence able to improve the accuracy of remote estimation and monitoring.

The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from antifatigue safe driving to intelligent route planning. However, ADSs are still plagued by increasing threats from different attacks, which could be categorized into physical attacks, cyberattacks and learning-based adversarial attacks. Inevitably, the safety and security of deep learning-based autonomous driving are severely challenged by these attacks, from which the countermeasures should be analyzed and studied comprehensively to mitigate all potential risks. This survey provides a thorough analysis of different attacks that may jeopardize ADSs, as well as the corresponding state-of-the-art defense mechanisms. The analysis is unrolled by taking an in-depth overview of each step in the ADS workflow, covering adversarial attacks for various deep learning models and attacks in both physical and cyber context. Furthermore, some promising research directions are suggested in order to improve deep learning-based autonomous driving safety, including model robustness training, model testing and verification, and anomaly detection based on cloud/edge servers.

Reliable forecasting of power output from solar Photovoltaic (PV) systems is indispensable for successful penetration of increasingly high solar PV capacity into the structure of power systems. Forecasting is as equally important for efficient demand management as solar power output is variable in nature. Farm level forecasting is the primary focus of the current literature and various methods are available. However, the grid operators need to have the forecasts at aggregated regional level (e.g. state level) for decision making. Aggregated system level forecasting has received very limited attention in literature with only a very few methods so far. In this paper, we consider the task of forecasting PV power production at aggregated regional level (at state level in Australia) and present a data-driven approach. To develop the prediction models, we investigate three widely used machine learning algorithms for farm level solar power forecasting and two state-of-the-art deep learning algorithms. As the inputs to the prediction algorithms, we study two different feature sets based on the lagged power data and their descriptive statistics. An evaluation of the presented approach using PV power generation data from Australian Energy Market Operator (AEMO) demonstrates up to 6.30% improvement in forecast skill over the baseline models.

Shield tunnelling machine is a giant engineering equipment working deep under the ground, whose maintenance is significant in ensuring the continually operation of the machine. However, the traditional regular maintenance by engineers takes long time and plenty of people. In this case, a more intelligent maintenance method is required. To fill this gap, this paper proposes an intelligent maintenance method based on knowledge graph, which captures and reuse the knowledge generated during maintenance process in order to intelligently recommend solutions for maintenance tasks. This method includes three stages, creating knowledge representation model, building knowledge graph, developing collaborative knowledge management system for implementation. A case study on a specific shield tunnelling machine is demonstrated in this paper, with results showing the feasibility and effectiveness of this method.

A reliable recognition of human activities using IoT devices (e.g., on-body wearable sensors) enables various applications, such as fitness tracking, bad habit detecting, healthcare support and elder care support. However, inaccurate results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy in the daily life activities classification, we propose in this paper countable and uncountable activities to better facilitate the understanding of the nature of daily life activities. We design global and local features and their integrated feature set for classifying countable and uncountable activities. The key idea is to examine human daily life activities from different perspectives and attempt to give a comprehensive description of the characteristics of each activity through leveraging the global and local features. By using only one simple accelerometer, our approach is evaluated to be able to recognize daily life activities with higher accuracy than the state of the art, based on one self-collected and another public available dataset.

With the advancement of the Internet of Things (IoT) technologies, the number of heterogeneous IoT applications requiring a variety of resources and services is increasing dramatically. Recently, the introduction of fog computing has further unlocked the potential of real-time services within the IoT context. On the basis of fog architecture, we herein propose a novel rate-adaptive fog service platform aiming at heterogeneous services provisioning and optimized service rate allocation. By forming several service groups in the fog network in which each service could be adequately provisioned, service consumers would always benefit from the fact that the majority of services produced by the IoT applications are in their proximity and thus are delivered to the destination promptly. Taking advantage of the well-known network utility maximization (NUM) approach, a service rate-adaptive algorithm is developed to empower fog nodes working together to adjust service delivery rate dynamically. Throughout this process, the algorithm takes the current network condition and constraint into account to ensure the rate is calibrated in favor of providing satisfactory quality of service (QoS) to each service receiver at the same time. Compared to other resource allocation strategies that mainly focus on allocating resources for a single network service, our proposed platform is capable of not only dealing with both the elastic and inelastic services but also handling the abrupt network changes and converging back to the global optimum rapidly.

In state-of-the-art deep learning, centralized deep learning forces end devices to pool their data in the cloud in order to train a global model on the joint data, while distributed deep learning requires a parameter server to mediate the training process among multiple end devices. However, none of these architectures scale gracefully to large-scale privacy and time-sensitive IoT applications. Therefore, we are motivated to propose a FOg-based pRivacy prEServing dEep lEarNing framework named FORESEEN, so as to achieve scalable, accurate yet private analytics. In FORESEEN, the intermediate fog nodes and the cloud collaboratively perform noisy training of deep neural networks (DNNs), while each end device and its connected fog node collaboratively perform fast, private yet accurate inference. To enhance robustness and ensure privacy, we put forward a collaborative noisy training algorithm and develop a novel representation perturber to perturb the extracted features by combining random projection, random noise addition and data nullification. To meet the required constraints of accuracy, memory and energy in IoT end devices, we build deep models with mixed-precision. Through these sophisticated designs, FORESEEN is able to not only preserve privacy but also maintain comparable inference performance. Extensive experimental results under different datasets, different inference schemes and different noise addition strategies validate the effectiveness of FORESEEN. Moreover, FORESEEN is capable of reducing the communication cost and providing inherent support for robustness and scalability.

Internet video traffic exhibits considerable variation as new video services continue to emerge. Some videos require strict real-time performance, while others may aim for a minimal packet loss rate or sufficient bandwidth. Therefore, it is important to develop fine-grained classification mechanisms to realize effective resource management and quality of service (QoS) provisioning. However, the existing methods for classifying video traffic always suffer from two problems: payload inspection and feature selection. In this paper, we propose a novel method that uses fractal characteristics to achieve traffic classification at a fine-grained level. This method requires neither payload signatures nor statistical features. Through rigorous analysis, we prove the feasibility of employing fractal characteristics for video traffic classification and further develop a theoretical framework for the proposed scheme. For the specific scenario of video flow classification, we improve the theory of fractals in terms of estimated spectrum, core domain, segmentation, and threshold setting. The results of an extensive experimental study on several real-world video traffic datasets show that the classification accuracy of the proposed scheme is higher than that of existing methods.

Flow aggregation is capable of reducing the burden on core routing and provide the scalability of network. Among the state-of-the-art methods of QoS (Quality of Service) mapping, the existing schemes lack effective means to guarantee the QoS of Internet flows in variable network environment. To tackle this problem, in this paper, we design a dynamic flow aggregation method that uses an enhanced rough${k}$-means algorithm (ERKM) to properly aggregate network flows and perform flexible QoS mapping across different networks. More importantly, it is able to adjust the allocation of flows according to the degree of membership in ever-changing Internet environment, and control the distance threshold parameterthto make the algorithm more flexible. The experimental results suggest that the proposed method outperforms other methods in terms of QoS support in the high-load and ever-changing networks. Moreover, it is validated that the proposed method can ensure the consistency and flexibility of QoS class mapping.

The current standalone deep learning framework tends to result in overfitting and low utility. This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates. Server-based solutions are prone to the problem of a single-point-of-failure. In this respect, collaborative learning frameworks, such as federated learning (FL), are more robust. Existing federated learning frameworks overlook an important aspect of participation: fairness. All parties are given the same final model without regard to their contributions. To address these issues, we propose a decentralized Fair and Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into federated deep learning models. In particular, we design a local credibility mutual evaluation mechanism to guarantee fairness, and a three-layer onion-style encryption scheme to guarantee both accuracy and privacy. Different from existing FL paradigm, under FPPDL, each participant receives a different version of the FL model with performance commensurate with his contributions. Experiments on benchmark datasets demonstrate that FPPDL balances fairness, privacy and accuracy. It enables federated learning ecosystems to detect and isolate low-contribution parties, thereby promoting responsible participation.

Fog computing faces various security and privacy threats. Internet of Things (IoTs) devices have limited computing, storage, and other resources. They are vulnerable to attack by adversaries. Although the existing privacy-preserving solutions in fog computing can be migrated to address some privacy issues, specific privacy challenges still exist because of the unique features of fog computing, such as the decentralized and hierarchical infrastructure, mobility, location and content-aware applications. Unfortunately, privacy-preserving issues and resources in fog computing have not been systematically identified, especially the privacy preservation in multiple fog node communication with end users. In this paper, we propose a dynamic MDP-based privacy-preserving model in zero-sum game to identify the efficiency of the privacy loss and payoff changes to preserve sensitive content in a fog computing environment. First, we develop a new dynamic model with MDP-based comprehensive algorithms. Then, extensive experimental results identify the significance of the proposed model compared with others in more effectively and feasibly solving the discussed issues.

There is a growing interest in the use of renewable energy sources to power fog networks in order to mitigate the detrimental effects of conventional energy production. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a fog node powered by renewable energy sources is studied. We develop a generic analytical model to study the energy sustainability of fog nodes powered by renewable energy sources, by generalizing the leaky bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. Based on the closed-form solutions of energy buffer analysis, i.e., the energy depletion probability and mean energy length, we study the energy sustainability in two special but real-happening scenarios. The experimental results show that with proper design the leaky bucket model effectively reflects the energy sustainability of data traffic in fog networks. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics in fog networks.

In current deep learning models, centralized architecture forces participants to pool their data to the central Cloud to train a global model, while distributed architecture requires a parameter server to mediate the training process. However, privacy issues, response delays, and computation and communication bottlenecks prevent these architectures from working well at the scale of Internet of Things devices. To counter these problems, in this paper we build a Fog-embedded privacy-preserving deep learning framework (FPPDL), which moves computation from the centralized Cloud to Fog nodes near the end devices. The experimental results on benchmark image datasets under different settings demonstrate that FPPDL achieves comparable accuracy to the centralized stochastic gradient descent (SGD) framework, and delivers better accuracy than the standalone SGD framework. Our evaluations also show that both computation and communication cost are greatly reduced by FPPDL, hence achieving the desired tradeoff between privacy and performance.

Real-time Internet of Things (IoT) applications have stringent delay requirements when implemented over distributed sensing and communication networks in smart traffic control. They require the system to reach a permissible neighbourhood of an optimum solution with a tolerable delay. The performance of such applications mostly depends on the delay introduced by the underlying optimization algorithms, with the localized computational capability. In this paper, we study a smart traffic control scenario-a real-time IoT application, where a group of autonomous vehicles independently decide on their lane velocities, in collaboration with road-side units to efficiently utilize intersections with minimal environmental impact. We decompose this problem as an unconstrained network utility maximization problem. A consensus-based, constant step-size gradient descent algorithm is proposed to obtain a near-optimal solution. We analyze the delay-accuracy tradeoff in reaching a near-optimal velocity. Delay is measured in terms of the number of iterations required before the scheduling operation can be done for a particular tolerance. The operation of the algorithm under quantized message passing is also studied. On contrary to the existing methods to intersection management problems, our approach studies the limit at which an optimization algorithm fails to cater for the requirements of a real-time application and must fall back for a pareto-optimal solution, due to the communication constraints. We used simulation of urban mobility to incorporate the microscopic behavior of traffic flows to our simulations and compared our solution with traditional and state-of-the-art intersection management techniques.

The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in a factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in the literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with a novel 3-layer decision: task offloading, path planning, and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.

Building Energy Management System (BEMS) is a vital approach in constructing a global energy-efficient environment. It can be operated by analyzing data collected from sensors located in designated indoor areas. The key is to improve the data processing results while reducing the total data processing/communication volume required in the whole Internet of Things (IoT) networks as much as possible. In this work, a novel in-network self-learning algorithm for BEMS through a collaborative Fog platform is proposed. In particular, we devise an emerging Fog computing enabled IoT network architecture, where most of data can be processed in the Sensor-to-Fog and Fog-to-Fog layers. Data processing on Cloud is only required if anomalous sensor data are detected, and thus, the energy consumption due to heavy data processing on Cloud will be significantly reduced. The proposed algorithm makes the best use of Fog node capability to realize distributed data collection and processing. Via Fog-to-Fog connections, it can examine the sensor data by collecting them from different search ranges, whose values are meanwhile optimized. Numerical experiments conducted in a real indoor environment demonstrate that our algorithm achieve a high prediction accuracy for anomaly detection even with relatively small sensor data for processing. The effectiveness of Fog node placement is also verified. The overall scheme is expected to be a feasible solution to construct a cost-effective IoT network to minimize energy consumption while maximizing the indoor user's comfort, from the perspective of achieving a high prediction accuracy in BEMS data monitoring.

The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.

This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.

For constrained end devices in Internet of Things, such as smart meters (SMs), data transmission is an energy-consuming operation. To address this problem, we propose an efficient and privacy-preserving aggregation system with the aid of Fog computing architecture, named PPFA, which enables the intermediate Fog nodes to periodically collect data from nearby SMs and accurately derive aggregate statistics as the fine-grained Fog level aggregation. The Cloud/utility supplier computes overall aggregate statistics by aggregating Fog level aggregation. To minimize the privacy leakage and mitigate the utility loss, we use more efficient and concentrated Gaussian mechanism to distribute noise generation among parties, thus offering provable differential privacy guarantees of the aggregate statistic on both Fog level and Cloud level. In addition, to ensure aggregator obliviousness and system robustness, we put forward a two-layer encryption scheme: the first layer applies OTP to encrypt individual noisy measurement to achieve aggregator obliviousness, while the second layer uses public-key cryptography for authentication purpose. Our scheme is simple, efficient, and practical, it requires only one round of data exchange among a SM, its connected Fog node and the Cloud if there are no node failures, otherwise, one extra round is needed between a meter, its connected Fog node, and the trusted third party.

Wireless sensor networks (WSNs) have been rapidly integrated into Internet of Things (IoT) systems, empowering rich and diverse applications such as large-scale environment monitoring. However, due to the random deployment of sensor nodes (SNs), physical topology of the WSNs cannot be controlled and typically remains unknown to the IoT cloud server. Therefore, in order to derive the physical topology at the cloud for effective real-time event detection, a cloud-orchestrated physical topology discovery scheme for large-scale IoT systems using unmanned aerial vehicles (UAVs) is proposed in this paper. More specifically, the large-scale monitoring area is first split into a number of subregions for UAV-enabled data collection. Within the subregions, parallel Metropolis-Hastings random walk (MHRW) is developed to gather the information of WSN nodes, including their IDs and neighbor tables. The collected information is then forwarded to the cloud through UAVs for the initial generation of logical topology. Thereafter, a network-wide 3-D localization algorithm is further developed based on the discovered logical topology and multidimensional scaling method (Topo-MDS), where the UAVs equipped with global positioning system are served as mobile anchors to locate the SNs. Simulation results indicate that the parallel MHRW improves both the efficiency and accuracy of logical topology discovery. In addition, the Topo-MDS algorithm dramatically improves the 3-D location accuracy, as compared to the existing algorithms in the literature.

Fog computing is an emerging architecture for bringing processing, storage, and control from the Cloud closer to the Things/Users. Fog has mostly been studied in the vertical continuum between the Things/Users and the Cloud to provide resources traditionally existing in the remote Cloud to the applications. This paper introduces ICN-Fog, a novel horizontal Fog-to-Fog layer enabled by Information-Centric Networking. ICN-Fog enriches applications with horizontal data transfer in the Fog layer, distributed processing among Fog nodes, and built-in mobility support thanks to the smart connectionless name-based Fog-to-Fog data communications. We explain the rationale behind our design and demonstrate the advantages of the proposed Fog architecture through two representative case studies.

Task offloading opens a gateway for robotic applications to leverage computation support from the cloud infrastructure. It exploits a trade-off between the robot's and cloud's processing capabilities, and the communication between the two entities plays a critical role in making these decisions. Two major factors that significantly influence communication are network connectivity (bandwidth) and mobility of the robot. We integrate these two factors with the offloading decisions to formulate an optimization problem. Our objective in this paper is to improve the quality of service (QoS) for a 25 node application taskflow, known as direct acyclic graph. We propose a genetic algorithm based approach to solve the optimization problem which performs a novel three-layer decision making: (i) whether to offload a task or not, (ii) path planning to reach a desired location for offloading/local execution, (iii) select access point to associate with for offloading. We simulate for a smart city scenario consisting of 36-cell workspace with obstacles and compare the offloading results with a well- established fixed movement offloading method. The outcomes of our study suggest that motion and connectivity aware offloading leads to more efficient performance in terms of improved QoS and minimum consumption of resources, i.e., energy, time or distance.

In this paper, we study the fixed-time cooperative tracking control problem for multi-robot systems with doubleintegrator dynamics. First, a novel distributed observer is proposed for each follower to estimate the leader state in a fixed time, then a local tracking controller based on sliding mode technique is proposed such that the estimated leader state is tracked in a fixed time. Both cases of a stationary leader and a dynamic leader are investigated. Since nonholonomic dynamics can better describe the mobile robots in reality, we further extend the results to achieve fixed-time cooperative tracking for multi-robot systems with nonholonomic dynamics. Different from the conventional finite-time cooperative tracking strategies, the fixed-time approach in this work guarantees that an upper bound of settling time can be prescribed without dependence on initial states of robots, which provides additional system information in advance. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

This paper gives models and analytic techniques for studying shaping and policing data traffic in fog networks. The traffic in these networks is expected to be highly diverse and bursty, and regulation will be required as an integral part of congestion control. We generalize the Leaky Bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. In particular, the Markov modulated fluid sources reflect the bursty characteristics of data traffic. To measure the performance of the model in shaping and policing traffic, we derive four performance metrics. The experimental results show that with proper design the Leaky Bucket model effectively controls a 4-way trade-off between throughput, loss probability, delay and burstiness of data traffic. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics.

Anomaly detection is important for time-critical Internet of Things (IoT) applications, such as healthcare and emergency management. The recent introduction of Fog computing architecture provides an efficient platform for delay sensitive IoT applications. Exploiting the advantages of Fog computing for anomaly detection provides the ability to detect abnormal patterns in an accurate and timely manner. Use of Centralized and Distributed anomaly detection methods suffer from significant latency and energy consumption issues. Hence, we propose a novel anomaly detection method, called Fog-Empowered anomaly detection, by harnessing the processing power of the Fog computing platform and using an efficient hyperellipsoidal clustering algorithm. The end nodes in the Fog computing architecture do not perform any processing or clustering on the data. The Fog layer and the Cloud layer nodes perform the clustering and anomaly detection process, thus helping to achieve anomaly detection in a timely manner. The evaluation using synthetic and real datasets demonstrates that our proposed approach achieves a significant reduction in latency and energy consumption compared to the Distributed and Centralized schemes, while achieving a comparable detection accuracy compared to a Centralized scheme.

Suppose a disaster happens, and several groups of robots are dispatched from distant control stations. To enable rescue staff to make collective decisions, reliable and robust connections need to be established among stations. Motivated by this scenario, a two-stage robot deployment strategy is proposed for wireless robotic networks (WRNs). In the first stage, robots in distant groups are merged into one group that covers a desirable area. Since connectivity alone cannot guarantee a high communication quality, in the second stage, the flow between any two stations is further optimized in terms of expected number of transmissions per successfully delivered packet. In both stages, a distributed collision-free controller is proposed to regulate the interactive force among robots. The stability issues of WRNs, where the proposed controller together with a class of interaction models based on an acute angle test is implemented for robots, are analyzed under both fixed and switching topology. In order to efficiently switch the neighbor set for each robot, a new energy function is constructed taking finite-time consensus into account. To guarantee that energy agreement is achieved before the next topology change, a fixed-time consensus approach is further proposed and an upper bound of the settling time for the energy agreement is obtained. Numerical simulations are provided to demonstrate the effectiveness of the two-stage deployment strategy.

Cloud robotics is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with cloud computing infrastructures. It further complements Internet of Things (IoT) to improve the performance of smart city applications. By offloading heavy data- intensive computation to the ubiquitous cloud, quality of service (QoS) guarantee can be ensured. Unlike their mobile counterpart, the robots have unique characteristics of mobility, skill- learning, data collection and decision-making capabilities, which makes offloading decisions significantly complex. This paper proposes a generic cloud robotics framework to realize smart city vision while taking into consideration its various complexities. Specifically, task offloading is formulated as a constrained optimization problem capable of handling Direct Acyclic Graph (DAG) known as task flow. Given the constraints, a genetic algorithm (GA) based scheme is further developed to solve the problem. The performance of the algorithm is verified by evaluating the results via three benchmarks. To the best of our knowledge, this is one of the first attempts of task offloading approach for smart city applications of cloud robotics.

Virtualizing Internet-of-Things (IoT) services is a concept of dynamically building customized high-level IoT services that rely on the real-time data streams flowing from low-level standalone IoT devices. IoT service virtualization is essential when a myriads of IoT devices can get online, interact with each other, exchange data, and based on them create one’s own service. Especially, when virtualization occurs across multiple externals domains, it is crucial for clients to verify the source of virtual services, i.e., whether they are built based on authentic original service sources. Also, original services’ sources must be constantly aware of the identity of entities who (recursively) virtualize their services. To address these issues, this paper proposes IoT service dependency tree (SDT) validation scheme. SDT uses service dependency trees and dependency signature trees, which enable clients to validate the original sources of a virtual IoT service, verify its service dependency relationships, and have original service sources to be constantly notified of the list of entities (recursively) virtualizing their services. This paper explains SDT scheme and presents use cases for IoT service virtualization where SDT can be applied. Our experimental analysis shows that SDT is scalable for practical use.

Today, Cloud Computing and the Internet of things are two “major forces” that drive the development of new Information Technology (IT) solutions. Many Internet of things (IoT) based large-scale applications rely on a cloud platform for data processing and storage. However, big data generated or collected by large-scale geo-distributed devices needs to be transferred to the cloud, often becoming a bottleneck for the system. In this paper, we propose a framework that integrates popular cloud services with a network of IoT devices. In the framework, novel methods have been designed for reliable and efficient data transportation. This framework provides a convergence of cloud services and devices that will ease the development of IoT based, cloud-enabled applications. We have implemented a prototype of the framework to demonstrate the convergence of popular cloud services and IoT technologies.

This paper develops a fair and efficient congestion control framework using robust sliding mode control. It not only considers the limitations of current optimal congestion control approach, but also guarantees the performance of heterogeneous applications with different Quality of Service (QoS) requirements. By further proposing enhanced sliding mode-like congestion control algorithms, the paper addresses two critical issues raised from previous work [1], namely, the rigorous stability of the system and the sensitivity of design parameter. The thorough treatment makes the framework practically applicable, as well as retains its appealing properties. Moreover, the paper summarizes the applications of sliding mode approach in communication networks and highlights its great potential ahead.

In this paper, we treat mobility as a network control primitive in wireless robotic networks (WRNs), which consist of a number of mobile robots. The aim is to achieve better communication performance by making use of the mobility of the robots. In the literature, it has been shown that for a graph-based connectivity model, an equal-space configuration of robot nodes is optimal in terms of energy efficiency. To be more realistic, we adopt a signal to interference plus noise ratio (SINR)-based physical model in this work. With this model, we show that the equal-space configuration of robots is no longer optimal from the perspective of network interference. Instead, an equal-SINR configuration of robots is demonstrated to be optimal for a unicast topology in WRNs. Based on our network interference reduction method, a distributed mobility control algorithm is proposed to achieve the equal-SINR configuration. Simulation results verify that the proposed algorithm can drive robots to the desired positions where the network interference is minimized.

Information-centric networking (ICN) is designed to decouple contents from hosts at the network layer, using in-network caching as a key feature to improve the overall performance. However, the en-route caching strategy used in many ICN implementations generally yields redundancies in the cached contents across different routers. There are also some recent works focusing on cache optimization by respectively exploiting either application layer or network layer, which we think is not sufficient to increase cache hit rate and reduce traffic. In this paper, we propose a novel caching scheme (CRCache) that utilizes a cross-layer design to cache contents in a few selected routers based on the correlation of content popularity and the network topology. Specifically, through exploiting information available at both application and network layers, CRCache aims to improve the cache hit rate and reduce the overall network traffic. We conduct a large scale and real traces-driven simulation with an underlying real Internet topology in China, and show that by using CRCache, the overall cache hit rate is increased by 62.5% and network traffic reduction is improved by at least 42% compared with recent single layer schemes.

Increasing population density in urban centers demands adequate provision of services and infrastructure to meet the needs of city inhabitants, encompassing residents, workers, and visitors. The utilization of information and communications technologies to achieve this objective presents an opportunity for the development of smart cities, where city management and citizens are given access to a wealth of real-time information about the urban environment upon which to base decisions, actions, and future planning. This paper presents a framework for the realization of smart cities through the Internet of Things (IoT). The framework encompasses the complete urban information system, from the sensory level and networking support structure through to data management and Cloud-based integration of respective systems and services, and forms a transformational part of the existing cyber-physical system. This IoT vision for a smart city is applied to a noise mapping case study to illustrate a new method for existing operations that can be adapted for the enhancement and delivery of important city services.

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, the concept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

A new sliding mode-based learning control scheme is developed for a class of uncertain discrete-time systems. In particular, a recursive-learning controller is designed to enforce the sliding variable vector to reach and retain in the sliding mode, and the system states are then guaranteed to asymptotically converge to zero. A recently introduced “Lipschitz-like condition” for sliding mode control systems, which describes the continuity property of uncertain systems, is further extended to the discrete-time case setting in this paper. The distinguishing features of this approach include: (i) the information about the uncertainties is not required for designing the controller, (ii) the closed-loop system exhibits a strong robustness with respect to uncertainties, and (iii) the control scheme enjoys the chattering-free characteristic. Simulation results are also given to demonstrate the effectiveness of the new control technique.

Emerging applications in Multihop Wireless Networks (MHWNs) require considerable processing power which often may be beyond the capability of individual nodes. Parallel processing provides a promising solution, which partitions a program into multiple small tasks and executes each task concurrently on independent nodes. However, multihop wireless communication is inevitable in such networks and it could have an adverse effect on distributed processing. In this paper, an adaptive intelligent task mapping together with a scheduling scheme based on a genetic algorithm is proposed to provide real-time guarantees. This solution enables efficient parallel processing in a way that only possible node collaborations with cost-effective communications are considered. Furthermore, in order to alleviate the power scarcity of MHWN, a hybrid fitness function is derived and embedded in the algorithm to extend the overall network lifetime via workload balancing among the collaborative nodes, while still ensuring the arbitrary application deadlines. Simulation results show significant performance improvement in various testing environments over existing mechanisms.

Proportional, TCP friendly (minimum potential delay) and max-min fairness are three most commonly used fairness criteria for resource allocation in communication networks. In this paper, we generalize the above fairness criteria in terms of utility and study the resource allocation problem for heterogeneous networks where contending users may have different Quality of Services (QoS) requirements and the utility functions may not necessarily satisfy the strict concavity condition, such as real-time applications. We propose a QoS based flow control algorithm and with different link price feedback mechanisms, utility weighted proportional, TCP friendly and max-min fairness is achieved in this unified approach. In addition, the new algorithm is not only suitable for elastic data traffic, but also capable of handling real-time applications, and therefore it can be treated as an efficient flow control mechanism to provide congestion control and QoS balance for Differentiated Services in the future Internet.

The capabilities of sensor networking devices are increasing at a rapid pace. It is therefore not impractical to assume that future sensing operations will involve real time (inelastic) traffic, such as audio and video surveillance, which have strict bandwidth constraints. This in turn implies that future sensor networks will have to cater for a mix of elastic (having no bandwidth constraint requirements) and inelastic traffic. Current state of the art rate control protocols for wireless sensor networks, are however designed with focus on elastic traffic. In this work, by adapting a recently developed theory of utilityproportional rate control for wired networks to a wireless setting, and combining it with a stochastic optimization framework that results in an elegant queue backpressure-based algorithm, we have designed the first-ever rate control protocol that can efficiently handle a mix of elastic and inelastic traffic in a wireless sensor network. We implement this novel protocol in a real world sensor network stack, the TinyOS-2.x communication stack for IEEE 802.15.4 radios and evaluate the real-world performance of this protocol through comprehensive experiments on 20 and 40-node subnetworks of USC's 94-node Tutornet wireless sensor network testbed.

Wireless sensor networks (WSNs) are well suited for environment monitoring. However, some highly specialized sensors (e.g. hydrological sensors) have high power demand, and without due care, they can exhaust the battery supply quickly. Taking measurements with this kind of sensors can also overwhelm the communication resources by far. One way to reduce the power drawn by these high-demand sensors is adaptive sampling, i.e., to skip sampling when data loss is estimated to be low. Here, we present an adaptive sampling algorithm based on the Box-Jenkins approach in time series analysis. To measure the performance of our algorithms, we use the ratio of the reduction factor to root mean square error (RMSE). The rationale of the metric is that the best algorithm is the algorithm that gives the most reduction in the amount of sampling and yet the the smallest RMSE. For the datasets used in our simulations, our algorithm is capable of reducing the amount of sampling by 24% to 49%. For seven out of eight datasets, our algorithm performs better than the best in the literature so far in terms of the reduction/RMSE ratio.