Multicore platforms based on NoC are promising architectures for safety-critical applications. Application execution performance is determined by task mapping, with reliable execution, real-time response, and energy efficiency as requirements. We can perform task duplication, DVFS, and multipath routing to meet these requirements during task mapping. Furthermore, the computation platforms have limited computation capacity and energy supply in several application domains. Some complex tasks can be offloaded from the edge device to the cloud for execution. However, such task offloading influences task mapping on the edge device. Existing approaches seldom consider the correlation of task offloading to the cloud and task mapping on the edge device. To address this limitation, we jointly consider task mapping inside the NoC-based multicore edge device and task offloading to the cloud to optimize energy consumption while satisfying reliability and real-time constraints. This problem is formulated as a mixed-integer nonlinear programming and linearized to find the optimal solution. We propose a novel three-step heuristic with a feedback mechanism to enhance task schedulability and reduce computation time. We evaluate the behavior of our approaches through exhaustive simulations. The results show that our approaches outperform existing methods in terms of energy efficiency, task reliability, and schedulability.

The explosive growth of user-generated time-series data fuels countless real-time analytics applications but also raises acute privacy risks. Local Differential Privacy (LDP) mitigates this risk by having each client perturb its own data, yet existing pattern-aware LDP techniques still process every stream element, making them unsuitable for resource-limited or latency-constrained environments. We propose Pattern-Aware Unified Local Sampling for Efficient Local Differential Privacy, called PULSE-LDP, a lightweight framework that inserts a Bernoulli sampling step at a per-dataset sampling raterselected via a lightweight per-dataset warm-up procedure before any pattern-aware perturbation. Only the sampled subset of sizer nundergoes local noise addition, and the full stream is reconstructed via piecewise linear interpolation. This design reduces both computation and memory requirements tor2andrtimes those of the unsampled pattern-aware baseline, respectively, while preserving the same ϵ-LDP guarantee and retaining essential temporal motifs. On four diverse public datasets, 15-minute electricity load, transformer telemetry, daily exchange rates, and ten-minute meteorological readings, PULSE-LDP withr= 0.8 incurs only a 7.8% increase in Mean Relative Error (MRE) and a 3.1% rise in Dynamic Time Warping (DTW) relative to the full-data pattern-aware baseline, while reducing runtime by 24.5%. These results demonstrate that PULSE-LDP makes pattern-aware LDP practical for IoT deployments, secure cloud services, and real-time encrypted search.

In smart classroom environments, dynamic facial expression recognition (DFER) is crucial for enhancing teaching quality and improving students' learning experiences. However, existing DFER models face significant challenges in computational efficiency and temporal modeling, which limit their practical application in resource-constrained settings. To address these issues, this paper proposes a novel lightweight DFER framework called Multi-Scale Dynamic Mamba (MSDM). The MSDM model combines a Multi-Scale Attention Fusion Module (MSAFM) to effectively integrate global and local facial features and a Dynamic Temporal Focus (DTF) mechanism to enhance the modeling of long-term facial expression dynamics. These components work together to highlight key facial muscle movements while reducing background interference. Additionally, we introduce Dual-Resolution Bidirectional Mamba (DR Bi-Mamba) blocks that process high- and low-resolution facial images in parallel for coarse-to-fine feature extraction. This bio-inspired strategy enhances robustness by effectively integrating global context and local details. To better align with the practical requirements of smart classroom scenarios, we have developed a dedicated classroom dataset, HM-Class, which addresses the mismatch between existing emotion categories and the high-frequency emotional states observed in educational contexts. Extensive experiments on seven in-the-wild datasets—four DFER datasets, two static facial expression recognition (SFER) datasets, and the HM-Class dataset—show that MSDM outperforms state-of-the-art methods with fewer parameters and lower computational costs. This study offers an efficient solution for affective computing in resource-constrained classroom environments and advances the practical application of DFER technology in educational settings.

Heterogeneous Multicore Platforms (HMPs) have been widely adopted to execute tasks across a range of applications. Under limited system resources and diverse application requirements, allocating and executing dependent Approximate Computing (AC) tasks on these platforms to achieve high Quality-of-Service (QoS) is challenging. Dynamic Voltage and Frequency Scaling (DVFS) and task migration have proven effective for improving QoS while balancing time and energy consumption. However, existing approaches often overlook the migration overhead and the resulting dynamic changes in task dependencies, which can adversely affect mapping outcomes. To address these issues, this article presents a novel AC task mapping method that maximizes system QoS under multiple constraints on HMPs, accounting for task migration overhead, DVFS, and changes in Directed Acyclic Graph (DAG) topology. We first formulate this joint design problem as a complex nonlinear programming problem. Next, we linearize the nonlinear terms without performance loss by introducing auxiliary variables and additional constraints. Building on this formulation, we propose an optimal (OPT) and a low-complexity Heuristic Algorithm (HEU), derived from problem decomposition and a greedy strategy, which divides the Mixed-Integer Non-Linear Programming (MINLP) problem into two smaller subproblems with fewer variables and constraints, solving them sequentially. The simulation results show that the proposed OPT method achieves higher QoS performance, measured at about 2.389 times on average and up to 4.115 times, while its feasibility is increased to about 3.263 times on average and up to 9.667 times, compared to other state-of-the-art methods. In addition, the average QoS of the proposed HEU method is about 0.577 times that of the proposed method, but its computation time is over a thousand times shorter.

The increasing integration of heterogeneous processors on a chip presents significant challenges for efficient management in Multi-Processor System-on-Chip (MPSoC) platforms. Network-on-Chip (NoC) architectures offer a flexible and scalable interconnection paradigm through router-based communication. However, mapping dependent, real-time tasks in NoC environments critically affects data processing and transmission efficiency. An optimized task mapping scheme must address constraints such as real-time deadlines, energy consumption, and reliability, which are key metrics for modern NoCs. Existing approaches often overlook the complex interplay between communication paths and their associated energy costs, resulting in suboptimal resource utilization. This paper proposes a comprehensive task mapping framework that jointly optimizes energy efficiency and reliability by integrating Dynamic Voltage and Frequency Scaling (DVFS), multi-path data routing, task allocation, scheduling, and partial task duplication. We formulate the problem as a complex combinatorial optimization task and transform it into a solvable form with reduced computational complexity. Simulation results demonstrate that the proposed method achieves superior energy efficiency by reducing energy consumption by up to 39.7%, reducing computation time, and improving task schedulability compared to existing state-of-the-art approaches.

The widespread adoption of 5 G networks and mobile devices has led to a surge in the generation of private data, creating massive data streams. Securing and continuously releasing histogram data over sliding windows in these streams has become a critical issue, as it enables understanding recent collective phenomena in data streams while preserving individual privacy. Existing state-of-the-art methods require buffering all data from each sliding window to reconstruct accurate histograms, which is unnecessary and significantly hampers efficiency. This paper proposes an online streaming sampling publication framework with differential privacy, named thePublishingApproach withSliding window estimation-count sketch(PAS), which constructs an approximate histogram without buffering each sliding window and subsequently generates publishable histograms. Specifically, we introduce a novel memory-efficient sketch structure called theSliding WindowEstimation-CountSketch(SES), which facilitates rapid retrieval of counts within sliding window intervals while providing guaranteed data protection. The output of this sketch structure approximates true counts while theoretically incorporating differentially private noise, thus ensuring$(\epsilon , \delta )$-differential privacy. Moreover, to improve the speed of histogram generation and reduce processing time in PAS, we propose an adaptive histogram generation algorithm based on SES. Extensive experiments are conducted to demonstrate the effectiveness of the proposed methods in comparison with other publication methods.

Recently, network-on-chip (NoC)-based multiprocessor system-on-chips (MPSoCs) have become popular computing platforms for real-time applications due to high communication performance and energy efficiency over traditional bus-based MPSoCs. Due to the nature of network structures, network congestion along with transient faults, can significantly affect communication efficiency and system reliability. Most existing works have rarely focused on the concurrent optimization of network contention, reliability, and energy consumption. Here, we study the problem of contention and reliability-aware task mapping under real-time constraints for dynamic voltage and frequency scaling-enabled NoC. The problem entails optimizing voltage/frequency on cores and links to reduce energy consumption and ensure system reliability, while task mapping and slack time are adopted to alleviate network contention and reduce latency. We aim to minimize computation and communication energy and balance workload. This problem is formulated as a mixed-integer nonlinear programming, and we present an effective linearization scheme that equivalently transforms it into a mixed-integer linear programming to find the optimal solution. To reduce computation time, we propose a three-step heuristic, including task allocation, frequency scaling and edge scheduling, and communication contention management. Finally, we perform extensive simulations to evaluate the proposed method. The results show we can achieve 31.6% and 21.7% energy savings, with 95.5% and 98.6% less contention than the existing methods.

The proliferation of location-aware devices has led to a wide-ranging applicability of trajectory data in diverse real-world scenarios. Despite the prevalent use of the k-means algorithm and differential privacy techniques in mainstream research for the generalization and protection of privacy-sensitive data, limitations persist in effectively reducing noise errors and enhancing overall algorithmic efficiency. This paper addresses the deficiencies observed in existing clustering methodologies applied to user trajectory data, focusing on data privacy preservation and enhanced utility. To this end, we propose a novel incremental clustering framework based on personalized differential privacy. The framework employs dynamic time warping for similarity assessment and incorporates temporal-based cluster protection mechanisms to fulfill privacy requirements. Moreover, it augments the Geo Indistinguishability (GI) privacy protection mechanism to tailor personalized privacy budgets. Subsequently, learning vector quantization is employed for incremental clustering synthesis of trajectory data, completing trajectory publication. Through experimental validation, our proposed model demonstrates significant improvements, with the data usability metric HD increasing by a minimum of 22% compared to existing algorithms, the privacy protection metric AMI improving by at least 18%, and the algorithm’s efficiency enhancing by no less than 20%.

Summary In recent years, data are typically distributed in multiple organizations while the data security is becoming increasingly important. Federated learning (FL), which enables multiple parties to collaboratively train a model without exchanging the raw data, has attracted more and more attention. Based on the distribution of data, FL can be realized in three scenarios, that is, horizontal, vertical, and hybrid. In this article, we propose to combine distributed machine learning techniques with vertical FL and propose a distributed vertical federated learning (DVFL) approach. The DVFL approach exploits a fully distributed architecture within each party in order to accelerate the training process. In addition, we exploit homomorphic encryption to protect the data against honest‐but‐curious participants. We conduct extensive experimentation in a large‐scale cluster environment and a cloud environment in order to show the efficiency and scalability of our proposed approach. The experiments demonstrate the good scalability of our approach and the significant efficiency advantage (up to 6.8 times with a single server and 15.1 times with multiple servers in terms of the training time) compared with baseline frameworks.

The problem of multiple integrity attacks (attackers) detection and identification (MIADI) in cyber–physical systems (CPSs) is still a challenging problem to date. The goal of this article is to develop a knowledge-based method capable of simultaneously detecting and identifying multiple integrity attacks aiming at different sensors in a CPS. In this article, with the help of labeled random finite set (RFS) theory, a new solution to solve the MIADI problem is proposed. The main contributions of this article lie in the following two aspects, the first is the novel formulation of the MIADI problem, in which labeled RFSs are used to model the behaviors of multiple integrity attackers for the first time, and the second is the proposed labeled RFS-based solution, which provides an elegant framework to cope with the MIADI problem. Numerical experiments are conducted and experimental results demonstrate the effectiveness of the proposed solution. This proposed solution further extends the feasibility of the labeled RFS theory in the context of CPSs cybersecurity.

Heterogeneous (HE) multicore platforms, such as ARM big.LITTLE, are widely used to execute embedded applications under multiple and contradictory constraints, such as energy consumption and real-time (RT) execution. To fulfill these constraints and optimize system performance, application tasks should be efficiently mapped on multicore platforms. Embedded applications are usually tolerant to approximated results but acceptable quality of service (QoS). Modeling-embedded applications by using the elastic task model, namely, imprecise computation (IC) task model, can balance system QoS, energy consumption, and RT performance during task deployment. However, state-of-the-art approaches seldom consider the problem of IC task deployment on HE multicore platforms. They typically neglect task migration, which can improve the solutions due to its flexibility during the task deployment process. This article proposes a novel QoS-aware task deployment method to maximize system QoS under energy and RT constraints, where the frequency assignment (FA), task allocation (TA), scheduling, and migration are optimized simultaneously. The task deployment problem is formulated as mixed-integer nonlinear programming. Then, it is linearized to mixed-integer linear programming to find the optimal (OPT) solution. Furthermore, based on the problem structure and problem decomposition, we propose a novel heuristic (HEU) with low computational complexity. The subproblems regarding FA, TA, scheduling, and adjustment are considered and solved in sequence. Finally, the simulation results show that the proposed task deployment method improves the system QoS by 31.2% on average (up to 112.8%) compared to the state-of-the-art methods and the designed HEU achieves about 53.9% (on average) performance of the OPT solution with a negligible computing time.

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform namedFedCubeand a Lyapunov-based data placement algorithm.FedCubeenables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches.

Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA) , a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods.

Task deployment plays an important role in the overall system performance, especially for complex architectures, including several cores with Dynamic Voltage and Frequency Scaling (DVFS) and Network-on-Chips (NoC). Task deployment affects not only the energy consumption but also the real-time response and reliability of the system. In this work, a task deployment approach is proposed to optimize the overall system energy consumption, including computation of the cores and communication of the NoC, under task reliability and real-time constraints. More precisely, the task deployment approach combines task allocation and scheduling, frequency assignment, task duplication, and multi-path data routing. The task deployment problem is formulated using mixed-integer non-linear programming. To find the optimal solution, the original problem is equivalently transformed to mixed-integer linear programming, and solved by state-of-the-art solvers. Furthermore, a decomposition-based heuristic, with low computational complexity, is proposed to deal with scalability. Finally, extended simulations evaluate the proposed methods.

Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

The emergence of connected vehicle (CV) technologies has created new traffic control opportunities, among them, is the potential to estimate volume without approach lane detection. Rather than requiring the expense and effort to install and maintain detector systems, this new “detector-free” method permits traffic volume to be estimated from CV GPS trajectory data. Unfortunately, however, CV GPS methods are limited not only to locations where CV GPS data can be recorded, but also limited to time when CV GPS data is recorded. The goal of this research was to overcome these limitations and permit volume estimation to be accomplished under any location or condition, including low-penetration CV environments. The contributions made by this work are significant in two respects. First, it creates an improved queue-based method to estimate intersection approach volumes during each signal cycle with sparse CV data. Second, the research demonstrates the application of a Bayesian deduction method to approximate volume with no CV trajectory data. To accomplish this, traffic volumes are assumed to be time-dependent Poisson distributed throughout the day, and CV data were used to estimate CV volume and further set as prior to deduce the time-dependent Poisson arrival rate. To verify and evaluate the accuracy and effectiveness of this new method under a range of potential traffic conditions, a simulation case study and a NGSIM case study were implemented. Results of both case studies resulted in estimated-to-actual arrival rate average errors as low as 4.2 percent and volume estimation errors as low as 0.9 percent.

On multicore platforms, reliable task execution, as well as low energy consumption, are essential. Dynamic Voltage/Frequency Scaling (DVFS) is typically used for energy saving, but with a negative impact on reliability, especially when the frequency is low. Using high frequencies to meet reliability constraints is not always feasible, while multiple replicas increase energy consumption. To minimize energy consumption, enhancing reliability, without violating real-time constraints, we propose an approach that combines distinct reliability enhancement techniques, under task-level, processor-level and system-level DVFS. Our task mapping problem jointly optimizes task allocation, task frequency assignment, and task duplication, under multiple constraints. This is achieved by formulating the task mapping problem as a mixed integer non-linear programming problem and equivalently transforming it into a mixed integer linear programming, that is optimally solved. From the obtained results, the proposed approach achieves better energy consumption and finds solutions, when other approaches fail.

Networked systems are useful for a wide range of applications, many of which require distributed and collaborative data processing to satisfy real-time requirements. On one hand, networked systems are usually resource constrained, mainly regarding the energy supply of the nodes and their computation and communication abilities. On the other hand, many real-time applications can be executed in an imprecise way, where an approximate result is acceptable as long as the baseline Quality of Service (QoS) is satisfied. Such applications can be modeled through imprecise computation (IC) tasks. To achieve a better tradeoff between QoS and limited system resources, while meeting application requirements, the IC-tasks must be efficiently mapped to the system nodes. To tackle this problem, we first construct an IC-task mapping problem that aims to maximize system QoS subject to real-time and energy constraints. Dynamic voltage and frequency scaling (DVFS) and multipath routing are explored to further enhance real-time performance and reduce energy consumption. Second, based on the problem structure, we propose an optimal approach to perform IC-task mapping and prove its optimality. Furthermore, to enhance the scalability of the proposed approach, we present a heuristic IC-task mapping method with low computation time. Finally, the simulation results demonstrate the effectiveness of the proposed methods in terms of the solution quality and the computation time.

Asymmetric Multicore Processors (AMP) are a very promising architecture to deal efficiently with the wide diversity of applications. In real-time application domains, in-time approximated results are preferred than accurate - but too late - results. In this work, we propose a deployment approach that exploits the heterogeneity provided by AMP architectures and the approximation tolerance provided by the applications, so as to increase as much as possible the quality of the results under given energy and timing constraints. Initially, an optimal approach is proposed based on problem linearization and decomposition. Then, a heuristic approach is developed based on iteration relaxation of the optimal version. The obtained results show 16.3% reduction in the computation time for the optimal approach compared to the conventional optimal approaches. The proposed heuristic approach is about 100 times faster at the cost of a 29.8% QoS degradation in comparison with the optimal solution.

Wireless charging provides dynamic power supply for wireless sensor networks (WSNs). Such systems, are typically considered under the scenario of wireless rechargeable sensor networks (WRSNs). With the use of mobile chargers (MCs), the flexibility of WRSNs is further enhanced. However, the use of MCs poses several challenges during the system design. The coordination process has to simultaneously optimize the scheduling, the moving time, and the charging time of multiple MCs under limited system resources (time and energy). Efficient methods that jointly solve these challenges are generally lacking in the literature. In this paper, we address the multiple MCs coordination problem under multiple system requirements. First, we aim at minimizing the energy consumption of MCs, guaranteeing that every sensor will not run out of energy. We formulate the multiple MCs coordination problem as a mixed-integer linear programming and derive a set of desired network properties. Second, we propose a novel decomposition method to optimally solve the problem, as well as to reduce the computation time. Our approach divides the problem into a subproblem for the MC scheduling and a subproblem for the MC moving time and charging time, and solves them iteratively by utilizing the solution of one into the other. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method in terms of solution quality and computation time.

In cyber-physical systems, mobile actuators can enhance system's flexibility and scalability, but at the same time incurs complex couplings in the scheduling and controlling of the actuators. In this paper, we propose a novel event-driven method aiming at satisfying a required level of control accuracy and saving energy consumption of the actuators, while guaranteeing a bounded action delay. We formulate a joint-design problem of both actuator scheduling and output control. To solve this problem, we propose a two-step optimization method. In the first step, the problem of actuator scheduling and action time allocation is decomposed into two subproblems. They are solved iteratively by utilizing the solution of one in the other. The convergence of this iterative algorithm is proved. In the second step, an online method is proposed to estimate the error and adjust the outputs of the actuators accordingly. Through simulations and experiments, we demonstrate the effectiveness of the proposed method.

Wireless sensor and actuator networks (WSANs) are emerging as a new generation of wireless sensor networks. Due to the coupling between the sensing areas of the sensors and the action areas of the actuators, the efficient coordination among the nodes is a great challenge. In this paper, we address the problem of distributed node coordination in WSANs aiming at meeting the user's requirements on the states of the points of interest (POIs) in a real-time and energy-efficient manner. The node coordination problem is formulated as a nonlinear program. To solve it efficiently, the problem is divided into two correlated subproblems: 1) the sensor-actuator (S-A) coordination and 2) the actuator-actuator (A-A) coordination. In the S-A coordination, a distributed federated Kalman filter-based estimation approach is applied for the actuators to collaborate with their ambient sensors to estimate the states of the POIs. In the A-A coordination, a distributed Lagrange-based control method is designed for the actuators to optimally adjust their outputs, based on the estimated results from the S-A coordination. The convergence of the proposed method is proved rigorously. As the proposed node coordination scheme is distributed, we find the optimal solution while avoiding high computational complexity. The simulation results also show that the proposed distributed approach is an efficient and practically applicable method with reasonable complexity.

Multicore architectures have great potential for energy-constrained embedded systems, such as energy-harvesting wireless sensor networks. Some embedded applications, especially the real-time ones, can be modeled as imprecise computation tasks. A task is divided into a mandatory subtask that provides a baseline quality-of-service (QoS) and an optional subtask that refines the result to increase the QoS. Combining dynamic voltage and frequency scaling, task allocation, and task adjustment, we can maximize the system QoS under real-time and energy supply constraints. However, the nonlinear and combinatorial nature of this problem makes it difficult to solve. This paper first formulates a mixed-integer nonlinear programming problem to concurrently carry out task-to-processor allocation, frequency-to-task assignment and optional task adjustment. We provide a mixed-integer linear programming form of this formulation without performance degradation and we propose a novel decomposition algorithm to provide an optimal solution with reduced computation time compared to state-of-the-art optimal approaches (22.6% in average). We also propose a heuristic version that has negligible computation time.

Multicore architectures are now widely used in energy-constrained real-time systems, such as energy-harvesting wireless sensor networks. To take advantage of these multicores, there is a strong need to balance system energy, performance and Quality-of-Service (QoS). The Imprecise Computation (IC) model splits a task into mandatory and optional parts allowing to tradeoff QoS. The problem of mapping, i.e. allocating and scheduling, IC-tasks to a set of processors to maximize system QoS under real-time and energy constraints can be formulated as a Mixed Integer Linear Programming (MILP) problem. However, state-of-the-art solving techniques either demand high complexity or can only achieve feasible (suboptimal) solutions. In this paper, we develop an effective decomposition-based approach to achieve an optimal solution while reducing computational complexity. It decomposes the original problem into two smaller easier-to-solve problems: a master problem for IC-tasks allocation and a slave problem for IC-tasks scheduling. We also provide comprehensive optimality analysis for the proposed method. Through the simulations, we validate and demonstrate the performance of the proposed method, resulting in an average 55% QoS improvement with regards to published techniques.

Wireless charging is a promising technology for provisioning dynamic power supply in wireless rechargeable sensor networks (WRSNs). The charging equipment can be carried by some mobile nodes to enhance the charging flexibility. With such mobile chargers (MCs), the charging process should simultaneously address the MC scheduling, the moving and charging time allocation, while saving the total energy consumption of MCs. However, the efficient solutions that jointly solve those challenges are generally lacking in the literature. First, we investigate the multi-MC coordination problem that minimizing the energy expenditure of MCs while guaranteeing the perpetual operation of WRSNs, and formulate this problem as a mixed-integer linear program (MILP). Second, to solve this problem efficiently, we propose a novel decentralized method which is based on Benders decomposition. The multi-MC coordination problem is then decomposed into a master problem (MP) and a slave problem (SP), with the MP for MC scheduling and the SP for MC moving and charging time allocation. The MP is being solved by the base station (BS), while the SP is further decomposed into several sub-SPs and being solved by the MCs in parallel. The BS and MCs coordinate themselves to decide an optimal charging strategy. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method.

Wireless sensor and actuator networks combine a large number of sensors and a lower number of actuators that are connected with wireless medium, providing distributed sensing and executing appropriate tasks in a special region of interest. To accomplish effective sensing and acting tasks, efficient coordination mechanism among the nodes is required. As an attempt in this direction, this paper develops a collaborative control and estimation mechanism, which addresses the nodes coordination in a distributed manner. First, we propose a regional controllability-based virtual force algorithm as an actuator deployment strategy to enhance area coverage after an initial random placement of actuators. During this process, a dynamic coordination mechanism is adopted to control nodes. This mechanism incorporates two components, namely, proportional-integral-derivative neural network and recursive least squares-based Kalman filter algorithms. Taking advantage of feedback control and online learning technology, the proposed coordination mechanism schedules the corresponding nodes on the basis of the characteristics of current events, utilizes proportional-integral-derivative neural network controller inside each actuator to improve system transient and steady-state responses, and deals with system state/parameter estimation problems according to the recursive least squares-based Kalman filter algorithm, so as to achieve better control accuracy. Simulations demonstrate the effectiveness of our proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.