Wireless rechargeable sensor networks (WRSNs) have become an important research topic as they show merit in long-term monitoring operations. Existing techniques focus on improving system performance, while the issue of thermal effects is overlooked, leading to discrepancies between theoretical results and real-world applications. In this work, we explore and exploit the impact of the thermal effect on charging performance. At first, the thermal effect is modeled based on the Newton-Richman cooling law, followed by a new theoretical charging model based on such effect. We jointly consider the influences of charger’s self-generated heat and ambient temperature on charging utility. To address the uncertainty of temperature variation problem, we developed an online learning scheme called tHermalEffectAdapTive charging algorithm (HEAT) based on the combined multi-armed bandit method. The proposed algorithm can dynamically schedule charging tasks adaptive to temperature fluctuations while guaranteeing a logarithmic regret bound. Extensive test-bed experiments and simulations are conducted. The results demonstrate that our scheme outperforms state-of-the-art methods by at least 24.9% in charging utility across various ambient temperature conditions.

Digital twin is emerging as a key technology to monitor the status of complex industry systems. Valuable insights, such as running statuses and anomalies, can be analyzed from the collected system status timely. Considering that the data updating from each system component (known as a physical object) to its digital twin is performed continuously, timely and accurate streaming analytics based on machine learning models is a key technology to analyze such data efficiently. In this paper, we focus on enabling low-delay yet highly-accurate streaming analytics for digital twin applications in mobile edge computing (MEC) networks. Specifically, we formulate a fundamental optimization problem of digital twin placements and model selections for streaming analytics, with the aim of minimizing both the analytic loss and the processing delay. To this end, we first consider the problem with a single query, for which, we propose an approximation algorithm with provable approximation ratio for a special case, and then devise an efficient algorithm for the original problem with a single query. We then study the online digital twin placement and model selection problem for streaming analytics with multiple queries under real scenarios, where resource demands of arrival queries and resource availability of MEC network are uncertain. We propose an online learning algorithm with a bounded regret to make admission policies. We finally evaluate the performance of the proposed algorithms by extensive simulations. Results show that the weighted sums of the total processing delay and the cumulative loss in the solution delivered by the proposed algorithms outperform their counterparts by 12.5% with a single query and 13.3% with multiple queries, respectively.

Wireless Power Transfer (WPT) has revolutionized the field of Wireless Rechargeable Sensor Networks (WRSNs), enabling sustainable operation of sensor nodes. Traditional mobile charging methods often require sensors to be within line-of-sight or physically accessed by the mobile charger, which may potentially lead to user safety or privacy concerns. Addressing this concern, this work is the first to introduce and validate the feasibility ofThrough-Wallcharging. We formulate theWireless charging thrOughWalls (WOW) problem to simultaneously enhance user safety and maximize charging utility. Our approach leverages fundamental principles of electromagnetics to construct an accurate charging model for Magnetic Resonance Coupling-based WPT systems. Additionally, we thoroughly analyze the impact of wall obstruction and provide a generalized framework for through-wall charging. By employing discretization techniques and approximation algorithms, we derive a near-optimal solution to the WOW problem. Extensive simulations and test-bed experiments demonstrate that our proposed approach reduces the reliance on physical access to devices, simplifies deployment in complex environments, and thereby optimizes the travel paths of mobile chargers and enhances the overall performance and lifetime of WRSNs. Compared to conventional methods, our method benefits from more reasonable scheduling order and path construction, achieving an average energy efficiency improvement of 27.8%.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, existing 2D charging methods suffer from significant errors in 3D scenarios, which leads to a huge gap between theoretical results and practical applications, hindering the widespread adoption of WRSNs. In this paper, we address the chargIng utility maximizatioN problem In 3D environmenT (INIT) and provide a general solution suitable for any type of transceiver antenna. Specifically, we first establish an accurate 3D charging model to quantify the received power of sensors in 3D environments. Secondly, we design an angle-distance discretization scheme to determine appropriate charging spots for the Mobile Charger (MC). Then, we transform the mobile charging problem into a submodular function maximization problem and propose an approximation algorithm with guaranteed performance to solve it. Finally, our method has been extensively evaluated through experiments and simulations and has demonstrated considerable advantages over other comparison algorithms in real-world 3D environments. On average, it has achieved an impressive 34.8% improvement in charging utility and a remarkable 56.1% reduction in the number of dead sensors.

Embedded real-time systems generally execute in a predictable and deterministic manner to deliver critical functionality within stringent timing constraints. However, the predictable execution behavior leaves the system vulnerable to schedule-based attacks. In this article, we present a multimode security-aware real-time scheduling scheme to counteract schedule-based attacks on multiprocessor real-time systems. To mitigate the vulnerability to the schedule-based attack, we propose a multimode scheduling method to reduce the accumulative attack effective window (AEW) of multiple victim tasks and prevent the untrusted tasks from executing during the AEW by distinctively scheduling mixed-trust tasks according to the system mode. To avoid the protection degradation due to the excessive blocking of untrusted tasks, we introduce a protection window for multiple victims on multiprocessors by analyzing the system protection capability limit under the system schedulability constraint. Furthermore, to maximize the protection capability of the multimode security-aware scheduling strategy on a multiprocessor platform, we also propose a security-aware packing algorithm to balance the workloads of mixed-trust tasks on different processors using a mixed-trust worst-fit decreasing heuristic strategy. The experimental results demonstrate that our proposed approach significantly outperforms the state-of-the-art method. Specifically, the AEW ratio and the AEW untrusted execution time ratio are reduced by 18.8% and 62.8%, respectively, while the defense success rate against ScheduLeak attack is improved by 16.3%.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as they can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 32.5% on average in charging utility in complicated environments.

As a novel solution for IoT applications, wireless rechargeable sensor networks (WRSNs) have achieved widespread deployment in recent years. Existing WRSN scheduling methods have focused extensively on maximizing the network charging utility in the fixed node case. However, when sensor nodes are deployed in dynamic environments (e.g., maritime environments) where sensors move randomly over time, existing approaches are likely to incur significant performance loss or even fail to execute normally. In this work, we focus on serving dynamic nodes whose locations vary randomly and formalize the dynamic WRSN charging utility maximization problem (termed MATA problem). Through discretizing candidate charging locations and modeling the dynamic charging process, we propose a near-optimal algorithm for maximizing charging utility. Moreover, we point out the long-short-term conflict of dynamic sensors that their location distributions in the short-term usually deviate from the long-term expectations. To tackle this issue, we further design an online learning algorithm based on the combinatorial multi-armed bandit (CMAB) model. It iteratively adjusts the charging strategy and adapts well to nodes’ short-term location deviations. Extensive experiments and simulations demonstrate that the proposed scheme can effectively charge dynamic sensors and achieve a higher charging utility compared to baseline algorithms in both long-term and short-term.

With widespread use of common-off-the-shelf components and the drive towards connection with external environments, the real-time systems are facing more and more security problems. In particular, the real-time systems are vulnerable to the schedule-based attacks because of their predictable and deterministic nature in operation. In this paper, we present a security-aware real-time scheduling scheme to counteract the schedule-based attacks by preventing the untrusted tasks from executing during the attack effective window (AEW). In order to minimize the AEW untrusted coverage ratio for the system with uncertain AEW size, we introduce the protection window to characterize the system protection capability limit due to the system schedulability constraint. To increase the opportunity of the priority inversion for the security-aware scheduling, we design an online feasibility test method based on the busy interval analysis. In addition, to reduce the run-time overhead of the online feasibility test, we also propose an efficient online feasibility test method based on the priority inversion budget analysis to avoid online iterative calculation through the offline maximum slack analysis. Owing to the protection window and the online feasibility test, our proposed approach can efficiently provide best-effort protection to mitigate the schedule-based attack vulnerability while ensuring system schedulability. Experiments show the significant security capability improvement of our proposed approach over the state-of-the-art coverage oriented scheduling algorithm.

Urban tracking plays a vital role for people’s urban life in intelligent transportation systems, e.g., public safety, case investigation, finding missing items, etc. However, the current tracking methods consume a large amount of communication and computing resources since they mainly offload all related sensing data, i.e., videos, generated by widely deployed cameras to the cloud where data are stored, processed, and analyzed. In this paper, we propose a graph optimized data offloading algorithm leveraging a crowd-AI hybrid method to minimize the data offloading cost and ensure the reliable urban tracking result. To be specific, we first formulate a crowd-AI hybrid urban tracking scenario, and prove the proposed data offloading problem in this scenario is NP-hard. Then, we solve it by decomposing the problem into two parts, i.e., trajectory prediction and task allocation. The trajectory prediction algorithm, leveraging the state graph, computes possible tracking areas of the target object, and the task allocation algorithm, using the dependency graph, chooses the optimal set of crowds and cameras to cover the tracking area while minimizing the data offloading cost separately. Finally, the extensive simulations with large real world data set are conducted showing that the proposed algorithm outperforms benchmarks in reducing data offloading cost while ensuring the tracking success rate in intelligent transportation systems.

Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.

Automotive systems are often subjected to stringent requirements on the maximum data age of certain cause-effect chains. In this paper, we present an efficient method for formally analyzing maximum data age of cause-effect chains. In particular, we decouple the problem of bounding the maximum data age of a chain into a problem of bounding the releasing interval of successive Last-to-Last data propagation instances in the chain. Owing to the problem decoupling, a relatively tighter data age upper bound can be effectively obtained in polynomial time. Experiments demonstrate that our approach can achieve high precision analysis with lower computational cost.

Wireless rechargeable sensor networks (WRSNs), which benefit from recent breakthroughs in Wireless Power Transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods concentrate on system performance improvement while little attention has been paid to security, making them vulnerable to novel attacks. In this paper, we develop a novel Charging Spoofing Attack (CSA), in which a mobile charger (MC) is charging a node intuitively. Nevertheless, it is launching an attack based on the nonlinear superposition principle of electromagnetic waves, causing the target node to be unable to receive any energy and finally exhausted in vain. First, we explain and model the nonlinear superposition effect through experiments, which points out the potential of launching such a novel attack. Second, we formalize the attacking problem as a charging uTility optImization problem with key noDe timE window constraints (TIDE). Then, we propose an approximation algorithm termed CSA to solve the TIDE problem with a bounded performance guarantee. Theoretical analyses are presented to exploit the feature of CSA. Finally, to demonstrate the outperformed features of our scheme, extensive simulations and test-bed experiments are conducted, revealing that CSA can exhaust at least 80% of key nodes without being detected.

Wireless Rechargeable Sensor Networks (WRSNs) have become an important research issue as it can overcome the energy bottleneck problem of wireless sensor networks. However, inaccurate discretization methods and imprecise charging models yield a huge gap between theoretical results and practical applications, making it difficult for wide adoptions. In this paper, we focus on designing a precise charging method for maximizing charging utility when line-of-sight (LOS) and none-line-of-sight (NLOS) charging cases exist in complicated environments. First, we design discretization methods for charging area and charging orientation for precisely constructing the charging model. Then, we develop a novel electromagnetic wave reflection model to describe the signal propagation model in the presence of obstacles. We formalize the mobile charging problem into a submodular function maximization problem which can be solved by a proposed algorithm with an approximation guarantee. Finally, extensive experiments and simulations demonstrate that our schemes outperform comparison algorithms by 31.45% on average in charging utility in complicated environments.

Mobile edge computing enables users to run resource-intensive applications at the network edge equipped with small server clusters. The mobile services are heterogeneous and edge servers are generally resource-limited. Only a subset of services can be processed by an edge server in a time slot. In this paper, we study the joint service placement and computation scheduling (JSPCS) problem to optimize both service quality and operation cost. We formulate the optimization problem to maximize the worst utility among all the services, under the constraints of multiple types of resources, and the JSPCS problem is proved to be NP-hard. By linear relaxation, we provide a dual decomposition approach to decouple this hard problem into a sequence of tractable sub-problems. We propose the Lagrange duality based joint optimal service placement and computation scheduling (LD-JSPCS) algorithm to derive the optimal solution to JSPCS problem with linear relaxation. By iteratively solving a series of feasibility problems, we prove the proposed algorithm guarantees the convergence to the optimum. Theoretical analysis and extensive simulations are performed, validating the efficiency of LD-JSPCS in service provision with limited resources.

Deep Neural Networks (DNNs) are increasingly deployed in battery-powered and resource-constrained devices. However, the most accurate DNNs usually require millions of parameters and operations, making them computation-heavy and energy-expensive, so it is an important topic to develop energy efficient DNN models. In this paper, we present an efficient DNN training framework under energy constraint to improve the energy efficiency of DNN inference. The key idea of this research is inspired by the observation that the input data of DNNs is usually inherently sparse and such sparsity can be exploited by sparse tensor DNN accelerators to eliminate ineffectual data access and compute. Therefore, we can enhance the inference accuracy within the energy budget by strategically controlling the sparsity of the input data. We build an energy consumption model for the sparse tensor DNN accelerator to quantify the inference energy consumption from the perspective of data access and data processing. In particular, we define a metric (named sporadic degree) to characterise the influence of the number of sporadic values in the sparse input on the energy consumption of data access for the sparse tensor DNN accelerator. Based on the proposed quantitative energy consumption model, we present an efficient pooling-based input mask training algorithm to optimize the energy efficiency of DNN inference by enhancing the input sparsity and reducing the number of sporadic values in the masked input. Experiments show that compared with the state-of-the-art methods, our proposed method can achieve higher inference accuracy with lower energy consumption and storage requirement owing to higher sparsity and lower sporadic degree of the masked input.

Subset selection problem for task scheduling with general cost constraints exists widely in IoT applications. Its objective is to select several profitable tasks to execute under routing and cost constraints such that the total profit is maximized. Most prior arts only focus on either online tasks or offline tasks, which are usually inapplicable in practical applications where online tasks and offline tasks co-exist. In this paper, we study the subset selection problem for HybrId Task Scheduling with general cost constraints (HITS), in which both online and offline tasks are scheduled to maximize the overall profit. We first divide the HITS problem into online and offline subproblems and propose two algorithms to solve them with bounded approximation ratios. Furthermore, we propose an approximation algorithm for the hybrid scenario where both online and offline tasks are considered. Extensive simulations show that our proposed algorithm outperforms baseline algorithms by 21.5% averagely in profit and also performs well in pure online/offline scenarios. We further demonstrate the feasibility of our algorithm through test-bed experiments in a realistic scene.

The rapid popularization of wireless power transfer (WPT) technology promotes the wide adoption of wireless rechargeable sensor networks (WRSNs). Traditional methods only focus on how to optimize network performance, and most of them overlook the energy waste issue induced by WPT. In this paper, we explore the potentials of recycling wasted energy when using WPT by means of freeloading. Specifically, with a slight modification on hardware, we expand the functionality of the mobile chargers (MCs), enabling them to harvest and recycle the WPT-induced wasted energy in the air to serve more sensors, which promotes energy efficiency. We model the problem, termed MEFree, as maximizing network energy efficiency by utilizing a limited number of freeloading MCs and scheduling their freeloading behaviors. Through jointly scheduling freeloading and charging tasks, the proposed scheme is able to solve the problem with a (1 − 1/e)/2 approximation ratio with a slightly relaxed budget. Extensive simulations are conducted and corresponding numerical results show that our proposed scheme significantly improves network energy efficiency by at least 18.8% and outperforms baseline algorithms by 19.1% on average in various aspects. Our test-bed experiments further demonstrate the practicability of our scheme in actual scenes.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

End-to-end latency analysis is one of the key problems in the automotive embedded system design. In this paper, we propose an efficient worst-case end-to-end latency analysis method for data chains of periodic real-time tasks executed on multiprocessors under a partitioned fixed-priority preemptive scheduling policy. The key idea of this research is to improve the analysis efficiency by transforming the problem of bounding the worst-case latency of the data chain to a problem of bounding the releasing interval of data propagation instances for each pair of consecutive tasks in the chain. In particular, we derive an upper bound on the releasing interval of successive data propagation instances to yield the desired data chain latency bound by a simple accumulation. Based on the above idea, we present an efficient latency upper bound analysis algorithm with polynomial time complexity. Experiments with randomly generated task sets based on a generic automotive benchmark show that our proposed approach can obtain a relatively tighter data chain latency upper bound with lower computational cost.

Benefitting from the recent breakthrough of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have become an important research topic. Most prior arts focus on system performance enhancement in an ideal environment that ignores impacts of obstacles. This contradicts with practical applications in which obstacles can be found almost anywhere and have dramatic impacts on energy transmission. In this paper, we concentrate on the problem of charging a practical WRSN in the presence of obstacles to maximize the charging utility under specific energy constraints. First, we propose a new theoretical charging model with obstacles based on Fresnel diffraction model, and conduct experiments to verify its effectiveness. Then, we propose a spatial discretization scheme to obtain a finite feasible charging position set for MC, which largely reduces computation overhead. Afterwards, we reformalize charging utility maximization with energy constraints as a submodular function maximization problem and propose a cost-efficient algorithm with approximation ratio (e-1)/2e (1 - ε) to solve it. Lastly, we demonstrate that our scheme outperforms other algorithms by at least 14.8% in terms of charging utility through test-bed experiments and extensive simulations.

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference.

Telecommunication networks are undergoing a disruptive transition toward distributed mobile edge networks with virtualized network functions (VNFs) [e.g., firewalls, intrusion detection systems (IDSs), and transcoders] within the proximity of users. This transition will enable network services, especially Internet-of-Things (IoT) applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this article, we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into integer linear programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a nontrivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on reinforcement learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real testbed. The experimental results demonstrate that the proposed algorithms are promising.

Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles' interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network.

Multiprocessor platforms have been widely applied in safety-critical domains to accommodate the increasing computation requirement of modern real-time applications. In this paper, we present a workload-aware harmonic partitioned multiprocessor scheduling scheme for periodic real-time tasks with constrained deadlines under the fixed-priority preemptive scheduling policy. In particular, two grouping metrics effectively integrating both harmonicity and workload characteristic are designed to guide our task partition. With those metrics, our scheme can greatly improve system utilization by taking advantage of the combination of harmonic relationship exploration and workload awareness. Experiments show that our proposed scheme significantly outperforms existing approaches in terms of schedulability.

Wireless rechargeable sensor networks (WRSNs), benefiting from recent breakthrough in wireless power transfer (WPT) technology, emerge as very promising for network lifetime extension. Traditional methods focus on scheduling algorithms and system optimization, and the issue of charging security/threat is ignored, causing it vulnerable to attacks. In this paper, we develop a novel attack for WRSN through Denial of Charge (DoC) aiming at maximizing destructiveness. At first, we form a generalized on-demand charging model, which provides fundamental basis for designing charging attacks. Then a request prediction method (RPM) is introduced for predicting the emergences of charging requests. Afterwards, a Collaborative DoC attacking algorithm (CoDoC) is developed, which tempers/modifies and generates fake charging requests, yielding normal nodes exhausted. Finally, to demonstrate the outperformed features of CoDoC, extensive simulations and test-bed experiments are conducted. The results show that, CoDoC outperforms in making sensor exhausted as well as causing missing events.

Multiprocessor platforms, widely adopted to realize real-time systems nowadays, bring the probabilistic characteristic to such systems because of the performance variations of complex chips. In this paper, we present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic realtime tasks on multiprocessors under the fixed-priority preemptive scheduling policy. The key idea of this research is to improve the overall schedulability by strategically arranging the workload among processors based on the exploration of the harmonic relationship among probabilistic real-time tasks. In particular, we define a harmonic index to quantify the harmonicity among probabilistic real-time tasks. This index can be obtained via the harmonic period transformation and probabilistic cumulative worst case utilization calculation of these tasks. The proposed scheduling scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task assignment. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over the existing harmonic partitioned scheduling algorithm for probabilistic real-time systems.

Benefited from recent breakthrough in wireless power transfer technology, the lifetime of wireless sensor networks (WSNs) can be prolonged significantly, generating the concept of wireless rechargeable sensor networks (WRSNs). While most recent works have been focusing on WRSNs with a single wireless charging vehicle (WCV), we investigate the issue of multiple WCVs' on-line collaborative charging schedules in this work. In our design, termed mTS, the network area is divided into subdomains for designated WCVs. Each WCV schedules its charging scheduling path by responding to the interdependency of temporal and spatial correlations from different charging requests. Higher priorities are given to sensor requests with a mixture of closer charging deadlines and closer distances. We further analyze the system performance with an M/M/n/mTS queueing model. Our further study through simulations revealed that our scheme excels in successful charging rate, sensor survival rate, and other related performance metrics. Our field experiments further confirmed these results and showed some further interesting findings on different charging hardware and methods.

Advances of mobile technology and global booming of “smartphone economy” promote a tremendous growth of smartphone-oriented applications with an exponential increase of mobile traffic in the past decade, resulting in expectable saturation of LTE spectrum in the next few years. Mobile-to-mobile (M2M) communications, capable of extending LTE capacity with enhanced spectrum efficiency, have been considered as a promise solution to this problem. One popular approach to deploy the M2M technology is to establish an M2M subsystem as an underlay to the current LTE network so that the M2M link can share the same radio resource with LTE regular links. Although this scheme can further explore the spectrum efficiency, it is challenging to design such an M2M subsystem without harmful interference to LTE regular users. We propose in this paper a novel proactive interference cancellation mechanism to form an interference-free underlay. The major innovation of the proposed framework is to use the codebook-based precoding technique to design appropriate precoders for eNB and M2M transmitter in order to achieve maximal reception interference suppression proactively at the transmitter side. Since the codebook-based precoding technique has already been employed in LTE, the proposed scheme is compatible to current LTE systems and applicable for real-world implementation. This is in strong contrast to several existing interference cancellation M2M schemes that highly rely on the unrealistic assumption on theoretical-oriented channel state information (CSI) feedback. Preliminary simulation results demonstrate the efficiency of the proposed scheme.

Real-time systems are increasingly running a mix of tasks with different criticality levels: for instance, unmanned aerial vehicle has multiple software functions with different safety criticality levels, but runs them on a single, shared computational platform. In addition, these systems are increasingly deployed on multiprocessor platforms because this can help to reduce their cost, space, weight, and power consumption. To assure the safety of such systems, several mixed-criticality scheduling algorithms have been developed that can provide mixed-criticality timing guarantees. However, most existing algorithms have two important limitations: they do not guarantee strong isolation among the high-criticality tasks, and they offer poor real-time performance for the low-criticality tasks. In this paper, we present a partitioned scheduling scheme for mixed-criticality tasks on multiprocessor platforms that addresses both issues. Our scheduling scheme consists of (i) a task-to-processor packing algorithm that takes into account the demands of tasks with respect to their criticality levels, and (ii) a mixed-criticality uniprocessor scheduling strategy that is based on task grouping. Our strategy associates each high-criticality task with a subset of the low-criticality tasks and encapsulates them in a task group, which is scheduled with the other task groups under the Earliest Deadline First (EDF) policy. Within each task group, the low-criticality task and the high-criticality tasks are scheduled using a server-based strategy, so as to enable more of the former to meet their deadlines without affecting the latter. We present a schedulability analysis for our scheduling strategy, and we show how tasks can be grouped using Mixed Integer Nonlinear Programming. Our evaluation shows that our proposed scheme significantly outperforms existing partitioned mixed-criticality scheduling algorithms, in terms of both the fraction of schedulable task sets and its ability to schedule low-criticality tasks.

Summary In peer‐to‐peer (P2P) networks, contributions are made by peers voluntarily for the autonomous character of peers. However, selfish peers may refuse to be cooperative when considering their limited transmission resources. Incentive mechanisms are always used to guarantee successful cooperations among peers. Although the inventive mechanisms have been widely investigated on the basis of game theory, most researches assume that peers are well mixed in the network, regardless of the influence of peers' transaction relationships. In this paper, a novel analysis framework based on spatial evolutionary game theory is proposed to verify the effectiveness of incentive mechanisms. In the framework, a transaction overlay network is used to model the transaction relationships of peers. The transactions between clients and servers are modeled as the donor‐recipient game to satisfy their asymmetric characters. Influences of the learning noise and some common behaviors of peers on incentive mechanisms are also considered. Moreover, in order to demonstrate the utility of the framework, a reciprocation‐based incentive mechanism, which considers the requestors' behaviors of providing and consuming services, is thoroughly investigated under the framework in scenarios with homogeneous and heterogeneous benefits of services. By using the framework, besides the effectiveness of incentive mechanisms, the detailed spatiotemporal evolutions of peers' strategies driven by incentive mechanisms can also be obtained. Copyright © 2014 John Wiley & Sons, Ltd.

Virtualization is an important enabling technology for many large data centers and cloud computing environments, and virtual machine (VM) migration plays a key role in the load balancing among the hosts of the data center. However, the existing load balancing schemes based on VM migration have serious influence on the fault-tolerant level of the services in the data center, and thus the reliability of the services cannot be guaranteed. In this paper, a novel guaranteeing fault-tolerant requirement load balancing scheme (GFTLBS) is proposed. GFTLBS migrates the VMs to balance the load without violating the fault-tolerant requirement of all services. The simulation results show that the scheme can guarantee the fault-tolerant requirements of all services while keeping the load balance.

As an important part of the smart space, body sensor networks (BSNs) provide continuous health monitoring and automation assistance for smart environment residents. A high degree of security and reliability for BSN is extremely required. An adaptive and flexible intrusion-tolerant scheme for BSN, namely ITFBS, is proposed. ITFBS dynamically detects intrusions according to the collected intrusion-related information, and it can provide an adaptive intrusion-tolerant strategy with passive replication by utilising two-step threshold-based intrusion detection and replicas classification. The correctness and effectiveness of ITFBS is theoretically proved, and the experimental results show that ITFBS can effectively tolerate intrusions with low power consumption and high adaptability.