Traditional Dynamic Voltage and Frequency Scaling (DVFS) does not adapt proactively to dynamic workload and thermal variations. It reacts statically, making it less effective in handling diverse tasks with varying performance and thermal demands. Hence, multiple solutions have been proposed to learn an optimal policy for each respective workload through Reinforcement Learning (RL). However, these approaches are typically developed for high-end microcontrollers, resulting in sub-optimal performance on lower-end devices. Moreover, the incomplete and noisy nature of the data used for training is usually overlooked, with the cutting-edge RL-based DVFS solution zTT formulating the problem under the assumption of a fully observable Markov Decision Process (MDP). In this work, we investigate the limitations of applying such framework on low-end devices (i.e, Jetson Nano 4GB) and reformulate the solution to Partially-Observable Markov Decision Process (POMDP), incorporating temporal dependencies across consecutive observations to compensate the limitations arising from the absence of full observability while enhancing the existent reward, and exploration & exploitation framework, addressing the limited computational capacity of low-end devices. Our approach improves the device’s performance by up to 17% compared to the previous solution while incurring only a negligible increase in power consumption.

Along with the development of the Industrial Internet of Things, human activity recognition (HAR) has received widespread attention in many fields. Support Vector Machine (SVM), is widely used by researchers for human activity recognition. However, the inherent difference of signal properties from different sensors and various orientations is potentially lost when using the vector-based SVM for human activity recognition. What's more, the outlier sensitivity problem of the standard SVM reduces the accuracy of human activity recognition. To tackle this problem, we present a tensor-based feature representation model and a weighted support tensor machine (WSTM) for human activity recognition. Specifically, tensor-based representations are first used to model features from different sensors and various orientations to retain the latent relationship. In addition, the weighted support tensor machine is proposed to classify the human activities in tensor space while avoiding the outlier sensitivity problem. Experimental results demonstrate the proposed WSTM algorithm.

As data and computational resources continue to expand, incorporating a variety of knowledge during the pre-training phase enhances large models, providing them with strong zero-shot capabilities. Due to the alignment of modal features by visual language models, zero-shot image captioning no longer necessitates pre-training on paired image-text labeled data, enabling accurate text description generation for images not encountered before. While recent research focuses on methods utilizing entity retrieval as anchors to bridge the gap between different modalities, these approaches often fall short of thoroughly analyzing the impact of entity retrieval recall on the zero-shot generation capabilities. To address this issue, we propose MERCap, a zero-shot image captioning method employing Multi-type Entity representation Retrieval. More specifically, we first approximate image representation using the CLIP representation of text and Gaussian noise to address the modality gap. Then, we train a GPT-2 decoder to reconstruct text using entities as hard prompts and CLIP representations as soft prompts. Additionally, we construct a domain-specific entity set, assigning multiple representations to each entity and refining their representation vectors through contrastive learning. During inference, we retrieve entities and input them into the decoder to generate corresponding captions. Extensive experiments validate that our approach is efficient, achieving a new state-of-the-art level in cross-domain captioning and demonstrating strong competitiveness in in-domain captioning compared to existing methods.

Video inpainting techniques typically serve to restore destroyed or missing regions in digital videos. However, such techniques may also be illegally used to remove important objects for creating forged videos. This letter proposes a simple yet effective forensic scheme for Video Inpainting LOcalization with ContrAstive Learning (ViLocal). A 3D Uniformer encoder is applied to the video noise residual for learning effective spatiotemporal features. To enhance discriminative power, supervised contrastive learning is adopted to capture the local regional inconsistency through separating the pristine and inpainted pixels. The pixel-wise inpainting localization map is yielded by a lightweight convolution decoder with two-stage training. To prepare enough training samples, we build a video object segmentation dataset (VOS2k5) of 2500 videos with pixel-level annotations per frame. Extensive experimental results validate the superiority of ViLocal over the state-of-the-arts.

Digital video inpainting technique has been substantially improved with deep learning in recent years. It may be used as malicious manipulation to remove important objects for creating forged videos. As such it is significant to blindly identify the inpainted regions in videos. In this paper, we present a Trusted Video Inpainting Localization network (TruVIL) with excellent robustness and generalization ability. Observing that high-frequency noise can effectively unveil the inpainted regions, we design deep attentive noise learning in multiple stages to capture the inpainting traces. Firstly, a multiscale noise extraction module based on 3D High Pass (HP3D) layers is used to create the noise modality from input RGB frames. Then the correlation between such two complementary modalities are explored by a cross-modality attentive fusion module to facilitate mutual feature learning. Lastly, spatial details are selectively enhanced by an attentive noise decoding module to boost the localization performance of the network. To prepare enough training samples, we also build a frame-level video object segmentation dataset (VOS2k5) with 2500 videos and pixel-level annotation for all frames. Both quantitative and qualitative evaluations on various inpainted videos verify the robustness against video compression and generalization ability of TruVIL.

Finding an optimal energy-efficient policy that is adaptable to underlying edge devices while meeting deadlines for tasks has always been challenging. This research studies generalized systems with multi-task, multi-deadline scenarios with reinforcement learning-based DVFS for energy saving for periodic soft real-time applications on edge devices. This work addresses the limitation of previous work that models a periodic system as a single task and single-deadline scenario, which is too simplified to cope with complex situations. The method encodes time series data in the Linux kernel into information that is easy to interpret for reinforcement learning, allowing the system to generate DVFS policies to adapt system patterns based on the general workload. For encoding, we present two different methods for comparison. Both methods use only one performance counter: system utilization, and the kernel only needs minimal information from the userspace. Our method is implemented on Jetson Nano Board (2GB) and is tested with three fixed multitask workloads, which are three, five, and eight tasks in the workload, respectively. For randomness and generalization, we also designed a random workload generator to build different multitask workloads to test. Based on the test results, our method could save 3%-10% power compared to Linux built-in governors.

Energy measurement of computer devices, which are widely used in the Internet of Things (IoT),is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. In this paper, we propose an easy-to-use approach to derive a software-based energy estimation model with external low-end power meters based on data-driven analysis. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection & profiling method and physical measurement are explored. Periodic Long-duration measurements are utilized in the experiments to derive and validate power models, allowing more accurate power readings from the low-end power meter. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved by the software-based power estimation compared to measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in a real environment. Our cost-effective method facilitates accurate instantaneous power estimation, which low-end power meters cannot directly provide.

This work aims at addressing carbon neutrality challenges through resource management with system software control. Reducing energy costs is vital for modern systems, especially those battery-powered devices that need to perform complex tasks. The technique of dynamic voltage or frequency scaling (DVFS) has been commonly adopted for reducing power consumption in cyber-physical systems to support the increasing computation demand under limited battery life. Dynamic slack becomes available when a task finishes earlier than its worst execution time. Dynamic slack management is an important factor for the DVFS mechanism. This paper proposes a dynamic slack-sharing (DSS) DVFS scheduling method that reduces CPU energy consumption by learning the slack-sharing rate. The DSS method automatically changes the slack sharing rate of a task on the fly in different situations through learning from experience to determine how much slack to use for the next task and how much to share. The method used for learning is Q-learning. Extensive experiments have been performed, and the results show that the DSS technique achieves more energy savings than the existing ones.

Internet of Things (IoT) is a technology that allows ordinary physical devices to collect, process, and share data with other physical devices and systems over the Internet. It provides pervasively connected infrastructures to support innovative applications and services that can automate otherwise intensely laborious manual effort. Edge computing (EC) complements the powerful centralized cloud servers by providing powerful computation capability close to the data source, minimizing communication latency, and securing data privacy. The energy consumption problem has continued to receive much attention from the IoT community in applying various techniques to reduce energy consumption while still meeting the computational demand. In this article, we propose an application-deadline-aware data offloading scheme using deep reinforcement learning and dynamic voltage and frequency scaling (DVFS) in an EC environment to reduce the energy consumption of IoT devices. The proposed scheme learns the optimal data distribution policies and local computation DVFS frequency scaling by interacting with the system environment and learning the behavior of the device, network, and edge servers. The proposed scheme was tested on multiple EC environments with different IoT devices. Experimental results show that this scheme can reduce energy consumption while achieving the IoT application and services timing and computational goals. The proposed scheme has substantial energy savings when compared with the native Linux governors.

Energy efficiency and autonomous power management are extremely important for mobile-edge computing. Reducing energy consumption of a number of applications running concurrently in mobile devices while maintaining performance poses a challenge to energy optimization due to the limited capacity of the embedded battery. To extend battery life and offer a long-lasting working energy, dynamic voltage and frequency scaling (DVFS) has been widely used in mobile devices for energy consumption minimization. However, most conventional DVFS techniques scale operating frequency based on static policies, and thus, they are difficult to be adapted to systems of varied conditions. In order to improve adaptivity, in this article, we proposed a Double-Q power management approach to scale operating frequency based on learning. The Double-Q method stores two Q tables and two corresponding update functions. In each decision point, either of Q tables is randomly chosen and updated, while the other is used for the measurement. This mechanism reduces the overestimation in Q values, consequently enhancing the accurateness of frequency predictions. To evaluate the effectiveness of our proposed approach, a Double-Q governor is implemented in the Linux kernel. Our approach is computationally light, and experimental results indicate that it achieves at least 5-18% total energy saving compared to on-demand and conservative governors, as well as Q learning-based method.

The promising Internet of Things (IoT) provides a powerful platform for practical smart applications. The limited resources of the IoT nodes as well as the emerged coverage holes pose a great challenge on the quality of service of the IoT. Mobile edge computing (MEC), which can improve the IoT nodes energy consumption efficiency and optimize the utilization effectiveness of the limited resources, provides a novel view for coping with the challenge. Based on the MEC, this paper focuses on how to solve the problem of dispatch of mobile edge nodes for confident information coverage holes repairing (DMEN-CICHR) with the goal of maximizing the network lifetime and guaranteeing the network connectivity. To deal with the DMEN-CICHR problem, we develop an energy-balanced and obstacle-adaptive mobile edge node dispatch algorithm called EBOADMEN-CICHR, which restricts the mobile edge nodes from moving too long distance by setting a bound for each CIC hole and repeatedly updating the bound by a competition mechanism. To guarantee the network connectivity, the EBOADMEN-CICHR recursively performs breadth first search on a constructed undirected graph to find all disconnected subgraphs and then dispatches some mobile edge nodes to connect those disconnected subgraphs until the whole network is connected. A number of experiments emulating the realistic scenarios in radiological pollution monitoring in uranium tailings are executed to verify the effectiveness of the proposed EBOADMEN-CICHR solution. Experimental results show the EBOADMEN-CICHR can perform better than other peer methods in term of higher energy efficiency and longer network lifetime.

The promising Industrial Internet of Things (IIoT) consisting of heterogeneous resource-restricted IoT nodes recently has attracted great attention from both academia and industry communities. However, the battery-powered, computing and communication resource-constrained, and randomly uneven distributed features of the IoT nodes pose several great tough hurdles, including the quality of services of real-time processing, energy efficiency, network lifetime, and coverage holes to the IIoT-based industrial applications. To deal with these challenges, based on the emerging edge computing paradigm and the novel confident information coverage (CIC) model, this paper investigates how to relocate redundant IoT edge nodes to provide timely CIC service in an offloading-assisted energy-balanced manner while extending the network lifetime, which is called as the CIC-based IoT edge node relocation (CICENR) problem. To effectively handle the CICENR problem, we propose an offloading-assisted energy-balanced IoT edge node relocation approach CIC-based offloading-assisted energy-balanced approach (CIC-OAEBA) and the other CIC-based direct replacement approach. Specially, the CIC-OAEBA adopts the Grid-Quorum strategy to quickly detect the redundant IoT edge nodes by offloading the communication-intensive and computing-intensive tasks from grid header nodes to peer IoT edge nodes, and make full use of the cascaded movement strategy to move the nearest redundant IoT edge nodes to the requesting CIC hole locations. Experimental results indicate the proposed approaches remarkably outperform other peer methods in terms of response time, energy efficiency, and especially the network lifetime and coverage performance.

Radio frequency identification (RFID) systems, as one of the key components in the Internet of Things (IoT), have attracted much attention in the domains of industry and academia. In practice, the performance of RFID systems rather relies on the effectiveness and efficiency of anti-collision algorithms. A large body of studies have recently focused on the anti-collision algorithms, such as the Q-algorithm ( QA ), which has been successfully utilized in EPCglobal Class-1 Generation-2 protocol. However, the performance of those anti-collision algorithms needs to be further improved. Observe that fully exploiting the pre-processing time can improve the efficiency of the QA algorithm. With an objective of improving the performance for anti-collision, we propose a Nested Q-algorithm ( NQA ), which makes full use of such pre-processing time and incorporates the advantages of both Binary Tree ( BT ) algorithm and QA algorithm. Specifically, based on the expected number of collision tags, the NQA algorithm can adaptively select either BT or QA to identify collision tags. Extensive simulation results validate the efficiency and effectiveness of our proposed NQA (i.e., less running time for processing the same number of active tags) when compared to the existing algorithms.

Reducing energy consumption is a vital and challenging problem for the edge computing devices since they are always energy-limited. To tackle this problem, a deep Q-learning model with multiple DVFS (dynamic voltage and frequency scaling) algorithms was proposed for energy-efficient scheduling (DQL-EES). However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. Additionally, it cannot distinguish the continuous system states well since it depends on a Q-table to generate the target values for training parameters. In this paper, a double deep Q-learning model is proposed for energy-efficient edge scheduling (DDQ-EES). Specially, the proposed double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. Furthermore, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, instead of the Sigmoid function in QDL-EES, to avoid gradient vanishing. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model. The proposed model is compared with DQL-EES on EdgeCloudSim in terms of energy saving and training time. Results indicate that our proposed model can save average 2%-2.4% energy and achieve a higher training efficiency than QQL-EES, proving its potential for energy-efficient edge scheduling.

Mobile cloud computing (MCC) is extensively ubiquitous in the mobile Internet era and embraces complex environments because of the heterogeneity of devices and complexity of communications. Balancing the costs of different influencing objectives (e.g., energy consumption, system reliability, and quality of experience (QoE)) in MCC faces great challenges. This paper focuses on reasonably allocating computational tasks to suitable cores of mobile devices or cloud in MCC to minimize the total energy consumption, and maximize the system reliability and QoE. Concretely, this paper 1 proposes a holistic mobile cloud optimization model including energy consumption, system reliability, and QoE; 2 presents a DVFS-enabled and thermal-aware global energy consumption model which simultaneously considers the synergy of multiple factors concerning mobile devices, cloud, and networks; 3 constructs a tensor-based representation model to comprehensively reflect the complex relationship of multiple influencing factors and cope with their heterogeneity; and 4 proposes a customized optimization framework and two heuristic single-objective optimization (SOO) and triple-objective optimization (TOO) algorithms based on simulated annealing. Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art scheduling schemes in SOO and the Pareto front in TOO can provide appropriate solutions to satisfy different application requirements.

Energy saving is a critical and challenging issue for real-time systems in embedded devices because of their limited energy supply. To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q-learning (QL-HDS) was proposed by combining energy-efficient DVFS techniques. However, QL-HDS discretizes the system state parameters with a certain step size, resulting in a poor distinction of the system states. More importantly, it is difficult for QL-HDS to learn a system for various task sets with a Q-table and limited training sets. In this paper, an energy-efficient scheduling scheme based on deep Q-learning model is proposed for periodic tasks in real-time systems (DQL-EES). Specially, a deep Q-learning model is designed by combining a stacked auto-encoder and a Q-learning model. In the deep Q-learning model, the stacked auto-encoder is used to replace the Q-function for learning the Q-value of each DVFS technology for any system state. Furthermore, a training strategy is devised to learn the parameters of the deep Q-learning model based on the experience replay scheme. Finally, the performance of the proposed scheme is evaluated by comparison with QL-HDS on different simulation task sets. Results demonstrated that the proposed algorithm can save average$4.2\%$energy than QL-HDS.

Big data has received considerable attentions in recent years because of massive data volumes in multifarious fields. Considering various “V” features, big data tasks are usually highly complex and computational intensive. These tasks are generally performed in parallel in data centers resulting in massive energy consumption and Green House Gases emissions. Therefore, efficient resource allocation considering the synergy of the performance and energy efficiency is one of the crucial challenges today. In this paper, we aim to achieve maximum energy efficiency by combining thermal-aware and dynamic voltage and frequency scaling (DVFS) techniques. This paper proposes: (a) a thermal-aware and power-aware hybrid energy consumption model synchronously considering the computing, cooling, and migration energy consumption; (b) a tensor-based task allocation and frequency assignment model for representing the relationship among different tasks, nodes, time slots, and frequencies; and (c) a big data Task Scheduling algorithm based on Thermal-aware and DVFS-enabled techniques (TSTD) to minimize the total energy consumption of data centers. The experimental results demonstrate that the proposed TSTD algorithm significantly outperforms the state-of-the-art energy efficient algorithms from total, computing, and cooling energy consumption perspectives, as well as cooling energy consumption proportion and total energy consumption savings.

The emergence of coverage holes will dramatically degrade the quality of service of the industrial wireless sensor networks (IWSNs). Based on the novel confident information coverage (CIC) model, this work focuses on how to heal the CIC holes in hybrid IWSNs containing both static nodes and mobile nodes. We pinpoint the CIC hole healing (CICHH) problem with the goal of selecting and dispatching some randomly scattered mobile nodes to the CIC holes detected by the stationary nodes such that the CIC holes can be repaired and the CIC performance can be satisfied, and prove its NP-completeness. For handling the CICHH problem, we devise two energy-efficient heuristic solutions including a centralized CICHH algorithm and a distributed one. Both the proposed schemes aim at efficiently healing the CIC holes while minimizing the total moving energy consumption of the dispatched mobile nodes, or maximizing the mobile nodes' average remaining energy after movement, or minimizing the maximum mobile energy consumption of each dispatched mobile node. Experimental simulation results show the proposed schemes can energy-efficiently heal the CIC holes and outperform three peer algorithms in terms of energy efficiency and coverage ratio.

The papers in this special section focus on low power dependable computing systems (LPDC). Faults (especially transient faults that lead to soft errors) have become more common due to the miniaturization of computing systems with continuously scaled technology sizes. Thus, it is imperative for most modern computing systems to deploy one or more types of fault-tolerance techniques. Traditionally, fault tolerance has been achieved through various error reduction, detection, and recovery techniques at different levels of the hardware/software stacks (e.g., circuit, architecture, operating systems, compiler, and application software), which generally incur power and energy overheads. Given the fact that energy has become a first-class system resource (especially for batterypowered mobile and IoT devices), it is important to understand the interdependencies between system reliability and power/energy consumption, and further investigate techniques that can address their tradeoffs. This special issue on Low-Power Dependable Computing (LPDC) seeks to tackle these challenges by exploring novel and bold ideas to achieve energy-efficient reliable computations in modern computing systems.

Cyber-physical-social systems (CPSS), an emerging computing paradigm, have attracted intensive attentions from the research community and industry. We are facing various challenges in designing secure, reliable, and user-satisfied CPSS. In this article, we consider these design issues as a whole and propose a system-level design optimization framework for CPSS design where energy consumption, security-level, and user satisfaction requirements can be fulfilled while satisfying constraints for system reliability. Specifically, we model the constraints (energy efficiency, security, and reliability) as the penalty functions to be incorporated into the corresponding objective functions for the optimization problem. A smart office application is presented to demonstrate the feasibility and effectiveness of our proposed design optimization approach.

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

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

Network-on-Chip (NoC) has emerged as interconnect paradigm in state-of-the-art multi/many core architectures. Voltage and frequency island (VFI) was recently adopted as an effective energy management technique for large scale multicore chip designs. Focusing on NoCand VFI-based multi/many core real-time systems with Dynamic Voltage and Frequency Scaling (DVFS) capability, we study both static and dynamic contention-aware energy management schemes for task set with precedence relationships and a common deadline. First, our static schemes utilize two approaches with contention awareness to obtain the mapping of tasks to cores together with scheduling of communications on NoC for minimizing makespan, and thus can potentially lower uniform scaled frequency for cores and links while meeting the timeliness. Next, different from other existing schemes, by incorporating the latency due to network congestions into the analysis, our dynamic contention-aware energy management schemes perform the allocation of feasible slack to tasks and communications simultaneously for further energy savings, subject to common voltage and frequency limitations of VFI and timing constraints of task set. The results through extensive simulations and case studies show that, compared to heuristicbased and INLP-based task mapping solutions (with pessimistic estimation of communication contention), our static scheme can obtain better energy savings (e.g., 25 percent more). The results also show that our dynamic scheme can save up to 45 percent more energy compared to our static scheme under deadline guarantee, while the online scheme ignoring the traffic congestions in NoC can result in serious deadline violation and usually more energy consumption (e.g., 15 percent more).

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.

Community Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely$MC^{3}$. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in$MC^{3}$. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds.

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.

Electronic textiles (e-textiles), known as computational fabrics, offer an emerging platform for constructing ambient intelligent applications. Computational nodes in e-textiles are driven by batteries. Unlike wireless sensor networks, not each computational node in e-textiles has its own battery. Instead, many computational nodes in e-textiles share a battery. Existing e-textiles use one fixed battery to drive a fixed set of computation nodes (or power consuming electronic components). The fixed battery-component connection may result in electronic components stopping functioning and/or energy waste in batteries when link connection problems occur. In this paper, we propose a new infrastructure of the power networks for e-textiles: flexible power network (FPN). Under the FPN infrastructure, a power consuming node (PCN) is not just connected to one single fixed battery. Instead, it is connected to multiple batteries and can obtain power energy from one of the available battery nodes (BNs) with the help of a battery selector. The electrical features of battery selectors and overcurrent protectors that protect the batteries from wasting the charge when short-circuit faults occur are illustrated. Moreover, by modeling the number of fault occurrence at conductive wires and nodes stochastically, an evaluation algorithm is proposed to analyze the reliability of FPN and to compare the metrics of different design schemes under the perspective of both the BNs and the PCNs. Experimental results show that our FPN is more dependable than some common e-textile electric networks published before with the occurrence of short- and/or open-circuit faults.

Dynamic voltage scaling (DVS) is an effective technique to reduce the energy consumption of CMOS powered embedded systems through software control. However, applying fixed priority DVS algorithms introduces increased number of preemptions, which, in turn results in extra time delay and energy cost. Effectively reducing the number of preemptions is therefore required. In this paper, we propose to integrate preemption threshold to fixed priority DVS scheduling algorithms to reduce such negative impact. Two preemption-aware algorithms ccFPPT and FPPT-WDA are studied. Performance evaluations in terms of both energy consumption and the number of preemptions are conducted among different fixed priority DVS algorithms, with or without preemption threshold. The experimental results show that our algorithms with preemption threshold can save up to 60\% number of preemptions and 20\% energy consumption over existing DVS algorithms.

An increasing number of real-time applications like railway signaling control systems and medical electronics systems require high quality of security to assure confidentiality and integrity of information. Therefore, it is desirable and essential to fulfill security requirements in security-critical real-time systems. This paper addresses the issue of optimizing quality of security in real-time systems. To meet the needs of a wide variety of security requirements imposed by real-time systems, a group-based security service model is used in which the security services are partitioned into several groups depending on security types. While services within the same security group provide the identical type of security service, the services in the group can achieve different quality of security. Security services from a number of groups can be combined to deliver better quality of security. In this study, we seamlessly integrate the group-based security model with a traditional real-time scheduling algorithm, namely earliest deadline first (EDF). Moreover, we design and develop a security-aware EDF schedulability test. Given a set of real-time tasks with chosen security services, our scheduling scheme aims at optimizing the combined security value of the selected services while guaranteeing the schedulability of the real-time tasks. We study two approaches to solve the security-aware optimization problem. Experimental results show that the combined security values are substantially higher than those achieved by alternatives for real-time tasks without violating real-time constraints.

This paper presents EDF schedulability driven security optimization in real-time systems. An increasing number of real-time applications like aircraft control and medical electronics systems require high quality of security to assure confidentiality and integrity of information. However, security requirements were not adequately considered in most existing real-time systems. We propose a group based security service model for real-time systems where the services are partitioned into groups. Services in the same security group provide the same type of security service but of different quality due to the different mechanism used. Service from different groups can be combined to achieve better security. The overhead model of the security services is also described. We consider EDF scheduling policy and develop a security aware EDF schedulability test. Two approaches: integer linear programming technique and an efficient heuristic search technique are proposed to select the best combination of security services for real-time systems while guaranteeing their schedulability.

RSA is a very popular public-key based cryptosystem. The security of RSA is relied on the difficulty of large integer factorization. The general number field sieve (GNFS) is an algorithm for factoring very large numbers, especially for integers over 110 digits. It is the asymptotically fastest known factoring algorithm. In this paper, we have successfully implemented the parallel general number field sieve (GNFS) algorithm and integrated with a new method called block Wiedemann's algorithm to solve the large and sparse linear system over GF(2) generated by the GNFS algorithm. The detailed parallel experimental results on a SUN cluster will be presented as well

Electronic textiles, known as computational fabrics, offer an emerging method for constructing wearable and large area applications. Because e-textiles are battery-driven and fault-prone systems, there is a need for developing a dependable infrastructure of the electric networks for e-textiles. In this paper, a new infrastructure of the power networks for e-textiles, flexible power network (FPN), is presented. Instead of drawing power from a fixed battery as in the conventional electric networks, the power consuming nodes in a FPN can obtain power energy from one of the choices of batteries available with the help of the battery selectors. We also introduce the over current protectors into the battery nodes (BN) to protect the batteries from wasting the charge when short-circuit faults occur. The electric features of battery selectors and over current protectors, the two types of important electric devices used in FPNs, are illustrated in the paper. We have performed simulation experiments and the results show that our FPNs are more dependable than some common electric networks published before in the cases of short- and open-circuit faults.

This paper describes the computer supported ubiquitous learning system and the social interaction between learners. We define the ubiquitous learning with five prime attributes, and present a generalized social interaction support model for the ubiquitous learning. Moreover, in order to support learners with increasing social skill, a solution for constructing social interaction in ubiquitous learning environment is designed, which includes three major functions; encounter, communication and collaboration support functions.

In the robot navigation problem, noisy sensor data must be filtered to obtain the best estimate of robot position. The discrete Kalman filter, which usually is used for prediction and detection of signals in communication and control problems has become a commonly used method to reduce the effect of uncertainty from the sensor data. However, due to the special domain of robot navigation, the Kalman approach is very limited. Here we propose the use of an iterative total least squares filter which is solved by applying the Lanczos bidiagonalization process. This filter is very promising for very large amounts of data and from our experiments we can obtain a more precise accuracy than with the Kalman filter.