Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm.

Two-tier end-edge collaborative computation offloading for edge computing

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.

Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers’ computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.

Decreased Emotional Dysregulation Following Multi-Modal Motion-Assisted Memory Desensitization and Reconsolidation Therapy (3MDR): Identifying Possible Driving Factors in Remediation of Treatment-Resistant PTSD

Multi-modal motion-assisted memory desensitization and reconsolidation therapy (3MDR), an interactive, virtual reality-assisted, exposure-based intervention for PTSD, has shown promising results for treatment-resistant posttraumatic stress disorder (TR-PTSD) among military members (MMs) and veterans in randomized controlled trials (RCT). Previous research has suggested that emotional regulation (ER) and emotional dysregulation (ED) may be factors which are correlated with symptom severity and maintenance of TR-PTSD. This embedded mixed-methods pilot study (n = 9) sought to explore the impact of 3MDR on ER and ED of MMs and veterans. Difficulties in Emotional Regulation Scale (DERS-18) data were collected at baseline, prior to each session, and at one week, one month, and three months postintervention and analyzed. Qualitative data collected from sessions, debriefs, and follow-up interviews were transcribed and descriptively analyzed. Results demonstrated statistically significant decreases in DERS-18 scores from preintervention to postintervention at each timepoint. Qualitatively, participants perceived improvements in ER within specified DERS-18 domains. We describe how 3MDR’s unique and novel approach addresses ED through cognitive–motor stimulation, narration, divergent thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. More studies are needed to better understand the underlying neurobiological mechanisms by which 3MDR addresses ER and PTSD.

Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.

Decreased Emotional Dysregulation Following Multi-modal Motion-assisted Memory Desensitization and Reprocessing Therapy (3MDR): Identifying Driving Factors in Remediation of Treatment-Resistant PTSD

Multi-modal Motion-assisted Memory Desensitization and Reprocessing Therapy (3MDR), an interactive, virtual-reality assisted, exposure-based intervention for PTSD, has shown promising results for treatment-resistant Posttraumatic Stress Disorder (TR-PTSD) among military members (MMs) and Veterans in Randomized Controlled Trials. Previous research has suggested that emotional regulation (ER) and emotional dysregulation (ED) may be factors which are correlated with symptom severity and maintenance of TR-PTSD. This embedded mixed-methods pilot study (n=9) sought to explore the impact of 3MDR on ER and ED of MMs and Veterans. Difficulties in Emotional Regulation Scale (DERS-18) data was collected at baseline, prior to each session, and at 1 week, 1 month and 3 months post-intervention and analyzed using a Wilcoxon signed-ranks test. Qualitative data collected from sessions, debriefs, and follow-up interviews were transcribed and descriptively analyzed. Results demonstrated statistically significant decreases in DERS-18 scores from pre-intervention to post-intervention at each timepoint. Qualitatively, participants perceived improvements in ER within specified DERS-18 domains. We describe how 3MDR’s unique and novel approach may address ED through cognitive-motor stimulation, narration, divergent thinking, reappraisal of aversive stimuli, dual-task processing, and reconsolidation of traumatic memories. Further investigation is underway to better understand the underlying neurobiological mechanisms by which 3MDR addresses ER and PTSD.

Orchestration of autonomous trusted third-party banking

Background Digital transformation is changing the structure and landscape of future banking needs with much emphasis on value creation. Autonomous banking solutions must incorporate on-the-fly processing for risky transactions to create this value. In an autonomous environment, access control with role and trust delegation has been said to be highly relevant. The aim of this research is to provide an end to end working solution that will enable autonomous transaction and task processing for banking. Method We illustrate the use case for task delegation with the aid of risk graphs, risk bands and finite state machines. This paper also highlights a step by step task delegation process using a risk ordering relation methodology that can be embedded into smart contracts. Results Task delegation with risk ordering relation is illustrated with six process owners that share immutable ledgers. Task delegation properties using Multi Agent Systems (MAS) is used to eliminate barriers for autonomous transaction processing. Secondly, the application of risk graph and risk ordering relation with reference to delegation of tasks is a novel approach that is nonexistent in RBAC. Conclusion The novelty of this study is the logic for task delegation and task policies for autonomous execution on autonomous banking platforms akin to the idea of federated ID (Liberty Alliance).

Dissociating the Neural Correlates of Planning and Executing Hierarchical Task Sets

Task processing and task representation, two facets of cognitive control, are both supported by lateral frontal cortex (LFC). However, processing and representation have largely been investigated separately, so it is unknown if they are distinguishable aspects of control or if they are complementary descriptions of the same mechanism. Here, we explored this by combining a hierarchical task mapping with a pre-cueing procedure. Participants made match/non-match judgments on features of pairs of stimuli. Cues presented at the start of each trial indicated the judgment domain (spatial/non-spatial), the response hand, both, or neither, giving variable amounts of information to the subject at each time point in the trial. Our results demonstrated that regions throughout LFC supported task processing, indicated by an influence of time point on their BOLD activity levels. A subset of regions in left caudal LFC also supported task representation, indicated by an interaction between time point and cue information; we termed this subgroup the "CuexTime" group. This interaction effect was not seen in the remaining LFC regions, which only showed a main effect of time consistent with involvement in task processing; we termed this subgroup the "Time" group. These results suggest that task representation is one component of task processing, confined to the "CuexTime group" in left caudal LFC, while other regions in our task support other aspects of task processing. We further conducted an exploratory investigation of connectivity between regions in the "CuexTime" and "Time" groups and their potential relationship to networks that support distinct cognitive control functions.

On the relationship of behavioral distraction and pupil dilation caused by emotional novel sounds

Effects of attentional distraction by unexpected and task-irrelevant sounds on task performance are discussed to comprise costs due to orienting of attention toward a distracting event and benefits due to enhanced level of arousal evoked by the processing of such events. Highly arousing distractor sounds may facilitate information and task processing resulting in reduced distraction effects compared to moderately arousing distractor sounds. By measuring pupil dilation responses as a marker of arousal and task performance as a marker of distraction, we disentangled orienting costs and arousal level changes through variations of the emotional content of distractor sounds. While participants (N=60) performed a visual categorization task, an auditory oddball sequence including standard sounds, highly arousing emotional and moderately arousing neutral novel sounds was presented. Multilevel analyses revealed prolonged reaction times to novel sounds compared to standard sounds. Distraction effects decreased when emotional novel sounds were presented compared to neutral novel sounds. Pupil dilation responses were increased in response to novel sounds compared to standard sounds. This increase was larger for emotional than for neutral novel sounds. None of the considered models supported a correlation at trial level between reduced distraction effects and arousal increase reflected by the pupil in response to emotional novel sounds, indicating at least partly independent underlying mechanisms. An exploratory analysis revealed an impact of the baseline pupil size, that indicates tonic level of arousal, on performance and distraction effects. Moreover, a positive correlation between the negative affect scale in the Adult Temperament Questionnaire and RTs was observed.