The interlinked processing units in modern Cyber-Physical Systems (CPS) creates a large network of connected computing embedded systems. Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) architecture is becoming a de facto computing platform for real-time applications due to its higher performance and Quality-of-Service (QoS). The number of processors has increased significantly on the multiprocessor systems in CPS; therefore, Voltage Frequency Island (VFI) has been recently adopted for effective energy management mechanism in the large-scale multiprocessor chip designs. In this article, we investigated energy-efficient and contention-aware static scheduling for tasks with precedence and deadline constraints on intelligent edge devices deploying heterogeneous VFI-based NoC-MPSoCs (VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike the existing population-based optimization algorithms, we proposed a novel population-based algorithm called ARSH-FATI that can dynamically switch between explorative and exploitative search modes at run-time. Our static scheduler ARHS-FATI collectively performs task mapping, scheduling, and voltage scaling. Consequently, its performance is superior to the existing state-of-the-art approach proposed for homogeneous VFI-based NoC-MPSoCs. We also developed a communication contention-aware Earliest Edge Consistent Deadline First (EECDF) scheduling algorithm and gradient descent--inspired voltage scaling algorithm called Energy Gradient Decent (EGD). We introduced a notion of Energy Gradient (EG) that guides EGD in its search for island voltage settings and minimize the total energy consumption. We conducted the experiments on eight real benchmarks adopted from Embedded Systems Synthesis Benchmarks (E3S). Our static scheduling approach ARSH-FATI outperformed state-of-the-art technique and achieved an average energy-efficiency of ∼24% and ∼30% over CA-TMES-Search and CA-TMES-Quick, respectively.

Wireless Sensor Network (WSN) consists of a large number of sensor nodes distributed over a certain target area. The WSN plays a vital role in surveillance, advanced healthcare, and commercialized industrial automation. Enhancing energyefficiency of the WSN is a prime concern because higher energy consumption restricts the Lifetime (LT) of the network. Clustering is a powerful technique widely adopted to increase LT of the network and reduce the transmission energy consumption. In this paper we develop a novel ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked based Clustering (NRC) to reduce the communication energy consumption of the sensor nodes while efficiently enhancing LT of the network. Unlike other population based algorithms ARSH-FATI-CHS dynamically switches between exploration and exploitation of the search process during run-time to achieve higher performance trade-off and significantly increase LT of the network. ARSH-FATI-CHS considers the residual energy, communication distance parameters, and workload during Cluster Heads (CHs) selection. We simulate our proposed ARSH-FATI-CHS and generate various results to determine the performance of the WSN in terms of LT. We compare our results with state-of-the-art Particle Swarm Optimization (PSO) and prove that ARSH-FATI-CHS approach improves the LT of the network by ∼ 25%.

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Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

Message transmission in vehicular networks is increasing in popularity which exploits the network nodes to transmit messages using cooperative communication in a multi-hop fashion. But the increasing number of malicious nodes in the high-speed Internet of Vehicles demands additional methodologies to quickly detect the presence of such nodes to avoid serious security consequences. Early detection of malicious nodes, and accurate assessment of complex data to assess the node reliability are of absolute importance in vehicular networks. To this end, this paper proposes a security scheme that uses evidence combination method to combine local data with external evidence to evaluate the reliability of multi-dimensional data received from other peer nodes. In addition, this paper uses European Telecommunications Standards Institute standard and Decentralized Environmental Notification Message, and proposes a trust calculation method based on collaborative filtering by introducing a small-time interval to detect the changes in the node behaviors. While the former solution helps more accurate computation of the direct trust value, the latter scheme can calculate the indirect trust based on recommendations received from neighbors, ultimately to obtain the global trust value. Finally, more effective traffic data can be obtained to help traffic prediction. Experiments conducted under various network scenarios demonstrate that our proposed scheme outperforms the existing trust models, such as precision or recall and can resist bad-mouth attacks, selective-misbehavior attacks, and time-dependent attacks, especially under larger proportions of malicious nodes. INDEX TERMS Cooperative communication, data processing, Internet of Vehicles, intrusion detection, security management.

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Microblogging, networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carries opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a Human-centric Social Computing (HCSC) model for hot event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are pre-processed through Hypertext Induced Topic Search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a Latent Dirichlet Allocation (LDA) based multi-prototype user topic detection method is used for identifying users with high influence in the network. Further, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot event detection and information propagation.