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Wireless sensor network found immense uses in the daily life. Also, the random deployment of nodes is a preferable option in many applications such as earthquake observation, military applications, and forest fire detection. It is expected that deployed nodes should be able to monitor the field of interest (FoI) with the optimum capacity. In order to maximize the coverage of area, each node should be repositioned to an optimal position inside the FoI. A modified particle swarm optimization (PSO) algorithm has been proposed to achieve optimize coverage while keeping the number of nodes minimum. It introduces the concept of negative velocity in order to avoid premature convergence of the algorithm. This paper describes a way to tackle the two dimensional obstacles present inside the FoI. The simulated results show a significant improvement in the performance with compared to the standard PSO in presence of obstacles. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging paradigm that decreases the computational burden of mobiles by task offloading. MEC is regarded as an effective method to offer computing capacities in close proximities to mobile users. The major issue in MEC is how to offload the heterogeneous task of mobile apps effectively from the user equipment to the MEC host. Some of the existing techniques contain feeble adaptability to new circumstance due to minimum sample effectiveness and require complete retraining. The purpose of MEC is to efficiently solve offloading issues such as network load and latency. In this paper, a deep belief network is proposed for solving the offloading issue in the clusters of numerous service node and several dependencies for a mobile task in huge-scale heterogeneous mobile edge computing. The deep belief network's weight is tuned optimally using Adaptive Harris Hawks optimization (AHHO) algorithm for solving the offloading issue optimally in the MEC environment. An AHHO algorithm is employed to improve the search performances of the standard algorithm. The limitations of the standard algorithm are poor stability among exploitation as well as exploration. Hence, two schemes such as the Gaussian mutation scheme and the cuckoo search are combined so as to form an adaptive Harris hawks optimization to enhance this stability. The task offloading issues are implemented using Google cluster trace and iFogSim. Furthermore, the simulation results depict that the offloading scheme based on the deep belief network-based adaptive Harris Hawks optimization approach has better results with respect to measures such as load balancing, energy consumption, average execution time, and latency than any other approach. [ABSTRACT FROM AUTHOR]

Ensuring security in IoT routing protocols is more challenging due to the fact that devices are mobile, resource constrained, and heterogeneous. The routing protocol for low-power and lossy networks (RPL) as the de facto routing protocol for IoT provides a little protection against routing attacks. On the other hand, the standard RPL because of the use of a single metric in routing has limitations that ultimately results in loss of network performance. To overcome the limitations of the use of a single metric and to prevent the consequences of routing attacks, we used the concept of trust and propose dynamic and comprehensive trust model for IoT (DCTM-IoT) and integrate it into RPL (DCTM-RPL). We provide a comprehensive hierarchical model for trusting of things in IoT, which has a multi-dimensional vision of trust. We put the combination of metrics and necessary activities to deal with attacks under the umbrella of trust level calculation. The performance of DCTM-RPL is compared with the standard RPL protocol in mobile environment and under routing major attacks. DCTM-RPL demonstrates its superior performance over the standard RPL protocol in the detection and isolation attacks. The DCTM-RPL, in addition to resistance mitigating routing attacks, improves network performance. [ABSTRACT FROM AUTHOR]

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique, which partitions sequential programs into multithreads to be executed in parallel. This paper presents different thread partitioning strategies for nonloops and loops. For nonloops, we propose a cost estimation based on combined run-time effects of various speculation factors to predict the resulting performance of candidate threads to guide the thread partitioning. For loops, we parallelize all the profitable loops that can potentially offer additional performance benefits by multilevel spawning in loop bodies, loop iterations, and inner loops. Then we select a proper thread boundary located in the front of loop branch instruction to reduce invalid spawning threads that waste core resources. Experimental results show that the proposed approach can obtain a significant increase in speedup and Olden benchmarks reach a performance improvement of 6.62 % on average. [ABSTRACT FROM AUTHOR]

The significant speed-gap between processor and memory makes last-level cache performance crucial for multi-core architectures (MCA). Non-uniform cache architecture (NUCA) has been proposed to overcome the performance limitations of MCA for many embedded applications. The cache is partitioned into sub-banks, with each sub-bank being an independently accessible entity connected with a fast on-chip network (NoC). This paper presents two NoC-assisted mechanisms to improve the performance and power consumption of NUCA coherence. The first mechanism provides priority-based communication based on the wormhole routing architecture to support NUCA coherence. High-priority coherent packets are transmitted first to save time. The second mechanism offers multicasting communication based on the proposed priority-based NoC to provide efficient cache coherency for NUCA. We dispatch and collect coherence packets at the collecting nodes (CN) to further decrease the number of coherent messages flowing in the NoC. Experimental results show that the priority-based transmission can improve performance by approximately 10 %. The proposed multicasting mechanism can further improve performance and decrease power consumption of the NoC in NUCA by approximately 15 %. The two proposed mechanisms can together enhance the performance by 25 % averagely. [ABSTRACT FROM AUTHOR]

Predicting distributed application performance is a constant challenge to researchers, with an increased difficulty when heterogeneous systems are involved. Research conducted so far is limited by application type, programming language, or targeted system. The employed models become too complex and prediction cost increases significantly. We propose dPerf, a new performance prediction tool. In dPerf, we extended existing methods from the frameworks Rose and SimGrid. New methods have also been proposed and implemented such that dPerf would perform (i) static code analysis and (ii) trace-based simulation. Based on these two phases, dPerf predicts the performance of C, C++ and Fortran applications communicating using MPI or P2PSAP. Neither one of the used frameworks was developed explicitly for performance prediction, making dPerf a novel tool. dPerf accuracy is validated by a sequential Laplace code and a parallel NAS benchmark. For a low prediction cost and a high gain, dPerf yields accurate results. [ABSTRACT FROM AUTHOR]