No abstract

No abstract

In last years, several approaches have been proposed for solving the Hardware/Software partitioning and scheduling problem in dynamically reconfigurable embedded systems (DRESs), directed by metaheuristic algorithms. Honey Bees Mating Optimization (HBMO) algorithm is one of these advanced methods. It is a nature inspired algorithm which simulates the process of real honey-bees mating. In this work, we propose a variant of the Honey-bee Mating Optimization Algorithm for solving Hardware/software (HW/SW) partitioning and scheduling problems in DRESs. The algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving these problems. More precisely, the proposed algorithm (HBMO_ DRESs) combines a Honey Bees Mating Optimization (HBMO) algorithm, the Tabu Search (TS) and Simulated Annealing (SA)). From an acyclic task graph and a set of Area-Time implementation trade off points for each task, the adopted method performs HW/SW partitioning and scheduling such that the global application execution time is minimized. Comparing the proposed method with Genetic Algorithm and Evolutionary Strategies (ES), the simulation results show that the proposed algorithm has better convergence performance.

The Internet of Things (IoT) is overrunning different domains and applications, where the use of wireless sensors and mobile devices is indispensable in such a mobile environment. These heterogeneous devices may generate a tremendous amount of content. Information-Centric Network (ICN) paradigm has been proposed to meet today's users and application requirements. The in-network caching is a fundamental feature supported by design in ICN that improves network performance by providing ubiquitous caching in the network layer. Since most IoT devices are resource-constrained with limitations in communication, processing, energy, and memory; the energy-efficiency is a prime concern in IoT deployment. Different factors may affect energy efficiency in ICN-based wireless IoT networks such as transport (communication), caching, and energy limitation. This research paper attempts to focus on the in-network caching in wireless IoT to maximize the energy-efficiency. We propose an Energy-aware caching placement scheme (EaCP) that aims to maximize the energy-saving by trading-off between content transmission energy and content caching energy. Compared to other strategies, the simulation results show significant improvements while ensuring low data replication and a high cache hit ratio.

In task scheduling, the job-shop scheduling problem is notorious for being a combinatorial optimization problem; it is considered among the largest class of NP-hard problems. In this paper, a parallel implementation of hybrid cellular genetic algorithm is proposed in order to reach the best solutions at a minimum execution time. To avoid additional computation time and for real-time control, the fitness evaluation and genetic operations are entirely executed on a graphic processing unit in parallel; moreover, the chosen genetic representation, as well as the crossover, will always give a feasible solution. In this paper, a two-level scheme is proposed; the first and fastest uses several subpopulations in the same block, and the best solutions migrate between subpopulations. To achieve the optimal performance of the device and to reshape a more complex problem, a projection of the first on different blocks will make the second level. The proposed solution leads to speedups 18 times higher when compared to the best-performing algorithms.