Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution.

Heterogeneous multicore and multiprocessor systems have been widely used for wireless sensor information processing, but system energy consumption has become an increasingly important issue. To ensure the reliable and safe operation of sensor systems, the task scheduling success rate of heterogeneous platforms should be improved, and energy consumption should be reduced. This work establishes a trusted task scheduling model for wireless sensor networks, proposes an energy consumption model, and adopts the ant colony algorithm and bee colony algorithm for the task scheduling of a real-time sensor node. Experimental result shows that the genetic algorithm and ant colony algorithm can efficiently solve the energy consumption problem in the trusted task scheduling of a wireless sensor and that the performance of the bee colony algorithm is slightly inferior to that of the first two methods.

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Controller Area Network (CAN) is broadly used for in-vehicle networking. Each message on the CAN bus is given a unique identifier as its priority. When more than one node starts to transmit at the same time, the message with the highest priority will be sent. The schedulability of the message set is significantly affected by the priority assignment algorithms employed. To ensure that the CAN system tolerate additional interference, we propose system robustness, a concept to represent the total robustness of the message set. Maximum system robustness of message set problem is NP-hard. We find that assigning a higher priority to the message with small transmission time or low utilization may lead to more system robustness. Based on this, this paper presents four optimal priority assignment approximation algorithms using the Audsley's priority assignment method: TMPA, UMPA, GTMPA, and GUMPA. TMPA and UMPA are heuristic algorithms. GTMPA and GUMPA are greedy algorithms. Experimental results show that, compared with the existing optimal algorithms, TMPA, GTMPA, and GUMPA can improve the system robustness of CAN message sets effectively.