Wireless Sensor Networks (WSNs) are prone to faults due to battery depletion of nodes. A node failure can disturb routing as it plays a key role in transferring sensed data to the end users. This paper presents a Fault-Tolerant and Energy-Aware Mechanism (FTEAM), which prolongs the lifetime of WSNs. This mechanism can be applied to clusterbased WSN protocols. The main idea behind the FTEAM is to identify overlapped nodes and configure the most powerful ones to the sleep mode to save their energy for the purpose of replacing a failed Cluster Head (CH) with them. FTEAM not only provides fault tolerant sensor nodes, but also tackles the problem of emerging dead area in the network. Our experimental results and simulations show that FTEAM outperforms conventional protocols in terms of network lifetime and energy consumption. In addition, an analytical evaluation using the Markov model is performed to determine the reliability of the FTEAM.
Mixed-Criticality Systems (MCSs) include tasks with multiple levels of criticality and different modes of operation. These systems bring benefits such as energy and resource saving while ensuring safe operation. However, management of available resources in order to achieve high utilization, low power consumption, and required reliability level is challenging in MCSs. In many cases, there is a trade-off between these goals. For instance, although using fault-tolerance techniques, such as replication, leads to improving the timing reliability, it increases power consumption and can threaten life-time reliability. In this work, we introduce an approach named Life-time Peak Power management in Mixed-Criticality systems (LPP-MC) to guarantee reliability, along with peak power reduction. This approach maps the tasks using a novel metric called Reliability-Power Metric (RPM). The LPP-MC approach uses this metric to balance the power consumption of different processor cores and to improve the life-time of a chip. Moreover, to guarantee the timing reliability of MCSs, a fault-tolerance technique, called task re-execution, is utilized in this approach. We evaluate the proposed approach by a real avionics task set, and various synthetic task sets. The experimental results show that the proposed approach mitigates the aging rate and reduces peak power by up to 20.6% and 17.6%, respectively, compared to state-of-the-art.
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