Evaluating the effectiveness of system scheduling and energy savings in embedded real-time systems with low computing resources is the problem addressed in this paper. In such systems, the characteristics of the implemented scheduling policy play a relevant role in both schedulability and energy consumption. Ideally, the scheduling policy should provide higher schedulability bounds and low runtime overheads, allowing for better usage of available slack in the schedule for energy saving purposes. Due its low overhead and simple implementation, the usual scheduling policy employed in real-time embedded systems is based on fixed priority (FPS). Under this scheme, as the priority of all system tasks are assigned at design time, a simple priority vector suffices to indicate the current ready task to run. System schedulability, however, is usually lower than that provided by dynamic priority schedulers (DPS) according to which task priorities are assigned at runtime. Managing dynamic priority queues incurs higher overheads, though. Deciding whether DPS is a possible choice for such embedded requires careful evaluation. We evaluate two implementations of Earliest-Deadline First (EDF), a classical DPS, implemented in FreeRTOS running on an ARM-M4 architecture. EDF is compared against an optimal FPS, namely Rate-Monotonic scheduling (RMS). Further, two mechanisms for energy savings are described. They differ by the manner they compute the slack available in an EDF schedule, statically (SS-EDF) or dynamically (DS-EDF). These two approaches are experimentally evaluated. Results indicate that EDF can be effectively used for energy savings.

Internet of Things (IoT) gateways bridging resource-constrained devices and the internet, running conventional operating systems and communication protocols, are valuable targets for malicious attackers. Once compromised, the gateway can no longer be trusted to deliver data accurately to the applications running on the Cloud. In this work we present an analysis of the Gateway Integrity Checking Protocol, a gossip protocol that collects data from subsets of IoT devices, to answer a challenge sent by an External Security Agent. The response is used to check the integrity of data arriving at the Cloud from the gateway. The evaluation of the time and energy overhead, and the impact of the proposed solution on the lifetime of the network through simulations is presented. A detailed qualitative comparison against other Intrusion Detection Systems in the IoT domain is made, showing that the proposed solution can provide customizable security levels with little overhead in terms of processing power and network traffic.

Modern embedded systems must have high reliability and performance. They should be able to tolerate both hard as well as soft errors occurring in the resources constituting the system. Reliability must be part of the system design and the system must consist of non expensive off-the-shelf resources. A system-level design process of reliable system demands efficient reliability evaluation of the explored design alternatives (DA). This work presents a new approach to accelerate the calculation of reliability and execution time of the system and thereby suggests the design space exploration for a reliable system. A new data structure denoted as system error decision diagram (SEDD) is proposed, which is based on both binary decision diagrams to model hard errors and zero-suppressed decision diagrams to model soft errors. The construction of the SEDD diagram and the calculation of reliability and execution time are explained in an algorithmic way. SEDD is found to be better in terms of memory requirements and construction time compared to other models available in the literature. Using SEDD and the corresponding algorithms, a nested genetic algorithm is constructed that designs system for lifetime reliability and execution time. The result of the design space exploration algorithm is a set of Pareto DAs. A so-called ‘human designer’ is thus able to select one of the best alternative that represents the given system requirements. The nested genetic algorithm and its benefits are illustrated using a real-life embedded application from automotive domain.