We propose a battery-aware multi-trip drone routing framework for post-disaster delivery that considers the mixed criticality of supplies under deadline and energy constraints. In this framework, each delivery request is assigned a criticality level and a delivery deadline, and drones are allowed to switch to a high-speed flight mode for urgent deliveries at the cost of increased energy consumption. The proposed optimization model simultaneously determines the number of drones, the delivery routes, and the flight speed mode for each segment, enabling a trade-off between energy consumption and urgency. Low-criticality deliveries may be skipped to prioritize highcriticality ones. Experimental results show that the proposed method improves the on-time delivery success rate for highcriticality items from 59.2 % to 89.3 %, compared to a baseline. In addition, the delivery rate for low-criticality items also slightly increases from 38.6% to 45.3%, supported by the increased number of drones. These results demonstrate the effectiveness of criticality-aware planning and provide insights into how to allocate and configure drone fleets for effective disaster response logistics.
This paper addresses the deadline-constrained task offloading and resource allocation problem in multi-access edge computing. We aim to determine where each task is offloaded and processed, as well as corresponding communication and computation resource allocations, to maximize the total saved energy for IoT devices, while considering task deadline and system resource constraints. Especially, our system allows each task to be offloaded to one of its accessible access points (APs) and processed on a server that is not co-located with its offloading AP. We formulate this problem as an Integer Nonlinear Programming problem and show it is NP-Hard. To address this problem, we propose a Graph-Matching-based Approximation Algorithm (GMA), the first approximation algorithm of its kind. GMA leverages linear relaxation, tripartite graph construction, and a Linear Programming rounding technique. We prove that GMA is a 1 - alpha/2 + epsilon approximation algorithm, where epsilon is a small positive value, and alpha (0 <= alpha < 1) is a system parameter that ensures the resource allocated to any task by an AP or a server cannot exceed alpha times its resource capacity. Experiments show that, in practice, GMA's energy saving achieves 97% of the optimal value on average.
Autonomous systems, such as autonomous vehicles and robots, typically use middleware like Robot Operating System (ROS) to manage communication, execution, and functionality. These systems sample sensing data at fixed rates from various sources and propagate it among independently developed tasks using a publish-subscribe scheme. A common issue is the nature of dynamic fluctuations in task execution time for processing this data, exacerbated by resource contention, dynamic environments, and scheduling strategies. This dynamism, combined with the lack of runtime coordination of task execution at the operating system level, leads to misaligned task execution rates, which is undetected by both middleware and operating systems. As a result, this misalignment (i.e., uncoordinated task execution) causes message drops, resource wastage, and degraded realtime performance. To address these challenges, we propose an Adaptive Task Execution rate Regulation (ATER) framework specifically designed for ROS 2-based systems. ATER consists of two key components: a runtime observer and a task regulator. It seamlessly integrates with the ROS 2 system without affecting its execution or requiring any modifications to its source code. By adapting the sensor data sampling rates at runtime, our framework effectively enhances the real-time performance of ROS 2-based systems through reduced message drops, efficient utilization of computational resources, and improved end-to-end latency.
We design and develop EtherTime, a tool for empirical evaluation of open-source implementations of the widely adopted Precision Time Protocol and Network Time Protocol standards. EtherTime is designed for distributed embedded systems networked over Ethernet. To demonstrate its benefits, we assemble a testbed of Raspberry Pi 4/5, Xilinx ZUBoard 1CG, and NVIDIA Jetson TK-1 boards, and carry out a measurement-based evaluation of four popular open-source implementations — PTPd, LinuxPTP, SPTP, and Chrony. EtherTime is successful in highlighting their limitations with regards to resource contention (specifically network and memory) and faults of various kinds (with/without hardware clock support). We open-source EtherTime and all datasets derived from this empirical study.
Nowadays, many functionalities are integrated in embedded real-time systems, leading to an increase in the number of their software and hardware components. In this context, the interest in mixed-criticality scheduling is growing [1].
