The future of automotive communication hinges on integrating legacy real-time protocols with advanced Ethernet technologies to empower Software-Defined Vehicles (SDVs). SDVs propose the adoption of the zonal computing architectures, which centralize vehicle functions into distinct zones connected by a high-speed communication backbone. While on the one hand, different zones can employ distinct network protocols like CAN, FlexRay, etc., Time Sensitive Ethernet (IEEE802.1Q) is being projected as the most promising protocol for the central backbone network. With such a heterogeneous distributed platform, SDVs demand reliable and deterministic end-to-end communication strategies, especially for zone-to-backbone or interzonal traffic via the central backbone. Although, there exist a few strategies for message transmission across heterogeneous network domains, they are ad-hoc in nature and oblivious to the precise demands of the control applications they cater to. These drawbacks may lead to poor bandwidth utilization and/or network congestion. Opposed to these ad-hoc techniques, this work proposes an optimal SMT (Satisfiability Modulo Theories) formulation for i) multiplexing periodic zonal message frames onto a minimum number of Ethernet frames, taking into account (m, k)-firmness-based relaxations on specific message flows, and ii) routing Ethernet frames between specified source and destination switches. Through extensive experimental evaluations of the proposed formulation using Z3 solver demonstrate substantial performance improvements, showing at least 30% gain in frame utilization under diverse traffic conditions and timing constraints. Our results validate the proposed approach as a robust solution for ensuring efficient and predictable real-time communication in futuristic SDVs.

ABSTRACT Internet‐of‐Things‐enabled frameworks have eased the development of complex systems, but they throw a significant challenge for efficient resource utilization, thereby improving the system performance. An intelligent scheduler is essential for managing the resources and allocating the same resources to different requests or tasks. This work proposes a generic methodology to optimize system performance metrics such as throughput, utilization, and reward achieved. We present an integer linear programming formulation of the problem to find an optimal solution. We present offline heuristic methods to quickly find reasonable solutions, given the intractable nature of the problem. These heuristics yield promising outcomes, with deviations from optimal solutions below 20% in scenarios with task overlap and high utilization. In scenarios with minimal overlap and utilization, deviations remain under 10%. However, as variables and constraints increase in ILP, the demand for time and memory resources rises substantially. We conduct a comparative analysis of heuristic performance across various scenarios and large test cases. Additionally, we extend our methods to handle resources in online mode, presenting an extensive comparative study with encouraging results.

Electric vehicles (EVs) provide a promising solution to global pollution by replacing conventional vehicles. Enough public charging stations must be built to encourage the broad adoption of electric vehicles (EVs). Additionally, efficient scheduling and planning of EV arrivals at charging stations with a variety of power sources is essential for improving overall operating efficiency. This paper presents a framework for optimizing both the profit and throughput of a charging station that incorporates a solar photovoltaic system and an energy storage system. We present an Integer Linear Programming model to find the optimal solution. We propose a heuristic strategy to efficiently find near-optimal solutions. Our experimental results demonstrate that the proposed approach can provide a reasonably good solution and for small inputs, the performance deviation is less than 5%. For larger inputs, a comparison of the heuristic and baseline is presented. In certain scenarios, the heuristic performs well, while the baseline exhibits better performance in others. We can use both approaches and select the one that yields better results.

Time Sensitive Ethernet is quickly emerging to be the preferred choice as the backbone network for in-vehicle communication, due to its high bandwidth, reliability, scalability, backward compatibility, and support for diverse traffic types. However, individual real-time control subsystems which may need to communicate via this backbone network, may be driven by non-Ethernet protocols like CAN, FlexRay, etc. Beyond ad hoc approaches, this highlights the need for a systematic message scheduling mechanism that enables seamless real-time message transmission over heterogeneous network domains. In this context, this work proposes a formal SMT (Satisfiability Modulo Theories) formulation for statically scheduling a set of persistent, periodic real-time messages. While the sources and destinations of these messages are in different CAN-network domains, the message transmissions must be conducted via an intermediate Time Sensitive Networking (TSN; IEEE802.1Q) backbone. Although, this formal strategy achieves high resource utilization while guaranteeing end-to-end timeliness, it is computationally exponential in nature with overheads becoming prohibitively expensive even for moderate problem sizes. Hence, we propose a lower overhead scheduling strategy called CAN-THER which can deliver efficient and quick solutions even for large problem sizes. The proposed scheduling strategies have been analyzed and compared using extensive simulation based experiments. Results reveal that CAN-THER is able to achieve performance that is up to 90% of the SMT formulation, while producing solutions at speeds that are approximately 104times faster for problems having up to 12 CAN flows and 15 TSN switches.

Higher reliability and lower energy consumption are conflicting, yet among the most important design objectives for the real-time systems. Moreover, in the domain of real-time systems, non-preemptive scheduling is relatively unexplored with objectives such as reliability and energy. Thus we propose an active replication based framework to schedule a set of periodic real-time tasks in the non-preemptive heterogeneous environment such that the given reliability and timing constraints are satisfied whereas the energy consumption is minimized. First, we formulate the problem as a constraint optimization problem that provides an optimal solution; however, it does not scale well. Thus, we also propose heuristics which apply reservation of processors and reallocation of jobs, to compute suboptimal solution efficiently in terms of energy consumption as well as schedulability. Heuristics make use of the interplay of task-level reliability target, reliability of replicas, number of replicas, reliability of tasks, and energy consumption. We perform an experimental study on the test cases generated by extending UUnisort algorithm[1]and observe the effect of various simulation parameters on energy consumption and schedulability.