An Energy-Aware Approach to Stream Processing and Collaborative Offloading in Internet of Vehicles

The Internet of Things (IoT) relies on embedded sensors in pervasive computing devices to interconnect heterogeneous systems, enabling smart environments. Despite these advancements, the Internet of Vehicles (IoV) faces challenges with scarce computational and energy resources, leading to increased service delays and data transfer overheads. Unstable networking and limited local task processing exacerbate these issues, especially when vehicle-to-vehicle connections fail. This research proposes a layered computational framework to support stream processing applications through a collaborative offloading approach. Instead of relying on traditional data re-transmission methods, our solution addresses re-transmission issues in stream processing through data-aware stream offloading and dynamic vehicle selection policy. These algorithms facilitate efficient data streaming among dynamically selected vehicular nodes in IoVs. Simulation-based experiments validate the efficacy of our approach, showing a 33.5 % efficiency improvement compared to traditional methods. Additionally, proposed framework demonstrates improvement in task offloading and execution time, task reception-delivery-failure rate, and energy consumption.