Task offloading in Vehicular Edge Computing (VEC) can significantly enhance Cooperative Perception (CP) for Autonomous Vehicles (AVs), improving situational awareness and traffic safety. However, the widespread adoption of VEC is often constrained by the high deployment costs of Roadside Units (RSUs). In this paper, we propose the Truthful and Quality-Aware Task Offloading (TQTO) scheme. TQTO leverages the prolific yet underutilized computational resources of parked vehicles for CP tasks in VEC to alleviate RSU scarcity. Using vehicle-to-vehicle (V2V) communication, parked vehicles can be strategically involved in CP processing and are incentivized to contribute their resources. TQTO introduces a distributed, truthful, double-auction-based multi-agent deep reinforcement learning framework that enables user vehicles to offload CP tasks to parked vehicles in a utility-maximizing manner, while respecting their individual budget constraints. Concurrently, TQTO considers the provider-side (i.e., parked vehicles) costs and ensures a truthful, incentive-compatible, and budget-balanced marketplace for VEC. A critical value-based payment mechanism is used to ensure fair compensation for parked vehicles and to align task requesters’ payments with their utility. TQTO formulates the task offloading problem as a Double Auction Quadratic Multiple Knapsack Problem (DA-QMKP) and solves it using a QMIX-based heuristic for scalable decision-making under partial observability. Extensive evaluations show that TQTO outperforms a prominent non-auction-based scheme by up to 39% in terms of social welfare.

Task offloading in Vehicular Edge Computing (VEC) enhances cooperative perception (CP) for Autonomous Vehicles (AVs), improving traffic awareness. However, the high cost of Roadside Unit (RSU) and the underutilization of parked vehicles pose challenges. Leveraging Vehicle-to-Vehicle (V2V) communication, parked vehicles can form collaborative worker groups for efficient perception aggregation. We propose CoGroup, a two-tier framework integrating task offloading and dynamic worker grouping. Modeled as a double quadratic multiple knapsack problem, it employs Hierarchical Reinforcement Learning (HRL): QMIX for decentralized task allocation and DQN for optimized worker grouping. Experiments show that CoGroup improves traffic awareness by 21% over non-cooperative methods, reducing RSU dependence and offering a scalable, cost-effective solution for next-generation VEC systems.

Task offloading in Vehicular Edge Computing (VEC) is crucial for enhancing cooperative perception (CP) in Autonomous Vehicles (AVs), thereby improving traffic situational awareness. However, existing approaches often neglect the balance between high-quality execution of interdependent tasks and conserving AVs limited budget, including communication and financial resources. To address this, we propose the Quality and Budget-Aware Task Offloading (QBATO) framework. QBATO is the first framework to balance the quality of cooperative perception with budget conservation. QBATO models the budget as a queue to ensure stability, balancing resource use while prioritizing situational awareness in CP. Additionally, QBATO enhances CP quality by predicting vehicles movements and estimating their regions of interest, thereby improving the Value of Information (VOI). The task offloading problem is modeled as a Quadratic Multiple Knapsack Problem (QMKP), an NPhard problem that optimizes vehicle allocation by evaluating the quality of assigning multiple vehicles to the same worker through a quadratic objective function.To manage resources effectively, we apply the queue stability Lyapunov drift-minus-bonus approach. We also introduce the QBATO-Heuristic (QBATO-H), which solves the problem in a decentralized, time-efficient manner using a multi-agent deep reinforcement learning technique that leverages the Q-Mixing Network (QMIX) method, which employs monotonic value decomposition to coordinate the actions of multiple agents. Extensive evaluations show that QBATO outperforms prominent quality and budget-oblivious schemes by up to 49%, 15%, and 35% in terms of budget conservation, situational awareness, and efficiency, respectively. QBATO-H also yields a small gap of up to 7% and 11% with QBATO in terms of budget conservation and efficiency, respectively.

Autonomous vehicles heavily rely on sensor data to make pivotal driving and traffic management decisions. However, the reliability of such data can be profoundly impacted by many impairments, such as the adverse environmental and weather conditions, the presence of obstacles, and the vehicle's limited view of road and traffic conditions of larger areas. Collaboration between vehicles can help improve the perception of vehicles beyond their line-of-sight, and increase accurate detection of objects. Vehicular Edge Computing (VEC) has emerged as a propitious computing paradigm that can foster the realization of autonomous vehicles. However, maximizing the cooperative perception of vehicles has been mostly overlooked. In this paper, we propose the Cooperative Perception-based Task Offloading (CPTO) scheme. CPTO enables task offloading in VEC with the goal of maximizing the cooperative perception of vehicles and minimizing the latency of perception aggregation, while abiding by a certain deadline. Towards that end, we formulate the task offloading problem as a multi-objective 0–1 integer linear program (0–1 ILP). We also propose a greedy heuristic, called the CPTO-Heuristic (CPTO-H) scheme, to solve the optimization problem. Extensive simulations show that CPTO significantly outperforms the baseline task offloading scheme in terms of perception intensity, service capacity, and satisfaction ratio. Furthermore, CPTO-H closely approaches the optimal solution, with a small gap of up to 3.7% and 2.4% in terms of perception intensity and satisfaction ratio, respectively.

Leveraging task offloading in Vehicular Edge Computing (VEC) via V2X can present unique and robust solutions to the challenges associated with cooperative perception in Autonomous Vehicles (AVs). However, making task offloading decisions that account for the risk of communication failure due to path loss, while adhering to the stringent QoS requirements of cooperative perception has been mostly overlooked. In this paper, we propose PLTO, a Path Loss-Aware Task Offloading scheme that accounts for path loss for Line-of-Sight (LOS), Obstructed LoS (OLoS), and Non-LoS (NLoS) propagation in vehicular communications. We formulate the task offloading problem as a 0–1 Integer Linear Program (0–1 ILP) that aims to minimize the path loss and response delay, while sustaining a certain satisfactory level of improved perception and situational awareness demanded by users. We also propose PLTO-Heuristic (PLTO-H), a scheme to solve the task offloading problem using the MTHG heuristic. Extensive simulations show that PLTO yields significant improvements of up to 17%, 10%, and 23% in terms of packet delivery ratio, Received Signal Strength Indicator (RSSI), and average response delay, respectively, compared to a baseline task offloading scheme that does not consider communication efficiency. In addition, PLTO-H achieves a near optimal solution, with a small gap of up to 6%, 5% and 1.2% in terms of packet delivery ratio, RSSI, and satisfaction ratio, respectively.

Edge Computing (EC) has paved the way toward the realization of the Internet of Things (IoT). This can be attributed to the ability of EC to bring the computational resources within close proximity to end-users, which significantly improves the response time. However, performance gain in EC can be compromised by service interruptions triggered by various dynamic changes. Consequently, reliable service migration is crucial in EC. However, most service migration schemes either fail to consider the profound impact of the dynamic computation load on service continuity or provide impractical and time-inefficient solutions based on optimization techniques. This paper proposes the Heuristic-based Load-induced Proactive Migration (HLPM) scheme. HLPM incorporates a Finite State Machine (FSM) to model the dynamic computation load. It then makes proactive migration decisions based on the underlying transition probabilities. The proactive migration problem is solved using the MTHG heuristic algorithm. Performance evaluation shows that HLPM produces a significant decrease of up to 97% in migration decision latency compared to conventional optimization techniques. Furthermore, the performance gap of HLPM with respect to the optimal migration solution is just 1.44% latency and 3.89% number of migrations.

The advent of the Internet-of-Things (IoT), which streams a wide range of computation-intensive applications with strict Quality of Service (QoS) requirements, has caused a paradigm shift from cloud computing to edge computing. Edge computing can drastically reduce latency and improve QoS. However, various dynamic changes can affect service continuity, thus requiring service migration. The dynamic computation load is one of the changes that are typically overlooked in service migration. In this paper, we propose the Dynamic Load-based Proactive Migration (DLPM) scheme. DLPM adopts a finite-state machine (FSM) that models the dynamic computation load, and proactively migrates computation tasks based on the associated transition probabilities. We formulate the service migration problem as an integer linear programming (ILP) optimization problem that aims to minimize the delay. We provide an analytical solution to the optimization problem using the KKT conditions and Lagrangian analysis. Performance evaluation shows that DLPM yields significant improvements in terms of delay and number of migrations compared to the reactive migration approach.