Vehicle ad-hoc networks (VANETs), which support various important applications for intelligent transportation systems (ITSs), consist of vehicle-to-vehicle and vehicle-to-infrastructure communications based on vehicles and roadside units (RSUs). Medium access control (MAC) plays a critical role in providing efficient broadcast services for VANETs. Unlike other types of networks, VANETs suffer from rapid changes in topology, resulting in frequent network disconnections caused by the high mobility of nodes. Hence, adaptive MAC protocols, which dynamically adjust the interval according to the traffic conditions, are essential to VANETs. This study proposes a cooperative and reliable RSU-assisted IEEE 802.11p-based multi-channel MAC protocol for VANETs, called RAM. In our proposal, an RSU is used to both calculate the optimized interval and keep track of the safety packet transmission. We also present a cooperative scheme for the retransmission of safety packets that failed to broadcast because of hidden nodes. The simulation results show that the RAM not only allows safety packets to be broadcast more efficiently using the existing MAC protocols, but also outperforms the existing MAC protocols in terms of the packet delivery ratios for safety and control packets.

In the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Currently, matching the incoming Internet of Things applications to the current state of computing and networking resources of a mobile edge orchestrator (MEO) is critical for providing the high quality of service while temporally and spatially changing the incoming workload. However, MEO needs to scale its capacity concerning a large number of devices to avoid task failure and to reduce service time. To cope with this issue, we propose MEO with fuzzy-based logic that splits tasks from mobile devices and maps them onto the cloud and edge servers to reduce the latency of handling these tasks and task failures. A fuzzy-based MEO handles the multi-criteria decision-making process to decide where the offloaded task should run by considering multiple parameters in the same framework. Our approach selects the appropriate host for task execution and finds the optimal task-splitting strategy. Compared to the existing approaches, the service time using our proposal can achieve up to 7.6%, 22.6%, 38.9%, and 51.8% performance gains for augmented reality, healthcare, compute-intensive, and infotainment applications, respectively.

One key element towards improving network performance with lower computing latency and better quality of service in vehicular edge computing (VEC) is offloading, which allows computation-intensive mobile applications to offload their tasks to VEC servers. In vehicular ad-hoc networks, VEC can offload the computation tasks to the road-side unit (RSU), which improves vehicular service and reduces energy consumption of the vehicle. In this letter, we investigate the impact of transmission time between vehicles and RSUs, which significantly impacts the offloading decision. In particular, due to the high mobility topology and IEEE 802.11p medium access control protocol, we propose an algorithm design of joint contention window control and offloading decision formulated as a mixed-integer non-linear problem to maximize system utility. The numerical results show that the proposed algorithm significantly outperforms benchmark policies in terms of the total processing completion time.

In the era of heterogeneous 5G networks, Internet of Things (IoT) devices have significantly altered our daily life by providing innovative applications and services. However, these devices process large amounts of data traffic and their application requires an extremely fast response time and a massive amount of computational resources, leading to a high failure rate for task offloading and considerable latency due to congestion. To improve the quality of services (QoS) and performance due to the dynamic flow of requests from devices, numerous task offloading strategies in the area of multi-access edge computing (MEC) have been proposed in previous studies. Nevertheless, the neighboring edge servers, where computational resources are in excess, have not been considered, leading to unbalanced loads among edge servers in the same network tier. Therefore, in this paper, we propose a collaboration algorithm between a fuzzy-logic-based mobile edge orchestrator (MEO) and state-action-reward-state-action (SARSA) reinforcement learning, which we call the Fu-SARSA algorithm. We aim to minimize the failure rate and service time of tasks and decide on the optimal resource allocation for offloading, such as a local edge server, cloud server, or the best neighboring edge server in the MEC network. Four typical application types, healthcare, AR, infotainment, and compute-intensive applications, were used for the simulation. The performance results demonstrate that our proposed Fu-SARSA framework outperformed other algorithms in terms of service time and the task failure rate, especially when the system was overloaded.