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.

Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social

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. INDEX TERMS VANET, medium access control, adaptive MAC protocol, Markov chain models.

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