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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The existing adaptive multichannel medium access control (MAC) protocols in vehicular ad hoc networks can adjust themselves according to different vehicular traffic densities. These protocols can increase throughput and guarantee a bounded transmission delay for real-time safety applications. However, the optimized control channel interval is computed based on the maximum throughput while ignoring the strict safety packet transmission delay requirements. In this paper, we analyze the effects of the throughput and strict safety packet transmission delay with adaptive multichannel MAC protocols, such as connectivity-aware MAC (CA MAC), adaptive multi-priority distributed MAC (APDM), multi-priority supported p-persistent MAC (MP MAC), and variable control channel interval MAC (VCI) protocols. The performance and analysis results show that: (a) under a low data rate condition, CA MAC does not guarantee a strict safety packet transmission delay; (b) APDM not only satisfies the safety packet transmission requirement, but also provides the lowest safety packet transmission delay; (c) under a high data rate condition, we suggest APDM for use as an adaptive MAC protocol because it allows for high throughput for nonsafety packets and preserves low safety packet transmission delay; (d) under a low data rate condition with various data packet sizes, we suggest MP MAC for high throughput, which satisfies the safety packet transmission requirement; and (e) under low vehicle density and low data rate conditions, VCI can support high throughput. A balance between transmission delay and throughput must be considered to improve the optimal efficiency, reliability, and adaptability. KEYWORDSVANET, multi-channel MAC, throughput, transmission delay Int J Commun Syst. 2020;33:e4172.wileyonlinelibrary.com/journal/dac

Online workload balancing guarantees that the incoming workloads are processed to the appropriate servers in real time without any knowledge of future resource requests. Currently, by matching the characteristics of incoming Internet of Things (IoT) applications to the current state of computing and networking resources, a mobile edge orchestrator (MEO) provides high-quality service while temporally and spatially changing the incoming workload. Moreover, a fuzzy-based MEO is used to handle the multicriteria decision-making process by considering multiple parameters within the same framework in order to make an offloading decision for an incoming task of an IoT application. In a fuzzy-based MEO, the fuzzy-based offloading strategy leads to unbalanced loads among edge servers. Therefore, the fuzzy-based MEO needs to scale its capacity when it comes to a large number of devices in order to avoid task failures and to reduce service times. In this paper, we investigate and propose an online workload balancing algorithm, which we call the fuzzy-based (FuB) algorithm, for a fuzzy-based MEO. By considering user configuration requirements, server geographic locations, and available resource capacities for achieving an online algorithm, our proposal allocates the proximate server for each incoming task in real time at the MEO. A simulation was conducted in augmented reality, healthcare, compute-intensive, and infotainment applications. Compared to two benchmark schemes that use the fuzzy logic approach for an MEO in IoT environments, the simulation results (using EdgeCloudSim) show that our proposal outperforms the existing algorithms in terms of service time, the number of failed tasks, and processing times when the system is overloaded.

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