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Despite the advantages of multi-access edge computing in enabling latency-sensitive services and extending the limited computing capabilities of network devices, access communication issues are still often causing the quality of the wireless channels to be severely degraded, preventing the edge resources from being efficiently utilized. Through the deployment of low-cost passive reflecting elements, the recent studies of intelligent reflecting surfaces (IRSs) in wireless networks have shown a great potential for enhancing the quality of the wireless channels and the transmission rates. In this work, motivated by the recent findings, we study the use of an IRS-aided edge computing system for enabling low latency and high reliability computation offloading in the context of a single-user network. Specifically, we optimize the phase shift of the IRS elements along with the device’s transmit power and offloading decision, with the objective of minimizing the device’s energy consumption. Due to the non-convexity of the problem, we propose a customized sub-optimal solution based on the alternating optimization approach, utilizing novel successive convex approximation techniques. Numerical analysis demonstrates the energy reduction and saving in network resources provided by the optimized use of the IRS, especially for offloading services with higher reliability.

Heterogeneous networks have allowed network operators to enhance the spectral efficiency and support large number of devices by deploying close small-cells. Recently, Multi-access Edge Computing (MEC) has become an enabler for modern latency-sensitive 5G services by pushing tasks computation to the network edge. In this paper, we study the problem of task offloading in a MEC-enabled heterogeneous network with low-cost wireless backhaul, where we minimize the total devices’ energy consumption while respecting their latency deadline. We explore the benefit of leveraging the macro-cell cloudlet for computing small-cell users’ tasks, where the allocation of backhaul radio resources is optimized. We also jointly optimize the partial offloading decision, transmit power, and the allocation of access radio and computational resources. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method that provides an approximate solution. Through numerical analysis, we perform simulations based on varying configurations, and demonstrate the performance and efficiency of our proposed solution.

Multi-access edge computing (MEC) has formed a major improvement in the existing mobile cloud computing paradigm, due to its ability in addressing the rising number of latency-sensitive services. However, bearing in mind the limited capacity that edge servers possess which offsets their benefits in the periods of high load, a hierarchical arrangement of the edge cloudlets has been studied and has shown to be successful in expanding their capabilities. Yet, considering the emerging business models in 5G networks, the cost disparity between the edge tiers has been until now ignored, leading to cost-inefficient solutions with respect to the network operators (NOs). In this paper, we consider an NO that is leasing resources of a high-tier central cloudlet for task offload, where we jointly minimize the NO’s computational cost and devices’ energy consumption in a multi-tier MEC system, by optimizing the offloading decision, the allocated transmission power and radio resources on the uplink channel, and the assigned servers’ computation, while respecting the devices’ latency requirement. We mathematically formulate our mixed-integer non-convex program and propose a Branch-and-Bound (BnB) algorithm for obtaining the optimal solution. Due to the BnB complexity, we propose a low-complexity algorithm based on the successive convex approximation method to solve and obtain a high-quality solution and also present an inflation-based algorithm for obtaining a polynomial-time and efficient solution. The numerical results show the performance and scalability of the algorithms, demonstrate their efficiency, and uncover insights for helping NOs to better manage their resources following various configurations.

Vasopressins are evolutionarily conserved peptide hormones. Mammalian vasopressin functions systemically as an antidiuretic and regulator of blood and cardiac flow essential for adapting to terrestrial environments. Moreover, vasopressin acts centrally as a neurohormone involved in social and parental behavior and stress response. Vasopressin synthesis in several cell types, storage in intracellular vesicles, and release in response to physiological stimuli are highly regulated and mediated by three distinct G protein coupled receptors. Other receptors may bind or cross-bind vasopressin. Vasopressin is regulated spatially and temporally through transcriptional and post-transcriptional mechanisms, sex, tissue, and cell-specific receptor expression. Anomalies of vasopressin signaling have been observed in polycystic kidney disease, chronic heart failure, and neuropsychiatric conditions. Growing knowledge of the central biological roles of vasopressin has enabled pharmacological advances to treat these conditions by targeting defective systemic or central pathways utilizing specific agonists and antagonists.

Multi-access Edge Computing (MEC) has enabled low-latency computation offloading for provisioning latency-sensitive 5G services that may also require stringent reliability. Given the growing user demands incurring communication bottleneck in the access network, Unmanned Aerial Vehicles (UAVs) have been proposed to provide edge computation capability, through mounting them by cloudlets, hence, harnessing their various advantages such as flexibility, low-cost, and line of sight communication. However, the introduction of UAV-mounted cloudlets necessitates a novel study of the provisioned reliability while accounting for the high failure rate of UAV-mounted cloudlets, that can be caused by various factors. In this paper, we study the problem of reliability-aware computation offloading in a UAV-enabled MEC system. We aim at maximizing the number of served offloading requests, by optimizing the UAVs' positions, users' task partitioning and assignment, as well as the allocation of radio and computational resources. We formulate the problem as a non-convex mixed-integer program, and due to its complexity, we transform it into an approximate convex program and provide a low-complexity iterative algorithm based on the Successive Convex Approximation (SCA) method. Through numerical analysis, we demonstrate the efficiency of our solution, and study the achieved performance gains for various latency and reliability requirements corresponding to different use cases in 5G networks.

By pushing computation to the mobile network edge, Multi-access Edge Computing (MEC) has been an enabler for the stringent latency and energy requirements of the new Internet of Things (IoT) services. On the other hand, ultra-dense heterogeneous networks with wireless backhaul have been proposed as a low-cost solution, allowing Network Operators (NOs) to extend the network capability, by deploying densified close-proximity small-cells and hence supporting a large number of low-latency low-energy IoT devices. In this paper, we study the problem of IoT task offloading in a MEC-enabled heterogeneous network, which to the best of our knowledge, is the first attempt to thoroughly explore the task offloading problem in a heterogeneous network with MEC support and wireless backhaul. We jointly optimize the offloading decision, transmission power, and the allocation of radio and computational resources, with the objective of minimizing the devices energy consumption, while respecting their latency deadline. We mathematically formulate our problem as a non-convex mixed-integer program, and due to its complexity, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) method for providing an approximate solution on the original problem. Through numerical analysis, we perform simulations based on multiple scenarios, and find out how NOs can respond to the requested load and help in minimizing the total devices energy consumption.

Given the inability of Mobile Cloud Computing (MCC) to guarantee the requirements of the delay-sensitive applications, Mobile Edge Computing (MEC) has been proposed to drastically reduce that latency. But since edge servers suffer from limited capabilities that offset the latency benefits in periods of high load, a hierarchical edge cloud architecture has been studied as a way to mitigate that problem. However, such model incurs different computational costs that depend on the cloudlet layer. In this paper, we jointly minimize the mobile devices' energy consumption and computational cost in a multilayered MEC, by optimizing their transmission power and the assigned server computation while respecting their latency threshold. We mathematically formulate the mixed integer non-convex program and propose an efficient algorithm based on Successive Convex Approximation (SCA) method to solve and obtain a high-quality solution. Through numerical results, we analyze different scenarios, and show the efficiency of our algorithm in providing an approximate solution that efficiently decreases the total energy consumption and computational cost.