In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning} framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.

With the rapid advancement of intelligent transportation systems (ITS) and vehicular communications, vehicular edge computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

Edge computing techniques allow to exploit the devices at the network borders for computing efforts in order to reduce the centralized cloud requests. A fog network is a feasible solution for implementing edge computing services. Within this scenario, the deployed Fog Nodes (FNs) are able to offload different portions of a single task to the available nodes to be processed at the network edge. However, to partially offload, FNs consume an extra amount of energy for transmission and reception of the tasks while saving energy by not processing the whole task on their own. Moreover, offloading requires an extra transmission and reception time to the task processing time. In this work, the focus is on a partial offloading approach where the trade off between FN energy consumption and task processing delay is considered when estimating the portion to be offloaded to the available devices at the edge of the network by comparing a centralized and a distributed architecture. Simulation results demonstrate the effectiveness of the proposed estimation solutions in terms of FN energy consumption, average task delay and network lifetime.

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods.

Edge Computing refers to a recently introduced approach aiming to bring the storage and computational capabilities of the cloud to the proximity of the edge devices. Edge Computing is one of the main techniques enabling Fog Computing and Networking. Among several application scenarios, the urban scenario seems one of the most attractive for exploiting edge computing approaches. However, in an urban scenario, mobility becomes a challenge to be addressed, affecting the edge computing. By gaining from the the presence of two types of devices, Fog Nodes (FNs) and Fog-Access Points (F-APs), the idea in this paper is that of exploiting Device to Device (D2D) communications between FNs for assisting computation offloading requests between FNs and F-APs by exchanging status information related to the F-APs. With this knowledge, this paper proposes a partial offloading approach where the optimal tasks amount to be offloaded is estimated for minimizing the outage probability due to the mobility of the devices. In order to reduce the outage probability we have further considered a relaying approach among F-APs. Moreover, the impact of the number of tasks that each F-AP can manage is shown in terms of task processing delay. Numerical results show that the proposed approaches allow to achieve performance closer to the lower bound, by reducing the outage probability and the task processing delay.