Multi-Access Edge Computing (MEC) is a paradigm that enables Internet-of-Things (IoT) applications and devices to run tasks in different locations, from IoT devices to servers in the Cloud. This way, less capable devices can offload computation loads to more powerful or available servers. However, choosing the optimal location for a particular task can be complex due to the features of each location and restrictions of the task. For this, numerous approaches in the literature adopt a centralized strategy for the computation offloading decision, which introduces a single point of failure and can be a bottleneck for resource-demanding applications. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to solve the choice of computing locations, and its assessment in a real testbed. This testbed is formed of an end-user device running the agent, which connects to a MEC server and a Cloud server through 5G. We compare the algorithm against four alternatives, one based on another DRL approach, and analyze their performance in terms of meeting the computing tasks’ requirements and energy consumption in the User Equipment (UE), where synthetically generated tasks are executed or offloaded. DRL algorithms are shown to provide the best tradeoff between performance and energy consumption in changing conditions.

Cooperative perception plays a vital role in extending a vehicle's sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to address the following fundamental question: What sensory data needs to be shared?, at which resolution?, and with which vehicles? To answer this question, in this paper, a novel framework is proposed to allow reinforcement learning (RL)-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages (CPMs) by utilizing a quadtree-based point cloud compression mechanism. Furthermore, a federated RL approach is introduced in order to speed up the training process across vehicles. Simulation results show the ability of the RL agents to efficiently learn the vehicles' association, RB allocation, and message content selection while maximizing vehicles' satisfaction in terms of the received sensory information. The results also show that federated RL improves the training process, where better policies can be achieved within the same amount of time compared to the non-federated approach.

Cooperative perception plays a vital role in extending a vehicle’s sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to address fundamental questions such as: what sensory data needs to be shared? at which resolution? with which vehicles? In this view, this paper proposes a reinforcement learning (RL)-based vehicular association, resource block (RB) allocation, and content selection of cooperative perception messages by utilizing a quadtree-based point cloud compression mechanism. Simulation results show the ability of the RL agents to efficiently learn the vehicles’ association, RB allocation and message content selection that maximizes the fulfillment of the vehicles in terms of the received sensory information.

Immersive virtual reality (VR) applications require ultra-high data rate and low-latency for smooth operation. Hence in this paper, aiming to improve VR experience in multi-user VR wireless video streaming, a deep-learning aided scheme for maximizing the quality of the delivered video chunks with low-latency is proposed. Therein the correlations in the predicted field of view (FoV) and locations of viewers watching 360° HD VR videos are capitalized on to realize a proactive FoV-centric millimeter wave (mmWave) physical-layer multicast transmission. The problem is cast as a frame quality maximization problem subject to tight latency constraints and network stability. The problem is then decoupled into an HD frame request admission and scheduling subproblems and a matching theory game is formulated to solve the scheduling subproblem by associating requests from clusters of users to mmWave small cell base stations (SBSs) for their unicast/multicast transmission. Furthermore, for realistic modeling and simulation purposes, a real VR head-tracking dataset and a deep recurrent neural network (DRNN) based on gated recurrent units (GRUs) are leveraged. Extensive simulation results show how the content-reuse for clusters of users with highly overlapping FoVs brought in by multicasting reduces the VR frame delay in 12%. This reduction is further boosted by proactiveness that cuts by half the average delays of both reactive unicast and multicast baselines while preserving HD delivery rates above 98%. Finally, enforcing tight latency bounds shortens the delay-tail as evinced by 13% lower delays in the 99th percentile.

Edge computing is an emerging concept based on distributed computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth-generation (5G) wireless systems and beyond. While the current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This paper will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultrareliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so on. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

In this paper, a novel proactive computing and mmWave communication for ultra-reliable and low latency wireless virtual reality (VR is proposed. By leveraging information about users' poses, proactive computing and caching are used to pre-compute and store users' HD video frames to minimize the computing latency. Furthermore, multi-connectivity is exploited to ensure reliable mmWave links to deliver users' requested HD frames. The performance of the proposed approach is validated on a VR network serving an interactive gaming arcade, where dynamic and real-time rendering of HD video frames is needed and impulse actions of different players impact the content to be shown. Simulation results show significant gains of up to 30% reduction in end-to-end delay and 50% in the 90thpercentile communication delay.

This work focuses on wide-scale freight transportation logistics motivated by the sharp increase of on-line shopping stores and the upsurge of Internet as the most frequently utilized selling channel during the last decade. This huge ecosystem of one-click-away catalogs has ultimately unleashed the need for efficient algorithms aimed at properly scheduling the underlying transportation resources in an efficient fashion, especially over the so-called last mile of the distribution chain. In this context the selective pickup and delivery problem focuses on determining the optimal subset of packets that should be picked from its origin city and delivered to their corresponding destination within a given time frame, often driven by the maximization of the total profit of the courier service company. This manuscript tackles a realistic variant of this problem where the transportation fleet is composed by more than one vehicle, which further complicates the selection of packets due to the subsequent need for coordinating the delivery service from the command center. In particular the addressed problem includes a second optimization metric aimed at reflecting a fair share of the net benefit among the company staff based on their driven distance. To efficiently solve this optimization problem, several nature-inspired metaheuristic solvers are analyzed and statistically compared to each other under different parameters of the problem setup. Finally, results obtained over a realistic scenario over the province of Bizkaia (Spain) using emulated data will be explored so as to shed light on the practical applicability of the analyzed heuristics.

Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

The detection of node clusters (communities) in graphs has been at the core of many modeling paradigms emerging in different fields and disciplines such as Social Sciences, Biology, Chemistry, Telecommunications and Linguistics. When evaluating the quality of a clustering arrangement unsupervised metrics can be utilized (e.g. modularity), which all rely on structural and topological characteristics of the cluster space rather than on an observed ground of truth that should be achieved. One of such metrics is the recently published Surprise, which evaluates how statistically unlikely a given clustering arrangement is with respect to a random network featuring the same distribution of nodes per cluster. To maximize this metric, a number of algorithms have been proposed in the literature, but their comparative performance varies significantly between networks of different shape and size. In this article a novel heuristic community detection approach is proposed as a means to achieve a universally well-performing tool for graph clustering based on Surprise maximization. The heuristic scheme relies on the search procedure of the so-called Firefly Algorithm, a nature-inspired meta-heuristic solver based on the collective behavior, mutual attractiveness and random yet controlled movement of these insects. The proposed technique emulates these observed behavioral patterns of fireflies in the genotype of the graph clustering problem rather than on an encoded representation of its search space (phenotype). Simulation results evince that the performance of our community detection scheme generalizes better than other schemes when applied over synthetically generated graphs with varying properties.

Tree based optimization problems stand for those paradigms where solutions can be arranged within a tree-like graph whose nodes represent the optimization variables of the problem at hand and their interconnecting edges topological and/or hierarchical relationships between such variables. In this context, a research line of increasing interest during the last decade focuses on the derivation of intelligent solution encoding strategies capable of 1) capturing all topological constraints of this particular class of graphs; and 2) preserving their connectivity properties when they undergo combination/mutation operations within approximative evolutionary solvers. This manuscript takes a step over the state of the art by shedding light on the heri-tability properties of the Dandelion tree encoding approach under avant-garde stochastically-controlled evolutionary operators. In particular we elaborate on the topological heritability of the so-called Harmony Memory Considering Rate (HMCR) exploitative operator of the Harmony Search algorithm, a population-based meta-heuristic algorithm that has so far shown to outperform other evolutionary schemes in a wide range of optimization scenarios. Results from extensive Monte Carlo simulations are discussed in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.