NOMA-based Joint Allocation and Offloading Strategy of Communication and Computing Resources

The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Due to the flexible mobility and agility, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial base stations (BSs) in future air-ground integrated wireless networks, providing temporary and controllable coverage and additional computation capabilities for ground Internet of Things (IoT) devices with or without infrastructure support. Meanwhile, with the breakthrough of artificial intelligence (AI), more and more AI applications relying on AI methods such as deep neural networks (DNNs) are expected to be applied in various fields such as smart homes, smart factories and smart cities to improve our lifestyles and efficiency dramatically. However, AI applications are generally computation-intensive, latency-sensitive, and energy-consuming, making resource-constrained IoT devices unable to benefit from AI anytime and anywhere. In this paper, we study mobile edge computing (MEC) for AI applications in air-ground integrated wireless networks. Our goal is to minimize the service latency while ensuring the learning accuracy requirements and energy consumption. To achieve that, we take DNN as the typical AI application and formulate an optimization problem that optimizes the DNN model decision, computation and communication resource allocation, and UAV trajectory control, subject to the energy consumption, latency, computation and communication resource constraints. Considering the formulated problem is non-convex, we decompose it into multiple convex subproblems and then alternately solve them till they converge to the desired solution. Simulation results show that the proposed algorithm significantly improves the system performance for AI applications.

Mobile cloud computing (MCC) is a new technology that has been developed to overcome the restrictions of smart mobile devices (e.g. battery, processing power, storage capacity, etc.) to send a part of the program (with complex computing) to the cloud server (CS). In this paper, we study a multi-cell with multi-input and multi-output (MIMO) system in which the cell-interior users request service for their processing from a common CS. Also, the problem of the optimum offloading is considered as an optimization problem with optimization parameters including communication resources (such as bandwidth, transmit power and backhaul link capacity) and computational resources (such as the capacity of cloud server) in the downlink network. The main goal is to minimize the total energy consumption by mobile users (MUs) for processing with the delay limitation for each use. This issue leads to a non-convex problem and to solve the problem, we use successive convex approximation (SCA) method. We finally show that the joint optimization of these parameters leads to reducing the energy consumption of the network with simulation examples.

The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i>-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

With the rapid growth of Internet traffic and smart mobile terminals, ultradense networks are adopted as the key technology of the fifth generation to enhance resource utilization and content distribution while causing serious energy efficiency problem. Mobile edge computing has recently drawn great attention for its advantages in reducing transmission delay and network energy consumption by implementing caching and computing abilities at the edge of mobile networks. To improve network energy efficiency and content transmission, in this paper, we propose a novel energy-efficient hierarchical collaborative scheme by considering the in-network caching, request aggregation, and joint allocation of caching, computing, and communication resources in a layered heterogeneous network including mobile users, small base stations, macro base stations, and the cloud. We formulate the energy consumption problem as a queuing theory-based centralized model, where the same content requests can be aggregated in the queue of each base station. Then, the optimal solution is analyzed based on the distribution characteristic of content popularity at the base stations. Simulation results show that the performance of our proposed model is much better than the existing cloud-edge cooperation solutions without considering the deployment of caching resource and request aggregation policies.

The development of an infrastructure for multiple autonomous agents, with an application to urban traffic signal control, is described. The agent-based infrastructure, Cybele, allows for distributed computing, interagent communication, agent migration, and computational resource allocation. The agents that are used to solve the traffic signal control problem are known collectively as DAARTS (Decentralized Adaptive Agents for contRol of Traffic Signals). DAARTS adopts a hierarchical multiagent-based architecture in which the lowest level (intersection agents) involves individual intersection-traffic dynamics and phase selection based on “local” information, while higher levels take into account the supervisory (network-level) dynamics. The controller design is based on a receding-horizon model predictive control approach. Coordination between intersections is achieved in a decentralized manner at the lowest level. The agents are integrated into a simulation test bed with the microsimulator CORSIM, using the DAARTS simulation tool kit. This kit enables communications between the CORSIM real-time extension, the communications management functions of Cybele, and the traffic control agents. The control process is truly distributed, and each of these components can reside on a different computer. Descriptions of all of the software components are given, and the control algorithm is discussed in detail. Some encouraging results from the simulation of a small network are included. Ongoing and future research activities are discussed.

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud and thus has the potential to enable computation-intensive and delay-sensitive applications via computation offloading. However, in multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. In addition, having access to complete network information that includes variables such as wireless channel state, available bandwidth and computation resources, is a major issue. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user end-edge-cloud orchestrated network where all devices and base stations have computation capabilities. Then, we formulate the joint computation offloading and resource allocation problem as a Markov Decision Process (MDP) and propose a new DRL algorithm to minimize system energy consumption. Numerical results based on a real-world dataset demonstrate that the proposed DRL-based algorithm significantly outperforms the benchmark policies in terms of system energy consumption. Extensive simulations show that learning rate, discount factor, and number of devices have considerable influence on the performance of the proposed algorithm.

Smart grids have various capabilities to meet the electricity demands of modern society in production and life, and real-time monitoring of smart grids is critical to enhance the reliability and operational efficiency of power utilities. With the development of artificial intelligence technology and cloud computing, several studies have proposed using the powerful computing ability of clouds to design fault detection systems based on deep learning. However, due to the transmission delay in the Internet backbone and the large amount of data uploaded to the system, various problems arise such as a large bandwidth load and poor real-time feedback from the cloud platform. To solve these problems, it is necessary to embed the artificial intelligence technology at the edge of a network to realize a de-centralized system. In this paper, we propose an edge computing assisted smart grid fault detection system that uses an embedded lightweight neural network device, which is placed near the edge of the monitored equipment to realize real-time monitoring. In addition, considering the limited communication resources, relatively low computation capabilities, and different monitoring accuracies of edge devices, we design an optimal allocation method for communication and computation resources, which can maximize the throughput of the system, and improve the resource utilization of the system while meeting the requirements of data transmission and delay processing. Finally, simulation experiments are carried out to show that compared with other structures of smart grid fault detection systems, our proposed system can transmit more data while meeting the requirements of the delay bound, reduce the time required for transmission, and enhance the real-time performance of smart grid fault detection systems.

Device-to-device (D2D) communications in cellular networks allow proximity users to communicate directly without going through the base station. Concentrating on scenarios where the direct communication between potential D2D users may not be reliable due to poor proximity and link condition, and paying attention to energy harvesting wireless communications, this study focuses on the green relay-assisted D2D communications, where long-term evolution-advanced layer-3 relays powered by renewable energy are employed. To achieve the trade-off between system capacity maximisation and user fairness, the authors formulate an offline resource block (RB) and power allocation problem under imperfect channel information with the objective of maximising the weighted sum-rate of all users. Then, based on battery dynamics, the original problem is reformulated as an online one with energy outage avoidance. To obtain distributed solutions, the online problem is further transformed into a convex one, and the rate outage-probability requirement is also converted for tractability. Finally, applying Lagrangian duality, the distributed RB and power allocation is derived, which is globally optimal to the reformulated online problem. Comparing with a baseline scheme where all users communicate directly without assistance of relays, numerical results show the superiority of the proposed approach in terms of the average achievable data rate.

Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation‐intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP‐hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

The incipient vehicular applications and the explosive growth of data generally led to the increase in demand for communication, computation, and storage resources, as well as the stringent performance requirement on latency and wireless network capacity. To address these challenges, Vehicular Edge Computing is an envisioned promising solution that extends the computation capability to the edge of the vehicular network and computation offloading service is provided in the proximity of the vehicles. Vehicular edge computing (VEC) and D2D communication have been designed to benefit from proximity gain to optimize computation offloading. Software-defined network (SDN) inside the vehicular network (SDVN) is developed in this article to rectify most problems in the vehicular network. Besides, the SDVN controller handles D2D pairing of vehicles and co-ordinates with the edge cloud for flexible allocation of computing resources among vehicles and mobile users. In this article, we combine D2D communication and VEC to further enhance the computation capacity of the vehicular network. Our goal is to optimize the number of devices the vehicular network can support under communication and computational limitation. We formulate the task offloading and associated resource allocation problem as a game with mixed strategies. The players involved in the game are the SDN controller handling the computation resource allocation at the network edge and a vehicle deciding on task offloading. The existence and uniqueness of Nash equilibrium are theoretically proved.

The allocation of computation and communication resources in a manner that optimizes aggregate system performance is a crucial aspect of system management. Wireless sensor network poses new challenges due to the resource constraints and real-time requirements. Existing work has dealt with the real-time sampling rate assignment problem, under single processor case and network case with static routing environment. For wireless sensor networks, in order to achieve better overall network performance, routing should be considered together with the rate assignments of individual flows. In this paper, we address the problem of optimizing sampling rates with dynamic route selection for wireless sensor networks. We model the problem as a constrained optimization problem and solve it under the network utility maximization framework. Based on the primal-dual method and dual decomposition technique, we design a distributed algorithm that achieves the optimal global network utility considering both dynamic route decision and rate assignment. Extensive simulations have been conducted to demonstrate the efficiency and efficacy of our proposed solutions.

The increasing number of computing platforms is critical with the increasing trend of delay-sensitive complex applications with enormous power consumption. These computing platforms attach themselves to multiple small base stations and macro base stations to optimize system performance if appropriately allocated. The arrival rate of computing tasks is often stochastic under time-varying wireless channel conditions in the mobile edge computing Internet of things (MEC IoT) network, making it challenging to implement an optimal offloading scheme. The user needs to choose the best computing platforms and base stations to minimize the task completion time and consume less power. In addition, the reliability of our system in terms of the number of computing resources (power, CPU cycles) each computing platform consumes to process the user’s task efficiently needs to be ascertained. This paper implements a computational task offloading scheme to a high-performance processor through a small base station and a collaborative edge device through macro base stations, considering the system’s maximum available processing capacity as one of the optimization constraints. We minimized the latency and energy consumption, which constitute the system’s total cost, by optimizing the computing platform choice, base station’s choice, and resource allocation (computing, communication, power). We use the actor-critic architecture to implement a Markov decision process that depends on deep reinforcement learning (DRL) to solve the model’s problem. Simulation results showed that our proposed scheme achieves significant long-term rewards in latency and energy costs compared with random search, greedy search, deep Q-learning, and the other schemes that implemented either the local computation or edge computation.