Optimal Resource Allocation Problem Research Articles

Efficient optimization techniques for resource allocation in UAVs mission framework.

This paper considers the generic problem of a central authority selecting an appropriate subset of operators in order to perform a process (i.e. mission or task) in an optimized manner. The subset is selected from a given and usually large set of 'n' candidate operators, with each operator having a certain resource availability and capability. This general mission performance optimization problem is considered in terms of Unmanned Aerial Vehicles (UAVs) acting as firefighting operators in a fire extinguishing mission and from a deterministic and a stochastic algorithmic point of view. Thus the applicability and performance of certain computationally efficient stochastic multistage optimization schemes is examined and compared to that produced by corresponding deterministic schemes. The simulation results show acceptable accuracy as well as useful computational efficiency of the proposed schemes when applied to the time critical resource allocation optimization problem. Distinguishing features of this work include development of a comprehensive UAV firefighting mission framework, development of deterministic as well as stochastic resource allocation optimization techniques for the mission and development of time-efficient search schemes. The work presented here is also useful for other UAV applications such as health care, surveillance and security operations as well as for other areas involving resource allocation such as wireless communications and smart grid.

Joint task assignment and resource allocation in VFC based on mobility prediction information

Vehicular Fog Computing (VFC) is a novel architecture that utilizes end-user clients or near-user edge devices to reduce the processing and transmission time of vehicular tasks. However, the high mobility of vehicles in VFC presents a challenge in coordinating vehicles and managing multi-dimensional resources. In this paper, we investigate the joint task assignment and resource allocation optimization problem by incorporating mobility prediction information. We propose two schemes that consider execution speed and accuracy. The first scheme decomposes the original problem into two sub-problems: subtask assignment and bandwidth resource allocation, solved by matching and convex optimization methods. The second scheme uses an iterative optimization process based on 0–1 integer linear programming. Simulation results based on real-world mobility datasets demonstrate the effectiveness and complementarity of the proposed schemes.

Energy-Efficient Mobile Edge Computing in RIS-Aided OFDM-NOMA Relay Networks

In this paper, the design of task offloading and computing strategy in a mobile edge computing (MEC) system deployed over a reconfigurable intelligent surface (RIS)-aided orthogonal frequency division multiplexing based non-orthogonal multiple access (OFDM-NOMA) relay network is studied, where both a relay node and an MEC server located at an access point help a user terminal accomplish task computing. Our objective is to minimize energy consumption for communication and computation at the user terminal and the relay subject to a given task executing delay constraint. To this end, a novel protocol is first proposed to enable the user terminal and the relay to fully utilize the high passive beamforming gain provided by RIS and the high spectral efficiency offered by OFDM and NOMA techniques for task offloading. Then, under the newly proposed protocol, a resource allocation optimization problem is formulated, where RIS reflection coefficients, task offloading and computing time allocation, subcarrier and power allocation, task partition, and CPU frequency of the MEC server are jointly optimized. Because the numerous optimization variables are coupled with each other and integer programming is involved for subcarrier allocation optimization, the formulated problem is a mixed integer programming and highly non-convex problem that is hard to tackle. To address this problem, an efficient algorithm is devised to achieve its solution by employing alternating optimization, layer optimization, big-M formulation and successive convex approximation techniques. Simulation results validate that substantial energy savings can be achieved by employing RIS and OFDM-NOMA techniques in wireless relaying-based MEC systems.

A Parallel Intelligence-Driven Resource Scheduling Scheme for Digital Twins-Based Intelligent Vehicular Systems

Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.

Resource Allocation for Integrated Sensing and Communication in Digital Twin Enabled Internet of Vehicles

With the development of the sixth-generation (6 G) network, virtualization remains critical. The key to future virtualization lies in the service provisioning capability of the network and the service requirements of end users, which will lead to virtualization of the network and end users. Therefore, this paper proposes a holistic network virtualization architecture that integrates digital twin (DT) and network slicing to achieve the network management of service-centric and user-centric. With the explosive growth of latency-sensitive and computing-intensive in-vehicle applications, limited in-vehicle computing resources are difficult to meet diverse network requirements, and vehicle edge computing (VEC) has become a potential solution. However, computation offloading may face the dilemma of excessive upload traffic and unbearable upload time. Therefore, in order to minimize the overall response time (ORT) of the system, this paper proposes a new environment aware offloading mechanism (EAOM) based on the integrated sensing and communication system (ISAC) to solve the joint optimization problem of task scheduling and resource allocation. Considering the mobility of vehicles and the time-varying of environment, the optimization problem is modeled as a Markov decision process, and an improved algorithm combining Shapley-Q value and deep deterministic policy gradient (DDPG) is used to solve it. The simulation results indicate the effectiveness and superiority of the scheme proposed in our work.

QoS-Aware Resource Allocation for Green Sensor-as-a-Service Provisioning in Vehicular Multi-Sensor-Cloud

This work addresses the problem of optimal resource allocation for provisioning green Sensor-as-a-Service (Se-aaS) in the presence of multiple sensor-cloud service providers (SCSPs) and vehicle owners (VOs) having heterogeneous sensors. To ensure high service availability, low carbon footprint, and optimal profit distribution, optimal usage of the available resources while maintaining optimal contribution of VOs in the multi-sensor-cloud (MSC) market is necessary. Additionally, a single SCSP may lose revenue due to the absence of compatible sensor-equipped vehicles (SVs) to serve user requests. To address these issues, we propose FRAME, a game-theoretic resource allocation scheme, for vehicular MSC. Initially, we use a single-leader-multiple-followers Stackelberg game to decide the optimal set of SVs for serving a request while ensuring high QoS, low energy consumption, and optimal profit distribution among the SCSPs and VOs. Further, to address the inadequacy of resources, we propose a collaboration scheme using the expected utility theory for multiple SCSPs and choose the optimal SCSP to serve the requests. We evaluate the performance of FRAME theoretically and experimentally and compare it with benchmark schemes. Simulation results depict a 4.14-27.3% increase in served requests, a 14.61-22.83% decrease in energy consumption, and optimal profit distribution among the SCSP and VOs.

Low-Latency Federated Learning With DNN Partition in Distributed Industrial IoT Networks

Federated Learning (FL) empowers Industrial Internet of Things (IIoT) with distributed intelligence of industrial automation thanks to its capability of distributed machine learning without any raw data exchange. However, it is rather challenging for lightweight IIoT devices to perform computation-intensive local model training over large-scale deep neural networks (DNNs). Driven by this issue, we develop a communication-computation efficient FL framework for resource-limited IIoT networks that integrates DNN partition technique into the standard FL mechanism, wherein IIoT devices perform local model training over the bottom layers of the objective DNN, and offload the top layers to the edge gateway side. Considering imbalanced data distribution, we derive the device-specific participation rate to involve the devices with better data distribution in more communication rounds. Upon deriving the device-specific participation rate, we propose to minimize the training delay under the constraints of device-specific participation rate, energy consumption and memory usage. To this end, we formulate a joint optimization problem of device scheduling and resource allocation (i.e. DNN partition point, channel assignment, transmit power, and computation frequency), and solve the long-term min-max mixed integer non-linear programming based on the Lyapunov technique. In particular, the proposed dynamic device scheduling and resource allocation (DDSRA) algorithm can achieve a trade-off to balance the training delay minimization and FL performance. We also provide the FL convergence bound for the DDSRA algorithm with both convex and non-convex settings. Experimental results demonstrate the derived device-specific participation rate in terms of feasibility, and show that the DDSRA algorithm outperforms baselines in terms of test accuracy and convergence time.

ISAC-Accelerated Edge Intelligence: Framework, Optimization, and Analysis

Conventionally, the sensing and communication stages for edge intelligence systems are executed sequentially, leading to an excessive time of dataset generation and uploading. To combat the weakness, this paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC), where the sensing and communication stages are merged to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. For the proposed ISAC-accelerated edge intelligence system, the resource allocation and beamforming should be jointly optimized to exploit the underlying ISAC benefits. We formulate a joint resource allocation and beamforming optimization problem. Despite the non-convexity, we obtain globally optimal solutions assuming that the constant maximal transmits power, and devise an alternating optimization algorithm for the original problem without such assumption. Furthermore, we analyze the ISAC acceleration gain of the proposed system over that of the conventional edge intelligence system. Both theoretic analysis and simulation results show that ISAC accelerates the conventional edge intelligence system when the duration of generating a sample is more than that of uploading a sample. Otherwise, the ISAC acceleration gain vanishes or even is negative. In this case, we derive a sufficient condition for positive ISAC acceleration gain.

Starling Flocks-Inspired Resource Allocation for ISAC-Aided Green Ad Hoc Networks

Efficient and intelligent environment awareness is one of the key aspects for enabling future green networks, especially for wireless ad hoc networks considering the limited resources, while poor environment awareness may lead to unbearable data transmission delay based on terrible routing strategies, which is unacceptable in some ad hoc networks. However, the sensing of communication environment and the performance of data transmission are tightly coupled with each other in physical reality. Therefore, traditional separate analyses of sensing and transmission cannot meet the low-latency and energy-efficient demand of future green ad hoc networks. In this article, we propose an environment awareness integrated sensing and communication (ISAC) network association architecture considering both the reward and cost of nodes’ sensing range and transmission strategy. In addition, the resource allocation optimization problem of the proposed ISAC architecture is formulated relying on the graph theory, which is efficiently solved by a novel low-complexity algorithm. Simulation results show that our proposed ISAC network association architecture is beneficial in terms of achieving lower transmission delay in comparison to the traditional methods which separately optimize the sensing and transmission process. Besides, a near-optimal latency performance is obtained with the aid of our optimization method with low-complexity.

Optimal resource allocation and adaptive robust control of technology innovation ecosystems based on cooperative game theory

This paper addresses a problem of optimal resource allocation for technology innovation ecosystem. Two layers goal are realized. First, an uncertain dynamical system is constructed with the Lotka–Volterra model for the description of the dynamics of the concerned innovation ecosystem, in which the growth rate and the symbiosis coefficient are specially treated as possibly (fast) time-varying uncertain parameters. Second, an adaptive robust resource allocation strategy is designed to drive the errors between the populations and the desired populations to be uniformly bounded and uniformly ultimately bounded, such that the first layer (basic) goal of resource allocation is achieved. Third, a two-player cooperative game is formulated for the seeking of the optimal control parameters, in which two (static) cost functions and a comprehensive performance index are constructed by performance analysis, and then the optimal control parameters (i.e., the Pareto-optimality) are obtained by minimizing the comprehensive performance index, such that the second layer (further) goal of optimal balance between the control inputs and the system performance is achieved. This work should be the first effort that addresses the optimal resource allocation and control for technology innovation ecosystem through a creative combination of dynamic modeling, control theory and game theory.

Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Recently, the technological development in edge computing and content caching can provide high-quality services for users in the wireless communication networks. As a promising technology, multi-access edge computing (MEC) can offload tasks to the nearby edge servers, which alleviates the pressure of users. However, various services and dynamic wireless channel conditions make effective resource allocation challenging. In addition, network slicing can create a logical virtual network and allocate resources flexibly among multiple tenants. In this paper, we construct an integrated architecture of communication, computing and caching to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited resources, this paper adopts an improved deep reinforcement learning (DRL) method, which fully jointly considers the diversity of user request services and the dynamic wireless channel conditions to obtain the mobile virtual network operator (MVNO) maximal profit function. Considering the slow convergence speed of the DRL algorithm, this paper combines DRL and ensemble learning. The simulation result shows that the resource allocation scheme inspired by DRL is significantly better than the other compared strategies. The output of the result of DRL algorithm combined with ensemble learning is faster and more cost-effective.

Integrated Sensing and Communication Aided Dynamic Resource Allocation for Random Access in Satellite Terrestrial Relay Networks

An integrated sensing and communication (ISAC) aided dynamic resource allocation is proposed for random access (RA) in Internet-of-Things (IoT) oriented satellite terrestrial relay networks (STRNs). Specifically, we design an ISAC aided RA architecture, which consists of sensing phase and RA phase. Relays can sense the number of active users by wireless sensing and assist RA with the sensed results. Active users are grouped into orthogonal clusters in the spectrum domain according to relays that they belong to. To appropriately utilize the bandwidth resources, a dynamic resource allocation optimization problem is formulated and solved, which is able to dynamically allocate bandwidth to each cluster according to the number of intra-cluster active users. Based on allocated bandwidth, active users can always perform optimal uplink RA, which maximizes the total system throughput. Simulation results show that the proposed scheme greatly outperforms the existing schemes in terms of total system throughput and packet loss rate (PLR).

A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services.

Wireless body area network (WBAN) is widely adopted in healthcare services, providing remote real-time and continuous healthcare monitoring. With the massive increase of detective sensor data, WBAN is largely restricted by limited storage and computation capacity, resulting in severely decreased efficiency and reliability. Mobile edge computing (MEC) technique can be combined with WBAN to resolve this issue. This paper studies the joint optimization problem of computational offloading and resource allocation (JCORA) in MEC for healthcare service scenarios. We formulate JCORA as a Markov decision process and propose a deep deterministic policy gradient-based WBAN offloading strategy (DDPG-WOS) to optimize time delay and energy consumption in interfered transmission channels. This scheme employs MEC to mitigate the computation pressure on a single WBAN and increase the transmission ability. Further, DDPG-WOS optimizes the offloading strategy-making process by considering the channel condition, transmission quality, computation ability and energy consumption. Simulation results verify the effectiveness of the proposed optimization schema in reducing energy consumption and computation latency and increasing the utility of WBAN compared to two competitive solutions.

Resource Allocation Based on Digital Twin-Enabled Federated Learning Framework in Heterogeneous Cellular Network

Federated learning (FL) allows user devices (UDs) to upload local model parameters to participate in a global model training, which protects UDs' data privacy. Nevertheless, FL still faces challenges such as core network congestion, UDs' limited resources and less efficient mapping between devices and cyber systems. Therefore, in this article, we integrate the digital twin (DT) and the mobile edge computing (MEC) technologies into a hierarchical FL framework in the heterogeneous cellular network scenario. When the UDs are not in the service range of the small base stations (SBSs), the framework allows macro base stations to assist UDs' local computation, thus reducing the transmission delay. It also protects the user privacy and allows more users to join in the training in order to improve the FL accuracy. In addition, we propose a deep reinforcement learning-based scheme to solve the joint optimization problem of dynamic UDs-stations association and resource allocation, thereby minimizing the energy consumption within a limited time delay. Simulation results show that our proposed scheme not only effectively reduces the task transmission failure rate and energy consumption compared with the baseline scheme, but also saves the communication cost through the DT network.

A two-stage stochastic optimization for disaster rescue resource distribution considering multiple disasters

In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%.

Multi-index evaluation learning-based computation offloading optimization for power internet of things

Cloud–edge-device collaborative computation offloading can provide flexible and real-time data processing services for massive resource-constrained devices in power internet of things (PIoT). However, the computation offloading optimization in PIoT still faces several challenges such as high computation offloading delay caused by uncertain information, coupling between task offloading and computation resource allocation, and degraded optimization performance due to the lack of multi-index consideration. To address the above challenges, we formulate a joint optimization problem of task offloading and computation resource allocation to minimize the average computation offloading delay. Specifically, a multi-index evaluation learning-based two-stage computation offloading (MINCO) algorithm is proposed to decouple the joint optimization problem into two-stage subproblems and solve them with evaluation and learning of multiple indexes including data flow characteristic, service priority, empirical average computation offloading delay, and empirical arm selection times. Simulation results show that compared with the baseline 1 and baseline 2 algorithms, MINCO improves the average computation offloading delay by 14.67% and 30.71%. Moreover, MINCO can evaluate different service priorities and data flow characteristics to meet different requirements of computation offloading delay.

Age of Information Aware Resource Allocation and Packet Sampling Control in Vehicular Networks

In this letter, we analyze the age of information (AoI) outage probability for the D/Geo/1 queueing system. Then, we formulate a problem of resource allocation and packet sampling rate optimization to maximize the sum ergodic capacity of V2N links while guaranteeing the AoI outage probability of V2V links in vehicular communication networks. For each possible V2N-V2V spectrum reusing pair, the optimal power allocation and packet sampling rate are obtained by a low-complexity algorithm. Afterwards, spectrum allocation is optimized by bipartite matching. Simulation results validate our AoI analysis and show the effectiveness of the proposed algorithm.

Secrecy performance analysis of IRS-assisted D2D communication underlaying cellular network

In this paper, we evaluate the secrecy performance of an intelligent reflecting surface (IRS)-assisted device-to-device (D2D) communication in spectrum-shared cellular networks. To this end, we derive novel closed-form expressions for the secrecy outage probability (SOP) and the asymptotic SOP in the presence of multiple eavesdroppers. In the continue, in order to dynamically access the spectrum band of the licensed users, we define the optimization problem of secrecy spectrum resource allocation to minimize the SOP as a mixed-integer linear programming (MILP) problem. Then, the globally optimal solutions to this problem are obtained by using the Hungarian algorithm. Numerical analyses show that increasing the reflective elements of IRS can improve the secrecy performance.

Distributed Time-Varying Quadratic Optimal Resource Allocation Subject to Nonidentical Time-Varying Hessians With Application to Multiquadrotor Hose Transportation

This article considers the distributed time-varying optimal resource allocation problem with time-varying quadratic cost functions and a time-varying coupled equality constraint for multiagent systems. The objective is to design a distributed algorithm for agents with single-integrator dynamics to cooperatively satisfy the coupled equality constraint and minimize the sum of all local cost functions. Here, both the coupled equality constraint and cost functions depend explicitly on time. The cost functions are in quadratic form and may have nonidentical time-varying Hessians. To solve the problem in a distributed manner, an estimator based on the distributed average tracking method is first developed for each agent to estimate certain global information. By leveraging the estimated global information and an adaptive gain scheme, a distributed continuous-time algorithm is proposed, which ensures the agents to find and track the time-varying optimal trajectories with vanishing errors. We illustrate the applicability of the proposed method in the optimal hose transportation problem using multiple quadrotors.

Joint CCI Mitigation and Power Control for MC-DS-CDMA in LEO Satellite Networks

We investigate a novel downlink multicarrier direct-sequence code division multiple access (MC-DS-CDMA) resource allocation scheme in the context of low earth orbit (LEO) satellite-ground integrated networks (SGINs). In contrast to the existing MC-DS-CDMA works which mainly focus on delay-tolerant services in terrestrial networks, we consider the heterogeneous delay traffic and take the unique characteristics of LEO satellite systems into account. Specifically, we exploit the channel information, the delay requirement, the buffer state, and the visible time of users to construct a utility function and formulate the resource allocation optimization problem in presence of co-channel interferences (CCI) in LEO satellite-ground heterogeneous systems. We transform the original nonconvex optimization problem into two convex ones and use the Lagrange dual decomposition method to derive the solution. We also design an efficient algorithm dynamically scheduling subcarriers, codes, and transmission power of MC-DS-CDMA. The simulation results confirm the superiority of our work in terms of lower average delay and higher overall throughput.