Optimal Energy-Centric Resource Allocation and Offloading Scheme for Green Internet of Things Using Machine Learning

Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor large-scale forest areas, and they are weak in predicting the specific areas of forest fires. In this article, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial Internet of Things (IIoTs), in which the data collected by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective is to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning-based cooperative particle swarm optimization (LCPSO) algorithm with a Markov random field (MRF)-based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into subsolution spaces according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple subsolution spaces cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.

The design, analysis and optimization of cooperative/relaying communication systems have recently become a very active research area within both the information theory as well as communications engineering societies. It is now well understood that relaying strategies can improve the coverage of wireless networks by providing higher data rates or better transmission reliability to user terminals at the edge of a wireless cell, or terminals having faded connectivity with the base station. Relaying technologies are also becoming part of the telecommunication standards. Although we can find studies, in the academic literature, on advanced relaying schemes, which are based on user terminals cooperating to help each other while applying decentralized resource allocation strategies, the first actual deployment step which will take place within the 3GPP long-term evolution (LTE)-advanced standard is based on fixed access points to do the relaying and within a centralized scheme in which the e-nodeB (base station with backhaul connection) takes the scheduling and resource allocation decisions. One major objective in 3GPP evolution is to utilize the scarce wireless system resources efficiently because achieving the high quality of service (QoS) targets through over-provisioning is uneconomical due to the relatively high cost for transmission capacity in cellular access networks. Our objective here is to obtain the optimal (from an information theory perspective) resource allocation schemes taking into considerations the system constraints that are relevant to the LTE-advanced standard. We have been able to derive optimal resource allocation polices that are provably based on closed-form formulations which are practical for implementation. They include the policies for (i) transmission mode selection (i.e. deciding whether the user needs the assistance of a relay or not), (ii) power allocation for the base station and the relays, and (iii) criterion for user scheduling over the available air-link resource units. Simulation results demonstrate that our proposed resource allocation scheme provides considerable throughput gains especially for users receiving low power through the direct link with the base station.

A growing number of mobile services demand intensive computation resources and high energy consumption. Traditional cloud-based task offloading incurs excessive transmission delay. With mobile edge computing, we can offload tasks to the nearby edge servers, thus mitigating long data transmission latency. Current task offloading and resource allocation approaches ignore the extra overhead brought by container instance startup. This naturally leads to a long overall latency and high energy consumption. Motivated by the limitations of existing literature, we address task offloading and resource allocation problem considering container instance startup time. We first examine the startup latency of containers started from different types of images, and present its impact on overall performance. Then, we formulate the problem as optimization problem. The problem is hard to address because mutual effects of offloading decision, resource allocation decision and container instance startup contribute to the overall performance. We resort to Gibbs sampling to decouple offloading decision and resource allocation. Finally, taking into account all potential conditions brought up by container instance startup, an optimal computation resource allocation and uplink power assignment scheme are derived through bisection, and Karush-Kuhn-Tucher (KKT) conditions. Comprehensive evaluations are conducted to verify the performance of the proposed approach, and demonstrate its superiority over current notable solutions.

We propose a cross-layer strategy that jointly optimizes user scheduling and resource allocation for adaptive modulation and coding (AMC) based orthogonal frequency multiple access (OFDMA). The objective of this paper is to maximize the system throughput as a function of the bit error rate (BER) and the spectral efficiency based on selected modulation and coding schemes (MCSs). The proposed optimal user scheduling and resource allocation (OSRA) strategy arranges the users in distinct queues according to their priorities and selects users in an optimal manner that guarantees fair services for users among different priority levels. Moreover, it allocates the available resources optimally over the utilized sub-channels. The transmitter of the investigated AMC-OFDMA system at the assigned base station (BS) divides the transmitted OFDMA frame into sub-channels depending on the number of the considered priority levels. Simulation results show that the performance of the investigated AMC-OFDMA system based on the proposed OSRA strategy outperforms the conventional approaches.

Modern vehicles have evolved into sophisticated mobile computing platforms executing computationally intensive and latency-sensitive applications. However, on-board units are limited in computational and storage capabilities. Mobile edge computing mitigates this issue, but the complex application structures, high computational demands, and limited resources in intelligent vehicles present significant challenges. To address task offloading and resource optimization for dependent tasks in vehicular applications under dynamically changing computational loads, this paper proposes a deep reinforcement learning-based method called the joint multi-dimensional decision algorithm for dependent task offloading and resource allocation (JMDD-DTORA). This method models and schedules tasks in different layers using directed acyclic graphs and a hierarchical concurrent task scheduling scheme. A three-level offloading framework dynamically adjusts task offloading paths to ensure load balancing and resource optimization. The approach improves the deep deterministic policy gradient algorithm for joint optimization of task offloading and resource allocation. Simulation results demonstrate that JMDD-DTORA outperforms other methods in key metrics of task offloading cost, proving its effectiveness in the Internet of Vehicles environment.

Multi-access for multi-mode terminals is possible in heterogeneous networks environment which becomes a critical issue for transmission in parallel with meeting user quality of service (QoS) performance requirements and throughput maximization. The previous optimal resource allocation schemes, which do not consider service type, may allocate scarce radio resources inefficiently. To solve this, we propose an optimal resource allocation algorithm with distinguishing the services traffic into two classes: Delay-Constraint (DC) and Best-Effort (BE). In our work, we formulate a mathematical optimal model to support such heterogeneous service requirements in multi-radio access scenario. Then, we develop a optimal radio resource allocation algorithm that achieves the goal of maximizing the total system throughput in heterogeneous networks, while efficiently satisfying the QoS requirement for DC services traffic and fairness for BE services traffic. Simulation results show that the proposed service traffic differentiation based radio resource allocation algorithm significantly outperforms other existing schemes.

We investigate an outage optimal adaptive resource allocation scheme for the upstream of two-hop OFDMA based decode-and-forward cooperative relay systems. The objective of this work is to design resource allocation strategy, which addresses the needs of the users minimizing their outage probability. This scheme utilizes the subchannel-pairing and proportional fairness in two-hop multiuser multirelay network to achieve the user required percentage throughput ( i.e., if the user's application can tolerate 5% outage then guaranteeing the 100% availability is actually a wastage of scarce radio resources). Using outage optimal resource allocation scheme, we achieve the complimentary fairness along with the required data rate on each node. Simulation results show that the proposed scheme achieve better throughput-fairness trade-off compared to proportional fair scheduling (PFS) and MaxMin resource allocation schemes.

Cooperative dynamic free-space optical (FSO) networks exploit the fact that atmospheric losses are distance dependent to enhance the performance of FSO networks. This enhancement is achieved by sharing the resources of shorter links among different nodes in the network. In this paper, two joint transceiver placement and resource allocation schemes are proposed to optimally place FSO redundant transceivers based on optimal resource allocation in cooperative dynamic FSO networks. Specifically, one scheme increases reliability and capacity, while the other increases reliability and fairness of cooperative dynamic FSO networks during severe weather conditions. The schemes are formulated as multi-objective and bi-level integer linear programming problems and solved using an exhaustive search to obtain optimal solutions. The numerical results reveal that higher reliabilities can be achieved with enhanced capacities and fairness using the first and second schemes, respectively. Furthermore, these improvements are achieved by using many fewer numbers of FSO redundant transceivers than those of random placement.

Mobile edge computing (MEC) is applied to 5G communication to meet the needs of large-scale data communication. Unmanned aerial vehicle (UAV) can be used as air base station to provide edge computing services for users in remote areas. In this paper, we consider a multi- UAV enabled MEC (i.e., Multi-UAV-MEC) network, where software defined network (SDN) is adopted to improve the quality of services (QoS) for all users, and we study the problem of task offloading and resource allocation. In the proposed network, UAV s act as MEC servers to provide computation offloading services for ground equipments (GEs), and a SDN controller is responsible for collecting network global information and providing offloading decision and resource allocation strategy for all GEs and UAV s. We aim to minimize the weighted sum of the task processing delay and energy consumption in the network, and the above problem is a mixed-integer and non-convex problem. To address this challenge, we transform the problem into a MDP, and propose a deep reinforcement learning (DRL)-based algorithm. The SDN controller is considered as a agent to learn the optimal offloading and resource allocation strategy. Simulation results show that the proposed DRL-based algorithm can achieve better performance than other baseline algorithms under different conditions.

In disaster-stricken areas that were severely damaged by earthquakes, typhoons, floods, mudslides, and the like, employing unmanned aerial vehicles (UAVs) as airborne base stations for mobile edge computing (MEC) constitutes an effective solution. Concerning this, we investigate a 3D air–ground collaborative MEC scenario facilitated by multi-UAV for multiple ground devices (GDs). Specifically, we first design a 3D multi-UAV-assisted air–ground cooperative MEC system, and construct system communication, computation, and UAV flight energy consumption models. Subsequently, a cooperative resource optimization (CRO) problem is proposed by jointly optimizing task offloading, UAV flight trajectories, and edge computing resource allocation to minimize the total energy consumption of the system. Further, the CRO problem is decoupled into two sub-problems. Among them, the MATD3 deep reinforcement learning algorithm is utilized to jointly optimize the offloading decisions of GDs and the flight trajectories of UAVs; subsequently, the optimal resource allocation scheme at the edge is demonstrated through the derivation of KKT conditions. Finally, the simulation results show that the algorithm has good convergence compared with other algorithms and can effectively reduce the system energy consumption.

Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

In this paper, the optimal placement and dynamic resource allocation problem has been investigated for multi-UAV enhanced reconfigurable intelligent surface (RIS) assisted wireless network with uncertain time-varying wireless channels. This paper aims to stimulate the potential of RIS by adding mobility to RIS through unmanned aerial vehicles (UAV). A novel UAV optimal placement and dynamic resource allocation technique needs to be developed jointly. A novel online rein-forcement learning based optimal resource allocation algorithm has been designed. Firstly, a deep Q-learning based K-means clustering algorithm is utilized to optimize the deployment of the multi-UAV. Then, an online actor-critic reinforcement learning algorithm is developed to learn the optimal transmit power control as well as mobile RIS phase shift control policy. Compared with conventional learning algorithms, the developed algorithm can learn the optimal resource allocation and multi-UAV placement for mobile RIS-assisted wireless networks in real-time even with uncertain and time-varying wireless channels. Eventually, numerical simulations are provided to demonstrate the effectiveness of developed schemes.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

In this paper, we study the resource allocation for simultaneous wireless information and power transfer (SWIPT) systems with the nonlinear energy harvesting (EH) model. A simple optimal resource allocation scheme based on the time slot switching is proposed to maximize the average achievable rate for the SWIPT systems. The optimal resource allocation is formulated as a nonconvex optimization problem, which is the combination of a series of nonconvex problems due to the binary feature of the time slot‐switching ratio. The optimal problem is then solved by using the time‐sharing strong duality theorem and Lagrange dual method. It is found that with the proposed optimal resource allocation scheme, the receiver should perform EH in the region of medium signal‐to‐noise ratio (SNR), whereas switching to information decoding (ID) is performed when the SNR is larger or smaller. The proposed resource allocation scheme is compared with the traditional time switching (TS) resource allocation scheme for the SWIPT systems with the nonlinear EH model. Numerical results show that the proposed resource allocation scheme significantly improves the system performance in energy efficiency.

In this letter, optimal power control and time resource allocation schemes are considered in wireless relay network, which two users communicate with each other assisted by an energy harvesting relay that gathers energy from the received signal by applying a time switching scheme and forwards the received signal by using the harvesting energy. It is focused on the optimal power control and time resource allocation for the system to cooperate the power transfer and information transmission among three nodes in Decode-and-Forward (DF) mode. Specifically, time ratios for three transmission phases are firstly investigated. Then, we proposed to formulate the power optimization problem with energy constraint. Since the transmission duration of the transmitter is not fixed, the energy constraint instead of power constraint is more reasonable to evaluate the system performance. Through some variables substitution, we demonstrate that it is a convex problem. It can be solved efficiently by convex optimization technology. Simulation results are demonstrated that the performance of the proposed algorithm outperform than the fixed and semi-fixed traditional methods.