Next-generation edge communication systems are expected to support large-scale connectivity and service provisioning for billions of smart terminal devices. Edge blockchain-based computing architectures enable coordinated management of homogeneous and heterogeneous resources, offering a promising framework for drones collaboration and resource sharing. Confronting the dual challenges of computational constraints and energy limitations in drone networks, this work formulates an optimized multi-auction resource allocation markets framework integrated with blockchain, establishing a new paradigm for large-scale autonomous system resource coordination. Firstly, focusing on computational resource allocation, we establish a double-auction primary market where drone miners (as buyers) procure computational services for blockchain consensus process, while edge service providers (as sellers) supply computation offloading services. Through optimal bidder matching, the primary market system achieves social welfare maximization with equilibrium strategies. For successful computational buyers, we then design a VCG (Vickrey-Clarke-Groves) auction-based energy compensation scheme that provides optimal battery service pricing in the secondary market. Extensive simulations validate the effectiveness in maintaining truthfulness, individual rationality, and validity.

Dueto its distributed characteristics, the development and deployment of the blockchain framework are able to provide feasible solutions for a wide range of Internet of Things (IoT) applications. While the IoT devices are usually resource-limited, how to make sure the acquisition of computational resources and participation of the devices will be the driving force to realize blockchain at the network edge. In this article, an edge computing-based blockchain framework is considered, where multiple edge service providers (ESPs) can provide computational resources to the devices for mining. We mainly focus on investigating the trading between the devices and ESPs in the computational resource market, where ESPs act as the sellers and devices act as the buyers. Accordingly, a sequential game model is formulated and by exploring the sequential Nash equilibrium (SE), the existence of the optimal solutions of selling and buying strategies can be proved. Then, a deep Q-network-based algorithm with modified experience replay update method is applied to find the optimal strategies. Through theoretical analysis and simulations, we demonstrate the effectiveness of the proposed incentive mechanism on forming the blockchain via the assistance of edge computing.

Fog computing system is able to facilitate computation-intensive applications and emerges as one of the promising technology for realizing the Internet of Things (IoT). By offloading the computational tasks to the fog node (FN) at the network edge, both the service latency and energy consumption can be improved, which is significant for industrial IoT applications. However, the dynamics of computational resource usages in the FN, the radio environment and the energy in the battery of IoT devices make the offloading mechanism design become challenging. Therefore, in this article, we propose a dynamic optimization scheme for the IoT fog computing system with multiple mobile devices (MDs), where the radio and computational resources, and offloading decisions, can be dynamically coordinated and allocated with the variation of radio resources and computation demands. Specifically, with the objective to minimize the system cost related to latency, energy consumption, and weights of MDs, we propose a joint computation offloading and radio resource allocation algorithm based on Lyapunov optimization. Through minimizing the derived upper bound of the Lyapunov drift-plus-penalty function, we divide the main problem into several subproblems at each time slot and address them accordingly. Through performance evaluation, the effectiveness of the proposed scheme can be verified.

Blockchain has been gradually applied to different Internet-of-Things platforms. As the efficiency of the blockchain mainly depends on the network computing capability, how to make sure the acquisition of the computational resources and participation of the devices would be the driving force. In this article, we focus on investigating incentive mechanism for rational miners to purchase the computational resources. An edge-computing-based blockchain network is considered, where the edge service provider (ESP) can provide computational resources for the miners. Accordingly, we formulate a two-stage Stackelberg game between the miners and ESP. The aim is to investigate SE of the optimal mining strategy under the two different mining schemes, in order to find the optimal incentive for the ESP and miners to choose autofit strategies. Through theoretical analysis and numerical simulations, we can demonstrate the effectiveness of the proposed scheme on encouraging devices to participate the blockchain.

In this paper, an energy-efficient resource allocation problem is investigated for the wireless power transfer (WPT)-enabled OFDMA multicell networks. In the considered system, multiple base stations (BSs) with a large number of antennas are responsible to provide WPT in the downlink, and the users can recycle and utilize the received energy for uplink data transmission. The role of BS is to execute WPT; thus, there are no data transmissions in the downlink. A time-division protocol is considered to divide the time of downlink WPT and uplink wireless information transfer into separate time slots. With the objective to improve the energy efficiency, we propose the time, subcarrier, and power allocation schemes and antenna selection algorithms. As the perfect channel state information (CSI) is hard to obtain in the practical systems, we also take the case where only estimated CSI is available into consideration when executing resources allocation decisions and analyze the corresponding performance. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem. Then, an efficient alternating direction method of multipliers-based distributed resource allocation algorithm is applied to address the transformed problem. Performance evaluations are conducted to demonstrate the advantages of the proposed schemes.

In this work, we propose a distributed user association and resource allocation scheme for the wireless virtualized information-centric networks with hybrid energy supply, where the base station (BS) equipped with caching and energy harvesting capabilities to reduce the COPEX and OPEX cost. In particular, with the objective to obtain the utility maximization for the network operators, a joint user association, caching, spectrum and power problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem with low complexity, the original problem is divided into two subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Extensive simulation studies demonstrate the advantages of our presented system architecture and proposed schemes.

In this study, an energy‐efficient optimisation scheme for a large‐scale multiple‐antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non‐linear fractional programming‐based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes.

Fog computing is considered as a promising technology to meet the ever-increasing computation requests from a wide variety of mobile applications. By offloading the computation-intensive requests to the fog node or the central cloud, the performance of the applications, such as energy consumption and delay, are able to be significantly enhanced. Meanwhile, utilizing the recent advances of social network and energy harvesting (EH) techniques, the system performance could be further improved. In this paper, we take the social relationships of the EH mobile devices (MDs) into the design of computational offloading scheme in fog computing. With the objective to minimize the social group execution cost, we advocate game theoretic approach and propose a dynamic computation offloading scheme designing the offloading process in fog computing system with EH MDs. Different queue models are applied to model the energy cost and delay performance. It can be seen that the proposed problem can be formulated as a generalized Nash equilibrium problem (GNEP) and we can use exponential penalty function method to transform the original GNEP into a classical Nash equilibrium problem and address it with semi-smooth Newton method with Armijo line search. The simulation results demonstrate the effectiveness of the proposed scheme.

Fog computing system is an emergent architecture for providing computing, storage, control, and networking capabilities for realizing Internet of Things. In the fog computing system, the mobile devices (MDs) can offload its data or computational expensive tasks to the fog node within its proximity, instead of distant cloud. Although offloading can reduce energy consumption at the MDs, it may also incur a larger execution delay including transmission time between the MDs and the fog/cloud servers, and waiting and execution time at the servers. Therefore, how to balance the energy consumption and delay performance is of research importance. Moreover, based on the energy consumption and delay, how to design a cost model for the MDs to enjoy the fog and cloud services is also important. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay, and payment cost of offloading processes in a fog computing system. Specifically, three queuing models are applied, respectively, to the MD, fog, and cloud centers, and the data rate and power consumption of the wireless link are explicitly considered. Based on the theoretical analysis, a multiobjective optimization problem is formulated with a joint objective to minimize the energy consumption, execution delay, and payment cost by finding the optimal offloading probability and transmit power for each MD. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme and the superior performance over several existed schemes are observed.

Wireless network visualization is envisioned as a promising framework to provide efficient and customized services for next-generation wireless networks. In wireless virtualized networks (WVNs), limited radio resources are shared among different service providers for providing services to different users with heterogeneous demands. In this work, we propose a distributed resource allocation scheme for a wireless virtualized small cell networks. The SBSs in the considered system are equipped with self-backhaul and energy harvesting capabilities in order to reduce the operation cost. In particular, with the objective to obtain the utility maximization, a joint user association, time, spectrum and power allocation problem is presented. To tackle the formulated mixed combinatorial and non-convex optimization problem, the original problem is divided into three low-complexity subproblems and we propose an alternating direction method of multipliers (ADMM)-based distributed algorithm to address them efficiently and effectively. Simulation studies are conducted to demonstrate the advantages of our presented system architecture and proposed schemes.

Mobile-edge cloud computing is a new cloud platform to provide pervasive and agile computation augmenting services for mobile devices (MDs) at anytime and anywhere by endowing ubiquitous radio access networks with computing capabilities. Although offloading computations to the cloud can reduce energy consumption at the MDs, it may also incur a larger execution delay. Usually the MDs have to pay cloud resource they used. In this paper, we utilize queuing theory to bring a thorough study on the energy consumption, execution delay and price cost of offloading process in a mobile-edge cloud system. Specifically, both wireless transmission and computing capabilities are explicitly and jointly considered when modelling the energy consumption and delay performance. Based on the theoretical analysis, the multi-objective optimization problem is formulated with the joint objectives to minimize the energy consumption, execution delay and price cost by finding the optimal offloading probability and optimal transmission power for each MD. The scalarization scheme and interior point method are applied to address the formulated problem. Through extensive simulations, the effectiveness of the proposed scheme can be demonstrated.

In this paper, we investigate the energy efficient resource allocation problem for the wireless powered OFDMA multi-cell networks. In the considered system, the users who have data to transmit in the uplink are empowered by the wireless power obtained from multiple base stations (BSs) with a large scale of multiple antennas in the downlink. A time division protocol is considered to divide the time of wireless power transfer (WPT) in the downlink and wireless information transfer (WIT) in the uplink into separate time slot. With the objective to improve the energy efficiency (EE) of the system, we propose the antenna selection, time allocation, subcarrier and power allocation schemes. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem and then address it through an efficient alternating direction method of multipliers based distributed resource allocation algorithm. Extensive simulations are conducted to show the effectiveness of proposed schemes.

This paper proposes an energy-efficient resource allocation scheme for a wireless power transfer (WPT) enabled multi-user massive MIMO system with imperfect channel estimation. In the considered system, the users who have data to be transmitted only can be empowered by the WPT in the downlink from a base station (BS) with a large scale of multiple antennas. The problem of optimizing the energy efficiency objective is formulated with consideration of beamforming design, antenna selection, power allocation and time division protocol based on the practical consideration, i.e., imperfect channel state information (CSI) at the BS. In particular, the proposed antenna selection scheme is to find the optimal number of antennas and then employ the energy beamforming. The nonlinear fractional programming based scheme is utilized to address optimization of energy efficiency and to find the optimal power and time allocation. Performance evaluation is presented to demonstrate the effectiveness of the proposed schemes.

In this paper, we address the energy efficient resource allocation problem for a secure orthogonal frequency division multiple access (OFDMA) relay system. In particular, we consider there is a eavesdropper near the base station (BS) which tries to overtake the information sent by BS. We formulate the joint optimization problem with the objective to optimize the energy efficiency of the considered system by considering subcarrier pairing, secret data rate and power allocation. In addition, the system can assign different priority to different users such that the security of information transmission can be guaranteed. The proposed iterative algorithm not only maximizes the system energy efficiency but can guarantee quality-of-service (QoS) properties. Simulation results show that the proposed scheme can obtain the energy efficiency maximization and also illustrate that the strategy of giving different priority to different users obtain a higher energy efficiency.