Existing zero-shot fault diagnosis methods are typically system-specific and numerically sensitive, which lack adaptive deployment capabilities across heterogeneous photovoltaic (PV) system scales and topologies. Multimodal large language models (MLLMs) emerge as a powerful solution in cross-system generalization, but their adoption in PV fault diagnosis has been limited by the lack of PV knowledge integration and challenges in processing diverse operating conditions. To bridge this gap, an MLLMs-empowered framework for zero-shot PV fault diagnosis is proposed for the first time, which jointly integrates data-driven and knowledge-driven schemes. First, a chain-of-thought-based data augmentation pipeline is constructed to achieve data-knowledge alignment and interpretable results. Second, a two-stage adaptation strategy is specifically designed for PV data to overcome system scales, diverse topologies, and numerical differences. It consists of a Kolmogorov–Arnold networks-based condition adaptive layer embedded in vision transformer and a low-rank adaptation-based PV domain fine-tuning. Third, we design a microservices-based architecture for PV-MLLM deployment that enables flexible component decoupling and adaptive inference, significantly reducing hardware requirements and resource consumption. The proposed method achieves 99.66% and 97.25% diagnostic accuracy on simulated and real-world datasets.

With the widespread application of Internet of thingstechnology in smart grid, the integration of numerous intelligent terminals exacerbates network congestion and data loss, leading to increased load tracking deviations. Simultaneously, the automatic generation control (AGC) employed for maintaining supply-demand balance faces high costs, slow response rates, and limited adaptability to renewable energy. Employing gas-to-power technology in conjunction with AGC can enhance overall system efficiency and stability. This paper proposes a power allocation and pricing strategy utilizing a two-level Stackelberg game framework to reduce utility costs while boosting profits for telecom operator and gas company. We develop an electricity cost model for utilities considering regulation errors from direct load control in smart grid. Using an iterative algorithm and backward induction, we derive the Nash equilibrium for the Stackelberg game. Simulation results show that this strategy reduces utility costs and increases profits for telecom operator and gas company.

Object instance segmentation is a key prerequisite for service robots to perform daily chores in unstructured environments. Traditional supervised learning-based segmentation solutions rely on massive annotated datasets, which are impractical for the wide variety of objects in real-world scenarios. To this end, we propose a novel zero-shot instance segmentation approach (TransZSIS) that enables precise instance segmentation without relying on external semantic embeddings or auxiliary information to address the unseen object instance segmentation (UOIS) problem. First, the RGB and depth images are segmented into irregular patches based on a super-pixel segmentation algorithm to generate a unified segmentation map, and then the comprehensive feature vectors of each patch is extracted and paired. Further, a Transformer-based architecture is introduced to capture the correlation between different patch-pair and the intrinsic characteristics of each patch-pair. To predict patch-pair relationships, TransZSIS uses a four-layer fully connected neural network (FCNN) to classify the transformer-encoded features and refine them with a graph-based processing tactic to achieve object instance segmentation. Extensive evaluations on both synthetic and real datasets demonstrate that TransZSIS achieves superior performance compared with state-of-the-art baseline methods. Also, we implement real experiments to verify that our solution can achieve robot grasping by segmenting unseen objects.

This article addresses the problem of prescribed-time stabilization of nonlinear systems with the features of unknown control directions and time-varying uncertain parameters based on a time-transformation method. The basic ideology relies on a newly established time-transformation method that incorporates the classical adaptive technique, converting the prescribed-time stable control problem of the original system into an asymptotically stable problem of its time-transformed stretched form. Unlike the existing literature, the time transformation method in this article directly gives the adaptive laws before and after the time transformation, which greatly reduces the complexity of designing the adaptive prescribed-time controller due to the fact that the design of the adaptive law in the stretched time domain only needs to satisfy the asymptotic stability criterion. Finally, the proposed methodology is validated by a simulation example.

Designing a pragmatic medium access control (MAC) protocol to operate at low collision rate while providing high throughput is significant for underwater acoustic networks (UANs). Though the centralized control approach could greatly reduce transmission collisions, it normally requires the real-time global network information, such as traffic load, remaining energy, network topology, etc., which is a harsh requirement especially in dynamic UANs. In contrast, the distributed control scheme appears to be more attractive and promising. However, due to the long propagation delay of acoustic signals in the water, the spatiotemporal uncertainty problem and the carrier sensing zone problem in UANs make the distributed MAC protocol unable to avoid frame collisions effectively, which degrades the network performance. For this, we propose an MAC protocol, called FCFS, using the First-Come-First-Served basis, which achieves low frame collision and high network throughput in UANs by adopting the mechanism of medium access reservation via an advance handshaking. Different from the traditional handshake-based protocols, FCFS is good at handling the hidden terminal problem by considering the complete exchange of channel access information among neighboring nodes. Simulation results confirm that the proposed protocol could improve the data reception rate and throughput compared to the related protocols. It also shows that our proposed scheme could maintain a stable performance under different network traffic loads.

Reliable bidirectional communication between the control center and manufacturing devices (MDs) along with efficient resource allocation are critical for the Industrial Internet of Things (IIoT). However, due to the limited network resources of IIoT devices and the intertwined nature of communication and computation resources, achieving efficient optimization of these resources presents significant challenges. In this article, we propose a multilayer communication architecture for the IIoT based on edge computing and cooperative communication technologies, where resource-constrained MDs upload data to edge servers (ESs) for processing. To address the issues of resource scarcity and coupling, we establish a bandwidth release model to analyze and quantify the relationship between computation and communication resources. This elucidates the interaction mechanisms between “transmission-computation” performance. Furthermore, to prevent excessive data upload to ESs, which could lead to node overload and network congestion, we employ game theory to develop a pricing mechanism for resource allocation, where ESs charge for computation services. Specifically, we construct a Lagrangian framework to determine a resource allocation scheme, aiming to maximize the utility of the factory. Simulation results indicate that compared to common methods employed in existing works, our strategy enhances factory utility by 29.79%.

Intelligent fault diagnosis of photovoltaic (PV) arrays in small-sample scenarios remains challenging due to poor model accuracy and generalization. Existing methods fail to simultaneously address issues of varied operation conditions and insufficient samples, leading to the limited applicability of models built by few-shot learning. In addition, factors, such as data transmission and computation costs, also need to be considered. Therefore, this article proposes a cloud-edge collaborative self-correcting-guided generalized contrastive learning framework for small-sample PV fault diagnosis. First, an end-to-end self-correcting model is proposed to eliminate the influence of variable environments. Then, a self-correcting scheme is integrated with contrastive learning to achieve model generalization, and a type screening method is designed to improve model accuracy. Furthermore, a fast fault filtering mechanism is proposed to enhance the algorithm efficiency with cloud-edge collaboration. Both simulation and real data are utilized to validate the proposed method.

Currently, the rapid development of Internet of Things (IoT) technology is promoting the development of smart grids. However, because of the numerous loads present and the surge of data in smart grids, network congestion, transmission delay, and insufficient channel resources pose pressing challenges for accurate, real-time transmission in load control communication systems. Mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) technology, as new types of communication architectures, can significantly improve the communication quality in such network system. In this article, we first establish an NOMA-MEC system model with relay assistance based on cloud-edge collaboration in smart grids, aiming to minimize the energy consumption cost of the system while satisfying constraints on relay power and computation latency. In addition, we formulate a powerful four-slot transmission strategy to support cooperative computation and communication in the proposed NOMA-MEC system with relay assistance. Since the optimization problem is nonconvex, an efficient joint cooperative computation and communication algorithm with relay assistance is developed to solve it. Numerical results show that the proposed resource allocation strategy can significantly improve the communication transmission quality and reduce the energy consumption cost of the system.

Mobile-edge computing (MEC), pushing the centralized cloud computing, storage, and communication capability to the edge close to vehicular terminals, is proposed as a promising solution to support computation-intensive and delay-sensitive services. This article proposes a cooperative MEC-enabled task offloading framework where the computational task of each vehicle is divided and computed by multiple collaborative MECs located on the roadside. However, existing MEC-enabled offloading research is based on offline settings or static networks and fails to address the dynamic communication environments. These dynamic environments involve variations in temporality (real-time channel state) and spatiality (uncertain data-queue backlogs as vehicles pass through different coverage areas of MECs). In the dynamic vehicular networks, the degradation of utility energy efficiency (UEE) and time delay is inevitable and significantly impacted. To tackle this issue, we propose an online dynamic scheme to solve the problem of maximizing UEE while meeting time-delay constraints. We then introduce a novel online dynamic optimization algorithm based on Lyapunov optimization theory to adaptively create strategies for task offloading and communication resource allocation in parallel. Numerical simulations demonstrate that the proposed algorithm achieves a balance between UEE and delay, striking a flexible tradeoff by tuning the control parameter$V$. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in terms of real-time communication and transmission capability.

Smart Grid needs real-time monitoring of power equipment status and optimization of power distribution. In order to meet the needs of mass communication and data transmission, smart grid needs sufficient spectrum resources to ensure fast and reliable data transmission. However, the limitation of spectrum resources and the interference caused by multisystem coexistence have become the bottleneck of the development of smart grid. This article pointed out the defect of the existing spectrum resources management mode. Based on the Taguchi Quality Assessment Theory, we developed a cost model for a utility company, which used a decoding and forward cooperative relay strategy to transmit downlink horizontal propagation to a data aggregation unit. This article analyzed the bilateral auction process between utility company and licensed user to maximize social welfare, and determined the optimal spectrum transaction to improve the utilization of spectrum resources.

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and further increase the cost of utility companies due to the purchase of additional automatic generation control services. This article considers a cooperative communication network consisting of multiple data aggregation units (DAUs) and multiple relays in smart grid, and each relay can forward data for all DAUs. We optimize the transmission power allocation of the relays to reduce the demand-side regulation errors and the cost of utility companies. However, additional cost is incurred due to the rental of relay in commercial networks. In order to minimize the total costs of utility companies, a two-layer game model is proposed and an iterative algorithm is developed. Simulation results show that the cost of utility companies can be reduced under the proposed scheme.

Owing to the openness and diversification of heterogeneous communication network, communication security becomes a pressing problem. In this paper, we consider a heterogeneous communication network in which spectrum resources are shared by electric power communication network and licensed network. First, we establish the utility companies’ cost model based on Taguchi loss function. Next, we utilize cooperative relay strategy to enhance the transmission quality and achieve high-speed information transmission in smart grids. Under the premise of ensuring high-quality transmission of electric power communication services, we propose a secure transmission strategy for information resource sharing and interference price trading that utilizes smart grid infrastructure and relay to interfere with eavesdroppers to improve the security rate of licensed user (LU), which achieves mutual benefits. Furthermore, the bernstein approximation method and the successive convex approximation are adopted to obtain the open-form expression of the constraint and transform the non-convex problem into the convex problem, respectively. A distributed robust power control algorithm is then proposed to obtain the optimal solutions. Finally, numerical results verify that the proposed secure scheme and algorithm can increase the secrecy rate at LU, reduce the total electricity cost, and improve both the profit of relay and the social welfare.

The rapid growth of the Industrial Internet of Things (IIoT) has brought attention to the critical issue of communication resource allocation. In industrial environments, efficiently utilizing communication resources to meet the demands of large-scale device connectivity and data transmission poses a significant challenge. This paper proposes a novel approach based on relaying with Intelligent Reflective Surfaces (IRS) to address the scarcity of communication resources in the IIoT. The method leverages decode-and-forward (DF) relaying and Intelligent Reflective Surfaces (IRS) to support data transmission over wireless channels and introduces a price mechanism to tackle the resource allocation benefit problem. Moreover, we employ the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a multi-objective optimization genetic algorithm, to achieve equitable resource allocation and balance the dual-objective benefit problem between edge servers and factories.

In the last few decades, independent consideration of underwater acoustic medium access control (MAC) layer has received much attention for designing a reliable data transmission protocol. Although it can simplify the system design, it is often insufficient for the enhancement of overall system performance. The focus of this article is on the cross-layer optimization to maximize the network throughput (NT) of clustered underwater acoustic sensor networks (UASNs) by jointly optimizing the sensor nodes’ slot scheduling and power allocation. The formulated problem is a mixed-integer nonlinear programming problem, which is NP-hard. An alternating-optimization-based centralized algorithm is proposed first to solve it, which can achieve the best NT performance but at the price of high complexity. Therefore, a multileader multifollower Stackelberg game-based distributed algorithm is also proposed to achieve a better tradeoff between system performance and complexity. Simulation results demonstrate that our proposed schemes considering the information causality constraint have a better NT performance than those without a “systematic” consideration over such joint optimization, like the CMS-MAC algorithm.

Mobile-edge computing (MEC) is a key technology to enable multitasking and low-latency user experiences for 5G Internet of Things (IoT) devices. The nonorthogonal multiple access (NOMA) technology is used in this context to enable large-scale connectivity and improve spectrum efficiency, with the unmanned aerial vehicle (UAV) serving as both computing units and relays for mobile users (MUs). Energy efficiency (EE) remains challenging given the limited energy available to the UAV and MUs. In this article, a UAV-assisted NOMA–MEC communication network architecture is studied to maximize the EE of the total system by jointly optimizing the user’s communication scheduling, resource allocation, and UAV flight trajectory. Among them, the resource allocation problem can further be divided into the transmit power optimization problem and the task computation allocation problem, whereby the corresponding time slot scheduling is obtained. The objective function is a nonconvex mixed-integer nonlinear fractional programming (MINLFP) problem, which is too complex to solve directly. Therefore, it is decomposed into more manageable subproblems and solved iteratively. Fractional problems are solved using the Dinkelbach method, which transforms their original subproblems into convex forms with methods such as successive convex approximation (SCA). Simulation results demonstrate the convergence of our proposed algorithm and its significant advantage over existing strategies in terms of EE.

In order to improve the profits of electricity utility company (EUC) and gas utility company (GUC) and reduce the electricity and heat cost of users, an energy trading and pricing scheme based on three-stage game is proposed. Firstly, a three-stage optimization problem is established, and the conversion between electricity and gas is considered. Meanwhile, the conversion factor is introduced and coordinated with the energy price to adjust the balance of supply and demand. Then, the equilibrium solution of the game is obtained by using Lagrange function and backward induction method. In addition, an iterative algorithm is developed to obtain the optimal conversion factor between electricity and natural gas. Numerical results show that the profits of EUC and GUC are increased by 31.6% and 14.4%, the electricity and heat profits of energy hubs (EHs) are increased by 3% and 6.4%, and the electricity and heat cost of users are reduced by 9.25% and 14.05%. Note to Practitioners—In the multi-energy market, trading and pricing strategies have attracted more and more attention. Based on this, many scholars only studied pricing strategy to balance the supply and demand. However, in the context of multi-energy coupling, the previous pricing strategy is not effective. In this paper, we propose a new pricing strategy, with which the conversion factor can cooperate with the energy price to achieve the balance between supply and demand. The new pricing strategy can increase the profits of electricity utility companies and gas utility companies and reduce the costs of users.

In smart grid, serious packet loss often occurs in the process of information interaction between the Utilities and customers, which results in supply-demand deviation and further increases the cost of the Utilities. In order to improve the information transmission performance of communication networks, the Utilities purchase relay service from telecom operator to help data aggregator units (DAU) transfer information to gateway (GW), so as to improve communication quality and reduce the cost of the Utilities. Second, in order to solve the problem of telecom operators’ energy reduction and reduce the cost of purchasing energy, we utilize energy supply point (ESP) to collect the surplus energy of retail customers for energy supply, and telecom operator pays a certain amount of remuneration to ESP in exchange for ESP to continuously supply energy to telecom operator. Then, we establish a three-stage game method and system model between the Utilities, telecom operator and ESP, and propose the relay power allocation and energy transaction pricing strategy. Due to the real-time change of energy demand, we consider two situations of energy oversupply and conservative supply, and use the backward induction method and iterative algorithm to obtain the equilibrium solution of the Stackelberg game, including the unit energy price of ESP, total power of telecom operator, the proportion of transmission power allocated to the relay service, and payment scheme of the Utilities. Simulation results show that the proposed algorithm can quickly and accurately converge to the optimal solution of the problem, and the method can improve the stability of demand-side regulation, reduce the cost of the Utilities and increase the profit of telecom operator.

The consensus based distributed frequency control strategy makes all measurements and control units in microgrids vulnerable to false data injection (FDI) attacks. This paper presents an observer-based resilient frequency control for estimating and compensating for FDI attacks on sensors and actuators in AC microgrids. Firstly, attacks of sensors and actuators are estimated online simultaneously by observers. Then a distributed H ∞ output feedback protocol is used to ensure that the frequency consensus tracking is achieved with cooperative uniform ultimate boundedness. The control strategy is completely distributed and does not require any quantitative information about attacks. Furthermore, there is no limit to the number and location of attacked units. Several case studies are provided to verify the effectiveness of the proposed resilient frequency control strategy.

With the increasing underwater applications, underwater wireless sensor networks (UWSNs) have become a research hotspot. Routing protocols used to keep network connectivity and reliable transmission are essential in UWSNs. Due to the specific limitations in UWSNs, such as serious ocean interference, high propagation latency, and dynamic network topology, it is challenging to balance multiple performances, such as real timeness and energy efficiency in a routing protocol. To this end, this article proposes a localization-free routing scheme, termed energy-efficient guiding-network-based routing (EEGNBR) protocol, to provide a time saving and reliable routing for UWSNs, which is a good choice for applications characterized by intermittent connectivity. For reducing the network delay, EEGNBR cites the advantageous distance-vector mechanism and establishes a guiding network to provide underwater sensor nodes with the shortest route (minimum hop counts) toward the sinks. Moreover, EEGNBR innovatively replaces the waiting mechanism used in traditional opportunistic routing with a novel data forwarding mechanism named concurrent working mechanism, which could greatly reduce the forwarding delay while guaranteeing reliable routing. In order to ensure routing reliability as well as avoid duplicate transmission, the forwarding protection mechanism is adopted to save energy consumption and extend the service life of the network. Simulation results show that EEGNBR performs significantly better than some classical related protocols in terms of network delay while maintaining comparable or even better energy consumption and packet delivery ratio.

The Internet of Underwater Things (IoUT) plays a key role in maritime monitoring systems, but energy-efficient data-uploading has been a challenging task owing to energy-constrained and expensive facilities, such as buoys and underwater sensors. In this article, we present an energy-efficient data collection scheme for unmanned aerial vehicle (UAV)-aided ocean monitoring networks (OMNs), where underwater acoustic and aerial radio frequency (RF) links are considered collaboratively. Our goal is to maximize energy efficiency (EE) of the entire OMN by jointly optimizing the transmit power of buoys and sensors, scheduling their transmissions, as well as designing the UAV's trajectory; the objective function is constrained by minimum throughput thresholds, power consumption budgets, and the UAV's kinematic conditions. Furthermore, we introduce a tradeoff between the energy consumption of buoys and sensors to bridge the gap between acoustic and RF links. The formulated problem is decomposed into three subproblems and they are solved alternatively. In each iteration, we leverage Dinkelbach's method and successive convex approximation (SCA) technique to tackle the fractional program (FP) and transform an original subproblem into a convex form, respectively. Extensive simulations confirm the convergence of our proposed scheme, reveal the influence of the tradeoff on EE, and show that our scheme outweighs other benchmarks in different scenarios.

The emerging hybrid backscatter and energy harvesting (EH) devices have been regarded as a promising scheme for green Internet of Things (IoT). In the interwoven primary and secondary wireless-powered IoT networks, we develop a novel hybrid scheme that integrates the backscatter communication and the harvest-then-transmit (HTT) protocol. In order to mitigate the adverse effect on the primary user (PU), the secondary users (SUs) are classified into “Cooperation Scenario” and “Coexistence Scenario” based on their different levels of interference to the PU. In the Cooperation Scenario, we propose a cooperation protocol where the SUs operate in the backscatter mode so as to relay information to the PU. Therefore, the SUs are rewarded for harvesting energy and obtaining the spectrum access time from the primary system. Also, in the Coexistence Scenario, a coexistence strategy is developed to enable the SUs to operate in either ambient backscatter or EH mode during the channel busy time. When the primary channel becomes idle, the SUs are capable of active transmission by using the harvested energy. For each scenario, we investigate the sum-throughput maximization problem of the secondary system. Employing the Lambert W function and the block coordinate descent method, the optimal time allocation can be obtained via the Lagrangian dual method. Numerical results validate that the proposed hybrid backscatter and HTT scheme improves the performance of the secondary system evidently compared with benchmark methods.

This article considers both the influence of relay power and load control period on the load tracking performance in smart grid communication. First, based on regulation errors caused by load control with imperfect channel state information (CSI), the load tracking cost model integrated of load control period and relay power is established in smart grid communication. Then, a coordination mechanism of cost-sharing contract (CSC) is presented. In the CSC, the utility companies select the preferred contractual terms offered by the telecom operator (TO) to reduce their costs and coordinate the whole network system simultaneously. Finally, the theoretical analysis and simulation demonstrate that the simultaneous consideration of the relay power and load control period can reduce the costs of the utility companies, increase the profit of the TO, and improve the social welfare. Besides, the proposed coordination mechanism of CSC can coordinate the whole network system.

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

The fast-growing demand of refueling electric vehicles (EVs) blocks the application and popularization of EVs. Battery swapping provides the EV users with a quick and convenient refueling way. In this article, an aggregative shared battery station (SBS) model is proposed, which is composed of a control center and a group of SBSs. With the SBS, the customers can rent the battery and pay a corresponding fee based on the swapped energy and satisfaction level. In order to enhance the SBS system responsiveness and reconfiguration to meet the changeable customers' battery demand and peak shaving and valley filling task, a two-stage framework for the multi-SBS is designed based on a self-adaptive dispatching strategy. On behalf of the SBS operator, an optimization objective function is established to maximize the operating revenue by optimizing the charging, discharging, and sleeping process of the batteries. Using the genetic algorithm, we perform extensive simulations to validate the optimization model and demonstrate the efficiency of the self-adaptive dispatching strategy. The results suggest that the proposed dispatching strategy is effective for scheduling SBSs to satisfy the EV refueling demand, provide peak shaving and valley filling service, and achieve the revenue maximization.

In recent years, cervical cancer has been one of the most common diseases in women's cancer. The advanced diagnosis of cervical precancerous lesions is essential for preventing cervical cancer. Its effectiveness and efficiency can be greatly improved by computer aided diagnosis, while challenged by the imprecise conclusions and uninterpretable process of diagnosis. To solve this problem, we propose a novel deep learning-based interpretable diagnosis system for pathology images, consisting of three interrelated models: an image model, an attention model and a conclusion model. Computer aided diagnosis improves the effectiveness and efficiency of the proposed image model uses a convolutional neural network (CNN) to ex-tract semantic features. Combining the model with the semantic attribute attention model, it aims to capture the discriminant relationship between se-mantic attributes by predicting the conclusion label through long-term and short-term memory (LSTM). The network is trained in an end-to-end manner, with different weights for each model. Experimental results on cervical intraepithelial neoplasia images, diagnostic reports and label datasets show that the proposed method achieves a significant improvement over traditional methods with a better interpretability.

In this article, we study an interference management and power allocation problem when electrical power communication (EPC) networks are densely deployed in the coverage of licensed networks. The purpose is to reduce the electricity cost and improve the licensed operator's profit subject to the quality-of-service (QoS) of licensed users (LUs). First, the electricity cost is modeled based on the Taguchi loss function, which links the cost to the communication errors in the EPC networks. The operator's profit is formulated by introducing a bonus-penalty mechanism, and a rational interference threshold (IT) of the licensed base station (LBS) is set to ensure the QoS of the LU. Second, we formulate the interference management and power allocation problem as a Stackelberg game, and a successive convex approximation algorithm is used to solve this problem to achieve the optimal IT and relay power. The simulation results indicate that the cost to the utility company is reduced and the profit of the LBS increases.

The electricity costs of the Utilities are increased with the deviations from the forecasted demand, which are caused by forecast errors and demand fluctuations. The forecast errors are deterministic and can be avoided by the Utilities with long-term operations in electricity markets, whereas the demand fluctuations are stochastic and unavoidable. Demand response can be used for mitigating the demand fluctuations by measuring the electricity usage of consumers and publishing control commands periodically. The performance of demand response is dependent on the quality of communications between the control center and the consumers. The two-way communications are established based on the data aggregator units (DAU) deployed by the Utilities. To improve the quality of communications, we utilize the base stations in telecom networks to forward the metering and control data for the DAUs. The telecom operators maximize their profits and decide the fractions of transmission power allocated for relaying, and then the Utilities select the payments to obtain the corresponding transmission power allocated for relaying. We characterize the electricity costs of the Utilities based on Taguchi loss function and establish a Stackelberg game between the telecom operators and the Utilities. We prove the existence and uniqueness of Nash equilibrium for the follower-level payment selection game and develop an iterative algorithm to search for the equilibrium. Then, the Stackelberg equilibrium can be obtained by a backward induction method. Numerical results show that the cost of the Utilities can be reduced and the profits of the telecom operators are increased.

Blockchain-based femtocell networks aim to build decentralized frameworks which enable easy deployment and low power consumption, thus they have been seen promising technologies to make up the coverage of cellular networks in the next generation communication system. This article aims to employ power control to support quality-of-service provisioning, especially the guarantee for the transmission rate of a macrocell user (MUE) and the time delay of femtocell users (FUEs) in two-tier femtocell networks, where the MUE and FUEs share the same communication channel. We formulate the interactions among the macrocell base station and FUEs as a Stackelberg game to maximize the utilities of MUE and FUEs by obtaining the optimal power allocation and pricing strategy. Considering the uncertainty of channel gain which is expressed as a function of transmission distance, we propose a worst-case method to transform the uncertain optimization problem into a deterministic one. We then design two algorithms by considering the dynamics of FUEs, i.e., FUEs may join and leave femtocells. Numerical results verify the convergence and superior performance of our proposed algorithms.

Although the Cognitive Radio Ad‐Hoc Network (CRAHN) is an effective technology to fully utilize the spectrum resource, the appearance of selfish nodes seriously reduces the communication efficiency of CRAHN and generates unfair resource competition. In this paper, a new incentive strategy is proposed to tackle selfish nodes in CRAHN. In our CRAHN model, the Secondary‐User (SU) cooperates with the Primary‐User (PU) in a spectrum leasing mode. Since PU can select multiple SUs as relays but only leases a common authorized spectrum usage time to SUs, the SU has the selfish tendency to reduce its power in relay task, which seriously damage the partnership between PU and SUs. We propose an evaluation coefficient to evaluate the behavior of each SU, where the evaluation coefficient establishes the reward and punishment mechanism to suppress the selfish behavior of SU in relay task. Meanwhile, in order to solve resource allocation problem, a Stackelberg game between PU and SUs is formulated and the optimal solutions are determined in a distributed manner. Simulation results validate that the incentive strategy can effectively suppress the selfish behavior of SUs, in the meantime, the total communication throughput is increased.

In this paper, a pricing mechanism is proposed for the electricity supply chain, which is consisting of one generation company (GC), multiple consumers, and competing utility companies (UCs). The UC participates in electricity supply chain management by a revenue sharing contract (RSC). In the electricity supply chain, the electricity real-time balance has an important role in the stable operation of the power system. Therefore, we introduce the demand response into the electricity supply chain to match supply with demand under forecast errors. Hence, we formulate a noncooperative game to characterize the interactions among the multiple competing UCs, which set the retail prices to maximize their profits. Besides, the UCs select their preferred contractual terms offered by the GC to maximize its profits and coordinate the electricity supply chain simultaneously. The existence and uniqueness of the Nash equilibrium (NE) are examined, and an iterative algorithm is developed to obtain the NE. Furthermore, we analyze the RSC that can coordinate the electricity supply chain and align the NE with the cooperative optimum under the RSC. Finally, numerical results demonstrate the superiority of the proposed model and the influence of market demand disruptions on the profits of the UCs, GC, and supply chain.

In this paper, we optimize power and spectrum allocation simultaneously to improve the demand-side communication quality in smart grid, to further reduce the cost of utility companies. The electricity cost is first modeled based on regulation errors caused by direct load control in the smart grid. Then the subbands are allocated to different data aggregator units according to the band confidence levels and the utility company's maximum cost. An algorithm is designed to optimize transmission power of the relay and refine the spectrum allocation to reduce the cost of utility companies. Simulation results demonstrate that the packet loss rate and cost of utility companies can be significantly reduced.

This paper presents an energy management method to optimally control the energy supply and the temperature settings of distributed heating and ventilation systems for residential buildings. The control model attempts to schedule the supply and demand simultaneously with the purpose of minimizing the total costs. Moreover, the Predicted Percentage of Dissatisfied (PPD) model is introduced into the consumers' cost functions and the quadratic fitting method is applied to simplify the PPD model. An energy management algorithm is developed to seek the optimal temperature settings, the energy supply, and the price. Furthermore, due to the ubiquity of price oscillations in electricity markets, we analyze and examine the effects of price oscillations on the performance of the proposed algorithm. Finally, the theoretical analysis and simulation results both demonstrate that the proposed energy management algorithm with price oscillations can converge to a region around the optimal solution.

Demand response can improve the stability of power system and reduce the operation cost. This paper design a pricing scheme to balance the energy demand and supply based on demand response in smart grid. In the demand side, a Bayesian game is formulated to model the interaction of multiple consumers because each consumer's utility function is private. The utility company announces a regulation price which can change the Bayesian Nash equilibrium and the energy demand of consumers. We develop an algorithm to update the regulation price until the energy demand match the energy supply. Numerical results demonstrate that the algorithm make the regulation price converge to a stable state and balance the energy supply and demand.

In smart grid, the frequency regulation can be provided by both the automatic generation control (AGC) and the demand-side regulation, and the regulation errors increase the electricity costs to the utility company. The demand-side regulation adopts a hierarchical communication architecture, and the data aggregator unit (DAU) may suffer from congestions which consequently increase the costs to the utility company for more AGC service except for the demand-side regulation. In this paper, we employed the base stations as relays and formulated the electricity costs-based upon the regulation errors and the packets loss model. Specifically, the utility company decides the relaying bandwidth to minimize its electricity costs, and the relay selects the base price to maximize its profits. The novelty of this paper is twofold. First, we formulate the interactions between the utility company and the relay as a bargaining problem. Second, we utilize the Nash bargaining solution (NBS) and Raiffa-Kalai-Smorodinsky (RBS) bargaining solution to achieve the Pareto-optimal outcome. Furthermore, we extended the results to the case with multiple DAUs and multiple relays. The numerical results demonstrate the cost reduction of the utility company and the profit increase of the relay under the NBS or RBS strategy. In addition, the NBS strategy can bring about more profits for the relay than the RBS strategy, while the RBS strategy can provide a fairer payoff allocation and lower costs to the utility company than the NBS strategy.

Summary Ultra dense deployment of base stations is one of most significant features in smart city communication networks. Aiming at the large‐scale wireless communication issue in smart city, we propose a distributed robust power allocation scheme with proportional fairness for cooperative orthogonal frequency‐division‐multiple‐access relay network. With the amplify‐and‐forward relay mode, all of the relays assist the information transmission simultaneously on orthogonal subcarriers. Considering the uncertainty of channel gains, first we aim at achieving the maximum utility subject to the constraints of outage probability threshold and power bound. Subsequently, the problem is transformed to a solvable convex optimization problem with determination constraints. The dual‐decomposition method is applied to solve the formulated optimization problem. To reduce the information exchange of the whole system, we propose a computationally efficient distributed iteration algorithm. Numerical results reveal the effectiveness of the proposed robust optimization algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

We study the problem of joint relay selection and power allocation in a underwater cooperative system with multiple users assisted by multiple relays. Due to the harsh underwater environments, the channel state information (CSI) at the transmitter is imperfect, which leads to the performance degrading in the underwater cooperative acoustic system. Therefore, we analyze the cooperative underwater acoustic channel with limited feedback to increase the sum-rate of the system. Meanwhile, different from other researches, we do not only focus on the single system scenario, but also consider the presence of nearby acoustic activities and the problem of joint relay selection and power allocation is solved in a cognitive acoustic (CA) scenario. Thus the codebook of interference CSI and the codebook of quantized relay selection and power allocation strategy are designed, respectively. Simulation results show that a few bits feedback can significantly improve the performance of the CA cooperative acoustic system.

This paper proposes a cooperative demand response (CDR) scheme for load management in smart grid. The CDR scheme is formulated as a constrained optimization problem that generates a Pareto-optimal response strategy profile for consumers. Comparing with the noncooperative response strategy (i.e., Nash equilibrium) obtained from the one-shot demand management game, the Pareto-optimal response strategy reduces the electricity costs to the consumers. We further develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behaviors of the selfish consumers. Furthermore, the CDR scheme is applied to achieve load management of industrial refrigerated warehouses. To implement the CDR scheme in large-scale systems, we group the refrigerated warehouses into clusters and utilize the CDR scheme within each cluster. Numerical results demonstrate that the CDR scheme can reduce the electricity costs, drop the electricity prices, and curtail the total energy consumption in comparison with the noncooperative demand response scheme.

This paper proposes a cooperative demand response scheme for price-anticipating buildings in smart grid. The cooperative demand response scheme is formulated as a constrained social optimization problem. We develop a cooperative strategy and obtain a Pareto-optimal solution from the constrained social optimization problem. Comparing with the Nash equilibrium obtained from the one-stage demand management game, the Pareto-optimal solution reduces the electricity costs to all the building managers. We further align this Pareto-optimal solution with the subgame perfect Nash equilibrium of a repeated demand management game and develop an incentive-compatible trigger-and-punishment mechanism to avoid the noncooperative behavior of the building managers. Numerical results demonstrate that the cooperative demand response scheme can reduce the electricity costs, the electricity price, and the total energy consumption.