To enhance the performance of stratospheric airships in Earth observation, the paper investigates the distributed cooperative tracking control problem for a heterogeneous stratospheric airship system under limited communication and computing resources. Initially, a distributed adaptive event-triggered consensus control is proposed to handle the limited communication and computing resources of the stratospheric airship system. Furthermore, to enhance the adaptability of the controller, the time-varying adaptive coupling weight is designed for each part of consensus error composition in both the controller and triggering function. Then, to improve the scalability and robustness of stratospheric airship clusters, a distributed event-triggered control is constructed that only uses state estimation and tracking errors of the neighbor airships. Additionally, the proposed distributed event-triggered control can realize the leader-following consensus for the cooperative tracking control of stratosphere airships, ensuring that each airship avoids the Zeno behavior. The necessary conditions and solid mathematical proof have been given to ensure the stability of the cooperative tracking stratosphere airship system. Finally, numerical simulations are given to illustrate the effectiveness of the proposed event-triggered cooperative tracking control of stratosphere airship for earth-observing.

In beyond 5G multi-cell networks, cell-edge users generally experience poor communication quality due to their greater distance from the base station (BS), and increased interference from neighboring cells, which severely impacts their user experience. To address this issue, achieving fair and efficient resource scheduling is key to ensuring the quality of service for edge users. Therefore, this paper formulates a joint optimization problem of spectral subband selection and power control, aiming to maximize the proportional fair sum rate of the multi-cell network. However, most existing algorithms require instantaneous global channel state information, which results in poor scalability and is impractical, especially in highly dynamic wireless network environments with user mobility. Noting that the considered problem can be modeled as a decentralized partially observable Markov decision process, we propose a multi-agent deep reinforcement learning (MADRL) scheme based on a hierar chical centralized training and distributed execution framework (MADRL-HE), enabling agents to make spectral subband and transmit power selections using only local information and some outdated non-local information. Simulation results demonstrate that the proposed scheme features excellent scalability and fast convergence. Moreover, its proportional fair sum rate performance consistently outperforms the existing MADRL scheme in dynamic environments and surpasses centralized iterative optimization schemes in most dynamic scenarios.

This paper considers a 5G dense cellular network scenario where wireless channels are dynamically changing. To address the resource allocation problems in 5G dense networks, a distributed real-time and fair resource allocation algorithm based on a single deep Q-network (DQN) and multiple local deep neural networks (DNNs) architecture (DRFRA-SDML) is proposed. Specifically, each downlink is modeled as an agent. Each downlink is equipped with a local deep neural network (DNN), allowing each agent to input locally observed information into the local DNN to select spectral subband and transmission power in real-time. In the core network, the global experience replay buffer is utilized to collect local experiences collected by all local agents to train a global weight vector of the train DNN, which is shared by all local DNNs. This ensures that the computational complexity of each local agent does not depend on the size of the cellular network, resulting in excellent scalability. To achieve fairness between user devices, a reward function based on fairness has been designed. The simulation results demonstrate that DRFRA-SDML significantly outperforms IFP-FRA and FP-FRA-D in terms of sum rate in most scenarios, while ensuring fairness among user devices and achieving markedly better real-time performance.

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.

With the emergence of 5G technology, the demand for data communication between mobile users has significantly increased, and the number of cellular network infrastructure and mobile devices has also grown rapidly. However, a large number of base stations conducting wireless communication simultaneously inevitably brings serious interference due to the limited spectrum resources and dense distribution. Since the channels in the 5G dense cellular network with mobile users are complex and it is difficult to capture the channel state, the power allocation scheme adapting to a dynamic environment has become an important issue. In this article, a multiagent deep Q-network (DQN) distributed algorithm based on environmental awareness (MADQN-EA) is proposed. Specifically, the downlink between each base station and the user is treated as an agent, and a multiagent distributed approach is developed to improve the scalability of the algorithm. In response to the time-varying nature of the 5G dense cellular network environment, an environmental awareness training method is adopted. This method provides the agent with the opportunity to observe more changes in the 5G dense cellular network environment during the training process. This design significantly enhances the robustness of the proposed algorithm under the changing channel conditions. The proposed MADQN-EA is compared to fractional programming with a perfect CSI (FP-PC), multiagent DQN with experienced instance transfer (MADQN-EIT), and the random power selection scheme (Random). Simulation results show that MADQN-EA is robust against dynamic environment and achieves a higher sum rate performance.

Considering the harsh and complex environments in Underwater Wireless Sensor Networks (UWSNs), where the malicious nodes exist, the secure routing design is investigated in this paper. The malicious nodes are a serious threat to the security of sensor networks. This paper proposes a secure routing protocol, named Trust Based Routing (TBR), that focus on how to evaluate and find the malicious nodes and then determine the reliable routing. The core idea of TBR is that a new trust management model is designed by considering the various historical behaviors of nodes in the interaction process, which is able to assess the trust value of a node based on past behaviors between nodes and protect against potential attacks in the network. And the extra factors such as the remained energy of nodes, the distance between nodes and the trustiness are included in the routing criterions. Finally, a strategy for selecting relay nodes is proposed based on these considerations. Simulation results show that the proposed TBR algorithm can effectively defend against attacks from inside the network and is also more efficient and reliable compared to other existing routing protocols.

Ambient backscatter communication (ABC) enables low-cost and energy-efficient connectivity for Internet of Things (IoT) devices by leveraging ambient radio-frequency (RF) signals. However, the passive nature and open wireless medium of ABC systems make them vulnerable to detection by unauthorized receivers (wardens). To mitigate this risk, covert communication, which conceals transmissions by embedding them within noise, offers a promising security enhancement for ABC systems. This paper proposes a jammer-assisted reflection coefficient optimization framework to enhance the covertness and reliability of ABC systems with an endogenous warden and an external jammer. Specifically, we consider two distinct jamming patterns: uniformly distributed and truncated exponentially distributed artificial noise power. We derive closed-form expressions for both the outage probability of the backscatter link and the minimum detection error rate at the warden under these jamming patterns. Based on these expressions, we determine the optimal reflection coefficients that maximize the effective covert rate while satisfying a predefined covertness constraint. Additionally, we introduce the concept of jamming cost to evaluate the efficiency and applicability of different jamming patterns in terms of the required jamming power to achieve a desired level of covertness. Numerical results validate the effectiveness of the proposed optimization framework and reveal that while uniform jamming provides stronger covertness and lower jamming cost, truncated exponential jamming achieves a lower outage probability. These findings provide key insights for designing secure and efficient ABC systems across diverse IoT deployment scenarios.

Secure data transmission and real-time state updates are critical yet challenging requirements for Cyber-Physical Systems (CPS) to maintain stability under adversarial conditions. While existing works have separately explored covert communications for security and Age of Information (AoI) optimization for timeliness, their interdependencies remain unaddressed, leading to suboptimal trade-offs between detection resistance, control performance, and resource efficiency. To bridge this gap, this paper proposes a novel co-design framework that jointly optimizes covert communication and AoI-aware control strategies. First, we rigorously derive a linear relationship between average AoI and control cost, termed the Cost of Update Delay (CoUD), which quantifies how outdated information exacerbates state fluctuations and increases stabilization efforts. Building on Kosta et al.’s Geo/Geo/1 queuing model, a closed-form expression for average AoI is further established as a function of sampling rate and packet delivery probability, explicitly linking communication parameters to control efficacy. Subsequently, this paper formulate a constrained optimization problem to minimize CoUD while guaranteeing covertness against eavesdroppers, leveraging Dinkelbach’s transformation and Lagrangian duality to decouple nonlinear constraints, derive optimal sampling rates, and transmission powers. Simulation results demonstrate that the proposed co-design framework achieves significant reductions in CoUD and superior freshness compared to baseline methods, while robustly maintaining covertness requirements. Notably, the proposed integration of sampling rate adaptation into the detection error rate model markedly enhances both AoI performance and resource efficiency, outperforming state-of-the-art disjoint designs. Therefore, this work provides a unified methodology to harmonize security, timeliness, and stability in resource-constrained CPS.

In this study, the task assignment problem for heterogeneous underwater vehicle collaborative system, which involves autonomous underwater vehicles (AUVs) and underwater gliders (UGs), is studied for high-efficiency data collection. UGs and AUVs show different motion modes. The advantages of different motion modes can be mutually complemented to achieve the preference-matched task assignment results, which show greatly promising prospect to enhance the data collection efficiency. Most of existing underwater task assignment algorithms focus on the single-type vehicles, which can not be applied to the heterogeneous system. To address this issue, a hierarchical learning algorithm is proposed. Firstly, based on the evaluated emergency degree of tasks, the preliminary-task-assignment hierarchy is proposed to assign the emergency tasks to AUVs and assign the non-emergency tasks to UGs, thereby achieving the preference-matched task assignment. Therefore, the collaborative efficiency of heterogeneous system can be enhanced. Then, in the UG-task-assignment hierarchy, the adaptive serial cluster mechanism is proposed to extract the high utility task-connectivity regions for UGs, thereby fully leveraging the UG advantages in region data collection. Furthermore, in the AUV-task-assignment hierarchy, the extended self-organizing mapping neural network is constructed to eliminate the disorganization of neuronal loops. As a result, the crossed paths of AUVs can be excluded to reduce the energy consumption. Finally, the superior performance is verified by numerical results.

This paper investigates a joint discrete-channel and continuous-power allocation problem for multi-user underwater acoustic communication networks. The unknown underwater acoustic Channel State Information (CSI) and the distributed optimization requirement make the proposed hybrid discrete-continuous optimization problem full of challenges. Firstly, an adversarial multi-player bandit game model is formulated, which enables each user to independently optimize its own strategy, thereby achieving the distributed decision. In the strategic game, the Multi-armed Bandit (MAB) learning theory is exploited to achieve the best response strategy of independent user without prior CSI. Secondly, an evolutive finite discrete strategy pool learning structure is proposed to achieve an efficient search for the hybrid discrete-continuous space. The constant evolvement of strategy pool endows the proposed MAB-based algorithm with the ability to search the whole continuous power space, thereby avoiding missing the superior strategy caused by the discretization of continuous space. Thirdly, a selection probability setting rule is proposed, which promotes the exploration-exploitation balance for the dynamic strategy pool, thereby improving the learning efficiency. Finally, simulation results demonstrate the superiority of the proposed algorithm.

This article deals with a joint multiple resources (relay, channel, and power) allocation problem for underwater acoustic (UWA) cooperative communication in time-varying Internet of Underwater Things scenarios. The strong coupling of multiple resources and the unknown time-varying characteristic of UWA communication scenes make the joint optimization problem full of challenges. To address this issue, the adversarial multiarmed bandit online learning model without any prior channel information and statistic assumptions is employed. Furthermore, a double closed-loop learning structure with multiple intelligent experts assistance is proposed. Multiple experts embedded in inner loop can intelligently learn the derived inferential information to provide more efficient advice for the player in outer loop, thereby enriching learning information and enhancing learning ability. In addition, the expert diversity learning mechanism is proposed to fully reflect the characteristics of seeking advantages and avoiding disadvantages in the double closed-loop learning structure. As a result, the learning speed and performance of the proposed algorithms are significantly improved. The superiorities of the proposed algorithms are demonstrated through numerical results.

In recent years, underwater acoustic sensor networks (UASNs) have played an increasingly important role in ocean exploration. However, underwater sensor networks suffers from severe propagation attenuation, limited energy and sensor mobility compared with terrestrial networks. Also, the value of sensing data is quite different in some applications of underwater data collection. To tackle these challenges, this paper proposes a hierarchical collection strategy based on value of information (VoI). Taking account of the mobility of nodes close to sea level, we divide the network into two layers according to the Ekman drift current model. In the upper layer, the nodes move violently with the sea water. We adopt opportunistic routing to allow these nodes to search for the appropriate next hop nodes actively. Meanwhile, nodes in the lower layer are clustered. According to the rarity of the data received in the historical data, we propose a novel mathematical formula to measure the VoI of the data, and define the ratio of received data value to energy consumption as the evaluation index of network energy efficiency. AUV-aided transmission and multi-hop transmission are utilized separately to determine the tradeoff between energy consumption and network performance. The choice of transmission mode depends on the VoI in a cluster. Simulation results indicate that the proposed strategy shows satisfactory performance in improving energy efficiency.

Increasing demands for Quality of Experience (QoE) lead to massive connectivity and intensive computation in future vehicular networks. This article proposes a device-to-device (D2D)-based mobile edge computing (MEC) network architecture to provide effective communication connections and sufficient computing abilities for vehicular networks. However, the available communication and computing resources are limited in the D2D-based vehicular MEC networks, and an imbalanced resource allocation always leads to suboptimal optimization of overall performances. To address this challenge, we formulate a Lyapunov optimization method-based resource allocation framework to balance communication and computing by compromising energy efficiency (EE) and time delay. However, the long-term resource allocation framework is ineffective when it ignores the dynamic characteristics of vehicular networks, i.e., channel state changes due to the movement of vehicles and a dynamic queue backlog with data fluctuations. Considering the time-varying channel state and dynamic queue backlog, the proposed framework aims to balance resource allocations while primarily maintaining network stability. Finally, we propose a Lyapunov optimization-based long-term dynamic resource allocation algorithm to develop real-time allocation strategies. Simulation results illustrate that the proposed algorithm balances communication and computing resources by tuning the control parameter V. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in real-time transmission and offloading ability.

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.

With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (${{\rm{E}}^{2}}\rm{L}$) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy.

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.

This article deals with the task assignment problem for multiple autonomous underwater vehicles to efficiently collect underwater data from sensors. We formulate a unified framework to consistently address the heterogeneous task assignment problem (nonemergency and emergency cases) without strictly distinguishing the mixed cases. First, a unified problem, which bridges the gap between different constraints and optimization objectives of different cases, is constructed. Then, the proposed reinforced self-organizing mapping algorithm is reinforced in three aspects: the regional learning rate, the self-configuring neuron (SCN) strategy, and the workload balance mechanism. Specifically, the proposed regional learning rate comprehensively considers the individual worth of tasks and the topology to generate the regional learning rate of dynamic task regions, which consists of dynamic remaining tasks and the reconstructed topology. Based on this idea, the constructed unified problem can be solved consistently. Furthermore, the proposed SCN strategy optimizes the neuron population both in quality and quantity, and guides the update of neurons with enriched historical information to improve the mapping ability. This strategy greatly improves learning efficiency and applicability in a wide range of scenarios. Meanwhile, the proposed workload balance mechanism takes into consideration of both the work capability and consumed energy to extend the continuous working capability. The numerical results validate the effectiveness and adaptability of the proposed unified task assignment framework.

This study deals with a joint channel selection and power allocation problem for time-varying underwater acoustic communication system. Without any prior channel information, designing a highly adaptable resource allocation algorithm to cope with the fast time-varying environment is a very challenging issue. To address this issue, a hierarchical learning approach, which is combined with adversarial multiarmed bandit theory and outdated pilot-based feedback information, is proposed. The proposed learning approach can online optimize joint resource allocate strategy without any prior channel state information. Specifically, a hierarchical self-reflection learning structure is proposed to offer different learning manners and spaces for the actual played information and outdated feedback information, thereby balancing the exploitation and exploration to cope with the time-varying environment effectively. Further, an integration learning structure is proposed to alleviate the solving difficulty and policy explosion of joint multiple substrategies problem. The user can rapidly achieve a few superior strategies in low-dimension space, then efficiently search the expected optimal strategy in high-dimension space, as a result, the learning efficiency is significantly improved. The proposed algorithms show strong tolerance for delay and noncomplete information due to the elaborate learning structures. The superiority of the proposed algorithms is demonstrated through numerical results.

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.

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 current Internet of Underwater Things (IoUT) for marine observations and emergency responses suffers from two critical issues: 1) energy efficient and 2) timely data collection. Autonomous underwater vehicles (AUVs), serving as tools for collecting and forwarding distributed data, can deal with the unbalanced power consumption in a traditional multihop underwater communication network. However, the low speed of the AUV has not been able to guarantee the timeliness of delay-sensitive data. In this article, we introduce a hybrid data collection scheme (HDCS), taking both real-time data collection and energy efficiency (EE) issues into consideration. All sensor nodes (SNs) are first clustered based on their locations in the network. We develop an analytic expression to describe the attenuation of Value of Information (VoI), involving the relationship between the importance degree and timeliness; initial VoI could be measured by historical data. The emergency can be recognized by the presented criterion, and the transmission mode of cluster heads (CHs) in the same layer is judged by CHs themselves according to VoI. The selected CHs shall transmit the urgent data via multihop routing to avoid over attenuation of VoI. The normal data are collected by AUVs visiting all remaining CHs, and the shortest trajectory is achieved by addressing a variation of the classic traveling salesman problem (TSP). Our simulation experiments show that this mechanism can effectively increase long-term VoI while significantly improving EE.

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 investigates the robust secure transmission problem in two-tier B5G heterogeneous networks with multiple noncollusive eavesdroppers and users, where two types of imperfect channel state information (CSI) scenarios, i.e., instantaneous and statistic CSI scenarios, are considered. Given the two-sidedness of co-channel interference in physical-layer security and the selfishness of femtocell base stations (FBSs), an imperfect-CSI-based noncooperative game framework is proposed to maximize the profits of the macro base station (MBS) and FBSs, while guaranteeing user’s Quality-of-Service (QoS) requirement in terms of outage probability. Specifically, based on the involved two CSI scenarios, the original game where the MBS and FBSs act as players is elaborated as two robust game problems. To address channel uncertainties in the objective function, the worst-case and the mean value of the channel gains are used separately. Besides, the remaining channel uncertainties embodied in the intractable outage probability constraints are treated in a unified way, i.e., the extended Bernstein approximation. The existence and uniqueness of the Nash equilibrium (NE) are analyzed, and the sufficient condition on the uniqueness of the NE is derived. Then, two robust iterative algorithms are given to approach the robust game equilibrium. Finally, numerical results are presented to verify the theoretical analysis and show the robustness and effectiveness of the proposed algorithms.

With the advent of Industry 4.0 and the popularization of smart terminal equipment, the interaction between industrial field information systems and production equipment has intensified. To meet the real-time transmission of time-triggered flow and the coordinated transmission of best effort flow, time-sensitive network-related technologies are used to implement flow queue forwarding by strictly following the gate control list. First, response-time analysis method is proposed to predict the upper bound of delay under a scheduling model following IEEE 802.1Qbv. Second, according to response-time analysis, a deadline monotonic scheduling algorithm with temporary priority expansion is proposed to divide the priority into more levels, which is not limited by the queue type, so as to ameliorate the transmission sequence of switch export flow. Finally, an adaptive priority adjustment scheduling algorithm with temporary priority expansion is designed to construct the optimal scheduling method further improving the scheduling success rate and reducing worst-case end-to-end delays. Compared with similar algorithms, the algorithm proposed improves the scheduling success rate by at least 30%, reduces the total delay by at least 21% and the TT flow delay by 11% in high network utilization conditions.

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.

This paper studied the problem of autonomous channel matching for device-to-device (D2D) pairs in a multiuser cellular network. The goal of each D2D pair is to match an optimal wireless channel to maximize its reward. The reward is defined as the rate of the D2D pairs and limited by the SINR (Signal to Interference plus Noise Ratio) of the cellular user on the current channel. This strategy maximizes a certain network D2D throughput in a distributed manner without requiring online coordination or message exchange among users. We describe this problem as a random non-cooperative game with multiple players (D2D pairs), where each player becomes a learning agent, whose task is to learn its best strategy (based on locally observed information). Then, we designed a multi-user learning algorithm based on double deep Q-network (DDQN), which converged to Nash equilibrium (NE) of mixed strategy. After simulation verification, the algorithm can enable each user to obtain a best strategy to obtain a high communication rate through online or offline learning. And the algorithm has a faster convergence speed compared to the similar method.

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.

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.

Femtocell network is regarded as the potential and effective technique to improve the capacity and coverage of traditional cellular networks. One of the challenges is how to access the network and manage the interference among different users. Compared with other access strategies, hybrid access strategy allows femtocell base stations (FBSs) to provide preferential access to femtocell users (FUEs) while other users can access nearby FBS with specific restrictions. In this paper, a robust downlink power control scheme is studied in two‐tier femtocell networks, where femtocells share the same frequency with macrocell. A hierarchical game framework that takes the different users' requirements into consideration is constructed. In addition, as the link gains are actually uncertain in dynamic environment, probabilistic constraints are used to describe the uncertainty. Then two sub‐problems are obtained to maximise the sum rate of macrocell and femtocells, respectively and guarantee the quality of service (QoS) of different users. To tackle the nonlinear and nonconvex optimization problem, successive convex approximation is introduced. And the practical iterative power allocation algorithm is provided. Finally, numerical results show that the proposed scheme is effective in aspect of energy saving and QoS guarantee under dynamic environment.

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.

A femtocell network which is supported by a macrocell base station and some femtocell base stations provides more reliable transmission, higher wireless capacity, and broader coverage. However, it may face eavesdropping risk, which provides an eavesdropper with a chance to overhear a macrocell user's confidential information. In this paper, we study a secrecy transmission problem for a downlink two-tier femtocell network with imperfect channel state information (CSI), where an eavesdropper wiretaps the legitimate macrocell user. More specifically, we aim to maximize the secrecy rate by jointly optimizing the power allocation and quality-of-service (QoS) requirement in terms of outage probability. We consider two types of CSI, i.e., instantaneous and statistic CSI, respectively, which can robustly guarantee the QoS of users in a complex communication environment. For the instantaneous CSI communication environment, where there exist estimated errors between instantaneous channel gains and their estimated values, we propose a novel conversion method to extract the approximate closed-form expressions of outage probability constraints. For the statistic CSI communication environment, where channel gains obey Rayleigh fading, we design a new method to obtain deterministic expressions by considering the expectations and variances of instantaneous channel gains. Then, the uncertainty and non-convexity of objective function are solved with the aid of variable substitution and Taylor expansion. Moreover, two iterative algorithms are proposed to derive the optimal transmission powers. Finally, we evaluate the proposed algorithms using large scale simulations, and present extensive evaluation results to demonstrate the effectiveness of our proposed algorithms.

In this study, a power allocation scheme for two‐tier femtocell networks is proposed to maximise the user utilities constrained with satisfactory quality of service, where femtocell users share the same frequency with macrocell users (MUEs). Since the environment changes and the channel gains cannot be assumed to be constants, a worst‐case method is used to address the uncertainty of the power allocation problem. A non‐cooperative game model is developed to maximise the utilities of femtocell users by letting the users competing the utilities with others. As the robustness can be affected by the changing gains of communication links, the robust Stackelberg game is proposed to model this hierarchical competition where the MUEs and femtocell users act as leaders and followers, respectively. Two effective pricing schemes are applied to maximise the utilities, when different user demands are required, where the uniqueness of Nash equilibrium is proved in the two schemes. Numerical results show the convergence of the Stackelberg game with uncertainty and also the results demonstrate the effectiveness of the power allocation algorithm.

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.

In this study, the authors study the power control for a two‐tier network system which is comprised of a central macrocell and several femtocells. In practice, the communication environment is fairly complex and dynamic which leads to the imperfect channel state information (CSI). To enhance the robustness of the two‐tier network system, the imperfect CSI in both signal links and interference links are considered and the uncertainties of the CSI are uniformly distributed in an ellipsoid uncertainty set. Then, a probability‐constrained optimisation problem is formulated to deal with the uncertainties and protect the quality‐of‐service of all users. A novel method is provided to convert probability constraints into deterministic ones. Based on them, they propose an iterative algorithm and an admission control algorithm to enhance the network efficiency. They also investigate the case when the distributions of the uncertainties of the CSI are unknown. Finally, numerical results are given to illustrate the effectiveness of the authors’ power control scheme.

This study presents a solution to the interference management scheme and resource allocation strategy for the two‐tier femtocell networks, where the femtocell users (FUEs) share the same frequency band with the existing macrocell users. It is assumed that the FUEs compete for the available spectrum to fulfil their own communication. And the macrocell base station protects itself by pricing the interference from the FUEs, which formulates the Stackelberg game. In this study, two effective pricing schemes, uniform pricing scheme and non‐uniform pricing scheme, combining with admission control are proposed to maximise the revenues and protect the quality of service requirements. The Stackelberg equilibriums for the proposed games are investigated. Besides, a novel distributed interference pricing algorithm is provided for the uniform pricing case. Numerical results show that, in two‐tier femtocell networks with shared spectrum, the proposed pricing schemes are effective in resource allocation and performance protection.

Abstract Cognitive radio (CR) networks comprise a number of spectrum agile nodes with the capability of spectrum detection. Applying techniques of spectrum sharing in CR networks can achieve the efficient utilization of network resources. Usually, data rates of user sessions are time varied because of the dynamic behaviors of CR networks. It is expected that the occurrence of link outage should be avoided and incorporated into the routing design under conditions of increasingly crowded spectrum. This paper proposes an integral framework, which considers these two correlated schemes (resource reconfiguration and robust routing) simultaneously. For that, the resource reconfiguration scheme is developed for the efficient usage of network resources and aims at reducing the occupancy of licensed bands. The link outage, resulting from random session rate, is confined within an acceptable range by using strategy of virtual ‘network portfolio’. A robust optimization approach is proposed to guarantee reliable data transmission among possible interfering links. Both these two items (resource reconfiguration and robust routing) are formulated in a framework of cross‐layer optimization. The evolutionary process of CR network states is provided in simulations, where the results show that the joint design proposal can achieve the least interferences among different licensed users while realizing robust routing. Copyright © 2013 John Wiley & Sons, Ltd.

In this study, the minimum transmission power of each femtocell user in a two‐tier network, in which femtocells and macrocell use the spectrum simultaneously, is investigated. That is, the authors are aiming at finding the power that characterises robustness and energy‐efficient when taking the uncertain gains into consideration. To solve the optimisation problem with uncertainty, an opportunistic power control strategy for the femtocells is introduced. Since the channel gain from a femtocell user to the femtocell base station is changeable with the environment, it is difficult to track channel gains instantaneously. For the reason that the mean channel gains, from the femtocell users to the femtocell base stations are usually achievable, the information of mean and probability density function are used to model the optimisation problem. Moreover, outage probability for each femtocell user's throughput is originated instead of considering the total throughput of the system, because high total throughput may not ensure that of each femtocell user. The signal‐to‐interference‐plus‐noise ratio of the macrocell is ensured by setting appreciate interference temperature. A distributed algorithm is designed to calculate the powers of femtocell users. Numerical results show the effectiveness of the proposed outage probabilistic method and the distributed algorithm.

In wireless sensor networks, how to efficiently use the energy of the nodes while assigning global unique ID to each node is a challenging problem. By analyzing the communication cost of the clustering and topological features of a sensor network, we present a distributed scheme of Energy Efficient Clustering with Self-organized ID Assignment (EECSIA). In the context of EECSIA, a network first selects the nodes in the high-density areas as cluster heads, and then assigns an unique ID to each node based on local information. In addition, EECSIA periodically updates cluster heads according to the nodes' residual energy and density. The method is independent of time synchronization, and it does not rely on the nodes' geographic locations either. Simulation results show that the scheme performs well in terms of cluster scale, and number of nodes alive over rounds.