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In this paper, we address key challenges in Proof-of-Stake (PoS) blockchains, with a particular focus on Ethereum 2.0. We introduce an innovative mechanism that combines Tullock contests and signaling games to optimize weight assignments based on security deposits from heterogeneous nodes. While Tullock contests motivate participants to allocate resources for potential rewards, signaling games enable efficient information transfer, thereby enriching decision-making. This approach enhances network security, efficiency, and resilience by incentivizing resource investment and facilitating effective information exchange. Our framework significantly outperforms existing methods, achieving a 45.43% increase in blockchain utility and a 47.92% rise in node utility. Additionally, it yields marked improvements in user participation rates (26.89 − 32.21%) and service coverage (24 − 29.54%), and also proves to be resilient against attacks from selfish nodes.

In this paper, we investigate frequency shift keying (FSK) over ambient orthogonal frequency division multiplexed (OFDM) signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient OFDM spectrum either up or down in frequency to disjoint subsets of the subcarriers allowing the implementation of FSK. We exploit the guard bands and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques that suffer from asymmetric error probabilities and rely on signal-to-noise ratio (SNR) dependent detection thresholds, the proposed scheme has symmetric error probabilities and allows simple detection without the need for a threshold. We present both binary and four-ary schemes, and analyze the error performance of the optimal noncoherent detectors. For the binary scheme, we obtain exact expressions for the average probability of error, while for the four-ary scheme, a union bound is used to characterize the error performance. Single and multi-antenna receivers are considered, and their performance is analyzed. Finally, we present simulation results to corroborate our analysis and investigate the effects of multiple system parameters. The results show that the proposed scheme outperforms the baseline energy detection based schemes available in the literature in various scenarios by up to 5 dB.

Ultra-reliable low latency communication (URLLC) is an innovative service offered by fifth-generation (5G) wireless systems. URLLC enables various mission-critical applications by facilitating reliable and low-latency signal transmission. Apart from reliability and latency, ensuring the security of URLLC data transmission has been a prominent issue in recent years. URLLC uses finite blocklength signals to achieve the stringent reliability and latency requirement which eliminates the possibility of using conventional complex secret key-based cryptographic techniques. In this regard, lightweight security mechanisms like physical layer Security (PLS) have emerged as a powerful alternative to the complex cryptographic security techniques for URLLC by exploiting the randomness of the wireless channel. Therefore, this survey presents a comprehensive and in-depth review of the state-of-the-art PLS enhancements utilized to unleash secure URLLC in 5G and upcoming sixth-generation (6G) wireless networks. Moreover, the survey discusses the impact of system design parameters on the PLS of URLLC. It also incorporates a detailed overview of the recent advancements in ensuring PLS for mission-critical applications, URLLC enabling technologies, and Machine Learning (ML) based intelligent PLS schemes for URLLC. Then, for the first time, the survey introduces the extended service class of URLLC in 6G, i.e., Hyper Reliable Low Latency Communication (HRLLC), and provides an outlook on the future security aspects while identifying various promising new technologies that can provide secure HRLLC in 6G. Finally, the survey discusses several key challenges and identifies interesting future research directions for developing efficient PLS schemes for 5G URLLC and HRLLC service in 6G wireless networks.

Quantum computing is commonly considered one highly efficient computing method with the potential to revolutionize computation technology and solve problems that are currently unsolvable. However, due to the limitation of hardware equipment and an immature experimental base, quantum technology is still in its early stages and is far from achieving the expected performance, especially in solving large-scale complex problems. To break through these barriers, we propose a parallelized quantum annealing algorithm based on Lagrangian relaxation. The proposed algorithm divides the large-scale problem into several small problems and then employs multiple quantum computers to solve them. Our proposed approach overcomes the limited number of qubits and allows quantum computing to solve largescale optimization problems. Additionally, we incorporate a local search method to ensure this Lagrangian relaxation based quantum algorithm achieves an optimal solution. We use the proposed parallelized quantum annealing algorithm to solve optimal scheduling problems in network function virtualization networks. The problem is expressed in a linear optimization model that is NP-hard. Our proposed algorithm presents excellent time performance in solving this virtualized network functions scheduling problem, compared with the Lagrangian relaxation based classical algorithm.

In recent years, deep neural networks have made significant progress in the field of automated disease recognition for medical imaging. Although methods based on deep neural networks reported good results in automated disease classification, many of them lack output interpretability. Unlike methods that only aim at predicting if an input image contains disease or not, our work focuses additionally on revealing the disease evidence and offering interpretability of decision-making. We assume that an abnormal image is the additive composition of a hypothetical but synthesizable normal counterpart and a related disease effect map. Therefore we propose a disease decomposition network (DDN) that can explicitly locate, separate the disease evidence from an abnormal image and synthesize this hypothetical normal counterpart simultaneously. The proposed DDN has two branches: one is for translating the input image to a normal image, and the other is for locating the underlying abnormalities accurately if the input is abnormal. The novelty of our proposed DDN is that it not only provides highly accurate visual disease evidence maps, but also generates corresponding healthy images that are close to real normal chest X-rays (CXRs). From an interpretability and user interface perspective, it is insightful and helpful to provide a relevant reference (albeit hypothetical) for better understanding the difference between a normal and diseased CXR image. We evaluated our method on an extensive and well-established public chest X-ray dataset. The results demonstrate that the proposed DDN can translate abnormal CXR images to healthy CXRs while also generating disease evidence maps with good localization performance over other state-of-the-art methods.

The deployment of 5G has exposed capacity constraints in realizing the key vision of the Internet of Everything (IoE). Therefore, the researchers are exploring potentials of Digital Twin Network (DTN) in wireless networks. DTN is a novel technology to create virtual replicas of physical environment for testing, optimizing, and managing wireless networks. The integration of Artificial Intelligence (AI) and DTN appears to be a promising approach to address communication systems by providing an efficient environment for testing and improving AI models before deployment in real networks for effective network management, optimal resource allocation, and precise behavior prediction. Therefore, AI-enabled DTN in 6G represents a compelling avenue to address multifaceted challenges faced by wireless networks. In this comprehensive work, we offer a holistic survey that delves into the state-of-the-art approaches for AI-enabled DTNs in 6G. Firstly, we discuss the evolution of wireless networks and concept of AI-enabled DTN in 6G. Secondly, we discuss the role of AI-enabled DTN in 6G and driving advancements in fundamental components of 6G including resource allocation, caching, data offloading, and data security. Thirdly, we conduct a detailed discussion on key enabling technologies for realizing the capabilities of AI-enabled DTN in 6G. Fourthly, several applications of AI-enabled DTN in 6G are discussed for the practical relevance and significance in various industries such as smart cities, healthcare, and transportation etc. Finally, we provide lessons learned and highlight existing challenges and research directions to embark on further research efforts in the realm of AI-enabled DTN in 6G.

While reconfigurable intelligent surface (RIS) technology has been shown to provide numerous benefits to wireless systems, in the hands of an adversary such technology can also be used to disrupt communication links. This paper describes and analyzes an RIS-based attack on multi-antenna wireless systems that operate in time-division duplex mode under the assumption of channel reciprocity. In particular, we show how an RIS with a non-diagonal (ND) phase shift matrix (referred to here as an ND-RIS) can be deployed to maliciously break the channel reciprocity and hence degrade the downlink network performance. Such an attack is entirely passive and difficult to detect and counteract. We provide a theoretical analysis of the degradation in the ergodic sum rate that results when an arbitrary malicious ND-RIS is deployed and design an approach based on the genetic algorithm for optimizing the ND structure under partial knowledge of the available channel state information. Our simulation results validate the analysis and demonstrate that an ND-RIS channel reciprocity attack can dramatically reduce the downlink throughput.

Decentralized exchanges (DEXes) provide effective price discovery and fair trading while dealing with the drawbacks of centralized exchanges, e.g., lack of transaction transparency and exclusive control of user assets and transaction fees. However, many DEXes suffer from frontrunning and transaction reordering, which fundamentally flaw their design. In this paper, we present a novel incentive mechanism design for mitigating frontrunning and transaction reordering even if frontrunners pay high transaction fees in DEXes. We utilize a weighted counting sort algorithm to order transactions based on the users’ multi-dimensional private information (e.g., transaction delay and confidentiality). To elicit users’ private information, we consider a multi-dimensional contract-theoretic design based on the users’ willingness to share their private information. We show that the miner can always maximize its utility under the complete and incomplete information scenarios. We implement solutions to our multi-dimensional contract and sorting algorithm on a decentralized oracle network to create a decentralized system and design a web application to extensively evaluate the performance of our proposed incentive mechanism. We further show that ordering transactions based on users’ private information increases the miner’s utility by $78.42\%-84.57\%$ and reduces the users’ cost by 64.47% compared with the state-of-the-art fair sequencing services, automated arbitrage market maker, and miner extractable value auctions.

This article explores the role of blockchains in the development of Web 3.0 and the Metaverse. The success of these technologies is dependent on the utilization of decentralized systems like blockchains, which can store and validate data on identities and reputations and facilitate the exchange of virtual assets. Full nodes, which store the entire blockchain state and validate all transactions, are essential for the decentralization and reliability of the network. However, operating a full node is resource-intensive and can be expensive. To tackle this challenge, we propose an incentive mechanism that utilizes contract-theoretic methods to economically motivate users to support the sustainability and growth of the blockchain network. Our contract design addresses the problem of information asymmetry (e.g., users' revenue-generating capabilities and efforts) between users and the blockchain network. Additionally, we recommend providing diverse incentives based on the user's revenue-generating capabilities and efforts to assist the blockchain network in funding incentives. Our experimental results demonstrate that our proposed mechanism increases the blockchain network's utility by $48.48\%-54.52\%$ and reduces the users' cost by $38.46\%-62.5\%$ compared with the state-of-the-art implementations such as Celo, Vipnode, and Pocket Network.

Federated learning (FL) is an on-device distributed learning scheme that does not require training devices to transfer their data to a centralized facility. The goal of federated learning is to learn a global model over several iterations. It is challenging to claim ownership rights and commercialize the global model efficiently and transparently. Additionally, incentives need to be provided to ensure that devices participate in the FL process. In this paper, we propose a smart contract-based framework called FL-Incentivizer, which relies on custom smart contracts to maintain flow governance of the FL process in a transparent and immutable manner. FL-Incentivizer commercializes and tokenizes the global model using FL-NFT (FL Non-Fungible Token) based on the ERC-721 standard. FL-Incentivizer uses ERC-20 compliant FL-Tokens to incentivize devices participating in FL. We present the system design and operational sequence of the FL-Incentivizer. We provide implementation and deployment details, complete smart contract codes, and qualitative evaluation of the FL-Incentivizer. After implementing FL-Incentivizer for a global iteration of a Federated learning task, we showed the FL-NFT on OpenSea and an FL-Token for a learner on MetaMask. FL-NFTs can be traded on markets such as OpenSea like other NFTs. While FL-Tokens can be transferred in the same manner as other ERC-20-based tokens.

Metaverse is envisioned as the next-generation paradigm that links a virtual world to the physical world, providing immersive experience for human activities in the hypothetical environment with augmented reality (AR) and virtual reality (VR) technology. Recent development of wireless multiaccess edge computing (MEC) has accelerated the realization of this vision. In this paper, we consider location-dependent AR services in MEC-enabled Metaverse systems. In such a system, each user performs simultaneous localization and communication first to estimate its position and request AR contents from the MEC server. Due to limited computation and communication resources, the MEC server adaptively adjusts the resolution of AR contents. However, two challenges arise in such a system. First, the localization error will degrade the user’s quality-of-experience (QoE); and second, the coupling of communication and computation complicates the resource management. To address these issues, we first model the QoE taking the localization errors into consideration, and then propose a minimum QoE maximization scheme to achieve fairness among users by jointly optimizing the waveform at users, the transmit power at the base station (BS), and the resolution of transmitted videos. Simulation results verify the effectiveness of the proposed scheme in comparison with benchmark schemes.

Future generation of Electric Vehicles (EVs) equipped with modern technologies will impose a significant burden on computation and communication to the network due to the vast extension of onboard infotainment services. To overcome this challenge, multi-access edge computing (MEC) or Fog Computing can be employed. However, the massive adoption of novel infotainment services such as Augmented Reality, Virtual Reality-based services will make the MEC and Fog resources insufficient. To cope with this issue, we propose a system model with onboard computation offloading, where an EV can utilize its neighboring EVs resources that are not resource-constrained to enhance its computing capacity. Then, we propose to solve the problem of computational task offloading by jointly considering the communication, computation, and control in a mobile vehicular network. We formulate a mixed-integer non-linear problem (MINLP) to minimize the trade-off between latency and energy consumption subject to the network resources and the mobility of EVs. The formulated problem is solved via the block coordination descent (BCD) method. In such a way, we decompose the original MINLP problem into three subproblems which are resource block allocation (RBA), power control and interference management (PCP), and offload decision problem (ODP). We then alternatively obtain solutions of RBA and PCP via the duality theory, and the third sub-problem is solvable via the relaxation method and alternating direction Lagrangian multiplier method (ADMM). Numerical results reveal that the proposed solution BCD-based algorithm performs a fast convergence rate.

With the increasing number of electric vehicles, deploying fixed charging stations (FCSs) has been a widely adopted solution for providing charging services to EVs. However, the charging requirement of EVs being near overload FCSs and/or in areas of inadequate charging infrastructures such as highways and rural areas will surpass the capabilities of FCSs. To address this challenge, the realized peer-to-peer (P2P) energy trading between electric vehicles (EVs) and mobile charging stations (MCSs) can be utilized to relieve the overload on FCSs, leverage under-utilized energy resources among cities and achieve trading benefits. By deploying this energy trading model, EVs purchase available energy from MCSs instead of FCSs to fulfill their charging demands. However, such an energy trade requires an incentive mechanism to ensure fair trading and prevent personal gain, which motivates us to study the incentive mechanism design for the energy trading model. In this paper, first, we consider an energy trading system involving multiple MCSs and EVs. Then, we formulate the incentive mechanism between MCSs and EVs as an auction game, in which the MCSs are auctioneers and EVs are bidders. In the formulated problem, each EV secretly submits its bid to MCSs, and each MCS distributively decides the winners without the knowledge of other MCSs. To achieve this, we design a distributed action-based energy trading mechanism that ensures fairness of providing charging service, validity in trading price’s resource determination, and nothing incentivizing the EVs to cheat on payment decisions. The proposed energy trading scheme achieves certain critical properties, including truthfulness, individual rationality, budget balance, and computational efficiency for both buyers and sellers. Finally, we perform simulation experiments to verify the proposed scheme’s effectiveness compared with the baseline in the literature.

Artificial intelligence (AI) provides a promising and novel direction to design future time-varying wireless networks by leading to significantly superior performances compared to conventional methods. In addition, the advanced deployment of unmanned aerial vehicles (UAVs) has boosted extensive novel research results and industrial products in terms of aerial-ground networks. However, with the rapid development of mobile networks and growing requirements for low-latency services, the conventional centralized aerial-ground network has failed to meet the time-varying expectations of mobile users in the dynamic network environment. To cope with the problems, the marriage of the aerial-ground network and innovative AI techniques, i.e., distributed artificial intelligence enabled aerial-ground network (DAIAGN), is proposed in this article, which consists of three vital components: deep reinforcement learning enabled distributed information sharing, edge intelligence enabled distributed security management, and multi-agent reinforcement learning enabled distributed decision making. The functions of the three components are elaborated, and recent related advances are surveyed in detail. A specific case study is also provided with respect to multi-agent reinforcement learning enabled distributed decision making. Furthermore, key challenges and open issues are also discussed to provide some guidances for potential future directions.

Chest X-ray is a prevalent medical imaging modality for detecting lung diseases. The clinical analysis of X-ray images is usually conducted by radiologists, who represent valuable human resources. In practice, situations with insufficient radiologists to timely analyze large quantities of X-ray data are very common. Accordingly, developing an automated computer-aided lung disease classification system is beneficial to facilitate diagnoses. However, due to restrictions of costs and time, collecting large amounts of accurately labeled X-ray images to train a machine learning based diagnosis system is challenging. Another limitation is the class imbalances present in datasets. Facing these challenges, we investigate the effectiveness of using generative models, particularly generative adversarial networks (GANs), to synthesize new data to tackle the issue of data paucity and class imbalances. To this end, it should be noted that few existing works have studied the effect of generated image quality on the performance of different learning models, particularly in medical imaging. Therefore, the current paper represents one of the first comprehensive investigations into the impact of synthetic image generation on classifier performance, which is empirically elucidated by a comparative analysis of a simpler deep convolutional GAN to a more complex progressive GAN design. Another contribution of this paper is a multi-scale convolutional neural network (CNN) architecture, which can take advantage of image features at different scales for better learning from scratch. Altogether, to verify the robustness of using GANs to augment datasets, we compare various data augmentation approaches, when applied to different network architectures, including transfer learning, learning-from-scratch CNNs, state-of-the-art ResNet, EfficientNet, DenseNet, and the proposed multi-scale CNN. Specifically, testing on two publicly available datasets, the obtained results show that using finer images synthesized from GANs with the proposed multi-scale CNN achieved good classification performance, under a wide range of operating conditions.

Over the last few years, interference has been a major hurdle for successfully implementing various end-user applications in the fifth-generation (5G) of wireless networks. During this era, several communication protocols and standards have been developed and used by the community. However, interference persists, keeping given quality of service (QoS) provision to end-users for different 5G applications. To mitigate the issues mentioned above, in this paper, we present an in-depth survey of state-of-the-art non-orthogonal multiple access (NOMA) variants having power and code domains as the backbone for interference mitigation, resource allocations, and QoS management in the 5G environment. These are future smart communication and supported by device-to-device (D2D), cooperative communication (CC), multiple-input and multiple-output (MIMO), and heterogeneous networks (HetNets). From the existing literature, it has been observed that NOMA can resolve most of the issues in the existing proposals to provide contention-based grant-free transmissions between different devices. The key differences between the orthogonal multiple access (OMA) and NOMA in 5G are also discussed in detail. Moreover, several open issues and research challenges of NOMA-based applications are analyzed. Finally, a comparative analysis of different existing proposals is also discussed to provide deep insights to the readers.

As an extensive prospect in communication technology, multi-hop unmanned aerial vehicle (UAV) faces some challenges as well. How to ensure the availability of time slots in high dynamic scenarios and the rationality and robustness of power allocation in massive scenarios remains to be resolved. In this paper, we propose a robust mean field game (MFG) framework to manage time slot resources and power resources. On the one hand, this framework can predict the potential conflicts of time slots and avoid them in time. On the other hand, it can well adapt to the massive scenarios with complex interference, in order to realize power control. The simulation results show that the robust MFG-based resource management scheme can not only reduce the probability of packet collision and packet loss, but also improve communication energy efficiency (EE) with better robustness.

In this paper, we present a distributed privacy-preserving quadratic optimization algorithm to solve the Security Constrained Optimal Power Flow (SCOPF) problem in the smart grid. The SCOPF problem seeks the optimal dispatch subject to a set of postulated constraints under the normal and contingency conditions. However, due to the large problem size and real-time requirement, a fast and robust technique is required to solve this problem. Moreover, due to privacy concerns, it is important that the data remains confidential and processed on local computers. Therefore, a fully privacy-preserving algorithm is proposed which performs computation directly over the encrypted SCOPF problem. The SCOPF is decomposed into smaller subproblems corresponding to individual pre-contingency and post-contingency cases using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. Both algorithms are presented for solving the SCOPF problem in a privacy-preserving and distributed manner. Security analysis shows that our algorithm can preserve both system confidentiality and data privacy. Performance evaluations validate the correctness and effectiveness of the proposed algorithm.

Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.

The development of Long Term Evolution (LTE) enables wireless communication with high transmission rate, low latency, and wide coverage area. These outstanding features of LTE support the next generation of vehicle-to-everything (V2X) communication, which is named LTE-V. Among the various technologies in LTE-V, placing relay nodes on vehicles is a promising approach to save power and energy, and extend the transmission range. In this paper, we consider the virtual relay node selection problem. In the problem, a base station transmits data to a vehicle relay (also known as helper) who will further disseminate the received data to the vehicular subscribers nearby. The selection of the vehicle relay node is a challenging issue since the utility of the selection can only be known after this action has been made. Another challenge of this problem is that the traditional pure optimization is inapplicable due to the imperfect information available. Motivated by the recent success of Alpha Go Zero, we employ deep reinforcement learning (DRL) as a powerful tool for facing the above challenges and solving the problem without global information. We build a bridge between the traffic information and decision of relay node selection based on the reality that the utility of vehicle relay is highly correlated with the traffic density. In our work, deep convolutional neural networks are first applied to extract traffic patterns and then learn the traffic and network topology. Deep learning (DL) acts as a role to map features inside traffic and communication topology to the decisions. Then Q-Learning dynamically updates the utilities of decisions by trials in the environment. Finally, the overall rewards can be calculated to measure these decisions, and the Q function is also updated. Simulation results based on real traffic data validate that our proposed approach can achieve high utility performance.

Through machine learning, this paper changes the fundamental assumption of the traditional medium access control (MAC) layer design. It obtains the capability of retrieving the information even the packets collide by training a deep neural network offline with the historical radio frequency (RF) traces and inferring the STAs involved collisions online in near-real-time. Specifically, we propose a MAC protocol based on intelligent spectrum learning for the future wireless local area networks (WLANs), called SL-MAC. In the proposed MAC, an access point (AP) is installed with a pre-trained convolutional neural network (CNN) model to identify the stations (STAs) involved in the collisions. In contrast to the conventional contention-based random medium access methods, e.g., IEEE 802.11 distributed coordination function (DCF), the proposed SL-MAC protocol seeks to schedule data transmissions from the STAs suffering from the collisions. To achieve this goal, we develop a two-step offline training algorithm enabling the AP to sense the spectrum with the aid of the CNN. In particular, on receiving the overlapped signal(s), the AP firstly predicts the number of STAs involving collisions and then further identifies the STAs' ID. Furthermore, we analyze the upper bound of throughput gain brought by the CNN predictor and investigate the impact of the inference error on the achieved throughput. Extensive simulations show the superiority of the proposed SL-MAC and allow us to gain insights on the trade-off between performance gain and the inference accuracy.

Fifth-generation (5G) and beyond networks are envisioned to provide multi-services with diverse specifications. Network slicing is identified as a key enabling technology to enable 5G networks with multi-services. Network slicing allows a transition from a network-as-an-infrastructure setup to a network-as-a-service to enable numerous 5G smart services with diverse requirements. Although several surveys and tutorials have discussed network slicing in detail, there is no comprehensive study discussing the taxonomy and requirements of network slicing. In this paper, we present and investigate key recent advances of network slicing towards enabling several Internet of Things (IoT) smart applications. A taxonomy is devised for network slicing using different parameters: key design principles, enablers, slicing resources levels, service function chaining schemes, physical infrastructures, and security. Furthermore, we discuss key requirements for network slicing to enable smart services. Finally, we present several open research challenges along with possible guidelines for network slicing.

An efficient content caching policy at the edge of the mobile cellular network can improve the quality of services of the mobile users and reduces network congestion at the backhaul. On the other hand, the wireless network virtualization emerges as a cutting-edge technique to address the limited network capacity problem due to the exponential growth of the mobile data traffic. In addition, the wireless network virtualization can bring huge benefits such as reducing the capital expenditure (CAPEX) and the operational expenditure (OPEX) as well as improving the network capacity. In this regard, one of the key requirements to recognize the benefits of the above mentioned two technologies is to have an applicable resource allocation framework, that enables the deployment of the content caching schemes in the virtualized wireless network. In this study, we investigate a novel joint radio resource allocation and content caching problem to efficiently utilize the radio resource blocks, the transmit power, and the available cache storage at the base stations (BSs). The goal of the formulated problem presented in this paper focuses on minimizing the delays experienced by the end mobile users of mobile virtual network operators (MVNOs). We show that the formulated problem is a non-convex, mixed integer non-linear problem (MINLP), which is NP-hard and simply intractable. Therefore, we deploy the block upper-bound minimization (BSUM) algorithm to solve the formulated problem. Numerical results show that our method outperforms the existing baseline resource allocation schemes, with up to 19% performance gain in terms of network delay.

Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where “fine” local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches.

Recently, drone small cells (DSCs) has been brought into significant focus, which is the one key enabler for potentially facilitating terrestrial wireless communication systems. Meanwhile, in ultra-dense unmanned networks, artificial intelligence (AI) has been a useful and efficient tool for control and management of the multi-agents. This paper investigates a downlink interference control problem in ultra-dense unmanned networks with AI-aided approach, that each DSC can adjust its altitude to increase the data-rate. This problem is formulated as a mean field game (MFG) framework, an AI-aided method to make decisions. In this framework, each DSC controls its velocity to minimize the cost over a period, where the cost function is composed by the data-rate and height adjusting consumption. Meanwhile, in this model, we adopt the mean-field approximation (MFA) approach to derive the interference introduced from a large number of DSCs. Besides, the control strategy is described and explained by using the related Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, respectively. Thus, a finite difference algorithm is proposed to solve the coupled partial differential equations, which can obtain the optimal altitude control strategy. The algorithm outputs show the optimal behaviors of DSCs in different environment scenarios. In additon, the simulation results verify that the proposed control strategy has better average signal to interference plus noise ratio (SINR) compared with the baseline method.

Multi-access edge computing (MEC) enables mobile users to offload their computation tasks to the server located at the edge of the cellular network. Thereby, MEC prolongs the battery lifespan of mobile devices and significantly enhances their computation capacities. However, a significant challenge is to offload the computation tasks to the MEC server in an energy-efficient manner. Meanwhile, in the fifth-generation (5G) networks, mobile users with different service requirements classified as enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) users will coexist in the current cellular network. Therefore, it is important to appropriately multiplex these users in the cellular network. In this work, we address the issues of these two promising technologies together. Firstly, we formulate an energy-efficient task offloading, and scheduling of eMBB and URLLC users as a mixed-integer non-linear problem. Then, we decompose the problem into multiple sub-problems in order to transform into convex form and alternately solve them until converging to the desired solutions by using the block coordinate descent (BCD) algorithm. Finally, we demonstrate numerical results to prove the superior effectiveness in the performance of our proposed algorithm over classical existing schemes.

This article proposes a resource allocation for multi carrier (MC) multiple input single output (MISO) non-orthogonal multiple access (NOMA) enabling simultaneous wireless information and power transfer (SWIPT) where a separated architecture (SA)-based heterogeneous network (HetNet) is considered. More precisely, we focus on designing MC MISO-NOMA scheme in HetNets, where each base stations (BSs) serves multiple information decoding (ID) and energy harvesting (EH) users with a non-linear energy harvesting model which are equipped with multi antennas. For this configuration, we propose a joint subcarrier assignment, beamforming design, and energy beamforming to maximize the total rate of the network. Moreover, we take into account the condition of cross-layer interference constraints to coordinate interference, satisfying a minimum data rate requirement, fulfilling a minimum harvested energy, and respecting the maximum allowed power transfer constraints. We also consider successive interference cancellation (SIC) decoding order to detect the signal of each user due to coupling between SIC order, beamforming design, and subcarrier allocation. As a result of interference in the rate function, our optimization problem is non-convex and thus challenging to solve. Subsequently, first we adopt semi-definite relaxation (SDR) for beamforming design and then an efficient algorithm via the majorization minimization (MM) approach based on the difference of convex (DC) programming is proposed to obtain a locally optimal solution for the original problem. Simulation results reveal the superiority of our proposed scheme as compared to other existing works addressed in the literature.

Leveraging the cognitive Internet of things (C-IoT), emerging computing technologies, and machine learning schemes for industries can assist in streamlining manufacturing processes, revolutionizing operational analytics, and maintaining factory efficiency. However, further adoption of centralized machine learning in industries seems to be restricted due to data privacy issues. Federated learning has the potential to bring about predictive features in industrial systems without leaking private information. However, its implementation involves key challenges including resource optimization, robustness, and security. In this article, we propose a novel dispersed federated learning (DFL) framework to provide resource optimization, whereby distributed fashion of learning offers robustness. We formulate an integer linear optimization problem to minimize the overall federated learning cost for the DFL framework. To solve the formulated problem, first, we decompose it into two sub-problems: association and resource allocation problem. Second, we relax the association and resource allocation sub-problems to make them convex optimization problems. Later, we use the rounding technique to obtain binary association and resource allocation variables. Our proposed algorithm works in an iterative manner by fixing one problem variable (for example, association) and compute the other (for example, resource allocation). The iterative algorithm continues until convergence of the formulated cost optimization problem. Furthermore, we compare the proposed DFL with two schemes; namely, random resource allocation and random association. Numerical results show the superiority of the proposed DFL scheme.

Game theory is a very profound study on distributed decision-making behavior and has been extensively developed by many scholars. However, many existing works rely on certain strict assumptions such as knowing the opponent's private behaviors, which might not be practical. In this work, we focused on two Nobel winning concepts, the Nash equilibrium, and the correlated equilibrium. We proposed a policy-based deep reinforcement learning model which instead of just learning the regions for corresponding strategies and actions, it learns why and how the rational opponent plays. With our proposed policy-based deep reinforcement learning model, we successfully reached the correlated equilibrium which maximizes the utility for each player. Depending on the scenario, the equilibrium can reach outside of the Nash equilibrium convex hull to achieve higher utility for the players, while the traditional non-regret algorithms cannot. In addition, we also proposed a mathematical model to inverse the calculation of the correlated equilibrium probability to estimate the rational opponent player's payoff. Through simulations, with limited interaction among the players, we showed that our proposed method can achieve the optimal correlated equilibrium where each player gains an equal or higher utility than the Nash equilibrium.

Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.

Recently, large-scale wireless seismometer array (WSA) have played an increasingly important role in oil/gas exploration industry and seismology research. However, due to limited wireless channel bandwidth, transmission latency and high power consumption caused by transmission conflicts in battery-powered seismometers, real-time large-scale WSA system remains unsolved. In this article, we introduce a LoRaWAN-based WSA system with high precise time slot deriving from a synchronization platform composed of GPS timing module and temperature compensated crystal oscillator (TCXO) clock counter. Using the global sharing time slot, we propose an Equal Air Time and CuckooHash (EAT&CH) algorithm for the WSA to resolve channel congestion and delays when multiple seismometers upload data simultaneously. In EAT&CH, an equal air-time spreading factor (SF) allocation method based on the coarse/fine-grained theory is employed to reduce the collision probability of data transmission. To improve the scalability of the WSA in terms of reducing both collision probability and data delay, a different carrier frequency (CF) channel with the same SF allocation plan depending on CuckooHash is designed to reduce the network latency. Simulation results show that the proposed algorithm achieves nearly 100% data extraction rate of the WSA with less than two times the delay, compared with the existing Adaptive Data Rate (ADR) mechanism.

In order to realize an intelligent transportation system (ITS) which will provide smooth urban traffic, autonomous driving, accurate route navigation, etc., enormous computations need to be migrated from cloud centers to edge nodes, especially for the services requiring stringent latency. In addition to base stations and road side units (RSUs), vehicles can be alteratively considered as a kind of computation resources. In this article, a hierarchical vehicular-based architecture which consists of cloud centers and vehicles is investigated. Computation offloading performance in the hierarchical architecture is also studied. In specific, the main components in vehicular networks and their characteristics on communication and computations are presented firstly. Several communication techniques that are essential in enabling computation offloading among these components are then discussed. Secondly, a hierarchical vehicular-based architecture, which integrates the main components, is constructed. Thirdly, a case study on computation offloading in the proposed architecture is conducted. In the concerned scenario, the computation offloading problem is modelled as a multi-dimensional multiple knapsack problem (MMKP). Two algorithms are investigated, among which, the first algorithm is a greedy heuristic method providing a sub-optimal solution with a low computational complexity. The second algorithm is a modified branch and bound (B&B) method, which can obtain the best solution with a high computational complexity. Numerical results are also presented to verify the performance of the two algorithms. It can be demonstrated that the proposed architecture can migrate more computations from cloud centers to vehicular nodes, when the computations require more communication resources.

COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this article, an optimization problem is formulated that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we solve the formulated problem by applying a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Furthermore, the sustainability of the lockdown policy is interpreted with the help of our proposed game-theoretic incentive model for maintaining social distancing where there exists a Nash equilibrium. Finally, we perform an extensive numerical analysis that shows the effectiveness of the proposed approach in terms of achieving the desired social-distancing to prevent the outbreak of the COVID-19 in a noncooperative environment. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

Encrypting information with pre-agreed key is an effective method to ensure information security. With the increasing requirement for key rate, this paper investigates the problem of simultaneously generating a secret key (SK) and a private key (PK) over three legitimate terminals with a trusted helper via public discussion that a passive eavesdropper completely access to. The legitimate terminals and the helper observe the correlated source sequences from the outputs of discrete memoryless source (DMS). The legitimate terminals wish to generate an SK that is concealed from the eavesdropper, and two designated legitimate terminals want to generate a PK that needs to be additionally keep secret from the third legitimate terminal. Under this model, the SK-PK capacity region is fully characterized, and the source coding scheme is further provided to show that the region is achievable. The security performance of the generated keys is analyzed in terms of key leakage rate.

Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.

In this paper, we consider a cloud radio access network-based system consisting of one network operator (NO) and several content providers (CPs). The NO owns a cloud cache and provides caching as a service for CPs, who provide contents to users. While the NO wishes to motivate CPs to rent its cache and maximize its profit, CPs want to optimize the service performance for users and their renting utilities. Due to the time separation between cache allocation and user association problems, we model the interactions between the NO and CPs as a hierarchical game, i.e., a cache renting scheme between the NO and CPs in the cache allocation problem and the willingness of CPs in the user association problem. In the cache allocation problem, we propose a contract theory-based incentive mechanism in which the NO designs and offers an optimal contract to various types of CPs. We then formulate the user association problem as a many-to-many matching game with externalities. To solve this matching game, we propose a matching algorithm that converges to a two-sided exchange stable matching with low complexity. The simulation results demonstrate that this proposed approach is beneficial to the NO's profit and incentivize the CP to rent the cache with truthful private information. In addition, the system performance of the proposed approach in terms of the total data rate-delay tradeoff outperforms than the benchmarks.

The network function virtualization (NFV) enables the virtual network functions (VNFs) to run as software components upon a virtualization system hosted in a cloud. Recently, due to its high flexibility and elasticity in terms of network services and functions deploying, NFV has been considered as one of the key enabling techniques of the fifth-generation (5G) wireless systems. Moreover, the creation of the core network functions is the key to the implementation of 5G. Currently, there are extensive studies researching into how to deploy cooperatively VNFs over a federated cloud for multiple cloud providers (CPs) to create the 5G Core. These studies have a critical assumption that each CP participating in the cooperation is fully informed of all the information about other CPs. However, the assumption may not hold in practice. Since CPs adopt diverse virtualization technologies (e.g., XEN, KVM, or containers), the cost to create an instance of one VNF for each CP may be different. Moreover, the cost information is private and will not be shared with each other. Consequently, it is difficult for each CP to determine whether to cooperate with others and the way of cooperation. Therefore, in this paper, we utilize the Bayesian coalition formation game (BCFG) to tackle this challenging situation with the unknown information about CPs and formulate the optimal coalition that can deploy cooperatively each VNFs of 5G Core. Specifically, we address the optimal number of VNF instances to instantiate, the number of virtual resources for each instance, and the placement decision in a specific CP over a federated cloud. We propose a BCFG-based non-myopic and full negotiation approach for the creation of the virtual 5G Core network functions. Simulation results demonstrate the comprehensive performance of the proposed method in terms of convergence properties and the profits that each participating CP can obtain.

With the development of communication infrastructure in smart grids, cyber security reinforcement has become one of the most challenging issues for power system operators. In this paper, an attacker is considered a participant in the virtual bidding procedure in the day-ahead (DA) and real-time (RT) electricity markets to maximize its profit. The cyber attacker attempts to identify the optimal power system measurements to attack along with the false data injected into measurement devices. Towards the maximum profit, the attacker needs to specify the relation between manipulated meters, virtual power traded in the markets, and electricity prices. Meanwhile, to avoid being detected by the system operator, the attacker considers the physical power system constraints existing in the DA and RT markets. Then, a bi-level optimization model is presented which combines the real electricity market state variables with the attacker decision-variables. Using the mathematical problem with equilibrium constraints, the presented bi-level model is converted into a single level optimization problem and the optimal decision variables for the attacker are obtained. Finally, simulation results are provided to demonstrate the performance of the attacker, which also provides insights for security improvement.

Heterogeneous Internet of Things (IoT) and multi-access mobile edge computing (MA-MEC) are believed as supporting technologies for building a smart city. The advancement and flourish of IoT are facilitating the entry of human society into the Internet of Everything era, which lay the foundation of the smart city. To address the conflict between computation capability and low-cost mobile devices in IoT, the MA-MEC is available for supporting the resource-limited and computation-sensitive services and applications by computation offloading and distributed content delivery/caching. However, deploying cloud computing capability within the radio access network may face serious security threats, which stem from not only the existing technologies and networks but also the MA-MEC-based IoT itself. Therefore, in this paper, the solutions to address the security threats are investigated from physical layer perspectives, since physical layer security technologies have the advantages of achieving perfect secrecy, low-computational complexity, and resource consumption, and good adaptation for channel changes. Specifically, we investigate the secure wiretap coding, resource allocation, signal processing, and multi-node cooperation, along with physical layer key generation and authentication, to cope with the emerging security challenges. Finally, the paper is concluded with some possible future research directions.

In this paper, we demonstrate the impact of channel aging on the spectral- and energy-efficiency performance of the multi-pair two-way massive MIMO relay (MTW-MMR) systems, where massive antennas are deployed at the relay and the amplify-and-forward (AF) protocol is operated. By assuming the maximum-ratio combining/maximum-ratio transmission (MRC/MRT) or zero-forcing reception/ zero-forcing transmission (ZFR/ZFT) relaying processing, we analyze the spectral efficiency (SE) and energy efficiency (EE) of the MTW-MMR systems experiencing both imperfect channel estimation and channel aging based on the four proposed power-scaling schemes. Furthermore, we compare the performance of the full-duplex (FD)-assisted MTW-MMR systems with that of the half-duplex (HD)-assisted MTW-MMR systems, showing that the FD mode outperforms the HD mode with the interference level not exceeding -15 dB. Our studies show that channel aging may significantly degrade the achievable performance in terms of SE and EE. However, the inter-pair interference caused by the other user pairs and the self-interference can be effectively mitigated by the MRC/MRT processing or ZFR/ZFT processing at the relay.

The fifth generation mobile networks (5G) envision to interconnect the massive number of devices with a wide range of characteristics and demands for 2020 and beyond. With more radio-frequency (RF) bands to support multiple frequency transmission, separate antennas, and RF chips may be required for widely separated frequency bands. The development of cognitive radio systems, in which a wireless transceiver automatically adapts its communication parameters to network and user demands, is therefore crucial to the successful roll-out of the 5G. This paper introduces an innovative CR system that performs the spectrum sensing for the joint energy harvesting and channel access. A prototype test bed is employed to detect and convert RF signals from different bands to a DC voltage for powering a sensor board that communicates over another channel to an access point. The system's objective is to use the harvested energy for concurrent data transmission. We model the selection of channels to maximize this objective as a multi-armed bandit (MAB) problem. Depending on the type of the MAB problem, three spectrum sensing strategies are developed for joint energy harvesting and channel access. The first one, applicable for stochastic MAB, conceptualizes some formulations of the exploration/exploitation balancing technique of the available frequency bands. The second strategy is an opportunistic sensing framework for the Markovian MAB when the state of the underlying Markov process is partially observed. The third strategy is based on the Gittins index allocation framework for the fully observed Markovian MAB. The simulation results show that lower regrets can be obtained with more information on the underlying Markov process of the MAB.

Intelligent transportation systems (ITSs), which can fulfill people's increasing requirements on mobility, have arisen as a popular research area. In order to implement the ITSs, a large amount of data needs to be executed within requested time. Due to the remote locations, cloud centers are no longer sufficient in data off-loading. A heterogeneous vehicular network, which consists of three kinds of computation resources, i.e., a cloud center, an edge node, and multiple vehicles, is, therefore, constructed to solve the aforementioned issues. First, considering the heterogeneity of each computation resource, the delay bound can be derived based on the martingale theory. Especially, data off-loading to the cloud center or to the edge node is assisted by road side units (RSUs). The communication link from the source node to the cloud center or to the edge node is modeled as a two-hop one. By utilizing the min-plus algebra, the two-hop link can be abstracted and analyzed in an equivalent single system further. Then, given a requested delay time, the maximum off-loading capacity of each resource can be obtained from the derived delay bound. When the tasks are smaller than the network available off-loading capacity, an optimal task allocation problem can, therefore, be constructed to minimize the overall delay violation probability. The numerical results are presented to show the performance of the proposed task allocation scheme in the heterogeneous vehicular networks. In addition, it can be verified that the proposed optimal task allocation scheme performs better than the benchmarks in the literature.

Hybrid precoding is an important issue in millimeter wave (mmWave) massive multi-input and multi-output (MIMO) system. Specially, energy-saving hybrid precoding architectures and efficient hybrid precoding schemes provide ideas for solving this issue. In this paper, we propose a hybrid precoding/combining architecture that is low-cost and easy to implement. Specifically, a hybrid precoding architecture is realized by the lens sub-arrays at the base station (BS). Moreover, a hybrid combining architecture applies the low-resolution analog-to-digital converters (ADCs) at the front end of the radio frequency (RF) chains at the receiving terminal. Based on the hybrid precoding/combining architecture, the hybrid precoder and combiner are jointly optimized to maximize the spectrum efficiency (SE) in the downlink systems, which is a combinatorial optimization problem due to hardware constraints. The cross-entropy (CE) approach in machine learning (ML) is a simple way to solve the combinatorial optimization problem benefiting from its adaptive update procedure. Therefore, we propose an adaptive hybrid precoder/combiner design scheme (AHDS), in which a hybrid precoding algorithm based on the improved CE (ICE) inspired by ML is adopted to design the optimal hybrid precoder, and an approximate optimization method (AOM) is suggested when designing the hybrid combiner. In general, compared with the existing hybrid design schemes, the proposed AHDS is demonstrated to have significant advantage in SE with low computational complexity.

Fog computing is an epitome to enable provisioning resources and services beyond the cloud, at the edge of the network, and nearer to end devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. However, the mobility of end users and/or fog nodes brings a major dilemma in the implementation of real-life scenarios. Therefore, we deliver the state-of-the-art research about mobility in fog computing. By identifying the mobility problems, requirements, and features of different proposals, we discover the open problems from subsisting studies and summarize the advantages of mobility for readers. This will help the researchers and developers avoid the current misapprehensions and capture the real-life scenarios, such as business, government, and education institutions. In spite of the extensive state-of-the-art research work, we also present the diverse mobility factors to investigate the correlation between dynamic nodes with fog nodes in order to accomplish better successful tasks. We conceive that the investigations of mobility factors in the fog computing will furnish the novel perspective on not only dynamic connections but also network dynamics. To revolutionize the follow-up research, we distinguish and foreground futurity directions concerning real-life scenarios of people and vehicles in a dynamic fog environment.

The smart field has drawn much attention for the field business functions to be tied into a smoothly operating system. However, its operation decisions still cannot meet the ever-increasing multiple data source requirements. This paper proposes a novel smart field cyber-physical system (SF-CPS), which comprises some sensors transmitting real-time sensed information between the marine terminals and the petroleum refinery, and an asset optimization-based decision maker using the pumping schedule to determine the best configuration. From a unique functional unit perspective, the resource allocation of volumes and qualities is implemented with the Dinkelbach method to address this enterprise-wide optimization. Taking advantage of the unloading flows at a low cost, we settle the state variables to the steady process of refinery planning, and then, the next multi-operations sequence follows the tailored outer approximation approach for decomposition to achieve high-cost efficiency. Moreover, the two-phase stochastic scheduling decisions coupled with inventory levels hosted on the SF-CPS platform can cope well with uncertainty in the process between the oil supply and maritime conditions. The experimental results validate the proposed techniques for typical oil and gas resources.

Device-to-device (D2D) communication is one of the emerging paradigms in 5G networks to facilitate ever-increasing demands for local area services. For content delivery, direct transmission via D2D communications among proximity nodes and cooperative content downloading from base stations can enhance system capacity, which on the other hand cause interference to regular cellular networks due to spectrum sharing. Random linear network coding is capable of improving robustness to the varying channel state with severe interference. It also increases collaborative efficiency between users for content sharing. This paper investigates cooperative relaying scheme (RNCC) for cooperative D2D communications, which is aided by the random linear network coding. This is achieved by allocating the downlink resource among multiple cellular users, cooperative relays, and D2D pairs. First a binary integer linear programming-based resource allocation problem is modeled by introducing D2D pairs' clustering concept, which is an NP-hard problem. For the optimal solution, we leverage the branch-and-cut algorithm. While for large scale networks, we contributed a distributed QoS-aware greedy algorithm, which yields close to optimal solution. Our proposed schemes are evaluated via the extensive simulations considering practical scenarios, and the system sum rate results demonstrate an enhancement of ~20% performance in comparison with several other practical strategies in literature, which validates our schemes' effectiveness.

In the Internet of Things (IoT), the malicious node with sensorial capability can smartly launch jamming attacks only when it detects the legitimate transmission, known as the reactive jamming. Compared with the conventional constant jamming model, the reactive nature enables highly efficient and long-lasting attacks with limited energy supply, which thus presents a significant threat upon the secure communications in IoT. In this paper, we investigate the anti-reactive-jamming transmission strategy for IoT by exploiting the inherent weakness of the jammer. Specifically, since the reactive jamming depends on the detection of the legitimate transmission, the legitimate user can elaborately determine its transmit power to tradeoff between its achieved signal-to-interference-plus-noise ratio and the probability to be detected and jammed by its adversary. Meanwhile, the jammer can smartly allocate the jamming power based on its observation of the legitimate transmission. We formulate the rivalry between the legitimate user and jammer as a hierarchical game, where the legitimate user takes action first as the leader while the jammer is the follower. We analyze the game equilibrium for both single-channel and multi-channel scenarios and derive the optimal transmission and jamming strategies for the legitimate user and jammer, respectively. Finally, we present the numerical results to evaluate the performance of the secure IoT communications under our proposal.

In ultra-dense small cell networks, interference mitigation is very important due to severe interference. Interference dynamics caused by time-varying environment should be aware and characterized when an interference-aware power control policy is designed to mitigate interference. Meanwhile, interference perception should not be naturally assumed to have complete information with certainty. Generally, it is known that a generic player will react to all the players actions and states in a power control game, which involves huge interference-related information exchange with dynamics and uncertainties. Therefore, to reduce requirements of complete information, we formulate a robust power control mean field game taking the uncertainties of both state dynamics and cost functions into consideration. To achieve the robust power control, we regard the power control problem as a game with players whose individual states are combined by a disturbance term and a Brownian motion. We derive the robust Fokker-Planck-Kolmogorov and Hamilton-Jacobi-Bellman equations, and based on which we propose the robust interference-aware power control algorithm. Simulation results demonstrate the improved performance and the robustness of the proposed algorithm.

Smart grids, which have the ability to detect and monitor necessary system parameters and user behaviors, have gradually become the development trend for future power networks. With increasing scale and access of new energy source, SGs become more unstable and vulnerable when changes of network topology occur abruptly. Among different kinds of abrupt changes, unexpected line outages may give rise to significant potential damages to SGs and lead to the very bad user experience. Thus, accurate and rapid detection of line outages turn into one of the important tasks and challenges in SGs. By utilizing the sparse property of line outages, compressive sensing (CS)-based line outage detection schemes have been proposed recently, in which the phase information collected from phasor measurement units is also fully utilized. In this paper, a novel block-wise CS (BW-CS)-based multiple line outage detection scheme is proposed for SGs. Firstly, by exploiting the sparsity of line outages and topology structure of the power grid, a novel reactance model is introduced, which makes the block-wise CS algorithm can be adopted. Then, by modifying one step of the conventional CS algorithm with the redefined element selection, criterion is modified to improve the recovery accuracy. Finally, the proposed scheme is extended into a three-phase power system. The simulation results show that our proposed BW-CS method can achieve superior precision with the similar complexity compared to the traditional methods.