Assessing the trustworthiness of network elements, including devices, communication links, and end-to-end paths, is critical for building secure and reliable networks. However, the heterogeneity of these components and the dynamic nature of their security states make trust assessment in large-scale networks highly challenging. This paper presents a configurable end-to-end network trustworthiness assessment scheme featuring a hierarchical, life-cycle-based architecture for evaluating trust from initial network deployment through long-term operation to retirement. In the proposed scheme, trustworthiness is assessed at three levels: device, link, and path. At the device level, a Dempster-Shafer evidence theory-based model integrates multi-dimensional trust attributes to quantify each device’s trustworthiness. At the link level, a dynamic multi-attribute model combines static and dynamic trust attributes and employs a clustering algorithm to calculate link trust scores. At the path level, a distributed fusion-based model uses fuzzy logic to evaluate end-to-end path trust by aggregating the trust levels of constituent devices and links. Simulation results demonstrate that the proposed scheme significantly improves both the accuracy and efficiency of trustworthiness assessment in large-scale networks.

To advance the development of maritime intelligent transportation systems (MITS), underwater wireless sensor networks (UWSNs), composed of numerous sensor nodes, have been widely deployed for underwater information perception. However, UWSNs face critical challenges in achieving cost-effective and timely data collection due to their large-scale deployment and stringent data timeliness requirements. To address this challenge, this paper proposes an efficient data collection scheme for UWSNs through the collaboration between uncrewed surface vehicles (USVs) and autonomous underwater vehicles (AUVs). Specifically, we first introduce a cooperative framework where AUVs select appropriate USVs to form USV-AUV clusters. Within each cluster, AUVs are responsible for sensing data collection, while USVs act as relay nodes, moving toward the destination (e.g., data center). We then devise an evolutionary game-theoretic cluster forming mechanism, deriving evolutionarily stable strategies (ESS) through replicator dynamics analysis, which guarantees a provable Nash equilibrium. Next, we present a hierarchical optimization method that models the interaction between UWSNs and the cluster as a two-agent Markov decision process, where a dual-agent Q-learning algorithm is designed to jointly optimize the decisions of both entities. Finally, extensive simulations demonstrate that the proposed scheme outperforms conventional methods in improving the efficiency of sensing data collection for UWSNs.

Generative artificial intelligence (GAI), e.g., diffusion model is recognized as a promising paradigm for enhancing intelligent transportation systems in vehicular networks. However, the existing implementation of GAI in vehicular networks is limited due to the massive data requirements of GAI and the considerable resources for model training, particularly in distributed vehicular network environments. Federated learning (FL) offers a promising solution by enabling distributed collaborative training for GAI. Therefore, in this paper we present an FL-empowered diffusion model training scheme for vehicular networks. Specifically, first, a novel utility evaluation model based on local model training accuracy is designed to assess the contribution of each vehicle's local model. The interactions between the edge computing servers and vehicles are modeled using a Stackelberg game, while a non-cooperative game determines the optimal strategy among vehicles. To account for the heterogeneity of vehicles and the uncertainty of associated risks, we incorporate prospect theory (PT) to represent subjective utility. Afterward, a backward induction mechanism is devised to determine the Stackelberg equilibrium for deriving the optimal decisions of edge computing servers and vehicles. Finally, simulations are conducted to illustrate that the proposed scheme significantly improves the sum utility rate in comparison to other baseline schemes.

With the advancement of communication technologies within the smart grid Internet of Things (SGIoTs), virtual power plants (VPPs), which aggregate distributed energy resources, are increasingly encouraged to participate in energy provisioning services. However, the inherent variability in energy supplies among different VPPs, combined with the dynamic and heterogeneous nature of energy demands, poses significant challenges for efficient energy provisioning in a competitive environment involving multiple VPPs and numerous energy users. To address these challenges, this paper proposes a cooperative energy provisioning scheme based on a coalition-Stackelberg game to enhance demand response management for VPPs in SGIoTs. Firstly, a coalition formation game model is employed to create VPP clusters, enhancing both the reliability of energy supply and the profitability of individual VPPs. Subsequently, a multi-leader multi-follower (MLMF) Stackelberg game is utilized to model the competitive interactions between VPP coalitions and energy users, aiming to maximize the respective utilities of both parties. The existence of the Stackelberg equilibrium is rigorously analyzed and derived through an alternating direction method of multipliers (ADMM)-based energy requirement decision algorithm and an asynchronous gradient descent iteration-based pricing algorithm. These methods enable the determination of optimal electricity prices for VPP coalitions and optimal energy requirement decisions for individual users. Finally, extensive simulations are conducted to demonstrate that the proposed scheme significantly enhances the utilities of both VPPs and energy users compared to conventional approaches.

Recently, quantum federated learning (QFL) is advocated to leverage the robust computing power of quantum edge computing devices (QECDs) within unmanned aerial vehicle (UAV)-assisted wireless networks, to enhance the efficiency of distributed learning. However, the presence of malicious and selfish behaviors among some QECDs poses significant challenges for QFL model training to achieve high accuracy and rapid convergence. To tackle this issue, we introduce a trustenhanced incentive scheme for QFL in the UAV-assisted wireless networks. Specifically, a QECD-empowered QFL framework is first presented in the UAV-assisted wireless networks, where the QECDs independently train local models with their private data by using the quantum computing capabilities, while UAVs aggregate these trained local models to update the global model. Then, to ensure security and eliminate malicious participants, we devise a Bayesian inference-based trust assessment mechanism to select honest QECDs for local model training. Furthermore, we design a Stackelberg game-based incentive mechanism to incentivize QECDs to cooperatively provide high-quality training services. Afterwards, through game analysis using the backward induction method, we prove the existence of a Stackelberg equilibrium. The optimal payment strategies of the UAVs are obtained using the deep Q-learning network (DQN) algorithm in dynamic networks, and the optimal training contribution strategy of each QECD is derived using the convex optimization method. Finally, extensive simulations demonstrate that the proposed scheme can significantly enhance the accuracy and training speed of QFL in UAV-assisted wireless networks.

Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.

The trustworthiness assessments of devices (e.g., routers, switches, and controllers, etc.) are essential to build a trusted network. However, due to the diversity of network devices and time-varying natures of security states, it is challenging to effectively assess the trustworthiness of devices in large-scale networks. To address this challenge, in this paper, we propose a device trustworthiness assessment scheme based on the Dempster-Shafer (D-S) evidence theory. Specifically, we first devise a whole life-cycle device assessment architecture to assess the trustworthiness of devices from startup, long-term operation to retirement. We then present a user-editable device assessment model to establish the trustworthiness attribute sets and trustworthiness level division strategies based on user requirements. Furthermore, we design a D-S evidence theory-based trustworthiness assessment algorithm to fuse the measurement values of multi-dimensional trustworthiness attributes for obtaining the trustworthiness level of devices. Finally, simulation results demonstrate that the proposed scheme can improve the accuracy and efficiency of device trustworthiness assessment.

Aerial intelligent reflecting surface (AIRS), integrating unmanned aerial vehicle (UAV) with IRS, has emerged as a promising paradigm to improve the transmission quality and security in emergency communication, space-air-ground-integrated network and mobile edge computing, etc. However, the size of a single AIRS is constrained by the limited energy and payload capacity of the UAV, as well as the path loss of the air-to-ground reflective link, which makes the gain from a single AIRS finite. To address these problems, we propose a hybrid aerial IRS-assisted cooperative secure transmission system, where an aerial active IRS and an aerial simultaneously transmitting and reflecting IRS (STAR-IRS) are employed to achieve reflection amplification and 360-degree ubiquitous coverage, respectively. Additionally, the cooperative beamforming gain generated by the secondary reflection between the hybrid AIRSs can further improve communication quality. Specifically, an optimization problem is proposed with the objective of maximizing the sum secrecy rate by jointly optimizing the transmitting beamforming and the reflection coefficients of each AIRS. We first reformulated the original non-convex problem by fractional programming method, and a three-layer alternating optimization algorithm is introduced to address the proposed problem with the successive convex approximation (SCA) as well as penalty convex-concave procedure (PCCP) techniques. Finally, extensive simulations are conducted to demonstrate that the proposed scheme substantially improves the sum secrecy rate compared to other baseline schemes.

Virtual Reality (VR) paves the way to link Meta-verse and the real world, allowing users to enjoy immersive experiences. However, delivering high-quality full spherical VR service within limited rendering energy is a challenge. Mobile edge computing (MEC) is a promising paradigm to provide rendering computation services to users. It is widely held that the rendering of panoramic video presents a significant impediment in the VR system, with disregard for the importance of the data correlation leading to excessive energy consumption caused by repeated rendering. In this paper, we propose a cooperative rendering scheme in mm Wave-enabled wireless networks with MEC via a coalition formation game, among which we focus on the data correlation of the background environment of VR streams. Specifically, we first devise a multiple MEC servers rendering framework, and we formulate an optimization problem to maximize the system utility, which contains energy savings for MEC servers and users' quality of experience (QoE). Then, considering the overlap of the VR streams requested by users in Metaverse, a coalition formation game is employed to model the cooperations among MEC servers, such that the user's QoE is significantly improved. The simulation experiments show that our proposed algorithm is superior to benchmark algorithms in improving the users' QoE and reducing the total energy consumption of MEC servers.

With the rapid development of artificial intelligence technology, unmanned surface vehicles (USVs) in marine Internet of Things (MIoTs) have become an important paradigm for marine environment exploration. However, in MIoTs, when collecting environmental information, USVs face a series of threats such as engine failure, grounding and collision, etc., resulting in damage to shipboard memory, vessel breakage and sinking, which may cause loss or damage of stored data. The USV fleet consisting of multiple USVs is recently advocated to enable collaborative communication and storage resource sharing. As such, in this paper, the USV fleet-assisted data backup scheme for the damaged USVs is proposed to guarantee the availability of stored data. First, a data backup framework for USV fleets is designed, where the USVs are classified into high-risk USVs and low-risk USVs according to the damage risk probability of sailing. Within the USV fleet, high-risk USVs (i.e., requesters) back up data to low-risk USVs (i.e., assistants) under emergency time. Second, the coalition game based on cost sharing is utilized to incentivize individual USVs to form the optimal USV fleets by maximizing the expected revenues, where the cost sharing fashion effectively ensures the stability of the coalitions. Finally, the joint optimization problem of the requesters’ allocating data decisions and the assistants’ receiving data decisions is formulated to maximize the average amount of data backup. The predictor-corrector interior point method (PIPM) and Q-learning method are leveraged to derive the reasonable solution of the formulated problem, with achieving the optimal allocating data decision and receiving data decision. Extensive simulation results demonstrate that the proposed scheme outperforms the benchmark schemes in terms of individual expected revenue, participation degree and the average amount of data backup.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provision anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV’s cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and promote trustworthy data dissemination in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions. To defend against free-riding attacks, we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game to identify malicious AVs in disseminating fake information. Furthermore, for lack of sufficient knowledge on parameters of the network model and the user cost model in dynamic game scenarios, a reinforcement learning based algorithm with hotbooting is developed to obtain the optimal strategies of subscriber AVs and publisher AVs with free-rider prevention. Extensive simulations are conducted, and the results validate that our SPAD can effectively prevent free-riders and enhance the dependability of disseminated contents, compared with conventional schemes.

Unmanned Aerial Vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, line-of-sight (LoS) characteristics of air-toground (A2G) channels, and low cost. However, the broadcast nature of wireless transmission and the LoS characteristics of A2G channels are vulnerable to eavesdropping attack, which leads to severe security issues. To enhance the security of UAV communication, we propose a framework that multiple UAVs cooperate to resist attacks (MURA). Specifically, we first propose an efficient incentive scheme based on the coalitional game to encourage UAVs to join the coalition. We prove that each UAV can maximize its utility by joining the coalition to form a grand coalition. Then, a secure UAV communication scheme is proposed to resist eavesdropping attack. Two types of scenarios are considered for UAV communication. In a completely rational scenario, in which participants make decisions aiming to maximize their utility, we utilize the Stackelberg game to model the interactions between UAVs and attacker. The existence and uniqueness of the equilibrium solution are proved, and the equilibrium solution is obtained. In an imperfectly rational scenario, the prospect theory (PT) is applied to capture the underlying rationality of the players. The PT valuations of the players, i.e., UAV and attacker, are deduced in detail. Meanwhile, the convergence of the PT valuations of UAV and attacker is proved. Finally, extensive simulation results show that the proposed scheme can effectively improve the utility of legal UAVs and ensure the security of the UAV networks compared with benchmarks.

This paper investigates the power efficiency of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted multi-user uplink channels. Existing research works usually focus on enhancing secrecy performance, and neglect measures to improve power efficiency. In this paper, the optimization problem is formulated to minimize the sum radio frequency (RF) power of multiple users in the uplink channel subject to secrecy outage probability constraint. This problem is solved by an alternating optimization (AO) algorithm that includes three optimization sub-problems, i.e., phase shift matrix, receiving matrix, and RF power optimization. Furthermore, to reduce the complexity of the proposed AO algorithm, a deep learning (DL)-based approach is proposed to optimize the sophisticated phase shift matrix optimization process. Simulation results demonstrate that the proposed scheme can significantly reduce the average RF power, and the DL-based scheme achieves similar performance as AO algorithm while reducing the time complexity significantly.

Underwater wireless acoustic networks (UWANs) integrating sensing and underwater acoustic communication technology have been widely used for the perception and collection of underwater information. However, due to the openness of UWAN's deployment environment and the broadcast nature of wireless communication, the data collection process of UWAN by using an autonomous underwater vehicle (AUV) may suffer from potential eavesdropping attacks, resulting in information leakage. In order to implement data secure collection, we propose a data secure collection scheme for UWAN assisted by unmanned surface vehicle (USV) to protect the security of data collection in the case of a malicious proactive eavesdropping AUV. Firstly, a friendly jamming strategy by USV for coping with the eavesdropping AUV is proposed. Secondly, the interaction between the data collection link and the eavesdropping link is modeled as a linear constrained bimatrix game problem, and the utility functions of the two links are established. After that, the equilibrium solution of the linear constrained bimatrix game is obtained by transforming the game into an optimal solution of a corresponding quadratic programming. The numerical results demonstrate that the proposed scheme can achieve higher utility of legitimate link compared with other schemes.

Federated learning (FL) is a promising distributed learning paradigm, which enables devices to collaboratively train an AI model without exposing participants' private data. However, FL is vulnerable to various attacks and thus remains exposed to privacy issues. For example, malicious parties can launch attacks to recover sensitive and private training data from the shared parameters. Leakage of privacy data can cause serious damage to data providers. Furthermore, user anonymity and data verification in FL also need to be considered. To tackle these problems, in this paper, a verifiable and privacy-preserving cooperative FL (VPPFL) scheme is proposed in UAV-assisted vehicular networks (UVNs). Specifically, to preserve the identity privacy of vehicles, elliptic curve cryptosystem (ECC) is used to generate pseudonyms for vehicles. To preserve the data privacy, Paillier homomorphic encryption algorithm is utilized to encrypt the updates of vehicles, whereby UAVs directly perform global aggregations on encrypted updates instead of raw ones. Additionally, pseudonym-based signature mechanism is presented for vehicles to generate verifiable signatures, so as to ensure the authenticity and validity of uploaded local model updates. Besides, to sufficiently use the multi-source data, multiple UAVs share the local updates packets with each other to execute global aggregation. Finally, simulations are carried out to demonstrate that the proposed scheme can achieve high accuracy and verification with providing strict privacy protection.

Multi-access Edge Computing has come forth as a promising paradigm to provide low-latency computing service to mobile end users. Its basic idea is to deploy computation resources at the edge of core networks such as wireless access points, and then users can offload their tasks to nearby edge nodes for processing. Plenty of works have well studied the task offloading problem, aiming to reduce task completion delays. Also, a few recent works have focused on task dispatching among edge nodes to balance their workloads and improve resource utilization. In this work, we jointly consider the task offloading and dispatching problem in an edge computing system with interconnected access points. Furthermore, we assume both end devices and access points are rational, which only care about their own benefits. To solve the joint problem, we firstly formulate it as a multi-leader multi-follower Stackelberg game, and rigorously prove the existence of a Stackelberg equilibrium. Then, we propose two algorithms for task offloading and dispatching, respectively. Extensive simulations are conducted to show the superiority of our proposed approach. We also demonstrate that an upper bound with a constant approximation ratio is achieved by our approach.

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.

Edge caching has been envisioned as a promising technology in heterogeneous networks (HetNets) to proximally cache (video) contents. Nevertheless, as massive resources (e.g., energy, storage, computing, and bandwidth) are consumed to cache contents, edge caching devices (ECDs) are unwilling to provide caching services. In addition, as the ECDs are usually deployed by untrusted third parties, the cached contents may be illegally accessed, which results in the mobile users’ privacy leakage. To efficiently address these problems, in this paper, we propose a novel secure edge caching scheme for video contents in HetNets. Specifically, to motivate the participation of ECDs, the Nash bargaining game is exploited to model the negotiations between the content provider and ECDs, where the optimal requested caching space of the content provider and the optimal caching price of each ECD are jointly analyzed. Apart from this, to protect the content secrecy, scalable video coding is employed to facilitate secure edge caching, where the ECDs are only utilized to cache the enhancement layers that cannot be independently decoded to reconstruct the original contents. Then, we formulate a non-convex 0–1 integer programming problem to optimize the enhancement layer caching on ECDs, and the modified alternating direction method of multipliers (ADMM) is used to solve the problem optimally. Finally, simulation results show that the proposed scheme provides secure and efficient content caching for mobile users.

Unmanned aerial vehicles (UAVs) have the potential to establish flexible and reliable emergency networks in disaster sites when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrusted environment and open-access UAV networks. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable for heavy security provisioning operations when performing complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-assisted disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs’ honest behaviors. Furthermore, we introduce a novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs’ heavy data processing and storage tasks. To offload computational tasks from the UAVs to ground vehicles with idle computing resources, an optimal allocation strategy is developed by choosing payoffs that achieve equilibrium in a Stackelberg game formulation of the allocation problem. For lack of sufficient knowledge on network model parameters and users’ private cost parameters in practical environment, we also design a two-tier deep reinforcement learning-based algorithm to seek the optimal payment and resource strategies of UAVs and vehicles with improved learning efficiency. Simulation results show that RescueChain can effectively accelerate consensus process, improve offloading efficiency, reduce energy consumption, and enhance user payoffs.

With the rapid advance of wireless communication technologies, the promising space-air-ground integrated networks (SAGINs) are advocated to provide ubiquitous multicast transmission services for the social community constituted by a group of mobile users that have strong social ties and similar content interests. However, due to the limited yet valuable spectrum resources, the network heterogeneity, and diverse service demands of mobile users, it is challenging to efficiently allocate bandwidth for social communities with the objective of achieving satisfactory quality of experience (QoE) in SAGINs. To address this problem, in this paper, we propose a hierarchical bandwidth allocation scheme to enable high-quality multicast services for social communities in SAGINs. Specifically, we first develop a hierarchical bandwidth allocation framework. Wherein, the low earth orbit (LEO) satellite is utilized to provide space-to-air (S2A) unicast bandwidth for unmanned aerial vehicles (UAVs) at a certain price. Each UAV is employed to provide air-to-ground (A2G) multicast bandwidth for ground social communities with a certain A2G multicast bandwidth charge. We then formulate the hierarchical bandwidth allocation problem as a four-stage Stackelberg game, where the target of each participant is to maximize its own utility. Afterward, through the game analysis by the backward induction method, the existence of the Stackelberg equilibrium is proved, where the closed-form solutions on the optimal policies of both the social communities and UAVs are derived by the convex optimization method, and the optimal pricing policies of the LEO satellite is achieved by a proposed gradient descent iteration algorithm. Finally, extensive experiments are conducted to demonstrate that the proposed scheme can greatly increase the utilities of social communities while consuming a less bandwidth compared to conventional schemes.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

The security of distributed communications in UAV rescue networks is promising to be provisioned by blockchain technology. However, due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blocks, which can result in severe security issues (such as Forged deposit address and Double spend attack). These issues has been neglected in literature. This paper proposes a UAVs assisted and incentive based blockchain offline transaction scheme to address the above issues when UAVs and ground users are offline. Particularly, we consider vehicle-to-vehicle (V2V) charging transactions in disaster areas. First, we built an offline channel between charging and discharging electric vehicles (EVs), and then, we design an accountable assertions based UAVs aided penalty algorithm to prevent various attacks. Then, considering selfishness of users, we formulate an incentive model based on Stackelberg game to encourage EVs to participate to the offline V2V charging transactions. Our simulation results demonstrate that our proposed scheme obtain the optimal utilities for EVs, which outperforms the conventional schemes.

Recently, unmanned aerial vehicle (UAV) swarm has been advocated to provide diverse data-centric services including data relay, content caching and computing task offloading in vehicular networks due to their flexibility and conveniences. Since only offloading computing tasks to edge computing devices (ECDs) can not meet the real-time demand of vehicles in peak traffic flow, this paper proposes to combine edge computing and UAV swarm for cooperative task offloading in vehicular networks. Specifically, we first design a cooperative task offloading framework that vehicles' computing tasks can be executed locally, offloaded to UAV swarm, or offloaded to ECDs. Then, the selection of offloading strategy is formulated as a mixed integer nonlinear programming problem, the object of which is to maximize the utility of the vehicle. To solve the problem, we further decompose the original problem into two subproblems: minimizing the completion time when offloading to UAV swarm and optimizing the computing resources when offloading to ECD. For offloading to UAV swarm, the computing task will be split into multiple subtasks that are offloaded to different UAVs simultaneously for parallel computing. A Q-learning based iterative algorithm is proposed to minimize the computing task's completion time by equalizing the completion time of its subtasks assigned to each UAV. For offloading to ECDs, a gradient descent algorithm is used to optimally allocate computing resources for offloaded tasks. Extensive simulations are lastly conducted to demonstrate that the proposed scheme can significantly improve the utility of vehicles compared with conventional schemes.

Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) have become a new paradigm for marine monitoring and disaster rescue. In AI-enabled UAV applications, UAVs generate amounts of computation-intensive tasks (e.g., image recognition, video processing, and path planning, etc.) that cannot be locally executed by UAVs in time. How to offload the computation-intensive tasks of UAVs timely and effectively has become an urgent challenge. Multiple unmanned surface vehicles (USVs) integrated into a USV fleet is appealingly advocated to provide abundant computation resources for computation tasks. In this paper, we propose a collaborative computation offloading scheme with UAVs and USV fleets in maritime communication networks. Specifically, we first propose a collaborative computation offloading framework, where UAVs act as the requesters of computation offloading, and USV fleets are the assistants. Then, to minimize the overall execution time of computation tasks, UAVs determine the optimal ratio of compu-tation tasks offloaded to USV fleets in the worst case. Afterwards, the first sealed reverse auction with reserve price is utilized to incentivize USV fleets to assist in executing computation tasks of UAVs, where the reserve price guarantees the satisfied benefits of UAVs. Simulation results demonstrate that the proposed scheme reduces the overall execution time and improves the expected revenue of the USV fleet as compared to conventional schemes.

At present, the crowdsourcing-based indoor navigation system (CINS) has attracted extensive attention from both industry and academia owing to its low-cost and high-accuracy performance. Unfortunately, the system that relies on crowdsourced data is vulnerable to the collusion attack, which leads to severe security issues. To address the security issues in the CINS, we propose to utilize a fully trusted fog server platform to advocate secure transactions between service requesters and responders. First, we propose a novel reputation incentive mechanism based on the behaviors of responders. Then, we employ the offensive and defensive game to model the interactions between the fog server platform and the responders, whereby a social welfare optimization problem is formulated to maximize the social welfare of the system. Next, the game equilibriums are found by using the replicator dynamic equation while the game stability is discussed. Finally, the simulation results show that the proposed mechanism can effectively encourage responders to provide positive navigation services and obtain more social welfare of the system compared with the conventional mechanisms.

Recently, mobile social cloud (MSC), formed by mobile users with social ties, has been advocated to allocate tasks of big data applications instead of relying on the conventional cloud systems. However, due to the dynamic topology of networks and social features of users, how to optimally allocate tasks to mobile users based on the trust becomes a new challenge. Therefore, this paper proposes a novel incentive scheme based on the trust of mobile users in the MSC to allocate the tasks of big data. First, a social trust degree is defined according to the social tie among users, the importance of task, and the available resources of networks. With the social trust degree, the task owner can select a group of mobile users as the candidates for task allocation. Second, a reverse auction game model is developed to study the interactions among the task owner and the candidates. With the reverse auction game model, the optimal strategy of task allocation can be obtained with a low cost for the task owner where the selected candidate of mobile users can also obtain the high profit. Finally, simulation experiments are carried out to prove that the proposal can outperform other existing methods with a low delay and a high efficiency to allocate tasks in the MSC.

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this article, we develop alightweightvehicularblockchain-enabledsecure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. First, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Second, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace misbehaviors and record data transactions for UAVs and vehicles with improved efficiency and security in reaching consensus. Third, since UAVs and vehicles have little explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes.

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme.

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, owing to their flexible mobilities and fast deployment. However, UAVs typically have very limited battery and computational capacities, which makes them harder to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue of the battery and computation resource limitation with a fog computing based UAV system. Specifically, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To avoid the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared to other benchmark schemes.

Unmanned aerial vehicles (UAVs) combining with crowdsensing technology has been viewed as a promising paradigm for performing sensing tasks in extreme scenarios such as earthquakes, etc. However, potential security issues could incur on data sharing between UAVs and task publishers owing to the vulnerability of central nodes and selfishness of distrusted UAVs. To cope with these problems, we propose a novel blockchain-based crowdsensing framework with reputation incentive (BCFR) in UAV-assisted mobile crowdsensing. Specifically, we first propose a novel reputation incentive scheme to choose UAVs with a high reputation to perform sensing tasks, thereby protecting data sharing between UAVs and task publishers from internal attack (i.e., some UAVs with insufficient resources may turn into malicious UAVs to provide wrong sensory data to the task publishers). Then, we design a blockchain-based secure data transmission scheme to securely record data transactions of UAVs. Furthermore, since UAVs with limited resources are difficult to perform compute-intensive mining tasks, edge computing is incorporated to increase the success probability of block creation. The interactions between UAVs and edge computing provider (ECP) are modeled as a two-stage Stackelberg game to motivate UAVs participating in the block creation process while providing high-quality services. Finally, we conduct extensive simulations to demonstrate that the proposed BCFR scheme can effectively improve successful mining probabilities and utilities of UAVs, and ensure the security of data sharing among UAVs and task publishers.

Autonomous vehicles (AVs) are coming with great potentials to bring safer, greener, and more convenient transportation systems. As AVs rely on radar, camera, and other advanced sensors to sense its surroundings, a salient challenge of AVs is the intrinsic limitations of onboard sensors (e.g., limited awareness range, blind spots, and failure in foggy days). To tackle this problem, we propose a collaborative data sharing scheme for AVs to make up for sensor deficiencies by promoting sensory information sharing in autonomous driving. However, this brings another fundamental issue on how to ensure trust in shared sensory data from distrustful collaborators and how to motivate AVs to participate in data sharing. This work studies this issue by modeling it as a quality-aware optimal sensing task scheduling problem. Specifically, we design an edge computing-enabled architecture where AVs can form collaborative sensing groups in executing sensing tasks. After that, a quality-aware auction-based incentive mechanism is developed to promote AVs’ participation and high-quality data sharing. We also design a reputation model to recruit trustworthy AVs to perform sensing tasks based on their behaviors and social identities. Due to the NP-hardness of problem, we devise a heuristic algorithm to determine the optimal winners and payments in auction with truthfulness and individual rationality guarantees. Lastly, extensive simulations validate that our approach can effectively improve sensing data quality and user utility, compared with conventional schemes.

Unmanned aerial vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, and characteristics of line of sight (LoS) propagation. However, wireless communication is vulnerable to eavesdropping attacks because of the broadcast characteristics. To enhance secure UAV communications with the ground nodes, we propose a novel framework that multiple UAVs cooperate to resist attack (MURA). First, we propose an incentive mechanism based on coalitional game to encourage legal UAVs to join the coalition. We prove that each legal UAV can only maximize its profits by joining the coalition to form a major coalition. Then, a secure UAV communication scheme is proposed to resist the eavesdropping attacks. Two types of scenarios are considered for the UAV communication: in a completely rational scenario, we utilize the Stackelberg game to model the interactions between the legal UAVs and attacker. In an imperfectly rational scenario, the cumulative prospect theory (PT) is applied to the game to capture the underlying rationality of the players. Finally, simulation results show that the proposed scheme can significantly improve the security of the UAV network compared with traditional schemes.

Recently, unmanned aerial vehicles (UAVs) have been employed to provide wireless communication services, which promotes the emergence of promising UAV-assisted heteroge-neous networks (UHetNets). However, due to the ever-increasing amount of data traffic and diverse wireless service demands of mobile users, it is challenging to efficiently allocate limited secure bandwidth for safe communication. To tackle this problem, in this paper, we propose a game theoretical secure bandwidth allocation scheme in UHetNets. Specifically, we first design a UAV-assisted bandwidth allocation framework, where each UAV as a flying base station reuses the secure spectrum to enhance the utilization rate of wireless resource. To allocate the restricted secure band-width, we further introduce the utility functions of both UAVs and mobile users, based on the real-time bandwidth capacity of each UAV and the demand of each mobile user. Stackelberg game is then utilized to model the dynamic interactions between UAVs and mobile users. Afterwards, we devise a gradient descent based optimal decision searching algorithm to achieve the Stackelberg equilibrium. The simulation results, at last, show the effectiveness of the proposed scheme to improve the utilities of both mobile users and UAVs.

Unmanned aerial vehicles (UAVs) have brought numerous potentials to establish flexible and reliable emergency networks in disaster areas when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrustful environment and open-access UAV networking. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable to execute heavy security provisioning operations when carrying out complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-aided disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a novel vehicular fog computing based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs' heavy data processing and storage tasks. To optimally stimulate vehicles to share their idle computing resources, we also design a two-layer reinforcement learning based incentive algorithm for UAVs and ground vehicles in the highly dynamic networks. Simulation results show that RescueChain can effectively accelerate consensus process, enhance user payoffs, and reduce delivery latency, compared with representative existing approaches.

The infectious disease presents great hazards to public health, due to their high infectivities and potential lethalities. One of the effective methods to hinder the spread of infectious disease is vaccination. However, due to the limitation of resource and the medical budget, vaccinating all people is not feasible in practice. Besides, the vaccinating effects are difficult to be timely observed through traditional ways, such as outpatient services. To tackle the above problems, we propose an e-healthcare mobile social Internet of Things (MSIoTs)-based targeted vaccination scheme to fast contain the spread of the infectious disease. Specifically, we first develop an e-healthcare MSIoT architecture by integrating the e-healthcare system and MSIoTs, whereby the spread status of the infectious disease is timely collected. Furthermore, a graph coloring and spreading centrality-based optional candidate searching algorithm is devised to hunt for the candidates that are powerfully capable of preventing infectious disease. Especially, in order to reduce the vaccination cost, we design an optimal vaccinated target selection algorithm to choose a minimum number of targets whose locations are differentially distributed. Extensive simulations demonstrate that the proposed scheme can effectively prevent infectious disease as compared to conventional schemes.

As both the scale of mobile networks and the population of mobile users keep increasing, the applications of mobile social big data have emerged where mobile social users can use their mobile devices to exchange and share contents with each other. The security resource is needed to protect mobile social big data during the delivery. However, due to the limited security resource, how to allocate the security resource becomes a new challenge. Therefore, in this paper we propose a joint match-coalitional game based security-aware resource allocation scheme to deliver mobile social big data. In the proposed scheme, first a coalition game model is introduced for base stations (BSs) to form groups to provide both wireless and security resource, where the resource efficiency and profits can be improved. Second, a matching theory based model is employed to determine the selecting process between communities and the coalitions of BSs so that mobile social users can form communities to select the optimal coalition to obtain security resource. Third, a joint matching-coalition algorithm is presented to obtain the stable security-aware resource allocation. At last, the simulation experiments prove that the proposal scheme outperforms other existing schemes.

Smart city emerges a promising paradigm for improving operational efficiency of city and comfort of people. With embedded multi-sensors, Unmanned Aerial Vehicles (UAVs) hold great potential for collecting sensing data and providing social services in smart city. However, due to the limited battery lifetime and processing capacities of UAVs, the efficient offloading scheme of UAVs is urgently needed in smart city. Therefore, in this article, a vehicle-assisted computing offloading architecture for UAVs is proposed to improve offloading efficiency by harnessing the moving vehicles in smart city. We first develop an offloading model for UAVs to determine the offloading strategy. Next, to select the optimal vehicles for offloading, we formulate a matching scheme based on the preference lists of UAVs and vehicles to derive the optimal matching between UAVs and vehicles. After that, to improve the offloading efficiency and maximize the utilities of UAVs and vehicles, the transaction process of computing data between UAVs and vehicles is modeled as a bargaining game. Moreover, an offloading algorithm for UAVs and vehicles is proposed to obtain the optimal strategy. Finally, simulations are performed to validate the efficiency of the proposed offloading scheme. The results demonstrate that the proposed offloading scheme can significantly save resource and improve the utilities of UAVs and vehicles.

The increasing development of Internet of Things (IoT) has led to the emergence of Internet of connected vehicles (IoCVs). These vehicles with various functionalities have the potential prospects for improving the quality of experience (QoE) of vehicle users. Moreover, the use of unmanned aerial vehicles (UAVs) in flying networks extends the connectivity and universality of IoT, and these UAVs with caching and communication capacities can support various services. However, due to the heterogeneity of vehicular networks and flying networks, the communication performance and content distribution between UAVs and IoCVs expose new challenges in heterogeneous networks (HetNets). Therefore, in this paper, a novel content distribution mechanism between UAVs and IoCVs is proposed to improve the QoE of vehicle users. Specifically, we first develop a novel content distribution architecture for UAVs and IoCVs in HetNets, where the content is distributed by UAV content providers to IoCVs. Next, we establish an optimization problem of content distribution between UAVs and IoCVs to minimize the transmission delay. In order to stimulate UAVs and IoCVs to join content distribution, the utilities of UAVs and IoCVs are formulated, respectively. Moreover, we design a coalition game between UAVs and IoCVs to determine the optimal strategy of content distribution. Finally, simulation results demonstrate that the proposed mechanism can significantly improve the performance of content distribution compared with the conventional mechanisms.

Natural disasters often cause huge and unpredictable losses to human lives and properties. In such an emergency post-disaster rescue situation, unmanned aerial vehicles (UAVs) are effective tools to enter the damaged areas to perform immediate disaster recovery missions, due to their flexible mobilities and fast deployment. However, the UAVs typically have very limited batteries and computational capacities, which make them unable to perform heavy computation tasks during the complicated disaster recovery process. This paper addresses the issue with a fog computing based UAV system. In specific, we first introduce the vehicular fog computing (VFC) system in which the unmanned ground vehicles (UGVs) perform the computation tasks offloaded from UAVs. To resolve the transmission competitions yet enable cooperations among UAVs and UGVs, a stable matching algorithm is developed to transform the computation task offloading problem into a two-sided matching problem. An iterative algorithm is then developed which matches each UAV with the most suitable UGV for offloading. Finally, extensive simulations are carried out to demonstrate that the proposed scheme can effectively improve utilities of UAVs and reduce average delay through comparison with conventional schemes.

With the technical advancement of transportation electrification and Internet of vehicle, an increasing number of electric vehicles (EVs) and related infrastructures (e.g., service stations with both charging and communication services) are deployed in the intelligent highway systems. Not only can EVs enter the service station areas for charging, but they can also upload/download cached data at service stations to access multiple networking services. However, as EVs are operated individually with their unique travelling patterns, questions arise as how to incent EVs so that both energy and communication resources are optimally allocated. In this paper, we propose an online pricing mechanism of EV charging and data caching for service stations along the highway. First, we design an online reservation system at each EV to decide the best service station to park when the EV enters the highway. Furthermore, based on the variant power system status, an online pricing mechanism is devised to update the charging and caching price based on Q-learning, by which EVs can be motivated to arrive at the designated station for services. Finally, simulation results validate the effectiveness of the proposed scheme in improving the station's utility.

The popularization of mobile devices connected to the network promotes the rise and development of the emerging mobile Internet of Things (MIoT). Crowdsensing is a promising mode to perceive data in MIoT, where the collection of sensing data is outsourced to the public crowd carrying mobile devices. However, this crowdsensing mode inevitably makes privacy compromised, due to the workers' sensitive information in the sensing data. As such, how to incentivize workers' participation with privacy preservation becomes a challenge. To tackle this problem, in this article, we propose an auction-based privacy-preserving incentive scheme (APIS) for sensing task allocation in MIoT. Specifically, integrating the idea of software-defined network (SDN), we first present a cloud and edge cooperation-based crowdsensing framework, where the cloud is designed as the controller to collect sensing results from the distributed edge nodes and each edge node outsources sensing tasks to participating workers. To motivate workers' participation, we devise a differential privacy-based auction mechanism, whereby each worker can utilize her privacy budget to control how much privacy can be leaked and decide the sensing precision by the sensing time. Moreover, to maximize the utility of the sensing platform, we design a greed-based algorithm to select the winning workers and determine payments to winners. Finally, we conduct extensive simulations to verify the effectiveness of APIS and demonstrate its superiority.

To improve mobile users' quality-of-experience (QoE) in the mobile cyber-physical system (MCPS), caching layered-coding contents on edge nodes that are close to mobile users has been advocated as a promising solution, which can efficiently lower the content delivery delay and mitigate the overhead of backhaul network. However, due to the complexity of trust management and the limited caching capacities of edge nodes, designing an efficient edge caching scheme for mobile users becomes a challenge. Meanwhile, the content caching in MCPS also faces some security problems, where edge nodes may return incorrect results or viruses to mobile users, and mobile users would deliberately refuse to pay for caching services. To tackle these problems, we propose a novel blockchain-based trustworthy edge caching scheme for mobile users in MCPS. Specifically, we first exploit blockchain to supervise the caching transactions between the edge nodes and mobile users in a distributed manner, whereby the caching service information cannot be modified and denied by any entities. Furthermore, we devise a trust management mechanism for mobile users to search the trustworthy caching services from diversified edge nodes, where the trust degree of the edge node is real-time evaluated and updated by mobile users based on the quality of caching service. To take full advantage of caching resources, we design a max-min-based resource allocation algorithm, with which the trustworthy edge node could fairly allocate its caching resource based on mobile users' optimal demands. The simulation results show that the presented scheme not only improves the utilities of edge nodes but also increases the QoE of mobile users.

Edge caching has become one of promising technologies in mobile social networks (MSNs) to proximally provide popular contents for mobile users. However, since caching contents inevitably consume resources (e.g., power, bandwidth, storage, etc.), edge caching devices maybe selfish to cheat the content provider for earning service fees. In addition, due to the open access of edge caching devices, the edge caching service is vulnerable to various attacks, such as man-in-the-middle attack and content tamper attack, etc., resulting in the degradation of content delivery performance. To efficiently tackle the above problems, in this paper, we propose a secure edge caching scheme for the content provider and mobile users in MSNs. Specifically, we first develop a secure edge caching framework consisting of the content provider, multiple edge caching devices, and some mobile users. To motivate the participation of edge caching devices, Stackelberg game is exploited to model the interactions between the content provider and edge caching devices. The content provider serves as the game-leader to determine the payment strategy of secure caching service and each edge caching device is the game-follower to make the strategy on the quality of secure caching service. Especially, the zero payment mechanism is adopted to suppress the selfish behaviors of edge caching devices. Apart from this, for lack of the knowledge on interactions between the content provider and edge caching devices in dynamic network scenarios, we also employ the Q-leaning to derive the optimal payment strategy of the content provider and the security strategy of edge caching device. Extensive simulations are conducted, and results demonstrate that the proposed scheme can efficiently motivate edge caching devices to provide the content provider and mobile users with high-quality secure caching services.

To meet the exponentially increasing mobile services and applications, heterogenous networks (HetNets) have been envisioned as a promising technology. In HetNets, multiple caching-enabled small-cell based stations (SBSs) are deployed within the coverage of a macro-cell base station (MBS) to cache multimedia contents for mobile users. However, due to security threats of untrusted SBSs, the cached contents may be illegally accessed by owners of these untrusted SBSs, resulting in the content privacy leakage. To tackle this problem, we propose a secure edge caching scheme for layered multimedia contents in HetNets. Specifically, considering the layered features of contents, we first develop a secure edge caching framework based on the cooperations of SBSs and MBS. In this framework, the critical base layer subfile of the content are directly delivered by the trusted MBS, whereas the enhancement layer subfiles are cached on untrusted SBSs. Furthermore, according to the limited caching capacities of SBSs and dynamic content demands of mobile users, we formulate the enhancement layer subfile caching problem as a non-convex 0-1 integer programming problem. To solve this problem, we devise a distributed alternating direction method of multipliers (ADMM) and secure the edge caching for each SBS to iteratively search the optimal caching strategy. Simulation results show that the proposed scheme provides secure and efficient multimedia content caching for mobile users.

With the rapid advances of wireless technologies and popularization of mobile devices, edge computing boosts mobile social networks (MSNs) to allow mobile users to deliver, share, and exchange contents with each other. In particular, with edge caching, various content services can be provided to mobile users with improved Quality-of-Experience (QoE). However, edge caching is vulnerable to cache pollution attack (CPAttack), degrading content delivery. To tackle these problems, in this paper, we propose a hidden Markov model (HMM) based detection scheme against CPAttack in edge computing enabled MSNs. Specifically, we first present the CPAttack model with the observations of malicious behaviors. According to the CPAttack model, the caching state of each edge device is characterized in terms of request rate and cache hit rate. The HMM is exploited to detect the CPAttack with observation sequence of caching states. The simulation results demonstrate that the proposed scheme can efficiently improve edge devices' capability to detect CPAttack.

Automatic detection of fight behaviors in surveillance videos is an important task for surveillance systems. In this work, we propose a novel localization guided framework for detecting fight actions in surveillance videos. Specifically, we exploit optical flow maps to extract motion activation information, which indicates the location of active regions. Then, a detection guided alignment module is designed to adjust the localized active regions. This approach employs a two-stream based 3D convolution network as the backbone network with a novel motion acceleration representation on the temporal stream. While most existing methods are still evaluated on three benchmark datasets which were not originally collected from surveillance scenarios, we present a novel Fight Action Detection in Surveillance-videos (FADS) dataset for this purpose. With a total of 1,520 video clips, the FADS is the largest known dataset in terms of number of surveillance videos with fight scenes. Experimental results on both the benchmark datasets and the FADS show that our proposed localization guided method outperforms state-of-the-art techniques.

The smart community (SC), as an important part of the Internet of Energy (IoE), can facilitate integration of distributed renewable energy sources and electric vehicles (EVs) in the smart grid. However, due to the potential security and privacy issues caused by untrusted and opaque energy markets, it becomes a great challenge to optimally schedule the charging behaviors of EVs with distinct energy consumption preferences in SC. In this paper, we propose a contract-based energy blockchain for secure EV charging in SC. First, a permissioned energy blockchain system is introduced to implement secure charging services for EVs with the execution of smart contracts. Second, a reputation-based delegated Byzantine fault tolerance consensus algorithm is proposed to efficiently achieve the consensus in the permissioned blockchain. Third, based on the contract theory, the optimal contracts are analyzed and designed to satisfy EVs' individual needs for energy sources while maximizing the operator's utility. Furthermore, a novel energy allocation mechanism is proposed to allocate the limited renewable energy for EVs. Finally, extensive numerical results are carried out to evaluate and demonstrate the effectiveness and efficiency of the proposed scheme through comparison with other conventional schemes.

Caching contents on edge computing-enabled small cell base stations (ECSBSs) has become a promising technology for mitigating burdens of macro cell base stations and offloading data from mobile users. However, as ECSBSs may be malicious, providing a secure caching scheme becomes a challenge. In this paper, we propose a novel secure caching scheme in heterogeneous networks for multihoming users. First, to provide the cached contents, a trust mechanism is designed to verify the reliability of each ECSBS. Then, in order to guarantee the integrity of cached contents and preserve the privacy of mobile users, a Chinese remainder theorem-based privacy preservation protocol is proposed. Next, we investigate the interactions among mobile users and ECSBSs by Stackelberg game, where the trusted ECSBSs are selected to provide caching resources for mobile users with multihoming access. In addition, we analyze the Stackelberg equilibrium to jointly maximize the utilities of ECSBSs and mobile users. Extensive simulations validate the efficiency of the proposed scheme with the reliability and effectiveness to cache contents.

In a smart community (SC) with renewable energy sources (RES), flexible charging service can be provisioned to electric vehicles (EVs), where EVs can choose clean energy, traditional energy, or the mixture of them on demand. Considering the existence of various entities in the SC and the limited generation capacity of RES, it becomes of significance yet very challenging to optimally schedule the charging service for EVs with different consumption preferences. In this paper, we propose a charging scheme for EVs in a SC integrated with RES by using a game theoretical approach. Firstly, a three-party energy network is proposed to model the interactions among the power grid, EVs, and aggregators in the smart grid. Secondly, the trust model is presented to improve safety of power trading by evaluating the reliability of aggregators. Thirdly, based on the four-stage stackelberg game, the optimal strategies of three energy entities are analyzed by solving the stackelberg equilibrium (SE). Furthermore, a weighted max-min fairness (WMMF) based algorithm is proposed to fairly allocate the limited renewable power for EVs. Finally, extensive simulations are carried out to evaluate and demonstrate the effectiveness of the proposed scheme through comparison with conventional schemes.

Recently, social network based e-healthcare service has emerged as a promising way to control the spread of infectious diseases. However, the large-scale deployment in reality faces a fundamental challenge to reduce the cost where social features of mobile users and the properties of networks should be considered. To tackle the above problem, this paper presents a green social cyber physical system (CPS) based e-Healthcare scheme to control infectious diseases. Firstly, based on the analysis of social features, the high influential users are selected to inoculate immune drugs when an infectious disease is identified. Secondly, we develop an epidemic spreading model with the dynamic equations to analyze the efficiency of immune strategy. With the proposed model, the spread of infectious diseases can be effectively monitored and the spreading range of the infectious can be predicted. In addition, simulation experiments prove that the proposal can be more efficient to prevent infectious diseases from being spread than conventional methods.

Caching contents in small cell base stations (SBSs), namely, caching-enabled SBSs, has become a promising technology for mitigating burdens of macro cell base stations and backbone links. However, as some SBSs may be malicious, how to provide a secure caching scheme becomes challenging. In this paper, we propose a novel secure caching scheme in heterogeneous networks for multi-homing users. Firstly, to achieve availability of cached contents, a trust mechanism is designed to verify the reliability of each SBS. Then, we investigate the interactions among mobile users and SBSs according to Stackelberg game, where the trusted SBSs are selected to provide caching space for mobile users with multi-homing access. In addition, we investigate the Stackelberg equilibrium (SE) to jointly maximize the utilities of SBSs and mobile users. Extensive simulations validates the efficiency of the proposed scheme by evaluating the reliability and effectiveness to cache contents.

Caching content with fog computing at the edge nodes has been a promising alternative to mitigate burdens of backbone networks and improve mobile users' quality of experience in mobile social networks (MSNs). However, as edge node may be vulnerable due to the attacks from malicious users, the design of secure caching schemes for the fog/edge enabled MSNs becomes a new challenge. In this paper, to tackle the above problem, we propose a secure caching scheme for disaster backup in MSNs with fog computing. Specifically, to protect the privacy, a partitioning and scrambling method is first designed to encrypt the contents. Then, the encrypted contents are replicated to multiple replicates, where these replicates are delivered and stored in different servers. Based on the recovery time objective and content delivery latency, an auction game model is developed to determine the optimal servers, where both edge nodes and cloud servers can obtain the maximum utilities. Extensive simulations are conducted to show the effectiveness and reliability of the proposed scheme.

To save energy during content delivery in green cities, caching contents on edge nodes that are placed near mobile social users has been advocated recently. However, how to allocate the limited caching resources with secure content delivery becomes a new challenge. Therefore, in this paper, we present a novel theoretical model to deliver secure content with edge nodes in order to save energy for green cities. First, we present a reverse auction game to encourage edge nodes to cooperatively provide caching services with incentives. With the model, mobile users can determine the candidate of edge node to cache content based on the interaction between mobile users and edge nodes. Second, a trust management method is designed to evaluate the reliability of the selected candidate of edge node by considering the direct trust evaluation. Finally, extensive simulations show that the proposal can save energy with a secure content delivery where both the delay to obtain the content and the caching ratio can be improved compared with the conventional methods.

With the rapid development of wireless communication and mobile devices, heterogeneous networks have emerged as a promising paradigm to enable users' data services. However, it lacks an experience blocking theory to optimize data services. Furthermore, due to the limited resources of spectrum, the spectrum sharing based on the quality of experience (QoE) in heterogeneous networks becomes a new challenge. Therefore, to tackle the above challenge, we present an experience blocking (EB) ratio based game-theoretic approach for spectrum sharing in heterogeneous networks where the small cell can lease the spare spectrum from macro cell. Specifically, firstly, a novel EB ratio based model is proposed to evaluate the efficiency of spectrum usage in a cell. Then a Stackelberg game is employed to formulate the interaction between macro cell and small cell according to the EB ratio. Finally, an EB table is given to evaluate the blocking status of a cell and simulation results show that the proposed scheme can improve the efficiency of spectrum sharing better than other schemes.

To improve the performance of mobile video delivery, caching layered videos at a site near to mobile end users (e.g., at the edge of mobile service provider's backbone) was advocated because cached videos can be delivered to mobile users with a high quality of experience, e.g., a short latency. How to optimally cache layered videos based on caching price, the available capacity of cache nodes, and the social features of mobile users, however, is still a challenging issue. In this paper, we propose a novel edge caching scheme to cache layered videos. First, a framework to cache layered videos is presented in which a cache node stores layered videos for multiple social groups, formed by mobile users based on their requests. Due to the limited capacity of the cache node, these social groups compete with each other for the number of layers they request to cache, aiming at maximizing their utilities while all mobile users in each group share the cost involved in the cache of video contents. Second, a Stackelberg game model is developed to study the interaction among multiple social groups and the cache node, and a noncooperative game model is introduced to analyze the competition among mobile users in different social groups. Third, leveraging the backward induction method, the optimal strategy of each player in the game model is proposed. Finally, simulation results show that the proposed method outperforms the exiting counterparts with a higher hit ratio and lower delay of delivering video contents.

To cope with the rapidly expanding network scale and population, the heterogeneous content centric networks (HCCNs) have emerged, where the naming content can be shared among different sub-networks by using interest packets. However, In the HCCNs, as these sub-networks are managed by different operators and some sub-network may exhibit selfish behaviors due to the limited resource, how to design a cooperative scheme for relay service to deliver naming content becomes a new challenge. Therefore, this paper proposes a bargaining game based cooperative scheme for relay service in HCCNs. Specifically, at first we present an incentive framework that each sub-network can obtain currency by providing other subnetworks with relay service. Then, by using a cooperative node as an agent, a sub-network can select an optimal adjacent subnetwork to obtain content. Next, a bargaining game is introduced to model the interaction between two sub-networks, which leads to a Subgame perfect Nash equilibrium as the agreement of two players to maximize their benefits. Finally, simulation experiments prove that the proposed scheme can outperform other conventional methods to reduce the delay and overhead.

Due to the rapid increase of mobile user population and the dynamical change of network topology in mobile social networks (MSNs), how to efficiently deliver content among mobile social users becomes a new challenge. In this paper, an incentive content delivery mechanism based on the weighted directed graph is proposed to encourage users to obtain and provide content in the MSNs. Specifically, firstly we introduce a weighted directed graph to study the features of obtaining and providing content among mobile social users. Secondly, based on the social features including the average closeness and vertex betweenness, we present the sealed-bid auction based incentive mechanism to overcome selfish behavior and efficiently deliver content in the MSNs. Finally, with a real dataset numerical experiments are carried out to prove that the proposal can accurately show the properties of the MSNs and can be efficient for content delivery.

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably.

Nowadays, due to the increasing population of mobile users and popularity of social applications, the social relationships among mobile users become more various and affect the information spreading in mobile social networks (MSNs) more deeply than before. For example, there are multiple social relationships between mobile users, such as friends, relatives, classmates and so on. However, as most of existing models mainly consider the relationship among mobile users to be the same during the information spreading, these models can not mimic the information spreading in the MSNs well, where multiple relationships among mobile users needs to be studied thoroughly. Therefore, in this paper, to resolve the above issue, we firstly divide social relationships into four types: blood relationship, geographical relationship, work relationship, and interest relationship. Then we develop an analytical model to evaluate the influences of multiple relationships on the information spreading process in MSNs. With real traces, simulation results show the accuracy of the presented model. Numerical results demonstrate that different social relationships have different effects on the information spreading.

With the development of mobile devices, especially the emergence of smart phones, the mobile social networks (MSNs) have emerged to provide a variety of mechanisms for users to share their content. However, because the number of the mobile users still keeps growing rapidly, the MSNs become more complex than before and the features including evolution and topology need to be studied for communication system optimization. Therefore, in this paper, a great deal of data on social interactions among mobile users are collected to reveal the evolution and topological features of the MSNs. Firstly, the evolution feature of the MSN with the time is detailedly studied. Then, the statistical features of MSN including degree distribution, node distance, node closeness, and betweenness are analyzed. From the results of the analysis on the evolution features, we find that the MSN will become complex over time. In addition, the analysis of the topological properties shows that the MSN is a typical scale-free network and has strong small-world features.