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By leveraging the power of crowd, the prevalence of mobile devices in mobile crowd-sensing (MCS) networks helps and provides a wide range of sensing services through collecting and sharing sensing data. However, the diverse behaviours of mobile users affect the quality of sensing service. Unreliable or malicious users and platforms could provide untrusted data to gain illegal benefits, which deteriorates the practicability of MCS. Besides, due to individual selfishness, mobile users are reluctant to participant in sensing tasks. It is desirable to design a trust and incentive scheme to improve the service efficiency of MCS. In this paper, we propose a novel trust-driven contract incentive framework in MCS, which guarantees the service quality, while stimulating mobile users to join sensing tasks. The basic idea is to establish a trust evaluation and contract incentive scheme for mobile users and platforms. We first design a trust evaluation scheme between mobile users and sensing platforms based on the historical interactions to derive the reliability value of sensing platform. Then, the trust threshold is formulated to filter out malicious sensing platforms. By considering the privacy preferences of mobile users, we establish a contract incentive scheme to maximize the utility of both mobile users and sensing platforms. The design objective is to derive a set of optimal contracts under both discrete and continuous contract models. Meanwhile, the designed contracts guarantee the individual rationality (IR) and incentive compatibility (IC) properties. Finally, simulations are conducted to evaluate the effectiveness of the proposed trust-driven contract incentive scheme, and results demonstrate that the proposed scheme can jointly improve the quality of sensing service and maximize the utilities of mobile users and sensing platforms.

By interacting, synchronizing, and cooperating with its physical counterpart in real time, digital twin is promised to promote an intelligent, predictive, and optimized modern city. Via interconnecting massive physical entities and their virtual twins with inter-twin and intra-twin communications, the Internet of digital twins (IoDT) enables free data exchange, dynamic mission cooperation, and efficient information aggregation for composite insights across vast physical/virtual entities. However, as IoDT incorporates various cutting-edge technologies to spawn the new ecology, severe known/unknown security flaws and privacy invasions of IoDT hinders its wide deployment. Besides, the intrinsic characteristics of IoDT such as \emph{decentralized structure}, \emph{information-centric routing} and \emph{semantic communications} entail critical challenges for security service provisioning in IoDT. To this end, this paper presents an in-depth review of the IoDT with respect to system architecture, enabling technologies, and security/privacy issues. Specifically, we first explore a novel distributed IoDT architecture with cyber-physical interactions and discuss its key characteristics and communication modes. Afterward, we investigate the taxonomy of security and privacy threats in IoDT, discuss the key research challenges, and review the state-of-the-art defense approaches. Finally, we point out the new trends and open research directions related to IoDT., Comment: 21 pages, 7 figures

The proliferation of smart devices has led to a huge amount of data streaming in the Internet of Things (IoT). However, the resource-limited devices cannot satisfy the demands of computing-in-tensive but delay-sensitive applications. The data delivery among devices may be tampered with by malicious users. These pose new challenges to provide secure and intelligent services in IoT. Blockchain and reinforcement learning (RL) are promising techniques for establishing a secure environment and intelligent resource management. In this article, we introduce a novel software defined networking (SDN)-enabled architecture for edge-cloud orchestrated computing to support secure and intelligent services in IoT. We first introduce the SDN-enabled architecture by integrating cloud computing, edge computing, and IoT networks. Then we provide several applications of SDN-enabled architecture in edge-cloud orchestrated computing. Next, we propose the blockchain and RL envisioned solutions to implement secure and intelligent services in IoT. Moreover, a case study of blockchain- and RL-enabled secure and intelligent computing offloading is presented to validate its effectiveness. We finally provide our conclusion and discuss several promising research directions.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning 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 UAV's 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., 15 pages, 11 figures, under review. arXiv admin note: substantial text overlap with arXiv:2209.13815

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 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 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, fairness, 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., Accepted by IEEE Globecom2022

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.

In this paper, the vector sensor array (VSA) and the scalar sensor array (SSA) with polarization sensitivity (PS) are placed under the same framework of polarization sensitive arrays (PSAs), whose direction-dependent PS factor is larger than zero. This commonality of their PS factors enables both VSA and SSA to perform polarization estimation effectively. Based on this new PSA model, a generalized monopulse algorithm is proposed to jointly estimate direction-of-arrival (DOA) and polarization parameters, by expanding the existing monopulse angle estimation in spatial domain to both spatial and polarization domains. Both the DOA and polarization parameters can be effectively and efficiently estimated by comparing the outputs of the sum and spatial/polarized difference beams with only one snapshot. Then, closed-form expressions for joint DOA and polarization estimation error variances are derived and the polarization estimation variance is proved to decrease monotonically with the PS factor. Simulations are carried out on an X-band 5 x 5 planar array composed of microstrip patch antennas to demonstrate the effectiveness of the proposed algorithm.

With the increasing demands of big data applications in the Internet of Things (IoT), various applications have emerged to provide mobile services for IoT devices. However, due to the increasing number of IoT devices, transmission delay, energy consumption, and heavy load become new challenges for offloading tasks. Therefore, this article introduces a novel energy-aware task offloading scheme in IoT to determine the optimal offloading strategy. First, the architecture of mobile edge computing (MEC) in IoT is investigated. Second, the challenges of task offloading in MEC for IoT are introduced. Third, the framework of task offloading for MEC is proposed, and the optimal offloading strategy for computing task is achieved. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of task offloading compared with the conventional scheme.

With the rapid increasing demands of social services, mobile crowd sensing (MCS) is getting growing attraction with the wide applicability of mobile applications. This effective paradigm depends on sensing services from crowd contributors for sensing data collection and sharing. However, on one hand, the sensing data is tampered by malicious users without the reputation evaluation. On the other hand, the privacy information is exposed when mobile users submit sensing data to others. Moreover, the efficiency of task assignment is low without incentives. Therefore, we propose a privacy preservation based scheme for task assignment in internet of things (IoT) to improve the performance of crowd sensing. Firstly, a novel trusted framework is developed to judge mobile user's reputation value. In the proposed framework, the reputation evaluation scheme is established based on the social trust, recommendation credibility, and history evaluation. Secondly, the location-privacy preservation algorithm (LPA) based differential privacy method is designed to protect mobile user's location privacy and select the credible users to complete sensing tasks. Thirdly, to incentivize mobile users, we model the transaction process between mobile requesters and mobile responders as the first-order sealed price auction game. After that, the task assignment based bidding-preferred (TAB) algorithm is proposed to assign tasks to mobile users. Finally, simulation results demonstrate that the proposed scheme can effectively improve the number of finished tasks and bring more utilities to mobile users compared with the conventional methods.

The unmanned surface vehicles (USVs) have been regarded as a promising paradigm to automatically perform emergency tasks in a dynamic maritime traffic environment. However, the performance of maritime communication between USVs and offshore platforms becomes a critical challenge, and the efficiency of task allocation for USVs in the smart ocean is low. In this article, a novel task allocation scheme for USVs in the smart ocean Internet of Things (IoT) is proposed to improve the efficiency of task allocation. First, the offshore platform is developed to provide maritime communication for USVs in the smart ocean IoT. Second, the network resource allocation process between USVs and offshore platforms is modeled as the second price sealed auction game, where the optimal bidding strategy of USV is derived by the $Q$ -learning to maximize the utilities of USVs and offshore platforms. Third, the task allocation scheme is proposed to improve the number of allocated tasks. Finally, the performance of the proposed scheme is conducted based on extensive simulations. The simulation results show that the proposed scheme can significantly improve the number of allocated tasks compared with the conventional schemes.

The rise of the intelligent Internet of Things (IoT) promotes the development of emerging technologies and industries with the integration of edge computing and cloud computing. This integrated paradigm becomes a promising prospect to continuously expand the service capabilities of IoT. However, edge-driven intelligent IoT systems raises serious security issues as the systems are prone to be compromised by malicious attacks. In this article, an edge-driven security framework is investigated for intelligent IoT systems. First, we introduce the architecture of edge-driven intelligent IoT, and present typical edge-driven intelligent IoT applications. Second, we point out the security threats in edge-driven intelligent IoT in terms of attack behaviors of adversaries. Third, we develop a case study of edge-driven intelligent IoT from the security perspective. Finally, we discuss future research directions in this emerging area.