This paper focuses on the QoS-aware secure routing design based on the physical layer security technology for a multi-hop wireless network consisting of legitimate nodes, malicious eavesdroppers, and selfish jammers. We first provide theoretical modeling for a given route to reveal how its end-to-end security/QoS performance is related to the transmitting power of legitimate nodes along the route and the jamming power of jammers in the network. We then design an incentive mechanism that stimulates jammers to generate artificial jamming for security enhancement, and also develop a non-cooperative game framework to resolve the jamming power setting issue here. Based on the security/QoS performance modeling of the route and jamming power setting, we further propose a theoretical framework to determine the optimal transmitting power of nodes along the route such that its optimal transmission security can be achieved under a QoS constraint. Finally, with the help of the power setting results of the given route, we formulate a shortest weighted path-finding problem to identify the optimal route for data delivery in the network, which can be solved by employing the Bellman-Ford or Dijkstra's algorithm. It is demonstrated that the proposed routing scheme is individually rational, stable, distributed and computationally efficient.

This paper focuses on the secure routing problem in decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers) and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra’s or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.

Motivated by the benefits of cognitive radio (CR), energy harvesting (EH), and backscatter communication (BC) technologies to support Internet of Things (IoT) systems, we investigate the backscatter-aided energy harvesting cognitive radio networks (EH-CRNs) in a multi-channel scenario. To achieve high throughput on various channels, we propose a novel hybrid communication scheme that the secondary transmitter (ST) selects one channel for spectrum sensing, and performs multiple actions based on the sensing result. To be specific, if the selected channel is detected as busy, the ST potentially performs underlay mode transmission, ambient backscatter communication (AmBC), or radio frequency (RF) EH. Otherwise, the ST performs interweave mode transmission. Based on the ST’s knowledge of the channel availability and the amount of the available energy, the decisions of channel and specific action selections are made. Furthermore, the sequential decision problem is formulated as a mixed observability Markov decision process (MOMDP), and addressed by the classic value iteration algorithm. The proposed scheme could be flexibly adapted to the changes in energy and channel availabilities. Simulations demonstrate the superiority of this scheme in terms of throughput, and show that even without channel selection, the proposed scheme conducted on the channels with different idle probabilities always achieves high throughput.

Mobile crowdsensing (MCS) has emerged as a popular and promising paradigm for solving challenging problems by utilizing collective wisdom and resources. However, the system architecture and operational rules for MCS have not been well-defined, and obtaining accurate and reliable results from conflicting data collected by workers is difficult due to discrepancies in sensor quality and privacy protection requirements. In this paper, we combine the methodologies of Dynamic Truth Discovery (DTD), Combinatorial Multi-Armed Bandit (CMAB), and Multi-Attribute Reverse Auction to develop a novel MCS ecosystem, with the objective of maximizing the sensing accuracy-aware utility under the budget constraint. We first establish the data collection model by jointly considering the task completion duration as well as the deviation caused by both endogenous errors and privacy protection-oriented injected noise. Then, we theoretically evaluate the accuracy of truth discovery and quantify the contribution of each worker to MCS to form the worker selection criterion. As the qualities of workers are initially unknown, the platform faces the exploration-exploitation dilemma. Therefore, we apply CMAB to transform the worker recruitment problem into a combinatorial arm-pulling problem and elaborately design an Upper Confidence Bound (UCB) algorithm to achieve a desirable exploration-exploitation tradeoff. Moreover, we design an auction-based payment method for the platform, stimulating workers to provide their quoted price honestly while enabling individual rationality. Extensive simulations and comparison results demonstrate the feasibility and effectiveness of our proposed MCS ecosystem.

In recent years, Cognitive Radio Networks (CRNs) have emerged as a transformative solution to address the growing demand for wireless spectrum resources and the inefficiencies of traditional static spectrum allocation policies

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EVs), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this article, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency,” such that an IDA-based data-energy transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.