Abstract Mobile crowdsensing (MCS) leverages the multi-sensory capabilities of mobile devices to collect diverse data efficiently. However, in MCS, inefficient task assignment strategies seriously affect the overall effectiveness, while efficient task assignment frequently requires the collection of sensitive information about users and tasks. In order to effectively trade-off task assignment efficiency and bilateral privacy security, we propose a privacy-preserving task assignment framework based on location fingerprinting. In this investigation, we propose a location fingerprinting-based for privacy preservation mechanism (LFPM) based on Monte Carlo stochastic algorithm to bidirectionally protect the location privacy of workers and tasks. Meanwhile, to overcome the challenge of utilizing location information while protecting privacy, a two-stage task allocation algorithm (TSTA) is proposed. This mechanism facilitates precise task assignment through segmental location fingerprinting, aiming to minimize the total cost of completing all tasks. We theoretically analyze its lightweight design and privacy features. Comparative experiments on real datasets show that this strategy achieves significant improvements in communication efficiency, computational performance, and task assignment accuracy compared to other methods.

With the continuous advancement of transportation systems, leveraging Vehicular Crowdsensing (VCS) for data collection and analysis within digital cities has become a promising paradigm. Most existing research focuses on improving task completion rates using location information but often overlooks the impact of drivers’ rational decision-making processes. To address this issue, we propose novel task scheduling and incentive mechanisms grounded in two distinct rational decision-making models. Specifically, drivers are categorized as Individual Rationality and Bounded Rationality based on their sensitivity to utility and cost. For drivers with Individual Rationality, who only accept tasks that ensure non-negative utility, we formulate the maximum weighted subset coverage problem (MWSC-Problem). On the other hand, for drivers exhibiting Bounded Rationality, who accept tasks with a certain probability influenced by their sensing utility and cost, we introduce the maximum probability task coverage problem (MPTC-Problem). The driver recruitment problem in both scenarios is proven to be NP-hard. For each case, we design customized scheduling and incentive algorithms to optimize both the platform’s task completion rate and cost efficiency. Meanwhile, the performance bounds and computational complexity of the proposed algorithms are theoretically analyzed. By extensive simulations on a real-world taxi dataset, the effectiveness of our strategies is validated.

Abstract The high efficiency of mobile crowdsensing (MCS) relies heavily on motivating users to participate in sensing tasks. Designing an auction-based incentive mechanism is a widely adopted approach. However, platforms operating in the unrestricted Internet environment are inevitably vulnerable to various types of malicious behaviors. While most existing studies focus solely on countering a single type of malicious behaviors, their approaches often lead to a decline in task acceptance rates, ultimately impacting the system’s utility. To address this challenge, we propose an incentive mechanism to resist multiple malicious user behaviors in a reverse auction, including monopoly and malicious competition. The PT-IM model is first introduced to identify and exclude monopolistic users through the calculation of tolerance price and user capability. Additionally, a novel task area division method is implemented within PT-IM to improve the task acceptance rate. Building on this foundation, we develop the enhanced model EPT-IM to further mitigate malicious competition among users through primary selection and secondary selection conditions. We conduct both theoretical and experimental analysis to evaluate EPT-IM. The results demonstrate that the proposed mechanism effectively resists malicious behaviors of users and surpasses other incentive mechanisms in terms of overall performance.

Zero-power communication, enabled by energy harvesting, backscattering, and low-power computing, is capable of fulfilling the requirements of emerging Internet of Things (IoT) communication scenarios that demand low cost, compact size, and minimal power consumption. Thus, it holds great potential as a transformative technology for the future of IoT. Trusted access and secure transmission remain essential in zero-power communication scenarios. Nevertheless, conventional complex security mechanisms become impractical due to limited power consumption and resources. This work presents a lightweight security protocol for authentication. Initially, a sliding window algorithm, utilizing the Hamming distance, is designed to generate the message digest. This algorithm leverages the remaining electric quantity of the transmitter as a secret parameter for authentication. Subsequently, a key distribution function based on the hash chain is employed to ensure the security of the session key. The protocol’s security attributes regarding transmitted data and its ability to withstand common attacks are demonstrated through formal security analysis and the utilization of the ProVerif analysis tool. Extensive simulations validate the efficacy of the proposed security algorithms, which are well suited for lightweight IoT devices with severely constrained resources and outperform benchmark algorithms.

Utilizing mobile crowdsensing (MCS) for data collection and analysis has become a prominent paradigm in the Internet of Things (IoTs). However, the existing research predominantly focuses on platform-user interactions, often neglecting the potential for user collaboration, which is crucial for improving data quality and task efficiency. In practical applications, mobile users tend to cooperate with familiar individuals based on their preferences in sensing tasks. To tackle this issue, we introduce a novel MCS model that integrates user cooperation, significantly enhancing the system's overall effectiveness. Specifically, users’ capabilities and costs are synthesized and managed through a cooperation degree matrix. Additionally, cooperation is updated based on historical behaviors and user preferences. To incentivize user participation, currencies are employed for recruitment. Within this framework, we investigate the maximum collaborative user selection (MCUS) problem, which is dedicated to the problem of maximizing the amount of recruitment under user cooperation. The MCUS problem is proved to be an NP-hard problem and thus intractable. To address this, we propose the minimum weighted cost replacement (MWCR) algorithm. Experimental results demonstrate that the MWCR algorithm exhibits low complexity and high efficiency across various scales, making it an excellent solution for collaborative crowd recruitment.

With the rapid growth of smart devices, mobile crowdsensing is becoming one of the most important and attractive paradigms to acquire information from physical environments. Low-quality data, a notorious but widely found issue, degrades the availability and preciseness of sensing services, especially for these complex sensing task scenarios. However, few existing incentive mechanisms frequently ignore the issue of data quality. In this paper, we define user reputation and user task preferences in a new perspective, while predicting the number of users likely to upload high-quality data by combining Poisson distribution. Then, the maximum expectation algorithm is employed to evaluate the parameter values of the Poisson distribution. Subsequently, a two-way auction mechanism is proposed, which encourages users to participate in the sensing task and improves the match between tasks and users. We adopt the number of high-quality data that the user may upload as a factor in the user’s offer to maximize the quality of data received by the platform. The analysis based on the model lays a theoretical foundation on the incentive process of mobile crowdsensing considering data quality. The evaluation results show that our mechanism outperforms other existing techniques, in terms of robustness and efficiency.

Exploiting mobile crowdsensing to conduct data collection and analysis brings unprecedented opportunities to promote the development of the Internet of Things(IoT). However, malicious platforms may provide untrusted data or illegally leak users’ information, which leads users in crowdsensing networks to be reluctant to participate in sensing activities. Besides, users are unwilling to report malicious platforms without sufficient incentives. To tackle the problem, a new incentive mechanism is proposed by modeling users’ preferences in this paper. Specifically, two scenarios are considered to detect malicious platforms when users join sensing activities according to the system grasps user’s information, i.e., complete information scenario and partial information scenario. Different incentive algorithms are designed for each scenario to optimize the systems incentive cost. In the complete information scenario, we minimize the total incentive cost by ranking users’ preferences. In the partial information scenario, uniform Distribution and Laplace Distribution are employed to model the distribution of users’ preferences to find the optimal cost. Specifically, we incorporate the concept of non-convexity into design the incentive mechanism, when user preferences obey the Laplace Distribution. By conducting an in-depth exploration the properties of Laplace Distribution, we can transform it into a convex problem to solve it efficiently. The analysis based on these mechanisms lays a theoretical foundation on the detection of malicious platforms. Furthermore, the soundness of modeling and the accuracy of analysis are verified through extensive simulation, which also guides the design of more sophisticated incentive schemes for the detection of malicious platforms.