Crowdsensing Research Articles

DTC-MDD: A spatiotemporal data acquisition technology for privacy-preserving in MCS

Some attackers in the Internet of Things submit falsified high-quality data to cause harm to users. To prevent malicious workers from reporting untruthful data for skyline computation in Mobile Crowd Sensing, we propose a double trust check-based spatiotemporal data acquisition scheme, DTC-MDD. In DTC-MDD, worker trust uses four-way validation to obtain reliable worker trust evaluations. Then, based on Probabilistic Skyline Calculation, we propose a worker selection algorithm to select high-trust, high-quality workers for data reporting. We also introduce the Non-Interactive Encrypted Integer Comparison Protocol to safeguard privacy between workers and users from malicious attacks. Finally, through extensive simulations on real datasets, DTC-MDD effectively enhances the quality and security of spatiotemporal data acquisition. DTC-MDD improved the data quality and reliability of candidate worker sets by 16.2% and 49.1%, respectively, and the data quality and reliability of the first skyline worker by 21.4% and 320.0%, respectively.

Computation offloading in blockchain-enabled MCS systems: A scalable deep reinforcement learning approach

In Mobile Crowdsensing (MCS) systems, cloud service providers (CSPs) pay for and analyze the sensing data collected by mobile devices (MDs) to enhance the Quality-of-Service (QoS). Therefore, it is necessary to guarantee security when CSPs and users conduct transactions. Blockchain can secure transactions between two parties by using the Proof-of-Work (PoW) to confirm transactions and add new blocks to the chain. Nevertheless, the complex PoW seriously hinders applying Blockchain into MCS since MDs are equipped with limited resources. To address these challenges, we first design a new consortium blockchain framework for MCS, aiming to assure high reliability in complex environments, where a novel Credit-based Proof-of-Work (C-PoW) algorithm is developed to relieve the complexity of PoW while keeping the reliability of blockchain. Next, we propose a new scalable Deep Reinforcement learning based Computation Offloading (DRCO) method to handle the computation-intensive tasks of C-PoW. By combining Proximal Policy Optimization (PPO) and Differentiable Neural Computer (DNC), the DRCO can efficiently make the optimal/near-optimal offloading decisions for C-PoW tasks in blockchain-enabled MCS systems. Extensive experiments demonstrate that the DRCO reaches a lower total cost (weighted sum of latency and power consumption) than state-of-the-art methods under various scenarios.

A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications

Mobile CrowdSensing (MCS) is a computational paradigm designed to gather sensing data by using personal devices of MCS platform users. However, being the mobility of devices tightly correlated with mobility of their owners, the locations from which data are collected might be limited to specific sub-regions. We extend the data coverage capability of a traditional MCS platform by exploiting unmanned aerial vehicles (UAV) as mobile sensors gathering data from low covered locations. We present a probabilistic model designed to measure the coverage of a location. The model analyses the user’s trajectories and the detouring capability of users towards locations of interest. Our model provides a coverage probability for each of the target locations, so that to identify low-covered locations. In turn, these locations are used as targets for the StationPositioning algorithms which optimizes the deployment of k UAV stations. We analyze the performance of StationPositioning by comparing the ratio of the covered locations against Random, DBSCAN and KMeans deployment algorithm. We explore the performance by varying the time period, the deployment regions and the existence of areas where it is not possible to deploy any station. Our experimental results show that StationPositioning is able to optimize the selected target location for a number of UAV stations with a maximum covered ratio up to 60%.

Task recommendation based on user preferences and user-task matching in mobile crowdsensing

Task recommendation based on user preferences and user-task matching in mobile crowdsensing

Mobile Sensoring Data Verification via a Pairing-Free Certificateless Signature Secure Approach against Novel Public Key Replacement Attacks

To achieve flexible sensing coverage with low deployment costs, mobile users need to contribute their equipment as sensors. Data integrity is one of the most fundamental security requirements and can be verified by digital signature techniques. In the mobile crowdsensing (MCS) environment, most sensors, such as smartphones, are resource-limited. Therefore, many traditional cryptographic algorithms that require complex computations cannot be efficiently implemented on these sensors. In this paper, we study the security of certificateless signatures, in particular, some constructions without pairing. We notice that there is no secure pairing-free certificateless signature scheme against the super adversary. We also find a potential attack that has not been fully addressed in previous studies. To handle these two issues, we propose a concrete secure construction that can withstand this attack. Our scheme does not rely on pairing operations and can be applied in scenarios where the devices’ resources are limited.

Chronological Review on the Implementation of EEG in the Public Realm Assessment

From 1900 to the present, numerous studies have been conducted in the public realm to assess its diverse characteristics. As time has progressed advancements in technology have led to the development of neuroscientific methods which can assess spatial characteristics, human perception, decision-making, and human spatial cognition. Electroencephalogram (EEG), Functional Magnetic Resonance Imaging (fMRI), Crowd Sensing, Support Vector Machine (SVM), and Agent-based model (ABM) are a few prominent examples and some of these methods have proven to be more efficient in producing precise, relevant and bigger data. This paper focuses on and highlights the use of Electroencephalogram (EEG) in the public realm. It is a pragmatic tool that can be used for the subconscious assessment of users which is an advantage over traditional methodologies. This paper reviews and brings together various studies held in the public realm to learn how EEG can be implemented for evaluation and to find a possible breakthrough for future research.

When Connected and Automated Vehicles Meet Mobile Crowdsensing: A Perception and Transmission Framework in the Metaverse

When Connected and Automated Vehicles Meet Mobile Crowdsensing: A Perception and Transmission Framework in the Metaverse

VDCM: A Data Collection Mechanism for Crowd Sensing in Vehicular Ad Hoc Networks

VDCM: A Data Collection Mechanism for Crowd Sensing in Vehicular Ad Hoc Networks

TRACE: Transformer-based continuous tracking framework using IoT and MCS

Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target tracking solutions are either energy-inefficient or are only effective for fixed-length trajectories, making them impractical for real-world applications. For robust predictive tracking, with irregular trajectory lengths, energy efficiency and accuracy are vital to ensure system’s longevity and reliability. In this work, using a combination of trajectory prediction and path correction techniques, a novel approach, TRACE, is proposed for continuously tracking a target in an environment. TRACE uses locations offered by IoT/MCS localization systems to make predictions about the target’s future movement. A transformer neural network is implemented to learn mobility patterns to predict the target’s future trajectory. To ensure accurate predictions, a path correction mechanism is devised, by updating the predicted trajectory using polynomial regression. Experiments are conducted using a real-world GeoLife dataset to evaluate the performance of the proposed approach. The results demonstrate that TRACE performs better than existing tracking techniques with an improvement in accuracy of about 50% while using 85% less energy, supporting the potential of the proposed approach for enhancing target tracking in IoT/MCS applications.

Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing

The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users’ needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.

A method of user recruitment and adaptation degree improvement via community collaboration in sparse mobile crowdsensing systems

The task allocation problem in sparse mobile crowdsensing is simplified as a subarea selection problem. However, the lack of participants in some high-value subareas leads to the low quality of the final inferred sensing map. To solve this problem, a method of user recruitment and adaptation degree improvement via community collaboration is proposed. Firstly, the adjacency matrix is constructed based on the social relationship of the participants, and then all the participants are classified into communities by the non-negative matrix factorization method of deep autoencoder-like; secondly, the perception platform matches the perception tasks with the centroids of the perception communities based on the different eigenvalues of the classified perception communities and the location characteristics of the perception tasks. After the matching is completed, some participants in the matched communities will be selected to complete the perceptual tasks under the constraint of perceptual cost; finally, the perceptual data provided by the participants is used to obtain the complete perceptual map using the compressed perceptual algorithm. We designed this user recruitment method to obtain high-quality sensing data by recruiting a small number of users after community classification based on their social relationships and then accurately inferring the entire perceptual map. The experimental results based on the Gowalla and U-Air datasets show that the user recruitment method proposed in this paper can infer accurate data with fewer sensing areas, which is significantly better than other comparison methods.

Ellipsoid-Pricing-Based Context-Feature Mechanisms for Noisy Sensing Tasks

Recently, online pricing receives extensive attentions for addressing more realistic mobile crowdsensing (MCS) applications. However, existing these works mainly focus on finding an optimal price by repeated price experiments of user’s acceptance decision, which incurs a desirable regret. In practice, there are also many other factors that will affect these regrets, such contextual features as discrete features and continuous features. In this paper, we investigate the impact of users’ context features and corresponding weights on selecting an optimal price. To apply for multi-dimensional contextual features and learned corresponding weights, three ellipsoid pricing based mechanisms are proposed for homogenous and heterogeneous noisy sensing tasks respectively. What’s more, performance analysis are given for satisfying basic properties of crowdsensing. Finally, extensive simulations show that our mechanisms outweigh than existing mechanisms.

Task Recommendation via Heterogeneous Multi-modal Features and Decision Fusion in Mobile Crowdsensing

In the decision-making process of the behavior of mobile crowdsensing, using a single view to learn a user's preference will lead to a mismatch between the user's wishes and the final task recommendation list, resulting in the low efficiency of the model recommendation. Aiming at the lack of perceptual representation and cognitive fusion of multimodal coupled information, a task recommendation method based on heterogeneous multimodal features and decision fusion is proposed. According to the content characteristics of multi-source data in the user's historical task set, several task-task similarity matrices are constructed to align feature dimensions and feature semantics. Using the improved similarity network fusion algorithm, networks composed of multiple content similarity matrices are effectively fused into a similarity network. Considering the influence of the time factor, the tasks that have had interest drift are filtered out from the set of tasks that the user has participated in. Finally, the updated similarity network is clustered to predict the current preference of the user for new tasks. Experimental results based on simulation and real datasets show that the proposed method can effectively improve the accuracy and efficiency of task assignments while improving user satisfaction.

Blockchain-based decentralized model for mobile crowd-sensing

Blockchain-based decentralized model for mobile crowd-sensing

Trust-Preserving Mechanism for Blockchain Assisted Mobile Crowdsensing

Blockchain is envisioned as one of the promising technologies to address trust concern brought by mobile crowdsensing (MCS), due to its auditability, immutability and decentralization. Nevertheless, blockchain cannot fundamentally guarantee that the valuable sensed data outside the chain can enter the chain, although data integrity and consistency can be ensured once it is confirmed inside the chain. In addition, simply applying blockchain in MCS while ignoring possible abnormal saboteurs hidden in numerous devices may mislead the normal operation of blockchain, resulting in untrustworthy interactions. Consequently, it is highly desirable to build a trust-preserving mechanism (TPM) to fully enjoy the benefits of using blockchain in MCS. To this end, we first resort to a probabilistic trust assessment inferred from the interaction outcomes in blockchain, to incentivize participants to maintain the trustworthiness of interactions. By inferring trust to aid decision-making, trust decision is further made, including leader election and transaction data generation, to filter untrusted nodes from participating in blockchain process. Finally, extensive simulations are conducted to validate the effectiveness and efficiency of TPM, and improve the performance in terms of contribution rate, consensus accuracy and system stability.

A Privacy-Preserving and Reputation-Based Truth Discovery Framework in Mobile Crowdsensing

In mobile crowdsensing (MCS), truth discovery (TD) plays an important role in sensing task completion. Most of the existing studies focus on the privacy preservation of mobile users, and the reliability of mobile users is evaluated by their weights which are calculated based on the submitted sensing data. However, if mobile users are unreliable, the submitted sensing data and their weights are also unreliable, which may influence the accuracy of the ground truths of sensing tasks. Therefore, this paper proposes a privacy-preserving and reputation-based truth discovery framework named PRTD which can generate the ground truths of sensing tasks with high accuracy while preserving privacy. Specifically, we first preserve sensing data privacy, weight privacy, and reputation value privacy by utilizing the Paillier algorithm and Pedersen commitment. Then, to verify whether the reputation values of mobile users are tampered with and select mobile users that satisfy the corresponding reputation requirements, we design a privacy-preserving reputation verification algorithm based on reputation commitment and zero-knowledge proof and propose a concept of reliability level to select mobile users. Finally, a general TD algorithm with reliability level is presented to improve the accuracy of the ground truths of sensing tasks. Moreover, theoretical analysis and performance evaluation are conducted, and the evaluation results demonstrate that the PRTD framework outperforms the existing TD frameworks in several evaluation metrics in the synthetic dataset and real-world dataset.

Freshness-Aware Incentive Mechanism for Mobile Crowdsensing With Budget Constraint

Freshness-Aware Incentive Mechanism for Mobile Crowdsensing With Budget Constraint

IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities

IoMT-Assisted Medical Vehicle Routing Based on UAV-Borne Human Crowd Sensing and Deep Learning in Smart Cities

APLDP: Adaptive personalized local differential privacy data collection in mobile crowdsensing

Local differential privacy (LDP) enables terminal participants to share their private data safely while controlling the privacy disclosure at the source. In the majority of current works, they assumed that the privacy preservation parameter is totally determined by the data collector and then dispatched to all participants in mobile crowdsensing. However, in the real world, due to different privacy preferences of participants, it is inelegant and unpromising for all participants to accept the same privacy preservation level during data collection. To address such issue, an adaptive personalized local differential privacy (APLDP) data collection scheme is proposed to realize personalized privacy preservation while achieving higher data utility, in which two different LDP perturbation methods (basic RAPPOR and k-RR) are adaptively chosen by the participants according to their different privacy preferences, as well as the best perturbation probability is adaptively adopted by the participants to perturb their private data. In such case, the adaptive boundary based on the minimum mean square error (MSE) is theoretically derived to allow the participant to adaptively choose the best perturbation method, and meanwhile, it allows the participant to adaptively choose the best perturbation probability. Then, two estimation mergence methods named the direct combination (DC) and the weighted combination (WC) are demonstrated to do efficient data aggregation. Experiments on both synthetic and real data sets show that the proposed APLDP scheme performs better than previous non-personalized proposals in terms of the MSE and the average error rate (AER), especially using WC estimation method.

Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling and drone-aided visual inspections, etc. However, dynamic perception and cooperative decision-making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this paper, we propose an incentive-based federated learning scheme for digital twin driven industrial mobile crowdsensing. Specifically, we first design a digital twin driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based inter-client incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of non-independent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 datasets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.