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The article "Ten quick tips to build a Model Life Cycle" published in PLoS Computational Biology discusses the importance of managing models in computational biology through their life cycle. The authors emphasize the need to integrate model development with data collection and provide 10 tips to facilitate collaborations and maximize model re-use. They highlight the significance of sharing code, considering data architecture, and deciding on acceptable model performance criteria. The article aims to enhance research practices by establishing a systematic approach to bridging data and models in biological data-driven research. [Extracted from the article]

The Internet of Things (IoT) enables healthcare systems to handle emergencies, where multiple authorities interact to perform tasks. Prevention of unauthorized access and defining access domains for legitimate users are crucial. Attribute-Based Access Control System (ABACS) techniques play a vital role in defining boundaries in a multi-agent environment. However, adopting traditional ABAC in IoT-based resource-constrained networks is not feasible. This research analyzes the effects of attributes as key performance metrics, including execution time, memory overhead, and computational complexities. To address these challenges, this research proposes a Physical-Social Attributes Access Control Policy (PS-ABACS) framework that secures Multiparty Computation (SMC), symmetric encryption, and randomization-based access control methods. PS-ABASC introduces a lightweight two-party set intersection technique to generate an access policy. The analysis shows that the proposed technique is efficient in computing access policy and session key generation, and less number of attributes based on randomness characteristics is appropriate for resource-constrained networks. Moreover, it demonstrates advancements by reducing memory usage up to 0.048 KB for 60 attributes. The framework generates session keys proficiently, encrypts data, and minimizes computational expenses through a randomized attribute vector. In terms of communication overhead, the framework surpasses expectations by supporting up to 100 attributes, resulting in a reduction of transmission costs to 1120 bits. Overall, this framework improves security, reduces resource consumption, and enhances data exchange efficiency in IoT ecosystems. [ABSTRACT FROM AUTHOR]

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of "showing the fake, and hiding the real" by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ε-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP. [ABSTRACT FROM AUTHOR]

Plants sense and respond to hyperosmotic stress via quick activation of sucrose nonfermenting 1-related protein kinase 2 (SnRK2). Under unstressed conditions, the protein phosphatase type 2C (PP2C) in clade A interact with and inhibit SnRK2s in subgroup III, which are released from the PP2C inhibition via pyrabactin resistance 1-like (PYL) abscisic acid receptors. However, how SnRK2s are released under osmotic stress is unclear. Here, we outline how subgroup I SnRK2s sense molecular crowding to interrupt PP2C-mediated inhibition in plants. Severe hyperosmotic stress triggers condensate formation to activate the subgroup I SnRK2s, which requires their intrinsically disordered region. PP2Cs interact with and inhibit subgroup I SnRK2s, and this interaction is disrupted by phase separation of SnRK2s. The subgroup I SnRK2s are critical for severe osmotic stress responses. Our findings elucidate a mechanism for how macromolecular crowding is sensed in plants and demonstrate that physical separation of signaling molecules can segregate negative regulators to initiate signaling. [ABSTRACT FROM AUTHOR]

Visual Mobile Crowd-Sensing (VMCS) collects photos by leveraging camera embedded in mobile users' phones. There are two important issues in VMCS: determining whether photos collected by mobile users meet the requirements or not and designing an appropriate mechanism to attract mobile users to provide photos that meet the requirements. In this article, we address those two issues when VMCS is applied to emergency monitoring applications. We first model an emergency scene as a disk region and define a coverage angle metric that quantifies the coverage ratio provided by each photo, then formulate a Maximize Coverage Angle with Limited Budget problem. The goal of this work is to recruit mobile users to provide panoramic view coverage for a disk while the total reward paid to participants does not exceed the budget. In our solution, we first propose a Coverage Angle Computation algorithm to calculate the coverage angle of each uploaded photo. Then two incentive mechanisms—the Guidance-based Incentive Mechanism and the Coverage Prediction Incentive Mechanism—are designed to encourage mobile users to upload photos with a coverage angle that are not provided by other mobile users. Finally, we design a mobile app called I-share in the Android system to implement the system. Meanwhile, we recruited students to install I-share and simulated the information interaction between mobile users and the server. We conducted experiments by using I-share without and with an embedded Coverage Angle Computation algorithm to validate the efficiency of the two incentive mechanisms. The experiment results demonstrate that our proposed incentive mechanisms effectively attract mobile users to provide panoramic view coverage of emergency scenes when the budget allows. Additionally, the Coverage Prediction Incentive Mechanism outperforms the Guidance-based Incentive Mechanism, offering a higher coverage ratio with lower rewards. [ABSTRACT FROM AUTHOR]

This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a new solution, the Volunteer Task Allocation Engine (VTAE), to address these challenges. This solution is not based on economic incentives, and it has two primary goals. The first one is to improve user experience by limiting the workload and creating a user-centric task allocation solution. The second goal is to create an equal distribution of tasks over the spatial locations to make the solution robust against the possible decrease in participation. Two approaches are used to test the performance of this solution against different conditions: computer simulations and a real-world experiment with real users, which include a qualitative evaluation. The simulations tested system performance in controlled environments, while the real-world experiment assessed the effectiveness and usability of the VTAE with real users. This research highlights the importance of user-centered design in citizen science applications with altruistic participation. The findings demonstrate that the VTAE algorithm ensures equitable task distribution across geographical areas while actively involving users in the decision-making process. [ABSTRACT FROM AUTHOR]

Marine spatial planning (MSP) is one of the most important tools for ensuring sustainable use of marine areas. Although MSP is a well‐established method, its adoption in rapidly developing countries is a challenge. One of the main concerns is data adequacy, as the MSP process typically requires a large amount of spatial data on human activities, biodiversity, and socio‐ecological interactions within the planning area. Drawing from an institutional cooperation project in Zanzibar, Tanzania, we share our experience and demonstrate how to fill in socio‐ecological data gaps to support the development of MSP in areas with limited data availability. We developed a rapid and cost‐effective system for collecting biological data, which, together with remote sensing and place‐based participatory mapping, helped formulate the first pilot ecologically informed MSP for Zanzibar. By sharing our results and experiences, we aim to provide best practices, lessons learned, and recommendations for future projects with a similar ecological setting and socio‐economic context. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach for securing mobile crowd sensing (MCS) systems, with a focus on improving the safety and efficiency of crowd management during the Hajj pilgrimage through the integration of deep learning techniques within an IOTA-based framework. The proposed method employs a logit-boosted convolutional neural network (Logit-CNN) model to address significant security threats, such as jamming, spoofing, and faked sensing attacks, which are prevalent in large-scale, dynamic, and heterogeneous networks. Through comprehensive performance evaluations, the Logit-CNN model demonstrated superior accuracy and reliability, achieving a 99.5% accuracy, 99% precision, and 98% recall, outperforming traditional security methods by significant margins. These results highlight the model's ability to provide real-time anomaly detection, ensuring enhanced security and resource allocation. Furthermore, the study underscores the practical implications of deploying deep learning models in MCS systems, offering valuable insights into the challenges of real-world implementation and suggesting pathways for future research to further refine these security measures. The integration of deep learning with MCS systems not only elevates the overall security and management of large-scale events like the Hajj but also paves the way for its application in other similar environments. [ABSTRACT FROM AUTHOR]

User location trajectory refers to the sequence of geographic location information that records the user's movement or stay within a period of time and is usually used in mobile crowd sensing networks, in which the user participates in the sensing task, the process of sensing data collection faces the problem of privacy leakage. To address the privacy leakage issue of trajectory data during uploading, publishing, and sharing when users use location services on mobile smart group sensing terminal devices, this paper proposes a privacy protection method based on generative adversarial networks and attention mechanisms (BiLS-A-GAN). The method designs a generator attention model, GAttention, and a discriminator attention model, DAttention. In the generator, GAttention, combined with a bidirectional long short-term memory network, more effectively senses contextual information and captures dependencies within sequences. The discriminator uses DAttention and the long short-term memory network to distinguish the authenticity of data. Through continuous interaction between these two models, trajectory data with the statistical characteristics of the original data is generated. This non-original trajectory data can effectively reduce the probability of an attacker's identification, thereby enhancing the privacy protection of user information. Reliability assessment of the Trajectory-User Linking (TUL) task performed on the real-world semantic trajectory dataset Foursquare NYC, compared with traditional privacy-preserving algorithms that focus only on the privacy enhancement of the data, this approach, while achieving a high level of privacy protection, retains more temporal, spatial, and thematic features from the original trajectory data, to not only guarantee the user's personal privacy, but also retain the reliability of the information itself in the direction of geographic analysis and other directions, and to achieve the win-win purpose of both data utilization and privacy preservation. [ABSTRACT FROM AUTHOR]

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. [ABSTRACT FROM AUTHOR]

The security and privacy of data are major concerns in the mobile crowdsensing (MCS) environment due to the huge amount of heterogeneous data received from various users and devices automatically or manually regarding their surrounding environment. User participation in the MCS approach is highly essential to have a vast dataset for analysis that will provide the required information or beneficial solution for society. However, it is difficult to achieve due to huge energy consumption, the need for internet connectivity for data transmission, and the security and privacy of data. Therefore, it is essential to have a network coverage model in which data transmission can be done with minimal energy consumption and the need for internet connectivity can be removed from the user's side. The user's sensitive data needs to be protected from internal and external attackers to improve the efficiency of the solution provided by the MCS environment with genuine data. This work is based on data collection from users based on their experience for a certain location using the hybrid network coverage model based on clustering, in which each location may have just one or multiple heterogeneous cluster heads. Discrete event-based CrowdSenSim Simulator has been used to design a simulation environment in urban spaces in which 2000 users will move to any location randomly among considered 40 locations and provide feedback data for the location. In this paper, a novel security mechanism based on multiple heterogeneous cluster heads per location has been presented, and it provides better security against attackers than the security model with one cluster head per location. The proposed multiple-cluster heads per location (MCHL)-based mechanism has been compared with the vulnerable one-cluster head per location (OCHL)-based mechanism on the basis of the average number of rounds attackers attacked, average number of locations attackers attacked, average coverage and average efficiency of attackers, and average efficiency of system security. [ABSTRACT FROM AUTHOR]

With the rapid development and growing popularity of Internet of Things (IoT), edge-assisted crowdsensing has emerged as a new mode of data collection and data processing. In an edge-assisted crowdsensing system, a reasonable data task allocation and pricing mechanism is urgently required to promote the active participation of each part of the system. However, existing mechanisms either did not consider the impact of data quality on participant profits or ignored some parts of the whole system. We therefore propose a novel task allocation and pricing mechanism based on the Stackelberg game model, considering all four parties (data requesters, crowdsensing platform, edge servers and IoT sensors) in an edge-assisted crowdsensing system. Specifically, we decompose the problem into three game sub-problems, and design our mechanism using KKT conditional approaches, with the aim of maximising the benefits of each party in the crowdsensing system. We demonstrate mathematically that the Stackelberg equilibrium can be achieved in all three games, and validate its performance through simulation experiments. [ABSTRACT FROM AUTHOR]

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers' collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers' data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/e)¼-approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines. [ABSTRACT FROM AUTHOR]

Mobile Crowd Sensing is a human-based data collection model, and the approach taken to recommend data collection tasks to users in order to maximize task acceptance rates is an important part of this research. Existing task recommendation methods are based only on intuitive data for unconscious analysis and decision-making, and lack the embodiment of cognitive intelligence. To address the above problem, a conscious task recommendation based on cognitive reasoning computing in Mobile Crowd Sensing has been proposed, using knowledge from cognitive science to simulate the human thinking process in order to achieve warm learning and conscious recommendation of sensing tasks. First, the task attributes are segmented into positive and negative attributes using a Kernel Density Estimation method based on bandwidth self-selection. Then, the user's attribute preferences are diagnosed by the Cognitive Diagnostic Method to obtain the user's preference vector. Finally, get the overall preference trend of users based on the Drift Diffusion Model, and make decisions according to whether the current task drift direction is consistent with the user preference trend. Simulation experiments were conducted using the Taobao dataset, MTurk dataset, and synthetic dataset, it was ultimately proven that conscious task recommendation combined with user cognitive ability effectively reduced RMSE and improved task acceptance rate. RMSE was 10.5%∼70.8% lower than other methods, and the task acceptance rate was basically over 80%, with most of the results being over 90%. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) is a combination of crowdsourcing ideas and mobile sensing devices, designed to enable rational allocation of resources at scale. However, the MCS platform is highly vulnerable to injection attacks from malicious participants and fake tasks that interfere with platform service capabilities and sensing activities. To this end, the participant and task submission process is modeled as a multivariate time series, and a detection model for malicious participants and fake tasks (MP-FTD) with a Gaussian prior on the attentional mechanism and a two-stage adversarial training process is proposed. The attention mechanism was corrected using Gaussian bias, and then the corrected attention mechanism was used to obtain the correlation discrepancies between the data. Using the adversarial training method of Generative Adversarial Networks (GAN), the output of the correlation discrepancy reconstruction phase is transformed into a focus score, to amplify the reconstruction error in the output of the focus score reconstruction phase, and to improve the differentiation between the injected data and normal data of malicious attackers. The detection of these malicious attackers will effectively improve the robustness of the sensing platform. Experiments on six real-world datasets showed that the average F1-score reached 93.44%, outperforming the current baseline method, and resulting in an average 12.07% improvement in participant assignment accuracy and an average 12.25% improvement in task assignment accuracy in task assignment experiments. [ABSTRACT FROM AUTHOR]

Featured Application: This work presents the use of device-free wireless sensing for crowd size estimation on subway platforms. Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment's state. [ABSTRACT FROM AUTHOR]

Summary: The Internet of Vehicles (IoV) is a hot research topic in intelligent transportation, and the research on new methods of intelligent data transmission is one of the key contents. Aiming at the application scenario of urban air quality monitoring data collection and transmission based on IoV, a new reliable data transmission method (Vehicular Grouping‐Communicated Data [VGCD]) based on crowd sensing strategy is proposed. This method is designed to utilize the intelligent perception and collaborative monitoring of vehicle to collect data, avoiding redundancy and overload of information; a reliable data transmission minimum delay hybrid routing method is proposed in the data transmission part, which combines encoding mechanism with routing design, integrates routing switching ideas, predicts vehicle adaptive connectivity based on fuzzy logic, and makes probability based routing decisions to minimize delay, achieving reliable and efficient data collection and transmission. The proposed new method has important theoretical significance and practical value for various applications such as dynamic remote perception monitoring based on the IoV in intelligent transportation. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users' data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Cryptologic Research (2097-4116) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The conventional cloud-end MCS system currently faces problems of excessive load, leading to a significant increase in delay and energy consumption during the data aggregation process, inevitably causing a decrease in data aggregation efficiency. To tackle this issue, this paper proposed a cloud-edge-end MCS computation offloading algorithm based on APDQN. Firstly, it established a utility function considering the balanced optimization of delay and energy consumption, with the maximization of system utility as an optimized goal. Secondly, improving the P-DQN algorithm, it proposed a computational offloading algorithm AP-DQN for combining resource allocation. This algorithm, leveraging the advantages of MCS, designated idle users as one of the offloading devices. Finally, the problem was solved using the proposed method. Experimental results show that, compared to existing algorithms, the proposed method significantly improves data aggregation efficiency and maintains excellent system stability. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) receives extensive interest due to enabling many novel applications at lower cost. However in pricing incentive scenes of MCS, utility from mobile users presents complex distributions due to mobility, changes of abilities and resource consumption such as device's energy and memory, especially for submodular MCS with more general distributions. However, existing works only focus on homogeneous and heterogeneous MCS, whether multi-request pricing scene or single-request pricing scene. To the end, in this paper, we investigate online pricing issues for submodular MCS. Moreover, we apply a multiple-stage budget-limited process and robust mean estimators to design budget-limited pricing incentive for submodular MCS. Extensive simulations demonstrate that our mechanisms outweigh existing benchmarks. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection. [ABSTRACT FROM AUTHOR]

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) utilizes the mobility of participating users and relies on the sensing ability of user devices to complete high-quality sensing tasks with limited cost. Designing an incentive mechanism that maximizes revenue for both service provider and users while ensuring the quality of sensing data and preserving users' privacy remains a challenge in many scenarios. In this paper, we try to design an privacy-preserving incentive scheme based on DRL and Stackelberg game model which is dedicated to MCS. The proposed incentive mechanism is based on a two-stage Stackelberg game, in which the service provider is the leader and the user devices are the followers. We construct the relationship between user devices as a non-cooperative game and prove the existence and uniqueness of Nash equilibrium (NE) in this game. Considering the cost and quality of sensing data, we use the reputation constraint mechanism as the evaluation standard of data quality, and include sensing cost as indicator. Different from the traditional NE derivation method, we adopt deep reinforcement learning (DRL) approach (called PPO-DSIM) to derive NE and the optimal sensing strategy while protecting the user's private information. Numerical simulation results show the convergence and effectiveness of the PPO-DSIM. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Online crowdsourcing platforms have rapidly become a popular source of data collection. Despite the various advantages these platforms offer, there are substantial concerns regarding not only data validity issues, but also the ethical, societal, and global ramifications arising from the prevalent use of online crowdsourcing platforms. This paper seeks to expand the dialogue by examining both the "internal" aspects of crowdsourcing research practices, such as data quality issues, reporting transparency, and fair compensation, and the "external" aspects, in terms of how the widespread use of crowdsourcing data collection shapes the nature of scientific communities and our society in general. Online participants in research studies are informal workers who provide labor in exchange for remuneration. The paper thus highlights the need for researchers to consider the markedly different political, economic, and socio-cultural characteristics of the Global North and the Global South when undertaking crowdsourcing research involving an international sample; such consideration is crucial for both increasing research validity and mitigating societal inequities. We encourage researchers to scrutinize the value systems underlying this popular data collection research method and its associated ethical, societal, and global ramifications, as well as provide a set of recommendations regarding the use of crowdsourcing platforms. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a new paradigm for intelligent mobile devices to collect and share various types of sensing data in the urban environment. The recent rapid development of unmanned aerial vehicle (UAV) technology facilitates the realization of crowdsensing because UAVs have high-efficiency mobility in the urban environment and have been used in various areas of aerial photography, agriculture, plant protection, express transportation, disaster relief, and so on. However, for UAVs, one of the key issues for the archival of efficient crowdsensing is task allocation, which must balance the task quality and cost. This paper first proposes a mathematical model for task allocation for UAVs in crowdsensing. Then, for the effectiveness of data sensing, three algorithms are proposed to allocate tasks for the purpose of minimizing the incentive cost while ensuring the quality of sensing data. The proposed algorithms include the minimum cost first (MCF) algorithm, which assigns a high priority to UAVs with the lowest cost, the maximum ratio first (MRF) algorithm, which assigns a high priority to UAVs with a high ratio of sensing quality and sensing cost, and a genetic algorithm-based one (GA-TA), which comprehensively considers the factors such as UAV sensing quality, sensing cost, and execution ability. Experimental results show that, compared with MCF and MRF, GA-TA achieves the lowest total sensing cost, average task cost, and total moving distance of UAVs, and the highest average contribution of UAVs. Considering all factors, GA-TA is the best task allocation algorithm for UAVs in crowdsensing on average. [ABSTRACT FROM AUTHOR]

Soundscape contains rich information from biology, geography, and human society, and it is a comprehensive index of the ecosystem. The monitoring of soundscape can thus be a significant content of environmental monitoring and management, and the generated soundscape data has been gradually applied in subjective evaluation of acoustic environment quality, soundscape planning, species identification, biodiversity assessment, human physical and mental health evaluation, and other aspects, with the value of which has become increasingly prominent. Nowadays, the technical parameters of the equipment that can collect soundscape data vary in the market. The ways to use the equipment are also diverse, which is not beneficial for the comparison of research results. The paper describes the critical indicators of the soundscape data acquisition equipment in detail, including the sensitivity, frequency response, and sampling rate of the microphone. The practice methods and advantages and disadvantages of three common soundscape data acquisition patterns, including manual investigation in open spaces, participatory sensing on a large scale, and long-term monitoring with fixed stations, are also summarized. With the increasing soundscape data, these issues are urgent to be considered to standardize data acquisition, including data format, data storage method, and metadata information, to improve the management and sharing of soundscape data. The biological and physiological characteristics should be taken into account regarding the heterogeneity of organisms’ vocalization. Meanwhile, the future direction of the soundscape monitoring network was presented, which hopes to improve the acquisition techniques of soundscape data in our country. [ABSTRACT FROM AUTHOR]

This paper focuses on human activity recognition (HAR) for elderly care using the Light Gradient Boosting Machine (LGBM) algorithm. HAR plays a vital role in monitoring and ensuring the well-being of older individuals. By analysing sensor data, this system can accurately detect activities such as jogging, walking, sitting, and standing. The proposed framework integrates LGBM with an Android application that reads user movement data, classifies activities, displays step counts per day, and provides rewards for achieving movement targets. To address privacy concerns, user data is anonymized using Elliptic Curve Cryptography (ECC) Blind Signature. The system leverages the power of artificial intelligence models in the Mobile Crowd Sensing (MCS) server to effectively distinguish between different activities with high accuracy and reliability. By remotely monitoring the elderly's activities, healthcare providers can ensure their safety. Experiment results show the practicality of proposed system by achieving overall accuracy of activity recognition when sitting, standing, walking, and jogging 97.5%. [ABSTRACT FROM AUTHOR]

The sensors available in the smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users' participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms. [ABSTRACT FROM AUTHOR]

The growing demand for effective healthcare has driven advances in digital health. This digitization supposes a challenge from the point of view of privacy and the treatment of sensitive personal data while providing non-intrusive and easy-to-use digital mechanisms. This paper presents Contigo: a health monitoring system that integrates a mobile application and a web platform for detecting anomalies using Federated Learning techniques. The mobile application collects health and personal data to train a personal predictive model. It is then anonymized and aggregated into a global model to improve efficiency, reducing adoption time for new users. At the same time, the web platform allows healthcare professionals to access the data for its analysis and validation. Contigo addresses the need for user-friendly digital mechanisms in healthcare, addressing privacy concerns while improving data-driven decision-making for professionals and personalized patient care. This approach ensures privacy and facilitates continuous model improvement, providing personalized, proactive, and non-intrusive patient health analytics.

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

This study proposes a novel spatiotemporal crowdsensing and caching (SCAC) framework to address the surging demands of urban wireless network traffic. In the context of rampant urbanization and ubiquitous digitization in cities, effective data traffic management is crucial for maintaining a dynamic urban ecosystem. Leveraging user mobility patterns and content preferences, this study formulates an offloading policy to alleviate congestion across urban areas. Our approach uses an AI-based method at the cell level, providing a practical and scalable solution that can be readily adapted to bustling metropolitan areas. The implementation of our model demonstrated its effectiveness in reflecting real-world urban dynamics, resulting in significant reductions in peak-hour traffic and robust performance across diverse urban settings. The deployment strategy initiates from densely populated transportation hubs, gradually expanding to broader urban areas. This systematic expansion adheres to a policy framework that emphasizes data privacy and sustainable urban development, ensuring alignment with societal needs and regulatory frameworks. By addressing technological efficacy and societal impact, this study enhances the understanding of urban wireless traffic management. It offers mobile network operators, policymakers, and urban planners a comprehensive strategy to harness the potential of spatiotemporal technology, thereby ensuring that cities remain dynamic, efficient, and well-prepared for the future of digital connectivity. [ABSTRACT FROM AUTHOR]

The physicochemical properties of the cellular environment impact on the biochemical and molecular functions of plant cells. Osmotic stress conditions cause severe changes in intracellular macromolecular crowding that plants must adapt to for survival. Recent work has demonstrated that plant cells have mechanisms to sense changes in macromolecular crowding, including plasma membrane and intracellular osmosensors. Tools to dynamically track changes in macromolecular crowding will further contribute to the identification of plant osmosensors. Osmotic stress conditions occur at multiple stages of plant life. Changes in water availability caused by osmotic stress induce alterations in the mechanical properties of the plasma membrane, its interaction with the cell wall, and the concentration of macromolecules in the cytoplasm. We summarize the reported players involved in the sensing mechanisms of osmotic stress in plants. We discuss how changes in macromolecular crowding are perceived intracellularly by intrinsically disordered regions (IDRs) in proteins. Finally, we review methods for dynamically monitoring macromolecular crowding in living cells and discuss why their implementation is required for the discovery of new plant osmosensors. Elucidating the osmosensing mechanisms will be essential for designing strategies to improve plant productivity in the face of climate change. [ABSTRACT FROM AUTHOR]

The lack of multidimensional knowledge means that current reasoning methods for rice fertilization cannot make decisions accurate when faced with complex spatiotemporal conditions in general. Therefore, we propose a reasoning method for rice fertilization strategy based on spatiotemporal knowledge graph. First, we systematically organize multisource expert knowledge about rice fertilization, and construct an ontology for rice fertilization consisting of five core elements: rice variety, planting environment, nutrition diagnosis, fertilization schemes, and time and place. Spatiotemporal differences in rice fertilization knowledge are expressed by assessing spatiotemporal concepts, relations, and state instances. Second, we propose a reasoning method for rice fertilization strategy based on the constructed knowledge graph. This method leverages a certainty factor model for nutrition diagnosis and integrates case‐based and rule‐based reasoning to determine fertilization schemes for different stages. Finally, taking Pucheng County, China, as an example, knowledge from crowd‐sensing data is obtained to construct a knowledge graph using the proposed method. The results demonstrate the method can support the expression and complex reasoning of rice fertilization decisions under different spatiotemporal conditions. [ABSTRACT FROM AUTHOR]

Developing mmWave radar sensors for indoor crowd motion sensing and tracking faces a critical challenge: the scarcity of large-scale, high-quality training data. Traditional human experiments encounter logistical complexities, ethical considerations, and safety issues. Replicating precise human movements across trials introduces noise and inconsistency into the data. To address this, this study proposes a novel solution: a movable platform equipped with a life-size mannequin to generate realistic and diverse data points for mmWave radar training and testing. Unlike human subjects, the platform allows precise control over movements, optimising sensor placement relative to the target object. Preliminary optimisation results reveal that sensor height impacts tracking performance, with an optimal sensor placement above the test subject yields the best results. The results also reveal that the 3D data format outperforms 2D data in accuracy despite having fewer frames. Additionally, analysing height distribution using 3D data highlights the importance of the sensor angle—15° downwards from the horizontal plane. [ABSTRACT FROM AUTHOR]

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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%. [ABSTRACT FROM AUTHOR]