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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]

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]

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]

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]

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]

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.

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]

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]

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]

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]

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]

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]

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]

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]

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.)

In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system's ability to defend against malicious user attacks was validated. The experimental results demonstrate the method's effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission. [ABSTRACT FROM AUTHOR]

The learning landscape concept reflects shifts in the methods of learning and conducting research in universities. Public spaces within university campuses should constitute an essential component of the learning landscape as arenas of planned and serendipitous encounters, which may foster creativity and trans-disciplinary networking. However, their spatial configurations remain an under-researched topic. This paper assesses the quality of public spaces on campus based on the results of a crowdsensing survey. The Third Campus of Jagiellonian University in Krakow was selected as a case study; this is one of the largest projects of this kind carried out in Poland since the political and economic transformation of 1989. The behaviour of users provides evidence of the generally low quality of the public spaces despite the advantages of the urban layout of the campus. The paper proposes recommendations that may bring the spatial organisation of the campus closer to a fully fledged learning landscape. [ABSTRACT FROM AUTHOR]

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]

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.

Traveling demand increased rapidly on the roads of big cities. Traffic congestion is observed on a regular basis and causes serious problems for citizens. It is a vital need in larger cities to deal with the problem of overcrowded roads because Traffic congestion has negative consequences on daily routines and activities. All of the factors that cause traffic-jam affect our lives physically, mentally, and economically. It has been observed that one of the factors of the traffic jam is traffic diversion. Traffic diversion reroutes all the traffic towards alternative tracks that causes traffic jams to the diverted road. It has been observed in the past that the researchers less considered traffic congestion due to traffic diversion. There is a need for analysis to determine the risk factor related to traffic diversion that impacts the causes of traffic jams. This paper focuses on proposing an architecture based on the Internet Cloud of Vehicles for traffic congestion control through the mobile crowdsensing technique. [ABSTRACT FROM AUTHOR]

The honking events' density reflects the level of traffic noise pollution, road congestion, etc in the urban areas. In this paper, we propose a participatory sensing based traffic honk monitoring system called HonkSense that uses smartphone equipped sensors (e.g. microphone, GPS, etc.). Citizens can take part in monitoring traffic noise pollution due to honking by recording ambient noise on the road. Application running on users' smartphones is used to extract features in real time from recorded audio and then send to the cloud for honk detection and decision making tasks. Here, Mel-Frequency Cepstral Coefficients (MFCCs) are utilized as feature for presenting audio signals in honk detection. This paper uses a deep Convolutional Neural Network (CNN) model that is deployed to cloud for detecting traffic honking events. The end-to-end system provides a privacy-preserving (anonymous data collection), low-power and low-cost solution for participatory sensing based traffic honk monitoring. We evaluate our proposed system on real world participatory sensing based road sound dataset collected by participants. It achieves a classification accuracy of 96.3%. The deep CNN is also evaluated on different benchmark datasets (namely ESC-50 and UrbanSound8K). The results are also compared with the baseline support vector machine (SVM) and k-nearest neighbors (KNN) classification models. Besides, state-of-the-art visualization techniques are used to explore spatial and temporal variability of honking events in urban areas using two case studies. [ABSTRACT FROM AUTHOR]

Collaborative crowdsensing is a team collaboration model that harnesses the intelligence of a large network of participants, primarily applied in areas such as intelligent computing, federated learning, and blockchain. Unlike traditional crowdsensing, user recruitment in collaborative crowdsensing not only considers the individual capabilities of users but also emphasizes their collaborative abilities. In this context, this paper takes a unique approach by modeling user interactions as a graph, transforming the recruitment challenge into a graph theory problem. The methodology employs an enhanced Prim algorithm to identify optimal team members by finding the maximum spanning tree within the user interaction graph. After the recruitment, the collaborative crowdsensing explored in this paper presents a challenge of unfair incentives due to users engaging in free-riding behavior. To address these challenges, the paper introduces the MR-SVIM mechanism. Initially, the process begins with a Gaussian mixture model predicting the quality of users' tasks, combined with historical reputation values to calculate their direct reputation. Subsequently, to assess users' significance within the team, aggregation functions and the improved PageRank algorithm are employed for local and global influence evaluation, respectively. Indirect reputation is determined based on users' importance and similarity with interacting peers. Considering the comprehensive reputation value derived from the combined assessment of direct and indirect reputations, and integrating the collaborative capabilities among users, we have formulated a feature function for contribution. This function is applied within an enhanced Shapley value method to assess the relative contributions of each user, achieving a more equitable distribution of earnings. Finally, experiments conducted on real datasets validate the fairness of this mechanism. [ABSTRACT FROM AUTHOR]

The security of the Earth system has been extensively discussed through the concept of planetary boundaries, which hypothesizes the Anthropocene as the crisis for pushing environmental variables beyond safe limits. Cities, as burgeoning population centers, warrant heightened attention to issues surrounding planetary boundaries and ecological security. While groundwork has been laid for environmental change detection, the acoustic or soundscape perspective is rarely considered. This paper presents abundant empirical evidence supporting the feasibility of leveraging the soundscape as a valuable lens for exploring ecosystem structures, functions, and their contribution to human well-being. Particularly, it proposes spatialized soundscape maps as practical tools to implement this innovative perspective. We elaborate on two key aspects: (i) soundscape as a reflection of ecosystem evolution, enabling evaluation of ecosystem structures, interactions, and the ecosystem's functions; (ii) soundscape has the attribution of providing cultural services, allowing assessment of its impact on human health. Consequently, we propose two paradigms: (i) "security in soundscape" and (ii) "security of soundscape", thereby initiated the concept of "soundscape for security". Furthermore, we outline two generalized pathways: (i) soundscape monitoring, encompassing long-term strategies for real-time tracking of ecosystem evolution, and (ii) soundscape perception, involving detailed surveys to investigate perception and public participatory sensing for large-scale evaluation of ecosystem cultural services. We argue that integrating soundscape considerations holds promise in urban ecological security initiatives and the pursuit of sustainable cities. Moving forward, collective efforts among academics are crucial to establish widely accepted protocols to maximize the value of soundscape for urban ecological security. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing leverages the power of a vast group of participants to collect sensory data, thus presenting an economical solution for data collection. However, due to the variability among participants, the quality of sensory data varies significantly, making it crucial to extract truthful information from sensory data of differing quality. Additionally, given the fixed time and monetary costs for the participants, they typically only perform a subset of tasks. As a result, the datasets collected in real-world scenarios are usually sparse. Current truth discovery methods struggle to adapt to datasets with varying sparsity, especially when dealing with sparse datasets. In this article, we propose an adaptive Hypergraph-based EM truth discovery method, HGEM. The HGEM algorithm leverages the topological characteristics of hypergraphs to model sparse datasets, thereby improving its performance in evaluating the reliability of participants and the true value of the event to be observed. Experiments based on simulated and real-world scenarios demonstrate that HGEM consistently achieves higher predictive accuracy. [ABSTRACT FROM AUTHOR]

Crowdsensing systems use a group of people to collect and share sensor data for various tasks. One example is the crowdsensing-based healthcare system, which provides smart services to patients and elderly people using wearable sensors. However, such a system faces a significant security challenge: how to authenticate the sensor device (patient) and exchange medical data securely over a public channel. Although considerable research has been directed towards authentication protocols for healthcare systems, state-of-the-art approaches are vulnerable to a series of attacks, including impersonation and stolen verifier attacks, and do not ensure perfect forward secrecy. In this paper, first, we elaborate two of such approaches. Then, we propose a Robust and Efficient Authentication scheme for Crowdsensing-based Healthcare systems, called REACH. We prove that REACH supports perfect forward secrecy and anonymity and resists well-known attacks. We perform various formal and informal security analyses using the Real-OR-Random (ROR) Model, BAN logic, and the well-known Scyther tool. We also show that REACH outperforms the related methods in incurring the minimum computational overhead and comparable communication overhead. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) has shown the greatest potential that allows smart devices to collect and share different sensing data. Mobile users (or participants) send the desired sensing data to the service providers and collect rewards. However, the reward needs to be given such as, it does not increase platform costs. On the other hand, the unsatisfactory reward may reduce the interest of the participant which may degrade the quality data. Therefore, increasing sensing data quality with a constrained budget is a crucial challenge. There has been extensive research on the reward mechanism for MCS, but, most of the work is on the basic assumption that participant will complete their assigned task positively. In this paper, we propose an efficient user selection mechanism for Mobile Crowdsensing System (MCS) by considering the Probability of Success (PoS) of users (i.e. participant may fail to complete the assigned task with some probability). For the selection of an efficient user, the proposed mechanism also accounts the parameters like data quality and platform cost. We also propose a reward calculation model for the selected users. Minimizing the platform cost with a constrained budget is an NP-hard problem. To provide a sub-optimal solution to this problem Chaotic Krill-Herd optimization algorithm is used. The extensive simulation results reveal that the proposed method outperforms the existing work by considerable margins. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model's performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications. [ABSTRACT FROM AUTHOR]

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant's exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human's micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models. [ABSTRACT FROM AUTHOR]

To solve the problem of privacy leakage and multi-task allocation in crowdsensing, this paper proposed an edge assisted crowdsensing location privacy protection(EALP) multi-task allocation mechanism. Firstly, considering the geography of tasks, this paper used improved fuzzy clustering(FCM) algorithm to cluster tasks locations, improved the clustering index, and enhanced the rationality of multi-task allocation. Secondly, to prevent collusion between the cloud and perceived users, it proposed a location privacy protection protocol in the task allocation phase. It deployed homomorphic encryption among the perceived users, cloud and edge nodes. The cloud could safely calculate the mobile distance of the perceived users without knowing their locations and the location of the task cluster center. Finally, it proposed a multi-task allocation optimization scheme based on ant colony algorithm, it balanced the interests of both platform and perceptive users by optimizing the path of execute tasks. Experiment results show that compared with similar methods, the proposed mechanism improves task completion rate while protecting location privacy, and reduces system perception costs and user mobility costs. [ABSTRACT FROM AUTHOR]

In event management, preventing excessive overcrowding is not only essential for providing comfort but also crucial for ensuring safety. However, understanding the crowd dynamics of participants in outdoor events can be challenging. One of the primary reasons is the limited availability of sensing systems suitable for outdoor use. Challenges include the need for power outlets and adapting to dynamic environmental conditions and unclear event boundaries. Consequently, there is still uncertainty about which measurements can be conducted to scientifically manage crowding based on sound principles. Therefore, there is a need for systems that are capable of discerning spatial and temporal heterogeneity in density and accurately estimating the number of people in regions of interest in both sparse and congested areas. In this study, we propose a novel approach for measuring and understanding crowd states at outdoor events. We designed a highly portable measurement system utilizing Bluetooth technology to monitor crowd density in real time, ensuring uninterrupted data collection even in remote event locations. This system stands out for its ability to operate effectively under diverse weather and lighting conditions without power outlets, making it highly adaptable to various outdoor settings. In our experiments, conducted at four distinct outdoor event locations, we used a 360° camera and LiDARs to validate the system. For instance, we deployed the system at 40-m intervals in a shopping district during a high-density parade. This deployment enabled us to capture the movement of the crowd and estimate the total number of people within the district. A key finding was the system's capability to detect temporal and spatial congestion in both sparse and crowded areas. The system's potential to estimate crowd sizes and manage diverse outdoor events marks an advancement over traditional methods like cameras and LiDARs. [ABSTRACT FROM AUTHOR]

Managing crowds is important not only during evacuation in disasters such as earthquakes and fires but also during normal situations. In particular, places where many people gather every day, such as stations or event venues, need such management to prevent crowd accidents. Moreover, efficient guidance that prevents people from waiting or queuing can improve facility services and lead to business opportunities. In this study, we propose a crowd management platform to prevent crowd accidents and provide efficient guidance to visitors. Specifically, we integrate real-time observations of crowd conditions, predictions, and risk assessments through simulation and crowd control in collaboration with security and facility managers. We also present the results of operating this platform in actual fields, which contribute to and support the safety and comfort of individuals. [ABSTRACT FROM AUTHOR]

Crowdsensing utilizes a range of sensing resources and participants, including mobile device sensors, to achieve collaborative sensing and information fusion. This enables it to handle complex social sensing tasks and provide more intelligent and real-time environment sensing services. Incentive mechanisms in crowdsensing are employed to address issues related to insufficient user participation and low-quality data submission. However, existing mechanisms fail to adequately consider reference points in user decision-making and uncertainty in the decision-making environment. This results in high incentive costs for the platform and limited effectiveness. On the one hand, the probabilities and utilities in the actual decision environment are defined based on user preferences, and uncertainty can lead to unpredictable impacts on users' future gains or losses. On the other hand, users identify their choices based on certain known values, namely reference points. The factors influencing user decisions are not solely the absolute final result level but rather the relative changes or differences between the final result and the reference point. Therefore, to resolve this problem, we propose TB-HQ, an incentive mechanism for high-quality cooperation in crowdsensing, which simultaneously considers the reference points adopted by users in decision-making and the uncertainty caused by their preferences. This mechanism includes a task bonus-based incentive mechanism (TBIM) and a high quality-driven winner screening mechanism (HQWSM). TBIM motivates users to participate in tasks by offering task bonuses, which alter their reference points. HQWSM enhances data quality by reconstructing utility functions based on user preferences. Simulation results indicate that the proposed incentive mechanism is more effective in improving data quality and platform utility than the comparative incentive mechanisms, with a 32.7% increase in data quality and a 77.3% increase in platform utility. [ABSTRACT FROM AUTHOR]

To address the threat posed by unknown signal sources within Mobile Crowd Sensing (MCS) systems to system stability and to realize effective localization of unknown sources in long-distance scenarios, this paper proposes a unilateral branch ratio decision algorithm (UBRD). This approach addresses the inadequacies of traditional sparse localization algorithms in long-distance positioning by introducing a time–frequency domain composite block sparse localization model. Given the complexity of localizing unknown sources, a unilateral branch ratio decision scheme is devised. This scheme derives decision thresholds through the statistical characteristics of branch residual ratios, enabling adaptive control over iterative processes and facilitating multisource localization under conditions of remote blind sparsity. Simulation results indicate that the proposed model and algorithm, compared to classic sparse localization schemes, are more suitable for long-distance localization scenarios, demonstrating robust performance in complex situations like blind sparsity, thereby offering broader scenario adaptability. [ABSTRACT FROM AUTHOR]

With the increasing popularity of mobile smart devices, Mobile Crowd Sensing (MCS) has gained significant attention from the research community. Worker recruitment is a key research problem in MCS systems, where platforms recruit suitable workers for tasks in specified locations. A recently proposed approach to worker recruitment is the socially-aware MCS model, which utilizes workers' social connections to expand the platform's worker pool. This approach effectively improves the quality of task sensing. In the past, most worker recruitment ignored the combined utility of all parties and the privacy of the worker location, instead considering the interests of only one of the task requesters, the platform, or the worker. Therefore, we propose a Socially-Aware and Privacy-Preserving Multi-Objective Worker Recruitment (SPMWR) model. The objective is to use social network-assisted recruitment to weigh the interests of workers and platforms while protecting worker location information. To address the model, we first introduce a differential privacy mechanism to protect worker location information. Then the Weighted Combinatorial Multi-Objective Genetic Algorithm (WCMOGA) is proposed, aiming to discover potentially better worker selection options as much as possible. The effectiveness of SPMWR is verified through comparative experiments on different scenarios with real data sets. [ABSTRACT FROM AUTHOR]

Mobile Crowd Sensing (MCS) has become a new archetype allowing individuals to effectively perform their sensing tasks by employing interested mobile users. This paradigm satisfies the requirements of the task requester, along with providing the willing participants with a way to generate profit by performing the specific tasks. Normally, an incentive is provided to the participants by the requester for processing the requested tasks. However, the requester may normally have a limited budget, so they prefer to make payments to the user providing good quality data instead of all the users participating in the process. Thus, selecting the most suitable user among the participant pool is required for executing the tasks efficiently in a short time. This paper presents an efficient online task allocation technique using a hybrid optimization approach. A novel Crow COOT Foraging Optimization (CCFO) algorithm is proposed for allocating tasks in MCS. The optimal user is chosen based on the fitness function devised using various aspects, like finish time, time of receiving task, time of sending task, makespan, monetary cost, ready time, and energy consumption. The CCFO algorithm is developed by modifying the C-BFO algorithm to the COOT algorithm to enhance the performance of the task allocation process. The presented CCFO technique for task allocation based on the fitness function evaluates makespan with the lowest value of 0.482. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for the wireless communications specially in sub-6 GHz frequency ranges has led to radio resource scarcity where opportunistic spectrum access is its main solution. An online spectrum decision and prediction system can assist cognitive radio users in seeking idle frequency bands for opportunistic use. However, previous studies have not considered the use of crowd-sensing technique to collect spectrum and contextual information to present a hybrid spectrum decision/prediction service. In this paper, we propose a novel cloud-based service for spectrum availability decision and prediction, which brings more contextual parameters into the decision with the aim of improving the quality of decision. Location, time, and velocity of sensing nodes, the density of buildings around sensing nodes, and weather status have been considered as context information. In the proposed method, spectrum availability data and some of the mentioned context parameters are collected through crowd-sensing. Artificial neural network (ANN) classifiers are suggested to decide about the status of spectrum bands in the proposed architecture. We also propose a spectrum prediction service in our architecture to predict the future of spectrum bands and recommend ANN and k-nearest neighbor algorithms for prediction. The proposed architecture has been implemented and evaluated. Experimental results show that using the addressed contextual information, the quality of spectrum availability decision is improved. [ABSTRACT FROM AUTHOR]

Utilizing readily accessible information and communication technologies (ICTs), such as mobile devices, applications, and simple Internet of Things (IoT) sensors, and harnessing their potential through Experimentation as a Service (EaaS), crowdsensing, and gamification, represents one of the most effective approaches to implementing co-creation in smart cities. The benefits of this bottom-up approach are closely related to accurately identifying the real needs of city residents and increasing the chances of designing and implementing solutions with genuine impact, ensuring equity, social inclusion, sustainability, and community resilience. This paper investigates the utilization of ICTs to support social sustainability by analyzing 157 smart city projects funded under the Horizon 2020 program at the European Union level and 5 smart city projects from Canada. The results reveal the utilization of technological solutions such as testbeds, living labs, EaaS, crowdsensing, open data, and more for co-creation in smart city projects. In the discussion part, we point out the importance of focusing on technologies that are familiar to the beneficiaries and on leveraging resources already available as wearable devices or in the citizens’ homes, the versatility of the technological solutions analyzed, the role of heterogeneous and open data, and cross-disciplinary teams in creating new perspectives on urban problems, reducing inequity in the development of solutions to solve them. The concerns raised and problems reported relate to the technology itself (errors in operation), users (difficulties in stimulating their involvement and keeping it constant), and data (quality of data collected, difficult to process, ethics and security of data collection and use). Based on our results, we extract, synthetize and present six distinct categories of lessons learned by the implementation teams of the analyzed projects.