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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Insufficient datasets due to a lack of data contribution from participants is a longstanding problem for data collection in participatory sensing. Empirical evidence has shown that non-monetary incentives can be a strong motivator to enhance participants’ contributions. The purpose of this paper is to examine the influence of non-monetary incentives for data collection in participatory sensing. Sequential explanatory design is employed where the study integrates both quantitative and qualitative data analysis. The study uses a partial least squares structural equation modeling (PLS-SEM) analysis for quantitative data, in which a survey (N=301) of respondents attempted to identify the non-monetary incentives that influence data collection performance. The quantitative findings are further analyzed in the qualitative study using thematic analysis. Quantitative findings show that all four non-monetary incentives significantly influence participatory sensing data collection. A follow-up qualitative study suggests a convergence of the quantitative findings where inverse influence exists between the intrinsic incentives (autonomy and mastery) and the extrinsic incentives (purpose, social) toward data collection performance. Quantitative and qualitative findings show that an intrinsic incentive is more important than an extrinsic one in participatory sensing. This paper contributes to the study of participatory sensing by proposing the non-monetary incentive for participatory sensing (NMIPS) framework for participatory sensing data collection. The use of a sequential explanatory research design in the study demonstrates the ability of the proposed framework, which covers a broad spectrum of non-monetary incentives and is able to explain the contradiction between intrinsic and extrinsic incentives in participatory sensing. Moreover, the framework offers practical contributions for various stakeholders. It aids system developers, campaign organizers, and public health officials by improving incentive design, participant recruitment, and program evaluation.

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.

This paper describes a data collection experiment and the resulting dataset based on Bluetooth beacon messages collected in an indoor museum. The goal of this dataset is to study algorithms and techniques for proximity detection between people and points of interest (POI). To this purpose, we release the data we collected during 32 museum's visits, in which we vary the adopted smartphones and the visiting paths. The smartphone is used to collect Bluetooth beacons emitted by Bluetooth tags positioned nearby each POI. The visiting layout defines the order of visit of 10 artworks. The combination of different smartphones, the visiting paths and features of the indoor museum allow experiencing with realistic environmental conditions. The dataset comprises RSS (Received Signal Strength) values, timestamp and artwork identifiers, as long as a detailed ground truth, reporting the starting and ending time of each artwork's visit. The dataset is addressed to researchers and industrial players interested in further investigating how to automatically detect the location or the proximity between people and specific points of interest, by exploiting commercial technologies available with smartphone. The dataset is designed to speed up the prototyping process, by releasing an accurate ground truth annotation and details concerning the adopted hardware.

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]

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]

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]

Road surfaces can contain a lot of anomalies such as potholes. Understanding and detecting such irregularities are a key towards building a safe road transport system and a step towards building a smart city. This paper makes an attempt to classify road surface images using transfer learning on pre-trained convolution neural network (CNN) based models. In this work, two types of transfer learning approaches namely fine tuning and feature extraction are employed on various state-of-the-art CNN architectures of recent past such as VGG-16 (by Visual Geometry Group), VGG-19, Inception, ResNet-50 (Residual Network), Xception, DenseNet-121, SEResNet-50 (Squeeze-and-Excitation ResNet model), and EfficientNet-B3. The performance of the models are evaluated based on real-world dataset of road surface images collected by the participants. The evaluation metrics considered are test accuracy, precision, recall, F1-score, specificity, area under the receiver operating characteristic curve (ROC-AUC) and also training and inference time measurements. It is observed that among all the deep CNN models, SEResNet-50 (F1-score = 0.954, ROC-AUC = 0.993), Xception (F1-score = 0.943, ROC-AUC = 0.987) and DenseNet-121 (F1-score = 0.941, ROC-AUC = 0.987) achieve the best performance in terms of the evaluation metrics while fine tuning is considered with data augmentation. Effectiveness of the deep CNN models are also evaluated on various benchmark datasets, namely Road Traversing Knowledge (RTK) dataset and pothole dataset. [ABSTRACT FROM AUTHOR]

To further evolve urban quality of life, this paper explores the potential of crowdsensing and crowdsourcing in the context of smart cities. To aid urban planners and residents in understanding the nuances of day-to-day urban dynamics, we actively pursue the improvement of data visualisation tools that can adapt to changing conditions. An architecture was created and implemented that ensures secure and easy connectivity between various sources, such as a network of Internet of Things (IoT) devices, to merge with crowdsensing data and use them efficiently. In addition, we expanded the scope of our study to include the development of mobile and online applications, emphasizing the integration of autonomous and geo-surveillance. The main findings highlight the importance of sensor data in urban knowledge. Their incorporation via Tepresentational State Transfer (REST) Application Programming Interface (APIs) improves data access and informed decision-making, and dynamic data visualisation provides better insights. The geofencing of the application encourages community participation in urban planning and resource allocation, supporting sustainable urban innovation. [ABSTRACT FROM AUTHOR]

As a special type of location-based service (LBS), crowdsensing becomes more prosperous in people's daily life. However, during the process of task distribution, the publisher's and workers' locations will be revealed to each other, and then their personal privacy is violated. So in this paper, in order to cope with the violation of location privacy in crowdsensing and provide privacy preservation service for both entities, an active oblivious transfer-based location privacy preservation crowdsensing scheme (short for AOTC) has been proposed. In this scheme, the oblivious transfer is used to encrypt the range of sensing grid of workers, and then matching sensing grids with the sensing region of the publisher without decryption. During the whole process, the process of location matching and results sending is disposed of by the entity of workers actively, so does not establish any data aggregation that can be used as the point of attack. As a result, the AOTC can guarantee the personal privacy of both entities in crowdsensing cannot be obtained by each other, and guarantee other workers also difficult to obtain the precise location of any workers. In addition, as workers send the sensing result to the publisher actively this scheme can also increase the probability of workers' participation potentially. At last, the theoretical privacy preservation ability of AOTC is analyzed in the section on security analysis with three types of privacy threats. Then the performance of AOTC is compared with other similar schemes in both privacy preservation and execution efficiency, so in simulation experiments, comparison results with brief analyses will confirm that the AOTC has achieved the desired effect and will further demonstrate the superiority. [ABSTRACT FROM AUTHOR]

Participatory crowdsensing (PCS) is an innovative data sensing paradigm that leverages the sensors carried in mobile devices to collect large-scale environmental information and personal behavioral data with the user's participation. In PCS, task assignment and path planning pose complex challenges. Previous studies have only focused on the assignment of individual tasks, neglecting or overlooking the associations between tasks. In practice, users often tend to execute similar tasks when choosing assignments. Additionally, users frequently engage in tasks that do not match their abilities, leading to poor task quality or resource wastage. This paper introduces a multi-task assignment and path-planning problem (MTAPP), which defines utility as the ratio of a user's profit to the time spent on task execution. The optimization goal of MATPP is to maximize the utility of all users in the context of task assignment, allocate a set of task locations to a group of workers, and generate execution paths. To solve the MATPP, this study proposes a grade-matching degree and similarity-based mechanism (GSBM) in which the grade-matching degree determines the user's income. It also establishes a mathematical model, based on similarity, to investigate the impact of task similarity on user task completion. Finally, an improved ant colony optimization (IACO) algorithm, combining the ant colony and greedy algorithms, is employed to maximize total utility. The simulation results demonstrate its superior performance in terms of task coverage, average task completion rate, user profits, and task assignment rationality compared to other algorithms. [ABSTRACT FROM AUTHOR]

As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. However, the sensing environment is often so complex that the observable and unobservable data collected are sparse and heterogeneous, affecting the accuracy of the reconstruction. In this paper, we focus on developing a robust multi-agent environment monitoring framework, called self-interested coalitional crowdsensing for multi-agent interactive environment monitoring (SCC-MIE), including environment reconstruction and worker selection. In SCC-MIE, we start from a multi-agent generative adversarial imitation learning framework to introduce a new self-interested coalitional learning strategy, which forges cooperation between a reconstructor and a discriminator to learn the sensing environment together with the hidden confounder while providing interpretability on the results of environment monitoring. Based on this, we utilize the secretary problem to select suitable workers to collect data for accurate environment monitoring in a real-time manner. It is shown that SCC-MIE realizes a significant performance improvement in environment monitoring compared to the existing models. [ABSTRACT FROM AUTHOR]

As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μ EMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches. [ABSTRACT FROM AUTHOR]

Adaptive task recommendation in Mobile crowd sensing (MCS) is a challenging problem, mainly because perceptual tasks are spatio-temporal in nature and worker preferences are dynamically changing. Although there have been some approaches to address the dynamics of task recommendation, these approaches suffer from several problems. First, they only learn the worker's past preferences and cannot cope with the situation where the worker's preferences may change in the next moment, and they only consider the current optimal recommendation instead of global optimization. Second, existing methods do not scale efficiently to the arrival of new workers or tasks, requiring the entire model to be retrained. To address these issues, we propose an adaptive task recommendation method (ATRec) based on reinforcement learning. Specifically, we formalize the adaptive task recommendation problem for each target worker as an interactive Markov decision process (MDP). Then, we use an improved matrix decomposition technique to construct worker-personalized latent factor states based on information such as task content and spatio-temporal context, enabling us to use a unified MDP to learn optimal strategies for different workers. After that, we design an adaptive update algorithm (AUA) based on Deep Q Network (DQN) to more accurately learn the dynamic changes of workers' preferences to adaptively update the task recommendation list of workers. In addition, we propose a personalized dimension reduction method (PDR) to reduce the size of the task set. Through comprehensive experimental results and analysis, we demonstrate the effectiveness of the ATRec approach. Compared with existing methods, ATRec can better solve the problem of adaptive task recommendation, and can more accurately predict workers' preferences and make recommendations. [ABSTRACT FROM AUTHOR]

Most of the existing MCS task allocation methods often only consider the unilateral utility of workers or platforms, and the composition of utility is not comprehensive enough. Therefore, this paper designed a heterogeneous utility mechanism for both workers and platforms based on the worker reputation index and task proficiency index. It proposed a dual-population competitive multi-objective evolutionary algorithm (DCMEA) to obtain the optimal worker and platform heterogeneous utilities. The algorithm firstly initialized the population using a stochastic greedy algorithm, then divided the population into a winner population and a loser population using a binary bidding tournament algorithm, and employed different evolutionary strategies for each population. Finally, this paper proposed the repair operator to make the invalid individuals in the evolution process satisfy the constraint, Experiments on real-world datasets show that DCMEA converges faster than the baseline algorithm, and can find more accurate and stable task allocation solution sets, while maintaining its performance in more complex scenarios. [ABSTRACT FROM AUTHOR]

Deep learning is widely utilized to acquire predictive models for mobile crowdsensing systems (MCSs). These models significantly improve the availability and performance of MCSs in real-world scenarios. However, training these models requires substantial data resources, rendering them valuable to their owners. Numerous protection schemes have been proposed to mitigate potential economic loss arising from legal issues pertaining to model copyright. Although capable of providing copyright verification, these schemes either compromise the model utility or prove ineffective against adversarial attacks. Additionally, the privacy concern surrounding copyright verification is noteworthy, given the increasing privacy concerns among model owners. This paper introduces a privacy-preserving testing framework for copyright protection (PTFCP) comprising multiple protocols. Our protocols adhere to the two-cloud server model, where the owner and the suspect transmit their model output to non-colluding servers for evaluating model similarity through the public-key cryptosystem with distributed decryption (PCDD) and garbled circuits. Additionally, we have developed novel techniques to enable secure differentiation for absolute values. Our experiments in real-world datasets demonstrate that our protocols in the PTFCP successfully operate under numerous copyright violation scenarios, such as finetuning, pruning, and extraction. [ABSTRACT FROM AUTHOR]

Data imputation is prevalent in crowdsensing, especially for Internet of Things (IoT) devices. On the one hand, data collected from sensors will inevitably be affected or damaged by unpredictability. On the other hand, extending the active time of sensor networks has urgently aspired environmental monitoring. Using neural networks to design a data imputation algorithm can take advantage of the prior information stored in the models. This paper proposes a preprocessing algorithm to extract a subset for training a neural network on an IoT dataset, including time window determination, sensor aggregation, sensor exclusion and data frame shape selection. Moreover, we propose a data imputation algorithm using deep compressed sensing with generative models. It explores novel representation matrices and can impute data in the case of a high missing ratio situation. Finally, we test our subset extraction algorithm and data imputation algorithm on the EPFL SensorScope dataset, respectively, and they effectively improve the accuracy and robustness even with extreme data loss. [ABSTRACT FROM AUTHOR]

As the number of smart cars grows rapidly, vehicular crowdsensing (VCS) is gradually becoming popular. In a VCS infrastructure, sensing devices and computing units hold on smart cars as well as cloud servers form an IoT-edge-cloud continuum to perform real-time sensing tasks. In order to encourage the smart cars to participate in the real-time VCS process, blockchain technology can be combined with VCS to provide an automated incentive for VCS data trading without relying on trusted third parties. However, directly using blockchain to enforce the VCS data trading process incurs expensive service fees and participants still can conduct various misbehavior. In this article, we propose a secure blockchain-based data trading system for VCS named BTT system to address the above issues. In particular, we first integrate the blockchain-based data trading process with a lightweight privacy-preserving truth discovery algorithm to ensure the accuracy of sensing data while preserving data privacy. We then propose a gas-aware optimization mechanism to minimize the gas consumption of the data trading process. Finally, we carefully design a distributed judgment mechanism to regulate all participants to behave correctly in the data trading process. To demonstrate the practicability of our design, we implement a prototype of the BTT system deployed on an Ethereum test network and conduct extensive simulations. [ABSTRACT FROM AUTHOR]

In mobile crowdsensing, the sensing platform recruits users to complete large-scale sensing tasks cooperatively. In order to guarantee the quality of sensing tasks, the platform needs to recommend suitable tasks to users. Existing task recommendation methods typically focus on unilateral factors, such as user preferences or task quality, leading to low platform utility and task acceptance rate respectively. To solve the above issue, this paper proposes a task recommendation method which takes both user preferences and user-task matching into consideration. Firstly, we apply the Deep Interest Network (DIN) in the context of mobile crowdsensing to recommend tasks according to user preferences. Secondly, the concept of user-task matching is introduced, in which both the task difficulty and the user reliability are taken into account. Finally, we propose task recommendation algorithms and conduct extensive experiments on a real dataset. The experimental results show that the proposed method can not only improve the utility of the platform significantly, but also improve the recommendation accuracy slightly under longer recommendation list. [ABSTRACT FROM AUTHOR]

The out‐of‐sight groundwater and visible but much less extensive surface waters on land constitute a linked terrestrial water system around the planet. Research is crucial for our understanding of these terrestrial water system links and interactions with other geosystems and key challenges of Earth System change. This study uses a scoping review approach to discuss and identify topical, methodological and geographical gaps and priorities for research on these links and interactions of the coupled ground‐ and surface water (GSW) system at scales of whole‐catchments or greater. Results show that the large‐scale GSW system is considered in just a small part (0.4%–0.8%) of all studies (order of 105 for each topic) of either groundwater or surface water flow, storage, or quality at any scale. While relatively many of the large‐scale GSW studies consider links with the atmosphere or climate (8%–43%), considerably fewer address links with: (a) the cryosphere or coastal ocean as additional interacting geosystems (5%–9%); (b) change drivers/pressures of land‐use, water use, or the energy or food nexus (2%–12%); (c) change impacts related to health, biodiversity or ecosystem services (1%–4%). Methodologically, use of remote sensing data and participatory methods is small, while South America and Africa emerge as the least studied geographic regions. The paper discusses why these topical, methodological and geographical findings indicate important research gaps and priorities for the large‐scale coupled terrestrial GSW system and its roles in the future of the Earth System. Plain Language Summary: The water on the land surface (surface water) and that beneath it (groundwater), along with the water that is continuously and increasingly used and managed in human societies, are connected and constitute a coherent natural‐social water system around the world. Many unknowns and open questions remain for how the small‐scale variations add up to large‐scale variability and change of this water system on land, as an integral part of the whole Earth System. Relevant research is crucial for reducing the unknowns and answering the questions, and this study's scoping review aims to assess how they have been addressed in published research so far. The aim is to identify key research gaps and priorities for further research on how the integrated water system on land functions and evolves on large scales, from whole hydrological catchments and in multiple catchments around the world up to global scale. The scoping review results show key research gaps and priorities to be the coupling of surface water and groundwater on land, and the interactions of this coupled water system with other parts and major challenges of the Earth System. Geographically, the gaps and priorities emerge as particularly large and urgent for South America and Africa. Key Points: Coupling of the ground‐surface water system is a key gap in terrestrial water research, particularly at large scalesResearch on terrestrial water interactions with other geospheres and key challenges of Earth System change is rare but impactfulMajor geographic gaps in research on the large‐scale coupled terrestrial water system emerge for South America and Africa [ABSTRACT FROM AUTHOR]

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Research has shown that incorporating haptics into virtual environments can increase sensory fidelity and provide powerful and immersive experiences. However, current studies on haptics in virtual interactions primarily focus on one-on-one scenarios, while kinesthetic haptic interactions in large virtual gatherings are underexplored. This study aims to investigate the impact of kinesthetic haptics on eliciting emotional responses within crowded virtual reality (VR) scenarios. Specifically, we examine the influence of type or quality of the haptic feedback on the perception of positive and negative emotions. We designed and developed different combinations of tactile and torque feedback devices and evaluated their effects on emotional responses. To achieve this, we explored different combinations of haptic feedback devices, including “No Haptic,” “Tactile Stimulus” delivering tactile cues, and “Haptic Stimulus” delivering tactile and torque cues, in combination with two immersive 360- degree video crowd scenarios, namely, “Casual Crowd” and “Aggressive Crowd.” The results suggest that varying the type or quality of haptic feedback can evoke different emotional responses in crowded VR scenarios. Participants reported increased levels of nervousness with Haptic Stimulus in both virtual scenarios, while both Tactile Stimulus and Haptic Stimulus were negatively associated with pleasantness and comfort during the interaction. Additionally, we observed that participants’ sense of touch being real was enhanced in Haptic Stimulus compared to Tactile Stimulus. The “Haptic Stimulus” condition had the most positive influence on participants’ sense of identification with the crowd. [ABSTRACT FROM AUTHOR]

Summary: The wetlands of the jalca ecoregion in the Andes of northern Peru form peat and play a major role in the hydrological ecosystem services of the ecoregion. Although peat is globally valued for carbon sequestration and storage, peatlands have not yet been mapped in the jalca. In this region, the Gocta waterfall, one of the 20 highest waterfalls in the world, depends on the jalca 's wetlands ecosystem. The local population depends on tourism to the waterfall and is concerned about preserving its drainage area. To inform conservation planning, in this study we delimited the drainage area of the Gocta waterfall and identified land tenure by applying Geographic Information System (GIS), remote sensing and participatory mapping techniques. Then, by classifying optical, radar and digital elevation models data, we mapped peatland in the jalca of the Gocta drainage area with an overall accuracy of 97.1%. Our results will inform conservation strategy in this complex area of communal, private and informal land tenure systems. At a regional level, this appears to be the first attempt at mapping peatlands using remote sensing imagery in the jalca ecoregion, and it represents a milestone for future efforts to map and conserve peatlands in other tropical mountain areas of the world. [ABSTRACT FROM AUTHOR]

The impetus to write this paper was an analysis of the expression as if (akoby) as a component of the "equivalence" principle and the determination of its legitimacy as a component of the "behavior" principle. The study is focused on the search for further connections between the verbal behavior of the speaker and the linguistic aspects of as if (akoby) because it is used as a manifestation of motivated, ego-related and assertive verbal behavior. As if (akoby) can be viewed as: (a) a component of the principle of verbal behavior that attributes an egocentric meaning to the speaker's behavior; (b) an egocentric expression of the hypotaxis with an implicit speaker and the semantics of circumstantial and characteristic contexts; (c) a pragmatic marker of the speaker's ego-related interpretation of facts. Interpretation, which is grasped at the following two levels, is the methodological framework of the monitored aspects: (i) in the sense of the ego-related evaluation of a fact or event by the speaker, i.e. in the sense of participatory interpretation, and (ii) in the sense of explaining the verbal behavior of the speaker, i.e. in the sense of searching for the motives, intentions and goals that form the preconditions for his/her verbal behavior. [ABSTRACT FROM AUTHOR]

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

A self-evolving reasoning method for task-user relationships based on the autonomic knowledge graph is proposed for the problem of existing task assignment methods, which cannot cope with dynamic environment changes in mobile crowd sensing. In particular, with the idea of "self-reflection," "self-configuration," and "self-adjustment" from autonomic computing, the concept of the autonomic knowledge graph is proposed for the first time. We apply it to build the complete cycle of the knowledge graph, which ensures the dynamic mapping between the task-user relationships in the mobile crowdsensing system. "Self-reflection" generates real-time responses to changes in the internal and external environment of the system. "Self-configuration" selects high-quality sensing users and filters the mobile crowd sensing knowledge graph paths to ensure the reliability of sensing users. Based on the filtered paths, "self-adjustment" computes the neighbor weight matrix of nodes to update node embeddings and exploit potential connections between task-user nodes. Finally, link prediction between task-user nodes achieves accurate and effective task recommendations. This self-evolving reasoning method of nodal relationships we designed combines autonomic computing with the knowledge graph. It forms a continuously operating loop that the next moment of sensing quality is assured and enhanced in the mobile crowd sensing system. Experiments based on the real-world Gollowa dataset, the public Last-FM dataset, and the Amazon-book dataset show that the proposed method can effectively improve sensing data quality and the accuracy of task recommendations. [ABSTRACT FROM AUTHOR]