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

In the 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]

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]

The inspection of bridge structural cracks is essential to the structural safety evaluation and could provide reference for preventive maintenance. The traditional bridge structure inspection methods rely heavily on trained engineers with professional equipment. While such kind of way could provide reliable crack inspection data, the enormous amount of existing bridges waiting for inspection challenges the efficiency of these methods. Fortunately, the development of smartphones facilitates the possibility of making the pedestrian taking smartphones a mobile sensing node, which is able to collect crack information such as images and locations. At the same time, the booming deep learning methods could offer remarkable crack detection capacity to deal with the crack images automatically. Given this consideration, this paper established a crowd-sensing-based system for bridge structural crack detection. The system was composed of the cloud-based management platform and the mobile based application (APP) for crack information collection. The mobile-based APP was used by the volunteer pedestrians to collect the crack images as well as the locations, and the location accuracy was estimated to be around 5~10 m. Meanwhile, the cloud-based management platform was used for the management of the users and the collected crack information uploaded by all of the volunteers. A deep neural network was used to deal with the crack detection tasks and evaluate the quality of the collected images to see if they could be fitted for crack detection in bridge inspection works. [ABSTRACT FROM AUTHOR]

Crime analysis is important for social security management. With the advance of crowd sensing techniques, abundant multisource crowd sensed data could be used for crime analysis. The occurrence of crimes usually has some patterns in terms of temporal and spatial aspects. Investigating the spatio-temporal correlation of crimes could provide more useful cues for crime analysis and help discover underlying crime patterns. In this paper, we conduct a spatio-temporal study to understand urban crimes leveraging multisource crowd sensed data, including crime data, meteorological data, POI distribution, and taxi trips. Specifically, we first present monthly temporal trends and spatial distribution of crimes. We then investigate the spatio-temporal correlation using meteorological data (e.g., weather conditions and air temperature) and POI distribution and taxi trips. It is found that taxi trips and air temperature have a strong correlation with the crime, and some POI categories have a valuable correlation with the crime, e.g., College & University. We also find that Overcast days would witness more crime than other weather conditions. [ABSTRACT FROM AUTHOR]

In crowd sensing systems, mobile users provide requesters with access to the resources of their mobile devices, such as the core processor, memory, and camera, to execute tasks in return for monetary payment. Existing works consider the offline setting where information about mobile users and requesters is publicly known. However, this assumption does not hold for crowd sensing systems in the real world, where mobile users and requesters can arrive and leave at any time. We address the problem of online task allocation and pricing in crowd sensing systems without making any assumptions about the future information of mobile users and requesters. We formulate this problem in an auction-based online setting and propose a feasible and online double auction mechanism. The proposed online mechanism considers one-to-many mapping, which permits multiple mobile devices to work together to complete the same task at different times in order to improve resource utilization. In addition, we show that the proposed mechanism maintains individual rationality, budget-balance, and computational tractability. Furthermore, we analyze the approximation ratio of the proposed approximation algorithm. The experimental results show that a user cannot increase her utility by untruthful declaration, and the proposed mechanism brings more economic benefit for the auctioneer and stimulates users to join the system. [ABSTRACT FROM AUTHOR]

Mobile Crowd sensing model is an emerging paradigm that requires mobile device users to perform tasks such as data aggregation and cloud computing operations and also provides significant role in several data-driven applications. For providing effective performance, the crowd sensing model gathers huge data from several mobile nodes in cost-effective manner. Moreover, while handling with high-density sensors, there are several security issues such as, single point of failures, data leakage and so on. Hence, a decentralized ledger mechanism called block chain technology is integrated with crowd sensing models for solving various security concerns, in recent scenarios. With that note, this paper proposes a new model called Block Chain based Advanced Data Security-Reward Model (DSecCS) for enhancing data security and attack resistance. Moreover, the mode combines quality of data, status and rewards to make the users to contribute their private data, when dampen the malicious activities. The proposed model comprises of three sections, as, Construction of Intellectual CS Model, Confusion Model and Incorporation of Block-Chain. The experimental results show that the proposed model produces effective results than compared works. [ABSTRACT FROM AUTHOR]

Mobile crowd sensing is an appealing model, wherein mass users utilize smart devices to perform tasks in mobile social networks. Most conventional method selects subset of participants for maximizing the coverage. However, due to the budget constraints, the selection of most suitable participants becomes major issue. This paper presents a novel online task assignment framework using newly devised optimization algorithm, namely Crow-based Bacterial Foraging Algorithm (C-BFO), which is designed by combining Crow Search Algorithm and Bacterial Foraging Optimization. Here, scheduling in the cloud computing environments is simulated as an optimization problem, which is modeled using the proposed C-BFO, considering fitness function like communication duration, data sending rate, bandwidth, makespan, data receiving rate, time of sending task, time of receiving task, finish time, and ready time. The effectiveness of the proposed online task assignment model is revealed through the comparative analysis based on makespan by altering tasks, mobile user and requesters. The proposed C-BFO method shows enhanced performance with minimal makespan of 0.478 by varying number of tasks, and minimal makespan of 0.481 by varying number of users, and minimal makespan of 0.490 by varying requesters. [ABSTRACT FROM AUTHOR]

We develop an auction framework for privacy-preserving data aggregation in mobile crowdsensing, where the platform plays the role as an auctioneer to recruit workers for sensing tasks. The workers are allowed to report noisy versions of their data for privacy protection; and the platform selects workers by taking into account their sensing capabilities to ensure the accuracy level of the aggregated result. Observe that when moving the control of data privacy from the data aggregator to the workers, the data aggregator has limited market power in the sense that it can only partially control the noise by judiciously choosing a subset of workers based on workers’ privacy preferences. This introduces externalities because the privacy of each worker depends on the total noise in the aggregated result that in turn relies on which workers are selected. Specifically, we first consider a privacy-passive scenario where workers participate if their privacy loss can be adequately compensated by the rewards. We explicitly characterize the externalities and the hidden monotonicity property of the problem, making it possible to design a truthful, individually rational and computationally efficient incentive mechanism. We then extend the results to a privacy-proactive scenario where workers have individual requirements for their perceivable data privacy levels. Our proposed mechanisms for both scenarios can select a subset of workers to (nearly) minimize the cost of purchasing their private sensing data subject to the accuracy requirement of the aggregated result. We validate the proposed scheme through theoretical analysis as well as extensive simulations. [ABSTRACT FROM AUTHOR]

• A novel vibration pollution control framework including measurement, assessment, and data fusion, is proposed. • 5960 vibration records comprising vibration signals, GPS, and vibration comfort evaluations, have been collected. • An example of the urban vibration pollution map covering 18.55 km2 is generated. Vibration pollution poses a significant threat to the physical and mental well-being of citizens, while also exhibiting an undeniable adverse effect on the sustainable development of cities. Consequently, there has been a notable rise in scholarly interest regarding this issue. The monitoring and evaluation of vibration pollution play an important role in vibration pollution control, providing a scientific basis for rational environment policy formulation. However, traditional vibration pollution monitoring is based on short-term observations at fixed locations with small sample sizes, which cannot truly reflect the overall long-term pollution situation of the entire region. At the same time, traditional vibration pollution assessment also overlooks the uncertainty of subjective reactions, including differences in individual feelings and ambiguity in conceptual judgments of vibration comfort. To address this issue, a novel vibration pollution control framework using crowd sensing for measurement, fuzzy set for assessment, Dempster-Shafer (DS) evidence theory for data fusion, and vibration pollution map for visualization, is proposed in this study. Using ubiquitous smartphones as sensing terminals, 5960 vibration records in real environments, including vibration signals, GPS, vibration comfort evaluations, and environmental factors, have been collected. Based on these data, the combination membership functions to evaluate single vibration pollution are established. Considering the time-varying nature of vibration pollution, an optimized DS evidence method for long-term vibration assessment is adopted. Finally, in order to verify the feasibility and effectiveness of the method proposed, a field test covering 18.55 km2 was conducted. Then, an example of the urban vibration pollution map is generated where the distribution of vibration pollution can be vividly displayed. [ABSTRACT FROM AUTHOR]

Participant selection method is one of the important contents of crowd sensing research. Existing research still has some shortcomings. Only the attributes such as task time or task area coverage are considered, which makes the selected participants perform tasks less efficiently. Therefore, in order to comprehensively consider the task time and task area coverage constraints, this paper proposed a selection method of crowd sensing participant based on the greedy ant colony algorithm ( PSGACO) to achieve the highest task execution efficiency and the minimum incentive cost of the crowd sensing platform. The method mainly selected the participants who were suitable for performing the publishing task by the concentration of the ant pheromone concentration of the candidate participants, and greatly improved the task execution efficiency. Finally, it compared the proposed PS-GA CO method with the common participant selection method through simulation experiments. The experimental results show that PS-GA CO is superior to the other two methods in terms of algorithm running time, task execution efficiency and incentive cost, and has a good application prospect for the crowd sensing participant selection. [ABSTRACT FROM AUTHOR]

Crowd sensing has emerged as a compelling paradigm for collecting sensing data over a vast area. It is of paramount importance for crowd sensing systems to provide effective incentive mechanisms. This paper studies the critical problem of maximizing the aggregate data utility under a budget constraint in incentive mechanism design in crowd sensing. This problem is particularly challenging given the redundancy in sensing data, self-interested and strategic user behavior, and private cost information of smartphones users. Most of existing mechanisms do not consider the important performance objective—maximizing the redundancy-aware data utility of sensing data collected from smartphones users. Furthermore, they do not consider the practical constraint on budget. In this paper, we propose an incentive mechanism based on a reverse auction framework. It consists of an approximation algorithm for winning user determination and a critical payment scheme. The approximation algorithm guarantees an approximation ratio for the aggregate data utility at polynomial-time complexity. The critical payment scheme guarantees truthful bidding. The rigorous theoretical analysis demonstrates that our mechanism achieves truthfulness, individual rationality, computational efficiency and budget feasibility. [ABSTRACT FROM AUTHOR]

Noise pollution poses a serious threat to people living in cities today. To alleviate the negative impact of noise pollution, an urban noise mapping can be helpful. In this paper, we present the design of NoiseSense, a crowd sensing system for housing a real-time urban noise mapping service. A major challenge in building such a system is caused by the sparsity problem of the limited noise measurement data from smartphones. To tackle this challenge, we propose a hybrid approach including a neighborhood-based noise level estimation method and a semi-supervised tensor completion algorithm for inferring noise levels for locations without measurements by smartphone users. This approach leverages a variety of urban data sources, such as Point of Interests, road networks, and check-in data. We also provide a noise prediction method for forecasting the noise levels in the next few hours. We implemented the system and developed an APP for smartphone users. We conducted experiments and field study. The experimental results show that the proposed approach is superior in inferring noise levels merely with sparse measurements from smartphone users. And the prediction approach also outperforms other baseline methods. [ABSTRACT FROM AUTHOR]

With the advancement of mobile crowd sensing systems and vehicular ad hoc networks, the human-carried mobile devices (e.g., smartphones, smart navigators, and smart tablets) equipped with a variety of sensors (such as GPS, accelerometer, and compass) can work together to collect sensory data consequently delivered to the cloud for processing purposes, which supports a wide range of promising applications such as traffic monitoring, path planning, and real-time navigation. To ensure the authenticity and privacy of data, privacy-preserving truth discovery has attracted much attention since it can find reliable information among uneven quality of data collected from mobile users, while protecting both the confidentiality of users’ raw sensory data and reliability. However, these methods always incur tremendous overhead and require all participants to keep online for interacting frequently with the cloud server. In this paper, we design an efficient and privacy-preserving truth discovery (EPTD) approach in mobile crowd sensing systems, which can tolerate users offline at any stage, while guaranteeing practical efficiency and accuracy under working process. More notably, our EPTD is the first solution to resolve the problem that users must be online all times during the truth discovery under a single cloud server setting. Moreover, we design a double-masking protocol to ensure the strong security of users’ privacy even if the cloud server colludes with multiple users. Extensive experiments conducted on real-world mobile crowd sensing systems also demonstrate the high performance of our proposed scheme compared with existing models. [ABSTRACT FROM AUTHOR]

Crowd-centric research is receiving increasingly more attention as datasets on crowd behavior are becoming readily available. We have come to a point where many of the models on pedestrian analytics introduced in the last decade, which have mostly not been validated, can now be tested using real-world datasets. In this survey, we concentrate exclusively on automatically gathering such datasets, which we refer to as sensing the behavior of pedestrians. We roughly distinguish two approaches: one that requires users to explicitly use local applications and wearables, and one that scans the presence of handheld devices such as smartphones. We come to the conclusion that despite the numerous reports in popular media, relatively few groups have been looking into practical solutions for sensing pedestrian behavior. Moreover, we find that much work is still needed, in particular when it comes to combining privacy, transparency, scalability, and ease of deployment. We report on over 90 relevant articles and discuss and compare in detail 30 reports on sensing pedestrian behavior. [ABSTRACT FROM AUTHOR]

With the popularization of wireless networks and mobile intelligent terminals, mobile crowd sensing is becoming a promising sensing paradigm. Tasks are assigned to users with mobile devices, which then collect and submit ambient information to the server. The composition of participants greatly determines the quality and cost of the collected information. This paper aims to select fewest participants to achieve the quality required by a sensing task. The requirement namely “t-sweep k-coverage” means for a target location, every t time interval should at least k participants sense. The participant selection problem for “t-sweep k-coverage” crowd sensing tasks is NP-hard. Through delicate matrix stacking, linear programming can be adopted to solve the problem when it is in small size. We further propose a participant selection method based on greedy strategy. The two methods are evaluated through simulated experiments using users’ call detail records. The results show that for small problems, both the two methods can find a participant set meeting the requirement. The number of participants picked by the greedy based method is roughly twice of the linear programming based method. However, when problems become larger, the linear programming based method performs unstably, while the greedy based method can still output a reasonable solution. [ABSTRACT FROM AUTHOR]

In VANET environment the data of consecutive vehicle position truly are discrete, so the key to the moving vehicle modeling is to efficiently reduces update frequency of the position data to progress the Major Attributes regarding Security like communication and database management load Privacy, authentication and confidentiality for all applications of VANET. Geograhically VANET might be of two types either Fully connected or sparsly connected. Its type depends on day time or saturation rate of market devices. Many solutions of security Problems in VANET exist but its implementation is very costly for both user and for Service Provider. [ABSTRACT FROM AUTHOR]

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods. [ABSTRACT FROM AUTHOR]