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

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 past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private information of users in federated learning has become an important research topic. Compared with the differential privacy (DP) technique and secure multiparty computation (SMC) strategy, the covert communication mechanism in federated learning is more efficient and energy-saving in training the machine learning models. In this paper, we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks. Different from the previous works about covert communication in federated learning, most of which are considered in a centralized framework and experimental-based, we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents, the time complexity of which is O(log n), approximating to the optimal solution. Secondly, for the federated learning without parameter server, which is a harder case, we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log log Δ log n) times, approximating to the optimal solution. Δ is the maximum distance between any pair of learning agents. Theoretical analysis and numerical simulations are presented to show the performance of our covert communication mechanisms. We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

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]

With the rapid development of mobile devices, aggregation security and efficiency topics are more important than past in crowd sensing. When collecting large-scale vehicle-provided data, the data transmitted via autonomous networks are publicly accessible to all attackers, which increases the risk of vehicle exposure. So we need to ensure data aggregation security. In addition, low aggregation efficiency will lead to insufficient sensing data, making the data unable to provide data mining services. Aiming at the problem of aggregation security and efficiency in large-scale data collection, this article proposes a data collection mechanism (VDCM) for crowd sensing in vehicular ad hoc networks (VANETs). The mechanism includes two mechanism assumptions and selects appropriate methods to reduce consumption. It selects sub mechanism 1 when there exist very few vehicles or the coalition cannot be formed, otherwise selects sub mechanism 2. Single aggregation is used to collect data in sub mechanism 1. In sub mechanism 2, cooperative vehicles are selected by using coalition formation strategy and auction cooperation agreement, and multi aggregation is used to collect data. Two sub mechanisms use Paillier homomorphic encryption technology to ensure the security of data aggregation. In addition, mechanism supplements the data update and scoring steps to increase the amount of available data. The performance analysis shows that the mechanism proposed in this paper can safely aggregate data and reduce consumption. The simulation results indicate that the proposed mechanism reduces time consumption and increases the amount of available data compared with existing mechanisms.

In response to the inadequacy of existing crowd sensing incentive methods to meet the privacy requirements of users in distributed environments, a crowd sensing privacy protection and incentive method called tangle network incentive policy (TNIP) based on the tangle network was proposed. Firstly, guided by the concept of privacy protection, the sensing network adopted the tangle network framework, which ensured network security through a distributed recording approach. Secondly, to address issues such as malicious competition and the proof of work threshold introduced by blockchain, a PoW (proof of work)control algorithm was designed to lower the participation threshold and mitigate malicious competition. Furthermore, the privacy of sensing data was strengthened by utilizing the ECDSA(elliptic curve digital signature algorithm) based digital signature method, enhancing the security of the data. Subsequently, a quantitative evaluation model was used to assess the risks of privacy leakage, and a trusted milestone transaction was employed to control network risks and enhance the privacy of the sensing network. Finally, by utilizing real-world datasets and conducting simulation experiments, a comparative analysis of the incentive effect and privacy protection methods proposed by TNIP was performed. The experimental results demonstrate that the TNIP method achieves an average participation rate improvement of 8. 79% and 5. 93% over the CSII(cross-space multi-interaction-based-dynamic incentive scheme) method and TCS (tangle-net crowd sensing) method, respectively, for the three categories of tasks. [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.

The crowd sensing technology can realize the sensing and computing of people, machines, and environment in smart industrial IoT-based coal mine, which provides a solution for safety monitoring through distributed intelligence optimization. However, due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines, the accuracy of human body position prediction and pressure value prediction is not high. To solve these problems, this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine. First, we propose a Particle Swarm Optimization-Elman Neural Network (PE) algorithm for the mobile human position prediction. Second, we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them, our proposed PE algorithm has the lowest average cumulative prediction error, and the trajectory fit rate is improved by 24.1%, 13.9% and 8.7% compared with Kalman filtering, Elman and Kalman plus Elman algorithms, respectively. Meanwhile, compared with single-input ARIMA, RNN, LSTM, and GRU, the RMSE values of our proposed ADI-LSTM are reduced by 36.6%, 52%, 32%, and 13.7%, respectively; and the MAPE values are reduced by 0.0003%, 0.9482%, 1.1844%, and 0.3620%, respectively.© 2015 Published by Elsevier Ltd.

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]

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.

To solve the problem that data privacy leakage of participants under the crowd-sensed data trading model, a privacy-protected crowd-sensed data trading algorithm was proposed.Firstly, to achieve the privacy protection of participants, an aggregation scheme based on differential privacy was designed.Participants were no longer needed to upload raw data, but analyzed and calculated the collected data according to the task requirements, and then sent the analysis results to the platform after adding noise in accordance with the privacy budget allocated by the platform to protect their privacy.Secondly, in order to ensure the credibility of participants, a reputation model of participants was proposed.Finally, in order to encourage consumers and participants to participate in transactions, a data trading optimization model was constructed by considering the consumer’s constraint on the result deviation，the participant’s privacy leakage compensation and platform profit, and a POA based on genetic algorithm was proposed to solve the model.The simulation results show that the POA not only protects the privacy of participants, but also increases the profit of the platform by 29.27% and 20.45% compared to VENUS and DPDT, respectively.

The surrounding environment of rivers in city is often damaged and polluted.How to effectively monitor rivers has gradually attracted the attention of public,government and academia.At present,traditional monitoring methods are facing with high cost,insufficient coverage and other defects.With the increasing popularity of intelligent mobile devices,a new idea of using crowd sensing to efficiently monitor the river environment is proposed in this paper.The problem can be described as the assumption that each river reach contains c monitoring points,and then r users are selected according to the movement tracks of a large number of users to jointly complete the monitoring of all river reaches in s periods.It is stipulated that the smaller the number of users r,the less the monitoring cost.The stepwise-greedy strategy,the global-greedy strategy and the integer-programming stra-tegy are designed to solve this problem,that is,to select the least number of participants to achieve the monitoring goal of “s durations-c ranges-r users”.In this paper,the above strategies are applied to environmental monitoring of some rivers in Taijiang,Fuzhou.Experimental results show that the above strategies can obtain better solutions than the random strategy,and the integer-programming strategy has the best performance.However,with the increase of the scale of the problem,the implicit enumeration algorithm used to solve the small-scale integer programming will be unable to solve the situation.Motivated by this,this paper designs a discrete particle swarm optimization algorithm based on greedy initialization(GI-DPSO).Although this algorithm can solve large-scale integer programming,it is time-consuming.Considering the monitoring cost and computational cost comprehensively,it is suggested that the integer-programming strategy can be adopted for small-scale problems and the global-greedy strategy can be adopted for large-scale problems.

Mobile crowd-sensing (MCS) is a solution to provide spectrum availability information for dynamic spectrum access in cognitive radio systems. In MCS-based spectrum monitoring, participants should report the location and time of spectrum sensing in addition to the status of the spectrum bands, which raises the need for privacy-preserving. On the other hand, it is required to mitigate the possibility of fake reports sent from malicious participants that is almost handled using trust mechanisms. The trust mechanisms should be resistant to possible wrong reports which are due to channel fading and/or noise too. Moreover, some incentive mechanisms are required to encourage mobile users to participate in the crowd-sensing process. However, preserving-privacy, managing trust, and providing proper incentive mechanisms altogether is a challenge in MCS-based spectrum monitoring systems that has not been appropriately considered yet in previous work. In this paper, we propose a method that includes a privacy-preserving protocol with secure rewarding capability as well as a trust mechanism against malicious participants for MCS-based spectrum monitoring. We exploit Dempster–Shafer theory besides the reputation of participants in an anonymous manner to decide about spectrum availability. Also, we take advantage of the Gompertz function when updating the reputation of participants to better handle the spectrum sensing errors. To evaluate the proposed method, we conduct simulations to analyze and compare the proposed trust and spectrum decision mechanisms. The results show that in the proposed method, although 40% of participants were malicious, in more than 95% of cases, we were able to make the right decision about the participant's behavior compared to the majority method where only in about 85% of cases, the decision was correct. Also, we use ProVerif automatic protocol verifier to formally evaluate some security features of the proposed anonymity protocol. Moreover, we conduct some experimental analysis to validate the proposed protocol. The evaluation results demonstrate the superiority of the proposed method regarding both performance criteria and security features compared to the baseline methods. [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]

As a novel sensing paradigm, crowd sensing systems have gained great attention and been widely adopted in the environmental monitoring and calculation areas. In crowd sensing systems, mobile users provide their multiple resources to the requesters to execute tasks. Existing studies focus on the divisible task or one-to-one mapping for single resource allocation. However, this assumption does not hold for crowd sensing systems. Owing to the task attribute, some tasks cannot be divided into multiple parts to run on different devices. In addition, a high performance mobile device can execute multiple tasks simultaneously. We address the problem of multi-resource allocation in crowd sensing systems for the auction-based model considering many-to-one mapping for indivisible tasks, where many-to-one mapping allows one mobile device to provide multiple resources to execute one or more tasks. In this article, we study, for the first time to the best of our knowledge, a truthful mechanism that stimulates mobile users and requesters to declare their true values. We design a truthful double auction mechanism together with a payment scheme tailored to fit it that would help researchers understand how a truthful double auction mechanism can be designed. In addition, we prove that our proposed mechanism maintains budget-balance, individual rationality, and computational tractability. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. Experimental results demonstrate that our proposed mechanism has high computation efficiency and good performance. [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.

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]

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As a typical example of an Intelligent Transport System (ITS) in a smart city, the bicycle sharing is developing so fast and changing the citizens’ travel habits to a large extent in China. However, its disorder development brings a heavy burden for city, and it is considered as a junk of city in many citizens’ opinions. In our previous work, we proposed the concept of Internet of Shared Bicycle (IoSB) to solve some problems in technique. In this paper, we devote to how to enable bicycle sharing playing a more important role in the construction of smart city, instead of being treated only as a type of traffic mode. We summarize some of the current research status of bicycle sharing in smart cities and put forward some of our own views. Then the Bicycle Sharing System (BSS) is be further studied and explored the possibility of being deployed as a terminal in the city in terms of technology. When a serious disaster comes, it serves as an auxiliary communication system for the city and becomes a measure of the city’s emergency network. In particular, we describe a special role of bicycle sharing during public health events. In addition, the feasibility in some interesting cases with great potential in the future is also discussed. [ABSTRACT FROM AUTHOR]

By fully utilizing the capability of the spreading intelligent terminals, crowd spectrum sensing is an efficient and cost-effective framework to realize large-scale and broadband spectrum sensing. However, traditional crowd sensing system relies on a centralized architecture, which not only face severe security and privacy issues, but also may not be able to attract enough users to participate in the sensing tasks due to the lack of effective incentive mechanisms and guaranteed rewards. In this paper, we propose a blockchain-based crowd spectrum sensing framework to achieve secure and privacy-preserving spectrum sensing with guaranteed rewards for participating users. Considering the constraint of the sensing task and the workload of users, the optimal pricing and sensing task allocation scheme under the minimum sensing task constraint is investigated by leveraging Stackelberg game model. We analyze the Nash equilibrium of the sub-games and derive the optimal pricing and sensing task allocation strategy, for both uniform pricing and non-uniform pricing schemes. Simulation results demonstrate the effectiveness of the proposed scheme and it is shown that the scheme can maximize the utility while ensuring the completion of the sensing tasks. [ABSTRACT FROM AUTHOR]

Nowadays, people choose to travel in their leisure time more frequently, but fixed predetermined tour routes can barely meet people's personalized preferences. The needs of tourists are diverse, largely personal, and possibly have multiple constraints. The traditional single-objective route planning algorithm struggles to effectively deal with such problems. In this paper, a novel multi-objective and multi-constraint tour route recommendation method is proposed. Firstly, ArcMap was used to model the actual road network. Then, we created a new interest label matching method and a utility function scoring method based on crowd sensing, and constructed a personalized multi-constraint interest model. We present a variable neighborhood search algorithm and a hybrid particle swarm genetic optimization algorithm for recommending Top-K routes. Finally, we conducted extensive experiments on public datasets. Compared with the ATP route recommendation method based on an improved ant colony algorithm, our proposed method is superior in route score, interest abundance, number of POIs, and running time. [ABSTRACT FROM AUTHOR]

Mobile Crowd Sensing (MCS) enables the platform to offer data-based service by incentivizing mobile users to perform sensing task and collecting sensing data from them. Most of the existing works on MCS only consider designing incentive mechanisms for a single MCS platform. In this paper, we study the incentive mechanism in MCS with multiple platforms under two scenarios: competitive platform and cooperative platform. We correspondingly propose new competitive and cooperative mechanisms for each scenario. In the competitive platform scenario, platforms decide their prices on rewards to attract more participants, while the users choose which platform to work for. We model such a competitive platform scenario as a two-stage Stackelberg game. In the cooperative platform scenario, platforms cooperate to share sensing data with each other. We model it as many-to-many bargaining. Moreover, we first prove the NP-hardness of exact bargaining and then propose heuristic bargaining. Finally, numerical results show that (1) platforms in the competitive platform scenario can guarantee their payoff by optimally pricing on rewards and participants can select the best platform to contribute; (2) platforms in the cooperative platform scenario can further improve their payoff by bargaining with other platforms for cooperatively sharing collected sensing data. [ABSTRACT FROM AUTHOR]

In the case of the sparse distribution of users in the perception area, predicting the user's location in advance is the key to improve the task completion rate of the crowd sensing system. This paper presents a user location prediction model based on the gated recurrent unit. Firstly, a model of the crowd sensing system is constructed, and the application of the participatory sensing based on location is realized. Secondly, the data set of the user's location is normalized, and by combining the multidimensional characteristics of the user's historical location data, a gated recurrent unit structure is constructed. Finally, the actual trajectory data set in the vehicles networks is used to train the model, and the Adam algorithm is used to optimize the performance parameters of the user position prediction model based on the gated recurrent unit. The simulation results show that, compared with the RNN model and the LSTM model, the prediction mean square error of the proposed model is reduced by 22% and 18% respectively, and has the advantage of strong implementability in processing sequence data. [ABSTRACT FROM AUTHOR]

Traffic violations like illegal parking, illegal turning, and speeding have become one of the greatest challenges in urban transportation systems, bringing potential risks of traffic congestions, vehicle accidents, and parking difficulties. To maximize the utility and effectiveness of the traffic enforcement strategies aiming at reducing traffic violations, it is essential for urban authorities to infer the traffic violation-prone locations in cities. Therefore, we propose a low-cost, comprehensive, and dynamic framework to infer traffic violation-prone locations in cities based on the large-scale vehicle trajectory data and road environment data. First, we normalize the trajectory data by map matching algorithms and extract key driving behaviors, i.e., turning behaviors, parking behaviors, and speeds of vehicles. Second, we restore spatiotemporal contexts of driving behaviors to get corresponding traffic restrictions such as no-parking, no-turning, and speed restrictions. After matching the traffic restrictions with driving behaviors, we get the traffic violation distribution. Finally, we extract the spatiotemporal patterns of traffic violations and build a visualization system to showcase the inferred traffic violation-prone locations. To evaluate the effectiveness of the proposed method, we conduct extensive studies on large-scale, real-world vehicle GPS trajectories collected from two Chinese cities, respectively. Evaluation results confirm that the proposed framework infers traffic violation-prone locations effectively and efficiently, providing comprehensive decision supports for traffic enforcement strategies. [ABSTRACT FROM AUTHOR]

To meet some real-time mobile crowd sensing (MCS) scenarios, there is a tendency to enhance the MCS system with mobile edge computing (MEC). One of the key challenges is how to select some satisfied participants in such an edge-cloud collaboration MCS system to effectively and real-timely handle dynamic and heterogeneous sensing tasks. In this paper, we propose a bilateral satisfaction aware participant selection mechanism in the edge-cloud collaboration MCS system. The participant selection process is coordinated by the cloud service platform and the MEC server. The cloud service platform sends the required data types to the MEC server and evaluates the user reputation through the user history task records. The MEC server generates a set of tasks and evaluates user fitness based on the user's real-time location. Then the MEC server obtains the user sensing cost based on the user status, and develops the task price model based on the user supply index and data demand index. Finally, the participant selection process is transformed into a game between users and the MEC server about the task reward, and the user who accepts the optimal task price is selected as the participant. The results show that the proposed participant selection strategy can effectively reduce the amount of data processed by the cloud platform, shorten the task completion time, and increase bilateral satisfaction.

Crowd sensing is a promising sensing paradigm,which mainly uses a variety of embedded sensors in a large number of mobile devices to accomplish data sensing tasks.One of the key issues in crowd sensing is how to effectively coordinate mobile device users to perform multiple sensory tasks simultaneously.By introducing a new data layer between the sensing task and the user,the similarity of the sensing task and the heterogeneity of the user were effectively utilized,and the joint task selection and user scheduling problems were established on the data layer,aiming at maximizing the social welfare of the whole system.This problem was difficult to solve due to its combinatorial nature and the presence of private information on both the sensing tasks and the users.In order to deal with these problems,a two-sided randomized auction mechanism was proposed,and it was proved that it can satisfy the desirable properties of the computational efficiency,the individual rationality,and the incentive compatibility in expectation.The simulation results show that the proposed stochastic auction mechanism can achieve nearly optimal social welfare,and the social welfare benefits brought by data reuse will increase significantly with the enhancement of task similarity.

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]

The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.

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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]

In urban transit systems, discovering anomalous bus-driving behaviors in time is an important technique for monitoring the safety risk of public transportation and improving the satisfaction of passengers. This paper proposes a two-phase approach named Cygnus to detect anomalous driving behaviors from bus trajectories, which utilizes collected sensor data of smart phones as well as subjective assessments from bus passengers by crowd sensing. By optimizing support vector machines, Cygnus discovers the anomalous bus trajectory candidates in the first phase, and distinguishes real anomalies from the candidates, as well as identifies the types of driving anomalies in the second phase. To improve the anomaly detection performance and robustness, Cygnus introduces virtual labels of trajectories and proposes a correntropy-based policy to improve the robustness to noise, combines the unsupervised anomaly detection and supervised classification, and further refines the classification procedure, thus forming an integrated and practical solution. Extensive experiments are conducted on real-world bus trajectories. The experimental results demonstrate that Cygnus detects anomalous bus-driving behaviors in an effective, robust, and timely manner. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)