Incentive mechanism design for citizen reporting application using Stackelberg game

<p>The growing utilization of smartphones equipped with various sensors to collect and analyze information around us highlights a paradigm called mobile crowdsensing. To motivate citizens’ participation in crowdsensing and compensate them for their resources, it is necessary to incentivize the participants for their sensing service. There are several studies that used the Stackelberg game to model the incentive mechanism, however, those studies did not include a budget constraint for limited budget case. Another challenge is to optimize crowdsourcer (government) profit in conducting crowdsensing under the limited budget then allocates the budget to several regional working units that are responsible for the specific city problems. We propose an incentive mechanism for mobile crowdsensing based on several identified incentive parameters using the Stackelberg game model and applied the MOOP (multi-objective optimization problem) to the incentive model in which the participant reputation is taken into account. The evaluation of the proposed incentive model is performed through simulations. The simulation indicated that the result appropriately corresponds to the theoretical properties of the model.</p>

Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices

The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users’ individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.

An Efficient and Secure Malicious User Detection Scheme Based on Reputation Mechanism for Mobile Crowdsensing VANET

With the increasing development of wireless communication technology and Vehicular Ad hoc Network (VANET), as well as the continuous popularization of various sensors, Mobile Crowdsensing (MCS) paradigm has been widely concerned in the field of transportation. As a currently popular data sensing way, it mainly relies on wireless sensing devices to complete large-scale and complex sensing tasks. However, since vehicles are highly mobile in this scenario and the sensing system is open, that is, any vehicle equipped with sensing device can join the system, the credibility of all participating vehicles cannot be guaranteed. In addition, malicious users will upload false data in the sensing system, which makes the sensing data not meet the needs of the sensing tasks and will threaten traffic safety in some serious cases. There are many solutions to the above problems, such as cryptography, incentive mechanism, and reputation mechanisms. Unfortunately, although these schemes guaranteed the credibility of users, they did not give much thought to the reliability of data. In addition, some schemes brought a lot of overhead, some used a centralized server management architecture, and some were not suitable for the scenario of VANET. Therefore, this paper firstly proposes the MCS-VANET architecture-based blockchain, which consists of participating vehicles (PVs), road side units (RSUs), cloud server (CS), and the blockchain (BC), and then designs a malicious user detection scheme composed of three phases. In the data collecting phase, to reduce the data uploading overhead, data aggregation and machine learning technologies are combined by fully considering the historical reputation value of PVs, and the proportion of data uploading is determined based on the historical data quality evaluation result of PVs. In the data quality evaluation phase, a new reputation computational model is proposed to effectively evaluate the sensing data, which contains four indicators: the reputation history of PVs, the data unbiasedness, the leadership of PVs, and the spatial force of PVs. In the reputation updating phase, to achieve the effective change of reputation values, the logistic model function curve is introduced and the result of the reputation updating is stored in the blockchain for security publicity. Finally, on real datasets, the feasibility and effectiveness of our proposed scheme are demonstrated through the experimental simulation and security analysis. Compared with existing schemes, the proposed scheme not only reduces the cost of data uploading but also has better performance.

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.