Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowd- sourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to con- sider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a Social Aware Crowdsourcing with Reputation Management (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for par- ticipant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports.
Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A reputation score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate reputation scores. Leveraging social networks as an underlying infrastructure for participatory sensing systems provides an effective means to have access to a reasonable number of participants, which is essential for the success of this new and exciting paradigm. Another important issue is assessing the quality of the contributions prepared by the participants, who are, on the other hand, the social network members. In this paper, we propose a trust framework for social participatory sensing systems. Our framework is aimed at quantifying the trustworthiness of contributions by considering the quality of the raw sensor readings contributed and the trustworthiness of the user contributing the sensor data, and combining them via a fuzzy inference engine to arrive at a final trust score for a contribution. It also assigns a reputation score to each user. Extensive simulations demonstrate the efficacy of our framework. Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. Different from other participatory sensing application challenges who consider user privacy and data trustworthiness, we consider network trustworthiness problem namely Sybil attacks in participatory sensing. Sybil attacks focus on creating multiple online user identities called Sybil identities and try to achieve malicious results through these identities. In this paper, we proposed a Cloud based Trust Management Scheme (CbTMS) framework for detecting Sybil attacks in participatory sensing network. Our CbTMS was proposed for performing Sybil attack characteristic check and trustworthiness management system to verify coverage nodes in the participatory sensing. To verify the proposed framework, we are currently developing the proposed scheme on OMNeT++ network simulator in multiple scenarios to achieve Sybil identities detection in our simulation environment. Mineralization of 14C-labeled tracers is a common way of studying the environmental fate of xenobiotics, but it can be difficult to extract relevant kinetic parameters from such experiments since complex kinetic functions or several kinetic functions may be needed to adequately describe large data sets. In this study, we suggest using a two-parameter, sigmoid Gompertz function for parametrizing mineralization curves. The function was applied to a data set of 252 normalized mineralization curves that represented the potential for degradation of the herbicide MCPA in three horizons of an agricultural soil. The Gompertz function fitted most of the normalized curves, and trends in the data set could be visualized by a scatter plot of the two Gompertz parameters (rate constant and time delay). For agricultural topsoil, we also tested the effect of the MCPA concentration on the mineralization kinetics. Reduced initial concentrations lead to shortened lag-phases, probably due to reduced need for bacterial growth... Participatory sensing is a revolutionary new paradigm in which volunteers collect and share information from their local environment using mobile phones. The inherent openness of this platform makes it easy to contribute corrupted data. This paper proposes a novel reputation system that employs the Gompertz function for computing device reputation score as a reflection of the trustworthiness of the contributed data. We implement this system in the context of a participatory noise monitoring application and conduct extensive real-world experiments using Apple iPhones. Experimental results demonstrate that our scheme achieves three-fold improvement in comparison with the state-of-the-art Beta reputation scheme. With the proliferation of sensor-embedded mobile computing devices, participatory sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in participatory sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately. Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal. Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.