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Fair, Secure and Trustworthy Crowdsensing in Spontaneous Localized SettingsAuthor: Moin Hussain Moti Date: 2020-06-25 Report no: IIIT/TH/2020/50 Advisor:Sujit Prakash Gujar AbstractCrowd sensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. Existing crowdsensing mechanisms are good for general purpose queries but lack any additional benefits for specialized settings. This mechanism focuses on spontaneous location specific queries, we call it Spontaneous Localized Settings. We define the required characteristics for this specific setting and evaluate existing mechanisms for the same. Agent participation in good numbers is quintessential for any crowdsensing mechanism to work. Since our setting is location specific and bounded by short time, it allows only agents nearby the query location to participate. Therefore, we emphasize on a fair rewarding mechanism and a secure and trustworthy framework to maximize agent participation. Consequently, fairness in reward dsitribution and trustworthy crowdsensing systems are the two central themes of this thesis. Peer prediction based mechanisms are widely used in crowdsensing systems to elicit truthful information from agents, however, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information elicitation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent’s history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports. Furthemore, it requires trustworthy framework and to guarantee that the collected data are accurately acquired and reported. Trustworthy and transparent frameworks can be implemented via smart contracts on blockchain to enable robust crowdsensing mechanisms. To achieve this goal, we develop Orthos, a blockchain-based trustworthy framework for spontaneous location-based crowdsensing without assuming any prior knowledge about them. We employ game-theoretic mechanisms to incentivize agents to report truthfully and ensure that the information is collected at the desired location while ensuring the privacy of the agents. We evaluate existing mechanisms based on all desirable characteristics for the settings and identify RPTSC as the most suitable option. Orthos implements the RPTSC mechanism using smart contracts. Additionally, as location information is exogenous to RPTSC, we design the Proof-of-Location protocol to ensure that agents gather the data at the desired locations. We built the decentralized applicaiton based on Orthos protocol and examine its performance on Rinkeby Ethereum testnet and conduct experiments with live audience Full thesis: pdf Centre for Data Engineering |
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