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Register a new userQuality adaptive online double auction in participatory sensing
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Added by
Sajal Mukhopadhyay
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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Agents (specially humans) with smart devices are stemming with astounding rapidity and that may play a big role in information and communication technology apart from being used only as a mere calling devices. Inculcating the power of smart devices carried by the agents in several different applications is commonly termed as participatory sensing (PS). In this paper, for the first time a truthful quality adaptive participatory sensing is presented in an online double auction environment. The proposed algorithm is simulated with a benchmark mechanism that adapts the existing McAfees Double Auction (MDA) directly in the online environment.
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As mobile devices have been ubiquitous, participatory sensing emerges as a powerful tool to solve many contemporary real life problems. Here, we contemplate the participatory sensing in online double auction environment by considering the location information of the participating agents. In this paper, we propose a truthful mechanism in this setting and the mechanism also satisfies the other economic properties such as budget balance and individual rationality.
A classical trading experiment consists of a set of unit demand buyers and unit supply sellers with identical items. Each agents value or opportunity cost for the item is their private information and preferences are quasi-linear. Trade between agents employs a double oral auction (DOA) in which both buyers and sellers call out bids or offers which an auctioneer recognizes. Transactions resulting from accepted bids and offers are recorded. This continues until there are no more acceptable bids or offers. Remarkably, the experiment consistently terminates in a Walrasian price. The main result of this paper is a mechanism in the spirit of the DOA that converges to a Walrasian equilibrium in a polynomial number of steps, thus providing a theoretical basis for the above-described empirical phenomenon. It is well-known that computation of a Walrasian equilibrium for this market corresponds to solving a maximum weight bipartite matching problem. The uncoordinated but rational responses of agents thus solve in a distributed fashion a maximum weight bipartite matching problem that is encoded by their private valuations. We show, furthermore, that every Walrasian equilibrium is reachable by some sequence of responses. This is in contrast to the well known auction algorithms for this problem which only allow one side to make offers and thus essentially choose an equilibrium that maximizes the surplus for the side making offers. Our results extend to the setting where not every agent pair is allowed to trade with each other.
We address the question of aggregating the preferences of voters in the context of participatory budgeting. We scrutinize the voting method currently used in practice, underline its drawbacks, and introduce a novel scheme tailored to this setting, which we call Knapsack Voting. We study its strategic properties - we show that it is strategy-proof under a natural model of utility (a dis-utility given by the $ell_1$ distance between the outcome and the true preference of the voter), and partially strategy-proof under general additive utilities. We extend Knapsack Voting to more general settings with revenues, deficits or surpluses, and prove a similar strategy-proofness result. To further demonstrate the applicability of our scheme, we discuss its implementation on the digital voting platform that we have deployed in partnership with the local government bodies in many cities across the nation. From voting data thus collected, we present empirical evidence that Knapsack Voting works well in practice.
Participatory budgeting is a democratic process for allocating funds to projects based on the votes of members of the community. However, most input methods of voters preferences prevent the voters from expressing complex relationships among projects, leading to outcomes that do not reflect their preferences well enough. In this paper, we propose an input method that begins to address this challenge, by allowing participants to express substitutes over projects. Then, we extend a known aggregation mechanism from the literature (Rule X) to handle substitute projects. We prove that our extended rule preserves proportionality under natural conditions, and show empirically that it obtains substantially more welfare than the original mechanism on instances with substitutes.
Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are indispensable to attract more user participation. Most of existing mechanisms only apply for the offline scenario, where the system has full information about the users sensing profiles, i.e., a set of locations or mobility as well as the type of smartphones used, and their true costs. On the contrary, we focus on a more real scenario where users with their own privacy concerns arrive one by one online in a random order. We model the problem as a privacy-respecting online auction in which users are willing to negotiate access to certain private information and submit their sensing profiles satisfying privacy concerns to the platform (the provider of crowd sensing applications) over time, and the platform aims to the total total value of the services provided by selected users under a budget constraint. We then design two online mechanisms for a budgeted crowd sensing application, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, constant competitiveness and privacy concerns. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
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