In recent years, online marketplaces have seen a large increase in business. Managing and making sellers' reputations available to buyers is vital for the success of these markets since buyers may be dealing with unknown online sellers. Typically, prospective buyers assess sellers' performance by looking at past buyers' reviews, prior to making a purchase. In current online marketplaces, buyers' reviews are associated with their identities, raising privacy issues. Several decentralized solutions have been proposed using blockchain technologies to solve this problem. However, some of these systems are not entirely decentralized or have some weaknesses such as requiring a trusted third party. In this paper, we propose a system for decentralized reviewing that trades off cost for unlinkability. Using blockchain-based smart contracts, we propose a fully decentralized marketplace to provide reviewers with a secure and trusted platform. Here, buyers can use one-time identities to provide feedback on their transactions. The system also ensures that a review can only be submitted after a transaction is completed, and that at most one review may be submitted per transaction. The system enforces these properties through blockchain technologies and smart contracts. In addition, we propose an innovative approach to encourage buyers to submit reviews. The system has been prototyped using Remix IDE.

Very recently, in an attempt to reduce the uncertainty associated with notoriously unreliable buyer feedback, researchers proposed a blockchain-based trust and reputation management system, where a Smart Contract manages all aspects of a transaction: setting up a contract, interacting with the two parties, and finally evaluating and providing feedback on the buyer/seller performance at the end of each transaction. They have also proposed a data structure that manages seller reputation scores inside the blockchain. In this paper, we provide two novel reputation management schemes that significantly improve on these methods. Our first such scheme is adaptive; the second one is randomized, wherein decisions about managing the underlying data structure are made based on coin flips. We provide analytical performance predictions and verify, empirically, by extensive simulation, the accuracy of our predictions. Our schemes are shown to result in more efficient query execution with enhanced block structure schemes.

Existing surveys look at parking solutions from the perspective of sensors, communication protocols, and the hardware-software interface. While this is a worthwhile approach, it suffers from three obvious shortcomings, namely that present-day sensors are likely to become obsolete in a few years, communication protocols get discontinued, and present-day software will almost certainly not run on tomorrow’s platforms. Consequently, these approaches are not promising for the Smart Cities of the near future. Unlike previous surveys, we look at parking in Smart Cities through the lens of market-based allocation of goods and services. In competitive markets prices act as signals used to allocate goods to those who value them most. In the case of parking spots, some drivers are willing to pay higher prices for the use of those parking spots that offer them the highest utility. What makes our survey unique is that we are looking at the recent literature with an eye for market-oriented solutions including pricing as an instrument for shaping traffic and for incentivizing socially-desirable driver behavior. We believe that one of the important contributions of any survey paper, over and above being a compendium of known art, is to suggest new lines of research. With this in mind, we have peppered the manuscript with slightly unorthodox perspectives. These perspectives are intended to be thought-provoking and to open new avenues for possible investigations.