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.

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.

The goal of a trust and reputation system is to provide buyers with a robust framework that allows them to select a seller based on accumulated evidence of the seller’s past behavior in the marketplace. The quality of such a trust and reputation system depends on the quality of the feedback provided by buyers. Being a subjective measure, the quality of buyer feedback is notoriously hard to assess. When feedback is provided by buyers from around the world, who may value different aspects of the same transaction differently, it is very hard to know when a buyer provides truthful feedback.In this work we assume a blockchain-based decentralized marketplace where a Smart Contract is associated with each transaction. In an attempt to reduce the uncertainty associated with buyer feedback, the novelty that we introduce in this work is that at the end of the transaction, the Smart Contract is responsible for providing feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the buyer and the seller have fulfilled their contractual obligations towards each other. While Smart Contracts are naturally associated with transactions, to the best of our knowledge, this is the first time Smart Contracts are empowered with providing transaction feedback.Our second main contribution is to show how to manage efficiently reputation scores in such a blockchain-based marketplace.

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we identify the challenges that must be overcome and propose possible technical solutions for them. We also derive analytical expressions for the success probability of an autonomous UAV mission and describe a machine-learning model to train the UAV.