The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for microtransactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of UAV to UAV communication, and the limited flight time of the UAVs on account of limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent works proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be re-payed back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional scheme.

Governments and public sector entities around the world are actively exploring new ways to keep up with technological advancements to achieve smart governance, work efficiency, and cost optimization. Blockchain technology is an example of such technology that has been attracting the attention of Governments across the globe in recent years. Enhanced security, improved traceability and lowest cost infrastructure empower the blockchain to penetrate various domains. Generally, governments release tenders to some third-party organizations for different projects. During this process, different competitors try to eavesdrop the tender values of others to win the tender. The corrupt government officials also charge high bribe to pass the tender in favor of some particular third party. In this paper, we presented a secure and transparent framework for government tenders using blockchain. Blockchain is used as a secure and immutable data structure to store the government records that are highly susceptible to tampering. This work aims to create a transparent and secure edge computing infrastructure for the work-flow in government tenders to implement government schemes and policies by limiting human supervision to the minimal.

We address the problem of estimation of the Fujisaki model parameters for F0 synthesis. For this, we propose the use of a very efficient search and optimization method termed the ‘direct-search’ (Hooke and Jeeves, 1961) which belongs to the class of derivative-free unconstrained optimization methods, in the sense that it is applicable for non-linear optimization problems which are not amenable for gradient-descent methods possibly due to lack of a functional form of the gradient or non-viability due to the large number of parameters. Specifically, direct-search comprises a sequential examination of trials involving comparison of each trial solution with the best previous solution and requires only the numerical function values or their relative rank orderings at each trial, and employs a strategy for determining subsequent trial solutions as a function of earlier results. In the case of Fujisaki model parameter estimation problem, (which has been conventionally solved by techniques such as analysis-by-synthesis (AbS) or closed-form solution for phrase and accent command amplitudes combined with AbS or hill-climbing on the other parameters), we show that direct-search offers a very efficient optimization solution, in terms of its robustness to initializations, which makes it effective to locate globally optimal solutions, and avoid settling into local optima. With respect to this behavior, we show that direct-search consistently yields solutions closer to a reference optima (derived by other means) in comparison to conventional gradient-search (applied on the partial derivatives of the Fujisaki error as a function of the underlying Fujisaki parameters).