Blockchain technology has expanded from a system of verifying and recording peer-to-peer cryptocurrency transactions to a general-purpose system that has the capability to execute self-enforcing agreements in a digital form, called a smart contract. Smart Contracts remove the need for a trusted third party by decentralizing the terms of contract between all nodes on a distributed network and distributing and verifying them as executable code. In this paper, three complementary research strands, which are present in the literature, are combined to give an overview of the smart contract research field: a systematic taxonomy of smart contract improvement and usage studies, the network-theoretic analysis of decentralized application (DApp) architecture using the contract and function call graph, and a bibliometric critical review of smart contract publications over the past decade. This paper explains how smart contracts work, asks some questions about the current smart contract development platforms, categorizes the current research efforts into modeling-driven, optimization-driven improvement categories and resource-driven and cross-organizational usage categories. This paper also examines small world and modular properties of the internal calls architecture of real-life DApps that directly affect security and resiliency. Applications in healthcare, supply chain management, finance, insurance, energy trading and philanthropy are mentioned, the various existing challenges such as reentrancy and other vulnerabilities, legal uncertainty, scalability limits, immutability trade-offs, oracles and inefficiency of consensus mechanisms examined. The paper finally presents a list of the research avenues that are emerging, such as Layer 2 scaling solutions, formal verification, AI-aided auditing, and quantum-resistant crypto.
Harshraj N. Gadbail, Rajendra M. Rewatkar, Nupur G. Kamdi et al.· 2026 International Conferenc...· 0 citations
The fast-growing Artificial Intelligence (AI) has led to the enormous processing requirements previously unknown, which cause high energy usage and related carbon emissions. Although the previous studies have concentrated on enhancing the efficiency of the algorithms, less attention has been given on the timing and placement of AI workloads. This paper introduces a Carbon-Aware AI Scheduler a full-stack application that optimizes the carbon footprint of AI workloads by utilizing smart time-shifting and geo-shifting operations. The system combines actual carbon intensity data, a hybrid model of forecasting which uses historical baselines and artificial diurnal curves, and daily job urgency and flexibility optimization algorithms. Experimental testing in over 50 regions throughout the U.S. shows that carbon emissions can be reduced by as much as 87 percent in case of geo-shifted workloads and by 2530 percent in case of time-shifted workloads in one region. The scheduler has a latency of sub-200 ms, which is acceptable in real-time application. Findings suggest that carbon-conscious scheduling, alongside the clear user feedback, may have a considerable effect on the behavior of developers and have a positive impact on the environmental impact of AI systems.
Harshraj N. Gadbail, Rajendra M. Rewatkar, Sujal Zade et al.· International Conference on...· 0 citations
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