Since the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. This article brings a comprehensive review of the fundamentals of distributed ledger and its variants. Analyzing 185 publications, ranging from academic journals to industry websites, it provides a comparative analysis of 130 consensus algorithms using a novel architectural classification. The distribution of the reviewed algorithms is analyzed in terms of the proposed classification and different application domains, along with the applicability of each class among the top 10 platforms in the most prominent blockchain application domains. Additional conclusions are drawn from the evolution of consensus mechanisms, and the analysis concludes envisaging future prospects for consensus as an important part of distributed ledger technology.

With the widespread adoption of blockchain platforms across various decentralized applications, the smart contract’s vulnerabilities are continuously growing and evolving. Consequently, a failure to optimize conventional vulnerability analysis methods results in unforeseen effects caused by overlooked classes of vulnerabilities. Current methods have difficulty dealing with multifaceted intrusions, which calls for more robust approaches. Therefore, overdependence on environment-defined parameters in the contract execution logic binds the contract to the manipulation of such parameters and is perceived as a security vulnerability. Several vulnerability analysis tools have been identified as insufficient to effectively identify certain types of vulnerability. In this paper, we perform a domain-specific evaluation of state-of-the-art vulnerability detection tools on smart contracts. A domain can be defined as a particular area of knowledge, expertise, or industry. We use a perspective specific to the area of energy contracts to draw logical and language-dependent features to advance the structural and procedural comprehension of these contracts. The goal is to reach a greater degree of abstraction and navigate the complexities of decentralized applications by determining their domains. In particular, we analyze code embedding of energy smart contracts and characterize their vulnerabilities in transactive energy systems. We conclude that energy contracts can be affected by a relatively large number of defects. It also appears that the detection accuracy of the tools varies depending on the domain. This suggests that security flaws may be domain-specific. As a result, in some domains, many vulnerabilities can be overlooked by existing analytical tools. Additionally, the overall impact of a specific vulnerability can differ significantly between domains, making its mitigation a priority subject to business logic. As a result, more effort should be directed towards the reliable and accurate detection of existing and new types of vulnerability from a domain-specific point of view.

As blockchain technology advances, so has the deployment of smart contracts on blockchain platforms, making it exceedingly challenging for users to explicitly identify application services. Unlike traditional contracts, smart contracts are not written in a natural language, making it difficult to determine their provenance. Automatic classification of smart contracts offers blockchain users keyword-based contract queries and a streamlined effective management of smart contracts. In addition, the advancement in smart contracts is accompanied by security challenges, which are generally caused by domain-specific security breaches in smart contract implementation. The development of secure and reliable smart contracts can be extremely challenging due to domain-specific vulnerabilities and constraints associated with various business logics. Accordingly, contract classification based on the application domain and the transaction context offers greater insight into the syntactic and semantic properties of that class. However, despite initial attempts at classifying Ethereum smart contracts, there has been no research on the identification of smart contracts deployed in transactive energy systems for energy exchange purposes. In this article, in response to the widely recognized prospects of blockchain-enabled smart contracts towards an economical and transparent energy sector, we propose a methodology for the detection and analysis of energy smart contracts. First, smart contracts are parsed by transforming code elements into vectors that encapsulate the semantic and syntactic characteristics of each term. This generates a corpus of annotated text as a balanced, representative collection of terms in energy contracts. The use of a domain corpus builder as an embedding layer to annotate energy smart contracts in conjunction with machine learning models results in a classification accuracy of 98.34%. Subsequently, a source code analysis scheme is applied to identified energy contracts to uncover patterns in code segment distribution, predominant adoption of certain functions, and recurring contracts across the Ethereum network.

Crowdsensing has been recently investigated as an incorporation of Human-Machine intelligence in which contribution of users is crucial. Indoor localization is one of the significant applications among divers applications that have been introduced in this area. Considering the slight infiltration of GPS signals in indoor environments crowdsensing and its promising indoor localization schemes have been utilized for providing precise localization services. Precision of crowdsensing indoor localization schemes and elimination of erroneous data collection is strongly dependent on the underlying task allocation mechanism. In this work, we have approached the localization precision as a consequence of task allocation mechanism of crowd-powered indoor localization schemes. Hence, we have proposed to tackle this issue by applying GWO (Gray Wolf Optimizer) algorithm on participants of crowdsensing scheme. It is expected that the GWO algorithm implicitly performs the task allocation procedure in account of its crowd-powered nature. Accordingly, we have applied GWO algorithm on a proposed indoor localization scenario to undertake the requirements for discrete task allocation mechanism. Implementation results demonstrated that the population-centric structure of the GWO algorithm significantly increments the accuracy of fingerprint collection mechanism which maintains an exceptional localization precision.