Blockchain technology, particularly Hyperledger Fabric (HLF), has emerged as a promising solution to enhance security and privacy in various domains, including Internet of Things (IoT) networks. Conflicting transactions in a HLF-based IoT network occur when multiple transactions attempt to modify the same asset or data concurrently. Conflicting transactions can lead to data inconsistencies, because the network may be unable to determine the correct order or the most preferred valid transaction. Existing conflict resolution mechanisms in HLF-based IoT networks often introduce considerable transaction latency, detect and resolve conflicting transactions in the late stages of the transaction lifecycle (ordering and validation), or require significant changes to the underlying HLF blockchain platform. To overcome these limitations, we propose an Early Conflict Resolution (ECR) mechanism that detects and resolves conflicts during the endorsement stage. The ECR mechanism uses a local cache (Sync.Map) and a dependency graph to efficiently detect conflicts by analyzing the Read-Sets (RS) and Write-Sets (WS) of transactions. ECR resolves conflicts in the detected conflicting transactions through transaction reordering or sequential processing. It also executes non-conflicting transactions in parallel to speed their processing. Our results show that the ECR mechanism improves transaction latency and the success rate for varying conflict rates, block sizes, and IoT devices compared to existing mechanisms.

Conflicting transactions pose significant challenges in Hyperledger Fabric (HLF)-based IoT networks, affecting performance and introducing security vulnerabilities that can facilitate malicious attacks. Traditional conflict resolution mechanisms resolve conflicts in the later stages of transaction processing (i.e., the ordering or validation stages), resulting in increased transaction latency, which impacts delay-critical IoT applications. This paper proposes an Early Conflict Resolution (ECR) mechanism that integrates conflict detection and resolution at the endorsement stage, enhancing throughput and reducing transaction latency. This paper also explores the impact of conflicting transactions on blockchain attack vectors, focusing on four pivotal attacks-block withholding, double spending, balance attacks, and Distributed Denial-of-Service (DDoS)-simulated to analyze their exploitation of transaction conflicts and their impact on IoT networks. The results show that the ECR mechanism significantly improves the success rate and transaction latency compared to existing mechanisms.

Since the emergence of Large Language Models (LLMs) popularized by the release of GPT-3 and ChatGPT, LLMs have shown remarkable promise in programming-related tasks. While code generation using LLMs has become a popular field of research, code evaluation using LLMs remains under-explored. In this paper, we focus on LLM-based code evaluation and attempt to fill in the existing gaps. We propose multi-agentic novel approaches using question-specific rubrics tailored to the problem statement, arguing that these perform better for logical assessment than the existing approaches that use question-agnostic rubrics . To address the lack of suitable evaluation datasets, we introduce two datasets: a Data Structures and Algorithms dataset containing 150 student submissions from a popular Data Structures and Algorithms practice website, and an Object Oriented Programming dataset comprising 80 student submissions from undergraduate computer science courses. In addition to using standard metrics (Spearman Correlation, Cohen’s Kappa), we additionally propose a new metric called as Leniency, which quantifies evaluation strictness relative to expert assessment. Our comprehensive analysis demonstrates that question-specific rubrics significantly enhance logical assessment of code in educational settings, providing better feedback aligned with instructional goals beyond mere syntactic correctness.

Peer-to-Peer (P2P) energy trading, facilitated by prosumers who both produce and consume energy, provides a new type of for energy trading. Prosumers generate renewable energy in various environments, from industrial to residential. Traditional centralized energy trading methods pose risks, such as single point of failure and security issues. In contrast, de-centralized energy trading methods that use blockchains provide high security and reliability. However, the blockchain technology is not without limitations; in particular, the blockchain-based authentication mechanisms face three limitations, namely, they do not fully protect prosumer privacy due to unencrypted transactions, they are susceptible to multiple security attacks, and their authentication processes demand high computational and communication resources. To address these limitations, this paper proposes a novel Privacy-Preserving Mutual Authentication (PPMA) mechanism for P2P energy trading in smart grid networks. By employing Elliptic Curve Cryptography (ECC), symmetric encryption, and hash functions, the PPMA mechanism provides secure, privacy-preserving, and cost-effective mutual authentication for prosumers in P2P energy trading. When integrated with a permissioned blockchain and smart contract, PPMA aims to facilitate secure and scalable P2P energy trading. The efficacy of the PPMA mechanism is evaluated through comprehensive security and cost analyses.

Providing low latency, high security, and high resource utilization for Internet of Things (IoT) networks is challenging due to the heterogeneous nature of these networks and the need for more standardization in security algorithms. Current edge computing-based IoT solutions decrease network latency and improve resource utilization but do not provide adequate security because they offer multiple attack surfaces for adversaries. Recent work uses blockchain technology to provide better security in IoT networks. However, blockchain-based solutions suffer from scalability problems and can increase latency. Sidechains are parallel blockchain networks typically used to increase the scalability of blockchain networks. We propose a novel Sidechain-based Access control and Trust evaluation mechanism for IoT networks (SATI) to decrease network latency and improve scalability, security, and energy efficiency. SATI uses a sidechain with the blockchain network to improve its scalability. It also uses edge computing to provide low network latency and high resource utilization in terms of CPU and memory usage. In addition, trust evaluation and attribute-based access control mechanisms are used to improve the security of the IoT network. We compare our work with existing mechanisms in terms of scalability, security, latency, and CPU and memory usage. In addition, we perform a formal security analysis of the SATI mechanism using reduction-based analysis and the Scyther verification tool.

The limited resources and low computational power of wireless sensor networks (WSNs) make them vulnerable to various security attacks. Conventional security mechanisms require too many resources to allow the reliable operation of WSNs due to their resource-constrained nature. In addition, multihop communication in WSNs creates a requirement for guaranteed Quality of Service (QoS). Therefore, providing security while maintaining QoS and energy efficiency in WSNs are important design considerations. To further increase the performance of WSNs, there is a need to overcome the energy-hole problem, which leads to poor coverage of the field of interest. An energy-hole problem is created because of using poor deployment strategies. In this article, we define a multiobjective WSN optimization problem and present a novel algorithm known as lightweight secure routing (LSR) to manage WSNs that directly addresses the multiobjective WSN optimization problem. Our LSR algorithm uses ant colony optimization (ACO), an adaptive security model based on direct and indirect trust calculations, an adaptive QoS model, a hybrid deployment model based on 2-D Gaussian and uniform distributions, and an adaptive connectivity model that uses an appropriate communicational radius to ensure high connectivity between sensor nodes to solve the multiobjective WSN optimization problem. We divide our simulation results into three analyses, namely, trust model analysis, network scalability analysis, and security risk analysis to show that LSR outperforms the existing techniques in terms of energy consumed to calculate trust values, trust values convergence, network lifetime, average routing delay, and packet delivery ratio.

Wireless mesh networks (WMNs) have recently emerged to be a cost-effective solution to support large-scale wireless Internet access. In this paper, the handoff support in Internet-based infrastructure mesh networks is investigated. Current handoff schemes concentrate on the link-layer handoff support and have not considered the Internet-domain handoff due to the mobility from mesh networks. This paper focuses on the handoff overhead analysis. A new analytical model is proposed to evaluate the signaling overhead percentage during handoffs in infrastructure mesh networks. The proposed model considers overhead from both the link layer and network layer. The designed analytical model is validated by simulation results. Conclusions from this study can provide great insights in designing new cost-effective handoff schemes in WMNs.