R.M.K. College of Engineering and Technology

A Mobile Ad Hoc Network (MANET) decentralized network requires an effective packet-transmission routing protocol. In MANET, routing protocols are vital in managing numerous resource-constrained nodes. As the network grows, increased overhead and congestion cause unpredictable routing, packet loss, and fluctuations. In this case, ensuring secure routing and efficient data management remains a significant challenge. We proposed a Trust is Valued via Fuzzy Logic Optimization (TB-FL) to address this. The proposed TB-FL is designed with trust-based node evaluation, which works efficiently to handle dynamic MANET architecture by ensuring secure packet transmission. Fuzzy Logic Optimization assesses network path trust levels, enhancing reliability. The TB-FL model continuously maintains trust assessments, adapting to dynamic network conditions. To determine the performance of the proposed TB-FL, a comparison work is carried out with the existing Adaptive Beaconing Strategy Based on the Fuzzy Logic Scheme for Geographical Routing (AFB-GPSR), Enhanced Virtual Cord Protocol (EVCP) and Optimized Control Interval-Optimized Link State Routing-Based Efficient Routing (OCI-OLSR) Mechanisms. Compared to existing approaches, the proposed ABC performs better with 100 nodes, including 99

A Wireless Sensor Network (WSN) refers to a decentralized network architecture where sensor nodes, administrative units, and sink nodes work together to observe conditions and gather data efficiently. Due to the rapid progress in wireless communication, WSNs have gained significant popularity across various industries, as they overcome the limitations of existing techniques in collecting and monitoring data in harsh environments. The rapid growth in data transmission has made security a critical concern in WSN. The prevailing methods used to analyze network traffic data exhibit poor detection accuracy during intrusion attacks. This paper proposes a novel hybrid framework named Snow Leopard Beetle Optimization_Convolutional eXtreme Gradient Boosting (SLepBO_ ConvXGB) for intrusion detection in WSN, which introduces a unique integration mechanism, combining the global exploration capability of Snow Leopard Optimization Algorithm (SLOA) with the local exploitation strength of Dung Beetle Optimization (DBO) to achieve faster and more stable convergence in routing decisions. The adaptive synergy between SLOA and DBO ensures optimized routing with reduced energy consumption and minimal packet delay, outperforming conventional routing approaches. Routing in the simulated WSN is achieved using Snow Leopard Beetle Optimization (SLepBO), taking multiple objective fitness parameters into account. Next, the input data undergoes normalization using the Tanh estimator, after which feature fusion is performed through a Deep Neural Network (DNN) with Jeffreys similarity to improve classification performance. Subsequently, intrusion detection is carried out using ConvXGB, with its performance optimized through SLepBO. Further, the SLepBO for routing achieved distance, residual energy, and delay of 0.100 J, 81.346 m, and 0.718 ms, whereas the SLepBO_ConvXGB for intrusion detection obtained 92.777

In network theory, the domination parameter is vital in investigating several structural features of the networks, including connectedness, their tendency to form clusters, compactness, and symmetry. In this context, various domination parameters have been created using several properties to determine where machines should be placed to ensure that all the places are monitored. To ensure efficient and effective operation, a piece of equipment must monitor its network (power networks) to answer whenever there is a change in demand and availability conditions. Consequently, phasor measurement units (PMUs) are utilised by numerous electrical companies to monitor their networks perpetually. Overseeing an electrical system which consists of minimum PMUs is the same as the vertex covering problem of graph theory, in which a subset D of a vertex set V is a power dominating set (PDS) if it monitors generators, cables, and all other components, in the electrical system using a few guidelines. Hypercube is a versatile, most popular, adaptable, and convertible interconnection network. Its appealing qualities led to the development of other hypercube variants. A fractal cubic network is a new variant of the hypercube that can be used as a best substitute in case faults occur in the hypercube. This article determines the power domination number of the fractal cubic network. Further, we investigate the resolving power dominating set (RPDS), which contrasts starkly with hypercubes, where resolving power domination is inherently challenging. The investigation extends to fault-tolerant power domination number of fractal cubic networks. Also, the new invariant called fault-tolerant resolving power domination number is introduced, and the exact value for the fractal cubic network is obtained.

Penetration testing is still both critical and labour intensive in modern cybersecurity practice. Current automated approaches based on monolithic large language models (LLMs) have problems with statelessness, tend to hallucinate dangerous commands, and are unable to reason over multi-hop attack paths that span a number of hosts and services. We present Trinity, a neuro-symbolic, multi-agent framework for stateful network penetration testing that addresses these limitations by means of three key innovations. Our Neuro-Symbolic Safety Loop enforces deterministic Pydantic-based validation on each generated attack command, blocking out-of-scope or denial-of-service operations before they can execute. Alongside this, Graph-Based Knowledge Hygiene keeps a Neo4j attack-path graph modelling verified relationships between hosts, open ports, and associated CVEs—ensuring the agent only reasons on the basis of empirically confirmed network facts. The framework also has a Self-Healing Execution mechanism for detecting tool-level failures (e.g., Nmap syntax errors, Metasploit connection timeouts) and regenerates corrected commands via an LLM feedback loop that is governed by a circuit-breaker pattern. LangGraph is used to orchestrate the agent as a stateful graph, with real-time vulnerability context drawn from a ChromaDB vector store filled in by live NVD feeds. In controlled multi-host lab scenarios, Trinity improved reconnaissance completeness and command reliability over stateless LLM baselines while maintaining high scope and safety compliance. This work sets a reproducible, safety-sensitive standard for autonomous network penetration testing agents.

Disaster monitoring and response are critical for lessening the effects that natural catastrophes have on the environment and populations, playing a vital role in disaster management, environmental protection, and long-term sustainable development. This research focuses on developing an automated feature extraction framework utilizing a U-Net architecture for segmenting disaster-affected areas from remote sensing images, specifically targeting floods, landslides, and wildfires. The proposed model is trained on a dataset of annotated remote sensing images and demonstrates high accuracy and robustness. The results prove that the model can successfully extract relevant features from remote sensing imagery, enabling timely and accurate identification of disaster-affected areas. The proposed single-stage semantic segmentation network achieves an accuracy of 97.3%, a recall of 95.5%, and an F1-score of 95.3, outperforming existing methods such as BRRNet, DRNet, and ENRU-Net. The use of the U-Net architecture is particularly motivated by its ability to capture both global contextual information and fine-grained spatial details, which are crucial for identifying disaster-affected regions in high-resolution remote sensing imagery. Furthermore, by leveraging transfer learning techniques, the dependency on large volumes of labeled data is significantly reduced, enhancing the practicality and scalability of the proposed approach. Overall, this framework supports intelligent disaster management strategies and contributes to Sustainable Cities by enabling rapid damage assessment, informed decision-making, and efficient resource allocation, thereby aligning with global goals for Climate Action and sustainable disaster resilience..