Journal of Interconnection Networks

In wireless sensor networks (WSN), the multifunctional low-power sensor nodes are linked to base stations and are highly responsible for sensing various information. However, maintaining the network’s lifespan is a major issue because of limited battery capacity and node failures. Thus, the proposed study introduced a new Scalable Trust-based Energy Efficient Routing Algorithm (STEERA) with Adaptive Grasshopper Algorithm (AGA) to enhance the network lifespan. At first, the trust values (Tv) are generated for each node to eliminate the malicious node with the help of penalty factor and volatilization. To make efficient routing process, the nodes are formed into cluster groups. A suitable cluster head is selected for each cluster to mitigate the energy consumption problem through the AGA approach by optimizing the parameters like Tv, distance and residual energy. Finally, the data packets are effectively transmitted through a multi-hop routing process by selecting an appropriate path and satisfying certain parameters through Fire Hawks Optimization (FHO). To simulate the proposed techniques, NS2 software is employed, and the performance is measured over various metrics like energy consumption, residual energy, the lifespan of a network, packet loss ratio and average end-to-end delay. The performance analysis shows that the proposed protocol design has obtained higher results than conventional routing protocols.

The integrity of a graph $G$ is defined as the minimum value of $\left|S\right|+m(G-S)$ taken over all $S\subseteq V(G)$, where $m(H)$ denotes the maximum cardinality of a component of graph $H$. In this paper, we investigate bounds on the maximum and minimum values of the weighted version of this parameter. We also consider the same question for the related parameter vertex-neighbor-integrity.

For a set ${𝒦}$ of connected graphs, a spanning subgraph $H$ of $G$ is called a ${𝒦}$-factor if each component of $H$ is isomorphic to a member of ${𝒦}$. In this paper, some sufficient conditions with regard to tight toughness, isolated toughness and binding number bounds to guarantee the existence of the $\{K_{1,j}:1\le j\le 2k\}$-factor and $\{P_2,P_5\}$-factor for any graph are obtained.

Nowadays, Wireless Sensor Networks (WSN) face more security threats due to the increased service of data transmission at high speed in almost all applications. The security of the network must be ensured by identifying abnormal traffic and current emerging threats. The most promising model for safeguarding the core network from outside attacks is Intrusion Detection Systems (IDS). This work focuses on the introduction of clustering-based intrusion detection in WSN. Initially, clustering takes place, where the nodes are grouped under certain constraints via selecting the optimal Cluster Head (CH). The considered constraints are energy, delay, distance, risk, and link quality. This optimal selection takes place by a new hybrid optimization algorithm termed as Truncate Combined Bald Eagle Optimization (TCBEO) algorithm. The subsequent process is intrusion detection, where a hybrid detection model combining a Convolutional Neural Network (CNN) & Bi-directional Gated Recurrent unit (Bi-GRU) is employed, which is trained with features like improved entropy and correlation taking into consideration of constraints like energy and distance, respectively. Eventually, the suggested work’s effectiveness is affirmed against existing techniques using various performance metrics.

In most Internet of Things (IoT) systems, Quality of service (QoS) must be confirmed with respect to the requirement of implementation domain. The dynamic nature of the IoT surroundings shapes it to complicate the fulfilment of these commitments. A wide range of unpredictable events endanger the quality of service. While execution the self-adaptive schemes handle with system’s unpredictable. In IoT-based Wireless Sensor Networks (WSNs), the significant self-management objectives are self-configuration (SC) and self-healing (SH). In this paper, Self-Configuration and Self-healing Framework using an extreme gradient boosting (XGBoost) Classifier are proposed. In this framework, the IoT traffic classes are categorized as several types under XGBoost classifier. In SC phase, the IoT devices are self-configured by allocating various transmission slots, contention access period (CAPs) on the basis of its categories with priorities. In SH phase, the source node cardinally establishes a confined route retrieval method if the residual power in-between node is truncated or the node has displaced far away. The proposed framework is executed in NS-2 and the results exhibit that the proposed framework has higher packet delivery ratio with reduced packet drops and computational cost. Therefore, the proposed approach has attained 24.7%, 28.9%, 12.75% higher PDR, and 16.8%, 19.87%, and 13.7% higher residual energy than the existing methods like Self-Healing and Seamless Connectivity using Kalman Filter among IoT Networks (SH-SC-KF-IoT), Provenance aware run-time verification mechanism for self-healing IoT (PA-RVM-SH-IoT), and Fully Anonymous Routing Protocol and Self-healing Capacity in Unbalanced Sensor Networks (FARP-SC-USN) methods, respectively.

The aim of the intelligent transport system (ITS) is the improvement of road safety. This system is based on intelligent vehicles composing a network called Vehicular Ad hoc NETwork (VANET). This network suffers from a disconnection problem due to its dynamic topology. Therefore, a framework of collaboration between the Wireless Sensor Network (WSN) and VANET called Hybr1id Sensor and Vehicular Networks (HSVN) has been proposed. In HSVN, the WSN can play the role of a relay between disconnected vehicles. This paper aims to propose a strategy allowing the exchange of messages between the two networks. That reduces the number of accidents and improves the management of road traffic. We can summarize our proposition in three essential points. First, an algorithm is proposed to decompose the vehicular network into clusters. This algorithm takes into account the mobile aspect of vehicles and the road model. Second, a data encoding model and a message model have been proposed to improve the quality of messages. That reduces the response time of drivers to a critical situation. Finally, an exchange algorithm is proposed to ensure the transmission of road messages between vehicles and sensors. Its principle of work is based on several scenarios defined relative to the network condition. Obtained results show an improvement in the delivery delays of road messages and the number of exchanged road messages between the vehicles.