Wireless Sensor Network

In many significant characteristics of the modern world, including particular industry, agriculture, and military applications, Wireless Sensor Networks (WSNs) have begun to emerge. Even though energy consumption is the greatest challenge confronting WSNs, it is essential to analyse the feasibility of the use of mobile components for the data collection on WSN networks. To collect information from their sensing terrain, WSNs use significant numbers of wireless sensor nodes. The method of acquiring data from the sensor nodes and forwarding this data to the node or base station is Data Collection (DC). In such methods, data collection is effective and efficient in data transfer for an extended WSN lifetime. The scientific and investigational analysis demonstrated that Routing Path Selection-DC has the robustness and low energy, is required to reduce the mobile sink's energy demand, which provides better results, and is adaptable dynamically to multiple situations, including the variance between the ground robot and the cost of energy. The research paper developed an approach that creates a data collection schedule that is collision-free where appropriate power limits are allocated and verifies its overall performance of virtual environment network latency.

Malware attack is growing day by day in cyberspace. And Wireless Sensor Network (WSN) is also facing a hazardous type of situation due to attack of malware (malicious code, virus, worm etc.). Malwares target sensor nodes easily because, nodes are equipped with limited resources. Hence, security of WSN against malware attack is one of the imperative requisite. Malware spreads in the entire network wirelessly, which initiates from single infectious node and spread in the whole WSN. In this way the complete network comes under the security threat. Therefore, it is mandatory to apply the security technique through which to secure WSN against malware attacks. To secure WSN due to malware attacks a quarantine based model has been proposed. The proposed model consists of various epidemic states namely: Susceptible Carrier - Infectious - Quarantine - Recovered - Susceptible (SCIQRS). The model explained the propagation dynamics of malware in WSN and proposed a technique to prevent its propagation. The technique of quarantine along with recovery is to much effective in prevailing of malware propagation in WSN. For the determination of WSN stability and equilibrium points the expression of basic reproduction number has been obtained. Malware propagation is affected by different network parameters, which has been also discussed. The comparative investigation of proposed model has been carried out with existing model. The proposed model has been substantiated by simulation outcomes

The majority of wireless sensor network (WSN) security protocols state that a direct connection from an attacker can give them total control of a sensor node. A high level of security is necessary for the acceptance and adoption of sensor networks in a variety of applications. In order to clarify this issue, the current study focuses on identifying abnormalities in nodes and cluster heads as well as developing a method to identify new cluster heads and find anomalies in cluster heads and nodes. We simulated our suggested method using MATLAB tools and the Database of the Intel Research Laboratory. The purpose of the performed simulation is to identify the faulty sensor. Using the IBRL database, sensors that fail over time and their failure model is the form that shows the beats in the form of pulses, we find out that the sensor is broken and is of no value. Of course, this does not mean that the sensor is invasive or intrusive. We have tried by clustering through Euclidean distance that identify disturbing sensors. But in this part of the simulation, we didn't have any data that shows disturbing sensors, it only shows broken sensors. We have placed the sensors randomly in a 50 x 50 space and we want to identify the abnormal node.

An Artificial Intelligence (AI) technique plays the most crucial factor to consider in energy utilization in a wireless sensor network (WSN). AI transforms industrial operations by optimizing the energy consumption in sensor nodes. As a result, it is crucial for improving sensor node location accuracy, particularly in unbalanced or Adhoc environments. Because of this, the purpose of this research is to improve the accuracy of the localization process in locations where sensor nodes encounter barriers or obstacles on a regular basis. The Bees Swarm Optimization (BSO) algorithm is used to segment sensor nodes in order to increase the accuracy of the Direction of Arrival (DoA) estimate between the anchor and unknown node pairs. Even in the presence of unbalanced conditions, the proposed DoA- BSO involving three separate bee colonies can identify plausible anchor nodes as well as segment nodes arranged in clusters. In order to obtain the intended result, the objective function is designed to take into consideration the hops, energy, and transmission distance of the anchor and unknown node pairs, among other factors. The studies are carried out in a large-scale WSN using sensor node pairs in order to determine the precision with which the DoA-BSO can be located. When comparing DoA-BSO to conventional approaches, the findings of the meta-heuristic algorithm show that it improves the accuracy and segmentation of nodes significantly

Recent developments in electronics and wireless communication play a leading role in manufacturing sensors with reduced power consumption that have wireless connectivity and limited processing capabilities. Due to the limitation of battery in sensor nodes, one of the main challenges in this type of network is energy consumption, which is directly related to the lifetime of the network.  Another important issue is to keep nodes connected in the network during data transmission. For these purposes, a connectivity control system is required. By improving the tree growth algorithm in the network graph, an optimal graph using a suitable path for data transmission in the network is designed. Connectivity control significantly improved system performance in terms of network power consumption and lifetime.   In this paper, a new algorithm for connectivity and linkage control, based on sequential mode is presented, which has achieved a significant improvement compared to an ordinary algorithm. The outcomes of the proposed algorithm on the selected model show 56% improvement in the remaining battery charge. In addition, the end-to-end delay was reduced by 0.5 m seconds in the network.

In intrusion detection applications, wireless sensor networks are commonly used. Many research literature papers are aimed at generating and evaluating the information on intruder detection in terms of probability of detection and false alarm rates. In two modalities, the model for acoustic signal and the sensor probability model, and in this research paper, the problems of passive motive intrusion detections have been solved. The aim is to establish a three-stage hierarchy to determine if mobile intruders are present. The sensor nodes at the fundamental level have a k-mean clustering grouping. For binary hypothesis testing, the strengths or probabilities in the cluster head are employed. Cluster leaders send their judgments to the Fusion Centre (FC) after completing a Likelihood Ratio Test (LRT) to ensure invaders are correctly inferred. A numerical analysis of the signals received determines the optimal value for probability computation. The resulting fusion rule maximizes detection likelihood regarding the allowed falsifying rates. The number of absolute sensor nodes determines the exact fusion rule. Compared to earlier fusion rules, simulation results show that the new fusion rule has a better ability to follow mobile invaders and enhanced accuracy and detection speed.