The necessity to tackle the increasing common concern about safety issues, urges the scientific community to come up with the development of innovative intruder detection and early warning security systems. One of the most effective technological solutions is provided by the application of (WSN)s. In this endeavor, most solutions have already adopted supercomputers and other computer resource systems to process the enormous amount of data. Alternatively, to this approach, simpler and more easily implementable solutions, such as the (WSN)mod method, are already being put to use. In particular, (WSN)mod is based on three key elements, the categorization of sensor inputs, the quantization of the inputs and a time-window processing. (WSN)mod was introduced as an advanced intrusion detection system that focused on the minimization of the false positive alerts. Building on the idea of (WSN)mod, in this paper we focus, identify and quantify measurable parameters that influence the detection reliability. In addition, the very promising test results of the method and the security system are presented in a range of environmental conditions.

Adequate coverage is one of the main problems for distributed wireless sensor networks and the effectiveness of that highly depends on the sensor deployment scheme. Given a finite number of sensors, optimizing the sensor deployment will provide sufficient sensor coverage and save power of sensors for movement to target location to adequate coverage. In this paper, we apply fuzzy logic system to optimize the sensor placement after an initial random deployment. Based on Voronoi diagrams and Fuzzy logic, we design two distributed algorithms for controlling the movement of sensors. Simulation results show that our approaches maximiz the sensor coverage.

Managing the production of greenhouse products requires knowledge of controlling many environmental factors and plant nutrition and fight against pests and plant diseases. Recognition pests and fight against them is one of the most important activities in the process of production of greenhouse products. Pre-knowledge of the demographic density of insect pests is one of the effective methods of pesticides and reduce their levels of use, especially for insect pest control toxins. Wireless sensor networking Technologies ((WSN)) is one of the new technologies used to sense the environment and collect and transmit information to the user or the central station to view and respond appropriately to an occurrence or phenomenon. In this study, the use of (WSN) in monitoring, timely diagnosis of greenhouse white flies, design and mapping of greenhouse contamination was investigated. For this goal, 3750 images of 15 sticky traps with white flies that attached to Melon greenhouse in Isfahan Agricultural Jihad Research Center were provide and transmitted online using a (WSN) to a computer located at a distance of 900 meters from the greenhouse. The color images of the sticky traps are acquired by using 15 digital cameras were converted to gray colored images using MATLAB software, then after image classification with Support Vector Machine (SVM) classifier based on their features, are divided into two categories of images: whiteflies affected image and whiteflies unaffected image. After identification of the white flies, number of pests was counted and infection maps of Greenhouse with ArcMap10. 2 software was drawn up. Assessment of the system showed that accuracy of SVM algorithm for categorizing images of sticky traps was 97. 73%, and the average values of statistic parameters of the Confusion matrix for 15 traps including sensitivity, accuracy, specificity and classification accuracy were 98. 46%, 83. 31%, 99. 08% and 97. 72% respectively. The overall accuracy of the system for detection and counting Greenhouse whitefly pests is 97. 71%. The average root mean square error (RMSE) in estimating of the number of white flight by image processing and direct counting was between 1 and 5. 03. Therefore, the system is suitable for detecting and tracing and counting the number of trapped white flies, and it is possible to design appropriate greenhouse poisoning plans to fight this pest.

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

Wireless sensor networks are composed of hundreds or thousands of small nodes called sensor that work together and are associated with a specific task or tasks to do. Each of these nodes includes sensor, processor, communication components, a small memory and a source of energy. It is expected that wireless sensor networks will be used widely in many applications in near future. Scalability and lifetime extension in wireless sensor networks are critical issues in designing and implementing a wireless sensor network ((WSN)).Relay Nodes (RNs) in (WSN) are nodes with higher useful power and radius, which considered as head of network. RNs are able to disseminate data in a higher level, network clusters, and network regions. In this paper, a novel algorithm is proposed to cover all sensor nodes with the least possible RNs. The number of RNs can be calculated through a proposed mathematical function which has the maximum rate of detection. The proposed approach shows high capability for fault tolerance in (WSN) when compared to other algorithms.

Wireless sensor networks ((WSN)s) have been employed in various real-time applications and addressed fundamental issues, such as limited power resources and network life. Several sensor nodes in a (WSN) monitor the actual world and relay discovered data to base stations. The biggest issue with (WSN) is that the sensors have a limited lifetime and use much electricity to relay data to the base station. This paper proposes an improved PSO-based Enhanced Distributed Energy Efficient Clustering (EDEEC) algorithm to extend the network's life and reduce power consumption. Clustering is the process of forming groups of sensor nodes. The cluster aims to improve the network's scalability, energy efficiency, and other characteristics. The particle swarm optimization algorithm is modified to obtain energy-efficient (WSN)s. The assessment is based on the essential (WSN) characteristics, including network lifetime and energy efficiency (power consumption). Compared to LEACH, HEED, and DEEC, our proposed IPSO-EDEEC uses less energy.