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Using enhanced ant colony optimization, this study proposes an efficient heuristic scheduling technique for cloud infrastructure that addresses the issues with nonlinear loads, slow processing complexity, and incomplete shared memory asset knowledge that plagued earlier resource supply implementations. The cloud-based planning architecture has been tailored for dynamic planning. Therefore, to determine the best task allocation method, a contentment factor was developed by integrating these three objectives of the smallest waiting period, the extent of commodity congestion control, and the expense of goal accomplishment. Ultimately, the incentive and retribution component would be used to modify the ant colony calculation perfume-generating criteria that accelerate a solution time. In particular, they leverage an activity contributed of the instability component to enhance the capabilities of such a method, and they include a virtual desktop burden weight component in the operation of regional pheromone revamping to assure virtual computers’ immense. Experiences with the routing protocol should be used to explore or demonstrate the feasibility of our methodology. In comparison with traditional methods, the simulation results show that the proposed methodology has the most rapid generalization capability, and it has the shortest duration of the project, the most distributed demand, and the best utilization of the capabilities of the virtual computer. Consequently, their hypothetical technique of optimizing the supply of resources exceeds world competition.

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph.

Edge detection is a vital feature in the field of Computer Vision for extracting meaningful information from digital images. The primary aim of our proposed technique is to obtain thin edges, so that the result derived is suitable more for further applications such as boundary detection, image segmentation, motion detection/estimation, texture analysis, object identification and so on. We have done comparative analysis for four edge detectors that use different methods for detecting edges and have tested their results under a variety of situations to determine which detector was preferable under different circumstances. For practical analysis it was used to detect the edges and results have been compared with the gradient based methods of edge detection like Sobel, Prewitt, Robert these are first order derivative methods. Testing and analysis on different satellite images by Laplacian and gradient based method have done by using the ideal thresholding parameter for good true edges.To evaluate the performance of Laplacian based algorithms and gradient based algorithms. There is a need of calculating the some parameter for calculating the image quality. These parameters are able to identify which algorithm is best suited in detecting edges for satellite images.

Internet of things (IoT) has shown a different research direction in the domain of farming and agriculture. Smart agriculture has reduced the farmer’s effort and improved their capability in managing their crops, soil, water, field monitoring, pesticide control, etc. IoT-based solutions have increased the farmer’s attention toward humidity, temperature, pH value and environment conditions that are the most important concern in agriculture. The unique features of Internet of things like faster access to application and data, reduced human efforts, efficient communication and the global connectivity through different devices have made it a fast-growing technology in providing agriculture solutions. This paper explored various IoT smart agriculture systems and the challenges faced in deploying these systems.

Wi-Fi is widely used internet source which use to provide internet access in many areas such as Stores, Cafes, University campuses, Restaurants and so on. Due to transmission of data over the air which makes network vulnerable so it becomes prone to various threats such as Evil Twin and etc. The Evil Twin is a kind of adversary which impersonates a legitimate access point (LAP) as it can happen by spoofing the name (SSID) and MAC address (BSSID) of a legitimate access point (LAP). And this attack can cause many threats such as MITM, Service Interruption, Access point service blocking. In this paper we review various Evil Twin Attack Detection Techniques and proposed methods with their comparisons.

The low cost and better processing power and energy efficiency makes it possible to use large numbers of sensors in a wide variety of applications including environmental monitoring, military, ecology, agriculture, inventory control, robotics health care and various types of pipelines. In monitoring the pipelines, a special class of sensor defined as Linear Wireless Sensor Networks (LSNs) is required having its special characteristics as far as routing is concerned. In this paper a three layered architecture has been proposed for monitoring, and protection of over-ground oil, gas, and water pipelines using wireless sensor networks. The three layers include on the ground, on the pipeline and underground layer. This paper also proposes a architecture a Leach Based Hierarchical Routing Protocol for linear structure wireless sensor networks (LBHRP) for the above mentioned architecture.

In recent years, the range of sensing technologies has prolonged rapidly, whereas the sensing devices are getting cheaper day by day. This has led to a fast expansion in condition monitoring of different systems, structures, vehicles, and machinery using these sensing devices. There are special kind of structures, which are linear in nature and these structures includes roads, bridges, pipelines (Air Conditioner/ Oil/ Gas/Water etc.) can be over ground or underground and Electric power distribution lines. Most of these structures use a static topology and deploy the sensors in a straight line named as a Linear Wireless Sensor Network (LWSN). The LWSN has attracted the researchers due to their numerous positive features including easy placement of sensors and robustness in diverse environments. In the presented article, an attempt has been made to study and analyze the various routing techniques and their parameters used in various applications, used by Linear Wireless Sensor networks (LWSN) for monitoring the condition of these linear structures. In addition, recent LWSN are categorized according to the matches found in the routing protocols, which would help as a landmark for other investigators so that they could further discover the LWSN routing techniques more intensely.

The recent advancements in the field of Wireless Sensor Networks (WSN) make the sensors smaller, cheaper and more energy efficient. These innovations made it attractive to use sensor networks in new applications like Border monitoring, Railway track monitoring, Oil and Gas pipeline monitoring etc. In the above mentioned applications, a common characteristic is their linear topology. Since last few years, a new subclass of sensor networks known as linear sensor networks (LSN) is emerging as a major focus area of research. Due to the special topology of LSNs, the issues and solutions required for LSNs are significantly different from ordinary sensor network. In the present exposition, the applications of LSN, major issues related to LSN, and the major research trends in this area have been summarized.