EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment

Edge Computing and Blockchain in Smart Agriculture Systems

The advancement of Internet-based technologies has made huge progress toward improving the accessibility of "smart agriculture." With the advent of unmanned and automatic management, smart agriculture is now able to accomplish monitoring, supervision, and real-time picture monitoring. It is not possible to know for sure that the data in a smart agriculture system is complete and secure from intrusion. This article investigates and assesses the potential of edge computing and blockchain for use in smart agriculture. We combine the advantages of blockchain technology and the edge computing framework to create a smart agriculture framework system that is based on a very straightforward analysis of the evolution of smart agriculture. The study proposes a thorough method for emphasizing the significance of agriculture and edge computing, as well as the advantages of incorporating blockchain technology in this context. This paper also proposes an intelligent agricultural product traceability system design: edge computing with blockchain for smart agriculture. The study concludes with a discussion of outstanding problems and difficulties that can arise during the creation of a blockchain-based edge computing system for smart agriculture systems

LOAD FLOW ANALYSIS FOR A 220KV LINE – CASE STUDY

In power engineering the power flow analysis (also known as load flow study) is an important tool involving numerical analysis applied to a power system. This project deals with a model of existing power system using the actual data taking care of all parameters required for the simulation and analysis. With the help of Maharashtra State Electricity Transmission co. Ltd., a model of 220KV lines, of Solapur District grid using MATLAB software will be modeled. In this project, an algorithm will be used for power flow study and data collection and coding required for modeling. Load flow studies will be carried out using Newton Raphson method and voltage profile of buses will be analyzed. New method for the improvement of voltage profile will be suggested and analyze using the developed model. The optimization techniques include power factor compensation, tap changing, up gradation of substation, up gradation of line and load shifting will be analyzed

LRDADF: An AI enabled framework for detecting low-rate DDoS attacks in cloud computing environments

DDoS attacks, also known as distributed denial-of-service attacks, pose a significant risk to networks in the cloud. The attackers aim to flood the target system with an overwhelming amount of data and requests until it becomes completely overloaded and unable to function properly. These attacks are becoming smarter and more dangerous all the time. A low-rate DDoS attack is one such strategy that makes detection difficult. At the same time, cloud infrastructure is rapidly evolving. Container-based technology makes it possible for cloud computing to use resources efficiently and scale services in a flexible way. Existing methods for detecting DDoS attacks in cloud computing are insufficient when adversaries use low-rate DDoS attacks. A method is required that can not only identify the attack but also prevent it to some extent. A Low-Rate DDoS Attack Detection Framework (LRDADF) was proposed for this purpose when adversaries use low-rate DDoS attacks. A comprehensive approach is required because low-rate DDoS attacks are difficult to detect. In addition to employing deep learning methods to detect such attacks, we proposed a mathematical model to realize a mitigation strategy. As a result, we proposed a new algorithm called the Hybrid Approach for Low-Rate DDoS Detection (HA-LRDD). The algorithm employs an AI-enabled method comprised of deep convolutional neural networks (CNN) and a deep auto encoder. We defined another algorithm called Dynamic Low-Rate DDoS Mitigation (DLDM), which mitigates the impact of an attack once it has been identified. It also ensures that the attack is defeated and that the infrastructure continues to operate. A comprehensive simulation study revealed that the proposed framework is capable of detecting and mitigating low-rate DDoS attacks to ensure an acceptable level of service in cloud computing environments

Carbazole–pyrrolo[2,1-c][1,4]benzodiazepine conjugates: design, synthesis, and biological evaluation

A series of carbazole–pyrrolobenzodiazepine conjugates (4a–g and 5a–f) have been designed, and synthesized as anticancer agents. These compounds are prepared by linking the C8-position of DC-81 with a carbazole moiety through simple alkane spacers as well as piperazine side-armed alkane spacers in good yields. The DNA binding ability of these conjugates has been determined by thermal denaturation studies and also supported by molecular docking studies. These conjugates showed potent anticancer activity with GI50 ranging from 5.27–0.01 μM. The FACS analysis and BrdU assay of selected conjugates (4c, 4f, 5a and 5f) on MCF-7 cell lines disclosed the increased G1 cell cycle arrest and one of the conjugates 5f has exhibited significant anticancer activity. The analysis of the intrinsic factors involved in causing the G1 arrest in MCF-7 cell lines by 5f conjugate has been demonstrated on the proteins which play a vital role in G1 arrest followed by apoptosis (Cyclin D1, CDK4, c-Jun, JunB, CREB, p53, JNK1/2, procaspase-7, cleaved PARP, pRb, and BAX). Thus, these PBD conjugates (in particular 5f) have promising potency for combating human carcinoma