A Key management system plays an important role in the process of wireless communication between the nodes of a Wireless Sensor Network (WSN). Unlike the wired networks, WSNs are more vulnerable to ...attacks from the malicious nodes. To overcome the shortcomings of the existing key management systems, this paper proposes an adaptive multilevel location-based key management system (AML-KBS), in which the keys are generated dynamically and shared among the nodes of the wireless networks. Since the proposed approach follows a location-based system for key management, the attackers can be differentiated based upon their location. Also, the proposed system has proven its withstanding against node capture attacks. Comparing with the existing approaches, the memory requirement of the proposed system has shown better improvement. Moreover, the proposed methodology provides better security mechanism than the existing key management systems.
Survey on software defect prediction techniques Mahesh Kumar Thota; Francis H Shajin; P. Rajesh
International Journal of Applied Science and Engineering,
01/2020, Volume:
17, Issue:
4
Journal Article
Peer reviewed
Recent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the ...demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V＆V (Verification ＆ Validation). Software testing is an integral part of software V＆V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.
