Aspects of Security for Accelerating Artificial Intelligence inside Internet of Things Centric Distributed Storage Network

Abstract

The centricity of in-network storage is a methodology that allows the generated sensor data readings to be stored in slices, known as data fragments. Within the sensor network, fragments are stored at various storage nodes. As a result, the data is more secure. The IoT network shows failures and unauthorized admittance to the users. The role played, herein, by the storage nodes make them prone to various security attacks. In this research work, the preliminary investigation and the methods to employ to provide the solution for security and privacy problems in distributed sensor network storage. Deep learning technologies that are already in use in ioT contexts cannot assign compute tasks reasonably which leads to resources wasting. Artificial intelligence is made up of capabilities that assist gadgets in learning and processing information in the same way that people do. A quantifiable blend of artificial intelligence with the internet of things, popularly known as AAIoT, is something that is holding a promise of a more connected future. This article proposes an AAIoT based method for allocating each network layer's inference computation in a multi-layer internet of things system to each device This is, to the best of my knowledge, the first attempt to track down this criminal. When considering the cost of computing and communication, the design of this study effort is a programming algorithm to lower the response time. The simulation findings demonstrate that a method could improve the system's response time significantly.