No abstract

In general, due to the complexity and limited computation capabilities, the security issues occur in Internet of Things (IoT). Security protocols are required to increase the security of the system. Therefore, in this article, an enhanced Elman spike neural network (EESNN) with green proof of work consensus algorithm (GPoW) is proposed for enhancing the security of IoT network. Initially, the generalized security mechanism as EESNN approach is proposed for the IoT network by categorizing the devices into malicious and benign. Then, the GPoW consensus algorithm is used for enhancing the security of the devices from malicious attacks. Subsequently, a coalition formation (CF) algorithm is used for reducing the excess energy consumption in a network. The proposed EESNN‐GPoW‐CF approach has effectively classified the malicious attacks and enhances the security of the IoT network. The simulation of this work is done in Python. From the simulation, the proposed EESNN‐GPoW‐CF approach attains high efficiency outcomes in terms of accuracy, recall, precision, PDR, PLR, throughput, overhead, computation time, and delay. Moreover, the proposed EESNN‐GPoW‐CF approach attains 3.1%, 5.3%, 7.4% high accuracy rate, and 7.5%, 12.5%, 14.7% lower computation time with 4.8%, 2.3%, 5.7% lower energy utilization than the existing methods, such as deep learning based blockchain for IoT security, deep reinforcement learning based blockchain for IoT security, and deep blockchain‐based trustworthy privacy preserving secured framework in IoT, respectively.

Due to intense competition, it has become inevitable for business organisations world over to become efficient and cost effective. Seamless cold chain infrastructure is critical for the success of cold chain business. Concurrent growth in demand for products such as fresh agricultural produce, frozen food, photographic films, chemicals and pharmaceutical drugs has lead to the necessity of managing cold chains intelligently. The managers of cold chains at strategic and functional level need actionable information for making their organisations agile. In the recent times, the concept of business intelligence has gained momentum and found application in diverse areas. In this paper, authors have made an effort to develop a framework of BI system for cold chains and highlighted the application areas of business intelligence for cold chains including a case study of cold chain business. He has 18 years of experience in the field of information technology. His areas of interest are business intelligence, information system, and software engineering. Applications of business intelligence in making cold chains seamless 165Karunesh Saxena is currently working as a Professor at the Faculty of Management Studies, Mohanlal Sukhadiya University, Udaipur and obtained his MBA and PhD (on TQM and ISO 9000). He has about 22 years of research and teaching experience. He has also worked in multi national companies of Germany and UK and gained five years of industrial experience. He is constantly teaching and guiding PhD students. He has been awarded major research project by UGC, New Delhi on emotional intelligence. He has published four books and one monograph. Along with this, he has published/presented about 57 research papers in various reputed journals of national and international level and given keynote address in international conference. He has recorded more than 70 higher educational films made for UGC Center for Educational Consortium (CEC) Countrywide Classroom Programme. He conducted more than 50 management development programmes for various companies.