B. S. E. Zoraida

Internet of Things is a paradigm-shifting innovation with far-reaching implications for many industries and spheres of contemporary life, including but not limited to communications, finance, and even national defense (through initiatives like the Internet of Battlefield / Military Things). Security, equivalence, potential consumption, and device heterogeneity are just a few of the IoT’s long-standing issues. When transmitting data over IoT and edge networks, security and privacy must be prioritised due to the scarcity of available devices and computing resources (e.g., processing and storage). Whether deliberate or accidental , data manipulation in an IoT network may have tangible effects. This study examines how blockchain technology and SDN could collaborate to provide solutions. With a secure, novel, and potentially efficient routing protocol, our blockchain-enabled IoT networks’ cluster-based SDN controller architecture is a positive development. This architecture does away with wasteful Proof-of-Work (POW) authentication in favour of a more secure distributed-trust-based approach. Even low-powered Internet of Things (IoT) devices may use blockchain technology. The experimental findings reveal EESCFD, AOMDV, SMSN, AODV, and DSDV regarding throughput, latency, and potential using that the cluster structure-based routing protocol outperforms. Our studies’ findings indicate that our proposed architecture achieves better than the conventional blockchain regarding transaction throughput and latency.

Objectives: Providing efficient energy management for smart home through appropriate scheduling of household appliances is addressed in this paper, with two objectives namely, electricity peak demand minimization and electricity cost minimization. Methods/Statistical Analysis: The residential load scheduling problem requires the prior knowledge about the residential electricity demand and electricity price information, for scheduling the appliances. Two different algorithms namely, Discrete Non-dominated Multi-objective Particle Swarm Optimization (DNMPSO) algorithm and Manhattan distance based Non-dominated Multi-objective Genetic Algorithm (MNMGA) are proposed to solve the problem in this paper. Findings: Both the algorithms were simulated in order to evaluate their performance. The Peak-To-Average Ratio value is used as the measure to assessing the peak load in electricity demand. Based on the results, it is observed that the DNMPSO algorithm obtains significant cost reduction with the acceptable Peak-To-Average Ratio value than the MNMGA algorithm. In addition to that, the DNMPSO algorithm provides better diversity than the MNMGA algorithm. Application/Improvements: The proposed algorithm can be used to enhance the energy management for residential load in smart grid. Further, the cost efficiency can be improved by incorporating renewable energy resource.

The biological deoxyribonucleic acid (DNA) strand is found to be a promising computing unit. An attempt has been made to solve Freeze-Tag Problem (FTP) using DNA. In this paper, the thermodynamic properties of DNA have been utilized along with other biochemical operations to obtain the optimal awakening schedule. Actual distance values are represented using the thermodynamic properties of DNA. The proposed method also finds the minimum spanning tree (MST) of the FTP to obtain the optimal awakening schedule. All possible Euler cycles of the different spanning trees of the problem are first generated. From this generated Euler cycle, the MST is obtained, which is the optimal awakening schedule to the problem. Moreover, the proposed approach can be adopted to solve many real-life applications like broadcasting and scheduling problems, with necessary modifications. In this work, an instance with seven robots is solved using DNA computing.