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In this fast-paced technological world, the Internet of Things is a ground breaking technology which finds an immense role in the present electronic world which includes different embedded sensors, devices and most other things which are connected to the Internet. The IoT devices are designed in a way that it helps to collect various forms of data from varied sources and transmit them in digitalized form. In modern era of IoT technology data security is a trending issue which greatly affects the confidentiality of important information. Keeping the issue in mind a novel light encryption strategy known as LCB is designed for IoT devices for optimal security. LCB exploits the benefits of Feistel structure and the architectural benefits of substitution permutation network both to give more security. Moreover, this newly designed technique is tested on (Virtex-7) XC7VX330T FPGA board and it takes much little area of 224 GE (Gate Equivalent) and is extremely fast with very less combinational path delay of 0.877 ns. An in-depth screening confirms the proposed work to promise more security to counter cryptographic attacks. Lastly the Avalanche Effect (AE) of LCB showed as 63.125% and 63.875% when key and plaintext (PT) are taken into consideration respectively. [ABSTRACT FROM AUTHOR]

The edge/fog computing has the potential to gear up the healthcare industry by providing better and faster health services to the patients. In healthcare systems where every second is crucial, the edge computing can be helpful to reduce the time between data capture and analytics in a powerful manner. In edge computing, the network edge devices are configured in such a manner that they can handle critical analysis and make necessary decisions instead of sending the captured health data directly to the cloud. However, lifetime of the edge network is a critical factor and thus an energy efficient network architecture has to be designed to achieve the above mentioned goal. In this regard, this research presents a new extremal optimization tuned micro genetic algorithm (EO-μGA) based clustering technique for the sake of efficient routing and prolonging network lifetime by saving the battery power of network edge devices. Moreover, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been considered for the selection of the cluster leader which will be responsible for managing intra-cluster and inter-cluster data communication. The simulation results show that the proposed EO-μGA based clustering model offers a higher network lifetime and a least amount of transmission energy consumption per node as compared to various state of the art optimization algorithms. [ABSTRACT FROM AUTHOR]

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade. [ABSTRACT FROM AUTHOR]

Throughput efficiency holds great importance in the present scenario of the cognitive radio system. In this paper, optimization of throughput in multi-user single relay cognitive radio system has been performed. In a multi-user cognitive radio network, collisions occur between data packets of multiple secondary users, which cause a reduction in the throughput of the network to a great extent. In this paper, a new analytical model for throughput of the multi-user cognitive radio network in terms of the probability of collision has been presented. The model is convenient for the formulation of the optimization problem in such a way that an optimal value of throughput can be achieved by keeping the probability of collision below the allowable limit. This leads to an enhancement in throughput as well as maintains system reliability in the cognitive radio network. Optimal power allocation scheme is used to allocate power among secondary users which makes the system energy efficient. The optimization of the throughput of the system is done using swarm intelligence-based optimization techniques like Particle Swarm Optimization (PSO), Human behavior based Particle Swarm Optimization (HPSO), Particle Swarm Optimization with Aging Leader and Challengers (ALCPSO) and Whale Optimization Algorithm (WOA). Simulation results illustrate that the system optimized with WOA have better throughput than that of the aforementioned optimization algorithms. Moreover, a comparative analysis of the achievable throughput in the multi-user cognitive radio system with the proposed scheme and that with the equal power allocation scheme is presented. The analysis reveals that the proposed scheme makes the system more efficient in terms of throughput than equal power allocation scheme. [ABSTRACT FROM AUTHOR]