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This paper presents an emerging reconfigurable hardware that potentially delivers flexible high pegormanee for cryptographic algorithms. MorphoSys, a dynamic reconfigurable architecture that sustains implementations that can yield into equally or even better pevforniance results than custom-hardware and yet preserves all the flexibility of general-purpose processors. With today's great demand for secure communications systems, networks and the Internet, there is a growing demand for real-time implementation of cvptographic algorithms. As a case study, this paper presents the mapping andperformance analysis for one of the Jive AES finalists cryptographic algorithms, namely RC6. Being an important and secure cryptographic algorithm, RC6 is considered well chosen to be mapped in order to test and evaluate the suitability of dynamic reconfigurable computer(RC) systems such as MorphoSys.

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Node failures in distributed storage systems are becoming a critical issue, and many erasure codes are designed to handle such failures. The purpose of this paper is to evaluate fractional repetition (FR) codes, a class of regenerating codes for distributed storage systems, as a practical solution. FR codes consist of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. We model the problem as an integer linear programming problem that uses modified versions of the fractional repetition code by allowing different block sizes, and minimizes the recovery cost of all single node failure scenarios. The contribution of this work is three fold: We generate an optimized block distribution schema that minimizes the total system repair cost in a data center and we present a full recovery plan for the system. In addition, we account for new-comer blocks and allocate them to nodes with minimal computations and without changing the original optimal schema. This makes our work practical to apply. Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. We show that our results are close to optimal.

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Non terrestrial networks (NTN) involving 'in the sky' objects such as low-earth orbit satellites, high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected to be integral components of next generation cellular systems. With the deployment of 5G services and beyond, NTNs are leveraged to assist as aerial base stations in providing ubiquitous network connectivity and service to ground users or be deployed as aerial users connected to the cellular network. NTN-aided wireless communication offers multiple benefits such as mobility, flexibility, resistance to ground physical attacks and wide coverage. However, due to their limited resources and the current design of terrestrial cellular systems that do not account for aerial users, and other restrictions such as service requirements, limited available power and storage resources on high-throughput satellites, resource allocation, location of the high altitude platform base station and the flight trajectory of the UAVs need to be intelligently controlled to satisfy various objectives both from an aerial base station and overall network perspectives. To achieve this, many works have explored Reinforcement Learning (RL) techniques to allow aerial platforms in non-terrestrial networks to learn from past observations and achieve some optimal control policy. In this paper and differently from prior surveys, we contribute a comprehensive review of the control objectives required by non-terrestrial platforms that have been solved using RL formulations. We provide an up-to-date overview of the latest applications of RL techniques for different NTN-aided wireless communication aspects. The survey focuses on Markov Decision Process (MDP) formulations in terms of states, actions, and rewards. We synthesize a taxonomy from the surveyed literature and provide a comprehensive representation of the current usages of RL in NTN-aided wireless communications. A qualitative analysis of the level of realism achieved in the works presented in the literature is provided based on several factors that pertain to the simulation environment, station deployment setting, wireless channel assumption, and energy considerations. We also curate a list of challenges that remain to be considered by the research community in order to achieve more efficient deployments and close the simulation-to-reality gap.