SIMON Lightweight Encryption Benchmarking on Wireless Aquascape Preservation In pervasive computing, the humancomputer interaction emphasizes on information and communication technology and user experience. Now it is possible to communicate scientific and engineering technique informally through leisure activities, for instance aquascaping. It is necessary to keep the aquascape environment fresh and healthy, and the fish have to be feed regularly. This paper proposes an autonomous aquascape preservation system based on Arduino controller connected to a remote Android smartphone. However, it is widely known that the wireless communication is not as reliable as the wired counterpart. An unauthorized party should not be able to take control of the wireless aquascape preservation system. SIMON lightweight cryptography is used to tackle security issues in constrained devices. From experiments result, the DS18B20 sensor is able to measure aquascape temperature precisely with approximately 0.5% tolerance. The Android graphical user interface application is userfriendly. Moreover, the SIMON lightweight encryption SIMON64128 is able to secure wireless communication channel efficiently with small hardware footprints.

RealTime Decision Making for Underwater Big Data Applications Using the Apriori Algorithm Underwater acoustic sensor networks (UWASNs) have been introduced as a new technology to extract the data for underwater realtime applications such as seismic monitoring, undersea monitoring and control, oil well inspection, military applications, and disaster prevention. This new technology adds more networking capabilities and enables realtime reporting. However, it is restricted to data sensing, forwarding and data transmission. Specifically, transmitting large volumes of data takes a long time and requires a lot of power to be executed. This has inspired our research activities to focus on building an underwater realtime computing system with the minimum execution time and low power consumption. In our research, valuable information is extracted underwater using data mining or compression algorithms. In our previous study, we proposed a set of realtime underwater embedded system (RTUWES) architectures that can handle multiple network configurations. In this study, we extend our research results to develop information extraction algorithms for big data underwater applications to meet realtime constraints. The system performance is assessed in terms of the endtoend delay and power consumption. We have built a simulator for practical study. The simulation results are presented to demonstrate the performance of our proposed system based on an information extraction algorithm.