With the economic crisis going around the world, a new approach, “build back better”, has been adopted as a recovery package for various systems. The tsunami detection and warning system is one such system, crucial for saving human lives and infrastructure. While designing a tsunami detection system, the social, economic, and geographical circumstances are considered to be vital. This research is focused on designing a low-cost early warning system mainly for underdeveloped countries, which are more prone to tsunami damage due to a lack of any reliable early warning and detection systems. Such countries require proper cost-effective solutions to address these issues. Previous research has shown that the existing systems are either very costly or hard to implement and manage. In this study, we present a wireless sensor networking model, which is an optimized model in terms of cost, delay, and energy consumption. This research contemplates the techniques and advantages of the intelligence of marine animals. We propose a fuzzy logic-based approach for early tsunami detection, using electromagnetic and pressure sensors, based on the behavioral attributes of turtles and real-time values of earthquakes and water levels.

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of issues. Stability of peers in a network is one of the major issues among these. Most of the existing approaches address this issue through older-stable principle. This paper first extensively investigates the older-stable principle to observe its validity in different scenarios. It is observed that the older-stable principle does not hold in several of them. Then, it utilizes machine learning approach to predict the stability of peers. This work evaluates the accuracy of several machine learning algorithms over the prediction of stability, where the Gradient Boosting Regressor (GBR) out-performs other algorithms. Finally, this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay. The results indicate that machine learning-based stability estimation significantly improves the system.