Underwater wireless acoustic sensor networks (UWASNs) have emerged as a powerful communication technology for discovering and extracting data in aquatic environments. UWASNs have numerous applications in areas such as fisheries, resource exploration, mine reconnaissance, oil and gas inspection, marine exploration and military surveillance. However, these applications are limited by the capacity of networks to detect, discover, transmit, and forward big data. In particular, transmitting and receiving large volumes of data requires great lengths of time and substantial power, and thus fails to meet the real-time constraints. This problem has motivated us to focus on developing an underwater computer-embedded system capable of efficient big-data management. Thus, we have developed methods to discover and extract valuable information beneath the ocean using data-mining approaches. Previously, we introduced real-time underwater system architectures (RTUSAs) that use a single computer. In this study, we extend our results and propose a new RTUSA for large-scale networks. This novel RTUSA uses multi-computers and aims to enhance the reliability of our proposed system. Determining the optimal location of computers with respect to their membership of acoustic sensor nodes, so as to minimize delay time, power consumption, and balance loads, are NP-hard problems. Therefore, we propose a heuristic approach that enables optimization of computer locations and their memberships of acoustic sensor nodes. We conduct simulations to show the merits of our findings and measure the performance of our proposed solution.
Underwater Wireless Sensor Networks(UWSNs) have emerged as a promising technology that is used to monitor underwater environment. Applications of UWSNs are numerous such as oil and gas pipeline monitoring, underwater animal detection, and object of interest detection. Automated Underwater Vehicles (AUVs) have been used to monitor underwater environment [13]. One of the significant challenges of AU...
In this paper, a derivative-based MUSIC (DB-MUSIC) algorithm for two-dimensional (2-D) direction-of-arrival (DOA) estimation is proposed using an L-shaped uniform array. It transforms the traditional 2-D search problem into a one-dimensional (1-D) one using a derivative based optimization method, taking into consideration the structure of the steering vector and the associated cost function. As a result, the proposed algorithm has a significantly low computational complexity with the additional benefit of no need for 2-D angle pairing. Simulation results show that the proposed algorithm has better estimation accuracy than some existing representative 2-D DOA estimation algorithms falling into the same category, i.e., low complexity through 1-D search with no need for pairing.
Nowadays, fires in forest areas are very frequent, mainly caused by climate change and bad practices by the people who live in these areas. In the world the climatic "El Niño" phenomenon has intensified in recent years, increasing the frequency of forest fires, due to high temperatures and prolonged periods of drought that occur. Most forest fires are detected visually and from the ground or from the air using a helicopter; this method is not very efficient since it takes too long to alert the relief corps and requires well-organized logistics. The lack of early detection means has been evident in the events that have occurred in recent months (last fires) and it can be concluded that there are not enough measures to counteract this problem.The purpose of this article is to evaluate the performance of different CNN models pre-trained in the classification of forest fire images, which can be applied in economic development cards such as a Raspberry.
Value Iteration (VI) is a powerful, though time consuming, approach to solve reinforcement learning problems modeled as Markov Decision Processes (MDPs). In this paper, we explore strategies to run the sate-of-the-art cache efficient algorithm for VI developed by us [1], [2] on a multicore processor. We demonstrate a speedup of up to 2.59 on a 10-core multiprocessor using 20 threads on popular benchmark data. The speedup for the parallelized portion of the computation is up to 5.89.
