A Survey on Underwater Acoustic Sensor Network Routing Protocols

Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research

Modeling, Development and Analysis Performance of an Intelligent Control of Photovoltaic System by Fuzzy Logic approach for Maximum Power Point Tracking

In this paper, we present an intelligent algorithm based upon Fuzzy Logic method for optimizing and improving the control performance of a   photovoltaic system. The main objective of the proposed system is to pursuit the maximum power point (MPPT) under different conditions like the change of sunshine and temperature. The system consists of a photovoltaic solar module connected to a DC-DC step-up converter "Boost" and a battery - like a load . The converter parameters and the inference rule table are determined to ensure maximum output power. The effectiveness of the proposed system is validated through computer simulations using MATLAB / Simulink software and the obtained results shows that our proposed system has the best performance in term maximum power

An efficient intelligent algorithm based on WSNs of the drug control system

U radu se predlaže novi algoritam, ACORS-ANNDPF za WSNs (bežične senzorske mreže), u svrhu povećanja stope uporabe WSNs i produženja životnog ciklusa Iot-a (Interneta stvari). Razvijen na temelju algoritma kolonije mrava, ovaj se poboljšani algoritam može primijeniti na izbor optimalne putanje i prepoznavanje optimalnog čvora za usmjeravanje u slučaju gubljenja čvora usmjeravanja. Kako bi se smanjilo vrijeme utrošeno na premiještanje skupine mreža, algoritam neuronske mreže odabire pokazatelje u skladu s aktualnim aplikacijskim okruženjem i podešava ih u svrhu optimiziranja podataka skupine. Nakon toga, autor provodi nekoliko simulacijskih eksperimenata i uspoređuje predloženi algoritam s drugim algoritmima. Rezultati pokazuju da se predloženim algoritmom osigurava visoka učinkovitost energije i balansirana potrošnja energije. Prema tome, zaključeno je da se predloženim algoritmom može poboljšati brzina uporabe mreže i povećati prijenosna funkcija mreže.A new algorithm, ACORS-ANNDPF for WSNs, is proposed in this paper to improve the utilization rate of WSNs and prolong the life cycle of the IoT. Developed on the basis of ant colony algorithm, the improved algorithm is applicable to the selection of the optimal path and identification of the optimal routing node in the case of losing the routing node. To reduce the time spent on transferring network packets, the indices are selected by the neural network algorithm in light of the actual application environment and adjusted to optimize the fusion of packet data. After that, the author carries out several simulation experiments and compares the proposed algorithm with other algorithms. The results demonstrate that the proposed algorithm ensures high energy efficiency and balanced energy consumption. Therefore, it is concluded that the proposed algorithm can improve network utilization rate and lead to better network transmission performance

RFID AUTOMATIC MULTI-ROLL DETECTION SYSTEM

Normally, a printer can have one or more input media rolls with different media types to simplify customer life. With several media inputs already loaded in the printer, the user can select which one he/she is willing to use in a simple way, without having to unload and load another media type. However, knowing in advance how many rolls are loaded and which media type is rolled in which media input roll is not an easy task for the printer. Current printer implements this feature manually. The user needs to select which media is loaded in which media input roll through the Front Panel or any other input interface. Do you imagine a scenario where the printer can automatically detect and know which media type is loaded in which input roll/position? The present disclosure proposes an intelligent algorithm based on RFID technology to automatically detect the position of the media loaded without user interaction. The present disclosure has been investigated and tested in Polestar program

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased

ANN-based robust DC fault protection algorithm for MMC high-voltage direct current grids

Fast and reliable protection is a significant technical challenge in modular multilevel converter (MMC) based DC grids. The existing fault detection methods suffer from the difficulty in setting protective thresholds, incomplete function, insensitivity to high resistance faults and vulnerable to noise. This paper proposes an artificial neural network (ANN) based method to enable DC bus protection and DC line protection for DC grids. The transient characteristics of DC voltages are analysed during DC faults. Based on the analysis, the discrete wavelet transform (DWT) is used as an extractor of distinctive features at the input of the ANN. Both frequency-domain and time-domain components are selected as input vectors. A large number of offline data considering the impact of noise is employed to train the ANN. The outputs of the ANN are used to trigger the DC line and DC bus protections and select the faulted poles. The proposed method is tested in a four-terminal MMC based DC grid under PSCAD/EMTDC. The simulation results verify the effectiveness of the proposed method in fault identification and the selection of the faulty pole. The intelligent algorithm based protection scheme has good performance concerning selectivity, reliability, robustness to noise and fast action

Recognition and classification of power quality disturbances by DWT-MRA and SVM classifier

Electrical power system is a large and complex network, where power quality disturbances (PQDs) must be monitored, analyzed and mitigated continuously in order to preserve and to re-establish the normal power supply without even slight interruption. Practically huge disturbance data is difficult to manage and requires the higher level of accuracy and time for the analysis and monitoring. Thus automatic and intelligent algorithm based methodologies are in practice for the detection, recognition and classification of power quality events. This approach may help to take preventive measures against abnormal operations and moreover, sudden fluctuations in supply can be handled accordingly. Disturbance types, causes, proper and appropriate extraction of features in single and multiple disturbances, classification model type and classifier performance, are still the main concerns and challenges. In this paper, an attempt has been made to present a different approach for recognition of PQDs with the synthetic model based generated disturbances, which are frequent in power system operations, and the proposed unique feature vector. Disturbances are generated in Matlab workspace environment whereas distinctive features of events are extracted through discrete wavelet transform (DWT) technique. Machine learning based Support vector machine classifier tool is implemented for the classification and recognition of disturbances. In relation to the results, the proposed methodology recognizes the PQDs with high accuracy, sensitivity and specificity. This study illustrates that the proposed approach is valid, efficient and applicable

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network