scholarly journals Efficient Enhanced Sleep Awake Scheduling Using Fuzzy Logic and Neural Networks : A Review

2021 ◽  
pp. 18-24
Author(s):  
Ms Mani ◽  
Lovepreet Kaur
Keyword(s):  
Fuzzy Logic ◽  
Forest Fire ◽  
Low Cost ◽  
Living Organism ◽  
Sensor Nodes ◽  
Environmental Application ◽  
Power Conservation ◽  
Processing Power ◽  
Proposed Model ◽  
Memory Power

WSN is a distributed network that consists of great amount of sensor nodes and has the capacity of sensing, processing and transmits the partially processed and required data only. Sensor nodes have a tiny size, low cost but along with it the constraints of sensor node is they have limited memory, power source which is irreplaceable so power conservation should primarily focused by sensor network protocols. The proposed model was deals with environmental application where detection of forest fire is analyzed by taking parameters such as temperature, humidity, wind speed and time using fuzzy logic as by detecting earlier of fire in forest it helps to prevent huge loss of living organism, infrastructure and property. After detection the proposed MSA (Modified Sleep Awake) model work in prolonging lifetime of WSN in forest fire application using selective sleep awake approach. Cloud computing help to overcome the limitation of WSN such as limited storage, processing, power life processing. The resource allocation problem is the major problem for a group of cloud user requests. The scheduling algorithms are termed as NP completeness problems in which FIFO scheduling is used by the master node to distribute resources to the waiting tasks. The problem like fragmentation of resources, low utilization of the resources such as CPU utilization, network throughput, disk I/O rate. In this paper different papers are reviewed and further it is implemented in research paper.

scholarly journals Determination of the K-optimal number of chains-based routing protocol formed by the K-Means algorithm for the WSN

2020 ◽  
Vol 19 (2) ◽  
pp. 917
Author(s):  
Samah Alnajdi ◽  
Fuad Bajaber
Keyword(s):  
Sensor Networks ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Low Cost ◽  
Optimal Number ◽  
Sensor Nodes ◽  
Main Task ◽  
Energy Efficient Routing ◽  
Processing Power ◽  
The Many

<span>Wireless sensor networks comprise of a large number of lightweight and relatively low-cost computational nodes which their main task is to sense the surrounding environment and collect the information to send it wirelessly to a central point to take the appropriate actions. Although these networks had been used in various applications, achieving this task is challenging due to the many constraints of sensor nodes including their limited processing power, communication bandwidth, and power supply. Therefore, an energy efficient routing protocols had to be developed specifically for sensor networks to insure longer lifetime and reasonable performance of the network. In this work, we propose an energy efficient hierarchical routing protocol using chain-based clustering. <span>By simulation on MATLAB, the proposed protocol proved to enhance the performance as it prolongs the lifetime of the network and decreases the energy consumption, the transmission delay, and the overhead compared to other existing protocols as it depends on some advanced methods including dynamic selection of number of chains method, k-means clustering method, advanced greedy chain construction method, and multi-factor based leader selection method.</span></span>

scholarly journals A INDOOR POSITION ROUTING (IPR) AND DATA MONITOR USING BLUETOOTH LOW ENERGY TECHNOLOGY (iBEACON-BLE): AN IMPLEMENTATION STUDY

2021 ◽  
Vol 5 (1) ◽  
pp. 38-61
Author(s):  
Batur Alp Akgül ◽  
Bülent HAZNEDAR ◽  
Abdurrahman YAŞAR ◽  
Mustafa Ersan ÇİNKILIÇ
Keyword(s):  
Mobile Application ◽  
Low Cost ◽  
Low Energy ◽  
Sensor Nodes ◽  
Smart Devices ◽  
Smart Device ◽  
Indoor Environments ◽  
Bluetooth Low Energy ◽  
Proposed Model ◽  
Mobile Industry

Rapid advancements in mobile industry have emerged new technological ideas and applications for researchers by allowing smart devices over the last decade. In recent years, the need for Indoor Position Routing (IPR) and Location-Based Advertisements (LBA) systems are increasingly common, IPR and LBA systems have been becoming very popular. Nowadays, it has become possible to create software and hardware applications for IPR and LBA in indoor environments, thanks to developments of different technologies. The development of the system should be based on low-cost technology, it should be suitable for integration and indoors operation. New options and possibilities for indoor locations are presented by the iBeacon-Bluetooth Low Energy (BLE) radio protocol. iBeacon-BLE supports portable battery-powered system that can be smoothly distributed at low cost giving it distinct advantages over Wi-Fi. Therefore, in this study, a technological infrastructure is created to solve the navigation problem in closed locations using iBeacon-BLE technology, a data monitoring information system is proposed for smart devices of currently available technology for IPR, LBA with using iBeacon-BLE. The localization of the objects based on iBeacon-BLE and their combination are determined using the measured data with the developed application. To build an IPR system for indoor environments, the available hardware, software, and network technologies are presented. The concept of the indoor monitoring system and the technologies can be used to develop the IPR system are presented. This system is made up of iBeacon-BLE sensor nodes, a smart device and a mobile application that provides IPR and LBA services by measuring the distance between Transmitter (TX) and Receiver (RX). The proposed model uses the trilateration method, it allows the mobile application to determine the exact location of the object at the micro-level size. The proposed model uses sensory data to identify and trilateration the object’s position.

scholarly journals Transport Protocols for Wireless Sensor Networks: State-of-the-Art and Future Directions

2007 ◽  
Vol 3 (1) ◽  
pp. 119-133 ◽  
Author(s):  
Justin Jones ◽  
Mohammed Atiquzzaman
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Congestion Control ◽  
Low Cost ◽  
Transport Layer ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Message Delivery ◽  
Processing Power ◽  
Wireless Medium

Characteristics of wireless sensor networks, specifically dense deployment, limited processing power, and limited power supply, provide unique design challenges at the transport layer. Message transmission between sensor nodes over a wireless medium is especially expensive. Care must be taken to design an efficient transport layer protocol that combines reliable message delivery and congestion control with minimal overhead and retransmission. Sensor networks are created using low cost, low power nodes. Wireless sensors are assumed to have a finite lifetime; care must be taken to design and implement transport layer algorithms that allow maximum network lifetime. In this paper we present current and future challenges in the design of transport layers for sensor networks. Current transport layer protocols are compared based on how they implement reliable message delivery, congestion control, and energy efficiency.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Robust Silent Localization of Underwater Acoustic Sensor Network Using Mobile Anchor(s)

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 727
Author(s):  
Rahul Mourya ◽  
Mauro Dragone ◽  
Yvan Petillot
Keyword(s):  
Channel Model ◽  
Low Cost ◽  
Sensor Nodes ◽  
Noise Model ◽  
Robust Estimator ◽  
Acoustic Sensor ◽  
Underwater Acoustic ◽  
Acoustic Channel ◽  
Localization Scheme ◽  
Mobile Anchor

Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs.

scholarly journals Tower Crane Location Optimization for Heavy Unit Lifting in High-Rise Modular Construction

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 121
Author(s):  
Hosang Hyun ◽  
Moonseo Park ◽  
Dowan Lee ◽  
Jeonghoon Lee
Keyword(s):  
Low Cost ◽  
Modular Construction ◽  
Algorithm Optimization ◽  
Location Selection ◽  
Project Completion ◽  
High Rise ◽  
Tower Crane ◽  
Proposed Model ◽  
Study Results ◽  
Crane Selection

Modular construction, which involves unit production in factories and on-site work, has benefits such as low cost, high quality, and short duration, resulting from the controlled factory environment utilized. An efficient tower crane lifting plan ensures successful high-rise modular project completion. For improved efficiency, the lifting plan should minimize the reaching distance of the tower crane, because this distance directly affects the tower crane capacity, which is directly related to crane operation cost. In situations where units are lifted from trailers, the trailer-to-tower crane distance can have a significant impact on the tower crane operation efficiency. However, optimization of this distance to improve efficiency has not been sufficiently considered. This research proposes a genetic algorithm optimization model that suggests optimized tower crane and trailer locations. The case study results show that through the proposed model, the project manager can reflect the optimal location selection and optimal tower crane selection options with minimal cost.

Integration of fuzzy logic in the SEIRD compartmental model for the analysis of intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics

2022 ◽  
Author(s):  
Zakaria Shams Siam ◽  
Rubyat Tasnuva Hasan ◽  
Hossain Ahamed ◽  
Samiya Kabir Youme ◽  
Soumik Sarker Anik ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Compartmental Model ◽  
Fuzzy Rule ◽  
Transmission Dynamics ◽  
Intervention Measures ◽  
Proposed Model ◽  
Total Death ◽  
Linguistic Intervention ◽  
Fuzzy Linguistic ◽  
Death Cases

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

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