A Chain-Based Wireless Sensor Network Model Using the Douglas-Peucker Algorithm in the Iot Environment

WSNs which are the major component in the IoT mainly use interconnected intelligent wireless sensors. These wireless sensors sense monitor and gather data from their surroundings and then deliver them to users or access connected IoT devices remotely. One of the main issues in WSNs is that sensor nodes are generally powered by batteries, but because of the rugged environments, it is difficult to add energy. The other one may cause an unbalanced energy consumption among sensor nodes due to the uneven distribution of sensors. For these reasons, the death of nodes by the energy exhausting and the performance of the network may rapidly decrease. Hence, an efficient algorithm study for prolonging the network lifetime of WSNs is one of important challenges. In this paper, a chain-based wireless sensor network model is proposed to improve network performance with balanced energy consumption via the solution of the long-distance communication problem. The proposed algorithm is consisted of three phases: Segmentation, Chain Formation, and Data Collection. In segmentation phase, an optimal distance tolerance is determined, and then the network field is divided into small sub-regions according to its value. The chain formation is started from the sub-region far away from the sink, and then extended, and sensed data are collected along a chain and transmitted to a sink. Simulations have been performed to compare with PEGASIS and Enhanced PEGASIS using an OMNET++ simulator. The simulation results from this study showed that the proposed algorithm prolonged the network lifetime via the achievement of the balanced energy consumption compared to PEGASIS and Enhanced PEGASIS. The proposed algorithm can be used in any applications to improve network performance of WSNs

A Novel Chain Formation Scheme for Balanced Energy Consumption in WSN-based IoT Network

In the Internet of Things (IoT) technologies, wireless sensor networks (WSNs) are one essential part. The IoT network commonly consists of WSNs, where hundreds or even thousands of small sensors are capable of sensing, processing, and sending environmental phenomena in the targeted region. The energy consumption imbalance of sensors becomes the cause of the network performance decrement, as sensor nodes have limited energy available for operation after being randomly deployed. Therefore, more research is necessary for the design of energy-efficient routing algorithms in energy-constrained WSNs. This paper focuses on the chain-based routing algorithm, which is a popular algorithm for achieving energy efficiency in WSN-based IoT network. Chain-based routing algorithms offer numerous advantages for WSNs, such as energy conservation and extended lifetime of WSNs. However, they face challenges due to the issue of internal communication imbalance. The objective of our study is to design a novel chain formation scheme that improves the energy consumption imbalance caused by internal communication in WSN-based IoT network. The proposed scheme is categorized in three phases (initial communication phase, chain formation phase, and data collection phase). In the first phase, the sink acquires their location information from sensors deployed in the sensing region. Then the sensing region is separated into sub-regions and with the number of sensor nodes is balanced employing the concept of the k-dimensional binary tree (K-D-B-tree). The sub-regions are organized into a binary tree structure, which is then formed into a chain. Lastly, data is collected along the chain, and the selected representative sensor transmits the collected data to the sink. We utilized the OMNET++ simulator and demonstrated effective simulation results in terms of network lifetime and average residual energy. In the simulation results, a novel chain formation scheme outperforms the power-efficient gathering in sensor information systems (PEGASIS) and the concentric clustering scheme for efficient energy consumption in the PEGASIS (CCS)

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been widely applied to deal with temporal problems, such as flood forecasting and financial data processing. On the one hand, traditional RNNs models amplify the gradient issue due to the strict time serial dependency, making it difficult to realize a long-term memory function. On the other hand, RNNs cells are highly complex, which will significantly increase computational complexity and cause waste of computational resources during model training. In this paper, an improved Time Feedforward Connections Recurrent Neural Networks (TFC-RNNs) model was first proposed to address the gradient issue. A parallel branch was introduced for the hidden state at time t-2 to be directly transferred to time t without the nonlinear transformation at time t-1. This is effective in improving the long-term dependence of RNNs. Then, a novel cell structure named Single Gate Recurrent Unit (SGRU) was presented. This cell structure can reduce the number of parameters for RNNs cell, consequently reducing the computational complexity. Next, applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties. Finally, the performance of our proposed TFC-SGRU was verified through several experiments in terms of long-term memory and anti-interference capabilities. Experimental results demonstrated that our proposed TFC-SGRU model can capture helpful information with time step 1500 and effectively filter out the noise. The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability

PHP12 THE PUBLIC'S PREFERENCE ON THE PRIORITIES IN HEALTH CARE

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Fade Lighting Control Method for Visual Comfort and Energy Saving

This study proposes a fade lighting control method to ensure the visual comfort of indoor occupants through gradual illuminance control while saving energy. The illuminance sensor measures the indoor illuminance and calculates the required illuminance for achieving a reference illuminance of 500 Lux. The control illuminance for each lighting is derived based on the required illuminance, and it is confirmed to fall within the threshold range of 20%. The illuminance values and time intervals for fade lighting control are calculated, ensuring that the amount of illuminance adjustment is divided by the size of the threshold range or less. In the performance evaluation, the proposed method (experimental group) was compared with the influence-based control method (control group). The result shows that this fade lighting control method minimizes the visual discomfort of occupants caused by sudden changes in lighting, and the same energy-saving of 11-42% is achieved as the control group

A study of the anti-inflammatory effects of the ethyl acetate fraction of the methanol extract of Forsythiae fructus

Background: The dried fruit of Forsythia suspensa (Thunb.) Vahl. (Oleaceae) are better known by their herbal name Forsythiae Fructus, and have a bitter taste, slightly pungent smell, and cold habit. FF has been widely used to treat symptoms associated with the lung, heart, and small intestine. Recently, bioactive compounds isolated from hydrophobic solvent fractions of FF have been reported to have anti-oxidant, anti-bacterial, and anti-cancer effects. Traditionally, almost all herbal medicines are water extracts, and thus, extraction methods should be developed to optimize the practical efficacies of herbal medicines.Materials and Methods: In this study, the anti-inflammatory effects of the ethyl acetate fraction of the methanol extract of FF (FFE) were assessed by measuring NO and PGE2 production by and intracellular ROS and protein levels of iNOS and COX-2 in RAW 264.7 cells.Results: FFE inhibited COX-2 expression in LPS-stimulated RAW 264.7 cells.Conclusion: In summary, FFE effectively reduced intracellular ROS and NO levels and inhibited PGE2 production by downregulating COX-2 levels.Keywords: Forsythiae Fructus, herb, inflammation, efficacy