scholarly journals Deep Learning-Based Multiparametric Predictions for IoT

2020 ◽  
Vol 12 (18) ◽  
pp. 7752
Author(s):  
Muhammad Ateeq ◽  
Muhammad Khalil Afzal ◽  
Muhammad Naeem ◽  
Muhammad Shafiq ◽  
Jin-Ghoo Choi
Keyword(s):  
Channel Coding ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Transmission Power ◽  
Signal To Noise ◽  
Performance Requirement ◽  
Packet Delivery ◽  
Key Indicator

Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.

scholarly journals Developing of Bluetooth mesh flooding between source-destination linking of nodes in wireless sensor networks

2021 ◽  
Vol 6 (9 (114)) ◽  
pp. 6-14
Author(s):  
Shaymaa Kadhim Mohsin ◽  
Maysoon A. Mohammed ◽  
Helaa Mohammed Yassien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Packet Delivery Ratio ◽  
Mesh Network ◽  
Wireless Sensor ◽  
Destination Node ◽  
Delivery Ratio ◽  
Area Network ◽  
Hop Count ◽  
Packet Delivery

Bluetooth uses 2.4 GHz in ISM (industrial, scientific, and medical) band, which it shares with other wireless operating system technologies like ZigBee and WLAN. The Bluetooth core design comprises a low-energy version of a low-rate wireless personal area network and supports point-to-point or point-to-multipoint connections. The aim of the study is to develop a Bluetooth mesh flooding and to estimate packet delivery ratio in wireless sensor networks to model asynchronous transmissions including a visual representation of a mesh network, node-related statistics, and a packet delivery ratio (PDR). This work provides a platform for Bluetooth networking by analyzing the flooding of the network layers and configuring the architecture of a multi-node Bluetooth mesh. Five simulation scenarios have been presented to evaluate the network flooding performance. These scenarios have been performed over an area of 200×200 meters including 81 randomly distributed nodes including different Relay/End node configurations and source-destination linking between nodes. The results indicate that the proposed approach can create a pathway between the source node and destination node within a mesh network of randomly distributed End and Relay nodes using MATLAB environment. The results include probability calculation of getting a linking between two nodes based on Monte Carlo method, which was 88.7428 %, while the Average-hop-count linking between these nodes was 8. Based on the conducted survey, this is the first study to examine and demonstrate Bluetooth mesh flooding and estimate packet delivery ratio in wireless sensor networks

scholarly journals Link Failure Detection and Classification in Wireless Sensor Networks using Classification Method

2019 ◽  
Vol 8 (12) ◽  
pp. 1132-1135
Keyword(s):  
System Performance ◽  
Detection Rate ◽  
Sensor Node ◽  
Weak Link ◽  
Failure Detection ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Link Failure ◽  
Packet Delivery

This paper develops a method to detect the failures of wireless links between one sensor nodes to another sensor node in WSN environment. Every node in WSN has certain properties which may vary time to time based on its ability to transfer or receive the packets on it. This property or features are obtained from every node and they are classified using Neural Networks (NN) classifier with predetermined feature set which are belonging to both weak link and good link between nodes in wireless networks. The proposed system performance is analyzed by computing Packet Delivery Ratio (PDR), Link Failure Detection Rate (LFDR) and latency report.

scholarly journals An Enhanced Underwater Linear Wireless Sensor Network Deployment Strategy for Data Collection

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Zahoor Ahmed ◽  
Kamalrulnizam Abu Bakar
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Research Work ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Deployment Strategy ◽  
Underwater Pipeline

The deployment of Linear Wireless Sensor Network (LWSN) in underwater environment has attracted several research studies in the underwater data collection research domain. One of the major issues in underwater data collection is the lack of robust structure in the deployment of sensor nodes. The challenge is more obvious when considering a linear pipeline that covers hundreds of kilometers. In most of the previous work, nodes are deployed not considering heterogeneity and capacity of the various sensor nodes. This lead to the problem of inefficient data delivery from the sensor nodes on the underwater pipeline to the sink node at the water surface. Therefore, in this study, an Enhanced Underwater Linear Wireless Sensor Network Deployment (EULWSND) has been proposed in order to improve the robustness in linear sensor underwater data collection. To this end, this paper presents a review of related literature in an underwater linear wireless sensor network. Further, a deployment strategy is discussed considering linearity of the underwater pipeline and heterogeneity of sensor nodes. Some research challenges and directions are identified for future research work. Furthermore, the proposed deployment strategy is implemented using AQUASIM and compared with an existing data collection scheme. The result demonstrates that the proposed EULWSND outperforms the existing Dynamic Address Routing Protocol for Pipeline Monitoring (DARP-PM) in terms of overhead and packet delivery ratio metrics. The scheme performs better in terms of lower overhead with 17.4% and higher packet delivery with 20.5%.

Design and Simulation of OFDM System Based on Matlab

2014 ◽  
Vol 635-637 ◽  
pp. 1081-1085
Author(s):  
Xin Xin Sha ◽  
Jian Zhou ◽  
Yuan Xue Song
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Channel Coding ◽  
Signal To Noise Ratio ◽  
Average Power ◽  
System Structure ◽  
Power Ratio ◽  
Basic System ◽  
Ofdm System ◽  
Signal To Noise

OFDM is a key modulation and multiplexing technique. The basic system structure of OFDM is introduced firstly. This paper chose appropriate implementation schemes for channel coding, PAPR(Peak To Average Power Ratio) reducing and synchronization of the OFDM system based on the minimum BER(Bit Error Rate). Finally, the paper realized the simulation and got the BER in different SNR(Signal To Noise Ratio) in the matlab environment .

Designing and Implementing a Lightweight WSN MAC Protocol for Smart Home Networking Applications

2016 ◽  
Vol 26 (03) ◽  
pp. 1750043 ◽  
Author(s):  
Ching-Han Chen ◽  
Ming-Yi Lin ◽  
Wen-Hung Lin
Keyword(s):  
Smart Home ◽  
Ieee 802.15.4 ◽  
Mac Protocol ◽  
Discrete Event ◽  
Packet Delivery Ratio ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Medium Access ◽  
Packet Delivery

Wireless sensor networks (WSNs) represent a promising solution in the fields of the Internet of Things (IoT) and machine-to-machine networks for smart home applications. However, to feasibly deploy wireless sensor devices in a smart home environment, four key requirements must be satisfied: stability, compatibility, reliability routing, and performance and power balance. In this study, we focus on the unreliability problem of the IEEE 802.15.4 WSN medium access control (MAC), which is caused by the contention-based MAC protocol used for channel access. This problem results in a low packet delivery ratio, particularly in a smart home network with only a few sensor nodes. In this paper, we first propose a lightweight WSN protocol for a smart home or an intelligent building, thus replacing the IEEE 802.15.4 protocol, which is highly complex and has a low packet delivery ratio. Subsequently, we describe the development of a discrete event system model for the WSN by using a GRAFCET and propose a development platform based on a reconfigurable FPGA for reducing fabrication cost and time. Finally, a prototype WSN controller ASIC chip without an extra CPU and with our proposed lightweight MAC was developed and tested. It enhanced the packet delivery ratio by up to 100%.

scholarly journals Convex Optimization for Distributed Acoustic Beamforming

2021 ◽  
Author(s):  
◽  
Dayle Raymond Jellyman
Keyword(s):  
Convex Optimization ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Small Decrease ◽  
Noise Performance ◽  
Signal To Noise ◽  
New Model ◽  
Noise Simulation ◽  
Simulation Results ◽  
Distributed Convex Optimization

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

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