Antenna beamwidth control for improving Signal-to-Noise ratio in wireless sensor networks

2010 ◽  
Author(s):  
K. Staniec ◽  
G. Debita
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Signal To Noise Ratio ◽  
Wireless Sensor ◽  
Signal To Noise ◽  
Noise Ratio

Signal Propagation and Analysis in Wireless Underground Sensor Networks

2019 ◽  
Vol 41 ◽  
pp. 60-78
Author(s):  
Adamu Murtala Zungeru ◽  
Joseph Chuma ◽  
Mmoloki Mangwala ◽  
Boyce Sigweni ◽  
Oduetse Matsebe
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Magnetic Induction ◽  
Signal To Noise Ratio ◽  
Path Loss ◽  
Signal Propagation ◽  
Pulse Power ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nonlinear Program

The most challenging issue in the design of wireless sensor networks for the application of localization in the underground environment, mostly for miner’s location, is the sensor nodes’ energy consumption, efficiency and communication. Underground Wireless Sensor Networks are active and promising area of application of Wireless Sensor Networks (WSNs), whereby sensor nodes perform sensing duties in the underground environment. Most of the communication techniques used in the underground environment experience a high path loss and hence, hinders the range needed for transmission. However, the available option to increase information transmission is to increase the transmission power which needs large size of apparatus which is also limited in the underground. To solve the mentioned problems, this paper proposed a Magnetic Induction based Pulse Power. Analytical results of the Magnetic Induction based Pulse Power with an ordinary magnetic induction communication technique show an improvement in Signal-to-Noise Ratio (SNR) and path loss with variation in distance between nodes and frequency of operation. This paper further formulates a nonlinear program to determine the optimal data (events) extraction in a grid based WUSNs.

scholarly journals Accurate detection of selective forwarding attack in wireless sensor networks

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882400
Author(s):  
Qiong Zhang ◽  
Wenzheng Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Performance ◽  
Signal To Noise Ratio ◽  
Energy Resource ◽  
Attack Detection ◽  
Wireless Sensor ◽  
Detection Accuracy ◽  
Wireless Devices ◽  
Selective Forwarding

Selective forwarding attack in wireless sensor networks shows great impact on network performance and consumes limited energy resource. In previous countermeasures, it is assumed that all nodes in the communication range can detect misbehaviors of the attacker. However, as wireless devices require certain signal-to-noise ratio to receive frames correctly, and interference among nodes is inevitable in densely deployed wireless sensor networks, it is very difficult for previous approaches to detect misbehaviors accurately. In this article, a scheme named E-watchdog is proposed to improve accuracy of selective forwarding attack detection. Detection agents that are closer to the attacker are used to detect misbehaviors, which can improve the detection accuracy and reduce the false alarm rate effectively. Moreover, to prevent collaborative selective forwarding attack, E-watchdog uses reports from more than one detection agents. Fake reports from attackers are filtered out through an election algorithm. Simulation results show that the E-watchdog reduces the false detection rate by 25% and improves the detection accuracy by 10% on the premise of increasing negligible energy consumption.

scholarly journals Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5327
Author(s):  
Wei Liu ◽  
Yu Xia ◽  
Daqing Zheng ◽  
Jian Xie ◽  
Rong Luo ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Environmental Changes ◽  
Signal To Noise Ratio ◽  
Link Quality ◽  
Wireless Sensor ◽  
Transitional Region ◽  
Environment Changes ◽  
Fluctuation Range ◽  
Link Quality Indicator

Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.

Adaptive Technique with Cross Correlation for Lowering Signal-to-Noise Ratio Wall in Sensor Networks

2019 ◽  
Vol 105 (3) ◽  
pp. 787-802 ◽  
Author(s):  
Rajiv Kapoor ◽  
Rashmi Gupta ◽  
Le Hoang Son ◽  
Sudan Jha ◽  
Raghvendra Kumar
Keyword(s):  
Sensor Networks ◽  
Cross Correlation ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Adaptive Technique ◽  
Noise Ratio

scholarly journals Optimized Non-Cooperative Spectrum Sensing Algorithm in Cognitive Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2174 ◽  
Author(s):  
Yangyi Chen ◽  
Shaojing Su ◽  
Huiwen Yin ◽  
Xiaojun Guo ◽  
Zhen Zuo ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Entropy Method ◽  
Wireless Sensor ◽  
Reconstruction Method ◽  
Sensing Technology

The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.

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