scholarly journals Combination of clustering algorithms to maximize the lifespan of distributed wireless sensors

2016 ◽  
Vol 5 (1) ◽  
pp. 63-72 ◽  
Author(s):  
Derssie D. Mebratu ◽  
Charles Kim
Keyword(s):  
Wireless Sensors ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Adaptive Clustering ◽  
Combination Approach ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract. Increasing the lifespan of a group of distributed wireless sensors is one of the major challenges in research. This is especially important for distributed wireless sensor nodes used in harsh environments since it is not feasible to replace or recharge their batteries. Thus, the popular low-energy adaptive clustering hierarchy (LEACH) algorithm uses the “computation and communication energy model” to increase the lifespan of distributed wireless sensor nodes. As an improved method, we present here that a combination of three clustering algorithms performs better than the LEACH algorithm. The clustering algorithms included in the combination are the k-means+ + , k-means, and gap statistics algorithms. These three algorithms are used selectively in the following manner: the k-means+ +  algorithm initializes the center for the k-means algorithm, the k-means algorithm computes the optimal center of the clusters, and the gap statistics algorithm selects the optimal number of clusters in a distributed wireless sensor network. Our simulation shows that the approach of using a combination of clustering algorithms increases the lifespan of the wireless sensor nodes by 15 % compared with the LEACH algorithm. This paper reports the details of the clustering algorithms selected for use in the combination approach and, based on the simulation results, compares the performance of the combination approach with that of the LEACH algorithm.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2021 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Public Dataset ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.

Distributed Landsilde Mornitoring by Wireless Sensor Nodes

2012 ◽  
Vol 594-597 ◽  
pp. 1069-1073 ◽  
Author(s):  
Cheng Hu ◽  
Shui Bao Zhang ◽  
Shou Zhi Xu ◽  
Bo Xu
Keyword(s):  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Monitoring ◽  
Monitoring Method ◽  
Wireless Sensor Nodes ◽  
Environmental Disasters ◽  
Strain Data ◽  
Monitoring Algorithm ◽  
Better Than

Wireless Sensor network (WSN) is an emerging technology widely applied in environmental disasters monitoring. With the constraint of computation resource, it face big challenge of stability and reliability of monitoring network. A statistical model of strain data and distributed monitoring algorithm for landslide based on WSN is studied in this paper. The strain data is modeled using variable mean of Gaussian process. Miss alarm rate and false alarm rate are introduced as critical performance parameters of landslide prediction. Comparing with centralized monitoring method, simulation result shows that distributed monitoring algorithm performs better than centralized monitoring.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2020 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, as a traditional machine learning method, is still playing a significant role in data analysis. The most of clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on manual identification of the elbow points on the visualization curve, which will lead to the experienced analysts not being able to clearly identify the elbow point from the plotted curve when the plotted curve being fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to work out a statistical metric estimating an optimal cluster number when clustering on a dataset. Firstly, the average degree of distortion obtained by Elbow method is normalized to the range of 0 to10; Secondly, the normalized results are used to calculate Cosine of intersection angles between elbow points; Thirdly, the above calculated Cosine of intersection angles and Arccosine theorem are used to compute the intersection angles between elbow points; Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a public well-known dataset demonstrated that the estimated optimal cluster number output by our newly proposed method is better than widely used Silhouette method.

scholarly journals Wireless medical sensors – context, robustness and safety

2015 ◽  
Vol 1 (1) ◽  
pp. 349-352
Author(s):  
Christian Bollmeyer ◽  
Mathias Pelka ◽  
Hartmut Gehring ◽  
Horst Hellbrück
Keyword(s):  
Wireless Sensors ◽  
Signal Propagation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Unknown Environment ◽  
Medical Sensors ◽  
Context Detection ◽  
Indoor Scenarios ◽  
Rf Signal

AbstractWireless medical sensors are an emerging technology. Wireless sensors form networks and are placed in an unknown environment. For indoor scenarios context detection of medical sensors, e.g. removal of sensors from a specific room, is important. Current algorithms for context detection of wireless sensors are based on RF signals, but RF signal propagation and room location show only a weak correlation. Recent approaches with RSSI-measurements are based on prior fingerprinting and therefore costly. In our approach, we equip wireless sensor nodes with a barometric sensor to measure pressure disturbances that occur, when doors of rooms are opened or closed. By signal processing of these disturbances our proposed algorithm detects rooms and estimates distances without prior knowledge in an unknown environment. Based on these measurement we automatically build a topology graph representing the room context and distances for indoor environment in a model for buildings. We evaluate our algorithm within a wireless sensor network and show the performance of our solution.

scholarly journals QoS Framework for a Multi-stack based Heterogeneous Wireless Sensor Network

2017 ◽  
Vol 7 (5) ◽  
pp. 2713 ◽  
Author(s):  
K. Panimozhi ◽  
G. Mahadevan
Keyword(s):  
Routing Protocols ◽  
Wireless Sensors ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Processing Power ◽  
Reactive Routing ◽  
Heterogeneous Wireless Sensor Network ◽  
Battery Energy

Wireless sensor nodes consist of a collection of sensor nodes with constrained resources in terms of processing power and battery energy. Wireless sensors networks are used increasingly in many industrial and consumer applications. Sensors detect events and send via multi hop routing to the sink node for processing the event. The routing path is established through proactive or reactive routing protocols. To improve the performance of the Wireless Sensor Networks, multi stack architecture is addressed. But the multi stack architecture has many problems with respect to life time, routing loop and QOS. In this work we propose a solution to address all these three problems of life time, routing loop and QOS in case of multi stack architecture.

scholarly journals A Quantitative Discriminant Method of Elbow Point for the Optimal Number of Clusters in Clustering Algorithm

2020 ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

Abstract Clustering, as a traditional machine learning method, is still playing a significant role in data analysis. The most of clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on manual identification of the elbow points on the visualization curve, which will lead to the experienced analysts not being able to clearly identify the elbow point from the plotted curve when the plotted curve being fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to work out a statistical metric estimating an optimal cluster number when clustering on a dataset. Firstly, the average degree of distortion obtained by Elbow method is normalized to the range of 0 to10; Secondly, the normalized results are used to calculate Cosine of intersection angles between elbow points; Thirdly, the above calculated Cosine of intersection angles and Arccosine theorem are used to compute the intersection angles between elbow points; Finally, the index of the above computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a public well-known dataset (Iris Dataset) demonstrated that the estimated optimal cluster number output by our newly proposed method is better than widely used Silhouette method.

scholarly journals A High Conversion Gain Envelope Detector with Wide Input Range for Simultaneous Wireless Information and Power Transfer System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 160
Author(s):  
Yan Li ◽  
Xin Liu ◽  
Xiaosong Wang ◽  
Qian Su ◽  
Shuaipeng Zhao ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Input Signal ◽  
Wireless Sensors ◽  
Input Power ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Conversion Gain ◽  
Wireless Sensor Nodes ◽  
Envelope Detector ◽  
Input Range

Wireless sensors networks (WSN) have been gradually facilitating the pervasive connectivity of wireless sensor nodes. A greater number of wireless sensors have been used in different aspects of our life. However, limited device battery life restricts the applications of large-scale WSN. This paper presents a batteryless envelope detector with radio frequency energy harvesting (RFEH) for wireless sensor nodes, which enables simultaneous wireless information and power transfer (SWIPT). The envelope detector is designed for small modulation index AM signals with large amplitude variations. Therefore, the envelope detector is supposed to have wide input range while achieving a high conversion gain. We proposed an adaptive biasing technique in order to extend the input range of envelope detector. The input differential pair is adaptively biased through a feedback loop to overcome the variation of bias point when the amplitude of input signal changes. The cross coupled rectifier and DC-DC boost converter with maximum power point tracking (MPPT) are presented against power conversion efficiency (PCE) degradation of RF rectifier with the input power varying. The adaptive biased envelope detector is theoretically analyzed by square law MOSFET model. Designed with 0.18 μm complementary-metal-oxide-semiconductor (CMOS) standard process, the power consumption of proposed envelope detector is 9 μW. Simulated with a 915 MHz AM input signal with 2 Mbps data rate and 0.05 modulation index, the proposed envelope detector achieves 20.37 dB maximum conversion gain when the amplitude of input signal is 0.5 V, and the PCE of energy harvesting circuits achieves 55.2% when input power is –12.5 dBm.

Ambient Energy Harvesting for Wireless Sensor Nodes: A Review

2014 ◽  
Vol 1025-1026 ◽  
pp. 1093-1098
Author(s):  
Mohamed Hanafiah bin Omar ◽  
Meng Hee Lim
Keyword(s):  
Energy Harvesting ◽  
Wireless Sensors ◽  
Business Processes ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensing ◽  
Power Devices ◽  
Wireless Sensor Nodes ◽  
Industrial Systems ◽  
Sensing Applications

Energy harvesting has generated a lot of interest in low power devices and wireless sensing applications as a viable replacement to the batteries that are required to power them. Wireless sensors nodes on the other hand have gain considerable interests from researchers and industries alike. Wireless sensing have the potential to improve productivity of industrial systems by providing greater awareness, control and integration of business processes. This paper attempts to provide an overview of the available technologies and at the same time deduce a practical energy harvesting platform as applied to wireless sensor nodes based on current research.

scholarly journals A Self-Organized and Smart-Adaptive Clustering and Routing Approach for Wireless Sensor Networks

2012 ◽  
Vol 8 (1) ◽  
pp. 156268 ◽  
Author(s):  
Kyuhong Lee ◽  
Heesang Lee
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Critical Factor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Self Organized ◽  
Fitness Value ◽  
Better Than

Efficient energy consumption is a critical factor for the deployment and operation of wireless sensor networks (WSNs). In general, WSNs perform clustering and routing using localized neighbor information only. Therefore, some studies have used self-organized systems and smart mechanisms as research methods. In this paper, we propose a self-organized and smart-adaptive clustering (SOSAC) and routing method, which performs clustering in WSNs, operates the formed clusters in a smart-adaptive way, and performs cluster-based routing. SOSAC is comprised of three mechanisms, which are used to change the fitness value over time, to back up routing information in preparation for any potential breakdown in WSNs, and to adapt to the changes of the number of sensor nodes for a WSN. We compared the performance of the proposed SOSAC with that of a well-known clustering and routing protocol for WSNs. Our computational experiments demonstrate that the network lifetime, energy consumption, and scalability of SOSAC are better than those of the compared method.

scholarly journals A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Congming Shi ◽  
Bingtao Wei ◽  
Shoulin Wei ◽  
Wen Wang ◽  
Hai Liu ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Machine Learning Method ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Public Dataset ◽  
Optimal Cluster ◽  
Better Than ◽  
Optimal Number Of Clusters

AbstractClustering, a traditional machine learning method, plays a significant role in data analysis. Most clustering algorithms depend on a predetermined exact number of clusters, whereas, in practice, clusters are usually unpredictable. Although the Elbow method is one of the most commonly used methods to discriminate the optimal cluster number, the discriminant of the number of clusters depends on the manual identification of the elbow points on the visualization curve. Thus, experienced analysts cannot clearly identify the elbow point from the plotted curve when the plotted curve is fairly smooth. To solve this problem, a new elbow point discriminant method is proposed to yield a statistical metric that estimates an optimal cluster number when clustering on a dataset. First, the average degree of distortion obtained by the Elbow method is normalized to the range of 0 to 10. Second, the normalized results are used to calculate the cosine of intersection angles between elbow points. Third, this calculated cosine of intersection angles and the arccosine theorem are used to compute the intersection angles between elbow points. Finally, the index of the above-computed minimal intersection angles between elbow points is used as the estimated potential optimal cluster number. The experimental results based on simulated datasets and a well-known public dataset (Iris Dataset) demonstrated that the estimated optimal cluster number obtained by our newly proposed method is better than the widely used Silhouette method.

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