Enhancement of Advanced Metering Infrastructure Performance Using Unsupervised K-Means Clustering Algorithm

Data aggregation may be considered as the technique through which streams of data gathered from Smart Meters (SMs) can be processed and transmitted to a Utility Control Center (UCC) in a reliable and cost-efficient manner without compromising the Quality of Service (QoS) requirements. In a typical Smart Grid (SG) paradigm, the UCC is usually located far away from the consumers (SMs), which has led to a degradation in network performance. Although the data aggregation technique has been recognized as a favorable solution to optimize the network performance of the SG, the underlying issue to date is to determine the optimal locations for the Data Aggregation Points (DAPs), where network coverage and full connectivity for all SMs deployed within the network are achieved. In addition, the main concern of the aggregation technique is to minimize transmission and computational costs. In this sense, the number of DAPs deployed should be as minimal as possible while satisfying the QoS requirements of the SG. This paper presents a Neighborhood Area Network (NAN) placement scheme based on the unsupervised K-means clustering algorithm with silhouette index method to determine the efficient number of DAPs required under different SM densities and find the best locations for the deployment of DAPs. Poisson Point Process (PPP) has been deployed to model the locations of the SMs. The simulation results presented in this paper indicate that the NAN placement scheme based on the ageless unsupervised K-means clustering algorithm not only improves the accuracy in determining the number of DAPs required and their locations but may also improve the network performance significantly in terms of network coverage and full connectivity.

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Elimination of redundant data to enhance wireless sensor network performance using Multi level data aggregation technique

2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt45670.2019.8944647 ◽

2019 ◽

Author(s):

R. Nagesh ◽

Sarika Raga ◽

Shakti Mishra

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Data Aggregation ◽

Network Performance ◽

Wireless Sensor ◽

Redundant Data ◽

Level Data ◽

Multi Level ◽

Aggregation Technique

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Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666191218143503 ◽

2019 ◽

Vol 10 ◽

Author(s):

Piyush Rawat ◽

Siddhartha Chauhan

Keyword(s):

Energy Consumption ◽

Network Performance ◽

Energy Levels ◽

Cluster Head ◽

Base Station ◽

Sensor Nodes ◽

Wireless Sensor ◽

Threshold Function ◽

Efficient Manner ◽

Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

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Toward a parametric approach for modeling local area network performance

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/1010673.800502 ◽

1982 ◽

Vol 11 (2) ◽

pp. 17-28 ◽

Cited By ~ 1

Author(s):

Peter S. Mager

Keyword(s):

Network Performance ◽

Local Area Network ◽

Local Area ◽

Area Network ◽

Parametric Approach

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Deployment Strategy in Distributed Underwater Sensor Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3643 ◽

2014 ◽

Vol 602-605 ◽

pp. 3643-3647

Author(s):

Li Yan Liu ◽

Rong Fu ◽

Yi He ◽

Ying Qian Zhang

Keyword(s):

Network Performance ◽

Heuristic Method ◽

Grid Method ◽

Sensor Nodes ◽

Triangular Grid ◽

Underwater Sensor Networks ◽

Network Coverage ◽

Coverage Ratio ◽

Initial Node ◽

Deployment Strategy

Distributed underwater sensor network coverage is divided into two main categories: deterministic coverage and stochastic coverage. A strategy is put forward to deploy determinate area by using a triangular-grid method. When a coverage ratio is known, the distance between nodes can be adjusted to meet the coverage ratio in the monitored area, and the least number of sensor nodes can be calculated. Also a heuristic method is proposed for stochastic area deployment strategy. It is under the premise that the initial node location randomly deployed is given, using Voronoi diagram, the not easiest monitored path is searched, and the network coverage performance is improved by configuring the new nodes in the path. Finally it is proved that network performance is more improved by the simulation experiments, when one to four nodes are configured in the easiest breach path.

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Performance Analysis of Wireless Local Area Network for a High-/Low-Priority Traffic Ratio at Different Numbers of Access Categories

Symmetry ◽

10.3390/sym13040693 ◽

2021 ◽

Vol 13 (4) ◽

pp. 693

Author(s):

Kvitoslava Obelovska ◽

Olga Panova ◽

Vincent Karovič

Keyword(s):

Network Performance ◽

Wireless Local Area Network ◽

Local Area Network ◽

Local Area ◽

Area Network ◽

Large Network ◽

Medium Access ◽

Massive Growth ◽

Adaptive Adjustment ◽

The Impact

The performance of Wireless Local Area Network (WLAN) is highly dependent on the processes that are implemented in the Medium Access Control (MAC) sublayer regulated by the IEEE 802.11 standard. In turn, various parameters affect the performance of the MAC sublayer, the most important of which is the number of stations in the network and the offered load. With the massive growth of multimedia traffic, research of the network performance depending on traffic types is relevant. In this paper, we present the impact of a high-/low-priority traffic ratio on WLAN performance with different numbers of access categories. The simulation results show different impact of high-/low-priority traffic ratio on the performance of the MAC sublayer of wireless LANs depending on different network-sizes and on network conditions. Performance of the large network with two access categories and with the prevalent high-priority traffic is significantly higher than in the case of using four categories on the MAC sublayer. This allows us to conclude that the performance improvement of the large network with the prevalent high-priority traffic can be achieved by an adaptive adjustment of the access categories number on the MAC sublayer.

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Performance evaluation of Distance based Angular Clustering Algorithm (DACA) using data aggregation for heterogeneous WSN

2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC) ◽

10.1109/iccpeic.2016.7557231 ◽

2016 ◽

Cited By ~ 1

Author(s):

Navjot Kumar ◽

Surinder Kaur

Keyword(s):

Performance Evaluation ◽

Data Aggregation ◽

Clustering Algorithm ◽

Using Data

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Cross Layer Optimization and Simulation of Smart Grid Home Area Network

Modelling and Simulation in Engineering ◽

10.1155/2018/3561739 ◽

2018 ◽

Vol 2018 ◽

pp. 1-14

Author(s):

Lipi K. Chhaya ◽

Paawan Sharma ◽

Adesh Kumar ◽

Govind Bhagwatikar

Keyword(s):

Smart Grid ◽

Network Performance ◽

Performance Enhancement ◽

Hierarchical Architecture ◽

Buffer Size ◽

Cross Layer ◽

Area Network ◽

Frame Aggregation ◽

Cross Layer Optimization ◽

Layered Approach

An electrical “Grid” is a network that carries electricity from power plants to customer premises. Smart Grid is an assimilation of electrical and communication infrastructure. Smart Grid is characterized by bidirectional flow of electricity and information. Smart Grid is a complex network with hierarchical architecture. Realization of complete Smart Grid architecture necessitates diverse set of communication standards and protocols. Communication network protocols are engineered and established on the basis of layered approach. Each layer is designed to produce an explicit functionality in association with other layers. Layered approach can be modified with cross layer approach for performance enhancement. Complex and heterogeneous architecture of Smart Grid demands a deviation from primitive approach and reworking of an innovative approach. This paper describes a joint or cross layer optimization of Smart Grid home/building area network based on IEEE 802.11 standard using RIVERBED OPNET network design and simulation tool. The network performance can be improved by selecting various parameters pertaining to different layers. Simulation results are obtained for various parameters such as WLAN throughput, delay, media access delay, and retransmission attempts. The graphical results show that various parameters have divergent effects on network performance. For example, frame aggregation decreases overall delay but the network throughput is also reduced. To prevail over this effect, frame aggregation is used in combination with RTS and fragmentation mechanisms. The results show that this combination notably improves network performance. Higher value of buffer size considerably increases throughput but the delay is also greater and thus the choice of optimum value of buffer size is inevitable for network performance optimization. Parameter optimization significantly enhances the performance of a designed network. This paper is expected to serve as a comprehensive analysis and performance enhancement of communication standard suitable for Smart Grid HAN applications.

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Medoid-based shadow value validation and visualization

International Journal of Advances in Intelligent Informatics ◽

10.26555/ijain.v5i2.326 ◽

2019 ◽

Vol 5 (2) ◽

pp. 76

Author(s):

Weksi Budiaji

Keyword(s):

Clustering Algorithm ◽

Unique Property ◽

Network Visualization ◽

Visualization Technique ◽

Internal Validation ◽

Neighborhood Graph ◽

Silhouette Index ◽

Shadow Value

A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed. Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV.

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An Efficient Leader Node Selection for Movable Nodes in WSN using PSO Technique

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9554.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 5072-5078

Keyword(s):

Energy Efficient ◽

Network Performance ◽

Clustering Algorithm ◽

Group Formation ◽

Residual Energy ◽

Sensor Nodes ◽

Delivery Ratio ◽

Efficient Prediction ◽

The Stability ◽

Effective Group

Wireless Sensor Networks (WSN) is a group of sensor devices, which are used to sense the surroundings. The network performance is still an issue in the WSN and an efficient protocol is introduced such as LEACH. To improve the stability, LEACH with fuzzy descriptors is used in preceding research. However the existing has drawback with effective group formation in heterogeneous WSN and also it is not achieved the Super Leader Node (SLH). To overcome the above mentioned issues, the proposed system enhances the approach which is used for increasing the energy consumption, packet delivery ratio, and bandwidth and network lifetime. The proposed paper contains three phases such as grouping formation, Leader Node (LN) selection, SLN selection with three main objectives:(i) to acquire Energy-Efficient Prediction Clustering Algorithm (EEPCA) in heterogeneous WSN for grouping formation (ii)To design Low Energy Adaptive Clustering Hierarchy- Expected Residual Energy (LEACH-ERE) protocol for LN selection.(iii)To optimize the SCH selection by Particle Swarm Optimization (PSO) based fuzzy approach. The clustering formation is done by Energy-Efficient Prediction Clustering Algorithm (EEPCA) in heterogeneous WSN. It is used to calculate the sensor nodes which have shortest distance between each node. The LEACH-ERE protocol was proposed to form a Leader Node (LN) and all the nodes has to communicate with sink through LN only. New SLN is elected based on distance from the sink and battery power of the node.

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