Mobility based network lifetime in wireless sensor networks: A review

The clusters number optimization problem is a problem that still requires continuous research so that the information produced can be a consideration. Cluster evaluation techniques with Sum of Square Error (SSE) and Davies Bouldin Index (DBI) are techniques that can evaluate the number of clusters from a data test. Research with these two techniques utilizes Stunting data from a number of regions in Indonesia. The result is information on stunting data which is formed from the optimal number of clusters where the largest SSE is formed at k = 5 and the smallest DBI is formed at k = 5, with values of 23.403 and 1,178 respectively. Changes in the number of clusters also influence the information produced and DBI is proven to produce optimal number of clusters that contain information with a better pattern because it has a small intra-cluster value. It is expected that the results of this study can show the performance of the two evaluation techniques in producing the optimal number of clusters so that grouping information is in accordance with the expected pattern.

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Optimasi Cluster Pada Data Stunting: Teknik Evaluasi Cluster Sum of Square Error dan Davies Bouldin Index

Increasingly emerging technologies in micro-electromechanical systems and wireless communications allows a mobile wireless sensor networks (MWSN) to be a more and more powerful mean in many applications such as habitat and environmental monitoring, traffic observing, battlefield surveillance, smart homes and smart cities. Nevertheless, due to sensor battery constraints, energy-efficiently operating a MWSN is paramount importance in those applications; and a plethora of approaches have been proposed to elongate the network longevity at most possible. Therefore, this paper provides a comprehensive review on the developed methods that exploit mobility of sensor nodes and/or sink(s) to effectively maximize the lifetime of a MWSN. The survey systematically classifies the algorithms into categories where the MWSN is equipped with mobile sensor nodes, one mobile sink or multiple mobile sinks. How to drive the mobile sink(s) for energy efficiency in the network is also fully reviewed and reported.

Optimasi jumlah cluster diperlukan untuk memastikan kebijakan yang dapat diambil terkait hasil pengelompokkan, termasuk memastikan kelompok wilayah dengan status ODP, PDP dan Positif Covid-19 di provinsi Riau. Pengelompokkan berdasarkan status pasien perlu dilakukan untuk menentukan tindakan pencegahan yang mungkin dapat diambil pemerintah. Davies Bouldin Index (DBI) merupakan teknik evaluasi cluster yang dapat digunakan pada algoritma clustering dengan pengukuran jarak Euclidean dan Manhattan. Penelitian ini dimaksudkan untuk mengetahui kinerja terbaik DBI pada kedua pengukuran jarak tersebut melalui pengujian data sebaran Covid-19 wilayah Riau. Hasil penelitian menunjukkan bahwa DBI terendah terdapat pada k=8 untuk Euclidean dan k=7 untuk Manhattan dengan nilai masing-masing sebesar 0,394 dan 0,434. Selain itu, DBI bekerja lebih baik pada Euclidean dibandingkan Manhattan karena memiliki nilai DBI lebih rendah pada semua k uji

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Perbandingan Euclidean dan Manhattan Untuk Optimasi Cluster Menggunakan Davies Bouldin Index: Status Covid-19 Wilayah Riau

Nowadays, the Internet of Vehicle around the road among the smart city's development. This platform relayed on the Internet of Things. The vehicle has a relationship to the primaries (Vehicle, RSU, Backhaul, and Cell tower), cloud, architecture, protocol, and macro connections. The proposed paper is VANET based target tracking clustering for significant topology changes in vehicle and vehicle affinities establishments. Numerous cluster lead can use to establish and maintains inter, intra-cluster connection for either local or global and both. Especially cluster gateways, heads, members can propagate data among themselves and maintains the affinity between the clusters.

Internet of Vehicle: Effects of Target Tracking Cluster Routing in Vehicle Network

Target tracking with the wireless sensors networks is to detect and locate a target on its entire path through a region of interest. This application arouses interest in the world of research for its many fields of use. Wireless sensor networks, thanks to their versatility, can be used in many hostile and inaccessible to humans environments. However, with a limited energy, they cannot remain permanently active, which can significantly reduce their lifetime. The formation of a cluster network seems an effective mechanism to increase network lifetime. We propose to build optimal dynamic clusters on the target trajectory. For increasing energy efficiency, our algorithm integrates for the first time, to our knowledge, strategies to avoid overlapping clusters and a model to wake up the sensors, adapting to the context of targets with large and variable speed.

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Dynamic Clustering Algorithm for Tracking Targets with High and Variable Celerity ATHVC

Target tracking with the wireless sensors networks is to detect and locate a target on its entire path through a region of interest. This application arouses interest in the world of research for its many fields of use. Wireless sensor networks, thanks to their versatility, can be used in many hostile environments and inaccessible to humans. However, with a limited energy, they cannot remain permanently active which can significantly reduce their lifetime. The formation of a cluster network seems an effective mechanism to increase network lifetime ". We propose to build optimal dynamic clusters on the target trajectory. For increasing energy efficiency, our algorithm integrates for the first time, to our knowledge, strategies to avoid overlapping clusters and a model to wake up the sensors, adapting to the context of targets with large and variable speed.

Dynamic Clustering Algorithm for Tracking Target with High and Variable Celerity

Dynamic cluster algorithm for improving percolation of targets in a sensor network (DC-AIPT)

The purpose of this study is to investigate knee kinematic signals clustering by principal component analysis. The aim is to identify meaningful patterns in normal gait knee flexion/extension and tibial internal/external rotation signals. To preserve all of the information contained in these kinematics signals, the analysis uses the entire angle curve over a gait cycle rather then a few features extracted from this curve as done traditionally. To reduce processing complexity, the data dimensionality is reduced without loss of relevant information by projecting the gait curve onto a subspace of significant principal components (PCs). Gait patterns are then extracted by a discriminant analysis of the set of training data based on the PCs sign. The analysis identified two representation patterns for each of the flexion/extension (sagital plane) and the tibial internal/external rotation (transverse plane). These patterns were validated both by the clustering silhouette width and clinical interpretation.

Knee kinematic signals clustering for the Identification of sagittal and transverse gait patterns

To maintain the proper functioning of critical applications based on Wireless Sensor Networks, we must provide an acceptable level of security while taking into account limited capabilities of the sensors. In this paper we propose a mobile approach to secure data exchanged by structured nodes in cluster. The approach is based on mobile nodes with significant calculation and energy resources that allow cryptographic key management and periodic rekeying. However, mobility in wireless sensor networks aims to increase the security and lifetime of the entire network. The technical methods used in this paper are based on cryptography elliptic curves and key management through a balanced binary tree. To compare the performance of the proposed approach with other mobile algorithms, we focus on the following metrics: the energy consumed by normal sensors and cluster heads, the number of packets exchanged during key installation, time to generate and distribute cryptographic keys and the memory used by the different sensors to store keys.

Mohamed Toumi

Papers

Dynamic Clustering Algorithm for Tracking Targets with High and Variable Celerity ATHVC

Dynamic Clustering Algorithm for Tracking Target with High and Variable Celerity

Dynamic cluster algorithm for improving percolation of targets in a sensor network (DC-AIPT)

Knee kinematic signals clustering for the Identification of sagittal and transverse gait patterns

How Mobile Nodes Influence Wireless Sensor Networks Security and Lifetime