Deployment Jaringan Sensor Nirkabel Berdasarkan Cakupan Area Sensor Node Menggunakan Algoritma Particle Swarm Optimization

Deployment merupakan topik penting dalam bidang Wireless Sensor Network (WSN) karena mencerminkan biaya dan kemampuan deteksi WSN. Perangkat deployment adalah masalah mendasar dari pengadaan WSN. Jenis, jumlah dan lokasi perangkat menentukan beberapa sifat intrinsik WSN seperti cakupan area, konektivitas, dan konsumsi energi. Sebuah skema Deployment yang tepat dapat mengurangi kompleksitas masalah dalam WSN seperti Perutean, fusi data, komunikasi, dll. Selain itu, dapat memperpanjang umur WSN dengan meminimalkan konsumsi energi. Pada penelitian ini, diajukan sebuah aplikasi sensor node deployment secara otomatis berdasarkan Algoritma Particle Swarm Optimization (PSO) dengan mempertimbangkan konsumsi energi sensor node. Secara khusus tujuan penelitian yang ingin dicapai adalah rancang bangun aplikasi untuk menempatkan sensor node pada lingkungan secara nyata sehingga kondisi optimal dari penempatan sensor node di suatu gedung tercapai. Luaran yang ditargetkan yaitu terciptanya suatu aplikasi sensor node deployment yang memperhatikan penyebaran node sensor dengan meminimalkan konsumsi energi sehingga dapat memperpanjang masa jaringan sensor nirkabel. Penelitian ini diharapkan dapat memudahkan dalam perancangan dan optimalisasi penempatan sensor node dalam lingkungan khususnya pada suatu gedung, serta dapat mendukung penelitian di bidang pervasive dan mobile computing

Deployment Wireless Sensor Network (WSN) Berdasarkan Konsumsi Energi Sensor Node

Sebuah skema Deployment yang tepat dapat mengurangi kompleksitas masalah dalam Wireless Sensor Network (WSN) seperti routing, fusi data, komunikasi dll . Selain itu dapat memperpanjang umur WSN dengan meminimalkan konsumsi energi. Dalam penelitian ini, mengajukan penyebaran aplikasi node sensor otomatis berdasarkan Algoritma Particle Swarm Optimization (PSO) dengan mempertimbangkan konsumsi energi dari node sensor. Deployment mempertimbangkan sensor node penyebaran dengan meminimalkan konsumsi energi sehingga dapat memperpanjang jaringan sensor nirkabel

Wireless Sensor Network Deployment

Wireless Sensor Networks (WSNs) are widely used for various civilian and military applications, and thus have attracted significant interest in recent years. This work investigates the important problem of optimal deployment of WSNs in terms of coverage and energy consumption. Five deployment algorithms are developed for maximal sensing range and minimal energy consumption in order to provide optimal sensing coverage and maximum lifetime. Also, all developed algorithms include self-healing capabilities in order to restore the operation of WSNs after a number of nodes have become inoperative. Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% - 90% energy savings can be provided in different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times. Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without self-localization systems, developed before our work