This is the tcl script for simulating a Wireless Sensor Network with some obstacles in the target area.
Archive for the ‘Wireless Sensor Networks’ Category
NS-2: WSN with obstacles
May 29, 2017Kuliah Umum di Program Pasca Sarjana Universitas Mercu Buana – Jakarta
March 27, 2016
Hari minggu, 27 Maret 2016, saya diberikan kesempatan yang baik oleh Prof. Mudrik Alaydrus untuk memberikan Kuliah Umum kepada teman-teman sivitas akademis Program Sarjana Teknik Elektro, Magister Manajemen Telekomunikasi, Universitas Mercu Buana, Jakarta.
Pada kesempatan kali ini, saya berbagi pengalaman dan ilmu pengetahuan mengenai riset yang telah saya lakukan selama di Perancis. Topiknya adalah mengenai “Optimizing and Enhancing the lifetime of WSN Deployment” dengan menggunakan pendekatan MOP (Multi-objective Optimisation Problem) dan Operator Calculus untuk mendefinisikan sebuah Path Centrality dari sebuah WSN.
Senang dan bahagia sekali melihat antusias teman-teman/adik-adik mahasiswa yang penuh semangat untuk melanjutkan studi ke jenjang yang lebih tinggi. Bon courage…!
Anda bisa mengunduh file presentasi dalam file pdf ini.
Search Based Software Engineering on Evolutionary Multi-Objective Approach
March 15, 2016
Here is the abstract of our paper entitle “Search Based Software Engineering on Evolutionary Multi-Objective Approach” which is accepted in IEEE International Converence on Communications 2016 and will be presented at Kuala Lumpur, Malaysia, on 23 May 2016.
The works on Search Based Software Engineering (SBSE) have been a big increase in the last decade. An approach to software engineering in which search based optimisation algorithms are applied to address problems in software engineering. SBSE has been applied to problems throughout the software engineering lifecycle, from requirements and project planning to maintenance and re-engineering. This paper provides a modification and an implementation of SBSE on evolutionary multi-objective based ap-proach for deployment of wireless sensor network (WSN) with the presence of fixed obstacle. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address the deployment problem. Two functions namely ranking function and fitness function are used to select the best optimal solution from Pareto optimal fronts.
Performance Analysis of Evolutionary Multi-Objective Based Approach for Deployment of Wireless Sensor Network with The Presence of Fixed Obstacles
November 11, 2014
Here is the abstract from our paper that already accepted and will be presented on Globecom 2014 that will be held in Austin, Texas, USA from 8 – 12 December 2014.
Abstract — In this paper, a study about wireless sensor network (WSN) deployment strategy is demonstrated and made workable for the use of multi-objective approach. The development of sensor nodes by considering multiple objectives and existence of fixed obstacles is an important optimization problem. There are two objectives in this study, connectivity and coverage as two fundamental issues in wireless sensor networks deployment. In this work a multi-objective evolutionary algorithms based on elitist non-dominated sorting genetic algorithm (NSGA-II) is proposed to address this problem. Two proposed functions, ranking function and fitness function, are used to determine the best optimal solution from Pareto optimal fronts. Further we presented simulation and analysis to verify and validate the deployment of wireless sensor network in area with the presence of permanent obstacles.
Keywords — Multi-objective optimization, Wireless Sensor Network, Deployment, Genetic Algorithm, Obstacle.
Evolutionary Multi-Objective Based Approach for Wireless Sensor Network Deployment
January 14, 2014
Here is the abstract from our paper that already accepted and will be presented on ICC 2014 (International Conference on Communication) that will be held in the beautiful city of Sydney, Australia from 10-14 June 2014.
Abstract: A multi-objective evolutionary algorithm is designed to address some problems in many fields. This paper is a study about deployment strategy for achieving coverage and connectivity as two fundamental issues in wireless sensor networks. To achieve the best deployment, our approach is based on elitist non-dominated sorting genetic algorithm (NSGA-II). There are two objectives in this study, connectivity and coverage. We defined a fitness function to achieved the best deployment of nodes. Further we performed simulation to verify and validate the deployment of wireless sensor network as an output from our proposed mechanism. We measured some performance parameters to investigate and analyze our proposed sensor-deployment. Our simulation results show that our proposed algorithm can maintain coverage and connectivity in given sensing area with a relatively small number of sensor nodes in a given area.
Sketch of Simple Program Genetic Algorithm
October 21, 2012
For almost 3 weeks I have been learning about Genetic Algorithm (GA), especially NSGA-2. So, I write this posting to help me to understand a little bit about GA. I found a good reference for beginners who learn about GA. A David A. Coley book, An Introduction to Genetic Algorithms for Scientists and Engineers. (more…)
How to Installing & Running LEACH on NS-2.34 (Step-by-Step)
May 16, 2012This is my experience when I installed LEACH on NS-2.34 in my Ubuntu 10.04 LTS. After googling I found some useful links that discussing about how to running Leach on NS-2 as follows :
So here is my note when I installing and running Leach on NS-2.34. (more…)
Introduction to Wireless Sensor Networks
February 25, 2012
A wireless sensor network is a collection of nodes organized into a cooperative network [10]. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omni-directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Systems of 1000s or even 10,000 nodes are anticipated. Such systems can evolutionize the way we live and work.
Currently, wireless sensor networks are beginning to be deployed at an ccelerated pace. It is not unreasonable to expect that in 10-15 years that the world will be covered with wireless sensor networks with access to them via the Internet. This can be considered as the Internet becoming a physical network. This new technology is exciting with unlimited potential for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces.
Since a wireless sensor network is a distributed real-time system a natural question is how many solutions from distributed and real-time systems can be used in these new systems? Unfortunately, very little prior work can be applied and new solutions are necessary in all areas of the system. The main reason is that the set of assumptions underlying previous work has changed dramatically. Most past distributed systems research has assumed that the systems are wired, have unlimited power, are not real-time, have user interfaces such as screens and mice, have a fixed set of resources, treat each node in the system as very important and are location independent. In contrast, for wireless sensor networks, the systems are wireless, have scarce power, are real-time, utilize sensors and actuators as interfaces, have dynamically changing sets of resources, aggregate behavior is important and location is critical. Many wireless sensor networks also utilize minimal capacity devices which places a further strain on the ability to use past solutions.
Introduction to Wireless Sensor Networks
February 25, 2012
A wireless sensor network is a collection of nodes organized into a cooperative network [10]. Each node consists of processing capability (one or more microcontrollers, CPUs or DSP chips), may contain multiple types of memory (program, data and flash memories), have a RF transceiver (usually with a single omni-directional antenna), have a power source (e.g., batteries and solar cells), and accommodate various sensors and actuators. The nodes communicate wirelessly and often self-organize after being deployed in an ad hoc fashion. Systems of 1000s or even 10,000 nodes are anticipated. Such systems can revolutionize the way we live and work.
