Since October 2017, I have been involving as a member of International Joint Research (IJR) project between 3 institutions (2 France, 1 Indonesian). Currently I’m an associate professor at UMB (maybe until [month] 20[xx] 🙂 ) and a PostDoc fellow at CESI Starsbourg, France, as a part of joint research between UMB-UHA-CESI. We have started our project on December 2017. (more…)
Posts Tagged ‘WSN’
IJR Project UMB-UHA-CESI
February 21, 2019Protokol Routing RPL
January 23, 2018Internet of Things (IoT) bukan lagi sebagai fantasi sains-fiksi. Dengan kemajuan teknologi yang sangat pesat memungkinkan terhubungnya semua objek sehari-hari ke Internet. Namun, diperlukan sebuah solusi yang interoperabel untuk memastikan optimal komunikasi antar objek-objek tersebut. Protokol routing adalah salah satu elemen kunci pada IoT. Karena protokol routing memungkinkan setiap objek menentukan bagaimana mencapai objek lain. Beberapa kendala pada protokol routing antara lain saluran komunikasi yang tidak stabil dan energi daya rendah yang harus diperhitungkan dalam pengembangan protokol routing yang sesuai untuk IoT. Pada artikel kali ini, saya akan menyajikan sekilas tentang protokol RPL yang dirancang khusus untuk jaringan berdaya rendah dan lossy (Low-power and Lossy Network disingkat LLN).
Development of a New Routing Protocol by using Multi-objective and Swarm Intelligent Approaches for Wireless Sensor Network on Maritime Environment Monitoring
December 1, 2017This is the abstract for my International Research Collaboration and Scientific Publication with two French Institutions as international collaborators.
ABSTRACT
As the largest archipelago country in the world, Indonesia has great potential to be the world’s maritime axis. The maritime axis is a strategic idea which embodied to ensure inter-island connectivity, shipping and fishery industries development, marine transportation improvement and focus on maritime security. So maritime monitoring is crucial in order to prevent sea pollution, fish theft, sovereignty offenses, territorial disputes, and piracy. Nowadays, maritime monitoring has been a very interesting issue over the last decade. With the rapid advancement of technology and the Internet, sensor network technology has become very important in monitoring the aquatic environment. Maritime responsibilities, such as vigilance and patrolling, wildlife monitoring, and aqua-culture inspection, mostly require large operational teams and expensive equipment. In this research proposal, we will study a scalable robotics system based on swarm intelligence. We take advantage of bio-inspired artificial evolution approaches in order to synthesize scalable and robust collective behaviors for the nodes or the drones. In this research proposal, we will study and propose a system which composed of potentially hundreds sensor nodes or thousands of autonomous drones. Each drone could equipped with a number of different sensors. The use of numerous nodes or drones introduces redundancy in the system, which decreases the impact of hardware declines and has the potential to improve operational efficiency by admitting a wider area to be covered simultaneously. On the other hand, automating such missions greatly minimizes the maintenance cost while increasing scalability and availability. The behaviors of nodes or drones are then combined hierarchically, allowing the overall behavior for a particular mission to be quickly configured and tested in simulation. The goals of this research is to developing a new mechanism which enable swarm intelligence of nodes or drones to operate as a robust wireless sensor network (WSN) for maritime environment monitoring.
Keywords: Maritime, Swarm Intelligence, Sensor, WSN
MATLAB : WSN SIMULATION
November 13, 2017The instruction is valid for Matlab version R2007b.
First dowload and save grTheory toolobox (save it in Tools): http://www.mathworks.com/matlabcentral/fileexchange/4266. Open the Matlab and go to the File/Set Path and click on the Add Folder. Insert the path to your grTheory toolbox. (more…)
Optimisasi Penyebaran Jaringan Sensor Pemantauan Maritim Menggunakan Algoritma Genetik
November 8, 2017Abstrak
Sebagai negara kepulauan terbesar di dunia, Indonesia memiliki potensi besar menjadi poros maritim dunia. Poros maritim merupakan sebuah gagasan strategis yang diwujudkan untuk menjamin konektifitas antar pulau, pengembangan industri perkapalan dan perikanan, perbaikan transportasi laut serta fokus pada keamanan maritim. Maka diperlukan pemantauan maritim untuk mencegah terjadinya hal-hal yang negatif seperti pencemaran laut, pencurian ikan, pelanggaran kedaulatan, sengketa wilayah, dan perompakan. Pemantauan maritim telah menjadi isu yang sangat menarik selama beberapa tahun belakangan ini. Dengan adanya kemajuan teknologi dan Internet yang sangat pesat, teknologi jaringan sensor nirkabel telah menjadi hal yang sangat penting pada pemantauan lingkungan perairan. Penelitian ini mengusulkan sebuah metode optimasi penyebaran jaringan sensor untuk pemantauan lingkungan maritim. Dengan menggunakan pendekatan algoritma genetik multi-obyektif evolusioner untuk mendapatkan solusi topologi jaringan sensor optimal dengan jumlah sensor seminimum mungkin, namun maksimum dalam hal jangkauan dan konektifitas.
Operator calculus approach for route optimizing and enhancing wireless sensor network
October 1, 2017This is the abstract of my paper which accepted and published at Journal of Network and Computer Applications,Vol 97, pp. 1-10. 2017. DOI: 10.1016/j.jnca.2017.08.007.
Abstract
Route optimization is one of important feature in wireless sensor networks in order to enhancing the life time of WSNs. Since Centrality is one of the greatest challenges in computing and estimating the important node metrics of a structural graph, it is necessary to calculate and determine the importance of a node in a network. This paper proposes an alternative way to optimizing the route problems which is based on multi-constrained optimal path (MCOP) and operator calculus approach. A novel routing protocol called Path Operator Calculus Centrality (POCC) is proposed as a new method to determine the main path which contains high centrality nodes in a wireless sensor network deployment. The estimation of centrality is using the operator calculus approach based on network topology which provides optimal paths for each source node to base station. Some constraints such as energy level and bit error rate (BER) of node are considered to define the path centrality in this approach. The simulation evaluation shows improved performance in terms of energy consumption and network lifetime.
Keywords
Wireless Sensor Network, Multi-constrained Optimal Path Centrality, Path Centrality, Operator Calculus
NS-2: WSN with obstacles
May 29, 2017
This is the tcl script for simulating a Wireless Sensor Network with some obstacles in the target area.
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.
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.
