Search Results for "code matlab simulation wireless network markov"

STEAM-Sim establishes a hardware/software/network co-simulation of wireless sensor networks. Original C code is used in simulation. The code is natively executed by the cpu where the simulation is run. The time annotation engine annotates the C source code used for simulation with the timing information as if the code is run on a microcontroller. The annotation process is fully automated. Hardware models are developed using the PAWiS framework and reflect the timing, functionality...

... is to provide a simple-to-understand IGA FEM code. The work is funded under the FP7 Initial Training Network Funding under grant number 289361 'Integrating Numerical Simulation and Geometric Design Technology.' If you use this code or inspired by it please cite the following reference in your work: VP Nguyen, C Anitescu, S Bordas, T Rabczuk. "Isogeometric analysis: an overview and computer implementation aspects". Mathematics and Computers in Simulation, (2015), pp. 89-116, Vol 117.

The project page provides the source code of custom built simulators used in our publications whose URL are given as follow: http://dx.doi.org/10.1016/j.comcom.2013.09.006 http://dx.doi.org/10.1109/PIMRC.2009.5449983 The titles of the papers are: Online XOR packet coding: Efficient single-hop wireless multicasting with low decoding delay (COMCOM VERSION). An efficient network coding based retransmission algorithm for wireless multicast (PIMRC VERSION). Help (README) files are included...

While wireless sensor networks are increasingly equipped to handle more complex functions, in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network life time especially in a heterogeneous settings. Clustered techniques have since been employed to optimize energy consumption in this energy constrained wireless sensor networks. We propose an Enhanced-SEP clustering algorithm in a three-tier node scenario...

... protocols. It utilizes a simplified approach which minimizes computational overhead-cost to self-organize the sensor network. Our simulation result shows a better performance with respect to energy consumption, which is reflected in the network lifetime in both homogeneous and heterogeneous settings when compared with the existing protocols. It is worthy of note that our approach approximates an ideal solution for balanced energy consumption in hierarchical wireless sensor networks.