Umber Noreen
ABSTRACT
Low power wide area networks (LPWAN) are the enabling technologies for large scale wireless sensor networks (WSNs). Effective cost, long range and energy efficiency of LPWANs make them most suitable candidates for smart city applications. These technologies offer novel communication paradigm to address discrete IoT's applications.
This paper presents the integration of physical layers based on ZigBee, Wi-Fi, and LoRa into a wireless sensor network simulator CupCarbon for IoT's applications. We have restructured the operations of PHYs, so it can be flexible and scalable to exploit the system services.
- Akyildiz, I.F., Su, W.J., Sankarasubramaniam Y., and E. Cayirci, E., A survey on sensor networks. IEEE Communications Magazine, pp. 102--114, (2002) Google Scholar
Digital Library
- Shen, C., Srisathapornphat, C., and Jaikaeo, C., Sensor Information Networking Architecture and Applications. IEEE Pers. Commun., pp. 52--59, (2001)Google Scholar
- Moe Z. Win, Pedro C. Pinto and Lawrence A. Shepp, A Mathematical Theory of Network Interference and Its Applications. Proceedings of the IEEE. vol.97, no. 2. pp.205--230, (2009)Google Scholar
- Raj Jain, Channel Models: A Tutorial. WiMAX Forum AATG, (2007)Google Scholar
- IEEE Std. 802.15.4-2003. IEEE Standard for Local and Metropolitan Area Networks: Specifications for LowRate Wireless Personal Area Networks. (2003)Google Scholar
- Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs), Published by The Institute of Electrical and Electronics Engineers IEEE, USA, (2003)Google Scholar
- Kurt S., Tavli B., Propagation model alternatives for outdoor Wireless Sensor Networks, ASELSAN Inc., Ankara, Turkey, (2013)Google ScholarCross Ref
- M. O. A. Kalaa, W. Balid, N. Bitar, and H. H. Refai.: Evaluating bluetooth low energy in realistic wireless environments. pages 1--6 (2016).Google Scholar
- Zhao Xiaohui Lei Yuan, Xiong construction. Wi-Fi based wireless sensor network design and research. pp. 18:192--197 (2009)Google Scholar
- L. Kleinrock and F. Tobagi. Packet switching in radio channels: Part I - carrier sense multiple-access modes and their throughput-delay characteristics. IEEE Transactions on Communications, 23(12), 1400--1416 (1975).Google ScholarCross Ref
- U. Noreen and S. Baig. Modified incremental bit allocation algorithm for powerline communication in smart grids. In 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), 1--6 (2013)Google Scholar
- U. Noreen, A. Bounceur, L. Clavier, and R. Kacimi.: Performance evaluation of IEEE 802.15.4 phy with impulsive network interference in cupcarbon simulator. International Symposium on Networks, Computers and Communications (ISNCC), 1--6 (2016)Google Scholar
- IEEE draft standard for local and metropolitan area networks - part 15.4: Wireless medium access control (mac) and physical layer (phy) specifications for low rate wireless personal area networks (wpans) amendment - physical layer (phy) specifications for low energy, critical infrastructure monitoring networks (lecim). 1--133 (2013).Google Scholar
- IEEE standard for local and metropolitan area networks, part 15.4: Low-rate wireless personal area networks (lr-wpans). IEEE Std 802.15.4-2011 (Revision of IEEE Std. 802.15.4-2006), 1--314 (2011).Google Scholar
- Hui Li and Dan Yu.: Comparison of ad hoc and centralized multihop routing. 2:791--795 (2002)Google Scholar
- Anis KOUBAA, Mario ALVES, Eduardo TOVAR. "IEEE 802.15.4 for Wireless Sensor Networks: A Technical Overview ". HURRAY-TR-050801 (2005)Google Scholar
- B. Reynders, W. Meert, and S. Pollin. Range and coexistence analysis of long range unlicensed communication. In 2016 23rd International Conference on Telecommunications (ICT), 1--6 (2016)Google Scholar
- Semtech. http://www.semtech.com/.Google Scholar
- Changsu Suh, Jung-Eun, et Joung Young-Bae Ko, New RF Models of the TinyOS Simulator for IEEE 802.15.4 Standard. Dept. of R and D, Hanback Electron. Co., Daejeon, (2007)Google Scholar
- cupcarbon. "CupCarbon: A Smart City and IoT Wireless Sensor Network Simulator", available at: http://www.cupcarbon.com (2016)Google Scholar
- Kamal Mehdi and Massinissa Lounis and Ahcene Bounceur and Tahar Kechadi. CupCarbon: A Multi-Agent and Discrete Event Wireless Sensor Network Design and Simulation Tool, In IEEE 7th International Conference on Simulation Tools and Techniques (SIMUTools'14), Lisbon, Portugal, (2014) Google ScholarDigital Library
Index Terms
- Modeling Interference for Wireless Sensor Network Simulators
Recommendations
Modeling Interference for Wireless Sensor Network Simulators
BDAW '16: Proceedings of the International Conference on Big Data and Advanced Wireless TechnologiesAny radio signal gets exposed to some attenuation over distance, and is received at the receiver as its distorted version and is superposed with other wireless signals transmitted in the neighborhood. In wireless communications, the useful signal is ...
Integration of OFDM Based Communication System with Alpha-Stable Interference using CupCarbon Simulator
ICC '16: Proceedings of the International Conference on Internet of things and Cloud ComputingDeploying sensor networks in an efficient and optimal way has become an important topic these days. A sensor network is basically an infrastructure which consists on number of nodes equipped with a sensor, a controller and a communication module. ...
The optimization of sensor relocation in wireless mobile sensor networks
Wireless Sensor Networks (WSNs) have been an active research area these years due to their broad range of potential applications. Several research issues, which include energy-aware routing, sensor deployment problems, data aggregation, etc., have been ...
Comments