3D Wireless: Modeling Wireless Performance by Combining Spatial and Temporal Behaviors

Performance characterization is a fundamental issue in wireless networks for real time routing, wireless network simulation, and etc. There are four basic wireless operations that are required to be modeled, i.e., unicast, anycast, broadcast, and multicast. As observed in many recent works, the temporal and spatial distribution of packet receptions can have significant impact on wireless performance involving multiple links (anycast/broadcast/multicast). However, existing performance models and simulations overlook these two wireless behaviors, leading to biased performance estimation and simulation results. In this paper, we first explicitly identify the necessary "3-Dimension" information for wireless performance modeling, i.e., packet reception rate (PRR), PRR spatial distribution, and temporal distribution. We then propose a comprehensive modeling approach considering 3-Dimension Wireless information (called 3DW model). Further, we demonstrate the generality and wide applications of 3DW model by two case studies: 3DWbased network simulation and 3DW-based real time routing protocol. Extensive simulation and testbed experiments have been conducted. The results show that 3DW model achieves much more accurate performance estimation for both anycast and broadcast/multicast. 3DW-based simulation can effectively reserve the end-to-end performance metric of the input empirical traces. 3DW-based routing can select more efficient senders, achieving better transmission efficiency.

[1]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[2]  Yunhuai Liu,et al.  CorLayer: a transparent link correlation layer for energy efficient broadcast , 2013, MobiCom.

[3]  Robert Tappan Morris,et al.  Opportunistic routing in multi-hop wireless networks , 2004, Comput. Commun. Rev..

[4]  Yunhuai Liu,et al.  CorLayer: A Transparent Link Correlation Layer for Energy-Efficient Broadcast , 2015, IEEE/ACM Transactions on Networking.

[5]  Tao Liu,et al.  Temporal Adaptive Link Quality Prediction with Online Learning , 2014, ACM Trans. Sens. Networks.

[6]  Miguel Á. Carreira-Perpiñán,et al.  M&M: multi-level Markov model for wireless link simulations , 2009, SenSys '09.

[7]  Tao Liu,et al.  Foresee (4C): Wireless link prediction using link features , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[8]  Bo Jiang,et al.  Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links , 2014, IEEE Trans. Computers.

[9]  Deborah Estrin,et al.  Temporal Properties of Low Power Wireless Links: Modeling and Implications on Multi-Hop Routing , 2005 .

[10]  Martin Vetterli,et al.  Valuable Detours: Least-Cost Anypath Routing , 2011, IEEE/ACM Transactions on Networking.

[11]  Maurizio Rebaudengo,et al.  Performance analysis of reliable flooding in duty-cycle wireless sensor networks , 2014, Trans. Emerg. Telecommun. Technol..

[12]  Klaus Wehrle,et al.  Bursty traffic over bursty links , 2009, SenSys '09.

[13]  David E. Culler,et al.  The dynamic behavior of a data dissemination protocol for network programming at scale , 2004, SenSys '04.

[14]  Donald F. Towsley,et al.  Performance modeling of epidemic routing , 2006, Comput. Networks.

[15]  Michael Brünig,et al.  Radio diversity for reliable communication in WSNs , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[16]  Azzedine Boukerche,et al.  Opportunistic Routing in Wireless Networks: Models, Algorithms, and Classifications , 2014 .

[17]  Philip Levis,et al.  The β-factor: measuring wireless link burstiness , 2008, SenSys '08.

[18]  Wei Dong,et al.  Exploiting link correlation for core-based dissemination in wireless sensor networks , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[19]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[20]  Euhanna Ghadimi,et al.  Low power, low delay: Opportunistic routing meets duty cycling , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[21]  Atilla Eryilmaz,et al.  Throughput-Delay Analysis of Random Linear Network Coding for Wireless Broadcasting , 2013, IEEE Transactions on Information Theory.

[22]  Yoshito Tobe,et al.  Link-Correlation-Aware Opportunistic Routing in Wireless Networks , 2015, IEEE Transactions on Wireless Communications.

[23]  Philip Levis,et al.  The κ factor: inferring protocol performance using inter-link reception correlation , 2010, MobiCom.

[24]  Neal Patwari,et al.  2008 International Conference on Information Processing in Sensor Networks Effects of Correlated Shadowing: Connectivity, Localization, and RF Tomography , 2022 .

[25]  Dong Nguyen,et al.  Wireless Broadcast Using Network Coding , 2009, IEEE Transactions on Vehicular Technology.