Skip to main content

Advertisement

Springer Nature Link
Log in

SiAc: simultaneous activation of heterogeneous radios in high data rate multi-hop wireless networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Wireless networks are now experiencing ubiquitous deployment of multiple heterogeneous radios on single wireless devices such as smartphones, laptops, etc. Simultaneous activation of a low-capability radio in addition to a high-capability radio, to transmit data originated from a single application on such a device, is yet to be studied in the literature. Moreover, such activation of multiple radios from the application layer is another aspect, which is yet to be examined. Therefore, we investigate simultaneous activation of heterogeneous radios from the application layer in this paper. Our study reveals that we can significantly improve network performance with the simultaneous activation, and the improvement can even be disproportionately higher than the capability of the additional low-capability radio. However, the activation requires a judicious exploitation of the heterogeneous radios, i.e., suitable splitting of data over the radios. Therefore, we propose a cross-layer mathematical model to estimate the optimal data splitting. Estimations from our proposed model exhibit only 3 % average error, which we verify through ns-2 simulation. Besides, we evaluate the notion of simultaneous activation over a number of different network topologies through both ns-2 simulation and real testbed experiment to demonstrate achieving the disproportionately high performance improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Here, the capability of a radio depends on several factors such as its bandwidth, transmission range, etc. For example, comparing such factors for 802.15.4 and 802.11b radios (as presented in [34]), we can consider 802.15.4 radio as the lower-capability radio compared to 802.11b radio.

  2. The number of hops depends on the number of transmission steps over the intermediate nodes. Here, the number of hops equals to one plus the number of intermediate nodes between the source and the destination. Consequently, an increase in the number of hops refers to an increase in the number of intermediate nodes, which inversely affects the effective bandwidth of a radio contributed to a data flow.

  3. The spatial reuse depends on the number of non-interfering transmission steps through the intermediate nodes. For example, in case of three intermediate nodes or equivalently four-hop data transmission of a flow, the spatial reuse comes into play resulting \(\tau =2\). Here, the value \(2\) refers to two simultaneously transmitting hops (\(1st\) and \(4th\) hops in this case) in the flow.

  4. In our study, we always take \(\tau =1\) as we find similar multiplicative effects of \(\tau\) in both numerator and denominator in our study (Eqs. 2, 3).

  5. Note that the notion of total delay, i.e., total time duration required for completing transmission of a certain volume of data, is completely different from end-to-end delay that we will focus later in this paper.

  6. Here, network throughput implies the total number of application-layer bits successfully transmitted in one second all over the network, average end-to-end delay implies the average of time durations incurred for successful end-to-end transmissions of packets carrying application-layer data, and average energy per bit implies average energy consumption all over the network incurred for successful transmission of one application-layer bit.

  7. If we would want to consider such additional flows carrying cross-traffic, we will have to modify our mathematical models accordingly for estimating optimal data splitting over the radios in presence of such additional flows.

  8. We calculate the average and standard deviation of the errors over all rows in Tables 3 and 4.

  9. The lowest value of network throughput gives corresponding worst performance, and the highest values of end-to-end delay and energy per bit give corresponding worst performances.

  10. Here, we mainly emphasize on congestion over wireless channels rather than congestion in queues storing packets in transit.

  11. Evaluation using such very high data rate transmission demonstrates the performance of SiAc over a multi-hop wireless network, which experiences saturation of its capacity.

References

  1. Adya, A., Bahl, P., Padhye, J., Wolman, A., & Zhou, L. (2004). A multi-radio unification protocol for ieee 802.11 wireless networks. In Broadband Networks, 2004. BroadNets 2004. Proceedings on first international conference (pp. 344–354). IEEE.

  2. Al Hanbali, A., Altman, E., & Nain, P. (2005). A survey of tcp over ad hoc networks. IEEE Communications Surveys & Tutorials, 7(3), 22–36.

    Article  Google Scholar 

  3. Ali, H. M., Busson, A., & Vèque, V. (2009). Channel assignment algorithms: A comparison of graph based heuristics. In Proceedings of the 4th ACM workshop on performance monitoring and measurement of heterogeneous wireless and wired networks (pp. 120–127). ACM.

  4. Amiri Nehzad, M. (2013). Channel assignment protocols for multi-radio multi-channel wireless mesh netwworks. Ph.D. thesis, Department of Computer Architecture, Universitat Politècnica de Catalunya.

  5. Anand, M., Nightingale, E. B., & Flinn, J. (2005). Self-tuning wireless network power management. Wireless Networks, 11(4), 451–469.

    Article  Google Scholar 

  6. Baker, N. (2005). Zigbee and bluetooth: Strengths and weaknesses for industrial applications. Computing and Control Engineering, 16(2), 20–25.

    Article  Google Scholar 

  7. Balasubramanian, A., Mahajan, R., & Venkataramani, A. (2010). Augmenting mobile 3g using wifi. In Proceedings of the 8th international conference on mobile systems, applications, and services (pp. 209–222). ACM.

  8. Barker, S. K., & Shenoy, P. (2010). Empirical evaluation of latency-sensitive application performance in the cloud. In Proceedings of the first annual ACM SIGMM conference on multimedia systems (pp. 35–46). ACM.

  9. Brunato, M., & Severina, D. (2005). Wilmagate: A new open access gateway for hotspot management. In Proceedings of the 3rd ACM international workshop on wireless mobile applications and services on WLAN hotspots (pp. 56–64). ACM.

  10. Carter, C., Kravets, R., & Tourrilhes, J. (2003). Contact networking: A localized mobility system. In Proceedings of the 1st international conference on mobile systems, applications and services (pp. 145–158). ACM.

  11. Chandra, R., & Bahl, P. (2004). Multinet: Connecting to multiple ieee 802.11 networks using a single wireless card. In INFOCOM 2004. Twenty-third annual joint conference of the IEEE computer and communications societies, Vol. 2 (pp. 882–893). IEEE.

  12. Cheng, Y., Li, H., Wan, P.-J., & Wang, X. (2012). Wireless mesh network capacity achievable over the csma/ca mac. IEEE Transactions on Vehicular Technology, 61(7), 3151–3165.

    Article  Google Scholar 

  13. Jacquet, P., Muhlethaler, P., Clausen, T., Laouiti, A., Qayyum, A., & Viennot, L. (2001). Optimized link state routing protocol for ad hoc networks. In Proceedings of IEEE International Multi Topic Conference, 2001 (IEEE INMIC 2001). Technology for the 21st Century (pp. 62–68). IEEE.

  14. Cloud in the City of London. www.thecloud.net.

  15. Coley, G. (2009). Beagleboard system reference manual (p. 81). BeagleBoard.org.

  16. Orebaugh, A., Ramirez, G., & Beale, J. (2006). Wireshark & Ethereal network protocol analyzer toolkit. Syngress.

  17. CTIA 2010: 4G VoIP services for Android mobile devices. http://www.phonesreview.co.uk/2010/03/24/ctia-2010-4g-voip-services-for-android-mobile-devices/.

  18. Draves, R., Padhye, J., & Zill, B. (2004a). The architecture of the link quality source routing protocol. Technical report MSR-TR-2004-57, Microsoft Research.

  19. Draves, R., Padhye, J., & Zill, B. (2004b). Routing in multi-radio, multi-hop wireless mesh networks. In Proceedings of the 10th annual international conference on mobile computing and networking (pp. 114–128). ACM.

  20. East bay community mesh network. http://510pen.org/en/node/1.

  21. Efstathiou, E. C., & Polyzos, G. C. (2003). A peer-to-peer approach to wireless lan roaming. In Proceedings of the 1st ACM international workshop on wireless mobile applications and services on WLAN hotspots (pp. 10–18). ACM.

  22. Energy Model Update in ns-2. http://www.isi.edu/ilense/software/smac/ns2_energy.html.

  23. Eswaran, S., Johnson, M. P., Misra, A., & La Porta, T. (2009). Distributed utility-based rate adaptation protocols for prioritized, quasi-elastic flows. ACM SIGMOBILE Mobile Computing and Communications Review, 13(1), 2–13.

    Article  Google Scholar 

  24. Fan, W. H., Liu, Y. A., & Wu, F. (2013). Optimal resource allocation for transmission diversity in multi-radio access networks: A coevolutionary genetic algorithm approach. Science China Information Sciences, 57(2), 1–14.

    Article  Google Scholar 

  25. Floyd, S., & Henderson, T. (1999). RFC 2582: The newreno modification to tcp’s fast recovery algorithm.

  26. Girod, L., Lukac, M., Trifa, V., & Estrin, D. (2006). The design and implementation of a self-calibrating distributed acoustic sensing platform. In Proceedings of SenSys (pp. 71–84). ACM.

  27. Gopinathan, A., Li, Z., & Williamson, C. (2009). Optimal multicast in multi-channel multi-radio wireless networks. In IEEE international symposium on Modeling, analysis and simulation of computer and telecommunication systems, 2009. MASCOTS’09 (pp. 1–10). IEEE.

  28. Han, H., Shakkottai, S., Hollot, C. V., Srikant, R., & Towsley, D. (2006). Multi-path TCP: A joint congestion control and routing scheme to exploit path diversity in the internet. IEEE/ACM Transactions on Networking (TON), 14(6), 1260–1271.

    Article  Google Scholar 

  29. Haque, E., Darus, A. B., Yaacob, M. M., & Ahmed, F. (2002). Application of acoustic sensing and signal processing for PD detection in GIS. In Proceedings of ICICS (pp. 745–749). IEEE.

  30. Higgins, B. D., Reda, A., Alperovich, T., Flinn, J., Giuli, T. J., Noble, B., et al. (2010). Intentional networking: Opportunistic exploitation of mobile network diversity. In Proceedings of MobiCom (pp. 73–84).

  31. How Wireless Mesh Networks Work. http://computer.howstuffworks.com/how-wireless-mesh-networks-work2.htm.

  32. Hsieh, H. Y., & Sivakumar, R. (2005). A transport layer approach for achieving aggregate bandwidths on multi-homed mobile hosts. Wireless Networks, 11(1–2), 99–114.

    Article  Google Scholar 

  33. Irwin, R., MacKenzie, A., & DaSilva, L. (2012). Traffic-aware channel assignment for multi-radio wireless networks. NETWORKING 2012 (pp. 331–342).

  34. Islam, A. B. M. A. A., Hossain, M. S., Raghunathan, V., & Hu, Y. C. (2011). Backpacking: Deployment of heterogeneous radios in high data rate sensor networks. In 2011 Proceedings of international conference on computer communications and networks (ICCCN) (pp. 1–8). IEEE.

  35. Islam, A. B. M. A. A., & Raghunathan, V. (2011). Assessing the viability of cross-layer modeling for asynchronous, multi-hop, ad-hoc wireless mesh networks. In Proceedings of the 9th ACM international symposium on mobility management and wireless access (pp. 147–152). ACM.

  36. Issariyakul, T., & Hossain, E. (2008). Introduction to network simulator ns2. Berlin: Springer.

    Google Scholar 

  37. Jain, K., Padhye, J., Padmanabhan, V. N., & Qiu, L. (2005). Impact of interference on multi-hop wireless network performance. Wireless Networks, 11(4), 471–487.

    Article  Google Scholar 

  38. Joo, C., & Shroff, N. B. (2009). Performance of random access scheduling schemes in multi-hop wireless networks. IEEE/ACM Transactions on Networking (TON), 17(5), 1481–1493.

    Article  Google Scholar 

  39. Jurdak, R., Ruzzelli, A. G., & O’Hare, G. M. P. (2010). Radio sleep mode optimization in wireless sensor networks. IEEE Transactions on Mobile Computing, 9(7), 955–968.

    Article  Google Scholar 

  40. Kandula, S., Lin, K. C. J., Badirkhanli, T., & Katabi, D. (2008). FatVAP: Aggregating AP backhaul capacity to maximize throughput. In Proceedings of NSDI (pp. 89–104). USENIX Association.

  41. Keshav, S. (1995). A control-theoretic approach to flow control. ACM SIGCOMM Computer Communication Review, 25(1), 188–201.

    Article  Google Scholar 

  42. Key, P., Massoulié, L., & Towsley, P. D. (2007). Path selection and multipath congestion control. In Proceedings of INFOCOM (pp. 143–151). IEEE.

  43. Kim, K. H., Zhu, Y., Sivakumar, R., & Hsieh, H. Y. (2005). A receiver-centric transport protocol for mobile hosts with heterogeneous wireless interfaces. Wireless Networks, 11(4), 363–382.

    Article  Google Scholar 

  44. Kim, W., & Park, J.-S. (2014). Cross-layer scheduling for multi-users in cognitive multi-radio mesh networks. Wireless Communications and Mobile Computing, 14(11), 1034–1044.

    Article  Google Scholar 

  45. Kowlgi, S., & Dave, V. Effect of data rate on throughput. www.cs.utexas.edu/ vacha/wireless/rate_throughput.ppt.

  46. Kumar, N., Chilamkurti, N., Park, J. H., & Park, D. S. (2013). Load balancing and adaptive scheduling for data intensive prioritised traffic in multi-radio multi-channel wireless mesh networks. International Journal of Ad Hoc and Ubiquitous Computing, 12(1), 3–13.

    Article  Google Scholar 

  47. Vaidya, H. (2009). Capacity of multichannel wireless networks under the protocol model. IEEE/ACM Transactions on Networking, 17(2), 515–527.

    Article  Google Scholar 

  48. Lavery, R. J. (2001). Throughput optimization for wireless data transmission. PhD thesis, Thesis, Polytechnic University, June 2001.

  49. Leung, K. C., Li, V. O. K., & Yang, D. (2007). An overview of packet reordering in transmission control protocol (tcp): Problems, solutions, and challenges. IEEE Transactions on Parallel and Distributed Systems, 18(4), 522–535.

    Article  Google Scholar 

  50. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., et al. (2005). Tinyos: An operating system for sensor networks. In Ambient intelligence (pp. 115–148).

  51. Liu, T., & Liao, W. (2008). Location-dependent throughput and delay in wireless mesh networks. IEEE Transactions on Vehicular Technology, 57(2), 1188–1198.

    Article  Google Scholar 

  52. Magalhaes, L., & Kravets, R. (2001). Transport level mechanisms for bandwidth aggregation on mobile hosts. In Network protocols ninth international conference on ICNP 2001 (pp. 165–171). IEEE.

  53. Manoj, B. S., & Baker, A. H. (2007). Communication challenges in emergency response. Communications of the ACM, 50(3), 51–53.

    Article  Google Scholar 

  54. Margi, C. B., Petkov, V., Obraczka, K., & Manduchi, R. (2006). Characterizing energy consumption in a visual sensor network testbed. Citeseer. In Proceedings of TridentCom.

  55. Matsunaga, Y., Merino, A. S., Suzuki, T., & Katz, R. H. (2003). Secure authentication system for public wlan roaming. In Proceedings of the 1st ACM international workshop on wireless mobile applications and services on WLAN hotspots (pp. 113–121). ACM.

  56. Mini Wireless N USB Adapter. http://www.trendnet.com/products/proddetail.asp?prod=190_TEW-648UB&cat=175.

  57. Miu, A., & Bahl, P. (2001). Dynamic host configuration for managing mobility between public and private networks. In Proceedings of the 3rd conference on USENIX symposium on internet technologies and systems, Vol. 3 (pp. 13–13). USENIX Association.

  58. Ozone’s mesh network in Paris. www.ozone.net/en/.

  59. Patani, J., Gabale, V., & Raman, B. (2013). Minimum weight multicast scheduling in multi-channel wireless mesh networks for real-time voice applications. In 2013 fifth international conference on communication systems and networks (COMSNETS) (pp. 1–9). IEEE.

  60. Perkins, C. et al. (2002). RFC 3344: Ip mobility support for ipv4. The Internet Society, http://tools.ietf.org/html/rfc3344.

  61. Perkins, C. E., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers. In Proceedings of SIGCOMM (pp. 234–244).

  62. Peterson, L. L., & Davie, B. S. (2007). Computer networks: A systems approach. San Francisco, CA: Morgan Kaufmann Publishers Inc.

    Google Scholar 

  63. Petrioli, C., Basagni, S., & Chlamtac, I. (2004). Bluemesh: Degree-constrained multi-hop scatternet formation for bluetooth networks. Mobile Networks and Applications, 9(1), 33–47.

    Article  Google Scholar 

  64. Polastre, J., Szewczyk, R., & Culler, D. (2005). Telos: enabling ultra-low power wireless research. In Fourth international symposium on information processing in sensor networks, IPSN (pp. 364–369). IEEE.

  65. Pollak, S., & Wieser, V. (2012). Channel assignment schemes optimization for multi-interface wireless mesh networks based on link load Chap 4. Winchester: InTech.

    Google Scholar 

  66. Portmann, M., & Pirzada, A. A. (2008). Wireless mesh networks for public safety and crisis management applications. IEEE Internet Computing, 12(1), 18–25.

    Article  Google Scholar 

  67. Ra, M. R., Paek, J., Sharma, A. B., Govindan, R., Krieger, M. H., Neely, M. J. (2010). Energy-delay tradeoffs in smartphone applications. In Proceedings of the 8th international conference on mobile systems, applications, and services (pp. 255–270). ACM.

  68. Rahmati, A., & Zhong, L. (2007). Context-for-wireless: Context-sensitive energy-efficient wireless data transfer. In Proceedings of the 5th international conference on mobile systems, applications and services (pp. 165–178). ACM.

  69. Raychaudhuri, D., Seskar, I., Ott, M., Ganu, S., Ramachandran, K., Kremo, H., et al. (2005). Overview of the orbit radio grid testbed for evaluation of next-generation wireless network protocols. In Wireless communications and networking conference, 2005 IEEE, Vol. 3 (pp. 1664–1669). IEEE.

  70. Robinson, J., Papagiannaki, K., Diot, C., Guo, X., Krishnamurthy, L. (2005). Experimenting with a multi-radio mesh networking testbed. In 1st workshop on wireless network measurements.

  71. Sakakibara, H., Saito, M., & Tokuda, H. (2006). Design and implementation of a socket-level bandwidth aggregation mechanism for wireless networks. In Proceedings of the 2nd annual international workshop on wireless internet (p. 11). ACM.

  72. Salem, N. B., Hubaux, J. P., & Jakobsson, M. (2004). Reputation-based wi-fi deployment protocols and security analysis. In Proceedings of the 2nd ACM international workshop on wireless mobile applications and services on WLAN hotspots (pp. 29–40). ACM.

  73. Shin, B., Han, S. Y., Lee, D. (2012). Dynamic link quality aware routing protocol for multi-radio wireless mesh networks. In 2012 IEEE 26th international conference on advanced information networking and applications (AINA) (pp. 44–50). IEEE.

  74. Si, W., Selvakennedy, S., & Zomaya, A. Y. (2010). An overview of channel assignment methods for multi-radio multi-channel wireless mesh networks. Journal of Parallel and Distributed Computing, 70(5), 505–524.

    Article  MATH  Google Scholar 

  75. Simultaneous Dual-N Band Wireless Router. http://www.linksysbycisco.com/UK/en/products/WRT610N.

  76. SMesh, Johns Hopkins University. http://www.smesh.org/.

  77. Stathopoulos, T., Lukac, M., Mclntire, D., Heidemann, J., Estrin, D., & Kaiser, W. J. (2007). End-to-end routing for dual-radio sensor networks. In Proceedings of INFOCOM (pp. 2252–2260). IEEE.

  78. The Dharamsala Community Wireless Mesh Network. http://drupal.airjaldi.com/node/56.

  79. Toh, C. K., Delwar, M., & Allen, D. (2002). Evaluating the communication performance of an ad hoc wireless network. IEEE Transactions on Wireless Communications, 1(3), 402–414.

    Article  Google Scholar 

  80. Tonnesen, A., Lopatic, T., Gredler, H., Petrovitsch, B., Kaplan, A., & Tucke, S. O. (2010). Olsrd: An ad hoc wireless mesh routing daemon. http://www.olsr.org.

  81. Two-ray ground reflection model. http://www.isi.edu/nsnam/ns/doc/node219.html.

  82. Urgaonkar, R., Manfredi, V., & Ramanathan, R. (2013). Scalability analysis of grid-based multi-hop wireless networks. In 2013 fifth international conference on communication systems and networks (COMSNETS) (pp. 1–10). IEEE.

  83. Wang, W., & Liu, X. (2006). A framework for maximum capacity in multi-channel multi-radio wireless networks. In IEEE Consumer communications and networking conference.

  84. WING—Wireless Mesh Network for Next-Generation Internet, Italy. http://www.wing-project.org/.

  85. Yu, Y., Huang, Y., Zhao, B., & Hua, Y. (2008a). Throughput analysis of wireless mesh networks. In IEEE international conference on acoustics, speech and signal processing, 2008. ICASSP 2008 (pp. 3009–3012). IEEE.

  86. Yu, H., Mohapatra, P., & Liu, X. (2008b). Channel assignment and link scheduling in multi-radio multi-channel wireless mesh networks. Mobile Networks and Applications, 13(1–2), 169–185.

    Article  Google Scholar 

  87. Zhang, X., Jeong, S., Kunjithapatham, A., & Gibbs, S. (2010). Towards an elastic application model for augmenting computing capabilities of mobile platforms. In Mobile wireless middleware, operating systems, and applications (pp. 161–174).

  88. Zhang, M., Lai, J., Krishnamurthy, A., Peterson, L., & Wang, R. (2004). A transport layer approach for improving end-to-end performance and robustness using redundant paths. In Proceedings of the annual conference on USENIX annual technical conference (pp. 8–8). USENIX Association.

  89. Zhang, J., Wu, H., Zhang, Q., & Li, B. (2005). Joint routing and scheduling in multi-radio multi-channel multi-hop wireless networks. In 2nd international conference on broadband networks, 2005. BroadNets 2005, (pp. 631–640). IEEE.

  90. Zhao, X., Castelluccia, C., & Baker, M. (1998). Flexible network support for mobility. In Proceedings of the 4th annual ACM/IEEE international conference on mobile computing and networking (pp. 145–156). ACM.

Download references

Author information

Authors and Affiliations

  1. School of ECE, Purdue University, West Lafayette, IN, 47907, USA

    A. B. M. Alim Al Islam & Vijay Raghunathan

  2. Department of CSE, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh

    A. B. M. Alim Al Islam

Authors
  1. A. B. M. Alim Al Islam
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Vijay Raghunathan
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to A. B. M. Alim Al Islam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Islam, A.B.M.A.A., Raghunathan, V. SiAc: simultaneous activation of heterogeneous radios in high data rate multi-hop wireless networks. Wireless Netw 21, 2425–2452 (2015). https://doi.org/10.1007/s11276-015-0923-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-015-0923-2

Keywords