Skip to main content
SpringerLink
Log in

Performance analysis of wireless sensor networks using queuing networks

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Wireless Sensor Networks (WSNs) are autonomous wireless systems consists of a variety of collaborative sensor nodes forming a self-configuring network with or without any pre-defined infrastructure. The common challenges of a WSN are network connectivity, node mobility, energy consumption, data computation and aggregation at sensor nodes. In this paper we focus on intermittency in network connectivity due to mobility of sensor nodes. We propose a new mathematical model to capture a given entire WSN as is with intermittency introduced between the communication links due to mobility. The model involves open GI/G/1/N queuing networks whereby intermittency durations in communication links are captured in terms of mobility models. The analytical formulas for the performance measures such as average end-to-end delay, packet loss probability, throughput, and average number of hops are derived using the queuing network analyzer and expansion method for models with infinite- and finite-buffer nodes, respectively. For models with 2-state intermittency, we analyze the performance measures by classifying these models into three types: namely, model with intermittent reception, model with intermittent transmission and/or reception, and model with intermittent transmission. We extend the analysis to multi-state intermittency models. We demonstrate the gained insight of WSNs through extensive numerical results.

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

Access this article

Log in via an institution

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Allgood, G., Manges, W., & Smith, S. (1999a). It’s time for sensors to go wireless. Part 1: Technological underpinnings. Magazine: SENSOR.

  • Allgood, G., Manges, W., & Smith, S. (1999b). It’s time for sensors to go wireless; Part 2: Take a good technology and make it an economic success. Magazine: SENSOR.

  • Basagni, S., Carosi, A., & Petrioli, C. (2009). Algorithms and protocols for wireless sensor networks. New York: Wiley.

    Google Scholar 

  • Belle, T. B. (2004). Modularity and the evolution of software evolvability. PhD thesis, Computer Science, The University of New, Mexico Albuquerque, New Mexico.

  • Bhat, V. N. (1994). Renewal approximations of the switched Poisson processes and their applications to queueing systems. Journal of the Operational Research Society, 45, 345–353.

    Article  Google Scholar 

  • Bisnik, N., & Abouzeid, A. (2009). Queueing network models for delay analysis of multihop wireless ad hoc networks. Ad Hoc Networks, 7, 79–97.

    Article  Google Scholar 

  • Boric-Lubecke, O., & Lubecke, V. (2002). Wireless house calls: using communications technology for health care monitoring. IEEE Microwave Magazine, 3, 8–16.

    Article  Google Scholar 

  • Boukerche, A. (Ed.) (2009). Wiley series on parallel and distributed computing. Algorithms and protocols for wireless sensor networks. New York: Wiley.

    Google Scholar 

  • Britton, C., Warmack, R., Smith, S., Oden, P., Brown, G., Bryan, W., Clonts, L., Duncan, M., Emery, M., Ericson, M., Hu, Z., Jones, R., Moore, M., Moore, J., Rochelle, J., Threatt, T., Thundat, T., Turner, G., & Wintenberg, A. (1998). Mems sensors and wireless telemetry for distributed systems. In V. Varadan, P. McWhorter, R. Singer, & M. Vellekoop (Eds.), Proc. SPIE: Smart structures and materials 1998: smart electronics and MEMs (pp. 112–123).

    Chapter  Google Scholar 

  • Caldentey, R. (2001). Approximations for multi-class departure processes. Queueing Systems, 38, 205–212.

    Article  Google Scholar 

  • Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2, 483–502.

    Article  Google Scholar 

  • Cao, G., & Kesidis, G. (2006). Sensor network operations. New York: Wiley-Interscience.

    Google Scholar 

  • Castalia (2009). A simulation for WSNs. http://castalia.npc.nicta.com.au/.

  • Cruz, F., Duarte, A., & van Woensel, T. (2008). Buffer allocation in general single-server queueuing networks. Computers & Operations Research, 35, 3581–3598.

    Article  Google Scholar 

  • Fricke, R. (2001). Wireless sensor review final report. Technical Report AFRL-HE-WP-TR-2001-0167, United States Air Force Research Laboratory.

  • Ghasemi, A., & Esfahani, S. (2006). Exact probability of connectivity in one-dimensional ad hoc wireless networks. IEEE Communications Letters, 10, 251–253.

    Article  Google Scholar 

  • Gross, D., Shortle, J., Thompson, J., & Harris, C. (2008). Fundamentals of queueing theory (4th ed.). New Jersey: Wiley-Interscience.

    Book  Google Scholar 

  • Hayes, J., & Ganesh Babu, T. (2004). Modeling and analysis of telecommunications networks. Hoboken: Wiley-Interscience.

    Book  Google Scholar 

  • Horton, M., Broad, A., Grimmer, M., Pister, K., & Sastry, S. (2002). Deployment ready multimode micropower wireless sensor networks for intrusion dection, classification, and tracking. In E. Carapezza (Ed.), Proc. SPIE: sensors and command, control, communications, and intelligence (C3I) technologies for homeland defense and law enforcement (pp. 290–295).

    Chapter  Google Scholar 

  • Ipto, D. (2009). Senseit: Sensor information technology program. http://www.darpa.mil/ipto/programs/sensit/.

  • Jelenković, P. R. (2007). Scalability of wireless networks. IEEE/ACM Transactions on Networking, 15, 295–308.

    Article  Google Scholar 

  • Kerbache, L., & MacGregor Smith, J. (1987). The generalized expansion method for open finite queueing networks. European Journal of Operational Research, 32, 448–461.

    Article  Google Scholar 

  • Kim, N. K., & Chae, K. C. (2003). Transform-free analysis of GI/G/1/K queue through the decomposed Little’s formula. Computers and Operations Research, 30, 353–365.

    Article  Google Scholar 

  • Kuo, C.-C. (2011). Optimal assignment of resources to strengthen the weakest link in an uncertain environment. Annals of Operations Research, 186, 159–173.

    Article  Google Scholar 

  • Labetoulle, J., & Pujolle, G. (1980). Isolation method in a network of queues. IEEE Transactions on Software Engineering, 6, 373–381.

    Article  Google Scholar 

  • Li, Y., Thai, M., & Wu, W. (eds.) (2008). Wireless sensor networks and applications signals and communication technology. Berlin: Springer.

    Google Scholar 

  • Ma, Y., & Aylor, J. H. (2004). System lifetime optimization for heterogeneous sensor networks with a hub-spoke topology. IEEE Transactions on Mobile Computing, 3, 286–294.

    Article  Google Scholar 

  • Mainwarning, M., Polastre, J., Szewczyk, R., Culler, D., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In Proceedings of the 1st ACM international workshop on wireless sensor networks and applications, Atlanta, Georgia, USA (pp. 88–97).

    Chapter  Google Scholar 

  • McDonald, A. B., & Znati, T. (1999). A mobility-based framework for adaptive clustering in wireless ad hoc networks. IEEE Journal on Selected Areas in Communications, 17, 1466–1487.

    Article  Google Scholar 

  • Mohimani, G. H., Ashtiani, F., Javanmard, A., & Hamdi, M. (2009). Mobility modeling, spatial traffic distribution, and probability of connectivity for sparse and dense vehicular ad hoc networks. IEEE Transactions on Vehicular Technology, 58, 1998–2007.

    Article  Google Scholar 

  • Perry, D., Stadje, W., & Zacks, S. (2002). Boundary crossing for the difference of two ordinary or compound Poisson processes. Annals of Operations Research, 113, 119–132.

    Article  Google Scholar 

  • Petriu, E., Georganas, N., Petriu, D., Makrakis, D., & Groza, V. (2000). Sensor-based information appliances. IEEE Instrumentation and Measurement Magazine, 3, 31–35.

    Article  Google Scholar 

  • Roy, R. R. (2011). Handbook of mobile ad hoc networks for mobility models. Berlin: Springer.

    Book  Google Scholar 

  • Schoeneman, J., Smartt, H., & Hofer, D. (2000). Wipp transparency project—container tracking and monitoring demonstration using the authenticated tracking and monitoring system (ATMS). Proc. waste management 2000 (UM2K) conference, Tucson, AZ, USA. Washington: US Department of Energy.

    Google Scholar 

  • Xu, N., Rangwala, S., Chintalapudi, K., Ganesan, D., Broad, A., Govindan, R., & Estrin, D. (2004). A wireless sensor network for structural monitoring. In Proceedings of the 2nd international conference on embedded networked sensor systems, Baltimore, MD, USA (pp. 13–24).

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Central Arkansas, Conway, AR, 72035, USA

    R. B. Lenin

  2. Engineering Power Systems–Power Generation, ABB India Corporate Research Center, Bangalore, India

    S. Ramaswamy

Authors
  1. R. B. Lenin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Ramaswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. B. Lenin.

Appendix A: QNA and expansion method

Appendix A: QNA and expansion method

For completeness purposes, we present the QNA and expansion methods here. Note that the computational complexity of both the QNA and expansion method is O(M 2), where M is the number of sensor nodes in the network.

1.1 A.1 QNA

The QNA is an approximation algorithm developed at Bell Laboratories to calculate the average queuing delay at each node of open queuing networks without intermittency and with large number of infinite buffer nodes.

figure a

1.2 A.2 Expansion method

The expansion method calculates the average delay at each node of open queuing networks without intermittency and with large number of finite buffer nodes. In this approach, an artificial node is added to each finite buffer node. Whenever a node’s buffer is full, the packet is routed to the associated artificial node. After incurring a random/deterministic delay, the packet attempts to rejoin the buffer and if the buffer is full, it is routed back to the artificial node. This process continues for the packets in the artificial node until the artificial node is empty.

figure b

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lenin, R.B., Ramaswamy, S. Performance analysis of wireless sensor networks using queuing networks. Ann Oper Res 233, 237–261 (2015). https://doi.org/10.1007/s10479-013-1503-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-013-1503-4

Keywords

Search

Navigation