Resource and performance tradeoffs in delay-tolerant wireless networks

ABSTRACT

Wireless and mobile network technologies often impose severe limitations on the availability of resources, resulting in poor and often unsatisfactory performance of the commonly used wireless networking protocols. For instance, power and memory/storage constraints of miniaturized network nodes reduce the throughput capacity and increase the network latency. Through various approaches and technological advances, researchers attempt to somehow compensate for such hardware limitations. However, this is not always necessary. Sometimes, the required performance of such networks does not need to adhere to the level of services that would be required for performance-critical applications. For example, for some applications of sensor networks, minimal latency is not a critical factor and it could be traded off for a more limited resource, such as energy or throughput. Such networks are termed delay-tolerant networks. Thus, to reduce the energy expenditure, transmission range of such sensor nodes would be quite short, leading to network topologies in which the average number of neighbors of the network nodes is very small. If the sensor nodes are mobile, then most of the time a node has <u>no</u> neighbors; only infrequently another node migrates into its neighborhood. This means that the classical networking approach of store-and-forward would not work well, as there is nearly never an intact path between a source and a destination. Several routing protocols have been proposed for this type of networking environment, one example is the Shared Wireless Infostation Model (SWIM), where a packet propagates through the network by being copied (rather than forwarded) from a node to a node, as links are sporadically created. The goal is that one of the copies of the packet reaches the destination. SWIM is an example of the way that non-critical performance could be traded off for insufficient resources, such as the tradeoffs between energy, delay, storage, capacity, and processing complexity. In this paper, we examine some of these tradeoffs, exposing the ways in which resources could be saved by compromising on the level of performance, as to satisfy the particular limitations of network technologies.

REFERENCES

• 1: N. Bansal and Z. Liu, "Capacity, Delay and Mobility in Wireless Ad-Hoc Networks," IEEE INFOCOM'03, April 2003
• 2: Christian Bettstetter, Smooth is better than sharp: a random mobility model for simulation of wireless networks, Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems, p.19-27, July 2001, Rome, Italy [doi>10.1145/381591.381600]
• 3: S. Cui, R. Madan, A. J. Goldsmith, and S. Lall, "Energy-delay Tradeoff for Data Collection in Sensor Networks," ICC'05, May 2005
• 4: D. J. Goodman, J. Borràs, N.B. Mandayam, and R.D. Yates "INFOSTATIONS: A New System for Data and Messaging Services," IEEE VTC '97 2, 1997
• 5: Matthias Grossglauser , David N. C. Tse, Mobility increases the capacity of ad hoc wireless networks, IEEE/ACM Transactions on Networking (TON), v.10 n.4, p.477-486, August 2002 [doi>10.1109/TNET.2002.801403]
• 6: J.D. Herdtner, E.K.P. Chong, "Scaling Laws in Ad Hoc Wireless Networks," http://www.math.colostate.edu/~estep/doe_multiscale/slides/herdtner.pdf
• 7: Ramana Rao Kompella , Alex C. Snoeren, Practical lazy scheduling in sensor networks, Proceedings of the 1st international conference on Embedded networked sensor systems, November 05-07, 2003, Los Angeles, California, USA [doi>10.1145/958491.958524]
• 8: P. Nain, D. Towsley, B. Liu, and Z. Liu, "Properties of Random Direction Models," INRIA technical report RR-5284, July 2004
• 9: C. Perkins, Ad Hoc Networking, Addison-Wesley, 2001 %, Upper Saddle River, NJ
• 10: R. Shah, S. Roy, S. Jain, and W. Brunette, "Data MULEs: Modeling a three-tier architecture for sparse sensor networks," IEEE SNPA Workshop, May 2003
• 11: T. Small, "Modeling Trade-offs in Networks with Intermittent Connectivity," Cornell University PhD thesis, August 2005
• 12: Tara Small , Zygmunt J. Haas, The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way), Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing, June 01-03, 2003, Annapolis, Maryland, USA [doi>10.1145/778415.778443]
• 13: A. Vahdat and D. Becker, "Epidemic routing for partially-connected ad hoc networks," Technical report, Duke University, 2000
• 14: W. Zhao , M. Ammar , E. Zegura, A message ferrying approach for data delivery in sparse mobile ad hoc networks, Proceedings of the 5th ACM international symposium on Mobile ad hoc networking and computing, May 24-26, 2004, Roppongi Hills, Tokyo, Japan [doi>10.1145/989459.989483]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.0 General

General Terms:
Algorithms, Design, Performance

Keywords:
SWIM, carry and forward, data MULEs, delay-tolerant networks, epidemic routing, message ferrying, resource tradeoffs, sensor networks, shared wireless infostation model, sparse networks, store and forward