Abstract
Limited memory capacity is one of the major constraints in Delay Tolerant Wireless Sensor Networks. Efficient management of the memory is critical to the performance of the network. This paper proposes a novel buffer management algorithm, SmartGap, a Quality of Information (QoI) targeted buffer management algorithm. That is, in a wireless sensor network that continuously measures a parameter which changes over time, such as temperature, the value of a single packet is governed by an estimation of its contribution to the recreation of the original signal. Attractive features of SmartGap include a low computational complexity and a simplified reconstruction of the original signal. An analysis and simulations in which the performance of SmartGap is compared with the performance of several commonly used buffer management algorithms in wireless sensor networks are provided in the paper. The simulations suggest that SmartGap indeed provides significantly improved QoI compared the other evaluated algorithms.
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References
Luo, C.-J., Zhou, M.-T., Cao, Z.-Y.: Disruption-Tolerant Wireless Sensor Networks for Wind Tunnel Monitoring. In: International Conference on Apperceiving Computing and Intelligence Analysis, pp. 408–411 (2008)
Pöttner, W.-B., Büsching, F., von Zengen, G., Wolf, L.: Data elevators: Applying the bundle protocol in delay tolerant wireless sensor networks. In: Mobile Adhoc and Sensor Systems (MASS), pp. 218–226 (2012)
Zennaro, M.: Wireless Sensor Networks for Development: Potentials and Open Issues. Ph.D. dissertation, KTH Royal Institute of Technology (2010)
Sachidananda, V., Khelil, A., Suri, N.: Quality of Information in Wireless Sensor Networks: A Survey. ICIQ 1, 1–15 (2010)
Liu, C., Wu, K., Pei, J.: An energy-efficient data collection framework for wireless sensor networks by exploiting spatiotemporal correlation. IEEE Transactions on Parallel and Distributed Systems 18(7), 1010–1023 (2007)
Humber, G., Ngai, E.C.-H.: Quality-Of-Information Aware Data Delivery for Wireless Sensor Networks: Description and Experiments. In: IEEE Wireless Communication and Networking Conference, pp. 1–6 (April 2010)
Alippi, C., Anastasi, G., Di Francesco, M., Roveri, M.: An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks With Energy-Hungry Sensors. IEEE Transactions on Instrumentation and Measurement 59(2), 335–344 (2010)
Nasser, N., Karim, L., Taleb, T.: Dynamic Multilevel Priority Packet Scheduling Scheme for Wireless Sensor Network. IEEE Transactions on Wireless Communications 12(4), 1448–1459 (2013)
Lyu, M.R.: Congestion performance improvement in wireless sensor networks. In: 2012 IEEE Aerospace Conference, pp. 1–9 (March 2012)
Lindgren, A., Phanse, K.K.: Evaluation of Queueing Policies and Forwarding Strategies for Routing in Intermittently Connected Networks. In: 1st International Conference on Communication Systems Software & Middleware, pp. 1–10. IEEE (2006)
Scherfke, S., Lünsdorf, O.: SimPy - Discrete Event Simulation for Python (2014), http://simpy.readthedocs.org/
Lindgren, A., Doria, A., Davies, E., Grasic, S.: Probabilistic Routing Protocol for Intermittently Connected Networks. RFC 6693 (Experimental), Internet Engineering Task Force (August 2012), http://www.ietf.org/rfc/rfc6693.txt
Spyropoulos, T., Member, S., Psounis, K.: Efficient Routing in Intermittently Connected Mobile Networks: The Multiple-Copy Case. EEE/ACM Transactions on Networking 16(1), 77–90 (2008)
Söderman, P.: UPS data set, figshare (May 2014), http://figshare.com/articles/UPS_data_set/1018702
Söderman, P.: Window data set, figshare (May 2014), http://figshare.com/articles/Window_data_set/1018703
Söderman, P.: Garden data set figshare (May 2014), http://figshare.com/articles/Garden_data_set/1018700
Söderman, P.: Ocean data set, figshare (May 2014), http://figshare.com/articles/Ocean_data_set/1018701
NOAA, National Data Buoy Center (2008), http://www.ndbc.noaa.gov/
Luo, C., Wu, F., Sun, J., Chen, C.: Compressive data gathering for large-scale wireless sensor networks. IEEE Transactions on Mobile Computing and Networking (800), 145–156 (2009)
Imielinski, T., Korth, H.F.: Dynamic Source Routing in Ad Hoc Wireless Networks. In: Mobile Computing, pp. 153–181 (1996)
Hempstead, M., Lyons, M.J., Brooks, D., Wei, G.-Y.: Survey of Hardware Systems for Wireless Sensor Networks. Journal of Low Power Electronics 4(1), 11–20 (2008)
Vuran, M.C., Akan, O.B., Akyildiz, I.F.: Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks 45(3), 245–259 (2004)
Al-Karaki, I., UI-Mustafa, R., Kamal, A.: Data aggregation in wireless sensor networks - exact and approximate algorithms. In: 2004 Workshop on High Performance Switching and Routing, HPSR, pp. 241–245 (2004)
Camp, T., Boleng, J., Davies, V.: A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2(5), 483–502 (2002)
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Söderman, P., Grinnemo, KJ., Hidell, M., Sjödin, P. (2015). Mind the SmartGap: A Buffer Management Algorithm for Delay Tolerant Wireless Sensor Networks. In: Abdelzaher, T., Pereira, N., Tovar, E. (eds) Wireless Sensor Networks. EWSN 2015. Lecture Notes in Computer Science, vol 8965. Springer, Cham. https://doi.org/10.1007/978-3-319-15582-1_7
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DOI: https://doi.org/10.1007/978-3-319-15582-1_7
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