Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Wireless Networks
Erschienen in: Wireless Networks 7/2019

24.01.2019

Minimum cost event driven WSN with spatial differentiated QoS requirements

verfasst von: Debanjan Sadhukhan, Seela Veerabhadreswara Rao

Erschienen in: Wireless Networks | Ausgabe 7/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In wireless sensor networks applications like rare-event detection, maximizing lifetime, minimizing end-to-end delay, and minimizing the network cost, are some of the most important quality of service requirements. In applications like disastrous or fire event detection, if an event is detected very close to the center facility, the event information should reach to the base-station much faster than an event detected far away. In this work, we are interested to find a minimum cost network for such applications. A stochastic approach is used to find the minimum cost network for given lifetime requirement and spatial differentiated delay constraints. We use Monte-Carlo simulations for validating our analysis. In order to show the effectiveness of our approach, we use network simulator-2 simulations.
Vorheriger Artikel 3D UAV placement and user association in software-defined cellular networks
Nächster Artikel Analysis of optimal threshold selection for spectrum sensing in a cognitive radio network: an energy detection approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
The e2e delay of a node is defined as the average time a packet takes from the node to reach the base-station. Moreover, the e2e delay of the network is defined as the maximum e2e delay encountered by any node in the network.
 
2
In this paper we assume that solving the problem of minimum cost network is as same as finding the critical sensor density because the number of nodes is directly proportional to the overall deplyment cost of the network.
 
3
This kind of deployment can be justified when the sensor nodes are air-dropped from a plane in a hostile environment.
 
4
Note that, a sensor node may not directly communicate to base-station, but multi-hop communication can be used to send the data-packet to he base-station.
 
5
Note that, \(t_D\) denotes the average transmission delay without accounting the randomness involved in wireless channel.
 
6
The corresponding expected e2e delay associated with a sensor density is estimated using the method described Sect. 3.1.
 
7
The CSD is the average number of sensors present in each \({\mathrm{m}}^2\) area.
 
Literatur
1.
AlSkaif, T., Bellalta, B., Zapata, M. G., & Ordinas, J. M. B. (2017). Energy efficiency of mac protocols in low data rate wireless multimedia sensor networks: A comparative study. Ad Hoc Networks, 56, 141–157.CrossRef
2.
Biswas, S., & Morris, R. (2005). Exor: Opportunistic multi-hop routing for wireless networks. ACM SIGCOMM Computer Communication Review, 35(4), 133–144.CrossRef
3.
Cai, H., Jia, X., & Sha, M. (2011). Critical sensor density for partial connectivity in large area wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 7(4), 35.CrossRef
4.
Chen, X., Ma, M., & Liu, A. (2017). Dynamic power management and adaptive packet size selection for iot in e-healthcare. Computers and Electrical Engineering, 65, 357–375.CrossRef
5.
Chen, Z., Ma, M., Liu, X., Liu, A., & Zhao, M. (2017). Reliability improved cooperative communication over wireless sensor networks. Symmetry, 9(10), 209.CrossRef
6.
Dorling, K., Messier, G. G., Valentin, S., & Magierowski, S. (2015). Minimizing the net present cost of deploying and operating wireless sensor networks. IEEE Transactions on Network and Service Management, 12(3), 511–525.CrossRef
7.
Ghaffarzadeh, H., & Doustmohammadi, A. (2014). Two-phase data traffic optimization of wireless sensor networks for prolonging network lifetime. Wireless Networks, 20(4), 671–679.CrossRef
8.
Hammoudeh, M., Al-Fayez, F., Lloyd, H., Newman, R., Adebisi, B., Bounceur, A., et al. (2017). A wireless sensor network border monitoring system: Deployment issues and routing protocols. IEEE Sensors Journal, 17(8), 2572–2582.CrossRef
9.
Huang, M., Liu, A., Wang, T., & Huang, C. (2018). Green data gathering under delay differentiated services constraint for internet of things. Wireless Communications and Mobile Computing
10.
Jang, B., Lim, J. B., & Sichitiu, M. L. (2008). As-mac: An asynchronous scheduled mac protocol for wireless sensor networks. In Proceedings of the 5th IEEE international conference on mobile ad hoc and sensor systems (pp. 434–441). IEEE.
11.
Jang, U., Lee, S., & Yoo, S. (2012). Optimal wake-up scheduling of data gathering trees for wireless sensor networks. Journal of Parallel and Distributed Computing, 72(4), 536–546.CrossRef
12.
Kim, J., Lin, X., Shroff, N., & Sinha, P. (2010). Minimizing delay and maximizing lifetime for wireless sensor networks with anycast. IEEE/ACM Transactions on Networking, 18(2), 515–528.CrossRef
13.
Kim, J., Lin, X., & Shroff, N. (2011). Optimal anycast technique for delay-sensitive energy-constrained asynchronous sensor networks. IEEE/ACM Transactions on Networking, 19(2), 484–497.CrossRef
14.
Lazos, L., & Poovendran, R. (2006). Stochastic coverage in heterogeneous sensor networks. ACM Transactions on Sensor Networks (TOSN), 2(3), 325–358.CrossRef
15.
Liu, A., Chen, Z., & Xiong, N. N. (2018a). An adaptive virtual relaying set scheme for loss-and-delay sensitive wsns. Information Sciences, 424, 118–136.MathSciNetCrossRef
16.
Liu, S., Fan, K. W., & Sinha, P. (2009). Cmac: An energy-efficient mac layer protocol using convergent packet forwarding for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(4), 29.CrossRef
17.
Liu, Y., Ota, K., Zhang, K., Ma, M., Xiong, N., Liu, A., et al. (2018b). Qtsac: An energy-efficient mac protocol for delay minimization in wireless sensor networks. IEEE Access, 6, 8273–8291.CrossRef
18.
Luo, H., Wu, K., Guo, Z., Gu, L., & Ni, L. M. (2012). Ship detection with wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(7), 1336–1343.CrossRef
19.
Mitton, N., Simplot-Ryl, D., & Stojmenovic, I. (2009). Guaranteed delivery for geographical anycasting in wireless multi-sink sensor and sensor-actor networks. In Proceedings of IEEE INFOCOM 2009 (pp. 2691–2695). IEEE.
20.
Paillard, G., Ravelomananana, V. (2008). Limit theorems for degree of coverage and lifetime in large sensor networks. In Proceedings of the 27th conference on computer communications. IEEE.
21.
Rossi, M., & Zorzi, M. (2007). Integrated cost-based mac and routing techniques for hop count forwarding in wireless sensor networks. IEEE Transactions on Mobile Computing, 6(4), 434–448.CrossRef
22.
Rossi, M., Zorzi, M., & Rao, R. R. (2008). Statistically assisted routing algorithms (sara) for hop count based forwarding in wireless sensor networks. Wireless Networks, 14(1), 55–70.CrossRef
23.
Salim, A., Osamy, W., & Khedr, A. M. (2014). Ibleach: Intra-balanced leach protocol for wireless sensor networks. Wireless Networks, 20(6), 1515–1525.CrossRef
24.
Sun, Z., Wang, P., Vuran, M. C., Al-Rodhaan, M. A., Al-Dhelaan, A. M., & Akyildiz, I. F. (2011). Bordersense: Border patrol through advanced wireless sensor networks. Ad Hoc Networks, 9(3), 468–477.CrossRef
25.
Tang, J., Liu, A., Zhao, M., & Wang, T. (2018). An aggregate signature based trust routing for data gathering in sensor networks. Security and Communication Networks
26.
Yadav, S., & Yadav, R. S. (2016). A review on energy efficient protocols in wireless sensor networks. Wireless Networks, 22(1), 335–350.CrossRef
27.
Yang, T., Mu, D., Hu, W., & Zhang, H. (2014). Energy-efficient border intrusion detection using wireless sensors network. EURASIP Journal on Wireless Communications and Networking, 1, 46.CrossRef
28.
Yen, L., Yu, C., & Cheng, Y. (2006). Expected k-coverage in wireless sensor networks. Ad Hoc Networks, 4(5), 636–650.CrossRef
29.
Zhang, H., & Hou, J. (2004). On deriving the upper bound of alpha-lifetime for large sensor networks. In Proceedings of the 5th ACM international symposium on mobile ad hoc networking and computing (pp. 121–132). ACM.
30.
Zhang, H., & Hou, J. (2006). Is deterministic deployment worse than random deployment for wireless sensor network? In Proceedings of IEEE INFOCOM (pp. 1–13). IEEE.
31.
Zhao, Y., Wu, J., Li, F., & Lu, S. (2010). VBS: Maximum lifetime sleep scheduling for wireless sensor networks using virtual backbones. In Proceedings of IEEE INFOCOM (pp. 1–5). IEEE.
Metadaten
Titel
Minimum cost event driven WSN with spatial differentiated QoS requirements
verfasst von
Debanjan Sadhukhan
Seela Veerabhadreswara Rao
Publikationsdatum
24.01.2019
Verlag
Springer US
Erschienen in
Wireless Networks / Ausgabe 7/2019
Print ISSN: 1022-0038
Elektronische ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-018-01926-z

Weitere Artikel der Ausgabe 7/2019

Wireless Networks 7/2019 Zur Ausgabe

OriginalPaper

Traffic-aware auto-configuration protocol for service oriented low-power and lossy networks in IoT

OriginalPaper

Friendship-based cooperative jamming for secure communication in Poisson networks

OriginalPaper

R-DRA: a replication-based distributed randomized algorithm for data dissemination in connected vehicular networks

OriginalPaper

A probabilistic home-based routing scheme for delay tolerant networks

OriginalPaper

Auto-organization approach with adaptive frame periods for IEEE 802.15.4/zigbee forest fire detection system

OriginalPaper

Design of routing protocol for multi-sink based wireless sensor networks

Neuer Inhalt

    Bildnachweise