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Energy conservation in query driven wireless sensor networks

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Abstract

Network topology used to connect sensor nodes is important factor in wireless sensor networks (WSNs) that affect the energy usage of any network. Proper topology connection thereby reduces energy usage of network by reducing number of packet transmissions. Therefore node connection must be optimized in wireless sensor networks. Hence we introduce a new multiple tree based architecture of sensor network, comprising of heterogeneous nodes, to develop an energy efficient query processing WSN application. This article proposes Multi-tree and Multiple-tree algorithms for improving network parameters in the usual data collection WSN application scenarios. A multi-tree simply suggests a strategy where similar parameter type sensors are arranged in a tree based topology so as to reduce the energy consumption as well as the end to end delay in data communication. The Multiple-tree exemplifies a more effective way of query result propagation in data centric applications where delay requirements are not very stringent.

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References

  • Andreou P, Zeinalipour-Yazti D, Pamboris A, Chrysanthis PK, Samaras G (2011) Optimized query routing trees for wireless sensor networks. Inform Syst 36(2):267–291

    Article  Google Scholar 

  • Arici T, Gedik B, Altunbasak Y, Liu L (2003) PINCO: a pipelined in-network compression scheme for data collection in wireless sensor networks. In: 12th international conference on computer communications and networks, pp 539–544

  • Bilal K, Khalid O, Erbad A, Khan SU (2018) Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers. ComputNetw 130:94–120

    Google Scholar 

  • Chakraborty S, Nandi S, Karmakar S (2011) A tree-based local repairing approach for increasing lifetime of query driven WSN. In: 14th international conference on computational science and engineering (CSE). IEEE, pp 475–482

  • Da Silva RI, Macedo DF, Nogueira JMS (2014) Spatial query processing in wireless sensor networks—a survey. Inf Fusion 15:32–43

    Article  Google Scholar 

  • Daniele M, Ortolani M, Re GL (2007) A network protocol to enhance robustness in tree-based WSNs using data aggregation. In: International conference on mobile adhoc and sensor systems, (MASS 2007), IEEE, pp 1–4

  • Dehkordi SA, Farajzadeh K, Rezazadeh J, Farahbakhsh R, Sandrasegaran K, Dehkordi MA (2020) A survey on data aggregation techniques in IoT sensor networks. WirelNetw 26(2):1243–1263

    Google Scholar 

  • Dong S, Sarem M, Zhou W (2020) Distributed data gathering algorithm based on spanning tree. IEEE Syst J. https://doi.org/10.1109/JSYST.2020.2983002

    Article  Google Scholar 

  • Fariborzi H, Moghavvemi M (2009) EAMTR: energy aware multi-tree routing for wireless sensor networks. IET Commun 3(5):733–739

    Article  Google Scholar 

  • Ferrández-Pastor FJ, Mora H, Jimeno-Morenilla A, Volckaert B (2018) Deployment of IoT edge and fog computing technologies to develop smart building services. Sustainability 10(11):3832

    Article  Google Scholar 

  • Fitzgerald E, Pióro M, Tomaszewski A (2018) Energy-optimal data aggregation and dissemination for the internet of things. IEEE Internet Things J 5(2):955–969

    Article  Google Scholar 

  • Georgios C, Zeinalipour-Yazti D, Gunopulos D (2010) Minimum-hot-spot query trees for wireless sensor networks. In: Proceedings of the ninth ACM international workshop on data engineering for wireless and mobile access. ACM, pp 33–40

  • Gnawali O, Rodrigo F, Kyle J, David M, Philip L (2009) Collection tree protocol. In: Proceedings of the 7th ACM conference on embedded networked sensor systems, pp 1–14

  • Gong B, Jiang T (2011) A tree-based routing protocol in wireless sensor networks. In: International conference on electrical and control engineering (ICECE), pp 5729–5732

  • Hawbani A, Wang X, Karmoshi S, Wang L, Husaini N (2015) Sensors grouping hierarchy structure for wireless sensor network. Int J DistribSensNetw 11(8):650519

    Google Scholar 

  • Hawbani A, Wang X, Kuhlani H, Karmoshi S, Ghoul R, Sharabi Y, Torbosh E (2018) Sink-oriented tree based data dissemination protocol for mobile sinks wireless sensor networks. WirelNetw 24(7):2723–2734

    Google Scholar 

  • Imai S, Varela CA, Patterson S (2018) A performance study of geo-distributed iot data aggregation for fog computing. In: 2018 IEEE/ACM international conference on utility and cloud computing companion (UCC Companion). IEEE, pp 278–283

  • Jabeen F, Nawaz S (2015) In-network wireless sensor network query processors: State of the art, challenges and future directions. Inf Fusion 25:1–15

    Article  Google Scholar 

  • Jarník V (1930) O jistémproblémuminimálním [About a certain minimal problem], PráceMoravskéPřírodovědeckéSpolečnosti (in Czech) 6: 57–63

  • Jin Y, Wang L, Kim Y, Yang X (2008) EEMC: an energy efficient multi-level clustering algorithm for large-scale wireless sensor networks. Sci Direct ComputNetw 52:542–562

    MATH  Google Scholar 

  • Johansson T, Osipov E, Carr-Motyckova L (2008) Interference aware construction of multi- and convergecast trees in wireless sensor networks. In: Next generation teletraffic and wired/wireless advanced networking, lecture notes in computer science, vol 5174, pp 72–87

  • Joohwan K, Lin X, Shroff NB, Sinha P (2010) Minimizing delay and maximizing lifetime for wireless sensor networks with anycast. IEEE/ACM Trans Netw (TON) 18(2):515–528

    Article  Google Scholar 

  • Khan AW, Bangash JI, Ahmed A, Abdullah AH (2019) QDVGDD: query-driven virtual grid based data dissemination for wireless sensor networks using single mobile sink. WirelNetw 25(1):241–253

    Google Scholar 

  • Li Y, Chen H, Mo S, Liu H (2014) Optimal query-driven data forwarding for delay-sensitive wireless sensor networks. WirelPersCommun 77(1):41–62

    Google Scholar 

  • Roy NR, Chandra P (2020) Analysis of data aggregation techniques in WSN. In: Khanna A, Gupta D, Bhattacharyya S, Snasel V, Platos J, Hassanien A (eds) International conference on innovative computing and communications. Advances in intelligent systems and computing, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0324-5_48

  • Satyanarayanan M (2017) Edge computing: vision and challenges

  • Shi W, Cao J, Zhang Q, Li Y, Xu L (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637–646

    Article  Google Scholar 

  • Snigdh I, Saurabh S (2014) A multi tree based approach for performance analysis in hierarchical wireless sensor networks. Int J Digit InfWirelCommun (IJDIWC) 4(4):438–446

    Google Scholar 

  • Sohraby K, Minoli D, Znati T (2007) Wireless sensor networks: Technology, protocols and applications. Wiley, New York

    Book  Google Scholar 

  • Sun J-Z (2008) QoS aware query processing algorithm for wireless sensor networks. J Comput 3(11):32–41

    Google Scholar 

  • Wang J, Jee-Hyong K (2012) ViTAMin: a virtual backbone tree algorithm for minimal energy consumption in wireless sensor network routing. In: IEEE international conference on information networking (ICOIN), pp 144–149

  • Wang Q, Yang W (2007) Energy consumption model for power management in wireless sensor networks. In: 4th annual IEEE communications society conference on sensor, mesh and ad hoc communications and network (SECON)

  • Yin L, Liu C, Guo S, Yang Y (2020) Sparse random compressive sensing based data aggregation in wireless sensor networks. ConcurrComputPractExp 32(3):e4455

    Google Scholar 

  • Yingchi M, Xiaofang L, Yi L (2010) Workload based query routing tree algorithm in wireless sensor networks. In: International conference on computational intelligence and software engineering (CiSE), pp 1–4. https://doi.org/10.1109/ICISE.2010.5691166

  • Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3(4):366–379

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, India

    Itu Snigdh, Sagar S. Surani & Nilesh K. Sahu

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  1. Itu Snigdh
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  2. Sagar S. Surani
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  3. Nilesh K. Sahu
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Correspondence to Itu Snigdh.

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Snigdh, I., Surani, S.S. & Sahu, N.K. Energy conservation in query driven wireless sensor networks. Microsyst Technol 27, 843–851 (2021). https://doi.org/10.1007/s00542-020-05073-4

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