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2023 | OriginalPaper | Chapter

An Overview of Data Aggregation Techniques with Special Sensing Intelligent Device Selection Approaches

Authors : G. Nirmala, C. D. Guruprakash

Published in: ICDSMLA 2021

Publisher: Springer Nature Singapore

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Abstract

The transfer of the data from one Sensing Intelligent Device (SID) to the other sensing intelligent devices (SIDs). The transmission of data happens from one area to a different area in a Sensing Intelligent Network (SIN). The data packets receiver is usually the destination Control Center. Various applications of SIN are disaster monitoring, war plane detection and sending the environmental hazards information along with various other applications. Due to the constraints which are involved in SIN like low battery, low transmission range it is important to avoid duplication of data as well as collect the data from the SIDs in an intelligent fashion. Each SIN has huge number of SIDs and hence they transmit data which is similar in nature and hence this causes the SIDs to choke and become irrelevant SIDs over a period. The number of such chocking instances can be reduced in the SIN with the help of data aggregation, intelligent selection of centralized SID for a specific SIN area with the help of machine learning techniques. This paper lists out various data aggregation techniques which are available in the literature and how they can be used to improve the relevant ratio value for SIN.

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Cam H, Ozdemir S, Nair P, Muthuavinashiappan D (2003) ESPDA: energy-efficient and secure pattern based data aggregation for wireless sensor networks. In: Proceedings of IEEE sensor-the second IEEE conference on sensors, Toronto, Canada, 22–24 Oct, 2003, pp 732–736

Jose J, Jose J, Muhammed Ilyas H (2016) Symmetric concealed data aggregation techniques in wireless sensor networks using privacy homomorphism: a review. In: 2016 International conference on information science (ICIS), Kochi, pp 275–280

Nagesh R, Raga S, Mishra S (2019) Elimination of redundant data to enhance wireless sensor network performance using multi level data aggregation technique. In: 2019 10th International conference on computing, communication and networking technologies (ICCCNT), Kanpur, India, pp 1–5

Rahman H, Ahmed N, Hussain MI (2016) A hybrid data aggregation scheme for provisioning Quality of Service (QoS) in Internet of Things (IoT). In: 2016 cloudification of the Internet of Things (CIoT), Paris, pp 1–5.https://doi.org/10.1109/CIOT.2016.7872917

Jhuang Y, Lin T, Nguyen TD, Chu S, Liu B, Pham V (2017) A new overlap circle technique for reducing data aggregation time in wireless sensor networks. In: 2017 International conference on system science and engineering (ICSSE), Ho Chi Minh City, pp 546–549. https://doi.org/10.1109/ICSSE.2017.8030934

Sun B, Ma C, Jin X, Ma Z (2016) Data aggregation scheme based on compressive sensing in wireless sensor network. In: 2016 9th International symposium on computational intelligence and design (ISCID), Hangzhou, pp 453–456. https://doi.org/10.1109/ISCID.2016.2112

Atoui I, Makhoul A, Tawbe S, Couturier R, Hijazi A (2016) Tree-based data aggregation approach in periodic sensor networks using correlation matrix and polynomial regression. In: 2016 IEEE International conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) and 15th international symposium on distributed computing and applications for business engineering (DCABES), Paris, pp 716–723. https://doi.org/10.1109/CSE-EUC-DCABES.2016.267

Al-Tabbakh SM (2017) Novel technique for data aggregation in wireless sensor networks. In: 2017 International conference on Internet of Things, embedded systems and communications (IINTEC), Gafsa, pp 1–8. https://doi.org/10.1109/IINTEC.2017.8325904

Bhatti J, Waraich S (2015) Trends towards optimistic data aggregation techniques. In: 2015 International conference on innovations in information, embedded and communication systems (ICIIECS), Coimbatore, pp 1–7. https://doi.org/10.1109/ICIIECS.2015.7193086

10.

Brindha N, Vanitha S (2017) The data aggregation approach to enhance the lifetime of wireless sensor network. In: 2017 International conference on innovations in green energy and healthcare technologies (IGEHT), pp 1–5. https://doi.org/10.1109/IGEHT.2017.8094108

11.

Zhou F, Chen Z, Guo S, Li J (2016) Maximizing lifetime of data-gathering trees with different aggregation modes in WSNs. IEEE Sens J 16(22):8167–8177. https://doi.org/10.1109/JSEN.2016.2609152

12.

Chauhan R, Gupta V (2012) Energy efficient sleep scheduled clustering & spanning tree based data aggregation in wireless sensor network. In: 2012 1st International conference on recent advances in information technology (RAIT), Dhanbad, pp 536–541. https://doi.org/10.1109/RAIT.2012.6194617

13.

Jain S, Bharot N (2019) K Medoids based clustering algorithm with minimum spanning tree in wireless sensor network. In: 2019 International conference on communication and electronics systems (ICCES), Coimbatore, India, pp 1771–1776. DOI: https://doi.org/10.1109/ICCES45898.2019.9002548

14.

Kamthe P, Wagh SJ (2016) Tree based aggregation for preventing compromised node's intention in WSN. In: 2016 International conference on automatic control and dynamic optimization techniques (ICACDOT), Pune, pp 996–1000. https://doi.org/10.1109/ICACDOT.2016.7877736

15.

Inanlou H, Shourmasti KS, Marjani H, Rezaei NA (2010) FFDA: a tree based energy aware data aggregation protocol in wireless sensor networks. In: 2010 International conference on wireless information networks and systems (WINSYS), Athens, pp 1–5

16.

Kamble S, Dhope T (2016) Reliable routing data aggregation using efficient clustering in WSN. In: 2016 International conference on advanced communication control and computing technologies (ICACCCT), Ramanathapuram, pp 246–250. https://doi.org/10.1109/ICACCCT.2016.7831640

17.

Fakhr FY, Roustaei R, Movaghar A (2010) The addition of data aggregation to non cluster based SPEED routing algorithm while keeping the functionality of available techniques in order to increase QoS, In: 2010 2nd International conference on computer engineering and technology, Chengdu, pp V2-696–V2-700. https://doi.org/10.1109/ICCET.2010.5485711

18.

Bendechache M, Le-Khac N, Kechadi M (2016) Hierarchical aggregation approach for distributed clustering of spatial datasets. In: 2016 IEEE 16th International conference on data mining workshops (ICDMW), Barcelona, pp 1098–1103. https://doi.org/10.1109/ICDMW.2016.0158

19.

Idrees AK, Al-Yaseen WL, Taam MA, Zahwe O (2018) Distributed data aggregation based modified K-means technique for energy conservation in periodic wireless sensor networks. In: 2018 IEEE Middle East and North Africa communications conference (MENACOMM), Jounieh, pp 1–6. https://doi.org/10.1109/MENACOMM.2018.8371007

20.

Wu O, Hu W, Maybank SJ, Zhu M, Li B. Efficient clustering aggregation based on data fragments. IEEE Trans Systems, Man, Cybern Part B (Cybern) 42(3):913–926. https://doi.org/10.1109/TSMCB.2012.2183591

21.

Dhivya S, Kumar SM (2013) Cluster based approximate data collection for wireless sensor networks. In: 2013 International conference on computer communication and informatics, Coimbatore, pp 1–4. https://doi.org/10.1109/ICCCI.2013.6466248

22.

Kader MSF, Hossain MM, Ghose S, Zafreen KR (2016) Selection of better strategy for self organized data aggregation techniques using evolutionary game theory. In: 2016 International conference on innovations in science, engineering and technology (ICISET), Dhaka, pp 1–4. https://doi.org/10.1109/ICISET.2016.7856501

23.

Ahmed RZ, Biradar RC (2015) Data aggregation for pest identification in coffee plantations using WSN: a hybrid model. In: 2015 International conference on computing and network communications (CoCoNet), Trivandrum. https://doi.org/10.1109/CoCoNet.2015.7411179

24.

Suryawanshi R (2016) H-WSN with maximized QoS using secure data aggregation. In: 2016 2nd International conference on contemporary computing and informatics (IC3I), Noida, pp 149–154. https://doi.org/10.1109/IC3I.2016.7917950

25.

Kumar N, Kaur S (2016) Performance evaluation of distance based angular clustering algorithm (DACA) using data aggregation for heterogeneous WSN. In: 2016 International conference on computation of power, energy information and communication (ICCPEIC), Chennai, pp 097–101. https://doi.org/10.1109/ICCPEIC.2016.7557231

26.

Ashouri M, Yousefi H, Hemmatyar AMA, Movaghar A (2012) FOMA: flexible overlay multi-path data aggregation in wireless sensor networks. In: 2012 IEEE symposium on computers and communications (ISCC), Cappadocia, pp 000508–000511. https://doi.org/10.1109/ISCC.2012.6249346

27.

Manjula SH et al (2009) Multi-hop routing for multi-stationed wireless sensor networks. In: 2009 IEEE International advance computing conference, pp 806–811. https://doi.org/10.1109/IADCC.2009.4809118

28.

Fitzgerald E, Pióro M, Tomaszwski A (2018) Energy-optimal data aggregation and dissemination for the Internet of Things. IEEE Internet Things J 5(2):955–969. https://doi.org/10.1109/JIOT.2018.2803792CrossRef

29.

Bista R, Kim Y, Chang J (2009) A new approach for energy-balanced data aggregation in wireless sensor networks. In: 2009 Ninth IEEE international conference on computer and information technology, Xiamen, pp 9–15. https://doi.org/10.1109/CIT.2009.33

30.

Sruthi SS, Geethakumari G (2016) An efficient secure data aggregation technique for Internet of Things network: an integrated approach using DB-MAC and multi-path topology. In: 2016 IEEE 6th International conference on advanced computing (IACC), Bhimavaram, pp 599–603. https://doi.org/10.1109/IACC.2016.116

31.

He W, Yang S, Yang L (2013) NMCA: neighbour-aware multi-path clustering aggregation in wireless sensor networks. In: 2013 10th IEEE international conference on networking, sensing and control (ICNSC), Evry, pp 798–803. https://doi.org/10.1109/ICNSC.2013.6548840

32.

Chen Z, Shin KG (2008) OPAG: opportunistic data aggregation in wireless sensor networks. In: 2008 Real-time systems symposium, Barcelona, pp 345–354. https://doi.org/10.1109/RTSS.2008.21

33.

Shao Y (2020) Combining routing and virtual machine selection algorithm based on multi-hop cloud wireless access network of mobile edge computing. J Eng 8:706–716. https://doi.org/10.1049/joe.2020.0002

34.

Luo M, Hou X, Yang J (2020) Surface optimal path planning using an extended Dijkstra algorithm. IEEE Access 8:147827–147838. https://doi.org/10.1109/ACCESS.2020.3015976CrossRef

35.

Chen L, Hu Y, Sun Y, Liu H, Lv B, Chen J (2020) An underwater layered protocol based on cooperative communication for underwater sensor network. In: 2020 Chinese control and decision conference (CCDC), Hefei, China, pp 2014–2019. https://doi.org/10.1109/CCDC49329.2020.9164029

36.

Liu Q, Zeng H, Chen M, Liu L (2020) Cost minimization in multi-path communication under throughput and maximum delay constraints. In: IEEE INFOCOM 2020—IEEE conference on computer communications, Toronto, ON, Canada, pp 2263–2272. https://doi.org/10.1109/INFOCOM41043.2020.9155519

37.

Mohan BA, Sarojadevi H (2016) A hybrid approach for data collection using multiple mobile nodes in WSN (HADMMN). In: 2016 IEEE international conference on recent trends in electronics, information and communication technology (RTEICT), Bangalore, pp 711–714. https://doi.org/10.1109/RTEICT.2016.7807916

38.

Akila V, Sheela T (2017) Preserving data and key privacy in data aggregation for wireless sensor networks. In: 2017 2nd International conference on computing and communications technologies (ICCCT), pp 282–287. https://doi.org/10.1109/ICCCT2.2017.7972286

39.

Kavya B, Vani V, Roopa H (2020) Efficient cluster head rotation based on residual energy to extend network lifetime. In: 2020 Second international conference on inventive research in computing applications (ICIRCA), Coimbatore, India, pp 774–777. https://doi.org/10.1109/ICIRCA48905.2020.9182843

40.

Lin D, Gao L, Min W. A social welfare theory-based energy-efficient cluster head election scheme for WSNs. IEEE Syst J. https://doi.org/10.1109/JSYST.2020.3010868

41.

Ye L, Zhang Y, Li Y, Han S. A dynamic cluster head selecting algorithm for UAV Ad Hoc networks. In: International wireless communications and mobile computing (IWCMC), Limassol, Cyprus, pp 225–228. https://doi.org/10.1109/IWCMC48107.2020.9148458

42.

Bensaid R, Bensaid M, Boujemaa H (2020) Fuzzy C-means based clustering algorithm in WSNs for IoT applications. In: 2020 International wireless communications and mobile computing (IWCMC), Limassol, Cyprus, pp 126–130. https://doi.org/10.1109/IWCMC48107.2020.9148077

43.

Poluru RK, Ramasamy LK (2020) Optimal cluster head selection using modified rider assisted clustering for IoT. IET Commun 14(13):2189–2201. https://doi.org/10.1049/iet-com.2020.0236

44.

Duarte FL, De Lamare RC. C-RAN-type cluster-head-driven UAV relaying with recursive maximum minimum distance. IEEE Commun. https://doi.org/10.1109/LCOMM.2020.3009845

45.

Sinha S, Parsai MP (2020) Cluster head selection in leach with minimum risk of failure. In: 2020 5th International conference on communication and electronics systems (ICCES), Coimbatore, India, pp 486–489. https://doi.org/10.1109/ICCES48766.2020.9137980

46.

Panchal A, Singh L, Singh RK (2020) RCH-LEACH: residual energy based cluster head selection in LEACH for wireless sensor networks. In: 2020 International conference on electrical and electronics engineering (ICE3), Gorakhpur, India, pp 322–325. https://doi.org/10.1109/ICE348803.2020.9122962

47.

Choudhury N, Matam R, Mukherjee M, Lloret J, Kalaimannan E (2020) NCHR: a non-threshold-based cluster-head rotation scheme for IEEE 802.15.4 cluster-tree networks. IEEE Internet of Things J. https://doi.org/10.1109/JIOT.2020.3003320

48.

Prasad NA, Guruprakash CD (2018) An ephemeral investigation on energy proficiency mechanisms in WSN. In: Proceedings of the 2017 3rd international conference on applied and theoretical computing and communication technology, ICATccT 2017. Institute of Electrical and Electronics Engineers Inc., pp 180–185. https://doi.org/10.1109/ICATCCT.2017.8389129

49.

Achyutha Prasad N, Guruprakash CD (2018) A relay node scheme for energy redeemable and network lifespan enhancement. In: 4th International conference on applied and theoretical computing and communication technology technically sponsored by IEEE Bangalore section IEEE. ISBN: 978-1-5386-7706-3, Sep 2018

50.

Achyutha Prasad N, Guruprakash CD (2019) A relay node scheme of energy redeemable and network lifespan enhancement for wireless sensor networks and its analysis with standard channel models. Int J Inno Technol Explor Eng 8:605–612

51.

Achyutha Prasad N, Guruprakash CD (2019) A relay mote wheeze for energy saving and network longevity enhancement in WSN. Int J Recent Technol Eng 8:8220–8227. https://doi.org/10.35940/ijrte.C6707.098319CrossRef

52.

Achyutha Prasad N, Guruprakash CD (2019) A two hop relay battery aware mote scheme for energy redeemable and network lifespan improvement in WSN. Int J Eng Adv Technol 9:4785–4791. https://doi.org/10.35940/ijeat.A2204.109119CrossRef

53.

Achyutha Prasad N Dr, Guruprakash CD Dr (2020) Delay optimization and energy balancing algorithm for improving network lifetime in fixed wireless sensor networks. In: Emerging trends in engineering and technology, Ch 3, first edn. Mangalam Publications, Delhi, pp 26–51

54.

Rekha VS, Siddaraju (2019) An ephemeral analysis on network lifetime improvement techniques for wireless sensor networks. Int J Inno Technol Explor Eng 8(9):810–814. 2278-3075

Title: An Overview of Data Aggregation Techniques with Special Sensing Intelligent Device Selection Approaches
Authors: G. Nirmala
C. D. Guruprakash
Publisher: Springer Nature Singapore
Book: ICDSMLA 2021
Print ISBN: 978-981-19-5935-6

Electronic ISBN: 978-981-19-5936-3

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-5936-3_58

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