Total views : 759

Extended Comb Needle Model for Energy Efficient Data Aggregation in Random Wireless Sensor Networks


  • Department of CSE, Malla Reddy College of Engineering, Dhulapally, Secunderabad -14, Telangana, India
  • Deparment of CSE, College of Engineering, JNTUH, Hyderabad-85, Telangana, India


Background/Objectives: Energy conservation in Wireless Sensor Network is essential to enhance its life. A sensor node consumes more energy for communication than performing data gathering or data processing. Data aggregation minimizes the data size for communication. Methods/Statistical Analysis: The Comb Needle model is available in literature to perform data aggregation for grid networks (regular deployment). Extended the Basic Comb Needle Model in randomly deployed sensor networks. The simple random network with Comb Needle Model is compared with simple random network without Comb Needle Model. The theoretical analysis and simulation study shows that Extended Comb Needle Model performs better data aggregation. Findings: When we apply the Proposed Model in random network, the communication cost, overhead, and energy consumption are significantly reduced. The simulation results for the proposed Extended Comb Needle Model prove that the energy consumption and overall communication costs are substantially minimized. The simulation comparison is done for simple random network with and without Comb Needle Model in terms of communication cost, energy consumption, delay, packet loss, packet delivery ratio, and throughput. We found that the communication cost is decreased from 82% to 58%. the average energy consumption is decreased from 80% to 40%. Delay is decreased from 76% to 20%. Packet loss in decreased from 67% to 12%. Packet Delivery Ratio is increased from 82% to 87%. And throughput is increased from 70% to 90%. Application/Improvements: Proposed Model optimizes WSN performance in terms of better packet delivery ratio, improved throughput, minimized energy consumption and reduced delay. Simulation results as well as theoretical analysis affirm the same.


Comb Needle Model, Energy Consumption and Communication Cost, Random Network,Wireless Sensor Network.

Full Text:

 |  (PDF views: 262)


  • Cardei M,Wu J, Lu M, Pervaiz M. Maximum network lifetime in wireless sensor networks with adjustable sensing ranges. IEEE International Conference on Wireless and Mobile Computing, Networking and Communications. 2005; (WiMob’2005) 3: 438-45.
  • Subramanian R, Fekri F. Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions. In: Proceedings of the 5th International Conference on Information Processing in Sensor Networks, ACM, 2006, p. 218-25.
  • Cuomo F, Abbagnale A, Cipollone E. Cross-layer network formation for energy-efficient IEEE 802.15. 4/zigbee wireless sensor networks. Ad Hoc Netw. 2013; 11(2): 672-86.
  • Camillò, Nati M, Petrioli C, Rossi M, Zorzi M. IRIS: integrated data gathering and interest dissemination system for wireless sensor networks. Ad Hoc Netw. 2013;11(2): 654–71.
  • Candes E, Wakin M. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008; 25(2): 21-30.
  • Davenport MA, Duarte M, Eldar YC, Kutyniok G. Introduction to compressed sensing. In: publications/ddekchapter12011.pdf, 2011; 1-68.
  • Donoho DL. Compressed sensing. IEEE Trans. Inform. Theory. 2006; 52(4):1289–1306.
  • Nyquist H. Certain topics in telegraph transmission theory. Trans. Am. Inst. Electr. Eng.1928; 47(2):617-44.
  • Gong D, Yang Y. Low-latency sinr-based data gathering in wireless sensor networks. INFOCOM. 2014; 13(6):3207 -21.
  • Le-Trung Q, Taherkordi A, Skeie T, Pham HN, Engelstad PE. Information storage, reduction and dissemination in sensor networks: a survey. In: IEEE Conference on Consumer Communications and Networking Conference. 2009, p. 1-6.
  • Yuan L, Zhu Y, Xu T. A multi-layered energy-efficient and delay reduced chain-based data gathering protocol for wireless sensor network. Mechtronic and Embedded Systems and Applications. 2008, p. 13-18.
  • Tang X, Xu J. Optimizing lifetime for continuous data aggregation with precision guarantees in wireless sensor networks. IEEE/ACM Trans. Netw. 2008; 16(4): 904-17.
  • Gong D, Yang Y, Pan Z. Energy-efficient clustering in lossy wireless sensor networks. J. Parall. Distrib. Comput. 2013; 73(9):1323-36.
  • Liu X, Huang Q, Zhang Y. Combs, Needles, Haystacks:Balancing Push and Pull for Discovery in LargeScale Sensor Networks. Proc. ACM Conf. Embedded Networked Sensor Systems (SenSys ’04). 2004 Nov., p. 12233.
  • Lin H-C, Li F-J, Wang K-Y. Constructing maximumlifetime data gathering trees in sensor networks with data aggregation. In: IEEE International Conference on Communications, 2010, p. 1-6.
  • Ratnasamy S, Karp B, Shenker S, Estrin D, Govindan R, Yin L, Yu F. Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table. Mobile Networks and Applications. 2003; 8(4):427-42.
  • Rachuri K, Franklin AA, Murthy CSR. Energy efficient Searching in delay-tolerant wireless sensor networks. In: 1st ACM international Workshop on Heterogeneous Sensor and Actor Networks. 2008, p. 37-44.
  • Saranya V, Matheswari N, Punidha R, Soundarya M. Tracking Dynamic Target in Wireless Sensor Networks. Indian Journal of Science and Technology. 2016 Jan; 9(1):7p. Doi no:10.17485/ijst/2016/v9i1/85787.
  • Vijayan K, ArunRaaza. A Novel Cluster Arrangement Energy Efficient Routing Protocol for Wireless Sensor Networks. Indian Journal of Science and Technology. 2016 Jan; 9(2): 9p. Doi no: 10.17485/ijst/2016/v9i2/79073.
  • Mary Livinsa Z, Jaya shri S. Monitoring Moving Target and Energy Saving Localization Algorithm in Wireless Sensor Networks. Indian Journal of Science and Technology. 2016 Jan; 9(3):5p. Doi no:10.17485/ijst/2016/v9i3/70102.
  • Munusamy K, Parvathi RMS, Chandramohan K. Least Power Adaptive Hierarchy Cluster Framework for Wireless Sensor Network using Frequency Division Multiplexing Channelization. Indian Journal of Science and Technology. 2016 Feb; 9(6):10p. Doi no:10.17485/ijst/2016/v9i6/80046.
  • Shanmukhi M, Ramanaiah OBV. Cluster-based CombNeedle Model for Energy-efficient Data Aggregation in Wireless Sensor Networks. Applications and Innovations in Mobile Computing (AIMoC). 2015, p. 42-47.
  • Shanmukhi M, Ramanaiah OBV. A Survey on Energy Efficient Data Aggregation Protocols for Wireless Sensor Networks. International Journal of Applied Engineering Research. 2016; 11(10): 6990-7002.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.