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Journal ofScience & Technology 101 (2014) 145-149 Energy Efficiency for Wireless Sensor Networks Based on Internet of Things Thu Ngo Quynh', Chung Nguyen Due, Anh Nguyen Quynh Hanoi University ofScience and Technology, No Dai Co Viet Sir, Ha Noi, Viet Nam Received: March 04, 2014: accepted' Aprd 22 2014 Abstract Recently, internet of Things (loT) enables the convergence of Wireless Sensor Networt Hyjj.gai,oi(j, nodes will transmit this value to the root If not, nodes not transmit data Step Root receives this difference Ag^ and evaluate the value of 2"'' sample by applying the equation 5^ (2) =g (l) + Ag^ ,giiC2) = g t l } -I- AgR Step Nodes and root clear the value of 1" sample and while keeping the value of 2'"' sample ig Data collected at sensors of 3"* scheme From this figure, we realize that linear predicted data redimction algorithm achieves better energy efficiency compared to the scenario without data driven techniques (4.07%) However, the difference of energy consumption between two methods is not much because of the complexity of this data reduction (large memory for saving n samples and complex calculations for linear predicted function) For 3"^ scenario, energy consumed is presented in the following figure: Fig 3, Simulated Topology Fig Energy of 2"'' and V scenarios Journal ofScience & Technology 101 (2014) 145-149 Fig Energy of 3'*' and ^' scenarios V] Fig Energy of all scenarios We realize that the data reduction method based on difference between two consecutive samples achieves also better energy efficiency than the P' scenario (5%) It can be explained simply because all three metrics change slowly and the energy consumed for transmitting difference between consecutive samples stays small [8] Next, we examine energy efficiency of 4"' scenario Obviously, in the entropy-based method energy consumed is small compared to the 1" scenario [10] More concretelly, the energy efficiency of all scenarios is presented together in the following The last scenario with entropy-based data reduction method achieves the best energy efficiency — 9% [9] [II] [12] Conclusion In this paper, we present three data processing methods, linear prediction, data reduction based on difference and data reduction based on entropythreshold These three methods are also intergrated with RPL routing protocol for transmitting data to the root by using IPv6 protocol Simulation results by Contiki show that entropy-based data reduction method can save to 9% energy consummed, while method using difference between consecutive samples and linear predicted function achieve only 5% and 4% [13] [14] [15] G Montenegro, N Kushalnagar, J Hui, and D Culler, "Transmission of IPv6 packets over IEEE 802.15 networks," IETF, RFC 4944, 2007, A- Brandt, J Hui, R Kelsey, P Levis, K Pister, R Slmik, JP Vasseur, R.Alexander, RPL, IPv6 Routing Protocol for Low-Power and Lossy Networks IETF RFC 6550, May 2012 V Raghunathan, C Schurghers, S Park, M Snvastava, Energy-aware wireless microsensor networks, IEEE Signal Processing Magazine (2002) 40-50 G Pottie, W Kaiser, Wireless integrated network seusors, Communication of ACM 43 (2000) 51-58 P, Levis, N Patel D, Culler, and S Shenker, Trickle: A self-regulatmg algorithm for code maintenance and propagation m wireless sensor networks In Proceedmgs of the USENIX NSDI Conference, San Francisco, CA, USA (2004) 15-28, A, Barbato, A Capone, M Barrano, N Figiani Resource Onented and Energy Efficienl Routing Protocol for IPv6 Wireless Sensor Networks, IEEE Online Conference on Green Communications (2013) 163-168 Cheng-Yen Liao, Lin-Huang Chang, Tsung-Han Lee, Shu-Jan Chen, An Energy-Efficiency-Oriented Routing Algorithm over RPL AIT Conference 2013, M,C Vuran, OB, Akan, l.F, Akyildiz, Spatiotemporal correlation- theory and applications for wireless sensor networks, Computer Networks Joumal 45 (3) (2004) 245-261 E Fasolo, M Rossi, J Widmer, M Zorzi, hi-network aggregation techniques for wireless sensor networks: a survey, IEEE Wireless Communications 14 (2007) 70-87S.S Pradhan, K Ramchandran, DisQributed source coding using syndromes (DISCUS) design and constraction, IEEE Transactions on Information Theory 49 (2003) 626-643 C Tang, C S Raghavendra, Compression Techniques for Wireless Sensor Networks, Book Wireless Sensor Networks, Kluwer Academic Pubhshers , (2004) 207231 (Chapter 10) M Wu, CW Chen, Multiple Bh Stream Image Transmission over Wireless Sensor Networks, Book Sensor Network Operations, IEEE & Wiley Interscience (20O6) 677-687 (Chapter 13) Z Xiong; A.D Livens, S Cheng, Distributed source codmg for sensor networks, IEEE Signal Processing Magazine 21 (2004)80-94, Pietro Gomzzi, Gianluigi Fenari, Paolo Medagliani, Jeremie Leguay Data Storage and Retrieval with RPL routing 9* Wireless Communications and Mobile Computing Conference IWCMC (2013) 1400-1404 Hiroaki Taka, Hideyuki Uehara, Takashi Ohira, Intermittent Transmission Method based on Aggregation Model for Cluslenng Scheme, IEEE ICUFN (2011) 107-111,
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