1. An Efficient Routing Protocol for Energy Management in WSN-Assisted loT Sarah Ammar Rafea and Abdulkareem A. Kadhim

2. Content Background 01 02 03 04 05 06 07 08 Problem Definition Aim of the work Methodology Publication References 07 08 Results Thesis contribution Conclusion Futur work

3. Background IoT

4. What is IOT? Its a technology that enable the intelligent objects (things) to collect and exchange data through the internet for different purposes Things refer to any physical object with a device that has its own IP address and can connect to a network that also sends/receives data via network

5. WSN & IoT 04 Internet of Things (IoT) system is observed to have the potential to improve the overall efficiency of many applications. WSNs distribute hundreds to thousands of inexpensive micro-sensor nodes in their areas, and these nodes are fundamental parts of IoT.

6. WSN Node Small Devices Communicating Node Low Cost Low Power Sensing Processing Energy Consumption

7. Connecting WSN to the Internet There are three main approaches to connect WSN to the Internet Single Gateway Multiple Gateway Direct Access Inflexible Delay

8. IoT- WSN Protocol stack Application layer Transport layer Network layer Link layer physical layer

9. Routing Protocol LEACH RPL LEACH Internet Engineering Task Force (IETF) Proactive routing protocol IPv6 based protocol Tree-like topology (DODAG) Used different Objective Functions

10. Routing Protocol for Low Power and Lossy Network (RPL) DODAG Information Object DODAG Information Solicitation DODAG Advertisement Object DAO-ACKnologment RPL Control messages DIO DIS DAO DAO- ACK Rank=0 Rank=1 Rank=2 Rank=3 Rank=2

11. Route Selection in RPL Objective Function (OF) Objective Function Zero (OF0) Minimum Rank with Hysteresis Objective Function (MRHOF) Hop count metric Expected Transmission count (ETX) Routing Metrics Node Metric Link Metric Hop and residual energy Throughput, latency and link reliability

12. Related Works 201220152016 2017 Hop count or Expected Transmission count (ETX) Objective Function Hop Count & Residual Energy Objective Function Node energy & power & link qual ity & success tran smission data rat e & congestion de tection factor New Metric Expected Transmi ssion Count (ETX ) & Received Sig nal Strength Indic ator (RSSI) Objective function RPL ELB- FLR LCI Tx- tuned 2018 Delay & Energy Objective function OFQS

13. Problem definition ?

14. Problem Definition In WSN assisted IoT (IP over WSN) the resources are constrained in many ways: Energy resources storage resources computing resources

15. Problem Definition (cont..) The routing protocol for low power and lossy network (RPL) is the most common protocols used for WSN assisted IOT. The Objective function of RPL protocol depend s on either: link quality hop count There is no consideration to energy metric

16. Problem Definition (cont..) Node-4 will not switch its preferred parent Hop count ETX Bad link quality for path from node-5 through node-3 Node-4 will not switch its preferred parent Select 3 hops with 3 ETX rather than 2 hops with 3.5 ETX

17. Aim of the work

18. Propose a new energy-efficient routing protocol for WSN assisted loT based on RPL to improve the performance of the network by reducing the required energy. modify RPL routing protocol to work in energy efficient way by taking into consideration the energy metric with link quality metric.

19. Thesis contribution

20. Running the RPL protocol on Cooja simulator. The proposal of two modified versions of RPL protocols called ETRPL and EERPL aiming to improve the IoT-WSN network performance. Implementing the proposed protocols in Cooja simulator. 01 02 03 As a result of implementing the proposed protocols, better performance is achieved compared to RPL protocol. 04

21. Methodology

22. Using Contiki OS which is one of most widely used operating system for WSN supporting IoT. Using Cooja simulator which is the most important tool in Contiki OS used to simulate & emulate WSN. Implement the proposed protocols and compare it with the existing protocols.

23. Methodology ETX and remaining energy (ETRPL) - Node-4 will switch its preferred parent ETX and consumed energy (EERPL) - Not select old preferred parent - Create new metric (EE) based on summation of ETX and energy consumption - Node 5 change its parent in case when energy consumption became 95% since the path cost will be (4+9.5+1.5=15). - Based on remaining energy of preferred parent

24. Results

25. Infographic Style Remaining Energy Packet Delivery Ratio Average Time Delay Number of Dead Nodes The main parameters 1 2 4 3 Evaluation Method

26. Main Assumptions ParameterSpecification or Value TopologyRandom Area 100mX100m 150mX150m 200mX200m Number of nodes20, 40 and 80 Simulation time60 Mins. Transmission range50m Interference range70m and without int.

27. Remaining Energy Results Without interference range With interference range for 20 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m531.21918861.36829.68856.53 150mX150m731.78781.21755.26777.31781.21 200mX200m449.15505.94512.15479.94607.37 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m567.842945.7889.4934912.58 150mX150m737.789826.2785.1827826.26 200mX200m559.473660.8671.1619.2629.05

28. Remaining Energy Results for 40 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75% EERPL 100mX100m 133.84461.64446.43379.35410.49 150mX150m 90.66406.46355.20376.38410.44 200mX200m 148.82246.41233.30238.87313.82 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m125.84 444.48 367.35322.30320.36 150mX150m73.97233.48220.41209.84242.31 200mX200m104.46188.53207.25181.71140.13 Without interference range With interference range

29. Remaining Energy Results for 80 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m 10.1683.4189.5580.9356.73 150mX150m 68.0192.6981.9668.01 200mX200m 73.8389.5076.8473.83 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m9.22728.8841.2422.5843.101 150mX150m9.3679.93613.629.367 200mX200m22.012724.9127.6224.9122.013 Without interference range With interference range

30. Packet Delivery Ratio Results for 20 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m99.72%100% 150mX150m99.54%99.64%99.55%99.64% 200mX200m98.98%99.36%99.54%98.26%98.82% AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m99.54%100% 100 150mX150m99.54%99.64%99.73%99.64% 200mX200m98.14%98.52%97.96%97.88%98.73% Without interference range With interference range

31. Packet Delivery Ratio Results for 40 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m98.74%99.6%99.69%99.28%99.56% 150mX150m97.39%99.5%98.73%98.77%99.73% 200mX200m96.42%98.02%97.83%97.62%98.12% AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m98.47%99.78%99.6%99.243%99.6% 150mX150m96.94%98.77%98.817%98.511%98.65% 200mX200m94.99%97.74%98.086%97.318%96.17% Without interference range With interference range

32. Packet Delivery Ratio Results for 80 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75% EERPL 100mX100m 96.66%99.22%99.27%98.95%99.59% 150mX150m97.97%98.82%98.5%97.97% 200mX200m97.1%97.24%97.28%97.108% 97.1% AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m89.64%99.177%99.296%98.947%97.2% 150mX150m89.99%91.074%92.508%89.994%89.99% 200mX200m90.81%89.6%90.11%89.6%90.81% Without interference range With interference range

33. Average Time Delay Results for 20 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m0.29090.15110.1360.17620.167 150mX150m0.27430.26510.29590.260.2651 200mX200m0.25030.40110.38910.26520.2914 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m0.14790.13286920.136780.132860.12385 150mX150m0.220110.27002470.3030.25810.27002 200mX200m0.23110.22074880.250510.219720.40338 Without interference range With interference range

34. Average Time Delay Results for 40 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m0.31840.32190.34150.3970.3384 150mX150m0.32140.51730.33760.47920.483 200mX200m0.51870.47320.69820.41480.6201 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m0.37910.29790.47150.48890.2983 150mX150m0.46420.42950.60600.55170.3921 200mX200m0.62080.53120.57461.94010.4142 Without interference range With interference range

35. Average Time Delay Results for 80 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m0.70451.01640.72970.73360.6877 150mX150m0.97131.22280.76090.9713 200mX200m1.17991.52741.59151.1799 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m11.82710.96970.76040.92931.173 150mX150m4.25925.79572.61504.2592 200mX200m1.35781.33561.20411.33561.3578 Without interference range With interference range

36. Number of Dead Nodes for 20 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m40000 150mX150m21211 200mX200m53365 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m40000 150mX150m31111 200mX200m76666 Without interference range With interference range

37. Number of Dead Nodes for 40 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m3212101332 150mX150m351116 35 200mX200m2823202328 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m327142015 150mX150m3423222419 200mX200m3325242729 Without interference range With interference range

38. Number of Dead Nodes for 80 nodes AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m7965626162 150mX150m67646567 200mX200m7067 70 AreaRPLETRPL 25%ETRPL 50%ETRPL 75%EERPL 100mX100m7970697479 150mX150m79 7879 200mX200m76 Without interference range With interference range

39. Conclusion

40. The energy consumption of ETRPL is better than that of RPL protocol. For small area of 100mX100m, an increase in the remaining energy of at least 36%, 64.7% and 87% is achieved using 20, 40 and 80 nodes, respectively. ETRPL also performed better than RPL in regards to average time delay, Packet Reception Ratio, and the number of dead nodes in small area. For large area of 200mX200m, the performance is degraded for some test conditions. The proposed ETRPL protocol is useful for IoT networks with relatively small areas with high number of nodes. 01 02 03 04 05 Regarding ETRPL

41. Conclusion The energy consumption of EERPL is better than that of RPL protocol. For small area of 100mX100m, an increase in the remaining energy of 37.7%, 67.39% and 82.08% is achieved using 20, 40 and 80 nodes, respectively. ETRPL perform better than EERPL in small area while EERPL perform better in large area with interferen ce range for 20 nodes and also in medium and large area with 40 nodes. The EERPL performs as RPL for most cases with 80 nodes. EERPL also performed better than RPL in regards to Time Delay, Packet Reception Ratio, and the number of dead nodes in small area. For large area of 200mX200 m, the performance is degraded for some test conditions. The proposed EERPL protocol is useful for IoT networks with relatively large area with small or medium number of nodes. 01 02 03 04 05 06 Regarding EERPL

42. Future works

43. Implementing ETRPL and EERPL protocol using real hardware devices. Propose another objective function by taking into consideration other nodes/link metric such as Received Signal Strength Indicator (RSSI). Use different node platform such as Zolertia Z1 mote rather than Tmote sky to see the performance of the proposed protocols. 01 02 03 Use the proposed protocols in different wireless environment such as fast fading channel to see the strength of the protocols in real environments. 04

44. References

45. [1] Z. Wang, L. Zhang, Z. Zheng, and J. Wang, “An Optimized RPL Protocol for Wireless Sensor Net works,” IEEE 22nd Int. Conf. Parallel Distrib. Syst., 2016. [2]Q. Q. Abuein and et al., “Performance Evaluation of Routing Protocol (RPL) for Internet of Thing s,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 7, pp. 17–20, 2016. [3]Z. Wang, L. Zhang, Z. Zheng, and J. Wang, “Energy balancing RPL protocol with multipat h for wireless sensor networks,” Peer-to-Peer Netw. Appl., pp. 1–16, 2017. [4] M. A. Mahmud, A. Abdelgawad, and K. Yelamarthi, “Energy efficient routing for Internet of Things (IoT) applications,” 2017 IEEE Int. Conf. Electro Inf. Technol., pp. 442–446, 2017. [5] J. Nassar, M. Berthomé, J. Dubrulle, N. Gouvy, and B. Quoitin, “Multiple Instances QoS Routing I n RPL: Application To Smart Grids,” pp. 0–16, 2018. [6] P. Sanmartin and et al., “Sigma routing metric for rpl protocol,” Sensors, vol. 18, no. 4. 2018. [7] S. Kharche and S. Pawar, “Stability model for RPL with minimum rank hysterisis objective functio n in 6LoWPAN,” Int. J. Pure Appl. Math., vol. 118, no. 5 Special Issue, pp. 731–737, 2018.

46. Thank you