1. 2015/10/1 A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks Tai-Jung Chang, Kuochen Wang, Yi-Ling Hsieh Department of Computer Science National Chiao Tung University Hsinchu, Taiwan February 2008, Computer Networks (The International Journal of Computer and Telecommunications Networking)

2. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 2 2015/10/1  This paper propose a Color-theory-based Energy Efficient Routing algorithm to prolong the life time of each sensor node.  This paper adopted a range-free Color-theory based Dynamic Localization to help identify each sensor node's location.  The uniqueness of our approach is that by comparing the associated RGB values among neighboring nodes.  Simulation results have shown that our routing algorithm can save 50% - 60% energy than ESDSR in mobile WSNs. Abstract

3. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 3 2015/10/1 Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Outline Conclusion 5

4. Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Conclusion 5

5. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 5 2015/10/1 Introduction  It is a great challenge for routing in WSNs due to the following reasons.  First, since it is not easy to build a whole network topology, it is hard to assign each routing path.  Secondly, sensor nodes are tightly constrained in terms of energy, processing, and storage capacities.  The proposed Color-theory-based Energy Efficient Routing algorithm (CEER) is based on CDL, in which the location of a sensor node is represented as a set of RGB values. CDL - Color-theory based Dynamic Localization Color-theory Color-theory based Dynamic Localization Color-theory-based Energy Efficient Routing algorithm

6. Outline Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Conclusion 5

7. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 7 2015/10/1  Color-theory RGB ➠ HSV Related Work (1/5) ➠ RGB ► Red ► Green ► Blue HSV ► Hue (360) ► Saturation ► Value (1,0,0) (0,1,0) (1,1,0) (0,0,1) (1,1,1) (0,0,0) (1,0,1) (0,1,1)

8. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 8 2015/10/1 Related Work (2/5)  Each node i maintains an entry of (R ik,G ik,B ik ) and D ik, where k represents the k th anchor.  D avg is the average hop distance, which is based on DV-Hop.  h ij is the hops between nodes i and j.  D ik represents the hop distance from anchor k to node i : D ik = D avg × h ij  Range represents the maximum distance that a color can propagate.  (R k, G k, B k ) are the RGB values of anchor k.  (H ik, S ik, V ik ) are the HSV values of anchor k received by the i th node. D ik (R ik,G ik,B ik ) k th …… A B C D E F G  Notations that are defined in CDL (R A, G A, B A )

9. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 9 2015/10/1 Related Work (3/5)  There are four phases involved in the CDL. In the first phase: The RGB values of anchors are assigned randomly from 0 to 1. Anchors broadcast RGB value. ➠ ➠ The neighbors receiving the packet would record the number of hops to the anchor. ➠ After completing the broadcast, each anchor receives a set of hop count values from all other anchors and is able to calculate the distances and hops to all other anchors. Upon obtaining the information from all anchors, each node computes the average hop distance. DV-Hop

10. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 10 2015/10/1 Related Work (4/5) In the second phase: Once the nodes nearby the anchors receive the RGB values and location information from the anchors, they convert the RGB values to corresponding HSV values. ➠ ➠ (H k, S k, V k )= RGBtoHSV (R k, G k, B k ) (1) (2) (R ik, G ik, B ik )= HSVtoRGB (H ik, S ik, V ik ) (3) (4) ➠ By the means of broadcasting, the RGB values would be received by the server eventually. Sensor node Anchor where n is the number of anchors that node i received. Sensor node ➠

11. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 11 2015/10/1 Related Work (5/5) In the third and fourth phase: ➠ The establishment of a location database is performed when the server obtains the RGB values and coordinates (locations) of all anchor. (X i, Y i ) is the coordinate of location i, and (X k,Y k ) is the location of anchor k. (H k, S k, V k )= RGBtoHSV (R k, G k, B k ) (6) (5) (7) (R ik, G ik, B ik )= HSVtoRGB (H ik, S ik, V ik ) (8) ➠ ➠ (4) where N is the number of anchors ➠

12. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 12 2015/10/1 Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Conclusion 5

13. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 13 2015/10/1 Proposed Approach (1/4)  The network model is described as follows:  A server is used for building a location database, localization, and collecting sensed data.  The area is divided into clusters and a cluster head is selected by the server for each cluster.  There are four anchors, which are placed in the four corners of the area. Anchors collect aggregate data received from cluster heads.  All sensor nodes have a uniform energy at the beginning.  Each node wakes up at a fixed rate to check if its RGB values have changed.  All sensor nodes are mobile.  CSMA/CA is used to avoid collision of packets.

14. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 14 2015/10/1  Setup Phase  First, we define a grid size as R × R, where R is a sensor node’s radio range. All sensor nodes in a cluster (grid) are within their cluster head’s radio range.  Secondly, each anchor floods its RGB values and average hop distance to each sensor node  Each sensor node can calculate its hop count to the anchor and adjust its RGB values.  By the means of broadcasting, the RGB values would be received by the server.  By looking up the location database, the server can calculate each sensor node’s location and choose a cluster head which is close to the center of the grid. Figure 1. Setup phase 1Figure 2. Setup phase 2 Proposed Approach (2/4)

15. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 15 2015/10/1  Data Dissemination Phase  After the setup phase, each cluster head will receive its cluster member’s information.  When a cluster head receives information from its cluster member, it will setup a timer.  When a cluster head wants to forward the aggregate data toward the server, two steps are performed in CEER. The sensor node selects its one hop neighbors that are closer to a nearby anchor than itself. The sensor node with the highest energy level is selected as the next hop. Figure 3. Data dissemination step 1 Proposed Approach (3/4) Figure 4. Data dissemination step 2

16. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 16 2015/10/1 Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Conclusion 5

17. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 17 2015/10/1 Simulation Result (1/2)  Simulation Model Table 2. Simulation parameters. ParameterValue Area size500 × 500 m 2 Node speedRandomly choose from [V min, V max ] Node transmission range (R)50 m Pause time 0 Measurement period 50 t u Update interval 50 t u Time slot length (time unit) tutu

18. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 18 2015/10/1  Refinement Phase Figure 5. Energy consumption vs. number of nodes. Simulation Result (2/2) Figure 6. Latency per packet vs. number of nodes. Without cluster heads

19. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 19 2015/10/1 Introduction 1 Related Work (CDL) 2 Proposed Approach (CEER) 3 Simulation Result 4 Conclusion 5

20. A color-theory-based energy efficient routing algorithm for mobile wireless sensor networks 20 2015/10/1 Conclusion  In this paper, we have presented a color-theory-based energy efficient routing algorithm (CEER) based on a color-theory-based dynamic localization algorithm (CDL).  Simulation results have shown that our routing algorithm can save 50% - 60% energy compared to ESDSR in mobile wireless sensor networks.  In addition, the latency per packet of CEER is 50% less than that of ESDSR.