Generating Contours in a Sensor Network Using Isovector Aggregation

1. Generating Contours in a Sensor Network Using Isovector Aggregation Cheng Zhong National Center for Geographic Information and Analysis, University of Maine, Orono Today, I am going to give a presentation about Isovector aggregation, a method for in-network contour generation in sensor networks.Today, I am going to give a presentation about Isovector aggregation, a method for in-network contour generation in sensor networks.

2. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion Here is the outline of my presentation. First is the brief background of my work. a simple example of isovector aggregation will also be given. There are also some preliminaries of my works ,including a neighborhood ring data structure and the time model for aggregation. Then I will explain Isovector aggregation in detail. The isovector aggregation is also evaluated and compared with existing aggregation techniques. The final part is the conclusion. Here is the outline of my presentation. First is the brief background of my work. a simple example of isovector aggregation will also be given. There are also some preliminaries of my works ,including a neighborhood ring data structure and the time model for aggregation. Then I will explain Isovector aggregation in detail. The isovector aggregation is also evaluated and compared with existing aggregation techniques. The final part is the conclusion.

3. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion Here is the outline of my presentation. First is the brief background of my work. a simple example of isovector aggregation will also be given. There are also some preliminaries of my works ,including a neighborhood ring data structure and the time model for aggregation. Then I will explain Isovector aggregation in detail. The isovector aggregation is also evaluated and compared with existing aggregation techniques. The final part is the conclusion. Here is the outline of my presentation. First is the brief background of my work. a simple example of isovector aggregation will also be given. There are also some preliminaries of my works ,including a neighborhood ring data structure and the time model for aggregation. Then I will explain Isovector aggregation in detail. The isovector aggregation is also evaluated and compared with existing aggregation techniques. The final part is the conclusion.

4. Introduction Motivation What does the whole field look like? Which region have values between 50 and 60? Try to answer these questions using contour maps We already know a lot of threshold based sensor network applications. A sensor node can report it�s value if the value is above the threshold. These approached are easy to implement. But such kind of approach can not provides enough information for us and it�s not energy efficient. We may want to know how the whole field looks like. Or we may want to know which regions have values between 50-60. These kinds of question cannot be answer efficiently by threshold approach. A contour map is a useful data representation schema that provides an efficient way to visualize the filed monitored by sensor networks. By Generating contour maps for the regions, we reduce data transmission greatly. Which bring great energy savings for the network. We already know a lot of threshold based sensor network applications. A sensor node can report it�s value if the value is above the threshold. These approached are easy to implement. But such kind of approach can not provides enough information for us and it�s not energy efficient. We may want to know how the whole field looks like. Or we may want to know which regions have values between 50-60. These kinds of question cannot be answer efficiently by threshold approach. A contour map is a useful data representation schema that provides an efficient way to visualize the filed monitored by sensor networks. By Generating contour maps for the regions, we reduce data transmission greatly. Which bring great energy savings for the network.

5. Introduction Motivation A contour map is a useful data representation schema that provides an efficient way to visualize the field monitored by sensor networks. Generate contour maps for the region Reduce data transmission We already know a lot of threshold based sensor network applications. A sensor node can report it�s value if the value is above the threshold. These approached are easy to implement. But such kind of approach can not provides enough information for us and it�s not energy efficient. We may want to know how the whole field looks like. Or we may want to know which regions have values between 50-60. These kinds of question cannot be answer efficiently by threshold approach. A contour map is a useful data representation schema that provides an efficient way to visualize the filed monitored by sensor networks. By Generating contour maps for the regions, we reduce data transmission greatly. Which bring great energy savings for the network. We already know a lot of threshold based sensor network applications. A sensor node can report it�s value if the value is above the threshold. These approached are easy to implement. But such kind of approach can not provides enough information for us and it�s not energy efficient. We may want to know how the whole field looks like. Or we may want to know which regions have values between 50-60. These kinds of question cannot be answer efficiently by threshold approach. A contour map is a useful data representation schema that provides an efficient way to visualize the filed monitored by sensor networks. By Generating contour maps for the regions, we reduce data transmission greatly. Which bring great energy savings for the network.

6. Introduction Only a few reporting nodes near contours are chosen. Each reporting message contains contour information rather than any single node's ID and value pair Contours are progressively merged and simplified in the network. The base station receives contours which require no further processing Here is a general description of our aggregation approach: We find by in network contour merging and simplification. data transmission is reduced greatly.Here is a general description of our aggregation approach: We find by in network contour merging and simplification. data transmission is reduced greatly.

7. Introduction Merge contour vectors and remove redundant points along the data routing tree In order to introduce the isovector aggregation, I use a simple straight line contour as the example. Suppose N3, N4, N5 and N6 are reporting nodes, N3 will generate a vecotr P1, P3, P1 is the mid point between N3 and the corresponding neighbor. Others are the same. Then N3 reports Vector P1, P3 to N1 N4 reports vector P4, P5 to node N1. Later N1 connect vector P1 p3 and vector P4 p5 to generate a new contour vector, then vector points p2,p3, p4 will be removed because they are redundant. We can see that a lot of redundant points in the network are removed progressively. Because we The total data transmission is reduced. And we can then save the network energy. In order to introduce the isovector aggregation, I use a simple straight line contour as the example. Suppose N3, N4, N5 and N6 are reporting nodes, N3 will generate a vecotr P1, P3, P1 is the mid point between N3 and the corresponding neighbor. Others are the same. Then N3 reports Vector P1, P3 to N1 N4 reports vector P4, P5 to node N1. Later N1 connect vector P1 p3 and vector P4 p5 to generate a new contour vector, then vector points p2,p3, p4 will be removed because they are redundant. We can see that a lot of redundant points in the network are removed progressively. Because we The total data transmission is reduced. And we can then save the network energy.

8. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion Now, lets look at the preliminaries the of Isovector aggregationNow, lets look at the preliminaries the of Isovector aggregation

9. Preliminaries Each node maintains a neighborhood ring data structure. Each entry value of the ring is set to null or the corresponding neighboring node value We defined a ring data data struture.For each node in the network , it maintains a neighborhood ring data structure. Each entry value of the ring is null or a neighboring node value. More specifically, if the corresponding neighboring node is in the same value range of this node, that entry is set to null. Otherwise, it will be the value of the corresponding neighboring node.We defined a ring data data struture.For each node in the network , it maintains a neighborhood ring data structure. Each entry value of the ring is null or a neighboring node value. More specifically, if the corresponding neighboring node is in the same value range of this node, that entry is set to null. Otherwise, it will be the value of the corresponding neighboring node.

10. Preliminaries Message types Query and negotiation messages are only broadcasted at the network initialization phase. Notification will be broadcast by a node if the nodes changed the value ranges. Vector is the reporting message sent by nodesQuery and negotiation messages are only broadcasted at the network initialization phase. Notification will be broadcast by a node if the nodes changed the value ranges. Vector is the reporting message sent by nodes

11. Preliminaries Time Model-Cascading Timer [1] In cascading timer, nodes wait to receive all data from their children before forwarding Using cascading timer, a node merges, simplifies and reports contour vectors only after it receives all reports from its children We use a tree-based data routing schema. In order to do the aggregation correctly. We need a right time model. So a parent should wait all children�s reports before it reports in each sampling round. The time model for data aggregation is cascading timer.We use a tree-based data routing schema. In order to do the aggregation correctly. We need a right time model. So a parent should wait all children�s reports before it reports in each sampling round. The time model for data aggregation is cascading timer.

12. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion Now let me introduce Isovector aggregation in detail.Now let me introduce Isovector aggregation in detail.

13. Isovector aggregation Reporting node selection Local contour generation In-network contour merging In-network contour simplification It mainly contains for parts. Select reporting nodes, generate local contours from the contour neighborhood ring. And also how we merge and simplify contours in the networkIt mainly contains for parts. Select reporting nodes, generate local contours from the contour neighborhood ring. And also how we merge and simplify contours in the network

14. Isovector aggregation Reporting node selection Contour detection is symmetric. We only choose contour nodes in the higher value ranges to report. Exception: non-consecutive value ranges First, let�s consider the reporting node selectionFirst, let�s consider the reporting node selection

15. Isovector aggregation Local contour generation Divide the ring into partitions by contour scale and null values, and generate a contour for each partition As I said before, each node maintains a ring data structure. This ring is divided into partitions. For each partition, a contour vector will be generated by counter clockwise order. We use the mid-points between the reporting node and neighboring nodes to represent contours. Each contour will have a head and a tail. Ring contour is a special case. The head and the tail is the same. On the one hand, Partition can be used to distinguish contours with different values. As I said before, each node maintains a ring data structure. This ring is divided into partitions. For each partition, a contour vector will be generated by counter clockwise order. We use the mid-points between the reporting node and neighboring nodes to represent contours. Each contour will have a head and a tail. Ring contour is a special case. The head and the tail is the same. On the one hand, Partition can be used to distinguish contours with different values.

16. Isovector aggregation Narrow contour case generate more than 1 vector. Vectors send by the same node can not be merged until they reach the base station On the other hand, Partition is also used to deal with narrow contour cases. For example. Suppose U only generate one contour vector � vector 1. and the right part of the broken line generates vector2. Later It is not easy for us to merge them together. And we cannot get the correct contour shape. If we use the partition, and we define, contours send by the same nodes can be merged in the network. Here, we have vector 0 and vector 1. later. When they meet vector 2. it is straightforward to connect points P3 and Pi, and connect P6 with Pj, because they are near to each other. On the other hand, Partition is also used to deal with narrow contour cases. For example. Suppose U only generate one contour vector � vector 1. and the right part of the broken line generates vector2. Later It is not easy for us to merge them together. And we cannot get the correct contour shape. If we use the partition, and we define, contours send by the same nodes can be merged in the network. Here, we have vector 0 and vector 1. later. When they meet vector 2. it is straightforward to connect points P3 and Pi, and connect P6 with Pj, because they are near to each other.

17. Isovector aggregation Contour vector merging If the distance is shorter than a predefined tolerance, connect them Vectors send by the same node should not be merged in the network We calculate all distances of head tail pairs of two contour vectors. And use the small one as the distance of two vectors. We calculate all distances of head tail pairs of two contour vectors. And use the small one as the distance of two vectors.

18. Isovector aggregation Contour vector simplification using Douglas-Peucker algorithm [2] Although a contour vector only contains important contour point information, such information may be still quite large. Without losing contour fidelity greatly, we consider how to weed out redundant points from a contour vector. we use the Douglas-Peucker line simplification algorithm for vector simplification in that it was best at choosing critical points when compared with others I want to say that, any other poly-line simplification method can be adopted if it can retain the shape of the original line.Although a contour vector only contains important contour point information, such information may be still quite large. Without losing contour fidelity greatly, we consider how to weed out redundant points from a contour vector. we use the Douglas-Peucker line simplification algorithm for vector simplification in that it was best at choosing critical points when compared with others I want to say that, any other poly-line simplification method can be adopted if it can retain the shape of the original line.

19. Isovector aggregation All nodes in the same operation until the base station receives all final reportsAll nodes in the same operation until the base station receives all final reports

20. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion We also evaluate isovector aggregation by theoretical analysis and simulations.We also evaluate isovector aggregation by theoretical analysis and simulations.

21. Evaluation Aggregation analysis Simulations (using NS2) Compared with Isolines aggregation and the no aggregation method We do the analysis from two aspects: network traffic generation and in-network traffic reduction. The simulations are don in NS2 environment. By isolines aggregation, nealy half of the nodes along the contour the contour will report at each sampling round. By no aggregation method, all nodes will report at each sampling round.We do the analysis from two aspects: network traffic generation and in-network traffic reduction. The simulations are don in NS2 environment. By isolines aggregation, nealy half of the nodes along the contour the contour will report at each sampling round. By no aggregation method, all nodes will report at each sampling round.

22. Evaluation O(n0.5) traffic generation [3] Analyzed in-network traffic reduction of Isovector in a simplified binary routing tree. Isovector (O(2m) to O(m*2m)) performs better than Isolines (O(m*2m) ) even in the worst case. From a recent paper, I know that only let contour nodes report will only incur O square root n traffic generation, where n is the total number of the nodes in the network. How many traffic can be reduced in the network by contour simplification and merging in determined by many factors, such as the tolerance for line simplification, the network structure. The contour shape. It is very hard to a generic analysis. I only analyzied it in a simplified binary tree structure. For time reason. I will not talk this analysis in detail here.From a recent paper, I know that only let contour nodes report will only incur O square root n traffic generation, where n is the total number of the nodes in the network. How many traffic can be reduced in the network by contour simplification and merging in determined by many factors, such as the tolerance for line simplification, the network structure. The contour shape. It is very hard to a generic analysis. I only analyzied it in a simplified binary tree structure. For time reason. I will not talk this analysis in detail here.

23. Evaluation Comparisons with other techniques Here I compared Isovector aggregation with some existing techniques. I find Only Isovector has square root n traffic generation and considers in network traffic reduction at the same timeHere I compared Isovector aggregation with some existing techniques. I find Only Isovector has square root n traffic generation and considers in network traffic reduction at the same time

24. Evaluation Simulations by NS2 (snapshot) 4000 points on the ma, similarity is calculated as the percent of points that are in the correct value ranges. We can see from the result that isovector send the lest data than Isolines and no aggregation methods. And it also achieves good map similarity.4000 points on the ma, similarity is calculated as the percent of points that are in the correct value ranges. We can see from the result that isovector send the lest data than Isolines and no aggregation methods. And it also achieves good map similarity.

25. Evaluation Simulations by NS2 (moving front) Here is a continuous monitoring cases. A moving from moves from left to right and changes sensor values from thirties to fifties, We samplings 9 times from the second 3 to second 11. We count the total data send by different methods and we also take map snapshots at second 9 for comparisons. As we can see, that Isovector achieves both best map similarity and less data transmission.Here is a continuous monitoring cases. A moving from moves from left to right and changes sensor values from thirties to fifties, We samplings 9 times from the second 3 to second 11. We count the total data send by different methods and we also take map snapshots at second 9 for comparisons. As we can see, that Isovector achieves both best map similarity and less data transmission.

26. Evaluation The impact of contour node densities No aggregation method is not influenced by contour node ratio because it always let all node report. With the increase of contour node density, more and more node can detect contours. So both isolines and isovector send much more data. But they still performance better than No aggregation greatly when 80% of the nodes in the network can detect contours. The isovector aggregation obviously has the best scalabilities.No aggregation method is not influenced by contour node ratio because it always let all node report. With the increase of contour node density, more and more node can detect contours. So both isolines and isovector send much more data. But they still performance better than No aggregation greatly when 80% of the nodes in the network can detect contours. The isovector aggregation obviously has the best scalabilities.

27. Outline Introduction Preliminaries Isovector aggregation Evaluation Conclusion Finally, it�s the conclusion of my presentaitonFinally, it�s the conclusion of my presentaiton

28. Conclusion Isovector aggregation generates, merges and simplifies contours in the network Evaluation results show that Isovector aggregation out-performs Isolines aggregation and the no aggregation method, and it is more scalable and energy efficient.

29. Conclusion A case that Isovector cannot deal with

30. Questions!