LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British Columbia, Canada GLOBECOM 2005

2 Outline Introduction Related Works Problem Formulation Centralized LPT Construction Distributed LPT Construction Experiments Conclusion

3 Introduction (1/2) Why data aggregation?  A event can trigger many nearby sensor nodes in sensor networks.  Transmitting consumes much higher energy than other actions.  Nearby sensor nodes aggregate data and remove any redundancy can reduce communication cost.

4 Introduction (2/2) The paper proposes the lifetime-preserving tree (LPT) for data aggregation. LPT aims to prolong lifetime of sources, the node with higher energy are chosen as the aggregation parent. When a node is no longer functional or a link is broken, the LPT will be re-constructed.

5 Related Works (1/2) Distributed aggregation algorithms  Energy-aware data aggregation tree (EADAT) algorithm [4] :  Tree-based solution: Choose a sink as root, each node has expiration time inversely proportional to energy. Use the time to control node select parent.  Energy-aware spanning tree construction (E-Span) algorithm [10] :  Tree-based solution: Choose the highest-energy node as root, and other nodes choose aggregation parent by shortest path to root.  Hybrid energy-efficient distributed clustering (HEED) approach [8] :  Cluster-based solution: nodes with higher energy has higher probability to become cluster head

6 Related Works (2/2) Related Researches  Dynamic convoy tree-based collaboration (DCTC) framework for tracking a mobile target [5] :  A dynamic tree is created by adding or pruning nodes near the target to track the moving target.  Energy-efficient area monitoring for sensor networks [6] :  Periodically searching the smallest subset of nodes that cover the monitoring area.

7 Problem Formulation (1/3) Not all tree structures are suitable for aggregation

8 Problem Formulation (2/3) LPT approach intend to extend the refresh time of a tree to reduce the cost of maintenance.  Assign nodes with higher energy to be the parents Energy definition  branch : route from a root to a leaf node  set of nodes along a branch rooted at node “x”  set of nodes in a tree rooted at node “y”

9 Problem Formulation (3/3) LPT construction problem :  : set of possible routes form “s” to “r”  : energy of a tree rooted at “z”  : energy of a branch “h” with the leaf node “f” and root “g”

10 Centralized LPT Algorithm (1/3) Centralized LPT construction 1. Arrange nodes in ascending energy levels 2. Start from the least-energy node 3. Remove all the links to the node except from its highest- energy neighbor 4. Check If the removal disconnects the existing graph true  current selected node is the bottleneck node, restore the removed links and selected one of the remained nodes as root, run shortest path algorithm and return. false  go to the next least-energy node and jump to step3. 5. If finally it comes to the last node, there is no bottleneck in the graph, select the last node as root, then run shortest path algorithm and return.

11 Centralized LPT Algorithm (2/3)  : the highest-energy neighbor of node n  : the link between n and j  : the highest-energy node of the N sources

12 Centralized LPT Algorithm (3/3)

13 Distributed LPT Construction (1/5) Step1 - Exploring the highest-energy branch from every source to any root (any node): 1. Each source node initiate a message containing its energy information and broadcast it. 2. When another source receives this message, it appends its energy information and broadcast only if : It has not received this message from a new initiating node Or it has forwarded the message having a lower energy  Eventually, the message with highest branch energy will arrive at the root.

14 Distributed LPT Construction (2/5)  : energy of node n  : pair of energy and ID information of node n  : branch energy from initial node i to node j through route k  : branch list from initial node i to node j through route k, the format is

15 Distributed LPT Construction (3/5) Step2 - Constructing a tree spanning for every source : 1. Each source node has an initial tree structure that only comprises of itself. 2. Each source node incrementally updates its tree : On receiving any message with an unknown initiating node When receiving node identifies a message with higher branch energy.  Each source node avoids creating loops during updates: Reject a new-arrived branch if each parent on the new-arrived branch does not match the route on the already-stored branch.

16 Distributed LPT Construction (4/5)

17 Distributed LPT Construction (5/5) Step3 - Searching a lifetime-preserving tree : 1. Each source node initially selected stored tree as its LPT 2. Each source node broadcast its tree structure 3. Each source node update the selected LPT and forward it when receiving a tree with higher energy 3. Finally, every source get highest-energy tree as its LPT  : LPT of node n  : energy of LPT n

18 Experiments (1/7) Simulation Model  Nodes number : M = 50, 100, 150, 200, 250  Nodes density : D = 50/160 2 (nodes/meter 2 )  Source nodes number : N = 0.1M  Sinks number : 5  Radio range : 40 meter  Implement on Directed Diffusion in ns-2 simulator  Each source generates random data reports (fixed 136 byte) in constant interval 1 packet/sec  Nodes energy : source  10 ~ 15J, others  much higher  Energy consumption : idle  35mW, receive  395mW, transmit  660mW, data processing and aggregation cost  ignore

19 Experiments (2/7) Tree energy: Distributed LPT vs. Centralized LPT

20 Experiments (3/7) Dissipated Energy

21 Experiments (4/7) Node lifetime (5 sources)

22 Experiments (5/7) Nodes lifetime (25 nodes)

23 Experiments (6/7) Average data packet transfer delay

24 Experiments (7/7) Average data packet delivery ratio

25 Conclusion The paper proposes a useful LPT for data aggregation. Average node lifetime using LPT are higher than that of Directed Diffusion and E-Span. LPT also maintains a low average packet transfer delay and a high packet delivery ratio.