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Energy Efficient Data Gathering Algorithms in Sensor Networks

Vikramaditya. Energy Efficient Data Gathering Algorithms in Sensor Networks. What is a Sensor Network?. Sensor networks mainly constitute of inexpensive sensors densely deployed for data collection from the field in a variety of scenarios

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Energy Efficient Data Gathering Algorithms in Sensor Networks

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  1. Vikramaditya Energy Efficient Data Gathering Algorithms in Sensor Networks

  2. What is a Sensor Network? • Sensor networks mainly constitute of inexpensive sensors densely deployed for data collection from the field in a variety of scenarios • A sensor node is an autonomous device with integrated sensing, processing, and communication capabilities

  3. Data gathering in Sensor Network • A typical application in sensor network is gathering sensed data at a distant base station. • Each sensor node has power control and the ability to transmit data to any other sensor node or directly to the BS. • In each round of this data gathering application, all data from all nodes need to be collected and transmitted to the BS, where the end user can access the data

  4. The difficulties in collect and transmit data in Sensor Network • Each node transmits its data directly to the Base Station? Not suggested • BS is usually far away from sensors such communication will be a high cost and drain the power quickly 2. Sensor's battery is not replaceable, and sensors may operate in hostile or remote environments • Energy consumption is considered as the most important concern in sensor network

  5. Approaches for Data Gathering

  6. Direct Approach • Each sensor sends its data directly to the base station • quickly drain the battery of the nodes and reduce the system lifetime • the only receptions in this protocol occur at the base station • Only the base station is close to nodes, or the energy required to receive data is large, direct approachmay be an acceptable (and possibly optimal) method of communication

  7. MTE Based Approach • Nodes route data to the Base Station through intermediate nodes • Intermediate nodes act as routers for other nodes’ data and work as sensing nodes as well • Intermediate nodes are chosen if and only if the transmit amplifier energy is minimized

  8. Clustering Based Approach • Nodes are organized into groups, or say clusters • Each cluster has a cluster head • Other nodes in the same cluster sends data to its cluster head • Cluster heads transmit the data to the Base Station • Only cluster heads can send data to the Base Station

  9. Clustering Based Approach (Continue) • Static & Dynamic Clustering Approach • The difference is the way to choose cluster heads • Static Clustering Approach has a fixed cluster heads • Dynamic Clustering Approach has dynamic cluster heads

  10. Clustering Approach An Example (Continue) All nodes marked with the same symbol belong to the same Cluster and the cluster-head nodes are marked with a ●.

  11. Chain Based Approaches • Each node receives from and transmits to close neighbours and takes turns being the leader for transmission to the Base Station • Assumed that all nodes have global knowledge of the network and employ the greedy algorithm • Starts with the furthest node from the BS to ensure that nodes farther from the Base Station have close neighbours • Data gathering is performed in rounds. In each round, each node receives data from one neighbour, fuses with its own data, and transmits to the other neighbour on the chain

  12. Chain Based Approaches An Example node c(2) is the leader. Node c(0) will pass its data to node c(1). Node c(1) fuses node c(0)’s data with its own and then transmits to the leader. Node c(3) and c(4) do the same thing. Node c(2) waits to receive data from both neighbours c(1) and c(3) and then fuses its data with its neighbours’ data. Finally, node c(2) transmits one message to the BS

  13. Energy-Efficient Data Gathering with Multiple Paths

  14. Multiple Path Construction Mechanism

  15. Data Forwarding Mechanism

  16. AN EXAMPLE SCENARIO

  17. Performance evaluation Environment • NS 2 Simulation • 200 Nodes • 1000 m x 1000 m Area • Each simulation runs for 300 Seconds • Each Node transmission range 250 m • CBR 40 Bytes Sized Traffic • Energy at Each Node 10 J • Energy Transmitting Data 0.6W • Energy Receiving Data 0.3 W

  18. Parameters Compared • Experiment 1 • Comparison EDGM with AODV a] Throughput b] Nodal Life • Experiment 2 • Energy Saving not considered. • Random Selection Technique was considered. a] Nodal Life in Dense Network, b] Nodal Life in Sparse Network.

  19. Throughput : EDGM vs. AODV

  20. Nodal Life: EDGM vs. AODV

  21. Nodal Life: Dense Network

  22. Nodal Life: Sparse Network

  23. Conclusion • Energy consumption is considered as the most important concern. • It is hard to say which approach we point in this paper is the best one. We only choose the approach fitting for the particular case. • Should talk more about there NS 2 Simulation should include Energy Equations if any included. • Static and Dynamic nodes evaluation should be included.

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