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A Low-Latency and Energy-Efficient Algorithm for Convergecast in Wireless Sensor Networks

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A Low-Latency and Energy-Efficient Algorithm for Convergecast in Wireless Sensor Networks

Authors

Sarma Upadhyayula, Valliappan Annamalai, Sandeep Gupta

Presented by

Bin Wang

Arizona State University

- Introduction
- Problem Description
- System Model
- Algorithm
- Results
- Conclusions and Future Work
- References

- Wireless Sensor Networks (WSN) – long life expectancy
- Power Anemic
- Communication consumes maximum energy
- Data aggregation (convergecast) is a frequent operation in WSN – important to minimize its energy consumption
- Prior Work: PEGASIS [Step02], LEACH[Wendi00], CCTCCA[Valli03]
- Typically, convergecast follows broadcast – broadcast tree for convergecast. [Bhas02]

- Prior work concentrate on energy efficiency alone.
- We have dual objective – Energy-Efficiency and Low-Latency
- Conventional approach not necessarily the best approach

- n nodes in the network
- Data from all the nodes to be collected at a central node
- Single Hop or Multi-Hop communication
- Energy consumed for communication is proportional to distance ( , between 2 and 4)[Wendi00]
- Objective # 1: Find a route connecting all nodes to central node consuming minimum energy.
- Objective # 2: Minimum latency

- Assumptions
- Nodes are static and clocks are synchronized
- Every node has only one transceiver
- A node can transmit or receive at a time but not both
- Intermediate nodes concatenate the data they receive during upstream transmission
- Intermediate nodes wait until it receives data from all the nodes in whose path it lies.

is the electrical energy required on the circuit of transceiver

is the amplification energy required to transmit a unit of data over

unit distance

k is the size of the data packet transmitted by a node

r is the distance between communicating nodes

- Energy Model [Wendi00]

- Latency Model [Valli03]
- Let be time taken to transmit longest data packet
- Latency is the total time required to transmit data from all the nodes to the central node

- Latency Model
- Balanced trees increases possibility of multiple simultaneous transmissions
- : number of children per node where is a positive integer
- If, due to the rule, a node will be left out of the tree – overlook the rule.

- Rationale for Tree Construction
- Broadcast trees may not be suitable for convergecast

- Constructs tree following greedy approach
- A set of nodes chooses closest neighbors as its children – subject to
- This process is followed iteratively until all the nodes in the network join the tree

Network

Tree

- A fixed number of CDMA codes are given
- Each node is assigned a triplet (Transmission Code, Reception Code, Transmission Time Slot)
- Reception code of a node and Transmission code of all its children are same
- A node uses a time slot and a code for transmission if
- Its parent is receiving using same code
- Choosing the code and time slot will avoid any collisions with all of its neighbors

Network

(Transmission Code, Reception Code, Transmission Time Slot)

Energy for Convergecast ( = 3)

- [Valli03] and [CCA] consumes almost same amount of energy for convergecast
- [CCA] gains upto 8% over [Imrich87] for network of size >150 nodes

Latency for Convergecast ( = 3)

- [CCA] is almost 4 times faster than [Valli03] and 2 times faster than [Imrich87]

- Proposed a tree construction and channel allocation algorithm for convergecast satisfying two objectives
- Showed that broadcast trees are not efficient for convergecast
- The proposed work should be studied for distributed manner
- Cluster based convergecast can be studied in future work

[Valli03] V. Annamalai., S.K.S. Gupta and L. Schwiebert “On Tree-Based Convergecasting in Wireless Sensor Networks”. IEEE Wireless Communications and Networking Conference 2003, New Orleans 2003.

[Imrich87] I. Chalmatac. and S. Kutten “Tree-Based Broadcasting in Multihop Radio Networks”. IEEE Transactions on Computers Vol. C-36, No. 10, Oct 1987.

[Wendi00] W. R. Heinzelman, A. Chandrakasan and H. Balakrishnan “Energy-Efficient Communication Protocol for Wireless Micro Sensor Networks”. Proceedingsof the Hawaii International Conference on System Science, Jan 2000.

[Step02]S. Lindsey, C. Raghavendra, K. M. Sivalingam “Data Gathering Algorithms in Sensor Networks Using Energy Metrics”. IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 9, Sept 2002.

[Bhas02] B. Krishnamachari, D. Estrin and S. Wicker “Impact of Data Aggregation in Wireless Sensor Networks”. International Workshop on Distributed Event-Based Systems (DEBS, ‘02) Vienna, Austria, July 2002.