Juan Rodriguez
Senior Software Engineer - M.Sc. in Computer Science.
Energy-aware routing based on graph theory for WSNs
Juan Rodriguez - Advanced Discrete Maths - Uninorte 2021
WSN ==
[1]
[1]
[2]
G = (V, E)
Constraints
- Limited battery
- Deployed randomly
- Fault tolerance
Energy-aware balancing routing
Prolong the lifetime of the network
Preserve qualify of service (packet delivery ratio)
State of art
[6]
Clustering
[5]
Hierarchical
Clustering
[3]
- Unequal Clustering Radius
- Vote Method
-
Forming Connected Graph
[4]
Hierarchical
[5]
[5]
BFS
[2]
[*]
[2]
[*]
IBFS
A priority queue is maintained based on minimum distance criteria
[4]
[4]
IBFS limitations
- It does not take into account the quality of service
-
Link reliability is missing
[2]
EBFS
[2]
[2]
IBFS vs EBFS
[2]
IBFS = 17−>15−>14−>10−>8−>4−>2
Total path length = 1.41 + 2.24 + 2.24 + 1.41 + 1.41 + 2.24 = 10.95 m.
Energy consumed = 1.9881 + 5.0176 + 5.0176 + 1.9881 + 1.9881 + 5.0176 = 21.071 J.
When all nodes are alive
EBFS = 17−>15−>11−>7−>20−>3−>2
Total path length = 1.41+2.24+2+2.24+2.24+2= 12.13 m.
Energy consumed = 1.9881 + 5.0176 + 4 + 5.0176 + 5.0176 + 4 = 24 J.
IBFS vs EBFS
[2]
IBFS = 17→14→10→8→4→2
Total path length = 4.47+2.24+1.41+1.41+2.24= 11.77 m.
Energy consumed = 33.99 J.
When node 15 becomes faulty
EBFS = 17→13→11→7→20→3→2
Total path length = 2.24+2+2+2.24+2.24+2=12.72m.
Energy consumed = 27.0528J.
IBFS vs EBFS
[2]
IBFS vs EBFS
[2]
[2]
[2]
[2]
Conclusions
[2]
Prolong the lifetime of the network ✅
Preserve qualify of service (packet delivery ratio) ✅
References
[1] Hegde, Rajeshwari & Sudha B G, Prema & Bhat, Mamatha. (2019). A Graph Theory Approach to Load Balancing in Wireless Sensor Network.
[2] Mahajan, S., Malhotra, J., & Sharma, S. (2015). Energy balanced optimum path determination based on graph theory for wireless sensor network. IET Wireless Sensor Systems, 5(6), 290–298. doi:10.1049/iet-wss.2014.0061.
[3] Xia, H., Zhang, R., Yu, J., & Pan, Z. (2016). Energy-Efficient Routing Algorithm Based on Unequal Clustering and Connected Graph in Wireless Sensor Networks. International Journal of Wireless Information Networks, 23(2), 141–150. doi:10.1007/s10776-016-0304-5.
[4] Mahajan, S., & Malhotra, J. (2011). Energy efficient path determination in wireless sensor network using BFS approach. Wireless Sensor Network, 3(11), 351
[5] Banerjee, I., Roy, I., Choudhury, A. R., Sharma, B. D., & Samanta, T. (2012). Shortest path based geographical routing algorithm in wireless sensor network. 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS). doi:10.1109/codis.2012.6422188.
[6] Baranidharan, B. (2011). A New Graph Theory based Routing Protocol for Wireless Sensor Networks. International Journal on Applications of Graph Theory In Wireless Ad Hoc Networks And Sensor Networks, 3(4), 15–26. doi:10.5121/jgraphoc.2011.3402
Energy balanced optimum path determination based on graph theory for WSNs
By Juan Rodriguez
