Wireless Sensor Networks (WSNs) Lifetime Maximization
Dr: Rafia Ghoul
Dr: Jing He
Prof. Chunhua Wang
Computer Science and Technology
College of Computer Science and Electronic Engineering
This thesis addresses the problem of Wireless Sensor Networks (WSNs) Lifetime Maximization, by proposing at
the first stage a new technique to extract the overlapped sub-regions in the article titled: 'Finding the
Overlapped Sub-regions C2& C3 and the Maximum Covered Regions in WSN by using Net Arcs (NA) Method’.
It uses the idea of graph theory and transforms the graph model from theory state to the real time, and added some techniques, to find the all overlapped covered sub-regions (2-Edges) and (3-Edges), by taking in consideration the connectivity of the network. Finding these sub-regions and ensuring the connectivity between them, helps the researchers to use other optimization methods for network lifetime maximization based on the coverage. At the same aim, on the second stage, this thesis proposed many tree-based routing protocols such as: EEBTR: Energy Efficient Balanced Tree-Based Routing Protocol, that is designed to achieve a potential balancing of energy usage in each level of the tree such that nodes in the same level have approximately the same number of children. Simulation results show that EEBTR improves GTR performance in terms of both network lifetime and energy-efficiency. After that, it proposed a balanced tree-based protocol with energy saver algorithm (BTRES) that aims to let all sensors at the same level of tree consume a similar energy, it uses the same idea proposed in EEBTR but with the addition of a saver energy algorithm to enhance the balanced tree-based protocol. Simulation results show that BTRES provides better performance than ETR and EEBTR in term of energy balancing and lifetime. Later, it proposed a “Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks (PDTR)” for homogeneous sensors. That builds a tree from the leaves to the head (sink), according to the best elements in the initial probabilistic routing table, measured by the product of hops-count distribution, and transmission distance distribution, to select the best tree-paths. Each sender node forwards the received data to the next hop via the deterministic built tree. After that, when any node loses of its energy, PDTR updates the tree at that node. This update links probabilistically one of that node’s children to a new parent, according to the updated probabilistic routing table, measured by the product of the updated: Hops-count distribution, transmission distance distribution, and residual energy distribution at the loss of energy. By implementing the control parameters in each distribution, PDTR shows the impact of each distribution in the routing path. These control parameters are oriented by the user for different performances. The simulation results prove that selecting the initial best paths to root the packets via unicast, then improving the tree at the node with loss of energy by rooting the packets via anycast, leads to better performance in terms of energy consumption and network lifetime.
Ghoul, Rafia., He, Jing., Toure, Fatoumata Dite Mama.: 'Finding the Overlapped Sub-regions C2& C3 and the Maximum Covered Regions in WSN by using Net Arcs (NA) Method’. Advances in Computer Science Research, vol 44, 3rd International Conference on Wireless Communication and Sensor Network (WCSN2016). Doi:10.2991/icwcsn-16.2017.140.
Ghoul, Rafia., He, Jing., Hawbani Ammar., and Djaidja, Sana.:' Energy Efficient Balanced-tree Based Routing Protocol for Wireless Sensor Network (EEBTR)’. Proceeding of the Future Technologies Conference (FTC) 2019. pp 795-822.
Rafia, Ghoul., Jing, He., Sana, Djaidja., Mohammed A. A. Al-qaness., and Sunghwan, Kim.: 'PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks’. Sensors 2020, 20(6), 1697; Doi:10.3390/s20061697.
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