PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

Abstract

This paper proposed a "Probabilistic and Deterministic Tree-based Routing for WSNs (PDTR)". The PDTR 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 e 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. of 22positions, through other localization technologies [2,3]. Other proposed WSN localization technologies are based on different measurement techniques such as the time-of-arrival (TOA), angle-of-arrival (AOA), and received signal strength indicator (RSSI) [4][5][6][7]. Later,in [8,9], the cooperative localization technology was developed for hybrid networks containing mobile and static sensors. Recently,in [10], the authors proposed a centralized expectation maximization-based passive localization technology for asynchronous receivers (EMplLaR). Being aware of the nodes' location allows the use of many power control techniques to reduce energy consumption.Data forwarding toward the sink via deterministic protocols may cause quick energy depletion for some nodes, due to the sender node's forwarder unique choice, which causes a topology disruption, thus a quick network death. Meanwhile, the probabilistic routing approach came with the improvement, such that each sensor has to select its forwarder from a set of its candidates according to their routing probabilities, based on its neighborhood knowledge, thus providing a longer network lifetime.From the existing probabilistic and deterministic protocols, we concluded that to minimize the energy consumption and prolong the network lifetime, the protocol should depend on three factors. Reference [11] proposed a Hop-based Routing Protocol based on an Energy Efficient Minimum Spanning Tree for WSN. From the simulation results, it was proved that the proposed protocol improved the network in terms of energy consumption and lifetime extension, hence the importance of the hop-count factor. Article [12] also considered this factor in the presented protocol called (STDD). The authors in [13] showed tha...

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“…The authors of [ 16 ] integrate multiple cognitive metrics, namely, the distance, number of associated nodes, and residual energy using the weighted-sum method. The authors of [ 17 ] integrate three cognitive metrics, such as the hop count, residual energy, and link quality using the heuristic coefficients. The authors of [ 18 ] consider four attributes, i.e., the link expiration time, trip time, node speed, and lifetime, and integrate them using an “if-else” statement.…”

“…We compare the performance of the proposed scheme with the linear weighted sum-based parent selection algorithm [ 17 ] and the MCMD-based parent selection algorithm. The linear weighted sum-based scheme considers the number of hops, buffer occupancy ratio, link quality, and residual energy as decision metrics.…”

“…To address this problem, some studies [ 14 , 15 , 16 , 17 , 18 ] attempt to select the best parent node taking into account multiple cognitive metrics. To jointly achieve multiple objectives, they employ a linear weighted-sum method in which each node calculates the weighted cost by integrating multiple metrics.…”

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