بروتوكول توجيه فعال وحساس للوقت في شبكة المتحسسات اللاسلكية

Rp. Efficient Time Sensitive Routing Protocol for Wireless Sensor Network pdf.

Abstract
Wireless sensor networks (WSNs) consist of hundreds or thousands of limited sensor devices which have many constraints that affect its performance. The most important concept in WSN is the way to deliver the gathered data from the network nodes to the Base Station. This is called routing techniques. The hierarchical routing topology is used widely by many researchers. Chain-Based and Cluster-Based have many drawbacks separately. To build an efficient routing protocol, the researchers have to pay attention to delay, which is a critical problem in WSNs. In order to reduce the delay of packet delivery as possible, it is essential to create effective routing protocols. This research provides mixed hierarchical routing protocols, which are Chain-Based and Cluster-Based. They are used in a deterministic deployment strategy with a fixed distance between sensor nodes. The proposed routing protocol, called Efficient Time-Sensitive Routing Protocol (ETSRP), has three mechanisms: Cluster-Chain Formation mechanism, Cluster-Head Election mechanism, and Inter-connection mechanism. The basic parameters used to elect the Cluster Heads are distance, remaining energy, and the number of connections in each node. The performance of ETSRP was compared with three routing protocols, which are Grid-Power-Efficient Gathering in Sensor Information Systems (Grid-PEGASIS), Tow Stage Chain Routing Protocol (TSCP), and Deterministic Chain Based Routing Protocol (DCBRP), based on Network simulator NS-3 using End-to-End Delay, power consumption, Cluster Heads power consumption, First Node Die (FND), and Energy× Delay performance metrics. The results of simulation show that the total End -to- End Delay for ETSRP is (0.021) less than the results of DCBRP, TSCP, and Grid-PEGASIS of total End -to- End Delay which are (0.036), (0.99) and (0.085) respectively. ETSRP can be used in many applications in real world to speed up in delivered data such as smart cities, farming and environmental sensing.