A Smart Sensing and Routing Mechanism for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) have long been used for many applications. The efficiency of a WSN is subject to its monitoring accuracy and limited energy capacity. Thus, accurate detection and limited energy are two crucial problems for WSNs. Some studies have focused on building energy-efficient transmission mechanisms to extend monitoring lifetimes, and others have focused on building additional systems to support monitoring for enhanced accuracy. Herein, we propose a distributed cooperative mechanism where neighboring sensors mutually confirm event occurrences for improved monitoring accuracy. Moreover, the mechanism transmits events in a time- and energy-efficient manner by using smart antennae to extend monitoring lifetimes. The results of the simulations reveal that monitoring lifetime is extended and time for event notifications is shortened under the proposed mechanism. The evaluations also demonstrate that the monitoring accuracy of the proposed mechanism is much higher than that of other existing mechanisms.

Charging Protocol for Partially Rechargeable Mobile Sensor Networks

“…This mechanism reduces the energy consumed for data transmissions. In Hung’s [ 5 ] proposed smart routing mechanism, different transmission periods are arranged among surrounding neighbors. The use of a smart antenna could substantially improve parallel transmissions.…”

“…Thus, limited sensor energy is a crucial challenge in the design of WSNs. To alleviate such energy limitations, many researchers have proposed energy-efficient routing mechanisms for WSN applications [ 4 , 5 , 6 , 7 ]. In addition, some researchers have proposed applying different sensor duty cycles to prolong the lifetime of WSNs [ 8 , 9 ].…”

Enhanced Routing Algorithm Based on Reinforcement Machine Learning—A Case of VoIP Service

“…In the latest decades, Machine Learning algorithms have come to be used in several applications [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Thus, these algorithms can be applied into routing control protocols [ 29 , 30 , 31 ], specifically Reinforcement Learning (RL) is increasingly being used to solve routing problems [ 32 , 33 , 34 ]. In RL, an agent must be able to learn how to behave in a dynamic environment through iterations [ 35 ].…”