The traditional Shadowing model relies too much on parameters and specific environment, so its application occasions in WSN are restricted. This paper studies the wireless signal propagation model and proposes the ranging model based on BP neural network model, it has the ability of autonomous learning according to different environments, and can improve the precision of measuring distance, then enhance the adaptability to the environment. The simulation results show that the ranging model based on BP neural network model can reduce the ranging error, which has the adaptability to the environment.

A Ranging Model Based on BP Neural Network

In duty-cycled wireless sensor networks, energy efficiency and packet latency are two most important metrics in the design of medium access control and routing algorithms. However, these two proble...

Optimal sleep time controller based on traffic prediction and residual energy in duty-cycled wireless sensor networks:

In many sensor network applications, sink node needs to actively communicate with other sensor nodes in order to perform data forwarding operations. For those applications, there is usually adelay-bounded associated with them and require the messages sent to be received within a designated time bound. In energy harvesting sensor networks, limited energy from environment necessitates sensor nodes to operate at alow-duty-cycle. Sensor nodes work active briefly and stay asleep most of time. Such low-duty-cycle operation leads to communication delays in comparison with the always-active networks. In this paper, we address the data forwarding problems in an energy harvesting sensor network where energy efficiency and data freshness need to be balanced. To solve this problem, we propose autility-based delay bounded scheme for data forwarding MaxOpUtility-based scheme. MaxOpUtility scheme offers the ability to increase reliability through relay nodes selection as well as to ensure the timeliness. In add...

Ambassa Joel Yves

Papers

Delay-Bounded Data Forwarding in Low-Duty-Cycle Sensor Networks