Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage

Area of Science:

• Sensor Networks
• Signal Processing
• Control Systems

Background:

• Sensor networks with shared channels face transmission constraints, especially with heterogeneous or distant sensors.
• These constraints necessitate strategies for efficient data sharing and processing in asynchronous environments.

Purpose of the Study:

• To address periodic estimation problems in sensor networks with a shared communication channel.
• To develop a novel transmission strategy to overcome inevitable transmission constraints.
• To present a periodic mixed storage strategy for updating sensor buffer information.

Main Methods:

• A stochastic competitive transmission strategy enabling asynchronous communication between sensors and the fusion center (FC).
• A periodic mixed storage strategy combining zero-input and hold-input mechanisms for buffer management.
• Derivation of a recursive Kalman filtering algorithm for the FC to generate state variable estimates.

Main Results:

• The proposed stochastic competitive transmission strategy effectively manages communication constraints.
• The periodic mixed storage strategy ensures efficient updating of stored information.
• The recursive Kalman filtering algorithm successfully generates periodic state estimates.

Conclusions:

• The developed strategies are effective for periodic estimation in sensor networks with shared channels.
• The asynchronous communication and data handling methods improve estimation accuracy under transmission constraints.