Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions.

Area of Science:

• Information Theory
• Wireless Communication Systems
• Internet of Things (IoT)

Background:

• Big data in IoT necessitates efficient data processing at fusion centers.
• Ensuring both data freshness and fidelity is crucial for real-time applications.
• Wireless Sensor Networks (WSNs) generate abundant data requiring optimized transmission strategies.

Purpose of the Study:

• To investigate methods for jointly optimizing data freshness and fidelity in WSNs.
• To develop a strategy for selecting the optimal number of sensor nodes for improved data quality.
• To address the challenges of transmitting timely and accurate data in IoT environments.

Main Methods:

• Utilizing Age of Information (AoI) for data freshness and Minimum Mean Square Error (MMSE) for data fidelity.
• Deriving explicit expressions for average AoI and MMSE in a WSN with selection combining (SC).
• Proposing a closed-form sub-optimal solution for the number of sensor nodes to balance AoI and MMSE.

Main Results:

• Explicit mathematical expressions for average AoI and MMSE were derived.
• A sub-optimal number of sensor nodes was identified to achieve a tradeoff between data freshness and fidelity.
• The proposed method demonstrated negligible errors in achieving the desired balance.

Conclusions:

• The proposed node number optimization effectively enhances both data freshness and fidelity.
• This approach is vital for improving the performance of data fusion in IoT applications.
• Balancing AoI and MMSE through sensor node selection offers a practical solution for WSN data quality.