Two novel subjective logic-based in-network data processing schemes in wireless sensor networks

Wireless sensor networks (WSNs) consist of connected low-cost and small-size sensor nodes. The sensor nodes are characterized by various limitations, such as energy availability, processing power, and storage capacity. Typically, nodes collect data from an environment and transmit the raw or processed data to a sink. However, the collected data contains often redundant information. An in-network processing scheme attempts to eliminate or reduce such redundancy in sensed data. In this paper, we propose two in-network data processing schemes for WSNs, which are built based on a lightweight algebra for data processing. The schemes bring also benefits like decreased network traffic load and increased reliability for a network. For trust generation, we employ subjective logic based calculations to mitigate trust fluctuations caused by internal and external factors. Numerical results based on synthetic data reveal the effectiveness and preciseness of our proposed schemes.