In-network Computation for IoT Data Processing with ActiveNDN in Wireless Sensor Networks

Abstract

In-network computation allows application functions to be computed within the network directly on raw sensor data, and publish real-time responses or alerts to users in the field. We propose to extend Named Data Networking (NDN) with in-network computation by embedding functions in an additional entity called Function Library, which is connected to the NDN forwarder in each NDN router. Function calls can be expressed as part of the Interest names with proper name prefixes for routing, with the results of the computation returned as NDN Data packets, creating an ActiveNDN network. Our main focus is on performing robust distributed computation, such as analysing and filtering raw data in real-time, as close as possible to sensors in an environment with intermittent Internet connectivity and resource-constrained computable IoT nodes. In this paper, we describe the design of ActiveNDN with a small prototype network as a proof of concept. Extensive simulation experiments were conducted to investigate the performance and effectiveness of ActiveNDN in large-scale wireless IoT networks. We also compared the real-time processing capabilities of ActiveNDN with those of centralised edge computing. It has been shown that with the proposed minimal changes to NDN, low latency can be achieved so that time-critical IoT data processing of sensor data can meet the required deadlines.