Information centric networking based collaborative edge computing framework for the Internet of Things
The Internet of Things (IoT) has connected billions of devices and its proliferation will continue. As IoT grows, so do the volumes of data it produced and exchanged. The challenge lies in efficiently processing the massive amounts of IoT data. Moreover, IoT applications prioritize extracting meaningful knowledge rather than building connections with multiple devices. This results in a mismatch between the host-centric nature of the current Internet and the information-centric demands of IoT applications. To address these challenges, this thesis presents an Information Centric Networking (ICN) based collaborative edge computing framework for distributed IoT data processing. Firstly, the functional architecture is investigated to enable in-network data processing in IoT edge environments. Within this architecture, three software components, namely Computation Manager, Computation Executor and Function Repository, collaborate to resolve, deploy and execute IoT jobs. This thesis leverages the powerful and prevalent MapReduce paradigm in the architecture design. The ICN-based implementation empowers MapReduce job execution by categorizing Computation Executors as mappers and reducers, developing a distributed computational job tree construction protocol for the Computation Manager, and defining an ICN naming scheme for request expression and data/function acquisition. The Function Repository is distributed and maintained by each Computation Executor, which retrieves and saves functions by parsing users' requests. Experimental simulations have verified the feasibility of the proposed design and demonstrated its effectiveness in reducing network traffic. Secondly, this thesis improves the proposed ICN-based computing framework by considering the resource constraints of heterogenous edge devices. It classifies edge devices into two types: processing-capable nodes (i.e. mappers and reducers) and forwarding-only nodes (called forwarders). Both types of nodes join in the computational job tree construction procedure. A job maintenance scheme is developed to disseminate IoT jobs to appropriate devices and coordinate their collaboration in serving multiple jobs simultaneously. Performance evaluation tests have confirmed the effectiveness of the proposed framework, indicating decreased network traffic compared to the centralized data processing approach. Thirdly, this thesis enhances the proposed framework to ensure exactly once data computation. Interruptions in IoT network connections during edge collaboration can lead to data loss or duplicated data transmission and processing, which is unacceptable for IoT applications with exactly once computation requirement. Although checkpoint-based schemes have been successfully developed in traditional big data processing frameworks to achieve exactly once data delivery/processing, it is challenging to directly apply these solutions in IoT scenarios due to the differences between IoT networks and datacentre environments. This thesis identifies three specific challenges of achieving exactly once computation in IoT collaborative edge scenarios and devises a five-phase protocol to address them. The proposed protocol consists of a job execution procedure for normal job operations and a job recovery procedure to handle network failures. Simulation tests have shown that the proposed design outperforms the checkpoint-based benchmark solution in terms of network traffic and job execution time.
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