A handheld diagnostic system for 6LoWPAN networks

Abstract

The successful deployment of low-power wireless sensor networks (WSNs) in real application environments is a much broader exercise than just the simple instrumentation of the intended monitoring site. Many problems, from node malfunctions to connectivity issues, may arise during commissioning of these networks. These need to be corrected on the spot, often within limited time, to avoid undesired delays in commissioning and yet a fully functional system does not guarantee that no new problems will occur after leaving the site. In this paper we present the first ever (to our knowledge) implementation of a handheld diagnostic system for fast on-site commissioning of low-power IPv6 (6LoWPAN) WSNs as well as troubleshooting of network problems during and after deployment. This system can be used where traditional solutions are insufficient to ascertain the root causes of any problems encountered at no additional complexity in the implementation of the WSN. The embedded diagnosis capability in our system is based on a lightweight decision tree that distills the functioning of communication protocols in use by the network, with a major focus on interoperable IPv6 standards and protocols for low-power WSNs. To show the applicability of our system, we present a set of experiments based on results from a real deployment in a large construction site. Through these experiments, important performance insights are gained that can be used as guidelines for improvement of operation and maintenance of 6LoWPAN networks.

Inter-Process Correlation Model based Hybrid Framework for Fault Diagnosis in Wireless Sensor Networks

“…An in-situ diagnostic system for low power IPv6 WSNs is presented in [15]. The system consists of traffic monitor to capture IEEE 802.15.4 data frames, a frame decoder and a decision tree.…”

“…Decision tree is found to be a natural contender for root cause analysis in WSNs [15] due to inherent relationships of network entities and data emulating a tree structure. In the hybrid framework, the decision tree utilizes inter-process correlation model for fault diagnosis.…”

Industrial IoT Monitoring: Technologies and Architecture Proposal

“…In [ 31 ], the authors analyse an extensive set of diagnostic tools. In this section, some of the tools described in [ 31 ], as well as more recent tools like [ 50 , 51 , 52 , 53 , 54 ], will be presented from a diagnostic target perspective, organizing their presentation into network-based, firmware-based, and hardware-based tools.…”