Autonomous monitoring framework for resource-constrained environments

Sajid Nazir* (Corresponding Author), H Hamdoun, Fabio Verdicchio, Gorry Fairhurst

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
17 Downloads (Pure)


The availability of low-cost imaging devices for embedded applications has enabled development of wireless monitoring systems capable of acquiring and transmitting both image and video data. Remote deployment of such systems is often constrained by limited power resources, thus a system must operate autonomously, balancing operational needs against available resources. This paper describes a framework for the design and implementation of an autonomous embedded remote monitoring system employing information-driven sensing to conserve energy and extend the system deployment lifetime.

The results from two case studies show improvements over a conventional system and other similar systems through the use of intelligent algorithms for reliable event detection and enhanced system operational lifetime by efficient utilisation of limited resources. The results are applicable to low-power battery-operated field devices offering better resource utilisation in disaster management systems, intelligent transportation and remote monitoring.
This paper describes the design of an autonomous embedded remote monitoring system based on Raspberry Pi that was developed for RemoteStream project and provides innovative generalized solutions to the deployment challenges. The design methodology is verified through architecting two case studies. The first study deals with motion-activated event monitoring, that is, the events are generated based on motion events taking place in the field of interest. The second study covers the threshold-activated event monitoring to detect onset of freezing conditions for ensuring serviceability of road and rail networks. The results show the improvements over non-adaptive system and highlight the importance of augmenting the system with intelligent algorithms for an enhanced lifetime through better utilization of the limited resources.
Original languageEnglish
Pages (from-to)137-155
Number of pages19
JournalCyber-Physical Systems
Issue number3
Early online date11 Sept 2018
Publication statusPublished - 2018

Bibliographical note

The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL:


  • embedded sensors
  • remote monitoring
  • adaptive processing
  • power management
  • event detection
  • algorithm


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