Section: New Results

Sensor networks monitoring

Participants : Alexandre Boeglin, Laurent Ciarletta, Olivier Festor, Abdelkader Lahmadi [contact] , Emmanuel Nataf, Bilel Saadallah.

Low Power and Lossy Networks (LLNs) are made of interconnected wireless devices with limited resources in terms of energy, computing and communication. The communication channels are low-bandwidth, high loss rate and volatile wireless links subject to failure over time. They are dynamic and the connectivity is limited and fluctuant over time. Each node may loss frequently its connectivity with its neighborhood nodes. In addition, link layer frames have high constrains on their size and throughput is limited. These networks are used for many different applications including industrial automation, smart metering, environmental monitoring, homeland security, weather and climate analysis and prediction. The main issue in those networks is optimal operation combined with strong energy preservation. Monitoring, i.e the process of measuring sampled properties of nodes and links in a network, is a key technique in operational LLNs where devices need to be constantly or temporally monitored to assure their functioning and detect relevant problems which will result in an alarm being for- warded to the enterprise network for analysis and remediation.

During the year 2012, we developed novel approaches for the monitoring of LLNs. We developed and designed a novel algorithm and a supporting framework [18] that improves a poller-pollee monitoring architecture. We empower the poller-pollee placement decision process and operation by exploiting available routing data to monitor nodes status. In addition, monitoring data is efficiently embedded in any messages flowing through the network, drastically reducing monitoring overhead. Our approach is validated through both simulation, implementation and deployment on a 6LoWPAN-enabled network. Both simulations and large-scale testbed experiments assess the efficiency of our monitoring scheme. Results also demonstrate that our approach is less aggressive and less resource consuming than its competitors.

We developed a first fully operational CCNx stack [40] on a wireless sensor network. We implemented CCNx as a native C experimental extension of Contiki, an operating system dedicated to Internet of Things applications. Our extension [33] is based on the reference implementation of CCNx modified to run as a network driver on top of different available MAC protocols implementations in Contiki. Our goal is to design a monitoring and configuration framework that benefits from the content-centric approach to efficiently collect desired management content and apply in-network processing functions for nodes configuration and monitoring. This includes extending naming schema with monitoring oriented processing functions, optimizing data interests to minimize the communication overhead.