DICE

Author:

Gună Ştefan1,Mottola Luca2,Picco Gian Pietro1

Affiliation:

1. University of Trento, Italy

2. SICS Swedish ICT, Stockholm, Sweden

Abstract

Wireless sensor networks (WSNs) enable decentralized architectures to monitor the behavior of physical processes and to detect deviations from a specified “safe” behavior, for example, to check the operation of control loops. Such correct behavior is typically expressed by global invariants over the state of different sensors or actuators. Nevertheless, to leverage the computing capabilities of WSN nodes, the application intelligence needs to reside inside the network. The task of ensuring that the monitored processes behave safely thus becomes inherently distributed, and hence more complex. In this article we present DICE, a system enabling WSN-based distributed monitoring of global invariants. A DICE invariant is expressed by predicates defined over the state of multiple WSN nodes, such as the expected state of actuators based on given sensed environmental conditions. Our modular design allows two alternative protocols for detecting invariant violations: both perform in-network aggregation but with different degrees of decentralization, therefore supporting scenarios with different network and data dynamics. We characterize and compare the two protocols using large-scale simulations and a real-world testbed. Our results indicate that invariant violations are detected in a timely and energy-efficient manner. For instance, in a 225-node 15-hop network, invariant violations are detected in less than a second and with only a few packets sent by each node.

Funder

Seventh Framework Programme

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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1. RF Information Harvesting for Medium Access in Event-driven Batteryless Sensing;2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN);2022-05

2. makeSense: Simplifying the Integration of Wireless Sensor Networks into Business Processes;IEEE Transactions on Software Engineering;2019-06-01

3. Systematically Ensuring the Confidence of Real-Time Home Automation IoT Systems;ACM Transactions on Cyber-Physical Systems;2018-07-31

4. ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications;2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS);2018-07

5. Making ‘Glossy’ Networks Sparkle: Exploiting Concurrent Transmissions for Energy Efficient, Reliable, Ultra-Low Latency Communication in Wireless Control Networks;Lecture Notes in Computer Science;2014

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