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Connectivity is at the heart of the future Internet-of-Things (IoT) infrastructure, which can control and communicate with remote sensors and actuators for the beacons, data collections, and forwarding nodes. Existing sensor network solutions cannot solve the bottleneck problems near the sink node; the tree-based Internet architecture has the single point of failure. To solve current deficiencies in multi-hop mesh network and cross-domain network design, we propose a mesh inside a mesh IoT network architecture. Our designed "edge router" incorporates these two mesh networks together and performs seamlessly transmission of multi-standard packets. The proposed IoT testbed interoperates with existing multi-standards (Wi-Fi, 6LoWPAN) and segments of networks, and provides both high-throughput Internet and resilient sensor coverage throughout the community.
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A multi-step and resilient predictive Q-learning algorithm for IoT: a case study in water supply networks
In this paper, we consider the problem of deriving recommended resilient and predictive actions for an IoT network in the presence of faulty components and malicious agents. The IoT, combining physical and cyber devices, is formulated as a directed graph with a known topology whose objective is to maintain a constant and resilient flow between a source node and a destination node. The optimal route through this network is evaluated via a predictive and resilient Q-learning algorithm which takes into account historical data about irregular operation, including faults and attacks. To showcase the efficacy of our approach, we utilize anonymized data from Arlington County, Virginia to obtain predictive and resilient scheduling policies for a smart water supply system while avoiding neighborhoods with leaks and other faults.
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- Award ID(s):
- 1851588
- PAR ID:
- 10078412
- Date Published:
- Journal Name:
- Proceedings of the 8th International Conference on the Internet of Things
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3277593.3277605
