The next generation of Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability to variations in link quality. We address this challenge by developing Receiver Oriented Policies (RECORP), which leverages the stability of IIoT workloads to build a solution that combines offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, RECORP policies share slots among multiple flows by assigning a coordinator and a set of candidate flows in the same slot. At run-time, the coordinator will dynamically execute one of the flows depending on what flows the coordinator has already received. The net effect of this strategy is that a node can dynamically repurpose the retransmissions remaining after receiving the data of an incoming flow to service other incoming flows opportunistically. Therefore, the flows that are executed in a slot can be adapted in response to the variable link conditions observed at run-time. Furthermore, RECORP also provides predictable performance: a policy meets the end-to-end reliability and deadline constraints of flows given probabilistic link qualities. When RECORP policies and schedules are configured to meet the same end-to-end reliability target of 99%, larger-scale multihop simulations show that across typical IIoT workloads, policies provided a median improvement of 1.63 to 2.44 times in real-time capacity as well as a median reduction of 1.45 to 2.43 times in worst-case latency.
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A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks
Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.
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- Award ID(s):
- 1750155
- NSF-PAR ID:
- 10094534
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Industrial Internet (ICII)
- Page Range / eLocation ID:
- 79 to 88
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%.more » « less
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null (Ed.)Emerging Industrial Internet-of-Things systems require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge is to provide predictable performance and agility in response to fluctuations in link quality, variable workloads, and topology changes. We propose WARP to address this challenge. WARP uses programs to specify a network’s behavior and includes a synthesis procedure to automatically generate such programs from a high-level specification of the system’s workload and topology. WARP has three unique features: (1) WARP uses a domain-specific language to specify stateful programs that include conditional statements to control when a flow’s packets are transmitted. The execution paths of programs depend on the pattern of packet losses observed at runtime, thereby enabling WARP to readily adapt to packet losses due to short-term variations in link quality. (2) Our synthesis technique uses heuristics to improve network performance by considering multiple packet loss patterns and associated execution paths when determining the transmissions performed by nodes. Furthermore, the generated programs ensure that the likelihood of a flow delivering its packets by its deadline exceeds a user-specified threshold. (3) WARP can adapt to workload and topology changes without explicitly reconstructing a network’s program based on the observation that nodes can independently synthesize the same program when they share the same workload and topology information. Simulations show that WARP improves network throughput for data collection, dissemination, and mixed workloads on two realistic topologies. Testbed experiments show that WARP reduces the time to add new flows by 5 times over a state-of-the-art centralized control plane and guarantees the real-time and reliability of all flows.more » « less
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Conclusions Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICII.2018.00017
