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Throughput extremization is an important facet of performance modeling for low-power wide-area network (LP-WAN) wireless networks (e.g., LoRaWAN) as it provides insight into the best and worst case behavior of the network. Our previous work on throughput extremization established lower and upper bounds on throughput for random access channel assignment over a collision erasure channel in which the lower bound is expressed in terms of the number of radios and sum load on each channel. In this paper the lower bound is further characterized by identifying two local minimizers (a load balanced assignment and an imbalanced assignment) where the decision variables are the number of radios assigned to each channel and the total load on each channel. A primary focus is to characterize how macro-parameters of the optimization, i.e., the total number of radios, their total load, and the minimum load per radio, determine the regions under which each of the local minimizers is in fact the global minimizer.
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Quick (and Dirty) Aggregate Queries on Low-Power WANs
Low-Power Wide-Area Networks (LP-WANs) are seeing wide-spread deployments connecting millions of sensors, each powered by a ten-year AA battery to radio infrastructure, often miles away. By design, iteratively querying all sensors in an LP-WAN may take several hours or even days, given the stringent battery limits of client radios. This precludes obtaining even an approximate real-time view of sensed information across LP-WAN devices over a large area, say in the event of a disaster, fault or simply for diagnostics.This paper presents QuAiL 1 , a system that provides a coarse aggregate view of sensed data across LP-WAN devices over a wide- area within a time span of just one LP-WAN packet. QuAiL achieves this by coordinating multiple LP-WAN radios to transmit their information synchronously in time and frequency despite their power constraints. We design each client's transmission so that the base station can retrieve an approximate heatmap of sensed data by exploiting the spatial correlation of this data across clients. We further show how our system can be optimized for statistical and machine learning queries, all while maintaining the security and privacy of sensed data from individual clients. Our deployment over a 3 sq. km. LP-WAN deployment around CMU campus in Pittsburgh demonstrates a 4x faster information retrieval versus the state-of- the-art statistical methods to retrieve the spatial sensor heatmap at a desired resolution.
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- PAR ID:
- 10194059
- Date Published:
- Journal Name:
- 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
- Volume:
- 2020
- Page Range / eLocation ID:
- 277 to 288
- 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/10.1109/IPSN48710.2020.00031
