Showing papers by "Huiping Cao published in 2022"

Backbone Index to Support Skyline Path Queries over Multi-cost Road Networks

Abstract: Skyline path queries (SPQs) extend skyline queries to multi-dimensional networks, such as multi-cost road networks (MCRNs). Such queries return a set of non-dominated paths between two given network nodes. Despite the existence of extensive works on evaluating different SPQ variants, SPQ evaluation is still very inefficient due to the nonexistence of efficient index structures to support such queries. Existing index building approaches for supporting shortest-path query execution, when directly extended to support SPQs, use unreasonable amount of space and time to build, making them impractical for processing large graphs. In this paper, we propose a novel index structure, backbone index , and a corresponding index construction method that condenses an initial MCRN to multiple smaller summarized graphs with different granularity. We also present efficient approaches to find approximate solutions to SPQs. Our extensive experiments on nine real-world large road networks show that our approaches can efficiently find meaningful approximate SPQ solutions by utilizing the compact index. The backbone index can be constructed with reasonable time, which dramatically outperforms the construction of other types of indexes for road networks. As far as we know, this is the first compact index structure that can support efficient approximate SPQ evaluation on large MCRNs.

PSXIII-7 Field Testing of Lora-wan Sensors for Real-Time Tracking and Biosensing of Brangus and Raramuri Criollo Cattle Grazing a Small Pasture

Abstract: The study’s objective was to investigate the use of Internet of Things (IoT) biosensors using Long Range Wide Area Network (LoRa-WAN) communication to compare the foraging behavior of two desert adapted cattle breeds. Two groups of mature Brangus (BA) and Raramuri Criollo (RC) cows were introduced into a small irrigated pasture (38ha) at the New Mexico State University’s Clayton Livestock Research Center during Dec-Jan, 2021 (41-days). The real-time position and activity count data (15- and 1-minute interval, respectively) were retrieved continuously from 4 BA and 5 RC cows collared with commercially available trackers (Abeeway Industrial-trackers). Response variables derived from GPS records included daily distance traveled (Dist) and daily area explored (Area). Response variables derived from accelerometers included daily percentage motion (%Act) and cumulative activity data (CumAct). All variables were grouped into six consecutive weekly periods and analyzed by a repeated measures mixed model that included the fixed effects of BREED, WEEK, and the BREED*WEEK interaction in a completely randomized design. A significant (P< 0.01) BREED*WEEK interaction was detected on all GPS-derived variables, with RC exhibiting a trend to explore larger areas than BA (P=0.06). For the accelerometer-derived variables, the BREED*WEEK interaction was only significant (P=0.01) for CumAct. Overall, BA spent a larger percentage of the day active (%Act; BA= 76.74 vs. RC= 73.16; P=0.02), while investing a significantly greater overall activity (CumAct; BA= 8816.63 vs. RC= 6311.31; P=0.03) compared to RC cows. The insensitivity of GPS-derived variables is hypothesized to be the result of aggregating errors associated with less frequent GPS location measurements coupled with position errors (~5m) and a low success rate of data packet transmission (~40%). The spatial scale of the study is hypothesized to have influenced the foraging behavior differences that were observed between the breeds, and the better performance of the accelerometer vs. GPS-derived variables.