TY - GEN
T1 - Data Acquisition for Real-Time Decision-Making under Freshness Constraints
AU - Hu, Shaohan
AU - Yao, Shuochao
AU - Jin, Haiming
AU - Zhao, Yiran
AU - Hu, Yitao
AU - Liu, Xiaochen
AU - Naghibolhosseini, Nooreddin
AU - Li, Shen
AU - Kapoor, Akash
AU - Dron, William
AU - Su, Lu
AU - Bar-Noy, Amotz
AU - Szekely, Pedro
AU - Govindan, Ramesh
AU - Hobbs, Reginald
AU - Abdelzaher, Tarek F.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/14
Y1 - 2016/1/14
N2 - The paper describes a novel algorithm for timely sensor data retrieval in resource-poor environments under freshness constraints. Consider a civil unrest, national security, or disaster management scenario, where a dynamic situation evolves and a decision-maker must decide on a course of action in view of latest data. Since the situation changes, so is the best course of action. The scenario offers two interesting constraints. First, one should be able to successfully compute the course of action within some appropriate time window, which we call the decision deadline. Second, at the time the course of action is computed, the data it is based on must be fresh (i.e., within some corresponding validity interval). We call it the freshness constraint. These constraints create an interesting novel problem of timely data retrieval. We address this problem in resource-scarce environments, where network resource limitations require that data objects (e.g., pictures and other sensor measurements pertinent to the decision) generally remain at the sources. Hence, one must decide on (i) which objects to retrieve and (ii) in what order, such that the cost of deciding on a valid course of action is minimized while meeting data freshness and decision deadline constraints. Such an algorithm is reported in this paper. The algorithm is shown in simulation to reduce the cost of data retrieval compared to a host of baselines that consider time or resource constraints. It is applied in the context of minimizing cost of finding unobstructed routes between specified locations in a disaster zone by retrieving data on the health of individual route segments.
AB - The paper describes a novel algorithm for timely sensor data retrieval in resource-poor environments under freshness constraints. Consider a civil unrest, national security, or disaster management scenario, where a dynamic situation evolves and a decision-maker must decide on a course of action in view of latest data. Since the situation changes, so is the best course of action. The scenario offers two interesting constraints. First, one should be able to successfully compute the course of action within some appropriate time window, which we call the decision deadline. Second, at the time the course of action is computed, the data it is based on must be fresh (i.e., within some corresponding validity interval). We call it the freshness constraint. These constraints create an interesting novel problem of timely data retrieval. We address this problem in resource-scarce environments, where network resource limitations require that data objects (e.g., pictures and other sensor measurements pertinent to the decision) generally remain at the sources. Hence, one must decide on (i) which objects to retrieve and (ii) in what order, such that the cost of deciding on a valid course of action is minimized while meeting data freshness and decision deadline constraints. Such an algorithm is reported in this paper. The algorithm is shown in simulation to reduce the cost of data retrieval compared to a host of baselines that consider time or resource constraints. It is applied in the context of minimizing cost of finding unobstructed routes between specified locations in a disaster zone by retrieving data on the health of individual route segments.
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U2 - 10.1109/RTSS.2015.25
DO - 10.1109/RTSS.2015.25
M3 - Conference contribution
AN - SCOPUS:84964690509
T3 - Proceedings - Real-Time Systems Symposium
SP - 185
EP - 194
BT - Proceedings - 2015 IEEE 36th Real-Time Systems Symposium, RTSS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE Real-Time Systems Symposium, RTSS 2015
Y2 - 1 December 2015 through 4 December 2015
ER -