Abstract
Hospitalized patients are often attached by numerous cables to medical monitoring equipment, which obstructs their mobility. Wireless patient monitoring technology can thus facilitate patient mobility and increase their autonomy. A body area network (BAN) is a base technology used for wireless patient monitoring which consists of a set of wearable medical sensor nodes (SNs) placed in, on, or around a patient's body. BANs form an integrated large-scale wireless hospital sensor network (WHSN), for which we utilize a smartphone as a master node (MN) due to its more advanced computing capabilities and connectivity as compared with other mobile devices. Since smartphones are battery-powered, however, the MN requires efficient power utilization. Large-scale WHSNs, in which different types of medical data are generated, demand smartphone MNs that are able to handle various medical data and utilize cellular network resources, due to their limited bandwidth. In this paper, we present a scheme that maximizes the lifetime of the whole integrated WHSN and of each BAN by first forming clusters consisting of MNs and then constructing minimum spanning trees (MSTs) with these clusters around access points (APs) to limit the energy consumption of the medical SNs. The effectiveness of our lifetime optimization scheme for BANs in an integrated WHSN is demonstrated by simulation results.
Original language | English |
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Pages (from-to) | 332-349 |
Number of pages | 18 |
Journal | Information sciences |
Volume | 282 |
DOIs | |
Publication status | Published - 2014 Oct 20 |
Bibliographical note
Funding Information:This research was supported by the MSIP (Ministry of Science, ICT and Future Planning) , Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1042) supervised by the NIPA (National IT Industry Promotion Agency).
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence