Wireless Sensor Networks for Healthcare

Ko, JeongGil; Lu, Chenyang; Srivastava, Mani; Stankovic, John A.; Terzis, Andreas; Welsh, Matt

doi:10.1109/jproc.2010.2065210

Cited by 597 publications

(313 citation statements)

References 55 publications

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“…Recently, the application of WSNs in healthcare has received a lot of attention and, just to cite two major examples, WSNs have been used to monitor the health conditions of patients in hospitals and to remotely monitor people under health risk when they are at home [1].…”

Section: Introductionmentioning

confidence: 99%

A Fast ILP-Based Heuristic for the Robust Design of Body Wireless Sensor Networks

D’Andreagiovanni¹,

Nardin²,

Natalizio³

2017

Applications of Evolutionary Computation

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Abstract. We consider the problem of optimally designing a body wireless sensor network, while taking into account the uncertainty of data generation of biosensors. Since the related min-max robustness Integer Linear Programming (ILP) problem can be difficult to solve even for state-of-the-art commercial optimization solvers, we propose an original heuristic for its solution. The heuristic combines deterministic and probabilistic variable fixing strategies, guided by the information coming from strengthened linear relaxations of the ILP robust model, and includes a very large neighborhood search for reparation and improvement of generated solutions, formulated as an ILP problem solved exactly. Computational tests on realistic instances show that our heuristic finds solutions of much higher quality than a state-of-the-art solver and than an effective benchmark heuristic.

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Section: Introductionmentioning

confidence: 99%

A Fast ILP-Based Heuristic for the Robust Design of Body Wireless Sensor Networks

D’Andreagiovanni¹,

Nardin²,

Natalizio³

2017

Applications of Evolutionary Computation

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“…In [8], some interesting challenges from the network perspective are identified, while interoperability issues and recent standardization efforts are presented in [9]. On a different level, a lot of research activity has been focused on the body area domain, on the design of medical sensor devices [10] and on the main advances and challenges in the field of WBANs [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Machine-to-machine (M2M) communications for e-health applications

Kartsakli

Lalos

Antonopoulos

et al. 2015

Machine-to-Machine (M2M) Communications

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The emerging technology of Machine-to-Machine (M2M) communications is bringing a paradigm shift in healthcare delivery. A broad range of e-Health applications can be conceived, with considerable benefits for both patients and healthcare providers. Many technological challenges have to be met, however, to ensure the widespread adoption of e-Health solutions in the future. In this context, we aim to provide a comprehensive overview on M2M systems for e-Health applications from a wireless communication perspective. We provide an overview of the candidate wireless technologies that are suitable for different parts of the M2M system architecture and then show how these technologies are seamlessly integrated to provide an end-to-end e-Health solution. In particular, we discuss end-toend solution designs and testbed implementations, we present the key security and privacy challenges associated with the sensitive nature of medical data and we summarize the most recent research projects dedicated to e-Health applications. Index TermsMachine-to-Machine (M2M), e-Health, WBAN, M2M Area Network, M2M Communications Network, IEEE 802.15.6, end-to-end communications

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“…With the rapid advancements in sensor and wireless communication technologies, various novel Cyber-Physical Systems (CPS) are emerging such as humanoid robots [11], wireless body area networks [8,16], smart home applications, intelligent vehicles and various intelligent control systems [26]. An important function of these systems is to provide a close monitoring on the status of the operation environments so that timely and appropriate responses can be generated to react to critical events occurring in the environments [2].…”

Section: Introductionmentioning

confidence: 99%

“…A common way to support real-time monitoring functions effectively in CPS is to use periodic application transactions which are defined according to the specific requirements of the monitoring tasks [16]. Each invocation of an application transaction has a hard deadline on its completion time [24].…”

Section: Introductionmentioning

confidence: 99%

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An effective fixed priority co-scheduling algorithm for periodic update and application transactions

et al. 2012

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An important function of many Cyber-Physical Systems (CPS) is to provide a close monitoring of the operation environment to be able to adapt to changing situations effectively. One of the commonly applied techniques for that is to invoke time-constrained periodic application transactions to check the status of the operation environment. The status of the environment is represented by the values of the physical entities in the operation environment which are maintained as real-time data objects in a real-time database. Unfortunately, meeting the deadlines of application transactions and maintaining the quality of real-time data objects are conflicting with each other, because they compete for the same computation resources. To address this problem of update and application transactions co-scheduling problem, in this paper, we propose a fixed priority co-scheduling algorithm called Periodic Co-Scheduling ( PCS). PCS uses periodic update transactions to maintain the temporal validity of real-time data objects. It judiciously decides the priority orders among all the update and application transactions so that the constructed schedule can satisfy the deadline constraints of all the application transactions and at the same time maximize the qualities of the real-time data objects to ensure the correct execution of application transactions. The effectiveness of the algorithm is validated through extensive simulation experiments.

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Wireless Sensor Networks for Healthcare

Cited by 597 publications

References 55 publications

A Fast ILP-Based Heuristic for the Robust Design of Body Wireless Sensor Networks

A Fast ILP-Based Heuristic for the Robust Design of Body Wireless Sensor Networks

Machine-to-machine (M2M) communications for e-health applications

An effective fixed priority co-scheduling algorithm for periodic update and application transactions

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