Battery-powered computing solutions have grown in
importance and utility across a wide range of applications in the
technology industry, including both consumer and industrial uses.
Devices that are not attached to a stable and constant power source
must ensure that all power consumption is minimized while necessary
computation and communications are performed. WiFi networking is
ubiquitous in modern devices, and thus the power consumption
necessary to transmit data is of utmost concern for these battery
powered devices. The Ad hoc OnDemand Distance Vector (AODV)
routing algorithm is a widely adopted and adapted routing system for
path finding in wireless networks. AODV’s original implementation did
not include power consumption as a consideration for route
determinations. The Energy Aware AODV (EA-AODV) algorithm was
an attempt to account for energy conservation by varying broadcast
power and choosing paths with distance between nodes as a
consideration in routing. Lightning Strike AODV (LS-AODV) described
in this paper is a proposed routing algorithm that further accounts for
energy consumption in wireless networking by balancing energy in a
network. Quality of service is maintained while energy levels are
increased through networks using the LS-AODV algorithm.
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Adaptive Sampling for Low-power Wearable and Implantable Devices
Wearable and implantable medical devices ranging from wellness monitors to deep brain stimulators are becoming increasingly vital and ubiquitous. Such devices continuously take measurements, which consumes battery. The power consumption is proportional to the amount of information collected and with the frequency of data collection. High power consumption leads to rapid discharging of battery limiting the usage of these devices. These signals are often transmitted wirelessly for analysis, as well as to keep track of the user’s record, which also significantly increases power consumption. In this project, we evaluated adaptively modifying the rate of data collection on these devices, in other words, the sampling rate, for electrophysiological monitoring as the relevance of the signal changes in time. We carried out these tests using a proof-of-concept prototype developed for this project. In particular, we reviewed the effects of such adaptive sampling on intracellular potentials, and motor unit action potentials (MUAPs). By doing so, we were able to reduce the amount of data by 48.95% and power by 41.50% for the MUAPs with an 8% sample loss within MUAPs, and by 69.20% and 57.14% for intracellular potentials with a 6.75% sample loss.
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- Award ID(s):
- 1852316
- PAR ID:
- 10128314
- Date Published:
- Journal Name:
- The 16th International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2019): The Sixth National Workshop for REU Research in Networking and Systems (2019 6th REU Research in N&S)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
