MARC 主機 00000nam a2200385 4500
001 AAI3561410
005 20140512081850.5
008 140512s2013 ||||||||s|||||||| ||eng d
020 9781303084942
035 (MiAaPQ)AAI3561410
040 MiAaPQ|cMiAaPQ
100 1 Gorlatova, Maria
245 10 Energy Harvesting Networked Nodes: Measurements,
Algorithms, and Prototyping
300 138 p
500 Source: Dissertation Abstracts International, Volume: 74-
09(E), Section: B
500 Adviser: Gil Zussman
502 Thesis (Ph.D.)--Columbia University, 2013
520 Recent advances in ultra-low-power wireless communications
and in energy harvesting will soon enable energetically
self-sustainable wireless devices. Networks of such
devices will serve as building blocks for different
Internet of Things (IoT) applications, such as searching
for an object on a network of objects and continuous
monitoring of object configurations. Yet, numerous
challenges need to be addressed for the IoT vision to be
fully realized
520 This thesis considers several challenges related to ultra-
low-power energy harvesting networked nodes: energy source
characterization, algorithm design, and node design and
prototyping. Additionally, the thesis contributes to
engineering education, specifically to project-based
learning.
520 We summarize our contributions to light and kinetic
(motion) energy characterization for energy harvesting
nodes. To characterize light energy, we conducted a first-
of-its kind 16 month-long indoor light energy measurements
campaign. To characterize energy of motion, we collected
over 200 hours of human and object motion traces. We also
analyzed traces previously collected in a study with over
40 participants. We summarize our insights, including
light and motion energy budgets, variability, and
influencing factors. These insights are useful for
designing energy harvesting nodes and energy harvesting
adaptive algorithms. We shared with the community our
light energy traces, which can be used as energy inputs to
system and algorithm simulators and emulators
520 We also discuss resource allocation problems we considered
for energy harvesting nodes. Inspired by the needs of
tracking and monitoring IoT applications, we formulated
and studied resource allocation problems aimed at
allocating the nodes' time-varying resources in a uniform
way with respect to time. We mainly considered
deterministic energy profile and stochastic environmental
energy models, and focused on single node and link
scenarios. We formulated optimization problems using
utility maximization and lexicographic maximization
frameworks, and introduced algorithms for solving the
formulated problems. For several settings, we provided low
-complexity solution algorithms. We also examined many
simple policies. We demonstrated, analytically and via
simulations, that in many settings simple policies perform
well
520 We also summarize our design and prototyping efforts for a
new class of ultra-low-power nodes - Energy Harvesting
Active Networked Tags (EnHANTs). Future EnHANTs will be
wireless nodes that can be attached to commonplace objects
(books, furniture, clothing). We describe the EnHANTs
prototypes and the EnHANTs testbed that we developed, in
collaboration with other research groups, over the last 4
years in 6 integration phases. The prototypes harvest
energy of the indoor light, communicate with each other
via ultra-low-power transceivers, form small multihop
networks, and adapt their communications and networking to
their energy harvesting states. The EnHANTs testbed can
expose the prototypes to light conditions based on real-
world light energy traces. Using the testbed and our light
energy traces, we evaluated some of our energy harvesting
adaptive policies. Our insights into node design and
performance evaluations may apply beyond EnHANTs to
networks of various energy harvesting nodes
520 Finally, we present our contributions to engineering
education. Over the last 4 years, we engaged high school,
undergraduate, and M.S. students in more than 100 research
projects within the EnHANTs project. We summarize our
approaches to facilitating student learning, and discuss
the results of evaluation surveys that demonstrate the
effectiveness of our approaches
590 School code: 0054
650 4 Engineering, Electronics and Electrical
650 4 Computer Science
690 0544
690 0984
710 2 Columbia University.|bElectrical Engineering
773 0 |tDissertation Abstracts International|g74-09B(E)
856 40 |uhttps://pqdd.sinica.edu.tw/twdaoapp/servlet/
advanced?query=3561410
912 PQDT
