17,197 research outputs found
Age Minimization in Energy Harvesting Communications: Energy-Controlled Delays
We consider an energy harvesting source that is collecting measurements from
a physical phenomenon and sending updates to a destination within a
communication session time. Updates incur transmission delays that are function
of the energy used in their transmission. The more transmission energy used per
update, the faster it reaches the destination. The goal is to transmit updates
in a timely manner, namely, such that the total age of information is minimized
by the end of the communication session, subject to energy causality
constraints. We consider two variations of this problem. In the first setting,
the source controls the number of measurement updates, their transmission
times, and the amounts of energy used in their transmission (which govern their
delays, or service times, incurred). In the second setting, measurement updates
externally arrive over time, and therefore the number of updates becomes fixed,
at the expense of adding data causality constraints to the problem. We
characterize age-minimal policies in the two settings, and discuss the
relationship of the age of information metric to other metrics used in the
energy harvesting literature.Comment: Appeared in Asilomar 201
Energy Management Policies for Energy-Neutral Source-Channel Coding
In cyber-physical systems where sensors measure the temporal evolution of a
given phenomenon of interest and radio communication takes place over short
distances, the energy spent for source acquisition and compression may be
comparable with that used for transmission. Additionally, in order to avoid
limited lifetime issues, sensors may be powered via energy harvesting and thus
collect all the energy they need from the environment. This work addresses the
problem of energy allocation over source acquisition/compression and
transmission for energy-harvesting sensors. At first, focusing on a
single-sensor, energy management policies are identified that guarantee a
maximal average distortion while at the same time ensuring the stability of the
queue connecting source and channel encoders. It is shown that the identified
class of policies is optimal in the sense that it stabilizes the queue whenever
this is feasible by any other technique that satisfies the same average
distortion constraint. Moreover, this class of policies performs an independent
resource optimization for the source and channel encoders. Analog transmission
techniques as well as suboptimal strategies that do not use the energy buffer
(battery) or use it only for adapting either source or channel encoder energy
allocation are also studied for performance comparison. The problem of
optimizing the desired trade-off between average distortion and delay is then
formulated and solved via dynamic programming tools. Finally, a system with
multiple sensors is considered and time-division scheduling strategies are
derived that are able to maintain the stability of all data queues and to meet
the average distortion constraints at all sensors whenever it is feasible.Comment: Submitted to IEEE Transactions on Communications in March 2011; last
update in July 201
Age-Minimal Transmission in Energy Harvesting Two-hop Networks
We consider an energy harvesting two-hop network where a source is
communicating to a destination through a relay. During a given communication
session time, the source collects measurement updates from a physical
phenomenon and sends them to the relay, which then forwards them to the
destination. The objective is to send these updates to the destination as
timely as possible; namely, such that the total age of information is minimized
by the end of the communication session, subject to energy causality
constraints at the source and the relay, and data causality constraints at the
relay. Both the source and the relay use fixed, yet possibly different,
transmission rates. Hence, each update packet incurs fixed non-zero
transmission delays. We first solve the single-hop version of this problem, and
then show that the two-hop problem is solved by treating the source and relay
nodes as one combined node, with some parameter transformations, and solving a
single-hop problem between that combined node and the destination.Comment: Appeared in IEEE Globecom 201
Optimal Energy Management Policies for Energy Harvesting Sensor Nodes
We study a sensor node with an energy harvesting source. The generated energy
can be stored in a buffer. The sensor node periodically senses a random field
and generates a packet. These packets are stored in a queue and transmitted
using the energy available at that time. We obtain energy management policies
that are throughput optimal, i.e., the data queue stays stable for the largest
possible data rate. Next we obtain energy management policies which minimize
the mean delay in the queue.We also compare performance of several easily
implementable sub-optimal energy management policies. A greedy policy is
identified which, in low SNR regime, is throughput optimal and also minimizes
mean delay.Comment: Submitted to the IEEE Transactions on Wireless Communications; 22
pages with 10 figure
Source-Channel Coding under Energy, Delay and Buffer Constraints
Source-channel coding for an energy limited wireless sensor node is
investigated. The sensor node observes independent Gaussian source samples with
variances changing over time slots and transmits to a destination over a flat
fading channel. The fading is constant during each time slot. The compressed
samples are stored in a finite size data buffer and need to be delivered in at
most time slots. The objective is to design optimal transmission policies,
namely, optimal power and distortion allocation, over the time slots such that
the average distortion at destination is minimized. In particular, optimal
transmission policies with various energy constraints are studied. First, a
battery operated system in which sensor node has a finite amount of energy at
the beginning of transmission is investigated. Then, the impact of energy
harvesting, energy cost of processing and sampling are considered. For each
energy constraint, a convex optimization problem is formulated, and the
properties of optimal transmission policies are identified. For the strict
delay case, , waterfilling interpretation is provided. Numerical
results are presented to illustrate the structure of the optimal transmission
policy, to analyze the effect of delay constraints, data buffer size, energy
harvesting, processing and sampling costs.Comment: 30 pages, 15 figures. Submitted to IEEE Transactions on Wireless
Communication
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