Jamie Steck
Junjie Su
CSE237d: Embedded System Design
April 29, 2008
Literature Survey:
Efficient Energy Management and Task Scheduling of a Solar-Powered System
Due to the diverse aspects of our project, we have chosen to research several different
areas including: system design considerations, task scheduling algorithms, embedded operating
systems, energy modeling circuits, and types of energy storage. In this paper, we summarize each
reference, focusing on the aspects of the paper that most relate to our project. At the end of each
summary, we evaluate the relevance of the paper and describe how our proposed project will both
differ from and improve upon previous work.
[1] presents an overview of design considerations for Energy Harvesting Embedded
Systems (EHES) at both the system level and the software level. First, at the system level, the
voltage must be high enough to power the system and must be converted using a voltage or
switching regulator. A regulator transforms the voltage received from the supply to a known,
controllable level that can be used by the storage component. Second, a technique called Maximum
Power Point Tracking (MPPT) seeks to maximize the power output by controlling the level at which
power is drawn from the energy source. MPPT requires that the input intensity be known, which
can be done either after conversion or by an alternate method, such as using a light sensor to
estimate solar power. The third system issue in [1] addresses the type of energy storage device, such
as a rechargeable battery, super-capacitor or fuel cell. On the software level, the paper describes the
challenges of a Power Management Policy in an EHES. The goal of an EHES is to maintain energy
neutrality, or only consuming the amount of energy that is harvested. To do this, the power of the
transducer must be analytically modeled. With this information, the Power Management Policy must
adapt the performance and power consumption of the system. In "Adaptive Duty Cycling for
Energy Harvesting Systems", an algorithm is presented that uses energy predictions to determine the
optimal duty cycling.
[1] gives background on the considerations for an EHES at a high level, energy-aware
view. Our project is essentially putting these considerations into action. The information regarding
the MPPT confirm the necessity of the second solar panel and the prediction algorithm to maximize
the power output. The power management policy mentioned is also highly indicative of the goal of
the Energy Management Policy.
[2] compares two different operating system approaches designed for embedded systems
as well as presenting OS extensions that enable effective power management in energy-restricted
systems. First, a general-purpose, multi-tasking OS can be adapted to support embedded systems.
General-purpose OSes, however, have high context switching overhead and do not have built in
energy-management mechanism. For example, eCos spends about 50% of the code size can be
attributed to communication overhead. A second approach to embedded system OS design is an
event-driven architecture, which can be easily modeled as a graph of components that communicate
via events. TinyOS specifically targets this type of communication, and when compared to eCos,
requires significantly less memory, has eight times less OS overhead, and uses twelve times less
power. Despite its advantages, however, TinyOS does not provide global system level scheduling
and power management. Possible extensions to TinyOS include: replacing the FIFO task scheduler,
implementing components in hardware, allowing some tasks to go to hardware instead of software,
adding event queues, and adding global power control mechanisms.
While [2] provides an important contrast between conventional and event-driven OSes,
we will not use TinyOS for our project. Previous students have tried to port it to the microcontroller
with no success. Nevertheless, some of these concepts may help in optimizing whatever OS we do
put on the micro-controller (likely freeRTOS.)
[3] describes SOS, a new operating system designed for sensor nodes. Due to the dynamic
nature of embedded systems, specifically sensor nodes, SOS is composed of a traditional core kernel
and loadable kernel modules. The SOS kernel supports the ability to insert and remove modules at
run-time, flexible priority scheduling, and dynamic memory. Modules are binaries that implement a
specific task or function. Modules can easily be inserted into the kernel at run-time using a userfriendly API. Modules not only can communicate via messages, but can also use faster function calls
to increase the speed and thus reduce overhead. SOS schedules tasks using priority queues, sharing
the processor through cooperative scheduling. SOS implements dynamic memory, which enables
data structures to grow or shrink at run-time. SOS provides three block sizes for memory allocation
and references free blocks using a linked list. [3] compares SOS to both TinyOS and Mate’. Their
experiments show that SOS is almost identical in energy consumption, and in light of overall power
consumption, these small differences are trivial. SOS, however, consumes significantly less energy
when updating code than TinyOS, and provides greater system flexibility.
[3] presents an attractive option for the OS in our project. SOS provides a high-level
programming interface for developing modules and integrating them into the SOS kernel at runtime; the source code for SOS is free and available at [4]. Currently, it appears that SOS is excessive
for the minimal needs of our system; however, it remains a viable alternative to our choice of
freeRTOS.
[5] presents the Lazy Scheduling Algorithm (LSA), an algorithm for an energy-driven
scheduling scenario. A previous work by the authors proves that the LSA is optimal for a system
with energy and timing constraints. [5], as follows, presents the implementation of the LSA followed
by some simulations. In the LSA, tasks are scheduled according to the current energy capacity of the
system, task deadlines, and the power dissipation of the tasks. A task is characterized by its arrival
time, energy demand, and deadline and is considered to be preemptive. The goal of the LSA is to
find an optimal start time for a task by considering both the task time requirements and the system
energy capacity. In order to find this optimal start time, the LSA requires knowledge of the power
flow for future times. [5] does not mention how to find this, but suggests an analysis of past
harvested power. It is interesting to note that if the energy capacity is zero, the algorithm results in
EDF (earliest deadline first) scheduling, and if the energy capacity is infinite, the algorithm results in
ALAP (as late as possible) scheduling. [5] concludes with simulation results that demonstrate that
LSA outperforms EDF.
[5] does not take into consideration dependencies between tasks, which is essential for the
SHM sensor node. Data cannot be processed until it is collected and so forth. It is reasonable that
this algorithm could be altered to include dependencies. For our proposed project, tasks do not have
deadlines; rather, the scheduler tries to schedule as much as possible in a given time according to
energy capacity.
[6] presents a modeling circuit that simulates the solar panel. Based on the modeling
circuit, the authors use simplified parameters extraction procedure to find out the value of the
parameters (IL, I0, Rsh, Rs). The reason to develop such modeling circuit is to find the MPP
(maximum power point) using very small Photovoltaic Cell (such as those in embedded system).
MPP tracking requires substantial amount of energy, which is hard to do in embedded system. The
modeling circuit works together with a lighting pilot cell to achieve MPP, which’s errors are within
5%.
The goal of this circuit is to keep tracking of the MPP. In our platform, we don’t use MPP
tracking because of supercapacitor’s voltage limitation. However, we can use this circuit to simulate
the solar panel behavior, and provide input to our microcontroller prediction. It is not easy to find
all the parameter in this circuit without some manufactory parameter specifications.
Everlast is a wireless sensor node that utilizes solar panel and supercapacitor to power the
node. [7] demonstrates the feasibility of operating on supercapacitor recharged by solar cell to power
a sensor node. It also introduces a PFM (pulse frequency modulated) regulator and a PFM
controller, which are responsible for controlling the voltage and current that charging the
supercapacitor in order to improve the charging efficiency.
Although Everlast sensor has different functionality comparing to our sensor, Everlast
sensor node employs the solar panel and supercapacitor for energy solution, which is very similar to
our sensor node. Currently, our sensor node’s power circuit tree is not working properly. The PFM
regulator and controller can be a solution for our sensor node to obtain better charging efficiency.
[8] presents a simple circuit that simulates the supercapacitor using the capacitor in series
of a resistor called equivalent series resistor (ERS), and it also provides a measurement circuit for the
ESR (equivalent series resistance) of the supercapacitor. Lastly, this paper demonstrates the SCESS
(supercapacitor energy storage system) that designed to achieve high energy efficiency up to 97.94%.
The idea of modeling the supercapacitor in one capacitor in series with an ESR can be
employed in our simulation circuit. Also, because we don’t have the ESR from the manufactory of
the supercapacitor, we have to implement the measurement circuit to estimate the ESR of the
supercapacitor.
[9] proposes a highly efficient solar energy harvest technique for embedded system, which
is the MPPT (Maximum Power Point Tracking) method. Power equals the voltage times the current.
Therefore, for certain voltage and current relationship, there exists a maximum power point. In this
paper, it finds out there is a linear relationship between maximum power voltage and the open
circuit voltage of the solar panel. Utilizing this property, they build a solar energy harvesting
platform to perform the Maximum Power Point Tracking. Getting the open circuit voltage from the
pilot cell, the tracker can adjust the operating voltage to oscillate around the maximum power
voltage point to achieve maximum power.
[9] proposes a very high efficient solar energy harvest technique, MPPT. Although in our
project, we won’t be able to use MPPT due to the extra energy consume in tracking MPP, we can
still obtain the idea of monitoring the open circuit voltage of the pilot solar cell to have a better
prediction of the future power.
References
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embedded systems. In Proceedings of the 2006 international Symposium on Low Power Electronics
and Design (Tegernsee, Bavaria, Germany, October 04 - 06, 2006). ISLPED '06. ACM, New York,
NY, 369-374.
[2] Li, S., Sutton, R., and Rabaey, J. 2003. Low power operating system for heterogeneous wireless
communication system. In Compilers and Operating Systems For Low Power, L. Benini, M.
Kandemir, and J. Ramanujam, Eds. Kluwer Academic Publishers, Norwell, MA, 1-16.
[3] Han, C., Kumar, R., Shea, R., Kohler, E., and Srivastava, M. 2005. A dynamic operating system
for sensor nodes. In Proceedings of the 3rd international Conference on Mobile Systems,
Applications, and Services (Seattle, Washington, June 06 - 08, 2005). MobiSys '05. ACM, New York,
NY, 163-176.
[4] SOS Embedded Operating System, https://projects.nesl.ucla.edu/public/sos2x/doc/index.html.
[5] Moser C, Brunelli D, Thiele L, Benini L (2006a) Lazy scheduling for energy-harvesting sensor
nodes. In: Fifth working conference on distributed and parallel embedded systems, DIPES 2006,
Braga, Portugal, 11–13 October 2006, pp 125–134.
[6] Dondi, D.; Brunelli, D.; Benini, L.; Pavan, P.; Bertacchini, A.; Larcher, L., "Photovoltaic cell
modeling for solar energy powered sensor networks," Advances in Sensors and Interface, 2007.
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[7] Simjee, F. and Chou, P. H. 2006. Everlast: long-life, supercapacitor-operated wireless sensor
node. In Proceedings of the 2006 international Symposium on Low Power Electronics and Design
(Tegernsee, Bavaria, Germany, October 04 - 06, 2006). ISLPED '06. ACM, New York, NY, 197202.
[8] Yun Zhong; Jiancheng Zhang; Gengyin Li; Aiguo Liu, "Research on Energy Efficiency of
Supercapacitor Energy Storage System," Power System Technology, 2006. PowerCon 2006.
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[9] D. Brunelli, S. Raggini, L. Benini , C. Moser and L. Thiele, “An efficient solar energy harvester
for wireless sensor nodes.”, Submitted to International Symposium on Low Power Electronics
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