Using prediction to conserve energy in recognition on mobile devices
ABSTRACT As devices are expected to be aware of their environment, the challenge becomes how to accommodate these abilities with the power constraints which plague modern mobile devices. We present a framework for an embedded approach to context recognition which reduces power consumption. This is accomplished by identifying class-sensor dependencies, and using prediction methods to identify likely future classes, thereby identifying sensors which can be temporarily turned off. Different methods for prediction, as well as integration with several classifiers is analyzed and the methods are evaluated in terms of computational load and loss in quality of context. The results indicate that the amount of energy which can be saved is dependent on two variables (the acceptable loss in quality of recognition, and the number of most likely classes which should be accounted for), and two scenario-dependent properties (predictability of the context sequences and size of the context-sensor dependency sets).
Conference Proceeding: Global Peer-to-Peer Classification in Mobile Ad-Hoc Networks: A Requirements Analysis.Modeling and Using Context - 7th International and Interdisciplinary Conference, CONTEXT 2011, Karlsruhe, Germany, September 26-30, 2011. Proceedings; 01/2011
Using Prediction to Conserve Energy in Recognition on Mobile Devices
Dawud Gordon, Stephan Sigg, Yong Ding, Michael Beigl
Karlsruhe Institute of Technology, TecO
Abstract—As devices are expected to be aware of their
environment, the challenge becomes how to accommodate these
abilities with the power constraints which plague modern
mobile devices. We present a framework for an embedded
approach to context recognition which reduces power con-
sumption. This is accomplished by identifying class-sensor
dependencies, and using prediction methods to identify likely
future classes, thereby identifying sensors which can be tem-
porarily turned off. Different methods for prediction, as well as
integration with several classifiers is analyzed and the methods
are evaluated in terms of computational load and loss in quality
of context. The results indicate that the amount of energy which
can be saved is dependent on two variables (the acceptable loss
in quality of recognition, and the number of most likely classes
which should be accounted for), and two scenario-dependent
properties (predictability of the context sequences and size of
the context-sensor dependency sets).
Keywords-context recognition; context prediction, machine
learning; embedded and mobile systems;
I. INTRODUCTION AND RELATED WORK
As concepts from pervasive and mobile computing be-
come more mainstream, the community seeks practical
approaches for realizing pervasive technology. Situational,
context or activity recognition techniques provide a method
for machines to recognize human and social situations,
allowing them to act proactively without contradicting or
offending their owners. Modern technological devices such
as smart phones or wireless sensor networks are now able to
handle these algorithms  as processing power and memory
improve over time according to Moore’s Law. Unfortunately,
energy storage and consumption on such devices are not
subject to the same doubling effects and are quickly becom-
ing the limiting factor in pervasive technology. This can be
seen clearly when reviewing the battery lifetimes for mobile
phones over the past 10 years. The cost of communication
in terms of energy consumption is another factor which
does not scale according to Moore’s Law, indicating that for
pervasive computing applications to be practical, methods
for low power situational recognition must be embedded in
Embedded classification for mobile devices is not a new
concept and goes as far back as 1997 , where Bouten el
al. used simple signal processing to measure activity levels
of users wearing a mobile device. Several methods for low
power embedded context classification have been introduced
in the community , and trade-offs that must be
made between classification quality and energy consumption
for embedded context recognition are discussed in , .
While these approaches effectively reduce power con-
sumption in certain situations, we propose a method for
further reduction based on the concept of context prediction.
Context prediction as studied in  can be used to make
a prediction about future situations based on situations
recognized in the past. By developing a dependence mapping
between contexts, features and sensors, we propose to use
context prediction to parameterize sensor usage, sampling
and feature generation in order to further reduce unnecessary
power consumption while minimizing the negative effect on
II. CONTEXT, FEATURE AND SENSOR MAPPINGS
The standard process for situational recognition using
machine learning algorithms is straightforward. Sensors are
sampled in parallel at an arbitrary rate for an arbitrary period
of time, after which the data is then saved as a discrete
multidimensional array, referred to as a sample window. This
window is processed using different algorithms to generate
so called features, e.g. standard deviation, average, FFT or
cepstral coefficients. Which features are used depends on the
application, i.e. which situations we want to recognize and
the type of sensor being used and are referred to all together
as a feature vector. The feature vector is then passed to a
machine learning algorithm whose task is to recognize which
situation was occurring during the sample window, based on
its feature vector.
When observing this chain of events in the context clas-
sification process, it should be clear to the reader that each
feature in the set of features used f ∈ F is implicitly mapped
onto a single sensor in the set of sensors s ∈ S, namely
the single sensor which generates the data for this feature,
producing the surjective mapping a of features onto sensors:
a: F → S
Mapping recognition classes onto the features is not as
simple and requires a bit more legwork. The concept of
selecting features which best suite an application is not new,
K¨ on¨ onen et al. provide an overview of feature selection algo-
rithms for embedded systems in . While these algorithms
potentially improve the quality of classification and reduce
Figure 1. (C)lass, (F)eature, (S)ensor Mappings (a,b) and Weights (Q)
the computational load, they do not provide a mapping of
features to classes by relevance or importance.
It should be clear to the reader that turning sensors on and
off will result in a dynamic feature vector length, and for
this reason we will consider classifiers which can natively
support this. Specifically, nearest-neighbor classifiers are
well suited to this task as omitting a feature represents a
dimensional reduction of the labeled training vector space,
and the missing features are simply excluded from the
distance calculation. Hidden Markov Models are also well
suited as the observational distributions for these variables
are ignored when calculating the probabilities of the hidden
states. Both of these examples lose only the information that
would have been gained from the missing features, but are
not further negatively affected .
In order to generate the weighted mapping, training data
is gathered for each class. After training the classifier over
all of the training data, each class is tested for dependency
against each feature. This is done by testing the trained
classifier against all of the training vectors for each class
and removing each feature one at a time (all other features
are reinstated), and the degree of dependency is inferred
using the the drop in accuracy when a feature is removed:
a large drop in recognition indicates a high dependency, a
small drop, low dependency.
The result of this is a weighted mapping b: C
classes C onto features F with quality values q ∈ Q where
q has a value from 0 to 1, namely the cost of that feature
for that class in percent loss in recognition accuracy. Both
mappings can be seen in Fig. 1, where Each class c ∈ C
is mapped onto each feature f ∈ F over the parameterized
mapping b with a quality weight qcifj∈ Q for class i and
feature j, and each feature is in turn mapped to one sensor
sk∈ S over the mapping a.
As each feature will incur a certain loss in recognition
q ≥ 0, and it is assumed that at least one q will be non-
zero for each class, it is necessary to decide what loss is
acceptable for each application: l. Using the mappings a
and b and weights Q we must now calculate the total cost ω
of each sensor with respect to each class, as this is required
in order to judge if turning off a sensor will violate l.
For each sensor sj the subset Fsj⊆ F is built which
− → F of
Figure 2. Integration of the Classification and Prediction Processes
represents all features generated using that sensor. For each
class ci, the subset Qcisj⊆ Q is then built, which contains
the costs q of all features f ∈ Fsj, or the cost of each feature
generated using sensor sjwith respect to class ci. Now the
total cost of a sensor with respect to a class can be calculated
with respect to the class ci, then in order to perform the
sensor optimization for class ci, the sensor sn|∀j;ωcisj≤
ωcisn, meaning the sensor (sj) of least importance to that
class (ci), can be turned off and ωcisjsubtracted from l.
This is repeated until l is depleted, meaning until removing
the next least pertinent sensor would violate the acceptable
loss threshold for the specified application.
Now, for each class ci, a set of sensors Scihas been
identified which is required in order to recognize that class,
and more importantly, we can identify sensors which are
not of interest given the acceptable accuracy loss l. The
next section will analyze the use of context prediction to
generate a set of classes which are likely to appear in the
next sample window, and will allow us to shut off sensors
which are not needed to conserve energy.
q∈Qcisjq. If Ωciis the set of ω for all sensors
III. CONTEXT PREDICTION
The general approach to using context prediction to con-
serve energy is simple: the system should be able to predict a
subset of classes which are likely to occur in the near future,
i.e. the next sample window. Using this information and the
mapping of classes onto features onto sensors, it is logical
that the set of sensors necessary to recognize that class can
be identified and activated as shown in Fig. 2, providing an
energy advantage over systems which use all sensors at all
times, while maintaining loss of accuracy below l.
Since we use prediction to deactivate sensors, we also
have to consider the impact of the absence of this sensor data
on prediction accuracy. This means that the input time series
dimension is dynamic, which might reduce the prediction
accuracy as the information provided is reduced. This affect
is especially serious when prediction is carried out before
feature data is aggregated or classified (low-level prediction).
In  we derived that prediction accuracy is affected by the
amount of pre-processing applied to the input time series,
though in this case, this dependency must be further explored
to account for fluctuation in the dimension of the input time
Unlike low-level prediction, a high-level approach (using
feature or context data) is typically applied to an input time
series with fewer dimensions and a reduced sample space.
Since the time series of feature values are already aggregated
and classified at high-level prediction, it is an inherently
easier task when compared to low-level prediction, and
the effects of the of the dynamic sensor configuration are
reduced. Several algorithms such as ARMA  which can
be applied to numerical time series data, can not be applied
to the symbolic context data which is likely to be found in
high-level context time series. A very general method for
context prediction on high-level data is to utilize a Markov
process , which is optimal in the sense that it can always
achieve the highest possible prediction accuracy for infinite
binary random sequences .
Using a Markov Chain trained during classifier training
for illustration, we could use this chain to predict the set
of states, or classes, which are likely to occur in the next
sample. As with the acceptable recognition loss, there is
once again an application-based trade-off which has to be
made: where to draw the line between what is a likely
enough state to be accounted for in the near future, meaning
that the sensors required for that class should be activated
before entering the next sampling phase. This decision can
be made by selecting set of the p most likely classes, where
1 ≤ p ≤ N for a scenario with N classes.
Computational Load: To generate the class to fea-
ture parameterized mappings, each iteration requires only
one classification step using the trained classifier, yielding
O(NM), where N is the number of classes and M is the
number of features. This is only possible if a classifier is
used which can handle a variable feature vector length. If
this is not the case, a new classifier must be trained for
each different variable combination in order to compare
classification rates for the class-feature mapping weights
which would increase computational load by the cost of
classifier training. This would not only greatly increase
computational load at training time, but memory usage at
runtime as N × M classifiers must be stored locally for
The task of prediction must be periodically carried out
on the node, which is why analyses of these processes is
also of interest for this work. A comparison between several
prediction approaches regarding their computational load is
not always straightforward, since the load is not always
directly comparable. Consider, for instance, the ARMA and
the Markov prediction methods. When the length of the
observed context time series is k and C denotes the number
of possible context values, context prediction using ARMA
methods has an asymptotic complexity of O(klog(k)),
plus the cost of classifying the predicted data. For Markov
prediction methods, the complexity is O(|C|) .
For high-level prediction, the prediction step itself is
computationally very simple and can easily be optimized.
A Markov Chain can be represented by an N × N matrix,
where N is the number of classes between which the system
should distinguish. After each classification, the probability
for each class can be retrieved from the table, and the p
highest probabilities are selected. This computation is a
linear search through one row or column of the matrix,
which grows as O(N) where N is once again the number of
classes which we are trying to distinguish. Once p has been
fixed, the p most likely next states for each state become
static and the operation becomes O(1).
Dependencies: In Sec. III, two variables were identified
which would allow the application designer to affect the
energy/accuracy trade off. The first is l, or the acceptable
recognition loss for the specific application or scenario.
Setting this value closer to 1 creates a system with possibly
low recognition rates, but higher energy savings, while for
a value closer to 0 the behavior approaches that of a system
without any energy optimization. The second variable which
can affect the trade off is p, or the set of states which should
be accounted for in the next sample window. Following
Sec. III, as this value approaches 1, the energy savings are
maximized, but so too is the possible loss in recognition
rates, as the set of recognizable classes is reduced to one.
As p approaches N the power savings and recognition loss
rates approach that of a system without optimization, e.g. 0.
Furthermore, two properties intrinsic to the particular
scenario were also identified which will affect the en-
ergy/accuracy trade off in the system. The first the pre-
dictability of the data, which is a function of the scenario
itself, as well as the prediction method being used , .
Unpredictable data will lead to large errors in the prediction
causing power savings to come at disproportionately high
recognition rate costs. On the other hand, if predictions
are accurate, power savings would be far more affordable.
The second property is the grouping of the sensor/feature
dependencies, or the size of S −Scifor each class, and the
costs ω of those sensors. To demonstrate, imagine a system
in which each class is equally dependent on each sensor,
meaning the sensor weights in Ω are equally distributed
for each class. Turning off any sensor could then lead to a
direct violation of the acceptable loss l, forcing the system
to maintain all sensors on at all times. In the opposite case,
imagine a system where each class is dependent to 100% on
only one sensor, meaning Ω contains one 1 and otherwise
0’s for each class. Assuming flawless prediction, only one
sensor would be on at any given time, optimizing energy
savings with no loss in classification accuracy.
V. THE NEXT STEPS
Although the main concepts have been presented here, a
full evaluation of the system is yet to be completed. This
will clarify the advantages and disadvantages of high-level
vs. low-level prediction methods as well as the dependencies
between energy savings, recognition accuracy loss and the
two system variables. The approach is two-fold, where
initially the system will be tested using simulation, followed
by an evaluation using a high-modality sensor board to
validate the simulation results under real conditions.
One other aspect which is being explored is the further
parameterization of the mappings a and b to reflect the cost
of each feature and sensor in terms of power consumption,
and to introduce this as a metric for further optimizing
sensor and feature selection, i.e. the system will be reticent
to use expensive sensors and features. Furthermore, until
now the recognition algorithm has been considered as the
only application running on the device with full control
over sensors. In reality, especially when considering smart
phones, other applications will run along side, or on top of
the recognition and have their own requirements for sensing.
The mappings can then be used to opportunistically improve
activity recognition by evaluating feature computational
costs when sensors are in use by other applications.
In this paper we have proposed a method for conserving
energy in context recognition by using prediction algorithms.
A method for mapping classes onto features and weighting
these according to incurred loss of recognition accuracy was
put forward. It was then shown how these mappings allow
predictions to be applied to the system to turn off unneeded
sensors. Two scenario-based properties which will affect
the success of the approach were identified, namely the
inherent predictability of the data, as well as the dependency
distributions of classes over the sensors. Furthermore two
system variables where introduced which will affect the ratio
of power consumption and recognition rates. Specifically,
these are the amount of loss in recognition rates which are
acceptable, as well as how many classes should be accounted
for based on each prediction. Finally, the next steps for
evaluating the effects of the methods and the correlations
between the crucial variables, prediction accuracy, classifi-
cation accuracy and energy savings was detailed.
The authors would like to acknowledge joint funding by
the European Commission under the ICT project “CHOSeN”
(Project No. 224327, FP7-ICT-2007-2) and by the Deutsche
Forschungsgemeinschaft (DFG) under the project “Sense-
Cast ” (BE4319/1).
 M. Berchtold, M. Budde, D. Gordon, H. Schmidtke, and
M. Beigl, “ActiServ: Activity Recognition Service for Mobile
Phones,” in ISWC’10: Proceedings of the Fourteenth Interna-
tional Symposium on Wearable Computers. Seoul, S. Korea:
IEEE Computer Society, 2010, pp. 83–90.
 C. Bouten, K. Koekkoek, M. Verduin, R. Kodde, and
J. Janssen, “A triaxial accelerometer and portable data pro-
cessing unit for the assessment of daily physical activity,”
Biomedical Engineering, IEEE Transactions on, vol. 44,
no. 3, pp. 136–147, March 1997.
 O. Cakmakci, J. Coutaz, K. V. Laerhoven, and H. werner
Gellersen, “Context awareness in systems with limited re-
sources,” in In Proc. of the third workshop on Artificial
Intelligence in Mobile Systems (AIMS), ECAI 2002, 2002,
 M. St¨ ager, P. Lukowicz, and G. Tr¨ oster, “Implementation
and evaluation of a low-power sound-based user activity
recognition system,” in ISWC ’04: Proceedings of the Eighth
International Symposium on Wearable Computers. Washing-
ton, DC, USA: IEEE Computer Society, 2004, pp. 138–141.
 A. Y. Benbasat and J. A. Paradiso, “A framework for the
automated generation of power-efficient classifiers for em-
bedded sensor nodes,” in SenSys ’07: Proceedings of the
5th international conference on Embedded networked sensor
systems. New York, NY, USA: ACM, 2007, pp. 219–232.
 A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta,
D. Siewiorek, A. Smailagic, M. Deisher, and U. Sengupta,
“Trading off prediction accuracy and power consumption for
context-aware wearable computing,” in Wearable Computers,
2005. Proceedings. Ninth IEEE International Symposium on,
Oct. 2005, pp. 20–26.
 M. St¨ ager, P. Lukowicz, and G. Tr¨ oster, “Power and accuracy
trade-offs in sound-based context recognition systems,” Per-
vasive and Mobile Computing, vol. 3, pp. 300 – 327, 2007.
 N. B. Bharatula, M. St¨ ager, P. Lukowicz, and G. Tr¨ oster,
“Empirical Study of Design Choices in Multi-Sensor Context
Recognition Systems,” in IFAWC: 2nd International Forum
on Applied Wearable Computing, Mar. 2005, pp. 79–93.
 S. Sigg, S. Haseloff, and K. David, “An alignment approach
for context prediction tasks in ubicomp environments,” IEEE
Pervasive Computing, vol. Oct-Dec 2010, 2010.
 V. K¨ on¨ onen, J. M¨ antyj¨ arvi, H. Simil¨ a, J. P¨ arkk¨ a, and M. Er-
mes, “Automatic feature selection for context recognition in
mobile devices,” Pervasive and Mobile Computing, vol. 6,
no. 2, pp. 181 – 197, 2010.
 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classifica-
tion, 2nd ed. New York: Wiley, 2001.
 S. Sigg, D. Gordon, G. von Zengen, M. Beigl, S. Haseloff,
and K. David, “Investigation of context prediction accuracy
for different context abstraction levels,” IEEE Transactions
on Mobile Computing, 2011, (to appear).
 C. Chatfield, The Analysis of Time Series: An Introduction.
Chapman and Hall, 1996, vol. 5.
 W. Feller, An Introduction to Probability Theory and its
Applications. Wiley, 1968.
 M. Feder, N. Merhav, and M. Gutman, “Universal prediction
of individual sequences,” IEEE Transactions on Information
Theory, vol. 38, no. 4, pp. 1258–1270, July 1992.