A Framework of Energy Efficient Mobile Sensing for
`Automatic User State Recognition∗
`
`Yi Wang†
`wangyi@usc.edu
`Quinn A. Jacobson§
`quinn.jacobson@nokia.com
`
`Jialiu Lin‡
`jialiul@cs.cmu.edu
`Jason Hong‡
`jasonh@cs.cmu.edu
`Norman Sadeh‡
`sadeh@cs.cmu.edu
`†Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA
`‡School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
`§Nokia Research Center, Palo Alto, USA
`
`Murali Annavaram†
`annavara@usc.edu
`Bhaskar Krishnamachari†
`bkrishna@usc.edu
`
`ABSTRACT
`Urban sensing, participatory sensing, and user activity recog-
`nition can provide rich contextual information for mobile
`applications such as social networking and location-based
`services. However, continuously capturing this contextual
`information on mobile devices consumes huge amount of en-
`ergy.
`In this paper, we present a novel design framework
`for an Energy Efficient Mobile Sensing System (EEMSS).
`EEMSS uses hierarchical sensor management strategy to
`recognize user states as well as to detect state transitions.
`By powering only a minimum set of sensors and using ap-
`propriate sensor duty cycles EEMSS significantly improves
`device battery life. We present the design, implementation,
`and evaluation of EEMSS that automatically recognizes a
`set of users’ daily activities in real time using sensors on an
`off-the-shelf high-end smart phone. Evaluation of EEMSS
`with 10 users over one week shows that our approach in-
`creases the device battery life by more than 75% while main-
`taining both high accuracy and low latency in identifying
`transitions between end-user activities.
`
`Categories and Subject Descriptors
`C.3.3 [Special Purpose and Application Based Sys-
`tems]: Real-time and embedded systems
`
`General Terms
`Design, Experimentation, Measurement, Performance
`
`∗
`
`We’d like to acknowledge partial support for this work from
`Nokia Inc and National Science Foundation, numbered NSF
`CNS-0831545.
`
`Permission to make digital or hard copies of all or part of this work for
`personal or classroom use is granted without fee provided that copies are
`not made or distributed for profit or commercial advantage and that copies
`bear this notice and the full citation on the first page. To copy otherwise, to
`republish, to post on servers or to redistribute to lists, requires prior specific
`permission and/or a fee.
`MobiSys’09, June 22–25, 2009, Kraków, Poland.
`Copyright 2009 ACM 978-1-60558-566-6/09/06 ...$5.00.
`
`Keywords
`Energy efficiency, Mobile sensing, EEMSS, Human state
`recognition
`
`1.
`
`INTRODUCTION
`As the number of transistors in unit area doubles every
`18 months following Moore’s law, mobile phones are packing
`more features to utilize the transistor budget. Increasing the
`feature set is mostly achieved by integrating complex sens-
`ing capabilities on mobile devices. Today’s high-end mobile
`device features will become tomorrow’s mid-range mobile de-
`vice features. Current sensing capabilities on mobile phones
`include WiFi, Bluetooth, GPS, audio, video, light sensors,
`accelerometers and so on. As such the mobile phone is no
`longer only a communication device, but also a powerful en-
`vironmental sensing unit that can monitor a user’s ambient
`context, both unobtrusively and in real time.
`On the mobile application development front, ambient
`sensing and context information [1] have become primary
`inputs for a new class of mobile cooperative services such
`as real time traffic monitoring [2], and social networking
`applications such as Facebook [3] and MySpace [4]. Due
`to the synergistic combination of technology push and de-
`mand pull, context aware applications are increasingly uti-
`lizing various data sensed by existing embedded sensors. By
`extracting more meaningful characteristics of users and sur-
`roundings in real time, applications can be more adaptive
`to the changing environment and user preferences. For in-
`stance, it would be much more convenient if our phones can
`automatically adjust the ring tone profile to appropriate vol-
`ume and mode according to the surroundings and the events
`in which the users are participating. Thus we believe user’s
`contextual information brings application personalization to
`new levels of sophistication. While user’s context informa-
`tion can be represented in multiple ways, in this paper we
`focus on using user state as an important way to represent
`the context. User state may contain a combination of fea-
`tures such as motion, location and background condition
`that together describe user’s current context.
`A big hurdle for context detection, however, is the limited
`battery capacity of mobile devices. The embedded sensors in
`the mobile devices are major sources of power consumption.
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`For instance, a fully charged battery on Nokia N95 mobile
`phone can support telephone conversation for longer than
`ten hours, but our empirical results show that the battery
`would be completely drained within six hours if the GPS
`receiver is turned on, whether it can obtain GPS readings
`or not. Hence, excessive energy consumption may become a
`major obstacle to broader acceptance of context-aware mo-
`bile applications or services, no matter how useful the service
`may be. In mobile sensing applications, energy savings can
`be achieved by shutting down unnecessary sensors as well
`as carefully selecting sensor duty cycles (i.e., sensors will
`adopt periodic sensing and sleeping instead of being sam-
`pled continuously). In this paper, we define sensor sampling
`duration as the length of the time a sensor is turned ON for
`active data collection. We define sensor sleeping duration
`as the time a sensor stays idle. The sensing and sleeping
`durations, or sensor duty cycles, are generally referred to as
`sensor parameters.
`To address the problem of energy efficiency in mobile sens-
`ing, we present the design, implementation, and evaluation
`of EEMSS, an energy efficient mobile sensing system that
`incorporates a hierarchical sensor management scheme for
`power management. EEMSS uses a combination of sensor
`readings to automatically recognize user state as described
`by three real-time conditions; namely motion (such as run-
`ning and walking), location (such as staying at home or on
`a freeway) and background environment (such as loud or
`quiet). The core component of EEMSS is a sensor manage-
`ment scheme which defines user states and state transition
`rules by an XML styled state descriptor. This state descrip-
`tor is taken as an input and is used by our sensor assignment
`functional block to turn sensors on and off based on a user’s
`current condition.
`The benefits of our sensor management scheme are three-
`fold. First, the state description mechanism proposed in
`this paper is a flexible way to add/update user states and
`their relationship to the sensors. For instance, to account
`for emerging application needs new states and sensors may
`be incrementally added to the state description. Second,
`to achieve energy efficiency, the sensor management scheme
`assigns the minimum set of sensors and heuristically deter-
`mines sampling lengths and intervals for these set of sensors
`to detect user’s state as well as transitions to new states.
`Lastly, our sensor management scheme can be easily ex-
`tended as a middleware that manages sensor operations and
`provides contextual information to higher layer applications
`with multiple types of devices and sensors involved.
`EEMSS is currently implemented and evaluated on Nokia
`N95 devices. In our EEMSS implementation, the state de-
`scription subsystem currently defines the following states:
`“Walking”, “Vehicle”, “Resting”, “Home talking”, “Home ent
`ertaining”, “Working”, “Meeting”, “Office loud”, “Place quiet”,
`“Place speech” and “Place loud”. All these states are speci-
`fied as a combination of built-in Nokia N95 sensor readings.
`The sensors used to recognize these states are accelerometer,
`WiFi detector, GPS, and microphone. EEMSS incorporates
`novel and efficient classification algorithms for real-time user
`motion and background sound recognition, which form the
`foundation of detecting user states. We have also conducted
`a field study with 10 users at two different university cam-
`puses to evaluate the performance of EEMSS. Our results
`show that EEMSS is able to detect states with 92.56% ac-
`curacy and improves the battery lifetime by over 75%, com-
`
`pared to existing results. Note that although in this paper
`we focus only on states that can be detected by integrated
`sensors on mobile devices, our sensor management scheme
`is general enough that one can apply our infrastructure to
`mobile sensing systems that involves more sensors and de-
`vices.
`In
`The remainder of this paper is organized as follows.
`Section 2, we present relevant prior works and their relations
`to our study. In Section 3, we describe the sensor manage-
`ment scheme which is the core component of EEMSS. In
`Section 4, we introduce a case study of EEMSS on Nokia
`N95 devices and present the system architecture and imple-
`mentation. In Section 5, we list the empirical results of dif-
`ferent sensor power consumptions as one of the motivations
`of our system design and discuss the sensor duty cycling
`impact on system performance.
`In Section 6, we propose
`novel real-time activity and background sound classification
`mechanisms that result in good classification performance.
`The user study is presented in Section 7, where we evalu-
`ate our system in terms of state recognition accuracy, state
`transition discovery latency and device lifetime. Finally, we
`present the conclusion and our future work direction in Sec-
`tion 8.
`
`2. RELATED WORK
`There has been a fair amount of work investigating multi-
`sensor mobile applications and services in recent years. The
`concept of sensor fusion is well-known in pervasive comput-
`ing. For example, Gellersen et al. [5] pointed out the idea
`that combining a diverse set of sensors that individually cap-
`tures just a small aspect of an environment may result in a
`total picture that better characterizes a situation than loca-
`tion or vision based context.
`Motion sensors have been widely used in monitoring and
`recognizing human activities to provide guidance to specific
`tasks [6, 7, 8]. For example, in car manufacturing, a context-
`aware wearable computing system designed by Stiefmeier et
`al. [6] could support a production or maintenance worker by
`recognizing the worker’s actions and delivering just-in-time
`information about activities to be performed. A common
`low cost sensor used for detecting motion is the accelerome-
`ter. With accelerometer as the main sensing source, activity
`recognition is usually formulated as a classification problem
`where the training data is collected with experimenters wear-
`ing one or more accelerometer sensors in a certain period.
`Different kinds of classifiers can be trained and compared in
`terms of the accuracy of classification [9, 10, 11, 12]. For
`example, more than 20 human activities including walking,
`watching TV, running, stretching, etc. can be recognized
`with fairly high accuracy [12].
`Most existing works to accurately detect user state require
`accelerometer sensor(s) to be installed on pre-identified po-
`sition(s) near human body. Our aim is to avoid the use
`of obtrusive and cumbersome external sensors in detecting
`user state. As such, we remove the need to strap sensors
`to human body. EEMSS is able to accurately detect human
`states, such as walking, running and riding a vehicle by just
`placing the mobile phone anywhere on the user’s body with-
`out any placement restrictions. In this context it is worth
`noting that Schmidt et al. [13] first proposed incorporating
`low level sensors to mobile PDAs/phones to demonstrate
`situational awareness. Several works have been conducted
`thereafter by using the commodity cell phones as sensing.
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`computing or application platforms [14, 15, 16, 17, 18, 19].
`For example, “CenceMe” [16] enables members of social net-
`works to share their sensing presence with their “buddies”
`in a secure manner. The system uses the integrated as well
`as external sensors to capture the users’ status in terms of
`activity, disposition, habits and surroundings. A CenceMe
`prototype has been made available on Facebook, and the
`implementation and evaluation of the CenceMe application
`has also been discussed [17]. Similarly, “Sensay” [15] is a
`context-aware mobile phone and uses data from a number
`of sources to dynamically change cell phone ring tone, alert
`type, as well as determine users’ “un-interruptible” states.
`“Sensay” requires input from an external sensor box which
`is mounted on the user’s hip area and the system design does
`not have energy efficiency concern. Moreover, the decision
`module of “Sensay” is implemented on a computer instead
`of mobile device. In comparison, our approach in EEMSS
`design uses the off-the-shelf mobile device and manage sen-
`sors in a way such that sensing is conducted in an energy
`efficient manner.
`Researchers from different fields have studied and used a
`large number of sensors including GPS, Bluetooth, WiFi de-
`tector, blood oxygen saturation sensor, accelerometer, elec-
`trocardiograph sensor, temperature sensor, light sensor, mi-
`crophone, camera, etc. in projects such as urban/paticipatory
`sensing [14, 20, 21], activity recognition[22, 23, 24], and
`health monitoring [25, 26, 27]. For example, Whitesell et
`al. [21] have designed and implemented a system that ana-
`lyzes images from air sensors captured from mobile phones
`and indoor air pollution information has been extracted by
`comparing the data to a calibrated chart. Targeting obesity
`problem in health monitoring domain, Annavaram et al. [24]
`showed that by using data from multiple sensors and apply-
`ing multi-modal signal processing, seemingly similar states
`such as sitting and lying down can be accurately discrimi-
`nated, while using only a single accelerometer sensor these
`states can not be easily detected. Wu et al. [27] have de-
`signed “SmartCane” system which provides remote monitor-
`ing, local processing, and real-time feedback to elder patients
`in order to assist proper usage of canes to reduce injury and
`death risks. While these works only focused on how to more
`accurately detect human context using one or more sensors,
`in this paper we emphasize both energy efficiency and state
`detection accuracy. In fact, in [17], the authors were well
`aware of the battery life constraint of mobile devices and
`different duty cycling mechanisms have been considered and
`tested for different physical sensors. However the lack of
`intelligent sensor management method still withholds the
`device lifetime by a significant amount.
`The problem of energy management on mobile devices has
`been well-explored in the literature such as [28, 29, 30, 31,
`32]. For example, Viredaz et al. [28] surveyed many fun-
`damental but effective methods for saving energy on hand-
`held devices in terms of improving the design and coop-
`eration of system hardware, software as well as multiple
`sensing sources. Event driven power-saving method is in-
`vestigated by Shih et. al.
`to reduce system energy con-
`sumptions [31].
`In their work, the authors focused on re-
`ducing the idle power, the power a device consumes in a
`“standby” mode, such that a device turns off the wireless
`network adaptor to avoid energy waste while not actively
`used. The device will be powered on only when there is an
`incoming or outgoing call or when the user needs to use the
`
`PDA for other purposes. To further explore the concept of
`event-driven energy management, a hierarchical power man-
`agement method was used in [32].
`In their demo system
`“Turdecken”, a mote is used to wake up the PDA, which in
`turn wakes up the computer by sending a request message.
`Since the power required by the mote is enough for holding
`the whole system standby, the power consumption can be
`saved during system idle time.
`In our system design, we build on many of these past ideas
`and integrate them in the context of effective power man-
`agement for sensors on mobile devices. In order to achieve
`human state recognition in an energy efficient manner, we
`have proposed a hierarchical approach for managing sensors,
`and do so in such a way that still maintains accuracy in sens-
`ing the user’s state. Specifically, power hungry sensors are
`only activated whenever triggered by power efficient ones.
`By only duty cycling the minimum set of sensors to detect
`state transition and activating more expensive ones on de-
`mand to recognize new state, the device energy consumption
`can be significantly reduced. A similar idea was explored by
`the “SeeMon” system [33], which achieves energy efficiency
`by only performing context recognition when changes occur
`during the context monitoring. However, “SeeMon” focuses
`on managing different sensing sources and identifying con-
`dition changes rather than conducting people-centric user
`state recognition.
`
`3. SENSOR MANAGEMENT
`METHODOLOGY
`In this section we will describe our design methodology
`for EEMSS framework. The core component of EEMSS is
`a sensor management scheme which uniquely describes the
`features of each user state by a particular sensing criteria
`and state transition will only take place once the criteria
`is satisfied. An example would be that “meeting in office”
`requires the sensors to detect both the existence of speech
`and the fact that the user is currently located in office area.
`EEMSS also associates the set of sensors that are needed to
`detect state transitions from any given state. For example,
`if the user is “sitting still” and in order to detect “movement”
`mode accelerometer must be sampled periodically.
`
`3.1 State and Sensor Relationship
`Sensor assignment is achieved by specifying an XML-format
`state descriptor as system input that contains all the states
`to be automatically classified as well as sensor management
`rules for each state. The system will parse the XML file
`as input and automatically generate a sensor management
`module that serves as the core component of EEMSS and
`controls sensors based on real-time system feedback.
`In
`essence, the state descriptor consists of a set of state names,
`sensors to be monitored, and conditions for state transitions.
`It is important to note the system designer must be well fa-
`miliar with the operation of each sensor and how a user
`state can be detected by a set of sensors. State description
`must therefore be done with care so as to not include all
`the available sensors to detect each state since such a gross
`simplification in state description will essentially nullify any
`energy savings potential of EEMSS.
`Figure 1 illustrates the general format of a state descrip-
`tor and the corresponding state transition process. It can
`be seen that a user state is defined between the “<State>”
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`that are required to detect that state and all the possible
`state transitions. In the second phase, the system designer
`must carefully set the sampling period and duty cycles to
`balance the state detection accuracy with energy efficiency.
`In our current implementation these values are set manu-
`ally based on experimentation. In this phase of system con-
`figuration we also design and test classification algorithms
`that recognize user status based on different sensor read-
`ings. These classification algorithms are pre-trained based
`on extensive experiments conducted by researchers. We will
`present the specific sensor parameters used in EEMSS in
`Section 5 and the classification algorithms in Section 6.
`
`3.3 Generalization of the Framework
`We would like to emphasize that the system parameters
`need only to be set once after the training phase and can
`be used repeatedly during the operation of the sensing sys-
`tem. However, we do recognize that the process of man-
`ually setting sensor duty cycles for all sensors and states
`may be cumbersome even if it is rare. We believe there
`are ways to semi-automate sensor assignment mechanism.
`In order to provide an automated sensor assignment mech-
`anism rather than manually specifying sensor parameters,
`a sensor information database could be built a priori on
`each mobile device that stores the sensor power consump-
`tion statistics and also how the data provided by one sensor
`can be approximated with the data from a different sen-
`sor. For instance, position data from GPS can be approxi-
`mated using cell tower triangulations. We envision that in
`future the sensor management effort will be pushed from the
`developer-end to the device-end where the sensor informa-
`tion database serves as a stand-alone sensor management
`knowledge center. In this scenario the sensor management
`scheme as well as the sensor sampling parameters could be
`generated or computed based on knowledge database with
`limited human input.
`As noted earlier our XML based state description mech-
`anism is highly scalable as new states can be added or up-
`dated easily. With each new state addition in our current
`implementation we need to define a classification algorithm
`that recognizes the new state. Once the classification algo-
`rithm is defined we can generate the sensor parameters after
`a brief training period.
`Various sensors makes the user’s contextual information
`available in multiple dimensions, from which a rich set of
`user states can be inferred. However, in most cases different
`users or higher layer applications may only be interested
`in identifying a small subset of states and exploit the state
`information for application customization. For example, a
`ring tone adjustment application, which can automatically
`adjust the cell phone alarm type, may only need to know the
`property of background sound in order to infer the current
`situation. A medical application may require the system
`to monitor one’s surrounding temperature, oxygen level and
`the user’s motion such as running and walking to give advise
`to patient or doctors. In a personal safety application, one
`factor that one may care is whether the user is riding a
`vehicle or walking alone such that the mobile client is able
`to send warning messages to the user when he or she is
`detected walking in an unsafe area at late night. These are
`all examples of mobile sensing systems with particular needs,
`by which our framework design can be potentially adopted.
`
`Figure 1: The format of XML based state descriptor
`and its implication of state transition.
`
`and “</State>” tags. For each state, the sensor(s) to be
`monitored are specified by “<Sensor>” tags. The hierarchi-
`cal sensor management is achieved by assigning new sensors
`based on previous sensor readings in order to detect state
`transition. If the state transition criteria has been satisfied,
`the user will be considered as entering a new state (denoted
`by “<NextState>”in the descriptor) and the sensor manage-
`ment algorithm will restart from the new state. For example,
`based on the sample description in Figure 1, if the user is
`at “State2” and “Sensor2” returns “Sensor reading 2” which
`is not yet sufficient for detecting state transition, “Sensor3”
`will be turned on immediately to further detect the user’s
`status in order to identify state transition.
`There are three major advantages of using XML as the
`format of state descriptor. First, XML is a natural lan-
`guage to represent states in a hierarchical fashion. Second,
`new state descriptors can be added and existing states can
`be modified with relative ease even by someone with lim-
`ited programming experience. Finally, XML files are easily
`parsed by modern programming languages such as Java and
`Python thereby making the process portable and easy to
`implement.
`
`3.2 Setting Sensor Duty Cycles
`Recall that in the first phase of state description the sys-
`tem designer will specify the list of states and the sensors
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`4. EEMSS IMPLEMENTATION – A CASE
`STUDY
`4.1 Description
`In this section we will describe a practical implementation
`of a state detection system using EEMSS framework. For
`this case study we focus on using only built-in sensors on
`Nokia N95 device to detect states. N95 has several built-in
`sensors, including GPS, WiFi detector, accelerometer, and
`embedded microphone. The goal of the case study is to con-
`duct a prototype implementation using EEMSS framework
`and to quantify the performance in terms of state recogni-
`tion accuracy, detection latency, as well as energy efficiency.
`As such we select a set of states that describe the user’s
`daily activities and have defined the state and sensor rela-
`tionships in XML using the format introduced in Section 3.
`Table 1 illustrates the set of user states to be recognized by
`EEMSS and three characteristic features that define each of
`these states. The three features are the location, motion and
`background sound information. The list of sensors necessary
`to detect these three features are also shown in Table 1. We
`selected a sample set of user states that can all be detected
`solely using the in-built sensors on N95 in this case study.
`For each user state, our EEMSS implementation moni-
`tors the characteristic features defining that state by reading
`a corresponding sensor value. For instance, various back-
`ground sounds can be detected and discriminated by sam-
`pling the microphone sensor. In addition to monitoring the
`current state, EEMSS also monitors a set of sensors that
`define a state transition. Recall that state description using
`hierarchical sensor management not only defines the set of
`sensors to be sampled, but also specifies possible state tran-
`sitions and the sensor readings that trigger the transition.
`If a state transition happens, a new set of sensors will be
`turned on to recognize one’s new activity. Here we select
`one of the user states (Walking) and illustrate how the state
`transition is detected when the user is walking outdoor. Fig-
`ure 2 shows the hierarchical decision rules. It can be seen
`that the only sensor that is being periodically sampled is
`GPS when the user is walking, which returns both the Geo-
`coordinates and the user’s speed information that can be
`used to infer user’s mode of travel. If a significant amount
`of increase is found on both user speed and recent distance
`of travel, a state transition will happen and the user will be
`considered riding a vehicle. Once GPS times out due to lost
`of satellite signal or because the user has stopped moving for
`a certain amount of time, a WiFi scan will be performed to
`identify the current place by checking the surrounding wire-
`less access points. Note that the wireless access point sets
`for one’s frequently visited places such as home, cafeteria,
`office, gym, etc. can be pre-stored on the device. Finally,
`the background sound can be further sensed based on the
`audio signal processing. We will quantify the accuracy and
`device energy efficiency in Section 7.
`It is important to note that the Nokia N95 device con-
`tains more sensors such as Bluetooth, light sensor, and cam-
`era. However, we chose not to use these sensors in current
`EEMSS case study implementation due to either low tech-
`nology penetration rate or sensitivity to the phone’s physical
`placement. For instance, experiments have been conducted
`where a mobile device will probe and count the neighboring
`Bluetooth devices, and the results show that the number
`of such devices discovered is very low (usually less than 5),
`
`Figure 2: The sequential sensor management rules
`used to detect state transitions when the user is
`walking outdoors.
`
`even though a big crowd of people is nearby. Light sen-
`sor is also not used in our study because the result of light
`sensing depends highly on whether the sensor can clearly
`see the ambient light or its view is obstructed due to phone
`placement in a pocket or handbag. Therefore it could po-
`tentially provide high percentage of false results. Moreover,
`since we focus on an automated real-time state recognition
`system design, the camera is also not considered as part of
`our study since N95 camera shutter requires manual inter-
`vention to turn on and off the camera. Even though these
`sensors have not been used in our case study, they still re-
`main as important sensing sources for our future study.
`4.2 Architecture and Implementation
`The main components of EEMSS, including sensor man-
`agement and activity classification, have been implemented
`on J2ME on Nokia N95 devices. The popularity of Java
`programming and the wide support of J2ME by most of the
`programmable smart phone devices ensure that our system
`design achieves both portability and scalability. However,
`the current version of J2ME does not provide APIs that al-
`low direct access to some of the sensors such as WiFi and
`accelerometer. To overcome this, we created a Python pro-
`gram to gather and then share this sensor data over a local
`socket connection.
`The system can be viewed as a layered architecture that
`consists of a sensor management module, a classification
`module, and a sensor control interface which is responsi-
`ble of turning sensors on and off, and obtaining sensed data.
`We also implemented other components to facilitate debug-
`ging and evaluation, including real-time user state updates,
`logging, and user interfaces. Figure 3 illustrates the design
`of the system architecture and the interactions among the
`components.
`As mentioned in the previous subsection, the sensor man-
`agement module is the major control unit of the system. It
`first parses a state description file that describes the sensor
`management scheme, and then controls the sensors based
`on the sensing criteria of each user state and state transi-
`tion conditions by specifying the minimum set of sensors to
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`State Name
`
`Working
`Meeting
`Office loud
`Resting
`Home talking
`Home entertaining
`Place quiet
`Place speech
`Place loud
`Walking
`Vehicle
`
`State Features
`Motion
`Location
`Still
`Office
`Still
`Office
`Still
`Office
`Still
`Home
`Still
`Home
`Still
`Home
`Still
`Some Place
`Still
`Some Place
`Still
`Some Place
`Keep on changing Moving Slowly
`Keep on changing Moving Fast
`
`Background Sound
`Quiet
`Speech
`Loud
`Quiet
`Speech
`Loud
`Quiet
`Speech
`Loud
`N/A
`N/A
`
`Sensors Monitored
`
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`Accelerometer, Microphone
`GPS
`GPS
`
`Table 1: The states and their features captured by our system (EEMSS).
`
`be monitored under different scenarios (states). The sen-
`sor management module configures the sensors in real-time
`according to the intermediate classification result acquired
`from the classification module and informs the sensor con-
`trol interface what sensors to be turned on and off in the
`following step.
`In our case study, the classification module is the con-
`sumer of the sensor raw data. The classification module first
`processes the raw sensing data into desired format. For ex-
`ample, the magnitude of 3-axis accelerometer sensing data is
`computed, and FFT is performed on sound clips to conduct
`frequency domain signal analysis. The classification module
`returns user activity and position feature such as “moving
`fast”, “walking”, “home wireless access point detected” and
`“loud environment” by running classification algorithms on
`processed sensing data. The resulting user activity and po-
`sition information are both considered as intermediate state
`which will be forwarded to the sensor management module.
`The sensor management module then determines whether
`the sensing results satisfy the sensing criteria and decides
`sensor assignments according to the sensor management al-
`gorithm.
`The sensor interface contains APIs that provide direct ac-
`cess to the sensors. Through these APIs, application can
`obtain the sensor readings and instruct sensors to switch
`on/off for a given duty cycle, as well as change the sample
`rate. As mentioned previously, due to J2ME limitations,
`GPS and embedded microphone are operated through J2ME
`APIs while accelerometer and WiFi detector are operated
`through Python APIs.
`
`5. ENERGY CONSUMPTION MEASURE-
`MENT AND SENSOR DUTY CYCLES
`In this section, we present our methodology to deter-
`mine the energy consumption of sensors used in the current
`EEMSS case study, in order to understand how to best co-
`ordinate them in an effective way. We conducted a series of
`power consumption measurements on different built-in sen-
`sors that are used in this case study, including GPS, WiFi
`detector, microphone and accelerometer. We discuss the im-
`plementation of duty cycling mechanisms on the sensors and
`the corresponding energy cost for each sensor sampling.
`The sensors on a mobile phone can be categorized into
`two classes. The first class includes the accelerometer and
`microphone. These sensors once turned on operate continu-
`ously and require an explicit signal to be turned off. More-
`
`Figure 3: System architecture of EEMSS implemen-