Cse rover-technology-report

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A
Seminar report
On
Rover Technology
Submitted in partial fulfillment of the requirement for the award of degree
Of CSE
SUBMITTED TO: SUBMITTED BY:
www.studymafia.org www.studymafia.org

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Acknowledgement
I would like to thank respected Mr…….. and Mr. ……..for giving me such a wonderful
opportunity to expand my knowledge for my own branch and giving me guidelines to present a
seminar report. It helped me a lot to realize of what we study for.
Secondly, I would like to thank my parents who patiently helped me as i went through my work
and helped to modify and eliminate some of the irrelevant or un-necessary stuffs.
Thirdly, I would like to thank my friends who helped me to make my work more organized and
well-stacked till the end.
Next, I would thank Microsoft for developing such a wonderful tool like MS Word. It helped
my work a lot to remain error-free.
Last but clearly not the least, I would thank The Almighty for giving me strength to complete
my report on time.

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Preface
I have made this report file on the topic Rover Technology; I have tried my best to elucidate all
the relevant detail to the topic to be included in the report. While in the beginning I have tried to
give a general view about this topic.
My efforts and wholehearted co-corporation of each and everyone has ended on a successful
note. I express my sincere gratitude to …………..who assisting me throughout the preparation of
this topic. I thank him for providing me the reinforcement, confidence and most importantly the
track for the topic whenever I needed it.

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CONTENTS
 INTRODUCTION
 LOCATION AWARE COMPUTING COMES OF AG
 REVIEW
 LOCATION-SENSING TECHNOLOGIES
 Coarse-Grained Systems
 Fine-Grained Systems
 DEPLOYMENT
 SENSOR FUSION
 ROVER ARCHITECTURE AND WORKING
 ROVER SERVICES
 ROVER ARCHITECTURE
 System Scalability
 ACTION MODEL
 Actions vs. Threads
 ROVER CLIENTS
 ROVER CONTROLLER
 ROVER DATABASE
 LOCATION SERVER
 MULTI-ROVER SYSTEM
 ROVER IMPLEMENTATION AND ANALYSIS
 COMMUNICATION INTERFACES
 INITIAL IMPLEMENTATION
 SYSTEM FUNCTIONALITY
 SYSTEM PERFORMANCE
 Analysis using Passive Monitoring
 Experimental configuration
 Results and discussion
 CONCLUSION AND FUTURE WORK
 REFERENCES

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INTRODUCTION
Location-aware computing involves the automatic tailoring of information and services based on
the current location of the user. We have designed and implemented Rover, a system that enables
location-based services, as well as the traditional time-aware, user-aware and device-aware
services. To achieve system scalability to very large client sets, Rover servers are implemented
in an “action-based” concurrent software architecture that enables fine-grained application-
specific scheduling of tasks. We have demonstrated feasibility through implementations for both
outdoor and indoor environments on multiple platforms.
A user is shopping in a mall. On entering a store, he pulls out a PDA and browses through
detailed information about the products on display. Satisfied with the information, through the
PDA, he makes an online purchase of the items of interest that will be subsequently shipped to
his home directly. As he walks on to the next store, which happens to be a video rental store,
information on newly-released movies in his favorite categories are downloaded automatically
into his PDA, along with their availability information. He chooses a couple of these movies and
indicates that he will pick them up at the storefront. His membership discounts are applied to the
bill, and he confirms the charge to his credit card. The intriguing aspect of this scenario is the
automatic tailoring of information and services based on the current location of the user. We
refer to this paradigm as location-aware computing. The different technology components
needed to realize location-aware computing are present today, powered by the increasing
capabilities of mobile personal computing devices and the increasing deployment of wireless
connectivity (IEEE
802.11 wireless LANs [7], Bluetooth [1], Infra-red [2], Cellular services, etc.) What has hindered
its ubiquitous deployment is the lack of system-wide integration of these components in a
manner that scales with large user populations. In this paper, we describe the design and initial
implementation
experience of such a system, which we call Rover, and discuss the impact such a system can
have on the next generation of applications, devices, and users.
Location-aware, in addition to the more traditional notions of time-aware, user-aware,
and device-aware. Rover has a location service that can track the location of every user, either by
automated location determination technology (for example, using signal strength or time

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difference) or by the user manually entering current location (for example, by clicking on a
map).
Available via a variety of wireless access technologies (IEEE 802.11 wireless LANs,
Bluetooth, Infrared, cellular services, etc.) and devices (laptop, PDA, cellular phone, etc.), and
allows roaming between the different wireless and device types. Rover dynamically chooses
between different wireless links and tailors application-level information based on the device and
link layer technology.
Scales to a very large client population, for example, thousands of users. Rover achieves
this through fine-resolution application-specific scheduling of resources at the servers and the
network.
We will use a museum tour application as an example to illustrate different aspects of
Rover. We consider group of users touring the museums in Washington D.C. At a Rover
registration point in a museum, each user is issued a handheld device with audio and video
capabilities, say an off-the-shelf PDA available in the market today. Alternatively, if a user
possesses a personal device, he can register this device and thus gain access to Rover. The
devices are traceable by the Rover system. So as a user moves through the museum, information
on relevant artifacts on display are made available to the user’s device in various convenient
forms, for example, audio or video clips streamed to the device. Users can query the devices for
building maps and optimal routes to objects of their interest. They can also reserve and purchase
tickets for exhibitions and shows in the museum later in the day. The group leader can coordinate
group activities by sending relevant group messages to the users. Once deployed, the system can
be easily expanded to include many other different services to the users. The next section gives a
description of the kinds of services that are available through Rover. The successive sections
provide an overview of the Rover architecture and a description of a concurrent software
architecture that has been used for system scalability. The following sections expand on
particular aspects of Rover, including clients, servers, data management and multi-Rover
systems. Then we describe our initial implementation experience and conclude with ongoing and
future work.

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LOCATION AWARE COMPUTING COMES OF AGE
REVIEW
At the core of invisible computing is context awareness, the concept of sensing and
reacting to dynamic environments and activities. Location is a crucial component of context, and
much research in the past decade has focused on location-sensing technologies, location-aware
application support, and location-based applications. With numerous factors driving deployment
of sensing technologies, location-aware computing may soon become a part of everyday life.
LOCATION-SENSING TECHNOLOGIES
A central problem in location-aware computing is the determination of physical location.
Researchers in academia and industry have created numerous location-sensing systems that
differ with respect to accuracy, coverage, frequency of location updates, and cost of installation
and maintenance.
Coarse-Grained Systems
For applications in open, outdoor areas, the Global Positioning System isa common
choice. A GPS receiver estimates position by measuring satellite signals’ time difference of
arrival. Although GPS offers near-worldwide coverage, its performance degrades indoors and in
high-rise urban areas, and receivers have a relatively long start-up time and high cost. In 1989,
Roy Want, Andy Hopper, and others pioneered the study of indoor location sensing with their
infrared based Active Badge system. This provides room-grained location using wall-mounted
sensors that pick up an infrared ID broadcast by tags worn by the building’s occupants. Many of
the location-sensing systems developed since then are based on radio. By using base station
visibility and signal strength, it is possible to locate Wi-Fi-enabled devices with accuracies from
several meters to tens of meters. Bluetooth technology, which offers a shorter range than Wi-Fi,
can give more accurate positioning, but at the expense of requiring more fixed base stations to
provide coverage. Inexpensive radiofrequency identification tags can be used for location
determination as well by placing RFID readers at doorways and other strategic points to detect
the passage of people or objects. Location information can also be derived from other types of

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RF infrastructures including those for mobile phones and TV broadcasts. These can be deployed
over a wide area with relative ease, in contrast to technologies such as RFID that have limited
transmission range. With mobile phones, Cambridge Positioning Systems has demonstrated
location accuracies of 20meters, while Rosum has achieved accuracies from 3 to 25 meters with
digital TV signals.
Fine-Grained Systems
Many of the above systems are based on technologies that were not developed with
location sensing in mind. Perhaps as a consequence they exhibit modest accuracy, generally
measured in meters. However, at least three types of systems have been designed specifically to
provide fine grained location sensing, achieving accuracies on the order of centimeters.
Ultrasound can be used to determine distances between mobile tags and known points in the
environment. A process akin to triangulation can thebe employed to derive a location estimate
for the tag. One type of ultrasonic ranging device is the Cricket indoor location system developed
at MIT ,which is set to become available for purchase from Crossbow this year. Some computer
vision-based systems are appealing because they do not require users to wear any sort of tag.
However, such systems have difficulty identifying and simultaneously tracking many subjects.
Vision-based systems using barcode-like tags tend to be more robust.. Ubisense, a company that
builds real-time local positioning systems, recently demonstrated a fine-grained tracking system
that uses ultra wide band radio signals. Unlike conventional radio signals, these signals can have
pulse durations short enough to allow accurate time-of-arrival and angle-of-arrival measurement,
with an accuracy of about 15 centimeters. In addition, ultra wideband technology does not
require a direct line of sight between tags and sensors.

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Fig 2.1 Location-sensing technologies
DEPLOYMENT
Figure 1 shows Each box’s horizontal span shows the range of accuracies the technology
covers; the bottom boundary represents current deployment, while the top boundary shows
predicted deployment over the next several years and the current and predicted deployment of
location-sensing technologies within the next two to three years. The widest existing
deployments are based on GPS, which is particularly suited for outdoor applications. These
include servicing applications centered on vehicle location such as route planning and fleet
tracking, as well as applications integrated into handheld GPS units. Other current deployments
are found in vertically integrated solutions and comprise a specific location-aware application,
appropriate location-sensitive ing hardware, and a custom software platform. A handful of firms
offer these systems in targeted application areas such as military training, human-body motion
capture, supply chain management, and asset tracking. Looking ahead, numerous factor are
accelerating the adoption of coarse-grained location-sensing technologies. To begin with, the

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recent explosion of Wi-Fi, Bluetooth, and other wireless networking technologies has led to
many end-user devices being equipped with RF hardware that can be used for location sensing.
In addition, the Enhanced 911 requirement—which mandates that US wireless carriers provide
location accuracies of 50 to 100 meters for emergency 911 calls by the end of 2005— is driving
incorporation of location sensing systems into mobile phones using GPS, base-station
triangulation methods, and a combination of these technologies known as Assisted GPS. Similar
requirements exist in the European Union.
SENSOR FUSION
Vertically integrated location-aware systems typically use one type of sensor for a single
application. However, in the near future, many kinds of location sensors may be available to a
particular client system. The task of
making sense of this vast amount of sometimes contradictory information, known as sensor
fusion, presents a major challenge. Borrowing from the field of robotics, location researchers
have settled on Bayesian inferencing as the preferred method for processing data from disparate
location sensors. Using Kalman filters, hidden Markov models, dynamic Bayes nets, and particle
filters, they have developed principled methods of incorporating sensor uncertainty as well as
limits on speed and travel paths. The result is a location measurement derived from multiple
sensors and constraints that uses a probability distribution rather than a single value to describe
the inherent uncertainty. For example, researchers at the University of Washington have
demonstrated an indoor location-measuring system that
processes data from multiple sources, including infrared and ultrasonic sensors, using a particle
filter. In addition, the system learns typical walking paths through the building to aid in location
estimation.

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ROVER ARCHITECHTURE DESCRIPTION
ROVER SERVICES
Rover offers two kinds of services to its users. We refer to them as basic data services and
transactional services.
1. Basic data services: Rover enables a basic set of data services in different media formats,
including text, graphics, audio, and video. Users can subscribe to specific data components
dynamically through the device user interface. Depending on the capabilities of the user’s
device, only a select subset of media formats may be available to the user. This data service
primarily involves one-way interaction; depending on user subscriptions, appropriate data is
served by the Rover system to the client devices.
2. Transactional services: These services have commit semantics that require coordination of
state between the clients and the Rover servers. A typical example is e-commerce interactions.
Services that require location manipulation are a particularly important class of data
services in Rover. Location is an important attribute of all objects in Rover. The technique used
to estimate the location of an object (some techniques are described in the Appendix)
significantly affects the granularity and accuracy of the location information. Therefore an
object’s location is identified by a tuple of Value, Error, and Timestamp. The error identifies the
uncertainty in the measurement (value). The timestamp identifies when the measurement was
completed. The accuracy of the location information is relevant to the context of its use. For
example, an accuracy of _ meters is adequate to provide walking directions from the user’s
current location to another location about 500 meters away. However, this same accuracy is
inadequate to identify the exhibit in front of the user. User input in these cases, helps
significantly improve the accuracy of user location information.
Map-based services are an important component of location manipulation services. Rover
maps can be subject to various operations before being displayed to users:
Filter: Objects in a Rover map have a set of attributes that identify certain properties of the
objects. Depending on the user’s context (which indicates the user’s interests), filters are

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generated for the attribute values of interest to the user. These filters are applied to maps to select
the appropriate subset of objects to display to the user. For example, one user may be interested
in only the restaurants in a specific area, while another user needs to view only the museum and
exhibition locations. The filters can be dynamically changed to appropriately change the objects
being displayed on the map.
Zoom: The zoom level of a displayed map identifies its granularity. The zoom level at a client
device is chosen based on the user’s context. For example, a user inside a museum gets a detailed
museum map, but when the user steps outside the museum, he gets an area map of all the
museums and other points of interest in the geographic vicinity. The zoom level can be
implemented as an attribute of objects, and appropriate filters can then be applied to display a
map at the desired zoom level.
Translate: This functionality enables the map service to automatically update the view of the
displayed map on the client device as the user moves through the system. When the location of
the user moves out of the central region of the currently displayed map, the system prepares a
new map display, which is appropriately translated from the previously displayed map.
ROVER ARCHITECTURE
A Rover system, depicted in Figure 1, consists of the following entities:
End-users of the system. Rover maintains a user profile for each end-user, that defines specific
interests of the user and is used to customize the content served.
Rover-clients are the client devices through which users interact with Rover. They are typically
small wireless handheld units with great diversity of capabilities in regard to processing, memory
and storage, graphics and display, and network interface. Rover maintains a device profile for
each device, identifying its capabilities and thus, the functionality available at that device.
Wireless access infrastructure provides wireless connectivity to the Rover clients. Possible
wireless access technologies include IEEE 802.11 based wireless LANs, Bluetooth and Cellular
services. For certain Quos guarantees, additional mechanisms need to be implemented at the
access points of these technologies for controlled access to the wireless interface.
Servers implement and manage the various services provided to the end-users. The servers
consist of the following:

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– Rover controller: is the “brain” of the Rover system. It provides and manages the different
services requested by the Rover clients. It schedules and filters the content sent to the clients
based on use and device profiles and their current locations.
– Location server: is a dedicated unit responsible for managing the client device location
services within the Rover system. Alternatively, an externally available location service can also
be used.
Fig 3.1 Physical architecture of the Rover System
– Media streaming unit: provides the streaming of audio and video content to the clients. In
fact, it is possible to use many of the off-the-shelf streaming-media units that are available today
and integrate them in the Rover system.
– Rover database: stores all content delivered to the Rover clients. It also serves as the stable
store for the state of the users and clients that is maintained by the Rover controller.
– Logger: interacts with all the Rover server devices and receives log messages from their
instrumentation modules.
System Scalability

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There are two potential bottlenecks that can hinder the scalability of such a system to large user
populations. One is the server system because it needs to handle a very large number of client
requests with tight real-time constraints. Another potential bottleneck is the bandwidth and
latency of the wireless access points.
For a server to handle such a large volume of real-time requests, in addition to adequate compute
power and appropriate data structures, it must have fine-grained real-time application-specific
scheduling of tasks to efficiently manage the available resources, both processing and
bandwidth. This leads us to divide server devices into two classes:-
- Primary servers, which directly communicate with the clients, and
- Secondary servers, which do not directly communicate with clients but interact with primary
servers to provide backend capabilities to the system.
The Rover controller, location server and media streaming unit are examples of primary servers,
while the Rover database and the logger are examples of secondary servers.
In order to meet the performance objectives, only the primary servers need to implement the
fine-grained real-time task scheduling mechanism. We have defined a concurrent software
architecture called the Action model that provides such a scheduling mechanism, and
implemented the Rover controller accordingly. The Action model, explained below, avoids the
overheads of thread context switches and allows a more efficient scheduling of execution tasks.
The Rover system exports a set of well defined interfaces through which it interacts with the
heterogeneous world of users and devices with their widely varying requirements and
capabilities. Thus new and different client applications can be developed by third-party
developers to interact with the Rover system.
A Rover system represents a single domain of administrative control that is managed and
moderated by its Rover controller. A large domain can be partitioned into multiple administrative
domains each with its own Rover system, much like the existing Domain Name System [9]. For
this multi-Rover system, we define protocols that allow interaction between the domains. This
enables users registered in one domain to roam into other domains and still receive services from
the system

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ACTION MODEL
In order to achieve fine-grained real-time application-specific scheduling, the Rover controller is
built according to concurrent software architecture we call the action model. In this model,
scheduling is done in “atomic” units called actions. An action is a “small” piece of code that
does not have any intervening I/O operations. Once an action begins execution, it cannot be pre-
empted by another action. Consequently, given a specific server platform, it is easy to accurately
bound the execution time of an action. The actions are executed in a controlled manner by an
Action Controller.
We use the term server operation to refer to a transaction, either client- or administrator-
initiated, that interacts with the Rover controller; examples in the museum scenario would be
register Device, get Route and locate User. A server operation consists of a sequence (or more
precisely, a partial order) of actions interleaved by asynchronous I/O events. Each server
operation has exactly one “response handling” action for handling all I/O event responses for the
operation; i.e., the action is eligible to execute whenever an I/O response is received.
A server operation at any given time has zero or more actions eligible to be executed. A
server operation is in one of the following three states:
- Ready-to-run: At least one action of the server operation is eligible to be executed but
no action of the server operation is executing.
-Running: One action of the server operation is executing (in a multi-processor setup,
several actions of the operation can be executing simultaneously).
-Blocked: The server operation is waiting for some asynchronous I/O response and no
actions are eligible to be executed.
The Action Controller uses administrator-defined policies to decide the order of execution of the
set of eligible actions. The scheduling policy can be a simple static one, such as priorities
assigned to server operations, but it can equally well be time based, such as earliest-deadline-first

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or involving real-time cost functions. In any case, the controller picks an eligible action and
executes it to completion, and then repeats, waiting only if there are no eligible actions
(presumably all server operations are waiting for I/O completions).
The management and execution of actions are done through a simple Action API defined
as follows:
-init (action id, function ptr): This routine is called to initialize a new action (identified by
action id) for a server operation. Function ptr identifies the function (or piece of code) to
be executed when the action runs.
-run (action id, function parameters, deadline, deadline failed handler ptr): This routine
is called to mark the action as eligible to run. Function parameters are the parameters
used in executing this instance of the action. Deadline is optional and indicates the time
(relative to the current time) by which the action should be executed. This is a soft
deadline, that is, its violation leads to some penalty but not system failure. If the action
controller is unable to execute the action within the deadline, it will execute the function
indicated by deadline failed handler ptr. This parameter can be NULL, indicating that no
compensatory steps are needed.
-cancel (action id, cancel handler ptr): This routine is called to cancel a ready-to-run
action provided it is not executing. Cancel handler ptr indicates a cleanup function. It can
be NULL.
Actions vs. Threads:
Our need to scale to very large client populations made us adopt the action model rather
than the more traditional thread model. We now provide some experimental justification.
There are several ways to use a thread model to implement the Rover controller. One is to
implement each server operation as a separate thread. Another is to have a separate thread for
each user. Both of these imply a large number of simultaneously active threads as we scale to
large user populations, resulting in large overheads for thread switching. A more sensible
approach is to create a small set of “operator” threads that execute all operations, for example,
one thread for all registerDevice operations, one for all locateUser operations, and so on. Here

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the thread switching overhead is modest but there are drawbacks. One is that, depending on the
threads package, it restricts our ability to optimize thread scheduling, especially as we transit to
time-based (rather than priority based) scheduling. More importantly, because each operator
thread executes its set of operations in sequence, this approach severely limits our ability to
optimally schedule the eligible actions within an operation
and across operations. Of course, each thread could keep track of all its eligible actions and do
scheduling at the action level, but this is essentially recreating the action model within each
thread.
Figure 3.2: Scenario A has 10,000 processor-bound Figure 3.3: Scenario B has 100 I/O bound
server
server operations where computation is inter- operations where computation is
interleaved
leaved with file write operations with network I/O interactions
We compare the performance of action-based and thread-based systems through
implementation. In this paper, we consider two kinds of server operations, one processor-bound
and the other, I/O-bound, both of which appear in the context of the Rover controller. Using
these server operations, we construct two corresponding scenarios:

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1. Scenario A: This is a computation-intensive scenario and has 10,000 processor-bound
server operations, where each of the server operations has three compute blocks,
interleaved with two file write operations (see Figure 3.2). In each of these server
operations, the second and the third I/O compute block do not need to await for the
prior file I/O write operation to complete.
2. Scenario B: This is an I/O-intensive scenario and has 100 I/O-bound server
operations, where each of the server operations has three compute blocks, interleaved
with two network I/O operations (see Figure 3.3). In each of these server operations,
the second and third compute blocks can be initiated only after the completion of the
prior network I/O operation. The network I/O interaction was implemented using
UDP. Since our focus is on the comparison of the action-based versus the thread-
based systems, we avoided issues of packet loss and re-transmissions by only
considering those experiments where no UDP packets were lost in the network.
We consider two execution platforms, referred to as M1 and M2 in the paper. M1 comprises of a
Intel Pentium III (600 MHz) processor and 96 MB of RAM which runs Linux. M2 comprises of
a Sun Ultra 5, with a Sparc (333 MHz) processor and 128 MB RAM and runs Solaris. For the
thread-based implementation, we used the Linux Threads library for the M1 platform and the
Pthreads library for the M2 platform, both of which are implementations of the Posix 1003.1c
threads package. This total execution time for the three compute blocks in each server operation
A was 0.1518 ms for M1 and 0.9069 ms for M2. The ping network latency for the network I/O in
server operation B varied between 30-35 ms.
We compared performances of an action-based implementation and a thread-based
implementation of the two scenarios for the different platforms. In the action-based
implementation each compute block is implemented as a separate action. In the thread-based
implementation, we experimented with a different number of threads, where each thread
executed an equal number of server operations for perfect load balancing between the different
threads.

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Table 1: Comparisons of overheads for action-based and thread-based systems (in ms)
Scenario Machine Specifications Action-based Thread-based (threads used)
1 5 10 50 100
A (Fig. 2) M1 Pentium/Linux 24.27 299.3 299.9 300.4 304.5
310.3
A (Fig. 2) M2 Sparc/Solaris 62.82 1000.9 1012.5 1041.6 1012.8 1031.25
B (Fig. 3) M1 Controller, M2 Database 11.61 3711.9 1302.2 1011.4 893.10 728.30
In Table 1, we present the overheads obtained in each case, where the overhead is the
total execution time minus the fixed, identical and unavoidable computation/communication
costs for the two scenarios. We report the mean execution overheads of a large number of runs,
which were required to obtain low variance. For the computation-intensive server operations
there are very little performance gains in trying to overlap computation with communication (file
I/O), and is not substantial enough to justify the overheads of a multi-threaded implementation.
Therefore, a thread-based system with a single thread achieves the best performance among the
thread-based implementations.
For the I/O intensive server operations, using a multi-threaded implementation is useful,
since computation and communication can be overlapped. Consequently, the best performance
for the thread-based system is achieved, when the maximum number of threads is used (one
thread for each server operation).
As can be observed in both scenarios, the action-based implementation still achieves
significantly (about an order of magnitude) less overhead as compared to the best thread-based
implementation.
ROVER CLIENTS
The client devices in Rover are handheld units of varying form factors, ranging from powerful
laptops to simple cellular phones. They are categorized by the Rover controller based on
attributes identified in the device profiles, such as display properties—screen size and color

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capabilities, text and graphics capabilities, processing capabilities — ability to handle vector
representations and image compression, audio and video delivery capabilities and user interfaces.
The Rover controller uses these attributes to provide responses to clients in the most compatible
formats.
For the wireless interface of client devices, we have currently considered two link layer
technologies — IEEE 802.11 Wireless LAN and Bluetooth. Bluetooth is power efficient and is
therefore better at conserving client battery power. According to current standards, it can provide
bandwidths of up to 2 Mbps. In contrast, IEEE 802.11 wireless is less power-efficient but is
widely deployed and can currently provide bandwidths of up to 11 Mbps. In areas where these
high bandwidth alternatives are not available, Rover client devices will use the lower bandwidth
air interfaces provided by cellular wireless technologies that use CDMA [11] or TDMA based
techniques. In particular, cellular phones can connect as clients to Rover, which implies that the
Rover system interfaces with cellular service providers.
Different air-interfaces may be present in a single Rover system or in different domains
of a multi-Rover system. In either case, software radios [8] is an obvious choice to integrate
different air-interface technologies. While the location management system is not tied to a
particular air interface, certain properties of specific air interfaces can be leveraged to better
provide location management (discussed in the Appendix).
ROVER CONTROLLER
The interaction of the Rover controller with all other components of the system is
presented in Figure 4. The Rover controller interacts with the external world through the
following interfaces:
Location Interface: This interface is used by the Rover controller to query the location service
about the positions of client devices. The location of a device is defined as a tuple representing
the estimate of its position (either absolute or relative to some well-known locations), the
accuracy of the estimate, and the time of location measurement. Depending on the technology
being used to gain position estimates, The accuracy of the estimate depends on the particulars of

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the location technology, for example, GPS [6], IEEE 802.11 signal strength, signal propagation
delays, etc. Rover takes into account this accuracy information when making location-based
decisions.
Admin Interface: This interface is used by system administrators to oversee the Rover system,
including monitoring the Rover controller, querying client devices, updating security policies,
issuing system specific commands, and so on.
-Content Interface: This interface is used by the content provider to update the content that is
served by the Rover controller to the client devices. Having a separate content interface
decouples the data from the control path.
Back-end Interface: This interface is used for interaction between the Rover controller and
certain external services as may be required. One such service is e-commerce, by which credit
card authorization for various purchases can be made. These services would typically be
provided by third-party vendors.
Server Assistants Interface: This interface is used for interaction of the Rover controller with the
secondary servers. e.g. the database and the streaming media unit.

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Fig 3.4 Logical architecture of a Rover system
Transport Interface: This is the communication interface between the Rover controller and the
clients, which identify data formats and interaction protocols between them.
ROVER DATABASE
The database in Rover consists of two components, which together decouple client-level
information from the content that is served.
One component of the database is the user info base, which maintains user and device
information of all active users and devices in the system. It contains all client-specific contexts of

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the users and devices, namely profiles and preferences, client location, and triggers set by the
clients. This information changes at a fairly regular rate due to client activities, e.g. the client
location alters with movements. The Rover controller has the most updated copy of this
information and periodically commits this information to the database. For many of these data
items (e.g. client location), the Rover controller lazily updates the database. These are termed as
volatile data since any change to these data items are not guaranteed to be accurately reflected by
the system across system crashes. For some others, (e.g. new client registration) the Rover
controller commits this information to the database before completing the operation. These are
termed as non-volatile data. The Rover controller, identifies some parts of the data to be volatile,
so as to avoid very frequent database transactions. The Rover controller does not guarantee
perfect accuracy of the volatile data, and thus trades off accuracy with efficiency for these data
components.
The other component in the database is the content info base. This stores the content that
is served by the Rover controller and changes less frequently. The content provider of the Rover
system is responsible for keeping this info base updated. In the museum example, this
component stores all text and graphical information about the various artifacts on display.
The Rover database implements an extended-SQL interface that is accessed by the Rover
controller. Apart from the usual SQL functionality, it also provides an API for retrieval of spatial
information of different objects and clients in the system.
The transactions of the Rover controller with the database are executed on behalf of the
different server operations. The transactions, by definition, are executed atomically by the
database. Additionally, each transaction is identified by two different flags that identify certain
properties for execution, as follows:
Lock-Acquiring: If this flag is set, the transaction is required to acquire relevant locks,
on behalf of the server operation, to read or write data to the database. It also requires that
these locks will be released by the server operation prior to its termination at the Rover
controller.

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Blocking: If a transaction issued by a server operation is unable to access or modify some
data due to locks being held by other server operations, it can either block till it
successfully reads the data, or it returns immediately to the server operation without
successfully execution. If the Blocking flag is set for a transaction, then the first option is
chosen for the transaction.
To avoid deadlocks, server operations acquire the relevant locks on data items stored in the
database using a Two Phase Locking protocol with a lexicographic ordering of lock acquiring for
data items. It is important to note that server operations may need to acquire locks at the
database, if and only if they need to access the stored data through multiple transactions and all
these transactions need to have the same data view. This is not required for the vast majority of
server operations that either make a single database transaction, or do not need its multiple
transactions to have identical views. None of the server operations in the current implementation
of Rover, required to acquire locks at the database. The transactions themselves might acquire
and release locks at the database during their execution, which are not visible to the server
operations at the Rover controller.
LOCATION SERVER
The location server is responsible for storing and managing user locations in the Rover
system. The system is designed to work in both indoors and outdoors environments. We have
experimented with RF-based systems that infer the location of a device based on the signal
strength of received RF signals of IEEE 802.11 wireless LAN frames.
In our RF-based techniques, the user location of a client is obtained without the use of
any additional hardware. It thus provides more ubiquitous coverage in campus-like environments
that already have a rich wireless LAN coverage for data transport. This can be contrasted to
alternative Infra-red tag-based systems [18, 11, 3] or ultra-sonic emitter and receiver based
systems [16] in which additional devices need to be attached to the infrastructure as well as the
clients. We have developed different RF-based technique in the context of the Rover system.
Techniques are categorized into:

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– Radio-map Techniques: Work in 2 phases: an offline phase and a location
determinationphase. During the offline phase, the signal strengths received from the
access points, at selected locations in the area of interest, are gathered as vectors and
tabulated over the area. During the location determination phase, the vector of samples
received from each access point is compared to the radio-map and the ”best” match is
returned as the estimated user location. We used two methods to calculate the best match:
o K-Nearest Neighbors (KNN): A distance function is defined to measure the
distance between any two data vectors. The nearest K vectors to the vector of
samples, received from each access point at the location determination phase, are
calculated. Then from the K vectors a vote is conducted to estimate the best user
location.
o Probabilistic Clustering-based: Baye’s theorem is used to estimate the probability
of each location within the radio-map. Then the most probable location, using the
vector of samples, is reported as the estimated user location. Refer to [21] for
more details
– Model-based Techniques: The relation between the signal strength received from an
access point and the distance to this access point is captured by some function (model).
By using three or more access points, the user location is estimated. Two methods are
used:
o Minimum Triangulation: Given each access point i located at coordinates xi; yi;
zi, the distance between the receiver and the access point (di) is modeled as:

where x; y; z are receiver’s coordinates, vi is the strength of the received signal,
and k is a constant. Due to problems in signal propagation like reflection and
multi pathes,we model the problem as finding a solution to the minimization
problem:

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
By solving the derivatives equations of f numerically, we can get the estimation
coordination of the receiver.
o Curve Fitting: The received signal power is modeled as:

where PL is the received power at certain position in decibels, d is the three
dimensional path length between the transmitter and the receiver, d0 is a reference
distance, and n represents the path loss exponent. We estimate the A and B
parameters for each access points using curve fitting techniques. Given the vector
of samples, we can estimate how far the receiver is from each access point to
estimate his location.
For indoor environments, we found that radio-map based techniques achieve better accuracy than
model-based techniques. This is because the relation between the signal strength hand distance in
indoor environments is complicated by to the multi-path effect and other phenomena which are
difficult to capture by simple models. On the other hand, model based techniques have the
advantage of not depending on the calibration process required to build the radio-map. This
advantage favors model-based techniques in outdoor environments, where the relation between
signal strength and distance can be captured by simple functions and the coverage area is large
making building the radio-map a time consuming process.
MULTI-ROVER SYSTEM
A single Rover system comprises of a single Rover controller, other server devices (e.g.,
Rover database and Rover streaming media unit), and a set of Rover clients. A single system is
sufficient for management of Rover clients in a zone of single administrative control. For
example, consider a Rover system in a single museum. All artifacts and objects on display in the

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museum are managed by a single administrative entity. There is a single content provider for this
system and a single Rover system is appropriate to serve all visitors to this museum.
However, each separate museum has its independent administrative authority. Therefore,
we can have a separate Rover system for each of the different museums that are administered
separately by each museum authority. This allows a decentralized administration of the
independent Rover systems, locally by each museum authority. However, it is important to
provide a seamless experience to visitors as they roam from museum to museum. A multi-Rover
system is a collection of independent Rover systems that peer with each other to provide this
seamless connectivity to the user population.
The design of a multi-Rover system is similar in spirit to the Mobile IP [10] solution to
provide network layer mobility to devices. Each client device has a home Rover system to which
it is registered. As the device physically moves into the zone of a different, or foreign Rover
system, it needs to authenticate itself with the Rover controller of the foreign system. Based on
administrative policies, the two Rover systems have service level agreements that define the
services that they will provide to clients of each other.
When the Rover controller of a system detects a foreign client device, it first checks
whether it has an appropriate service-level agreement with the Rover controller of the device’s
home system. If one exists, the Rover controller of the foreign system requests transfer of
relevant state about the client device from the Rover controller of the home system and
subsequently provides necessary services to it. Rover controllers of different Rover system use
the Inter-Controller protocols to interact.

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ROVER IMPLEMENTATION AND ANALYSIS
COMMUNICATION INTERFACES
Fig 4.1 Rover’s controller interacts with other parts of the system and the external world
For the wireless interface to client devices, we considered two link-layer technologies: IEEE
802.11 and Bluetooth. Bluetooth is power efficient and therefore better at conserving client
battery power. According to current standards, Bluetooth can provide bandwidths up to 2 Mbps.
In contrast, IEEE 802.11 is less power efficient but widely deployed and currently provides
bandwidths up to 11 Mbps. In areas where these high-bandwidth alternatives are not available,
Rover client devices will use the lower bandwidth interfaces that cellular wireless technologies
provide. Figure 4.1 shows how Rover’s controller interacts with other parts of the system and
with the external world. The controller uses the location interface to query the location service
about the positions of client devices and the transport interface to identify data formats and
interaction protocols for communicating with the clients. It uses the server assistants’ interface

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to interact with secondary servers like the database and the streaming media unit and the back-
end interface to interact with external services, such as credit card authorization for e-commerce
purchases. Third-party providers typically offer these external services. System administrators
can use the admin interface to oversee the Rover system, including monitoring the Rover
controller, querying client devices, updating security policies, issuing system-specific
commands, and so on. The content interface lets content providers update the information and
services that the Rover controller serves to client devices. Having a separate content interface
decouples the data from the control path.
INITIAL IMPLEMENTATION
Fig 4.2 Rover client screen shots taken from a demonstration at the McKeldin mall of the
University of Maryland campus. (a) Rover client running the client software showing the mall
map. (b) A notification to the client about a nearby food stall. The user associated with the client
had previously set a trigger notification request when he is close to a food stall. (c) The user had
issued a query operation about the sites of interest in his vicinity. On receiving the response from
the Rover system, the client has highlighted the relevant sites. (d) An active chat session between
this user and another user is marked as a dotted line connecting both users.

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We have successfully built Rover prototype systems and tested them in the
campus of University of Maryland College Park. The implementation has been demonstrated for
both indoor and outdoor environments. A preliminary test implementation was developed on
Windows based systems (Windows 2000 for the controller and Windows CE for the client
devices). The current implementation of the Rover system has been developed under the Linux
operating system. The Rover controller is implemented on a Intel Pentium machine running Red
Hat Linux 7.1 and the clients are implemented on Compaq iPAQ Pocket PC (model H3650)
running the Familiar distribution (release versions 0.4 and 0.5) of Linux for PDAs1. Wireless
access is provided using IEEE 802.11 wireless LANs. Each Compaq iPAQ is equipped with a
wireless card which is attached to the device through an expansion sleeve.
We have experimented with a set of 8 client devices and have tested various
functionalities of the system.
Figure 4.3: View of the display of a Rover-client
For our outdoor experiments, we interfaced a GPS-device (Garmin e-Trex) to the
Compaq iPAQs and obtained device location accuracy of between 3-4 meters. The display of the
iPAQ Rover-client displays the locations of the different users (represented by the dots) on the
area map as shown in Figure 4.3. The indoor Rover system is implemented for the 4th floor of
the A.V.Williams Building (where the Computer Science Department is located), whose map is
shown in Figure 6. In this implementation, the location service is being provided using signal

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strength measurements from different base stations. There are about base stations that are
distributed all over the building and typically the client device can receive beacons from five or
six of the base stations. We are able to get an accuracy of better than a meter in this environment,
using very simple signal-strength based estimation techniques.
In both these cases, we implemented the basic functionality of the Rover system. They include:
 User activation/de-activation and device registration/de-registration procedures.
 Periodic broadcast of events of interest from the Rover controller to the users in specific
locations.
 Interaction between users. This can be either simple text messaging or voice chat. Users
can optionally make their location visible to other users. In the museum example, a tour
group coordinator can use this feature to locate all the other members of the group.
 Users can request alerts from the Rover controller when certain conditions are met. The
conditions may be time, location or context dependent. This can be used to provide
notification to ticket holders of an approaching show time. Clearly, for the users who are
further away from the show venue, this notification needs to be provided early enough, so
that they have enough time to reach the venue.
 An administrator’s console allows a global view of all users and their locations in the
system. The administrator can directly interact with all or a specific subset of the users
based on the location or other attributes of the users.
Currently, we are implementing more functionality in the Rover controller.
System Functionality
The Rover system provides different capabilities to the users, which can be categorized as
follows:
 System Admin Operations are available only to the authorized system administrator.
These set of operations are—register new user/device, update user/device attributes and
de-register user/device. The administrator is also able to query the Rover server system
about the state and information specific to any and all Rover clients in the system.

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 User Access Operations are the basic set operations that every user avails to access
theRover system. They include the user login and user logout operations.
 Trigger Operations allow users to set context-specific alerts. The triggers are activated
based on user interests and depend on current time and/or location of the user. An user
can enable triggers by specifying the relevant time or space-dependent condition. When
the trigger condition is satisfied the Rover server system sends appropriate notification to
the particular user (Figure 2(b)).
 Query Operations allow users to acquire information about different aspects of the system
and the environment. For example, an user can request information about all or a subset
of all active users in the system. Figure 2(c) shows a client screen shot in response to a
client query on sites of interest in its vicinity.
 Location Update Operation inform the server system about the client’s location using.
 Audio Chat Operations enable direct audio communication between clients. Audio chat
between clients is initiated with the coordination of the Media Manager. Once an audio
chat is initiated, the clients interact directly with each other without intervention of the
rover server system. Figure 2(d), shows the display at a client that is involved in anaudio
chat with another client. The dashed line indicates an active chat session between the
clients 4.

SYSTEM PERFORMANCE
To assess the performance and scalability of the Rover System we take two approaches:
a) Active Monitoring where we instrumented the controller to collect different
performance statistics (e.g. queue lengths for each component, the response time for each
operation, etc.).
b) Passive Monitoring which is described in on next page.

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Fig. 4.4. Passive monitoring analysis: (a) Performance model for passive monitoring. (b)
Typical asymptotic bounds.
Analysis using Passive Monitoring
In this approach, no instrumentation code is introduced in the server system. Instead, we
use a client load generator to stress test the server and observe two different metrics — the
response time obtained by individual clients and the number of clients that can simultaneously
served by the system without significantly impacting the performance of the clients.

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We model the Rover system as a single-server multi-client system as shown in Figure
4.4(a). The Rover System is modeled as a central subsystem consisting of two devices, the Rover
controller and the Rover database, and N terminal subsystems. Each of the N terminals is a client
of the Rover System and perform the cycle of issuing a request, waiting for the response and
processing the response (think time). Wireless network models the communication channel
between the server and the clients. Since we are interested in assessing the performance of the
Rover system we do not explore the affects of communication channel in this paper.
Using a technique called operational law [10] to analyze such systems, it can be shown that the
response time observed by clients increase marginally with increasing the number of clients up to
a critical client population. Let D denotes the time required by the system to process a single
client operation, and Dmax denotes the time required at the bottleneck server of a multi-server
system. For the single server model, Dmax = D. If N* indicates the critical number of clients
that the system can support without impacting the response time for the clients and Z the think
time used by the clients between operations, then operational analysis suggests that:
N* =(D + Z)/Dmax
The graphical representation of N* is shown in Figure 4.4(b).
Experiment Configuration
The central subsystem runs on Pentium IV 1.5 GHz desktop machine with 256 MB of
RAM running the Linux OS with kernel version 2.4.7. A second machine is used to behave as a
set of clients (client loader). The client loader runs on a Pentium III 800 MHz laptop with 128
MB of RAM and running a Linux OS with kernel version 2.4.2. The client machine uses 802.11b
wireless network to connect to the network.
The response time for each operation were collected as observed at the database, the
controller and the client (points A, B, C respectively in Figure 3(b)). Instead of collecting
response time for each of the system operations, we experimented with three different operations
representing three different categories:
1. GetAllLoginUsers: Gets the position of all users who are logged into the system. This
operation is controller intensive and does not involve the database.
2. VectorMap: Gets the vector map of an area. This operation is computationally intensive at the
database side.

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3. Locate: Locates the object containing the given point. This operation involves the database
though it is not very computationally intensive at either the database or the controller sides.
Results and Discussion
In this section we show the results obtained for each of the above operations.
GetAllLoginUsers
Figure 4.5(a) shows the response time at the controller plotted against the number of
clients in the system for different think times (Z = 100ms; 200ms; 300ms). The total
Fig 4.5 GetAllLoginUsers operation: (a) Controller response time, (b) Client response
time, and (c) Response timewhen Z=200ms.

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service time, D of the controller is observed to be around 300 microseconds5. For only one
device in the system Dmax = D. Using Equation 1 where D = 300 microseconds and a think time
Z = 200 milliseconds, we get N¤ to be approximately 667 requests. Hence the server can support
667 requests without any significant delays. In an actual deployment, the think time would be of
the order of 10’s of seconds and that would give an even higher value of N*(of the order of
thousands of clients). Figure 4.5(b) shows the response time for the client side.Figure 4.5(c)
shows the response time behavior of both the controller and the client when the think time is Z =
200 milliseconds. As the graph shows the controller graph stays almost horizontal as the number
of clients are increased which shows the controller can handle a large number of clients. On the
other hand, the client graph grows with the number of clients. This can either be due to the effect
of the wireless hop involved or the processing involved at the OS level. The performance of the
802.11b wireless network has not been taken into account and is left for future work.
VectorMap
Figure 4.6 shows the response time at the different Rover components. For the database
the total service demand time is observed to be around 0.5 seconds. Using equation 1, we can
predict the knee-point to be at N¤ = 3 with a think time Z = 1 second. We should note that the
VectorMap operation is an infrequent operation and has been used only to assess the
performance of the system in the extreme case. In an actual deployment, the duration between
subsequent VectorMap operation requests (Z) would be in the order of minutes.
Figure 4.6 shows the response time observed at the database, the controller, and the client
when the think time is Z = 1 second. The difference in the database response and the controller
response could be explained by the fact that at the controller all the data is touched and a copy is
created for debugging purpose. Once this debugging code is taken out the controller graph
should follow the database graph very closely.

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Fig. 4.6. VectorMap operation: (a) Database response time, (b) Controller response time,
(c) Client response time, and(d) Response time when Z=1s.
Locate
Similar to the analysis of the previous operations, we show the response time at the
database, the server and the client for a think time of 200 milliseconds in Figure 4.7. With
database service demand time (D) is 200 microseconds and the think time (Z) is 200
milliseconds, we get N* to be approximated 1000 requests the database can handle without any
significant delays. The difference between the database graph and the controller graph can be
explained by the same reasoning as given in the analysis of VectorMap request.

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Fig.4.7. Response time of Locate (Z=200ms)

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CONCLUSION AND FUTURE WORK
Location-aware computing involves the automatic tailoring of information and services
based on the current location of the user. We have designed and implemented Rover, a system
that enables location-based services, as well as the traditional time-aware, user-aware and
device-aware services. To achieve system scalability to very large client sets, Rover servers are
implemented in an “action-based” concurrent software architecture that enables fine-grained
application-specific scheduling of tasks. We have demonstrated feasability through
implementations for both outdoor and indoor environments on multiple platforms.
Rover is currently available as a deployable system using specific technologies, both
indoors and outdoors. Our final goal is to provide a completely integrated system that operates
under different technologies, and allows a seamless experience of location aware computing to
clients as they move through the system. With this in mind, we are continuing our work in a
number of different directions. We are experimenting with a wide range of client devices,
especially the ones with limited capabilities. We are also experimenting with other alternative
wireless access technologies including a Bluetooth-based LAN. We are also working on the
design and implementation of a multi-Rover system.
We believe that Rover Technology will greatly enhance the user experience in a large
number places, including visits to museums, amusement and theme parks, shopping malls, game
fields, offices and business centers. The system has been designed specifically to scale to large
user populations. Therefore, we expect the benefits of this system to be higher in such large user
population environments.

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REFERENCES
 www.google.com
 www.wikipedia.com
 www.studymafia.org

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