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Sensor Grid: Integration of Wireless Sensor
Networks and the Grid
Authors: Hock Beng Lim, Yong Meng Teo, Protik Mukherjee,
Vihn The Lam, Weng Fai Wong, and Simon See
Presentation: Maria Vanina Martinez
Wireless Sensor Networks
• WSNs can be seen as platforms with the potential to
couple the digital world to the physical world.
• They are possible due to the development of new
technologies such as MEMS sensor devices,
wireless networking, and lower-power embedded
processing.
• WSNs are composed by small, low-cost, low-power
and self-contained devices that have the capability
to sense, process data, and communicate via
wireless connections.
WSN Applications
• Applications require interaction between the user
and the physical environment.
• WSN applications include environmental and habitat
monitoring, healthcare, military survelliance, tracking
of goods, etc.
• Each sensor has limited capabilities, but when a
large number is deployed and aggregated over a
wide area, WSNs become important distributed
computing resources.
Grid Computing
• Grid computing is an approach for the coordinated
sharing of distributed and heterogeneous resources.
• It seeks to solve large-scale problems in dynamic
virtual organizations.
• There exist many kinds of grids, but most of the
existing developments are based on data and
computation grids.
• Examples: SETI@home, GIMPS, etc.
Rationale for Sensor Grids
• All the data collected by sensors (it can be a lot) can
be processed, analyzed, and stored using the grid’s
resources.
• It is possible for different users and applications to
flexibly share sensors.
• There are computationally powerful sensor devices,
so it is more efficient to off-load specialized tasks to
sensor devices (i.e. image and signal processing)
• Sensor Grids provide seamless access to a wide
variety of resources in a pervasive manner.
Rationale (Cont.)
• Advanced techniques in AI, data fusion, data mining,
and distributed database processing can be used to:
– make sense of all the collected data
– generate new knowledge about the environment
• Results can be used to:
– optimize the operation of the sensors
– influence the operation of actuators to change the
environment
The Paper’s Contribution
• The paper proposes a Sensor Grid architecture:
Scalable Proxy-based aRchItecture for seNsor Grid
(SPRING).
• The main idea is to use proxy systems as interfaces
between the WSN and the grid fabric.
• The authors present a series of challanges and
design issues, addressing them while describing the
architecture.
• They developed a sensor grid testbed to study the
design issues and improve the architecture.
Design Issues and Challenges
• Grid APIs for Sensors
• Network Connectivity and Protocols
• Scalability
• Power Managment
• Scheduling
• Security
• Availability
• Quality of Service
Grid APIs for Sensors
• Adopt grid standards and APIs for integration.
• The Open Grid Service Architecture (OGSA) is
based on standards and technologies like XML,
SOAP, and WSDL.
• If sensor data were available in the OGSA
framework, it would be easier to exchange and
process data on the grid.
• It is not possible to encode the data in XML format
within SOAP envelopes in sensors.
• Grid services are too complex to be implemented on
sensors.
Network Connectivity and Protocols
• Network connections in grids are usually fast and
reliable.
• The network connectivity in WSN is dynamic, and it
might be intermitent and susceptible to faults (noise,
degradation).
• Grid networking protocols are based on standard
Internet protocols (TCP/IP, HTTP, FTP, etc).
• WSN are based on proprietary protocols (MAC
protocol and routing protocols).
• Efficient techniques to interface both kinds of
protocols are needed.
Scalability
• The Sensor Grid should allow the easy integration of
multiple WSNs with grid resources.
• These WSNs may be owned by different virtual
organizations (VO).
• Enable applications to access sensor resources
across increasing number of heterogeneous WSNs.
Power Managment
• Applications running on sensors must trade off
between sensor operation and battery life.
• Sensor nodes should provide adaptive power
management facilities that can be accessed by
applications.
• In the Sensor Grid, the availability of sensors does
not depend only on their load, but also on their
power consumption.
• The Sensor Grid’s resource management
component has to take this into account.
Scheduling
• Scheduling of nodes in WSNs facilitates power and
sensor resources management.
• A scheduler is needed in Sensor Grids for an
efficient use of sensor resources by applications that
collect data.
• Applications and users may submit many different
kinds of jobs.
• The Scheduler must manage them in very different
ways, since they may have different requirements.
Security
• Organizations may share resources only if the
process is guaranteed to be secure.
• There are various proposals for security on Grids,
such as Grid Security Infrastructure (GSI), the
Security Assertion Markup Language (SAML), etc.
• WSNs are prone to security problems.
• Techniques to address these problems are sensor
node authentication, encryption of data, and secure
MAC and routing protocols.
• Sensor Grids require that security techniques of
both sides be integrated seamlessly and efficiently.
Availability
• Applications running on sensor nodes are prone to
failure.
• If a node is running out of power, or has failing HW,
it should be possible to migrate jobs to another
node.
• If possible, it would be convenient to replicate
services in order to preserve results.
• The system should be able to recover and restart
the interrupted jobs if unexpected interruptions
occur.
Quality of Service
• Quality of Service determines whether a sensor grid
can provide sensor resources on demand and
efficiently.
• The QoS requirements of sensor applications must
be described in a high-level manner.
• High-level requirements should be mapped into lowlevel QoS parameters that specify the amount of
resources to be allocated.
• Service descriptions are also needed to express
what the service does, how to access it, and the QoS
parameters of the service.
Quality of Service (Cont.)
• It is also necessary to consider resource
reservation, changes in resource availability, in
network topology, and in network bandwidth and
latency.
• Mechanisms to enforce QoS have been developed
separately for WSNs and grids.
• In Sensor Grids, the QoS should be enforced in a
coordinated manner, integrating mechanisms from
both parts.
Sensor Grid Organization
• A sensor Grid consists of WSNs and conventional
grid resources such as computers, servers, and disk
arrays for processing and storing sensor data.
• Resources are shared, and possibly owned, by
several virtual organizations (VO).
• Users from various VOs may access the resources
in the sensor grid, even if the resources are not
owned by their VO.
• The following figure shows a Sensor Grid and its
components.
Organization of a Sensor Grid
The SPRING Framework
• The paper proposes a proxy-based approach for a
sensor grid architecture.
• It allows sensor devices to be made available on the
grid in the same way that conventional grid services
are provided.
• Sensor services are resource-constrained.
• The proxy can support various different WSN
implementations, which provides interoperability.
• The following figure shows the SPRING Framework.
The SPRING Framework
SPRING Features
• SPRING is a layered architecture approach.
• Each layer represents the main software
components that are used to build a Sensor Grid.
• Each layer defines services that are accessible via
APIs by the application or other layers.
• The Grid Interface layer supports a standard grid
middleware (i.e. Globus Toolkit) that enables
different types of resources to communicate over the
grid network.
The SPRING Framework
SPRING Features – User Side
• The User Access layer provides an interface that
enables the submission of applications for
execution.
• The applications may consist of sensor jobs that
execute over the WSN to collect data, or
computational jobs to process the sensor data.
• Sensor jobs are not multitasking in nature, and
require specific durations and time slots.
• The Grid Meta-scheduler layer is used to schedule
and route jobs according to their required resources.
The SPRING Framework
SPRING Features – WSN Side
• The WNS Proxy acts as an interface between the
WSNs and the grid.
• The proxy has several important functions:
– It exposes the sensor resources as conventional grid
services, making them available for any application.
– It translates the sensor data from its native format to a
suitable OGSA format, such as XML.
– It provides the interface between the sensor network
protocols and the Internet protocols.
The WSN Proxy Functions (Cont.)
– It mitigates the effects of unexpected sensor network
disconnections (buffering, caching, link management).
– New WSNs can be integrated to the sensor grid just
by adding proxy systems.
– The WSN Proxy also provides other services to
address power management, scheduling, security,
availability, and QoS for the underlying WSNs.
SPRING Features – WSN Side
• The WSN Management layer provides an
abstraction of the specific APIs and protocols to
access and manage the heterogeneous sensor
resources.
• It manages the configuration of sensor nodes and
provides status information about them.
• It also accepts sensor job requests from the grid and
invokes the specific commands to execute the jobs
on the sensor nodes.
SPRING Features – WSN Side
• The WSN Scheduler is the local resource scheduler
for the WSN:
– It implements the low-level scheduling algorithms for
sensor power and resource management.
– It controls the scheduling of sensor jobs requested by
the user.
– Considering the job parameters, it checks the
resource availability and reserves them.
– It works jointly with other Proxy Components to
provide services for availability and QoS.
Proxy Software Architecture
Proxy Components
• The Data Management component:
– Converts sensor data from its native format to a gridfriendly format.
– Performs data fusion and optimizations to improve the
quality of the collected data.
– It supports several methods for transferring the
sensor data to the user application, such as using
GridFTP, or data streams.
• The Information Services component advertises the
available sensor resources as grid services,
following the OGSA standards.
Proxy Components (Cont.)
• The WSN Connectivity component provides services
to interface the WSN protocols and the grid
networking protocols:
– Buffers the transmission of sensor data, caches the
routing information of sensor nodes, and manages
the ad hoc sensor network links.
• The Power Management component:
– Keeps track of the power consumption of the sensor
nodes.
– It works together with the WSN Scheduler to preserve
power on the sensor nodes.
Proxy Components (Cont.)
• The Security component implements OGSA-compliant
grid security technologies.
• The Availability component:
– Monitors the sensor nodes for failing HW or weak power
levels, and migrates the jobs to more reliable nodes.
– It can replicate services and manage recovery of interrupted
jobs.
• The QoS component, together with the Scheduler and
the WSN Connectivity component:
– Performs the reservation and allocation of sensor resources
based on QoS requirements of sensor jobs.
– It adapts networking conditions to provide the desired QoS.
The SPRING Framework
SPRING Features – Resource Side
• The Resource Management layer provides APIs to
access and manages the resources for the grid job
executions.
• These resources are distributed and heterogeneous
computational and storages devices.
• The Resource Scheduler performs scheduling over
grid jobs based on local usage policies.
Implementation
• The authors developed a prototype sensor grid
testbed.
• They used the testbed to study the design issues
using real hardware.
• They completely implemented the Grid Interface
layer common to all the parts in the framework, and
the layers from the user and resource sides.
• In the WSN Proxy they implemented the WSN
Scheduler, and the WSN Management layer.
• Current work is being dedicated to implementing the
Proxy components.
Conclusions
• The integration of wireless sensor networks with grid
computing greatly enhances the potential of both
technologies for new and powerful applications.
• Sensor grids will attract growing attention from both
the research community and the industry.