Authors: Hock Beng Lim, Yong Meng Teo, Protik Mukherjee, Vihn The Lam, Weng Fai Wong, and Simon See

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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

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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.

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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.

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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_at_home, GIMPS, etc.

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Rationale for Sensor Grids

• All the data collected by sensors (it can be a
  lot) can be processed, analyzed, and stored using
  the grids 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.

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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

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The Papers 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.

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Design Issues and Challenges

• Grid APIs for Sensors
• Network Connectivity and Protocols
• Scalability
• Power Managment
• Scheduling
• Security
• Availability
• Quality of Service

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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.

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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.

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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.

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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 Grids resource management component
  has to take this into account.

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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.

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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.

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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.

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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
  low-level 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.

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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.

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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.

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Organization of a Sensor Grid
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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.

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The SPRING Framework
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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.

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The SPRING Framework
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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.

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The SPRING Framework
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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.

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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.

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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.

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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.

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Proxy Software Architecture
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Proxy Components

• The Data Management component
• Converts sensor data from its native format to a
  grid-friendly 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.

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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.

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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.

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The SPRING Framework
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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.

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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.

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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.