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: 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 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.

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 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.

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.