Sensor grid

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A sensor grid integrates wireless sensor networks with grid computing concepts to enable real-time data collection and the sharing of computational and storage resources for sensor data processing and management. It is an enabling technology for building large-scale infrastructures, integrating heterogeneous sensor, data and computational resources deployed over a wide area, to undertake complicated surveillance tasks such as environmental monitoring.

Contents

Concept and history

The concept of a sensor grid was first defined in the Discovery Net project where a distinction was made between “sensor networks” and “sensor grids”. [1] [2]

Briefly whereas the design of a sensor network addresses the logical and physical connectivity of the sensors, the focus of constructing a sensor grid is on the issues relating to the data management, computation management, information management and knowledge discovery management associated with the sensors and the data they generate, and how they can be addressed within an open computing environment. In particular in a Sensor Grid is characterized by:

Uses

The sensor grid enables the collection, processing, sharing, visualization, archiving and searching of large amounts of sensor data. [3] [4] [5] [6] There are several rationales for a sensor grid. First, the vast amount of data collected by the sensors can be processed, analyzed, and stored using the computational and data storage resources of the grid. Second, the sensors can be efficiently shared by different users and applications under flexible usage scenarios. Each user can access a subset of the sensors during a particular time period to run a specific application, and to collect the desired type of sensor data. Third, as sensor devices with embedded processors become more computationally powerful, it is more efficient to offload specialized tasks such as image and signal on the sensor devices. Finally, a sensor grid provides seamless access to a wide variety of resources in a pervasive manner. Advanced techniques in artificial intelligence, data fusion, data mining, and distributed database processing can be applied to make sense of the sensor data and generate new knowledge of the environment. The results can in turn be used to optimize the operation of the sensors, or influence the operation of actuators to change the environment. Thus, sensor grids are well suited for adaptive and pervasive computing applications.

Typical sensor grid architecture Sensor Grid architecture-new.jpg
Typical sensor grid architecture

Applications

A sensor grid based architecture has many applications such as environmental and habitat monitoring, healthcare monitoring of patients, weather monitoring and forecasting, military and homeland security surveillance, tracking of goods and manufacturing processes, safety monitoring of physical structures and construction sites, smart homes and offices, and many other uses currently beyond our imagination. Various architectures that can be used for such applications as well as different kinds of data analysis and data mining that can be conducted. Examples of architectures that integrate a mobile sensor network and Grids are given in [7]

See also

Related Research Articles

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Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid computers have each node set to perform a different task/application. Grid computers also tend to be more heterogeneous and geographically dispersed than cluster computers. Although a single grid can be dedicated to a particular application, commonly a grid is used for a variety of purposes. Grids are often constructed with general-purpose grid middleware software libraries. Grid sizes can be quite large.

<span class="mw-page-title-main">System on a chip</span> Micro-electronic component

A system on a chip or system-on-chip is an integrated circuit that integrates most or all components of a computer or other electronic system. These components almost always include on-chip central processing unit (CPU), memory interfaces, input/output devices and interfaces, and secondary storage interfaces, often alongside other components such as radio modems and a graphics processing unit (GPU) – all on a single substrate or microchip. SoCs may contain digital and also analog, mixed-signal and often radio frequency signal processing functions.

Autonomic computing (AC) is distributed computing resources with self-managing characteristics, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. Initiated by IBM in 2001, this initiative ultimately aimed to develop computer systems capable of self-management, to overcome the rapidly growing complexity of computing systems management, and to reduce the barrier that complexity poses to further growth.

<span class="mw-page-title-main">Telematics</span> Interdisciplinary field that encompasses telecommunications

Telematics is an interdisciplinary field encompassing telecommunications, vehicular technologies, electrical engineering, and computer science. Telematics can involve any of the following:

Wireless sensor networks (WSNs) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location. WSNs can measure environmental conditions such as temperature, sound, pollution levels, humidity and wind.

Sensor web is a type of sensor network that heavily utilizes the World Wide Web and is especially suited for environmental monitoring. OGC's Sensor Web Enablement (SWE) framework defines a suite of web service interfaces and communication protocols abstracting from the heterogeneity of sensor (network) communication.

Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data.

A wireless ad hoc network (WANET) or mobile ad hoc network (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such as routers or wireless access points. Instead, each node participates in routing by forwarding data for other nodes. The determination of which nodes forward data is made dynamically on the basis of network connectivity and the routing algorithm in use.

Grid-oriented Storage (GOS) was a term used for data storage by a university project during the era when the term grid computing was popular.

The Internet of things (IoT) describes devices with sensors, processing ability, software and other technologies that connect and exchange data with other devices and systems over the Internet or other communications networks. The Internet of things encompasses electronics, communication and computer science engineering. Internet of things has been considered a misnomer because devices do not need to be connected to the public internet, they only need to be connected to a network, and be individually addressable.

A virtual sensor network (VSN) in computing and telecommunications is an emerging form of collaborative wireless sensor networks. In contrast to early wireless sensor networks that were dedicated to a specific application, VSNs enable multi-purpose, collaborative, and resource efficient WSNs. The key idea difference of VSNs is the collaboration and resource sharing. By doing so nodes achieve application objectives in a more resource efficient way. These networks may further involve dynamically varying subset of sensor nodes and/or users .
A VSN can be formed by providing logical connectivity among collaborative sensors. Nodes can be grouped into different VSNs based on the phenomenon they track or the task they perform. VSNs are expected to provide the protocol support for formation, usage, adaptation, and maintenance of subset of sensors collaborating on a specific task(s). Even the nodes that do not sense the particular event/phenomenon could be part of a VSN as far as they are willing to allow sensing nodes to communicate through them. Thus, VSNs make use of intermediate nodes, networks, or other VSNs to efficiently deliver messages across members of a VSN.

A wireless identification and sensing platform (WISP) is an RFID device that supports sensing and computing: a microcontroller powered by radio-frequency energy. That is, like a passive RFID tag, WISP is powered and read by a standard off-the-shelf RFID reader, harvesting the power it uses from the reader's emitted radio signals. To an RFID reader, a WISP is just a normal EPC gen1 or gen2 tag; but inside the WISP, the harvested energy is operating a 16-bit general purpose microcontroller. The microcontroller can perform a variety of computing tasks, including sampling sensors, and reporting that sensor data back to the RFID reader. WISPs have been built with light sensors, temperature sensors, and strain gauges. Some contain accelerometers. WISPs can write to flash and perform cryptographic computations. The WISP was originally developed by Intel Research Seattle, but after their closure development work has continued at the Sensor Systems Laboratory at the University of Washington in Seattle.

Urban computing is an interdisciplinary field which pertains to the study and application of computing technology in urban areas. This involves the application of wireless networks, sensors, computational power, and data to improve the quality of densely populated areas. Urban computing is the technological framework for smart cities.

Discovery Net is one of the earliest examples of a scientific workflow system allowing users to coordinate the execution of remote services based on Web service and Grid Services standards. The system was designed and implemented at Imperial College London as part of the Discovery Net pilot project funded by the UK e-Science Programme. Many of the concepts pioneered by Discovery Net have been later incorporated into a variety of other scientific workflow systems.

<span class="mw-page-title-main">Body area network</span> Small-scale computer network to connect devices around a human body, typically wearables

A body area network (BAN), also referred to as a wireless body area network (WBAN) or a body sensor network (BSN) or a medical body area network (MBAN), is a wireless network of wearable computing devices. BAN devices may be embedded inside the body as implants or pills, may be surface-mounted on the body in a fixed position, or may be accompanied devices which humans can carry in different positions, such as in clothes pockets, by hand, or in various bags. While there is a trend toward the miniaturization of devices, in particular body area networks which consist of several miniaturized body sensor units (BSUs) together with a single body central unit (BCU), larger decimeter sized smart devices still play an important role in terms of acting as a data hub or data gateway and providing a user interface to view and manage BAN applications, in-situ. The development of WBAN technology started around 1995 around the idea of using wireless personal area network (WPAN) technologies to implement communications on, near, and around the human body. About six years later, the term "BAN" came to refer to systems where communication is entirely within, on, and in the immediate proximity of a human body. A WBAN system can use WPAN wireless technologies as gateways to reach longer ranges. Through gateway devices, it is possible to connect the wearable devices on the human body to the internet. This way, medical professionals can access patient data online using the internet independent of the patient location.

A mobile wireless sensor network (MWSN) can simply be defined as a wireless sensor network (WSN) in which the sensor nodes are mobile. MWSNs are a smaller, emerging field of research in contrast to their well-established predecessor. MWSNs are much more versatile than static sensor networks as they can be deployed in any scenario and cope with rapid topology changes. However, many of their applications are similar, such as environment monitoring or surveillance. Commonly, the nodes consist of a radio transceiver and a microcontroller powered by a battery, as well as some kind of sensor for detecting light, heat, humidity, temperature, etc.

Data mining, the process of discovering patterns in large data sets, has been used in many applications.

<span class="mw-page-title-main">Subsea Internet of Things</span>

Subsea Internet of Things (SIoT) is a network of smart, wireless sensors and smart devices configured to provide actionable operational intelligence such as performance, condition and diagnostic information. It is coined from the term The Internet of Things (IoT). Unlike IoT, SIoT focuses on subsea communication through the water and the water-air boundary. SIoT systems are based around smart, wireless devices incorporating Seatooth radio and Seatooth Hybrid technologies. SIoT systems incorporate standard sensors including temperature, pressure, flow, vibration, corrosion and video. Processed information is shared among nearby wireless sensor nodes. SIoT systems are used for environmental monitoring, oil & gas production control and optimisation and subsea asset integrity management. Some features of IoT's share similar characteristics to cloud computing. There is also a recent increase of interest looking at the integration of IoT and cloud computing. Subsea cloud computing is an architecture design to provide an efficient means of SIoT systems to manage large data sets. It is an adaption of cloud computing frameworks to meet the needs of the underwater environment. Similarly to fog computing or edge computing, critical focus remains at the edge. Algorithms are used to interrogate the data set for information which is used to optimise production.

References

  1. ↑ M Ghanem; Y Guo; J Hassard; M Osmond; M Richards (2004), "Sensor grids for air pollution monitoring", In Proc. 3rd UK E-Science All Hands Meeting, Nottingham, UK, 2004., CiteSeerX   10.1.1.127.1135
  2. ↑ M Richards; M Ghanem; M Osmond; Y Guo; J Hassard (2006), "Grid-based analysis of air pollution data", Ecological Modelling, 194 (1–3): 274–286, doi:10.1016/j.ecolmodel.2005.10.042
  3. ↑ H.B. Lim, et al. Sensor Grid: Integration of Wireless Sensor Networks and the Grid, In Proc. of the IEEE Conference on Local Computer Networks 30th Anniversary (LCN’05), October 2005.
  4. ↑ Hingne, V.; Joshi, A.; Houstis, E.; Michopoulos, J. On the Grid and Sensor Networks. In Proc. Of International Workshop on Grid Computing. pages 166-173. Nov. 2003.
  5. ↑ M. Gaynor, et al. Integrating wireless sensor networks with the grid. IEEE Internet Computing, pages 32–39, Jul/Aug 2004.
  6. ↑ H. B. Lim, K. V. Ling, W. Wang, Y. Yao, M. Iqbal, B. Li, X. Yin, and T. Sharma, "The National Weather Sensor Grid," Proc. of the 5th ACM Conference on Embedded Networked Sensor Systems (SenSys 2007), Nov 2007.
  7. ↑ Ma, Y.; Richards, M.; Ghanem, M.; Guo, Y.; Hassard, J. (2008). "Air Pollution Monitoring and Mining Based on Sensor Grid in London". Sensors. 8 (6): 3601–3623. Bibcode:2008Senso...8.3601M. doi: 10.3390/s8063601 . PMC   3714656 . PMID   27879895.

Further reading

For sensor grid architectures, designs and applications, see the following:

  1. Crisisgrid (2007): http://www.crisisgrid.org
  2. The National Weather Sensor Grid: https://archive.today/20130418144026/http://nwsp.ntu.edu.sg/
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