NISTIR 7908

A Sensor Ontology Literature 

Review

Roger Eastman

Craig Schlenoff

Stephen Balakirsky

Tsai Hong

http://dx.doi.org/10.6028/NIST.IR.7908
NISTIR 7908

A Sensor Ontology Literature 

Review

Roger Eastman

Craig Schlenoff

Stephen Balakirsky

Tsai Hong

Intelligent Systems Division

Engineering Laboratory

http://dx.doi.org/10.6028/NIST.IR.7908

April 2013

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
1

A Sensor Ontology Literature Review

Roger Eastman, Craig Schlenoff, Stephen Balakirsky, Tsai Hong

Abstract : The purpose of this paper is to review existing sensor and sensor network 

ontologies to understand whether they can be reused as a basis for a manufacturing 

perception sensor ontology, or if the existing ontologies hold lessons for the 

development of a new ontology. We develop an initial set of requirements that 

should apply to a manufacturing perception sensor ontology. These initial 

requirements are used in reviewing selected existing sensor ontologies. This paper 

describes the steps for 1) extending and refining the requirements; 2) proposing 

hierarchical structures for verifying the purposes of the ontology; and 3) choosing 

appropriate tools and languages to support such an ontology. Some languages could 

include OWL (Web Ontology Language) [1] and SensorML (Sensor Markup 

Language) [2]. This work will be proposed as a standard within the IEEE Robotics 

and Automation Society (RAS) Ontologies for Robotics Automation (ORA) Working 

Group [3]. 

1. Overview of Sensor Ontology Effort

Next generation robotic systems for manufacturing must perform highly complex 

tasks in dynamic environments. To improve return on investment, manufacturing 

robots and automation must become more flexible and adaptable, and less

dependent on blind, repetitive motions in a structured, fixed environment. To 

become more adaptable, robots need both precise sensing for parts and assemblies, 

so they can focus on specific tasks in which they must interact with and manipulate 

objects; and situational awareness, so they can robustly sense their entire 

environment for long-term planning and short-term safety. 

Meeting these requirements will need advances in sensing and perception systems 

that can identify and locate objects, can detect people and obstacles, and, in general,

can perceive as many elements of the manufacturing environment as needed for 

operation. To robustly and accurately perceive many elements of the environment 

will require a wide range of collaborating smart sensors such as cameras, laser 

scanners, stereo cameras, and others. In many cases these sensors will need to be 

integrated into a distributed sensor network that offers extensive coverage of a 

manufacturing facility by sensors of complementary capabilities. 

To support the development of these sensors and networks, the National Institute of 

Standards and Technology (NIST) manufacturing perception sensor ontology effort 

looks to create an ontology of sensors, sensor networks, sensor capabilities, 

environmental objects, and environmental conditions so as to better define and 

anticipate the wide range of perception systems needed. The ontology will include:
2

• detailed standard knowledge representations of sensor physical dimensions, 

weight, resolution, associated system performance, and operating 

conditions; 

• representations of system capabilities, to categorize the functions that 

individual and groups of sensors can perform; and 

• representations of sensor embeddings in sensor networks and the 

manufacturing environment. 

The ontology will have three primary objectives. The first is to form the basis for 

new measurement methods to evaluate each perception system’s (sensors and 

algorithms) ability to perform their required tasks. Prototypes of these perception 

systems are being developed but science-based approaches to accurately and 

traceably measure their performance do not exist. The NIST sensor ontology effort 

is part of a larger project to develop the metrics and methods that underpin such 

approaches. Its focus will be the ability to detect people and objects as they move 

about the workspace. The project will build a testbed in which to conduct 

experiments to assess that ability. Project results will provide scientific foundations 

for new standards that enable the use of perception systems in manufacturing 

applications. One aspect of the testbed will be to test the systems ability to locate 

people. For this, the project will work to create ground truth for tracking the 

location of people, and metrics for evaluating systems against this ground truth. The 

sensor ontology will help to categorize sensors and their capabilities.

The second objective will be to support the design and calibration of networks by 

reasoning about required capabilities. By mapping sensors into capabilities and 

measured performance, the ontology will enable a facility designer to consider 

required capabilities at an abstract level and reason about the best performing and 

lowest cost combination of sensors to meet facility needs. 

Similarly, the third objective is to enable an operating robotic system to reason 

about the available and running sensor systems to apply them to current perception 

needs, querying and retasking sensors as needed. 

The purpose of this document is to review existing sensor and sensor network 

ontologies to understand whether they can be reused as a basis for a manufacturing 

perception sensor ontology, or if the existing ontologies hold lessons for the 

development of a new ontology. Before reviewing the existing ontologies for their 

application to manufacturing, this document states requirements that should apply 

to a manufacturing perception sensor ontology. Section 2 develops these 

requirements, and Section 3 uses these requirements in reviewing selected existing 

sensor ontologies. Subsequent documents will focus on a detailed set of 

requirements for the manufacturing sensor ontology and the contents/structure of 

the manufacturing sensor ontology once it is developed.
3

2. High Level Requirements for a Manufacturing Sensor 

Ontology

While sensors have similar characteristics across applications, from earth science to 

military to manufacturing, a sensor ontology for manufacturing will have unique 

needs and uses. Sensors will be (typically) installed in an indoor facility, used to 

perceive objects and manufacturing processes, designed to meet appropriate power 

and operating conditions, and repurposed as product lines undergo change. Key 

here are the required capabilities of a sensor to perceive objects and processes as 

distinct from other applications. In a Geographic Information System (GIS) earth 

science application, sensors usually measure basic physical properties 

(temperature, air pressure) meant to represent large areas, while in manufacturing 

a sensor may need to perceive the position and identity relatively small parts. It 

may be possible to integrate sensor ontologies from different domains, but the 

integrated representation should be suitable in each.

The intended purposes of the manufacturing perception sensor ontology are to:

• Provide a basis for measurement techniques to evaluate system 

performance.

• Support design of sensor networks for manufacturing applications.

• Categorize and organize calibration techniques for sensors and sensor 

networks.

• Reason about available sensors and capabilities to meet goals during 

operation. 

• Create mechanisms and languages for querying sensors.

• Provide logs and data provenance for potential legal and regulatory review.

Based on these purposes, we can consider the sensor and system attributes that 

should be recorded in the ontology. Table 1 gives a potential list of attributes that 

could be associated with sensors and sensor systems. These are a high-level set of 

requirements and will be refined in subsequent phases of the effort.

The output of the sensors and sensor systems are important, and are described by 

five attributes in the table: Immediate data, Derived data, Algorithms, 

Integration/fusion and Capabilities. Immediate data describes the raw, unfiltered 

data; Derived data describes data and interpretations computed from the original 

data; Algorithms describes the different, possibly alternative, procedures for 

computing derived data; Integration/fusion describes derived data that comes from 

multiple sensor sources; and Capabilities describes the functional applications to 

which immediate and derived data can be put. It may be possible to collapse the five 

levels into fewer, but the current five are a working set of categories. It may be 

possible to put data into a classic hierarchical model, such raw data points, derived 

geometric entities like planes and surfaces, and semantic interpretations like objects 

and behaviors.
4

Table 1 - General Attributes for Sensors and Sensor Systems/Networks

Attributes Comments

Physical Power, weight, size

Operating conditions Environmental conditions required for operation

Immediate data Characteristics of data, resolution (time/spatial/latency/frame 

rate/other)

Derived data Results computed from raw data, both physical and semantic

Algorithms Alternative algorithms for producing derived data

Integration/fusion Data produced by combining data from multiple sensors

Capabilities Functional applications of raw and derived data

Communication Physical and logical protocols, and interoperability

Processing On board processing power of sensors and network nodes

Calibration Individual and joint sensor calibration information/algorithms

Provenance Record of sensor and processing history of raw and derived data

Confidence Levels of confidence in derived data

Algorithms are presented as a distinct attribute since a particular interpretation of 

the data, such as the presence of a person, may be computed in different ways from 

the raw data. For purposes of tracking provenance and confidence, it may be 

important to understand the processing steps that lead to the conclusion.

Capabilities are the interpretation, or affordances, of data as related to the goals of 

the overall robotic systems. The presence of a planar surface may be the derived 

data computed from a depth sensor; the fact that the surface affords a robot a 

platform for wheeled motion is a capability the sensor system provides the robot.

As a first step to combining these attributes into a hierarchy, Table 2 presents a 

basic outline. 

In addition to the concepts and attributes that must be supported by the ontology, a 

second question to answer is the nature of the ontology itself. What are the syntactic 

and semantic requirements for the ontology representation? 

Given that sensor technologies are constantly changing, and that a manufacturing 

ontology should be applicable to different industrial domains, the ontology should 

be readably extensible. In particular, the capabilities that must be described will 

need to be adapted to the application domain. Based on the previously stated 

purposes for the manufacturing ontology, we know it should support reasoning with 

suitably complete and consistent descriptive logic. To describe dynamic 

environments and actions, the logic should manage temporal elements. 
5

Table 2 - Sensor Requirements

Proposed

1. Three

Perception

Levels

1.1 Level 1: Raw data level 

1.2 Level 2: Primitive level

1.3 Level 3: Object level

2. Sensor 

Characteristics

2.1 Operating conditions

2.2 Resolution

2.3 Power, Weight, Size

2.4 Sensor hierarchy

2.5 Sensor communication

2.6 Raw data confidence

2.7 Onboard processing

3. Sensor 

Network

3.1 Sensors

3.2 Algorithms

3.3 Capabilities

3.4 Communications/Interoperability

3.5 Calibration algorithms

3.6 Network topology/geometry

3.7 Processing resources

4. Algorithms

4.1 Provenance

4.2 Derived data confidence

4.3 Input requirements

4.4 Capabilities

4.5 Performance 

4.6 Hardware/OS requirements

3. Review of Sensor Ontologies

In reviewing the literature on sensor ontologies, we selected seven research 

programs of interest and one review article. In this section we will look at each 

program and its relevance to the NIST sensor ontology project. 

3.1 Compton et al. Sensor Ontology Survey

The survey article by Compton [4] gives a useful start at terminology appropriate 

across sensor ontologies, and assisted in constructing this review. It surveys 12 

ontologies published from 2001 to 2009, looking at the range and expressive power 

of the ontologies as well as concepts these ontologies cannot describe accurately. 

The survey looks in particular at sensor networks and the use of ontologies in their 
6

operation and description. Many existing sensor ontology articles, and the Compton 

et al survey, are focused on the Semantic Web and the efforts to support 

autonomous agents that can find and use sensors attached to the Internet. This is a 

much larger and amorphous environment than a fixed manufacturing facility 

network, but similar concepts are needed for both.

Some of the terminology defined in the survey includes:

Sensor: a source that produces a value representing a quality of a phenomenon. It 

includes a range of instruments, including transducers, sensor devices, and 

computations. Comment: in this case, sensors are defined as combinations of 

transducers and processing. In some cases, sensors are defined more restrictively as 

transducers.

Sensor network: networks of connected sensors and associated devices. Nodes

(devices with attached sensors) interact to form a sensor network, and deliver data, 

through a gateway, to a base station. The base state and associated services then 

offer live and historical data, analysis, interpretation, and prediction.

Semantics: The semantic approach to information systems design uses declarative 

descriptions of information and processing units, allowing (semi-)automatic 

satisfaction of declaratively described requirements. Declarative descriptions 

enable both domain-independent and domain-specific reasoning of various forms 

(logic-based or otherwise) to be applied in processes such as entity identification, 

search, and query and workflow generation. Semantics enables reasoning, including 

search, logical reasoning, and domain reasoning. Semantics serve a dual role: it is 

part mark-up and elicitation of information and part logic for reasoning.

Semantic Sensor Network: requires declarative specifications of sensing devices, 

the network, services, and the domain and its relation to the observations and 

measurements of the sensors and services. Processing tools, logical and otherwise, 

can then be used to answer queries, infer further information, search for and 

identify particular resources, or generate workflows, all of which might require 

reasoning and inference in analyzing the specifications, links between entities and 

data, allowing users to develop, use, and adapt sensor networks, while abstracting 

away the low-level details and difficulties of the network and its multiple devices.

Semantic Sensor Network Architecture: multiple layers of semantics and 

technology to provide infrastructure and services.

Three Layer Architecture: a particular model of data, processing, and application, 

to support network-internal processing, inference and integration, and services 

respectively.

Moving from the survey article to specific ontologies, below are seven research 

programs with a short review of each.
7

3.2 Ontology-Driven Adaptive Sensor Networks

Avancha et al. [5] describe an ontology for adaptive sensor networks, with nodes 

that react to available power and environmental factors, calibrate for accuracy, and 

determine suitable operating states. The focus of this ontology is on adapting a 

wireless sensor network to current operating conditions while maintaining 

calibration and communication. The usefulness of this work for the NIST effort is in 

the concepts related to a sensor network and communication.

3.3 A Formal Pedigree Ontology for Level-One Sensor Fusion

Matheus et al. [6] is intended for maintaining provenance of data from multiple 

sensors in a naval military application, so a user can understand how to judge and 

interpret sensor results. By associating the originating sensor output with data

already represented in the ontology, the ontology can let users decide which of 

multiple sensor streams to trust. For example, if multiple sensors are providing 

tracks of a ship or airplane, the ontology can describe appropriate metadata so the 

system can annotate track displays with provenance and confidence information. 

The usefulness of this work for the NIST effort is in the provenance concepts.

3.4 An Ontology-Centric Approach to Sensor- Mission Assignment

Gomez et al [7] and Preece et al [8] describe an ontology for automatically selecting 

sensors for task assignment based on sensor fitness for the task description. The 

system was designed to fulfill military missions given high-level descriptions of 

needed capabilities and available resources. These papers also describe reasoning

algorithms to carry out the matching of sensor capabilities and transportation 

platforms to the mission under resource constraints. The emphasis is on premission planning, not during mission execution. The usefulness of this work is in the 

mission to sensor matching for system design.

3.5 A Universal Ontology for Sensor Networks Data 

Eid et al [9, 10] propose a two-tier framework for a sensor ontology. In their 

framework, the sensor hierarchy, data, and extension ontologies (lower tier) all 

reference the Standard Upper Merged Ontology (SUMO) (upper tier). SUMO [11] is 

the Suggested Upper Merged Ontology, which provides common high-level concepts 

that can be extended to produce domain specific ontologies, such as a sensor 

ontology. The lower tier ontology is divided into three parts: a Sensor Hierarchy 

Ontology (SHO) to describe transducer classes and attributes; a Sensor Data 

Ontology (SDO) to describe data; and a format for Extension Plug-in Ontologies 
8

(EPO) that allows developers to integrate domain-specific elements with the 

universal ontology. The usefulness of this article for the NIST effort is to provide a 

framework for integrating and extending ontologies, and using SUMO in a sensor 

ontology.

3.6 The Semantic Sensor Network Ontology

Neuhaus and Compton [12] is a generic ontology for describing sensors and 

deployments to be used in data integration, search, classification, and workflows. 

The ontology comprises four core clusters of concepts: (1) those concepts 

describing the domain of sensing (Feature); (2) those describing the sensor 

(Sensor); (3) those describing the physical components and location of the sensor 

(SensorGrounding); and (4) those describing functions and processing 

(OperationModel and Process). More specifically, the ontology discusses:

• The sensor: To provide wide application of the ontology, Sensor has to 

include more than just the physical instrument but also the associated 

processing chain included in the measurement. It links together what it 

measures (the domain), the physical sensor (the grounding), and its 

functions and processing (the models).

• The concrete sensor: The Sensor Grounding models the concrete realization of 

a sensor. The grounding represents its physical implementation, including 

size, shape, materials, and location, as well as the concrete aspects of 

accessing data from the sensor, including the types and expected formats of 

input when calling functions, the format of output, and the details for 

accessing the sensor (e.g., radio, network, or physical access). The Operation

Model describes how the sensor actually performs its measurement.

• The abstract sensor: The need to model single sensors, compound sensors,

and larger processing chains and models suggests a continuum of 

abstractions for describing functions and processes. The Process concept 

models processes from abstract and atomic to compound, allowing for a 

single specification to be described at multiple levels of abstraction and to 

use multiple different types of abstractions in describing its processing.

These clusters cover a wider range of concepts than the other ontologies. It is able to 

describe most of the spectrum of sensor concepts, including composition and 

structure. The ontology can describe more sophisticated forms of structural and 

sequencing composition, with, for example, sequence, conditional, and repetition for 

process composition. A combination of OntoSensor (described in the next section)

and this ontology represents the current level of expressive capability for semantic 

sensors. However, currently this ontology cannot describe the components of 

platforms.
9

3.7 Sensor Ontologies: From Shallow to Deep Models 

Russomanno et al. [13, 14] describe OntoSensor, an ontology intended as a general, 

knowledge base of sensors for query and inference. It is very general, and is 

comprised of definitions of concepts and properties adopted in part from SensorML 

[2] extensions to the Institute for Electrical and Electronics Engineers (IEEE) SUMO 

ontology [11], references to ISO 191151, and constructs from the Web Ontology 

Language [1].

SensorML is a specification of a generic data model in Unified Modeling Language 

(UML) for capturing classes and associations that are common to all sensors. 

SensorML is part of an Open Geospatial Consortium (OGC) initiative to contribute to 

the development of a Sensor Web “through which applications and services will be 

able to access sensors of all types over the Web.”2

OntoSensor extends the IEEE SUMO upper-level ontology by making some 

OntoSensor classes extensions of classes defined in SUMO. SensorML framework 

references some concepts that are defined in International Organization of 

Standardization (ISO) 19115, which defines schema required for geographic 

information and services.

Other relevant projects and articles that have not been further explored due to the 

fact that they appeared too far outside of the scope of manufacturing include:

• The Coastal Environmental Sensing Networks (CESN) project at the 

University of Massachusetts (Boston) is developing technology for sensor 

networks for coastal observing and is described in [15].

• The OOSTethys community is developing open-source resources to help 

install, integrate, and update standards-compliant Web services for 

oceanographic observing, with a particular emphasis on OGC standards and 

is described in [16].

3.8 IEEE 1451.4

IEEE 1451.5 [17] is a standard for adding plug and play capabilities to analog 

transducers. While not an ontology per se, it does provide a set of sensor specific 

data requirements that can be used to build the ontology. 

The underlying mechanism for plug and play identification is the standardization of 

a Transducer Electronic Data Sheet (TEDS). A TEDS contains the critical information 

needed by an instrument or measurement system to identify, characterize, interface, 

 1 http://www.iso.org/iso/catalogue_detail.htm?csnumber=26020

2 http://xml.coverpages.org/ni2008-01-31-a.html
10

and properly use the signal from an analog sensor. The Basic TEDS uniquely 

identifies the transducer and includes the Manufacturer ID, model number, version 

letter, version number, and serial number of the device. Sensor specific templates 

are also included which include information such as minimum and maximum 

voltage output, resistance values, current, and temperature. 

4. Summary

Each of the ontologies described in the previous section have elements useful in 

constructing a manufacturing perception sensor ontology, such as task assignment 

or provenance metadata. The two most general and comprehensive ontologies (as it 

pertains to the development of a manufacturing sensor ontology) appear to be the

Neuhaus/Compton and OntoSensor efforts, and would be the most promising starts 

for building the manufacturing sensor ontology. The other efforts can give insights 

for the design of particular elements. 

The next step in the NIST Sensor Ontology effort will be to further extend and refine 

the requirements by verifying the purposes of the ontology, reviewing the attributes 

in Section 2 for completeness, coverage, and redundancy, and putting them into 

appropriate hierarchies. Also, effort will be directed at choosing appropriate tools 

and languages for supporting the ontology, such as OWL and SensorML. After an 

initial design and choice of tools, the ontology will be implemented in the selected 

tool and validated on selected cases. 

Once an initial version of the manufacturing perception sensor ontology is 

completed, it will be proposed as a standard within the IEEE Robotics and 

Automation Society (RAS) Ontologies for Robotics Automation (ORA) Working 

Group [3]. The goal of this working group is to develop a standard ontology and 

associated methodology for knowledge representation and reasoning in robotics 

and automation, together with the representation of concepts in an initial set of 

application domains. The standard provides a unified way of representing 

knowledge and provides a common set of terms and definitions, allowing for 

unambiguous knowledge transfer among any group of humans, robots, and other 

artificial systems. It is expected that the manufacturing perception sensor ontology 

will be developed and proposed in late 2013.

Bibliography

[1] F. Harmelen and D. McGuiness. (2004). OWL Web Ontology Language 

Overview, W3C web site: http://www.w3.org/TR/2004/REC-owl-features20040210/. 
11

[2] M. Botts and A. Robin, "Sensor Model Language (SensorML) implementation 

specification," 2007.

[3] C. Schlenoff, E. Prestes, R. Madhavan, P. Goncalves, H. Li, S. Balakirsky, T. 

Kramer, and E. Miguelanez, "An IEEE Standard Ontology for Robotics and 

Automation," presented at the International Conference on Intelligent Robots 

and Systems (IROS), Vilamoura, Algarve (Portugal), 2012.

[4] M. Compton, C. Henson, H. Neuhaus, L. Lefort, and A. Sheth, "A Survey of the 

Semantic Specification of Sensors," presented at the 2nd International 

Workshop on Semantic Sensor Networks, at 8th International Semantic Web 

Conference, 2009.

[5] A. Avancha, C. Patel, and A. Joshi, "Ontology-driven adaptive sensor 

networks," presented at the 1st Annual International Conference on Mobile 

and Ubiquitous Systems, Networking and Services, 2004.

[6] C. Matheus, D. Tribble, M. Kokar, M. Ceruti, and S. McGirr, "Towards a formal 

pedigree ontology for level-one sensor fusion," presented at the 10th 

International Command & Control Research and Technology Symposium, 

2005.

[7] M. Gomez, A. Preece, M. Plohnson, G. d. Mel, W. Vasconcelos, C. Gibson, A. BarNov, K. Borowiecki, T. L. Porta, and D. Pizzocaro, "An ontology-centric 

approach to sensor- mission assignment," presented at the 16th 

International Conference on Knowledge Engineering and Knowledge 

Management, 2008.

[8] A. Preece, M. Gomez, G. d. Mel, W. Vasconcelos, S. Sleeman, S. Colley, G. 

Pearson, T. Pham, and T. L. Porta, "Matching sensors to missions using a 

knowledge-based approach," presented at the SPIE Defense Transformation 

and Net-Centric Systems, 2008.

[9] M. Eid, R. Liscano, and A. E. Saddik, "A novel ontology for sensor networks 

data," presented at the IEEE International Conference on Computational 

Intelligence for Measurement Systems and Applications, 2006.

[10] M. Eid, R. Liscano, and A. E. Saddik, "A universal ontology for sensor 

networks data," presented at the IEEE International Conference on 

Computational Intelligence for Measurement Systems and Applications, 

2007.

[11] A. Pease, I. Miles, and J. Li, "The Suggested Upper Merged Ontology: A Large 

Ontology for Semantic Web and its Applications," in Working Notes of the 

AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, 

Canada, 2002.

[12] H. Neuhaus and M. Compton, "The semantic sensor network ontology: A 

generic language to describe sensor assets," presented at the AGILE 

Workshop: Challenges in Geospatial Data Harmonization, 2009.

[13] D. Russomanno, C. Korthari, and O. Thomas, "Building a sensor ontology: “A 

practical approach leveraging ISO and OGC models," presented at the 2005 

International Conference on Artificial Intelligence, 2005.

[14] D. Russomanno, C. Kothari, and O. Thomas, "Sensor ontologies: from shallow 

to deep models," presented at the Thirty-Seventh Southeastern Symposium 

on System Theory, 2005.
12

[15] T. D. C. Little, J. Knorad, and P. Ishwar, "A Wireless Video Sensor Network for 

Autonomous Coastal Sensing," presented at the Conference on Coastal 

Environmental Sensing Networks (CESN), 2007.

[16] E. Bridger, L. E. Bermudez, M. Maskey, C. Rueda, B. L. Babin, and R. Blair, 

"OOSTethys - Open Source Software for the Global Earth Observing Systems 

of Systems," presented at the American Geophysical Union, Fall Meeting 

2009.

[17] IEEE, "IEEE standard for a smart transducer interface for sensors and 

actuators wireless communication protocols and transducer electronic data 

sheet (TEDs) formats - IEEE Std 1451.5," ed, 2007, pp. C1 - 236.