Adding some smartness to devices and everyday things
Summary (4 min read)
1. Introduction
- It is now widely acknowledged that some awareness of the context in which mobile systems are used can produce added value and foster innovation in many application domains.
- Secondly, components may be equipped with explicit location sensors, i.e. receivers for specific location services, such as GPS.
- And their focus in this paper is on augmentation of mobile system components for awareness of context beyond location.the authors.
- Diverse sets of sensors and perception techniques are integrated to the end of shifting complexity in contextawareness from algorithmic level to architectural level.
- In the subsequent sections, the authors will briefly discuss related work on sensor-augmented mobile artifacts, and then report experience first from the TEA project and secondly from Mediacup work.
3. TEA - an add-on device for contextawareness
- The general motivation underlying the TEA project is to make personal mobile devices smarter.
- The assumption is that the more a device knows about its user, its environment and the situations in which it is used the better it can provide assistance.
- The cornerstones of the TEA device concept are: Integration of diverse sensors, assembled for acquisition multi-sensor data independently of any particular application.
- Implementation of hardware, i.e. sensors and processing environment, and software, i.e. methods for computing situational context from sensor data, in an embedded device A specific objective underlying sensor integration is to address the kind of context that can not be derived from location information at all, for example situations that can occur anywhere.
- The aim is to derive more context from a group of sensors than the sum of what can be derived from individual sensors.
3.1. TEA architecture
- TEA is based on a layered architecture for sensor-based computation of context as illustrated in figure 1, with separate layers for raw sensor data, for features extracted from individual sensors (‘cues’), and for context derived from cues.
- The data supplied by sensors can be very different, ranging form slow sensors that supply scalars (e.g. temperature sensor) to fast and complex sensors that provide a large amount of more or less structured data (e.g. a camera or a microphone); also the update time varies from sensor to sensor.
- This way, the cue layer strictly separates the sensor layer and context layer which means context can be modeled in abstraction from sensor technologies and properties of specific sensors.
- Again, the architecture does not prescribe the methods for calculation of context from cues; rule-based algorithms, statistical methods and neural networks may for instance be used.
- The context calculation, i.e. the reasoning about cues to derive context, may be described explicitly, e.g. when cues are known to be relevant indicators of a certain real world situation, or implicitly in methods that learn context from example data.
3.2. Initial exploration of the approach
- To study the TEA approach, the authors have developed two generations of prototype devices and used them for exploration of multi-sensor data, and for a validation of TEA as add-on device for mobile phones.
- The TEA device was developed in two generations.
- The first generation device was developed for exploration of a wide range of sensors and their contribution to contextawareness.
- For this study a number of situations that the authors considered relevant for personal mobile devices were defined (e.g. user is walking, user is in a conversation, other people are around, user is driving a car, etc.).
- The data was then subjected to statistical analysis to determine for each sensor or sensor group whether its inclusion increased the probability of recognizing situations.
3.3. Prototype implementation and validation
- The initial exploration of sensors and their contribution to awareness of typical real-world situations served to inform development of the second generation device optimized for smaller packaging, and shown in figure 2.
- The sensors are read by a microcontroller, that also calculates the cues and in some applications also the contexts.
- Typical cues for audio that are calculated on the fly are the number of zero crossing of the signal in a certain time (indicator of the frequency) and number of direction changes of the signal (together with the zero crossings this is a indicator of the noise in the audio signal).
- The prototype is independent of nay specific host and has been used in conjunction with a palmtop computer, a wearable computer and mobile phones.
- The TEA device has been added to a mobile phone to automate activation of such profiles which otherwise have be activated manually by the user.
3.4. Application in mobile telephony
- An interesting application domain for context-aware mobile phones as enabled by TEA is the sharing of context between caller and callee.
- To study context-enhanced communication, the authors have implemented the WAP-based application “context-call”.
- The application however does not establish the call straightaway but instead looks up the context of the callee and provides this information to the caller.
3.5. Discussion of TEA experience
- The authors experience gathered in the TEA project supports the case for investigation of context beyond location, and for fusion of diverse sensors as approach to obtain such context.
- The authors have used the approach for obtaining strictly location-independent context such as “in a meeting”, “in a conversation”, “user is walking” which can not be derived from location information.
- This initial experience is valuable, however it is clearly not sufficient to derive any methodology for systematic application of sensor fusion for context-aware applications.
- From this experience the authors can derive some indication as to which sensors are of particular interest for the overall objective of capturing real world situation.
- In addition the authors found that perception can be improved by using not just diverse sensors but also multiple sensors of the same kind, in particular microphones and light sensors with different orientation.
4. Mediacup – embedding awareness technology in everyday artifacts
- The Mediacup project was conducted in parallel to TEA, and while also investigating embedded awareness technology it is motivated differently.
- TEA is about making artifacts smarter, i.e. to improve the functionality the artifact offers their user.
- In contrast, the Mediacup project is about using artifacts to collect context information transparently, i.e. without changing the function and use of the artifact.
- The core idea is that by embedding awareness technology in the everyday things people use the authors can obtain context on everyday activity so to speak at the source.
- This approach assumes a distributed system in which some artifacts are augmented to collect context information, while other artifacts are computationally augmented to use such context.
4.1. Aware artifacts model
- The context-awareness model investigated in the Mediacup project is based on the following concepts: Artifacts are augmented with an awareness of their own local context.
- To this end artifacts are equipped with sensors but also with a processing environment and software for autonomous calculation of artifactspecific context from sensor data.
- Artifacts broadcast their context in their local environment.
- To this end aware artifacts are augmented with basic communication capabilities.
- Any applications, appliances or information artifacts in the environment can use the locally available context, without further knowledge of the artifacts from which the context originate.
4.2. Mediacup – Awareness embedded in coffee cups
- For exploration of the aware artifacts model the authors have augmented coffee cups representing non-digital everyday artifacts with awareness technology.
- The Mediacups, as the authors call the augmented mugs, contain hardware and software for sensing, processing and communicating the state of the cup as context information.
- Also, the adaptation speed of the sensor is very slow, and therefore it is read only every two seconds.
- The transceivers are based on HP’s HSDL 1001 IrDA chip and have a footprint of about 1,5m².
- They are connected through a CAN bus (car area network) and a gateway to the local ethernet, in which collected context is broadcast in UDP packets.
4.3. Experience from design and use
- Like TEA, the Mediacup project served to gather extensive experience with sensor-based context- awareness.
- The Mediacup provides substantial experience on different issues, i.e. on the embedding of awareness technology in ‘unpowered’ artifacts, on issues surrounding transparency of technology, and on a paradigm shift in use of sensors for context-awareness.
- The used microcontroller runs with a reduced clock speed of only 1 MHz; this reduces the power consumption to below 2mA at 5.5V in processing mode.
- First fitting in a battery that runs for the live time of the cup or second recharging the cup with no additional attention of the user.
- Another example, that came up with use experience with an early battery-powered prototype, was that power provision needs to be transparent.
5. Discussion and conclusion
- In the TEA and Mediacup projects the authors have gathered substantial experience with sensor-based contextawareness and embedding of awareness technology in mobile artifacts.
- The authors have gained important insights into sensor fusion for awareness of situational context, into architectural issues, into embedded design of awareness technology, and into a new perspective on context-enabled environments and applications.
- The authors work to date was not specifically focussed on architectural issues.
- However their experience highlights substantial challenges for perception techniques to perform in low-end computing environment.
- The aware artifacts model is a first exploration in this direction, studying a shift from context-aware applications with sensor periphery to dynamic systems of specialized appliances and artifacts, some of which are augmented to capture context while others are augmented to use context.
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