Pervasive and Ubiquitous Computing Databases: Critical Issues and Challenges

Pervasive and Ubiquitous Computing Databases: Critical Issues and Challenges

Michael Zoumboulakis (University of London, UK) and George Roussos (University of London, UK)
DOI: 10.4018/978-1-60566-242-8.ch086
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Abstract

The concept of the so-called Pervasive and Ubiquitous Computing was introduced in the early nineties as the third wave of computing to follow the eras of the mainframe and the personal computer. Unlike previous technology generations, Pervasive and Ubiquitous Computing recedes into the background of everyday life: “it activates the world, makes computers so imbedded, so fitting, so natural, that we use it without even thinking about it, and is invisible, everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere” (Weiser 1991). Pervasive and Ubiquitous Computing is often referred to using different terms in different contexts. Pervasive, 4G mobile and sentient computing or ambient intelligence also refer to the same computing paradigm. Several technical developments come together to create this novel type of computing, the main ones are summarized in Table 1 (Davies and Gellersen 2002; Satyanarayanan 2001).
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Introduction

The concept of the so-called Pervasive and Ubiquitous Computing was introduced in the early nineties as the third wave of computing to follow the eras of the mainframe and the personal computer. Unlike previous technology generations, Pervasive and Ubiquitous Computing recedes into the background of everyday life: “it activates the world, makes computers so imbedded, so fitting, so natural, that we use it without even thinking about it, and is invisible, everywhere computing that does not live on a personal device of any sort, but is in the woodwork everywhere” (Weiser 1991). Pervasive and Ubiquitous Computing is often referred to using different terms in different contexts. Pervasive, 4G mobile and sentient computing or ambient intelligence also refer to the same computing paradigm. Several technical developments come together to create this novel type of computing, the main ones are summarized in Table 1 (Davies and Gellersen 2002; Satyanarayanan 2001).

Table 1.
Elements of pervasive and ubiquitous computing
1. Physical and Virtual Integration
Sensing
    Information gathering in the physical world and its representation in the virtual world
Actuation
    Decisions in the virtual world and their tangible results in the physical world
Awareness and Perception
    Using sensed data to maintain a higher-level model of the physical world and argue about
Ambient Displays
    Display information from the virtual world on physical artifacts
World Modeling
    Representations of physical spaces
2. System Components
Platforms
    Mobile, wearable or implantable hardware devices with small form factor
Sensors and Actuators
    Hardware and software platforms for sensing and actuation
Software Architectures
    Adaptable, large scale, complex software infrastructures and development
Connectivity
    High-speed, low power wired and wireless communications systems
3. Deployment
Scalability
    System adaptation to cater for massive scale in terms of space, devices and users
Reliability
    Security and redundancy technologies for continuous operation
Maintenance
    Structured maintenance processes for reliability and updates
Evaluation
    Methods to evaluate the effectiveness of information systems outside the laboratory
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Background

One of the major challenges in turning the Pervasive and Ubiquitous Computing vision into reality is the development of distributed system architectures that will support effectively and efficiently the ability to instrument the physical world (Estrin et al 2002, National Research Council 2001). Such architectures are being developed around two core concepts: self-organizing networks of embedded devices with wireless communication capabilities and data-centricity. To augment physical artifacts with computational and communications capabilities it is necessary to enable miniaturized hardware components capable of wireless communication. However, these same characteristics that allow for instrumentation of physical objects also impose significant constraints. Systems architectures require significant changes due to the severely limited resources available on these devices. One possible solution is offered by the emergence of data-centric systems. In this context, data-centric refers to in-network processing and storage, carried out in a decentralized manner (Estrin et al 2000).

Key Terms in this Chapter

Data aggregation: Data aggregation is the process in which information is gathered and expressed in a summary form. In case the aggregation operator is decomposable partial aggregation schemes may be employed in which intermediate results are produced that contain sufficient information to compute the final results. If the aggregation operator is non-decomposable then partial aggregation schemes can still be useful to provide approximate summaries.

In-Network Processing: In-network processing is a technique employed in sensor database systems whereby the data recorded is processed by the sensor nodes themselves. This is in contrast to the standard approach, which demands that data is routed to a so-called sink computer located outside the sensor network for processing. In-network processing is critical for sensor nodes because they are highly resource constrained, in particular in terms of battery power and this approach can extend their useful life quite considerably.

Sensor and Actuator Networks: An ad-hoc, mobile, wireless network consisting of nodes of very small size either embedded in objects or freestanding. Some nodes have sensing capabilities, which frequently include environmental conditions, existence of specific chemicals, motion characteristics and location. Other nodes have actuation capabilities, for example they may provide local control of mechanical, electric or biological actuators and optical long-range communications.

Sensor Query Processing: Sensor query processing is the design of algorithms and their implementations used to run queries over sensor databases. Due to the limited resources of sensor and actuator nodes query processing must employ in-network processing and storage mechanisms

Sensor Databases: The total of stored sensor data in a sensor and actuator networked is called a sensor database. The data contains both metadata about the nodes themselves, for example a list of nodes and their related attributes, such as their location, and sensed data. The types and the quantity of sensed data depend on the particular types of sensors that participate in the network.

Service-Oriented Architectures: A service is an information technology function that is well defined, self-contained, and does not depend on the context or state of other services. A service-oriented architecture is an approach to building information technology systems as a collection of services which communicate with each other. The communication may involve either simple data passing between services or it could involve infrastructure services which coordinate service interaction. SOA is seen as a core component of Ubiquitous Computing infrastructures.

Pervasive and Ubiquitous Computing: Pervasive and Ubiquitous computing is a vision of the future and a collection of technologies where information technology becomes pervasive, embedded in the everyday environments and thus invisible to the users. According to this vision, everyday environments will be saturated by computation and wireless communication capacity and yet they would be gracefully integrated with human users.

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