Method for adaptively modifying the observed collective behavior of individual sensor nodes based on broadcasting of parameters

Abstract

A method for continually controlling the collective behavior of a set of computing devices in a distributed data processing system. A gateway node disseminates a specification request comprising a set of parameters to a set of computing devices. The gateway node may be unaware of the number and identity of individual computing devices. Each computing node receiving the request determines whether its attributes satisfy the predicates expressed in the specification request. If so, the node processes the parameters in the specification request and modifies its own behavior based on the values in the parameters of the specification request. Subsequently, the gateway node may also observe the quality of information (QoI) values communicated from the set of computing devices. The gateway node iteratively modifies the parameters disseminated in subsequent specification requests based on a divergence between a computed quality of information value and a desired quality of information value.

Description

BACKGROUND OF THE INVENTION [0001] 1. Field of the Invention [0002] The present invention relates generally to computer and communications networks, and more specifically to the emerging field of “sensor networks”, typically consisting of a large number of individual “sensor” devices, which send back samples of some environmental state to certain designated nodes. [0003] 2. Description of the Related Art [0004] Sensor networks are an emerging and very promising new category of computer networks, characterized by the development of very-low cost sensor devices with combined sensing and communication (often wireless) capabilities. Most applications of sensor networks rely on combining information from multiple sensor devices to establish or infer some composite state or event of the sensed environment (often called the “sensing field”). Examples include the desire to “compute an average of readings taken by 10 different temperature sensors,” or to “compute the minimum out of 30 distin...

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Benefits of technology

[0019] Another aspect of this invention relates to the continual monitoring of the QoI obtained from the activated set of sensors, and the subsequent adjustment of the parameters in subsequent specification requests. Clearly, such continual monitoring allows the gateway node to rapidly re-tune the behavior of the sensor network and maintain conformance to the target QoI objective, even though the underlying sensor network may change dynamically. To make the convergence process faster, the gateway node may maintain some “state” that it infers about the current network resources, and make the adjustment of parameters a function of this estimated “state”. Many well-known techniques from control theory, such as Proportional-In...

Problems solved by technology

Examples include the desire to “compute an average of readings taken by 10 different temperature sensors,” or to “compute the minimum out of 30 distinct humidity sensor readings.” Scalability is a major technical challenge in such future networks, which are projected to contain thousands, and possibly hundreds of thousands, of tiny sensor nodes deployed fairly densely over the sensing field.

A key characteristic of many such operating environments is that such networks often exhibit significant redundancy, in that most applications do not normally require the use of sensor data from all of the available sensor nodes.

Indeed, the physical density of sensor networks may often vary greatly over the sensing field due to choice (e.g., network designers may deploy more nodes in an area where finer location accuracy is required), lack of precise control (e.g., when nodes over a remote terrain are deployed by being dropped from an airplane), or failures (e.g., when sets of sensor nodes turn out to be defective or die due to exhaustion of battery resources).

The main challenge in supporting such energy-efficient operation is that, while applications may be able to express the amount of resources (such as the number or resolution of the sensor nodes), they typically have no idea of the actual number or layout of the sensors deployed.

Accordingly, an application may not be able to decide on the appropriate settings (e.g., on or off, high or low zoom) for each individual sensor.

However, such a mechanism is not useful in future sensor network environments for two distinct reasons.

For example, the sensor network substrate can be modified either due to the occasional addition of new nodes, the death or removal of existing nodes and due to other unforeseen reasons (such as catastrophic node failures).

This imposes a substantial reporting overhead and cannot scale to large sensor network environments, since every such change in the network topology must be propagated to the database.

If a static configuration scheme is employed instead, it may quickly become inappropriate for the given operating environment.

However, if subsequently 50 nodes suffer a catastrophic failure (e.g., due to a natural disaster), the application will be left...

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