WISENET - TinyOS based wireless network of sensors

Patnode, D.; Dunne, Jonathan; Malinowski, A.; Schertz, D.R.

doi:10.1109/iecon.2003.1280614

Cited by 10 publications

(3 citation statements)

References 2 publications

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“…Elman neural network is a feedback neural network, Elman type recurrent neural network is generally divided into four layers: input layer, hidden layer (or known as the middle layer), following a layer (or called the context layer) and output layer, as shown in Figure 3 instructions. The input layer, hidden layer and output layer feed-forward network connection is similar to the model's hidden layer feed forward networks add a layer to undertake, as a step delay operator,which is used to remember the previous hidden layer units The output value of the moment and return to the input,which is characterized by the hidden layer output layer through the delay and to undertake the storage, connected to the hidden layer from the input, since even the way that makes the network sensitive to the historical data, internal feedback joined the network increased the network itself the ability to handle dynamic information, so as to achieve the purpose of dynamic modeling [6].…”

Section: Delman Neural Network Modelmentioning

confidence: 99%

The Application of the Combinatorial and Optimal Networks on the Prediction of the Consumer Price Index

Luo

Huang

2012

AMR

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Consumer price index (CPI) was a main scientific ground for manager to put forward price policy, wage policy, and national economy development strategy. A lot of study and prediction were carried out for CPI by many scientists, and corresponding achievement was obtained. However, the simple network was applied to the past perdition methods, and the prediction results was not good. The prediction method based on the combinatorial and optimal networks of BP network, Elman network, RBF network and GRNN network was established and applied to the prediction of consumer price index. The simulation results showed that the effect of the combinatorial and optimal method was better than that of single method.

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Section: Delman Neural Network Modelmentioning

confidence: 99%

The Application of the Combinatorial and Optimal Networks on the Prediction of the Consumer Price Index

Luo

Huang

2012

AMR

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“…A number of difficulties have been encountered during the course of this work, some of which have affected other research groups in their attempt to port TinyOS-1.x to an 8051-based architecture [4] [5]. TinyOS-2.0, commonly referred to as T2, addresses some of the issues faced by these groups, and presents some new ones.…”

Section: Difficulties Encounteredmentioning

confidence: 99%

Shaping TinyOS to deal with evolving device architectures

Beck

Johnson

2007

Proceedings of the 4th Workshop on Embedded Networked Sensors

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This paper outlines the issues involved and difficulties encountered porting TinyOS to the CC2430 System-on-Chip platform. Core issues that limit the portability of TinyOS-2.x are identified, and suggestions made as to how these might be addressed. Specifically, TinyOS's reliance on the GNU Compiler Collection being available for the wireless sensor network device, the use of connection-dependent abstractions and their impact on system-on-chip devices, and the reuse of components within chip families. The issues are associated with both TinyOS-2.0 and the nesC language that it is written in.

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“…The TinyOS operating system [2] (used in many wireless sensor nodes, such as the Crossbow motes [3]) has a layered software stack, where data from the sensors (and any other hardware) is directly processed in the application layer [4]. In energy harvesting nodes such as Heliomote [5] and Prometheus [6] the application must monitor voltage levels and incorporate cursory temperature compensation.…”

Section: Introductionmentioning

confidence: 99%

A Structured Hardware/Software Architecture for Embedded Sensor Nodes

Merrett

Weddell

Harris

et al. 2008

2008 Proceedings of 17th International Conference on Computer Communications and Networks

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Abstract-Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components.

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WISENET - TinyOS based wireless network of sensors

Cited by 10 publications

References 2 publications

The Application of the Combinatorial and Optimal Networks on the Prediction of the Consumer Price Index

The Application of the Combinatorial and Optimal Networks on the Prediction of the Consumer Price Index

Shaping TinyOS to deal with evolving device architectures

A Structured Hardware/Software Architecture for Embedded Sensor Nodes

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