TinyOS Programming

Levis, Philip; Gay, David

doi:10.1017/cbo9780511626609

Cited by 247 publications

(157 citation statements)

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“…5) is built around the 32-bit ARM5 Marvell PXA271 XScale R processor running TinyOS . 16 The PXA271 processor, which can operate in a low voltage (0.85V), low frequency (13MHz) mode, hence enabling very low power operation, is really a multi-chip module including 256kB SRAM, 32MB SDRAM and 32MB of FLASH memory. An 802.15.4-compliant radio is integrated into the Imote2 system too.…”

Section: Wi-flip: a Wireless Focal-plane Low-power Image Processormentioning

confidence: 99%

Focal plane generation of multi-resolution and multi-scale image representation for low-power vision applications

et al. 2011

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Early vision stages represent a considerably heavy computational load. A huge amount of data needs to be processed under strict timing and power requirements. Conventional architectures usually fail to adhere to the specifications in many application fields, especially when autonomous vision-enabled devices are to be implemented, like in lightweight UAVs, robotics or wireless sensor networks. A bioinspired architectural approach can be employed consisting of a hierarchical division of the processing chain, conveying the highest computational demand to the focal plane. There, distributed processing elements, concurrent with the photosensitive devices, influence the image capture and generate a pre-processed representation of the scene where only the information of interest for subsequent stages remains. These focal-plane operators are implemented by analog building blocks, which may individually be a little imprecise, but as a whole render the appropriate image processing very efficiently. As a proof of concept, we have developed a 176x144-pixel smart CMOS imager that delivers lighter but enriched representations of the scene. Each pixel of the array contains a photosensor and some switches and weighted paths allowing reconfigurable resolution and spatial filtering. An energy-based image representation is also supported. These functionalities greatly simplify the operation of the subsequent digital processor implementing the high level logic of the vision algorithm. The resulting figures, 5.6mW@30fps, permit the integration of the smart image sensor with a wireless interface module (Imote2 from Memsic Corp.) for the development of vision-enabled WSN applications.

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Section: Wi-flip: a Wireless Focal-plane Low-power Image Processormentioning

confidence: 99%

Focal plane generation of multi-resolution and multi-scale image representation for low-power vision applications

et al. 2011

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“…Figure 4 shows a simplified version of the application's wiring. As mentioned in [12], components need to be explicitly wired together. The interface for acquiring sensor data was designed with the following goals: Additional readings need to be added without major changes to the application code, sensor readings should always be linked to an IPFIX Field and Enterprise ID.…”

Section: Design Goals and Implementation Decisionsmentioning

confidence: 99%

“…Generic components can be instantiated with parameters [12]. This allows for a consistent linking of an IPFIX Field ID / Enterprise ID combination with a sensor, since every instance of IPFIXDataSampler can report exactly one value.…”

Section: Design Goals and Implementation Decisionsmentioning

confidence: 99%

“…Since nesC interfaces are bidirectional, this also allows for multiple calls to a single method call, meaning multiple methods can be invoked with a single command. The ability to have multiple callers is described as Fan-in and the concept of multiple calls is called Fan-out [12]. By simply wiring multiple components providing IPFIXDataSampler to ControllerC one can make effective use of the Fan-out concept as shown in Figure 5.…”

Section: Design Goals and Implementation Decisionsmentioning

confidence: 99%

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Gathering Sensor Data in Home Networks with IPFIX

Kothmayr

Schmitt

Braun

et al. 2010

Lecture Notes in Computer Science

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Abstract. New developments in military, health and home areas call for new approaches for data acquisition in real-time. Such application areas frequently include challenging requirements for collection, processing and analysis of environmental data. Wireless Sensor Networks can collect such environmental data efficiently. Collected sensor node data needs to be transmitted in an efficient way due to limitations of sensor node resources in battery power and available bandwidth. In this paper, we present a method for efficient transmission of sensor measurement data using the IETF standard IPFIX. We show that its template based design is suitable for efficient transmission of senor data with low bandwidth consumption. In this paper, we present the protocol and its implementation in Wireless Sensor Networks (WSNs). Additionally, a header compression scheme is introduced which further reduces communication cost during data transmission.

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“…Running an autonomous middleware such as Sensomax [1], [2], often requires resources that are capable of multi-tasking and running algorithms for aggregation and decision-making. Probably the most widely used hardware resources in WSN research are the Mica family [3] such as the Micaz and Mica2 motes that are very often used with TinyOS [4], which is an operating system originally developed for Mica nodes. When it comes to memory and processor, Mica motes are extremely resource constrained.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Data Storage Estimation for Multiple Concurrent Applications Using Probability Distribution Modeling in WSNs

Haghighi¹

2013

JACN

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Abstract-Wireless sensor networks (WSN) have become a mainstream technology for environmental monitoring and observing various variables of interest over extended periods of time via large-scale networks of sensors. WSNs have a wide range of applications including wildfire detection, healthcare, military, and habitat monitoring. In all such application areas, gathering and then relaying captured data to a central unit is often considered the primary task of the network. Scientific analysis however often requires WSNs to capture and store variables for long periods of time. Storing and managing flows of data tend to be challenging issues because WSNs often consist of nodes with limited processing, memory, and power resources. Therefore the software layer in WSNs needs to implement an efficient data storage allocation mechanism in order to provide sufficient memory space for multiple applications. In this paper we propose a novel statistical approach for estimating applications storage requirements. Our proposed mechanism has been originally developed and implemented in a new WSN middleware called Sensomax, which is an agent-based decentralized middleware with multiple concurrent applications support for dynamic data gathering in WSNs. The mechanism described here proved to be an effective technique for proactively allocating memory to multiple applications with different operational paradigms.

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TinyOS Programming

Cited by 247 publications

References 0 publications

Focal plane generation of multi-resolution and multi-scale image representation for low-power vision applications

Focal plane generation of multi-resolution and multi-scale image representation for low-power vision applications

Gathering Sensor Data in Home Networks with IPFIX

Dynamic Data Storage Estimation for Multiple Concurrent Applications Using Probability Distribution Modeling in WSNs

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