Egs: A Cortex M3-Based Mote Platform

Ko, JeongGil; Wang, Qiang; Schmid, Thomas; Hofer, Wanja; Dutta, Prabal; Terzis, Andreas

doi:10.1109/secon.2010.5508223

Cited by 13 publications

(5 citation statements)

References 7 publications

(6 reference statements)

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“…Using a frequency domain technique (e.g., FFT) could be a solution, but it is computationally prohibitive for the TelosB platform that we use. Emerging sensor mote platforms, such as the Egs platform [10] based on the Cortex M3 [2], could make this technique feasible.…”

Section: Discussionmentioning

confidence: 99%

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Lim

Terzis

Wang

2010

IEEE Local Computer Network Conference

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Abstract-Most target tracking applications developed for wireless sensor networks thus far employ passive sensors which detect the target's emissions. Their performance is thereby dictated by the magnitude of the target's signal and the receivers' sensitivity. Instead, we propose using a network of low-power Doppler radars that actively measure the target's radial velocity. Nodes combine their measurements to solve a system of nonlinear equations that estimate the target's position and velocity. Because these equations have no closed-form solutions we solve them using numerical methods. These methods however can lead to local minima or even diverge in the presence of even small measurement noise. For this reason we couple the proposed numerical method with an Extended Kalman filter that models the target's movement along a straight line. Clearly the target does not always follow a linear path and so we augment this simple Kalman filter with a method that detects the target's turns and updates the filter's dynamical model accordingly. The combination of the two approaches improve tracking accuracy compared to the numerical solution alone. Results from simulations and a prototype implementation suggest that the combined solution can effectively track a mobile target with average localization error as low as 25 cm in a 10×10 m outdoor field.

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Section: Discussionmentioning

confidence: 99%

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Lim

Terzis

Wang

2010

IEEE Local Computer Network Conference

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“…In fact, the 32-bit CPUs of newer WSN platforms are significantly more powerful than 8-and 16-bit CPUs featured in MicaZ and similar products. For instance, platforms such as Egs [29] are capable of encoding nearly a thousand 100-byte packets with RS code in just 1 second and can decode more than 200 packets with 30-byte erasures [30] within the same amount of time. Second, field devices of a WSN are active only occasionally, and execution of the LPED algorithm within 10 ms (timeslot) is not a must.…”

Section: A Lped Execution Timementioning

confidence: 99%

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

Barać

Caiola²,

Gidlund

et al. 2014

IEEE Sensors J.

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Wireless sensor network communication in industrial environments is compromised by interference, multipath fading, and signal attenuation. In that respect, accurate channel diagnostics is imperative to selecting the adequate countermeasures. This paper presents the lightweight packet error discriminator (LPED) that infers the wireless link condition by distinguishing between errors caused by multipath fading and attenuation, and those inflicted by interfering wideband singlechannel communication systems (e.g., IEEE 802.11b/g), based on the differences in their error footprints. The LPED uses forward error correction in a novel context, namely, to determine the symbol error density, which is then fed to a discriminator for error source classification. The classification criteria are derived from an extensive set of error traces collected in three different types of industrial environments, and verified on a newly collected set of error traces. The proposed solution is evaluated both offline and online, in terms of classification accuracy, speed of channel diagnostics, and execution time. The results show that in ≥91% of cases, a single packet is sufficient for a correct channel diagnosis, accelerating link state inference by at least 270%, compared with the relevant state-of-the-art approaches. The execution time of LPED, for the worst case of packet corruption and maximum packet size, is below 30 ms with ≤3% of device memory consumption. Finally, live tests in an industrial environment show that LPED quickly recovers from link outage, by losing up to two packets on average, which is only one packet above the theoretical minimum.Index Terms-IWSN, interference recognition, error discrimination, FEC, bit error traces. 1530-437X

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“…Another node in this class is Egs [5], based on a Cortex-M3 Atmel SAM3U2C µC, whose maximum operating frequency scales up to 96MHz and whose internal memory is composed by 36KB of RAM and 128KB of Flash, plus an external 2Gb NAND Flash. The microcontroller consumes 48mA in active state, while in wait mode (a sort of deep sleep) its consumption decreases to 15µA.…”

Section: Wsns Nodesmentioning

confidence: 99%

Enabling ultra-low power operation in high-end wireless sensor networks nodes

Brandolese

Fornaciari

Rucco

et al. 2012

Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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This paper presents a prototype hardware/software architecture for minimizing energy consumption on high-end microcontrollers, while simplifying the development of applications providing a general-purpose-like programming environment. The key features enabling this twofold goal are operating system support to processes, optimized sensing and hibernation of the system state. To balance performance offered by high-end microcontrollers with the need for ultra-low power operation-especially required by WSNsit is essential to minimize the duty cycle by keeping the microcontroller in a low-power state as long as possible, without affecting the performance required by the application. To cope with the increasing leakage current observed in today's microcontrollers a software-based hibernation mechanism has been implemented to transparently save the memory of processes in a non-volatile memory, allowing to completely switch off the microcontroller. To further avoid costly process loading overheads during the sleeping periods, we devised a smart sensing mechanism capable of gathering data from peripherals without restoring the state of processes. Preliminary results show that the lifetime of the proposed node architecture is of the same order of magnitude of that of ultra low-power nodes and significantly better than that of high-end ones.

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Egs: A Cortex M3-Based Mote Platform

Abstract: Abstract-We introduce the

Cited by 13 publications

References 7 publications

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Tracking a non-cooperative mobile target using low-power pulsed Doppler radars

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

Enabling ultra-low power operation in high-end wireless sensor networks nodes

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