Low Power or High Performance? A Tradeoff Whose Time Has Come (and Nearly Gone)

Ko, JeongGil; Klues, Kevin; Richter, Christian; Hofer, Wanja; Kusý, Branislav; Bruenig, Michael; Schmid, Thomas; Wang, Qiang; Dutta, Prabal; Terzis, Andreas

doi:10.1007/978-3-642-28169-3_7

Cited by 33 publications

(21 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…It has been a major concern to employ an energy efficient networking protocol for the construction of WSNs since most of sensor nodes are powered by batteries. It may be desirable to minimize overhead for message exchange between the nodes, which may consume much more energy than data sensing and processing in the WSN [3,4]. Recent demands for the construction of large scale WSNs have required the network scalability with energy efficiency as a major design challenge [5].…”

Section: Introductionmentioning

confidence: 99%

Distributed Scalable Network Association in Wireless Sensor Networks

Kim

Han

Bang

et al. 2012

2012 IEEE International Conference on Green Computing and Communications

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) need to employ a network association mechanism that can securely allocate a unique address to each node while minimizing energy consumption. However, conventional centralized and broadcasting based network association mechanisms may consume large energy, and distributed network association mechanisms may suffer from addressing failure when applied to large scale WSNs. In this paper, we consider distributed network association with scalability for the construction of a large scale WSN. The proposed mechanism constructs a large scale addressing tree before network initialization. Based on the addressing tree, it allows each router to have its own addressing space which will be used for unique addressing of its child nodes. Then, a novel parent/child selection algorithm can assign routers to be located throughout the whole deployment area. Thus, nodes can join the network in the presence of sufficient neighbor routers and receive a unique address in a distributed manner. Finally, the performance of the proposed scheme is evaluated by computer simulation, showing remarkable performance improvement over conventional schemes when applied to a large scale WSN.

show abstract

“…Applications can control high-level robot motion through a set of specialized interfaces. In addition, ArmBot is powered by an ARM Cortex-M3 microprocessor, a versatile processor core, recently popularized in sensor network platforms [3]. The processor provides enough computational power for complex data processing and motion control algorithms.…”

Section: Introductionmentioning

confidence: 99%

ARM-based robot platform for sensor networks

Antaw

Kusý

2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

Self Cite

View full text Add to dashboard Cite

Robot platforms have been proposed to address challenges of wireless sensor network deployments in dynamic environments. Enabling a fraction of nodes to be mobile can lead to improvements in sensing coverage, network lifetime, and network connectivity. We present the design of ArmBot, a Cortex-M3 based robot platform, that natively supports TinyOS applications and system services. ArmBot is cost-efficient as it is built from off-the-shelf hardware components and versatile due to its power-efficient ARM processor core. We evaluate the platform in the context of core sensor network services, such as time-synchronization, and analyze its energy efficiency under varying mobility and computation loads.

show abstract

Low Power or High Performance? A Tradeoff Whose Time Has Come (and Nearly Gone)

Cited by 33 publications

References 22 publications

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

Channel Diagnostics for Wireless Sensor Networks in Harsh Industrial Environments

Distributed Scalable Network Association in Wireless Sensor Networks

ARM-based robot platform for sensor networks

Contact Info

Product

Resources

About