Programming Sensor Networks with State-Centric Services

Lachenmann, Andreas; Müller, Ulrich Christian; Sugar, R.; Latour, Louis de Lary de; Neugebauer, Matthias; Gefflaut, Alain

doi:10.1007/978-3-642-13651-1_22

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…Unlike [23,26], the SF shared memory model proposed in this paper enables the designer to evaluate existing algorithms in the context of sensor networks. Moreover, since the programs are written in abstract models considered in distributed systems, it is straightforward to verify the correctness of the programs as well as to manipulate the programs to meet new properties.…”

Section: Programming Abstractions Based On Language Aspectsmentioning

confidence: 99%

“…Furthermore, in this model, the abstraction of collaboration groups hides the designer from issues such as communication protocols, event handling, etc. Similarly, in [23], a state centric programming model called uDSSP is proposed where an application is composed of several state centric services. Each service interact with other services by subscribing to their states.…”

Section: Programming Abstractions Based On Language Aspectsmentioning

confidence: 99%

“…Programming approaches proposed in [4,8,17,18,23,26,[31][32][33][34][35][36] fall under the category of abstractions based on language aspects. State centric approaches.…”

Section: Programming Abstractions Based On Language Aspectsmentioning

confidence: 99%

See 2 more Smart Citations

“Slow is Fast” for wireless sensor networks in the presence of message losses

Hajisheykhi

Zhu

Arumugam

et al. 2015

Journal of Parallel and Distributed Computing

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h i g h l i g h t s• None of the existing computational models consider message loss/collision in the distributed systems.• WAC model is a model that considers message loss in distributed systems. However, it reduces the performance. • Our work is a variation of the shared memory model, namely SF shared memory model. • It can improve the performance in the presence of message loss.• We present an analytical proof (and evaluations for three protocols) for our SF model. a b s t r a c tWe present a new shared memory model, SF shared memory model. In this model, the actions of each node are partitioned into slow actions and fast actions. By contrast, the traditional shared memory model only includes fast actions. Intuitively, slow actions can utilize slightly stale state information to execute successfully. However, fast actions require that the state information they use is most recent.We show that the use of slow actions can substantially benefit in improving performance of programs from the shared memory model to WAC model that has been designed for sensor networks. To illustrate this, we use three protocols concerning problems that need to be solved in sensor networks. We show that under various message loss probabilities, densities, etc., slow actions can improve the performance substantially, since slow actions reduce the performance penalty of fast actions under heavy message loss environments. Moreover, the effectiveness of the slow action increases when there is a higher probability of message loss.

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Section: Programming Abstractions Based On Language Aspectsmentioning

confidence: 99%

Section: Programming Abstractions Based On Language Aspectsmentioning

confidence: 99%

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“Slow is Fast” for wireless sensor networks in the presence of message losses

Hajisheykhi

Zhu

Arumugam

et al. 2015

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

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“…Most of WSN middlewares focus on data management (e.g., COUGAR [Bonnet et al 2001], TINYDB [Madden et al 2005], Mires [Souto et al 2006], and PerLa [Schreiber et al 2012]) and runtime node programming or configuration (e.g., Impala [Liu and Martonosi 2003], Maté [Levis and Culler 2002], Agilla [Fok et al 2005], RemoWare [Taherkordi et al 2013], and REED [Fei and Magill 2012]). There are also other middlewares trying to improve the simultaneousity of application programs [Horré et al 2008] and support complex in-network processing [Lachenmann et al 2010]. We are not aware of any middleware design that targets at reducing the levels of user intervention throughout the lifetime of WSNs.…”

Section: Related Workmentioning

confidence: 99%

MC ²

Xia

Yang

et al. 2014

ACM Trans. Sen. Netw.

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Real-world, long-running wireless sensor networks (WSNs) require intense user intervention in the development, hardware testing, deployment, and maintenance stages. A majority of network design is network centric and focuses primarily on network performance, for example, efficient sensing and reliable data delivery. Although several tools have been developed to assist debugging and fault diagnosis, it is yet to systematically examine the underlying heavy burden that users face throughout the lifetime of WSNs. In this article, we propose a general Multimode user-CentriC (MC 2 ) framework that can, with simple user inputs, adjust itself to assist user operation and thus reduce the users' burden at various stages. In particular, we have identified utilities that are essential at each stage and grouped them into modes. In each mode, only the corresponding utilities will be loaded, and modes can be easily switched using the customized MC 2 sensor platform. As such, we reduce the runtime interference between various utilities and simplify their development as well as their debugging. We validated our MC 2 software and the sensor platform in a long-lived microclimate monitoring system deployed at a wildland heritage site, Mogao Grottoes. In our current system, 241 sensor nodes have been deployed in 57 caves, and the network has been running for over five years. Our experimental validation shows that the MC 2 framework shortens the time for network deployment and maintenance, and makes network maintenance doable by field experts (in our case, historians).A preliminary result of this work was appeared in ACM SIGMOBILE Mobile Computing and Communications Review [Xia et al. 2010]. , 2014. MC 2 : Multimode user-centric design of wireless sensor networks for long-term monitoring.

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Programming Sensor Networks with State-Centric Services

Cited by 2 publications

References 11 publications

“Slow is Fast” for wireless sensor networks in the presence of message losses

“Slow is Fast” for wireless sensor networks in the presence of message losses

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