ProSe: A Programming Tool for Rapid Prototyping of Sensor Networks

Arumugam, Mahesh; Kulkarni, Sandeep S.

doi:10.1007/978-3-642-11528-8_12

Cited by 5 publications

(6 citation statements)

References 37 publications

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“…These simulation results with the help of ProSe [7] and TOSSIM [25] validate the analytical results.…”

Section: Contributions Of the Papersupporting

confidence: 83%

“…Since the programs in Sections 4.1, 4.2, and 4.3 utilize guarded commands, we use ProSe [7], which is a programming tool for sensor networks, to transform the programs to nesC/TinyOS platform [15]. Then, using TOSSIM [25], we simulate the generated programs and evaluate the effect of having slow actions in each program.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…An advantage of ProSe is that it facilitates the designer to reuse existing faulttolerant/self-stabilizing protocols from the literature in the context of sensor networks. Moreover, since the programmer only specifies the abstract protocol, ProSe helps to overcome deficiencies of existing event-driven programming platforms (e.g., nesC/TinyOS) that require the programmers to deal with several challenges, including, buffer management, stack management, and flow control [7].…”

Section: Prosementioning

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

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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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“…These simulation results with the help of ProSe [7] and TOSSIM [25] validate the analytical results.…”

Section: Contributions Of the Papersupporting

confidence: 83%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Prosementioning

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

Self Cite

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“…So, minimizing the memory space used by each node enable to minimize the amount of information that is exchanged between nodes. Indeed, protocols are typically written in the state model, where the state of each node is available for reading to every neighbor, and all existing stabilization-preserving compilers [25,8,3,26] expect this communication model. Second, minimizing memory space enables to significantly reduce the cost of redundancy when mixing self-stabilization and replication, in order to increase the probability of masking or containing transient faults [18,17,9].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these algorithms are designed for a communication model that is more powerful than the classical state model used in this paper. (The state model is the model used in most available compilers for actual networks [25,8,3,26]). More specifically, Mayer et al [24] use the message passing model, and Awerbuch and Ostrovsky [5] use the linkregister model, where communications between neighboring nodes are carried out through dedicated registers.…”

Section: Introductionmentioning

confidence: 99%

Brief announcement

Blin

Tixeuil

2013

Proceedings of the 2013 ACM Symposium on Principles of Distributed Computing

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This paper focuses on compact deterministic self-stabilizing solutions for the leader election problem. Self-stabilization is a versatile approach to withstand any kind of transient failures. Leader election is a fundamental building block in distributed computing, enabling to distinguish a unique node, in order to, e.g., execute particular actions. When the protocol is required to be silent (i.e., when communication content remains fixed from some point in time during any execution), there exists a lower bound of Ω(log n) bits of memory per node participating to the leader election (where n denotes the number of nodes in the system). This lower bound holds even in rings.We present a new deterministic (non-silent) self-stabilizing protocol for n-node rings that uses only O(log log n) memory bits per node, and stabilizes in O(n log 2 n) time. Our protocol has several attractive features that make it suitable for practical purposes. First, the communication model matches the one that is expected by existing compilers for real networks. Second, the size of the ring (or any upper bound for this size) needs not to be known by any node. Third, the node identifiers can be of various sizes. Finally, no synchrony assumption besides a weak fair scheduler is assumed. Therefore, our result shows that, perhaps surprisingly, trading silence for exponential improvement in term of memory space does not come at a high cost regarding stabilization time, neither it does regarding minimal assumptions about the framework for our algorithm.

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Compact Deterministic Self-stabilizing Leader Election

Blin

Tixeuil

2013

Lecture Notes in Computer Science

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ProSe: A Programming Tool for Rapid Prototyping of Sensor Networks

Cited by 5 publications

References 37 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

Brief announcement

Compact Deterministic Self-stabilizing Leader Election

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