Introducing DOWSN: Distributed optimization in wireless sensor networks

Iacca, Giovanni

doi:10.1109/ukci.2012.6335776

Cited by 3 publications

(11 citation statements)

References 34 publications

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“…In line with these studies, in our previous work [21] we introduced DOWSN, Distributed Optimization in Wireless Sensor Networks (pronounced "dawson"), a decentralized, island-model framework designed to perform online optimization processes in a WSN. It should be noticed that, compared to the works presented in [44] and [45], DOWSN does not require any specific assumption on the fitness function.…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization in wireless sensor networks: an island-model framework

Iacca¹

2013

Soft Comput

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Wireless Sensor Networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational Intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (Distributed Optimization for WSN) is an islandmodel infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of 15 continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.

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Section: Introductionmentioning

confidence: 99%

Distributed optimization in wireless sensor networks: an island-model framework

Iacca¹

2013

Soft Comput

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“…[6]). In agreement with [59], the significant (and potentially only) published research employing distributed GP for WSN are Johnson et al (2005) [62], Weise et al (2006,2008,2009,2014) [143,145-150], Valencia et al (2007Valencia et al ( ,2010 [134,137] (research within this thesis) and Iacca (2012Iacca ( ,2013 [59,60].These are now discussed (excluding this research) with particular attention paid to whether they are:• In situ (i.e. implemented on the embedded devices)…”

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confidence: 81%

“…[6]). In agreement with [59], the significant (and potentially only) published research employing distributed GP for WSN are Johnson et al (2005) [62], Weise et al (2006,2008,2009,2014) [143,[145][146][147][148][149][150], Valencia et al (2007Valencia et al ( ,2010 [134,137] (research within this thesis) and Iacca (2012Iacca ( ,2013 [59,60].…”

Section: Gp On Wsn Motessupporting

confidence: 68%

“…The Distributed Optimization for WSN (DOWSN) framework [59] was developed as an DOWSN online, distributed optimisation process for WSNs specifically targeted to address online dynamic problems requiring local computations on the motes (e.g. energy-aware routing, localisation, clustering, data fusion, scheduling, security, Quality of Service).…”

Section: Distributed Optimization For Wsn (Dowsn)mentioning

confidence: 99%

“…It is clear that GP on small robotic platforms pioneered research in this field with research dating back to the early 1990s and is still continuing today. GP on WSN motes, while less developed, has a natural synergy with the intrinsic parallel nature of GP, and EA more generally [59]. Research with mote-class devices started during the 2000s and received recent research interest by this thesis and the others with some success.…”

Section: Summary Of Related Workmentioning

confidence: 99%

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In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

Valencia¹

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This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices.The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic -specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their "native" logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device.IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance.Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and iii iv Abstract slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions.Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an "acceptable" average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were com...

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Distributed Embodied Evolution over Networks

Yaman,

Iacca

2020

Preprint

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In most network problems, the optimum behaviors of agents in the network are not known before deployment. In addition to that, agents might be required to adapt, i.e. change their behavior based on the environment conditions. In these scenarios, offline optimization is usually costly and inefficient, while online methods might be more suitable.In this work we propose a distributed embodied evolutionary approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment. Our numerical results show that the local exchange of information, performed by means of crossover of behavior parameters with neighbors, allows the network to converge to the global optimum more efficiently than the cases where local interactions are not allowed, even when there are large differences on the optimal behaviors within a neighborhood.

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Introducing DOWSN: Distributed optimization in wireless sensor networks

Cited by 3 publications

References 34 publications

Distributed optimization in wireless sensor networks: an island-model framework

Distributed optimization in wireless sensor networks: an island-model framework

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

Distributed Embodied Evolution over Networks

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