Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

Caputo, Davide; Grimaccia, Francesco; Mussetta, Marco; Zich, R.E.

doi:10.1155/2010/523943

Cited by 14 publications

(18 citation statements)

References 28 publications

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“…For each sender, an optimal route is defined by a single run of GSO and the network status and power level of each node are updated consequently. The best route is chosen by GSO according to fitness function which implements the set of rules presented in Section 5 and in [4]. Figure 3 shows the connectivity of the network and the energy-efficient distributed clustering scheme proposed by the optimization algorithm.…”

Section: Implementation Results and Discussionmentioning

confidence: 99%

“…According to [4] a simple radio model has been used in this work, where the radio dissipates E el = 50 nJ/bit to run both the transmitter and receiver circuitry, and E amp = 100 pJ bit·m 2 for transmit amplifier to achieve an acceptable signal to noise ratio. An r 2 energy path loss due to channel transmission is also assumed and thus to transmit a k-bit message to a distance d using this model, the single sensor node expends:…”

Section: Network Model and Assumptionmentioning

confidence: 99%

“…The nodes receive the information sent so they know if they will participate in the next route. The control overhead to synchronize the network clock is taken into account within the system simulation, as described in [4] and further detailed in Section 6.…”

Section: Network Model and Assumptionmentioning

confidence: 99%

“…In order to prove the performances of the GSO we considered different metrics criteria, such as Maximum Available Power (MAP), Minimum Communication Power (MCP), Minimum Hop Number (MHN), and their combinations, as already described in [4].…”

Section: Network Model and Assumptionmentioning

confidence: 99%

“…For this reason the authors decided to investigate the effectiveness of evolutionary algorithms to optimize network lifetime in a limited power supply framework, mainly focusing on communication strategies [4]. With the continuing advances in network design, RFIDs, smart phones, and other wireless mobile devices, optimization in the communication layer is needed not only to extend the system lifespan, but also to increase reliability, availability and integration efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

2012

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Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, etc. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences.

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Section: Implementation Results and Discussionmentioning

confidence: 99%

Section: Network Model and Assumptionmentioning

confidence: 99%

Section: Network Model and Assumptionmentioning

confidence: 99%

Section: Network Model and Assumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

2012

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Non‐stationary signal classification via modified fuzzy C‐means algorithm and improved bacterial foraging algorithm

Sahu

Biswal²,

Choubey

2016

Int J Numerical Modelling

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Summary In this paper, empirical mode decomposition is applied to decompose the non‐stationary power signals that results in a set of maximum and minimum points while satisfying the properties of the sifting process. Further, the empirical mode decomposition method is implemented to extract various intrinsic mode functions from the non‐stationary signal disturbance waveforms that are already superimposed by various undulating modes. A novel short‐time modified Hilbert transform with an equivalent window is applied on all the intrinsic mode functions to extract the modified Hilbert energy spectrum and instantaneous magnitude response. Distinct features are derived from the short‐time modified Hilbert energy spectrum for automatic classification of non‐stationary power signals. The features obtained from the short‐time modified Hilbert transform are found to be different, understandable, and immune to noise. These features are then applied to the modified fuzzy C‐means based improved bacterial foraging optimization algorithm for improving the classification accuracy of the disturbances. Extensive simulation results yield excellent visual detection, localization, and classification of the different types of non‐stationary power signal disturbances. Copyright © 2016 John Wiley & Sons, Ltd.

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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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Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

Cited by 14 publications

References 28 publications

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

Non‐stationary signal classification via modified fuzzy C‐means algorithm and improved bacterial foraging algorithm

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

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