Knowledge-based duty cycle estimation in wireless sensor networks: Application for sound pressure monitoring

Cuevas-Martínez, J.C.; Canada-Bago, J.; Fernández-Prieto, Jose-Ángel; Gadeo-Martos, M. A.

doi:10.1016/j.asoc.2012.10.005

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…The same authors of [22] adapt the management application protocol so that WSN node can infer itself the best sleep mode interval (SMI) using a fuzzy system [23]. The system takes as inputs sound pressure value (their use case), the variation of sound pressure, and battery level.…”

Section: Wsn Self-managementmentioning

confidence: 99%

Autonomic Context-Aware Wireless Sensor Networks

et al. 2015

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Autonomic Computing allows systems like wireless sensor networks (WSN) to self-manage computing resources in order to extend their autonomy as much as possible. In addition, contextualization tasks can fuse two or more different sensor data into a more meaningful information. Since these tasks usually run in a single centralized context server (e.g., sink node), the massive volume of data generated by the wireless sensors can lead to a huge information overload in such server. Here we propose DAIM, a distributed autonomic inference machine distributed which allows the sensor nodes to do self-management and contextualization tasks based on fuzzy logic. We have evaluated DAIM in a real sensor network taking into account other inference machines. Experimental results illustrate that DAIM is an energy-efficient contextualization method for WSN, reducing 48.8% of the number of messages sent to the context servers while saving 19.5% of the total amount of energy spent in the network.

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Section: Wsn Self-managementmentioning

confidence: 99%

Autonomic Context-Aware Wireless Sensor Networks

et al. 2015

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“…Wireless Sensor Networks(WSNs) have been applied in many respects including health monitoring, environmental monitoring, military surveillance, and many others as Internet of Thing (IoT) [1]- [5]. Energy efficiency has become the most key issue for WSNs.…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Routing for the Mobile Sink in Wireless Sensor Networks With Obstacles

Xie

Pan

2016

IEEE Access

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In wireless sensor networks (WSNs), the assistance of exploiting the sink mobility to extend network lifetime have been well identified. This paper delivers an energy-efficient routing system stationed on the cluster-based approach considering obstacles for the mobile sink in WSNs. Based on cluster method, the node is elected as a cluster head which collects data from their respective cluster members and forwards the data to the mobile sink. In this paper, the mobile sink initiates the data-gather route periodically from the beginning site, then directly gets data from these cluster heads in a single-hop range, and lastly backs to the initial site. However, having obstacles in WSN's the complexity of the scheduling problem increases the traditional algorithms are difficult to resolve.

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“…Wireless Sensor Networks have been introduced in many domains including health care systems, natural conditions monitoring, military applications, and others as Internet of Things [1][2][3]5]. The most key issue for wireless sensor networks is energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cluster Based K-Medoids Approach to Discriminate Travelling Time in Wireless Sensor Network

Prasad¹,

Kumar²

2018

IJIES

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As cluster heads chosen by nodes, gathers the data from their individual groups and exchanges the collected data to the mobile sink as proposed in the heuristic tour planning technique. In this research, the mobile sink starts the collection of data irregularly from the initial site, which is stored in cluster heads in an individual hop extend and at last returns to the initial site. In existing approaches, scheduling time for data delivery is less. To increase the data delivery, there is a need in extending the concept to higher level routing process. To solve this issue, this paper proposed a collection oriented distribution scheme with k-medoids (CODS-KM). The method can be exhausted for distribute the collection points in network in a particular manner. In this method, k-medoids algorithm for clustering and cluster head process is used. To collect the data purpose, it can be used CH (Cluster Head), CP (Collection Point) and RP (Rendezvous Point). The proposed system helps in mitigation of travelling time when compared to existing approaches. To find the effectiveness of proposed algorithm, NS2 (Network simulator-2) software is used for simulation verification.

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Knowledge-based duty cycle estimation in wireless sensor networks: Application for sound pressure monitoring

Cited by 18 publications

References 19 publications

Autonomic Context-Aware Wireless Sensor Networks

Autonomic Context-Aware Wireless Sensor Networks

Cluster-Based Routing for the Mobile Sink in Wireless Sensor Networks With Obstacles

Cluster Based K-Medoids Approach to Discriminate Travelling Time in Wireless Sensor Network

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