Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Haro, Benjamín Béjar; Zazo, Santiago; Palomar, Daniel P.

doi:10.1109/tsp.2013.2288080

Cited by 51 publications

(16 citation statements)

References 28 publications

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“…More explicitly, the time-span until the specific instant, when the first data collection failure occurred. NL- 13 The longest time period, over which the QoS constraint, such as the signal-to-noise ratio (SNR) requirement is satisfied [30].…”

Section: Nl-1mentioning

confidence: 99%

“…Feng et al [126] also proposed an algorithm providing a carefully balanced selection of the transmitters for maximizing the NL, where the NL is doubled compared to direct or multihop transmissions through a particular receiver that is located far-away from the sensing field. Similarly, Bejar-Haro et al [30] designed an energy-efficient collaborative beamforming scheme for transmitting data to a far-away base station for the sake of NL maximization, while satisfying the target QoS requirement, such as the SNR requirement. Additionally, Han et al [127] aimed for maximizing the NL by exploiting collaborative beamforming and cooperative transmission techniques that can be invoked by the closely located sensors in order to reduce the trafficload and to prevent the relaying of data by the specific sensors having critical battery charges.…”

Section: Beamformingmentioning

confidence: 99%

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A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

545

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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Section: Nl-1mentioning

confidence: 99%

Section: Beamformingmentioning

confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

545

229

View full text Add to dashboard Cite

show abstract

“…Developing methods to maximize network lifetime by saving energy and balancing energy consumption remains a challenge [7,49]. Table 2.…”

Section: Limited Battery Powermentioning

confidence: 99%

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

et al. 2018

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Abstract:A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensors that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, humidity, and pressure. Many problems, including data routing, power consumption, clustering, and selecting cluster heads (CHs), may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. One of the important hierarchical protocols that are used to reduce power consumption in WSNs is low-energy adaptive clustering hierarchy (LEACH). This paper presents a comprehensive study of clustering protocols for WSNs that are relevant to LEACH. This paper is the first to emphasis on cluster formation and CHs selection methods and their strengths and weaknesses. A new taxonomy is presented to discuss LEACH variants on the basis of different classes, and the current survey is compared with other existing surveys. A complete comparison of the location, energy, complexity, reliability, multi-hop path, and load balancing characteristics of LEACH variants is conducted. Future research guidelines for CHs selection and cluster formation in WSNs are also discussed.

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“…The node location is based on a uniform distribution in both 2D coordinates or 3D coordinates, with no movement once the nodes are settled down. This approach is widely used in the definition of WSN experimental scenarios, as can be found in [7] or [8]. In this scenario, each node has limited power autonomy, which must fulfill the operation requirements for both data transmitting to the High Energy Communication Node (HECN) and environment sensing.…”

Section: Collaborative Beamforming In Wsnmentioning

confidence: 99%

“…The problem has to cope with the air interface (the radio signals) and how the radiation pattern (beam) is set up to reach the desired QoS with minimal energy consumption. The technique that enables this energy-efficient wireless communication is beamforming [7], which consists of several coordinated antennas radiating jointly to generate a directive beam for covering a given area with very accurate precision. The goal is to reduce the energy consumption of the sensors by performing collaborative beamforming.…”

Section: Introductionmentioning

confidence: 99%

Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks

Porcel-Rodríguez

Valenzuela-Valdés

Padilla

et al. 2016

Sensors

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Energy efficiency is a critical issue for wireless sensor networks (WSNs) as sensor nodes have limited power availability. In order to address this issue, this paper tries to maximize the power efficiency in WSNs by means of the evaluation of WSN node networks and their performance when both clustering and antenna beamforming techniques are applied. In this work, four different scenarios are defined, each one considering different numbers of sensors: 50, 20, 10, five, and two nodes per scenario, and each scenario is randomly generated thirty times in order to statistically validate the results. For each experiment, two different target directions for transmission are taken into consideration in the optimization process (φ = 0° and θ = 45°; φ = 45°, and θ = 45°). Each scenario is evaluated for two different types of antennas, an ideal isotropic antenna and a conventional dipole one. In this set of experiments two types of WSN are evaluated: in the first one, all of the sensors have the same amount of power for communications purposes; in the second one, each sensor has a different amount of power for its communications purposes. The analyzed cases in this document are focused on 2D surface and 3D space for the node location. To the authors’ knowledge, this is the first time that beamforming and clustering are simultaneously applied to increase the network lifetime in WSNs.

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Energy Efficient Collaborative Beamforming in Wireless Sensor Networks

Cited by 51 publications

References 28 publications

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges

Clustering and Beamforming for Efficient Communication in Wireless Sensor Networks

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