A Stochastic Game Approach for Collaborative Beamforming in SDN-Based Energy Harvesting Wireless Sensor Networks

Bao, Xuecai; Liang, Hao; Liu, Yuan; Zhang, Fenghui

doi:10.1109/jiot.2019.2930073

Cited by 28 publications

(8 citation statements)

References 40 publications

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“…Furthermore, Ahmed et al and Huang et al conducted research on Gaussian and arbitrary distribution, respectively [42,43]. From the discussions of the above and other authors, it has been verified that the CB can form a directional beam, as shown in Figure 3, and is prominently helpful to achieve power improvement [44] in the direction of a desired AP (theoretically, proportional to the square of the number of sensors) [45][46][47][48][49][50][51][52][53][54][55][56][57]. Their work also show that the CB can increase the success probability of long-range direct transmission regardless of distribution of sensors.…”

Section: Introductionmentioning

confidence: 91%

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

Park

2021

Applied Sciences

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Collaborative beamforming (CB) enables uplink transmission in a wireless sensor network (WSN) composed of sensors (nodes) and far-away access points (APs). It can also be applied to the case where the sensors are equipped with beam-switching structures (BSSs). However, as the antenna arrays of the BSSs are randomly headed due to the irregular mounting surface, some sensors form beams that do not illuminate a desired AP and waste their limited energy. Therefore, to resolve this problem, it is required to switch the beams toward the desired AP. While an exhaustive search can provide the globally optimal combination, a greedy search (GS) is utilized to solve this optimization problem efficiently. Simulation and experimental results verify that under certain conditions the proposed algorithm can drive the sensors to switch their beams properly and increase the received signal-to-noise ratio (SNR) significantly with low computational complexity and energy consumption.

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Section: Introductionmentioning

confidence: 91%

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

Park

2021

Applied Sciences

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“…Recently, Collaborative Beamforming (CB) has been proposed as an alternative valuable technology for constructing reliable and energy-efficient communication routes between WSN nodes and distant sinks [2] , [3] , [4] .…”

Section: Introductionmentioning

confidence: 99%

Design and analysis of a multiple collaborative beamforming scheme in the realm of Wireless Sensor Networks featuring 3-dimension node configuration

Maina

Langat

Kihato

2022

Heliyon

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“…However, it is noted that the amount of energy extracted by energy harvester from the surroundings is typically random, and this implies that the sensor can harvest energy but not every time instant [24]. Taking such random features into consideration, in recent years, the filtering problems with an energy harvesting sensor (EHS), as a new challengeable research topic, have got ongoing attention and many remarkable outcomes have been published, see for instance [22,[25][26][27][28][29]. Specially, in [28], the sensor energy-induced missing measurement rate, for the first time, is derived recursively and then the corresponding filtering problem for non-linear timedelayed systems has been investigated thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors

Karimi

2021

IET Control Theory & Appl

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This paper is concerned with the recursive fusion estimation-based mobile robot localization (RL) problem by employing multiple energy harvesting sensors (EHSs). In the addressed RL problem, multiple sensors with energy harvesting capacity are deployed to produce measurements used for RL. When the sensors own sufficient energy, the sensors can output measurements and then send them to the corresponding local filter. Otherwise, the sensor energy-induced missing measurement phenomenon will occur. In order to obtain the missing measurement rate, at each time instant, the relationship between the totality of the sensor energy and its probability distribution is derived recursively. This paper aims at seeking out a practicable solution to the addressed mobile RL problem. First, in the presence of the sensor energy-induced measurement missing phenomenon, an upper bound (UB) of the local localization error covariance is recursively acquired. Then, such a derived UB is minimized by suitably devising the desired local filter parameter. Subsequently, the covariance intersection fusion method is adopted to achieve the addressed RL problem. In the end, a simulation is conducted to verify the practicability of the developed RL scheme.

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A Stochastic Game Approach for Collaborative Beamforming in SDN-Based Energy Harvesting Wireless Sensor Networks

Cited by 28 publications

References 40 publications

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices

Design and analysis of a multiple collaborative beamforming scheme in the realm of Wireless Sensor Networks featuring 3-dimension node configuration

Recursive fusion estimation for mobile robot localization under multiple energy harvesting sensors

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