Enhancing Energy Efficiency via Cooperative MIMO in Wireless Sensor Networks: State of the Art and Future Research Directions

Peng, Yuyang; Al-Hazemi, Fawaz; Boutaba, Raouf; Tong, Fei; Hwang, Il-Sun; Youn, Chan‐Hyun

doi:10.1109/mcom.2017.1600837

Cited by 30 publications

(17 citation statements)

References 13 publications

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“…The discussion about CMIMO is mostly geared on the selection of nodes as candidates for CNs [32] and channel state estimation models [31][32][33][34][35][36]. The selection of CNs has been very diverse, starting by utilizing intelligent systems such as GA, PSO, Fuzzy, and NN [7], [42][43][44][45][46]57]. The channel state estimation model is common and adopts MIMO real hardware models.…”

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

“…Nonetheless, the approach can vary; for example, it utilizes residual energy, link credibility, and other parameters that can be accessed from nodes. Until today, the study of cooperative routing generally deals with channel problems and the selection of cooperative nodes [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. The study of channels is carried out to strengthen the argumentation of virtual methods or logical channels that are more flexible than creating physical channels that require more complicated and sophisticated hardware.…”

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

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A New Bio-Inspired for Cooperative Data Transmission of IoT

et al. 2020

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The original queen honey bee migration (QHBM) was developed for independent action on solving efficient mobile routing in WSN. In this paper, we enhanced the original QHBM using Binary testing injection on the cooperative node's selection on IoT system. We also added a new cost function for making nodes' coalition, implementing the threshold value for modified QHBM (mQHBM for short), and demonstrating the mQHBM-CMIMO in fair comparison with another previous algorithms. Our research portrayed that mQHBM can perform better than its competitors such as Fuzzy-BT, Neuro Fuzzy and PSO in terms of network lifetime and the end to end delay.

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Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

Section: Figure 1 Cluster-based Data Transmissionmentioning

confidence: 99%

A New Bio-Inspired for Cooperative Data Transmission of IoT

et al. 2020

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“…Therefore, the MIMO scheme is suitable only for long‐range communication, whereas the SISO scheme is suitable for short‐range communication [3]. Thus, to achieve a long transmission range, more cooperative sensor nodes should be utilized to reduce the transmission energy via antenna diversity, and for the short transmission range, fewer cooperative nodes are chosen for reducing the circuit energy consumption [4].…”

Section: Introductionmentioning

confidence: 99%

Energy‐efficient data transmission technique for wireless sensor networks based on DSC and virtual MIMO

Singh

Amin

2020

ETRI Journal

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In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple‐input multiple‐output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space‐time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple‐input multiple‐output (MIMO) data transmission technique in the WSNs. The DSC‐MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC‐MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single‐input single‐output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

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“…Wireless Sensor Network (WSN) is one of the most adopted networking technologies that have found its identity from the arena of research to commercial products and services [1]. At present, there are numbers of organization developing sensor nodes with different capabilities of sensing different physical attributes.…”

Section: Introductionmentioning

confidence: 99%

A novel predictive optimization scheme for energy-efficient reliable operation of a sensor in dynamic scenarios

Jayasri¹,

Rao²

2020

IJECE

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Wireless Sensor Network (WSN) has been studied for more than a decades that resulted in evolution of the significant applications towards assisting in sensing physical information from human inaccesible area. It was also observed from existing sysem that energy attribute is the root cause of majority of the problems associated with WSN that also gives rise to various operational reliability issue. Therefore, the prime goal of the proposed study is to present a novel predictive optimization approach of data fusion in order to jointly address the problems associated with energy efficiency and reliable operation of sensor nodes in WSN. An analytical research approach is carried out in order to ensure that a time-based synchronization scheme contributes to offer an evolutionary approach towards significant energy optimization. A simulation-based benchmarking analysis is carried out to find that proposed system offers good energy-efficient performance in comparison to existing approaches.

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Enhancing Energy Efficiency via Cooperative MIMO in Wireless Sensor Networks: State of the Art and Future Research Directions

Cited by 30 publications

References 13 publications

A New Bio-Inspired for Cooperative Data Transmission of IoT

A New Bio-Inspired for Cooperative Data Transmission of IoT

Energy‐efficient data transmission technique for wireless sensor networks based on DSC and virtual MIMO

A novel predictive optimization scheme for energy-efficient reliable operation of a sensor in dynamic scenarios

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