Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

Naeem, Muhammad; Pareek, Udit; Lee, Daniel; Anpalagan, Alagan

doi:10.3390/s130404884

Cited by 23 publications

(18 citation statements)

References 36 publications

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“…Mahapatra et al [60] discussed three different schemes (i.e., wake-up radio (WUR), wireless energy harvesting (WEH) and error control coding (ECC)) to improve the performance of green WSNs while reducing the CO 2 emissions. However, multi-objective substructure resource allocation was proposed for green cooperative cognitive radio (CR) sensor networks [61]. On the other hand, authors in [62]proposed hybrid transmission protocol to maximize lifetime reliability guarantees for WSNs [62].…”

Section: Green Wireless Sensor Network Technologymentioning

confidence: 99%

Greening internet of things for greener and smarter cities: a survey and future prospects

et al. 2019

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Tremendous technology development in the field of Internet of Things (IoT) has changed the way we work and live. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also consumes energy, embraces toxic pollution and E-waste. These place new stress on the environments and smart world. In order to increase the benefits and reduce the harm of IoT, there is an increasing desire to move toward green IoT. Green IoT is seen as the future of IoT that is environmentally friendly. To achieve that, it is necessary to put a lot of measures to reduce carbon footprint, conserve fewer resources, and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, communications, sensors, clouds, and internet are alongside energy efficiency and reducing carbon emission. This paper presents a thorough survey of the current on-going research work and potential technologies of green IoT with an intention to provide some clues for future green IoT research.

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Section: Green Wireless Sensor Network Technologymentioning

confidence: 99%

Greening internet of things for greener and smarter cities: a survey and future prospects

et al. 2019

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“…The spectrum available will be extremely crowded in the near future [3]. The emerging wireless communication systems will have to support, besides data traffic generated by human, also machine-generated traffic [4].…”

Section: Introductionmentioning

confidence: 99%

An Auction-Based Spectrum Leasing Mechanism for Mobile Macro-Femtocell Networks of IoT

Chen

Xing

Qiu

et al. 2017

Sensors

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Abstract:The Internet of Things (IoT) is a vision of the upcoming society. It can provide pervasive communication between two or more entities using 4G-LTE (Long Term Evolution) communication technology. In 4G-LTE networks, there are two important problems: helping manage the spectrum demands of IoT devices and achieving much more revenue with the limited resource. This paper proposes a pricing framework to investigate the spectrum leasing in mobile heterogeneous networks with single macrocell and multiple femtocells. We modeled the leasing procedure between the macrocell service provider (MSP) and femtocell holders (FHs) as an auction to motivate the MSP to lease its spectrum resource. All FHs act as the bidders, and the monopolist MSP acts as the auctioneer. In the auction, FHs submit their bids to rent the spectrum resource so that they can make a profit by selling it to their subscribers. The MSP determines the spectrum leasing amount and chooses the winning FHs by solving the dynamic programming-based 0-1 knapsack problem. In our proposed framework, we focus on the spectrum pricing strategy and revenue maximization of the MSP. The simulation results show that the proposed scheme provides effective motivation for the MSP to lease the spectrum to FHs, and both the MSP and FHs can benefit from spectrum leasing.

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Section: Introductionmentioning

confidence: 99%

“…Cooperative communication is one of the fastest-growing areas of wireless communications and is likely to be a key enabling technology for efficient spectrum utilization in future [10]. The basic idea of cooperative communication is that all mobile users or nodes in a wireless network can help each other to send signals to the destination cooperatively, and therefore the throughput of wireless network can be improved greatly by creating multiple reception of the same data at the various terminals and transmission paths [11,12].In Quan et al[13] the authors propose a multiband joint detection framework that maximizes the aggregate throughput over multiple frequency bands by selecting the optimal sensing thresholds; however, the longer sensing time may decrease the time of data transmission. Instead of selecting a sensing threshold, other authors [14-18] investigate a sensing-throughput tradeoff scheme which maximizes CRN throughput by searching an optimal sensing time.…”

mentioning

confidence: 99%

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

Liu

2014

Int J Network Mgmt

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A conventional cognitive radio network (CRN) uses the spectrum of the licensed primary network (PN) on the premise of detecting the absence of the PN by the spectrum sensing of the sensor node (SN). In this paper, a cooperative multiband CRN is considered, wherein the SNs are allowed to use some time of the transmission slot to relay PN data by cooperative communication, while using the remaining time of the transmission slot to forward its own data, over multiple sub-bands during each frame, if the presence of PN is detected by cooperative spectrum sensing of the SNs in the sensing slot. A new sensing-throughput tradeoff scheme is formulated as a multi-variable optimization problem, which maximizes the average aggregate throughput of the CRN over all the sub-bands by jointly optimizing spectrum sensing time and sub-band transmission power, subject to the constraints on the average aggregate throughput of the PN, the maximal aggregate power of each SN, and the false alarm and detection probabilities of each sub-band. The bi-level optimization method is adopted to obtain the optimal solution by dividing the multi-variable optimization problem into two convex single-variable suboptimization problems. The simulations show that there exists the optimal sensing time and sub-band transmission power that maximize the average aggregate throughput of the CRN and, compared with the conventional scheme, the throughput obtained by the proposed scheme is outstanding. INTRODUCTIONWith the high development of wireless communications and the rapid increase of user requirements, traditional static spectrum allocation methodology has caused a shortage of spectrum resources [1]. However, recent measurements by the Federal Communications Commission (FCC) have shown that 70% of the allocated spectrum in the USA has not been well utilized [2]. Hence, based on cognitive radio, the cognitive radio network (CRN), as a novel intelligent wireless sensing network, is proposed to overcome the scarcity problem of the spectrum by reusing frequency bands allocated to the primary network (PN) [3]. In order to avoid causing severe interference to the PN, before communications, the sensor node (SN) needs to identify the idle spectrum by performing spectrum sensing [4].Energy detection is the most common method in the spectrum sensing of CRN because of its simple implementation without any prior information of the PN's signal, but it shows lower correct detection performance if the PN is shadowed or in serious fading as a hidden terminal [5,6]. Cooperative spectrum sensing is proposed to overcome the hidden terminal problem, which may generate sensing diversity gain by combining the sensing results of multiple SNs [7]. In cooperative spectrum sensing, each SN performs energy sensing independently, and then reports its local sensing result to a coordinator that combines all the received sensing results for obtaining a final decision on the presence of the PN [8,9]. Cooperative communication is one of the fastest-growing areas of wireless communicati...

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Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

Cited by 23 publications

References 36 publications

Greening internet of things for greener and smarter cities: a survey and future prospects

Greening internet of things for greener and smarter cities: a survey and future prospects

An Auction-Based Spectrum Leasing Mechanism for Mobile Macro-Femtocell Networks of IoT

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

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