A channel assignment algorithm for cognitive radio wireless sensor networks

Askari, Mehdi; Kavian, Yousef S.; Kaabi, Hooman; Rashvand, Habib F.

doi:10.1049/cp.2012.0583

Cited by 19 publications

(3 citation statements)

References 2 publications

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“…When occurring during the hand-off, the buffer overflows result in packet losses and affects the transmission reliability. Works that make use of this criterion for the resource allocation in CRSN applications have been reported in [61], [63], [66], [71]. The authors in [64] presented a reduced handoffs technique using a home gateway (HGW) for a home area network in a cognitive radio-based SG.…”

Section: Reduced Adaptive Modulation Overhead and Probability Of Detectionmentioning

confidence: 99%

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Ogbodo¹,

Dorrell²,

Abu‐Mahfouz³

2019

Preprint

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A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

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Section: Reduced Adaptive Modulation Overhead and Probability Of Detectionmentioning

confidence: 99%

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Ogbodo¹,

Dorrell²,

Abu‐Mahfouz³

2019

Preprint

View full text Add to dashboard Cite

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“…Usually, there are many alternative licensed users for a WSN. In a CR-based WSN [ 4 , 5 , 6 ], the spectrum demand for a licensed user directly depends on unit price and spectrum efficiency. To attract more buyers, licensed users need to compete with each other in the terms of spectrum price.…”

Section: Introductionmentioning

confidence: 99%

A Strategic Bargaining Game for a Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks

Mao

Cheng

Zhao

et al. 2017

Sensors

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In Wireless Sensor Networks (WSNs), unlicensed users, that is, sensor nodes, have excessively exploited the unlicensed radio spectrum. Through Cognitive Radio (CR), licensed radio spectra, which are owned by licensed users, can be partly or entirely shared with unlicensed users. This paper proposes a strategic bargaining spectrum-sharing scheme, considering a CR-based heterogeneous WSN (HWSN). The sensors of HWSNs are discrepant and exist in different wireless environments, which leads to various signal-to-noise ratios (SNRs) for the same or different licensed users. Unlicensed users bargain with licensed users regarding the spectrum price. In each round of bargaining, licensed users are allowed to adaptively adjust their spectrum price to the best for maximizing their profits. . Then, each unlicensed user makes their best response and informs licensed users of “bargaining” and “warning”. Through finite rounds of bargaining, this scheme can obtain a Nash bargaining solution (NBS), which makes all licensed and unlicensed users reach an agreement. The simulation results demonstrate that the proposed scheme can quickly find a NBS and all players in the game prefer to be honest. The proposed scheme outperforms existing schemes, within a certain range, in terms of fairness and trade success probability.

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“…Most existing studies focus on multiple sellers selling spectrum to one buyer or one seller selling spectrum to multiple buyers. 11,12 This article solves the problem of multiple sellers selling spectrum to multiple buyers in WSN using game theory and the MKP algorithm. It improves the practicality of the spectrum sharing model by considering the importance and urgency of a node's information and imposing a maximum waiting period for a node.…”

Section: Introductionmentioning

confidence: 99%

Multiple-seller and multiple-buyer spectrum sharing model in cognitive radio-based wireless sensor network

Mao

Cheng

Zhao

et al. 2017

International Journal of Distributed Sensor Networks

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As the use and scale of wireless sensor networks has grown, the lack of transmission spectrum has become a key factor affecting wireless sensor network quality of service. An effective solution is to collect unused spectrum from primary users (i.e. licensed users) and use that spectrum for wireless sensor networks. In this article, for a cognitive radio-based wireless sensor network, a spectrum sharing strategy between primary users and clusters is proposed. First, as a secondary user (i.e. an unlicensed user), the base station plays a Bertrand game with the monopolists (the primary users) to gain spectrum access on behalf of the clusters. Then, the base station allocates the spectrum band to the cluster heads using the ant colony optimization-based multiple knapsack problem. After obtaining the spectrum, each cluster head programs its nodes' spectrum according to a timing sequence. Simulation results show that the Bertrand game model is a good choice for determining spectrum pricing and that the ant colony optimization-based multiple knapsack problem algorithm is an efficient way to allocate spectrum dynamically.

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A channel assignment algorithm for cognitive radio wireless sensor networks

Cited by 19 publications

References 2 publications

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

A Strategic Bargaining Game for a Spectrum Sharing Scheme in Cognitive Radio-Based Heterogeneous Wireless Sensor Networks

Multiple-seller and multiple-buyer spectrum sharing model in cognitive radio-based wireless sensor network

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