Dynamic Spectrum Access and Management in Cognitive Radio Networks: References

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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A Systematic Review of IoT Solutions for Smart Farming

Cognitive radio (CR)-based Internet of Things (IoT) system is an effective step toward a world of smart technology. Many frameworks have been proposed to build CR-based IoT systems. The CR-based IoT frameworks are the key points on which this survey focuses. Efficient spectrum sensing and sharing are the main functional components of the CR-based IoT. Reviews of recent SS and sharing approaches are presented in this survey. This survey classifies the SS and sharing approaches and discusses the merits and limitations of those approaches. Moreover, this survey discusses the design factors of the CR-based IoT and the criteria by which the proper SS and access approaches are selected. Furthermore, the survey explores the integration of newly emerging technologies with the CR-based IoT systems. Finally, the survey highlights some emerging challenges and concludes with suggesting future research directions and open issues.

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Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey

Target coverage and data collection are two fundamental problems for wireless sensor networks (WSNs). Target coverage is needed to select sensors in a given area that can monitor a set of interesting points. Data collection is needed to transmit the sensed data from sensors to a sink. Since, in many applications, sensors are battery powered, it is expected that a WSN can work untended for a long period. This paper addresses the scheduling problems for both target coverage and data collection in WSNs with the objective of maximizing network lifetime. First, a polynomial-time approximation scheme is developed for the case where the density of target points is bounded, and then, a polynomial-time constant-factor approximation algorithm is developed for the general case. It is also proved that it is ${\cal NP} $ -hard to find a maximum lifetime scheduling of target cover and data collection for a WSN, even if all the sensors have the same sensing radius and the same transmission radius. Further, the practical efficiency of our algorithms is analyzed through simulation. These extensive simulation results show better performances of our algorithms compared with other research findings.

Maximum Lifetime Scheduling for Target Coverage and Data Collection in Wireless Sensor Networks

With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

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Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6g networks

In this article, we consider a stochastic model of wireless sensor networks (WSNs) in which each sensor node randomly and alternatively stays in an active mode or a sleep mode. The active mode consists of two phases, called the full-active phase and the semi-active phase. When a referenced sensor node is in the full-active phase of the active mode, it may sense data packets, transmit the sensed packets, receive packets, and relay the received packets. However, when the phase of the sensor node switches from the full active phase to the semi-active phase, it is only able to transmit/relay data. When the referenced sensor node is in a sleep mode, it does not interact with the external world. In this article, first, we develop a stochastic model for the sensor node of a WSN, and then we derive an explicit expression of the stationary distribution of the number of data packets in the sensor node. Furthermore, we figure out some important performance measures, including the sensor node’s energy consumption for transmission, the energy consumption of the sensor operations, and the average energy consumption of the sensor node in a cycle of active and sleep modes. Also, a numerical analysis is provided to validate the proposed model and the results obtained. The novel aspects of our research are the development of a stochastic model for WSN with active and sleep features and the development of important analytical formulae for evaluating the energy consumption of a WSN. These results are expected to be useful as significant contributions to the fundamental theory of the design of various WSNs with active and sleep mode considerations.

https://link.springer.com/content/pdf/10.1186%2F1687-1499-2012-282.pdf

Modeling and energy consumption evaluation of a stochastic wireless sensor network

We consider an M/G/1 retrial queue with general retrial times, and introduce working vacations and vacation interruption policy into the retrial queue. During the working vacation period, customers can be served at a lower rate. If there are customers in the system at a service completion instant, the vacation will be interrupted and the server will come back to the normal working level. Using supplementary variable method, we obtain the stationary probability distribution and some performance measures. Furthermore, we carry out the waiting time distribution and prove the conditional stochastic decomposition for the queue length in orbit. Finally, some numerical examples are presented.

An m/g/1 retrial queue with general retrial times, working vacations and vacation interruption

The transmission times with generally random distributed for both primary user (PU) and secondary user (SU) in a cognitive radio system with a single PU band is investigated. To characterize the random access protocols of wireless networks, a retrial queueing system is employed to model the decentralized and centralized behavior of SUs. Based on the tradeoff between the service reward and the delay cost, each SU must decide whether to join or balk the system upon arrival based on different levels of information provided by the system. Two situations in which SUs have no information or partially observable information are studied. In each situation, two kinds of strategies are considered: 1) individual equilibrium strategies (i.e., non-cooperative strategies) that maximize SUs’ own profit and 2) socially optimal strategies (i.e., cooperative strategies) that maximize the expected social welfare. Moreover, comparisons are carried out between these two kinds of strategies. The results indicate that the equilibrium joining probability in the non-cooperative case is not less than the optimal joining probability in the cooperative case. To regulate the SUs’ behavior, an equilibrium pricing scheme is proposed to make these strategies coincide. In addition, we observe that the expected delay and the mean number of SUs in the system only depend on the first two moments of the SU’s transmission time. Numerical examples also show that the partially observable queue is more profitable from the perspective of the social planner.

Strategic Joining and Optimal Pricing in the Cognitive Radio System With Delay-Sensitive Secondary Users

A novel model is developed and investigated for a duty-cycle wireless sensor network (WSN), where each sensor node can stay at one of four statuses (i.e., full-active phase, two semi-active phases each with different functions, and a sleep phase) to make the system more efficient. The explicit result of the joint probability for data packet numbers and the sensor phases is achieved by suitably constructing a multi-dimensional Markov process. Furthermore, various energy consumption measures, such as energy consumption per unit time in each of the three energy consumptions and energy consumption switching from one phase to another, are obtained. System performance measures, such as the average delay time of a data packet in the system, throughput of the system, and the probability of the sensor node staying at any of the phases, are also achieved. The numerical analyses are provided to validate the model and the analytic results. The proposed model and the analysis method are expected to be applied to the design and analysis of WSN models with various phases of the sensor.

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Energy Consumption Analysis of a Duty Cycle Wireless Sensor Network Model

In a cognitive radio (CR) system, excessive access services for secondary users (SUs) lead to a substantial increase in congestion and the retrial phenomenon, both of which degrade the performance of CR networks, especially in overload conditions. This paper investigates the price-based spectrum access control policy that characterizes the network operator’s provision to heterogeneous and delay-sensitive SUs through pricing strategies. Based on shared-use dynamic spectrum access (DSA), the SUs can occupy the dedicated spectrum without degrading the operations of primary users (PUs). The service to transmission of SUs can be interrupted by an arriving PU, while the interrupted SUs join a retrial pool called an orbit, later trying to use the spectrum to complete the service. In the retrial orbit, the interrupted SU competes fairly with other SUs in the orbit. Such a DSA mechanism is formulated as a retrial queue with service interruptions and general service times. Regarding the heterogeneity of delay-sensitive SUs, we consider two cases: the delay-sensitive parameter follows a discrete distribution and a continuous distribution, respectively. In equilibrium, we find that the revenue-optimal price is unique, while there may exist a continuum of equilibria for the socially optimal price. In addition, the socially optimal price is always not greater than the revenue-optimal price, and thus the socially optimal arrival rate is not less than the revenue-optimal one, which is contrary with the conclusion, i.e., the socially optimal and revenue-optimal arrival rates are consistent, drawn in the literature for homogeneous SUs. Finally, we present numerical examples to show the effect of various parameters on the operator’s pricing strategies and SUs’ behavior.

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Wei Wayne Li

Papers

Modeling and energy consumption evaluation of a stochastic wireless sensor network

An m/g/1 retrial queue with general retrial times, working vacations and vacation interruption

Strategic Joining and Optimal Pricing in the Cognitive Radio System With Delay-Sensitive Secondary Users

Energy Consumption Analysis of a Duty Cycle Wireless Sensor Network Model

Optimal Pricing Strategies in Cognitive Radio Networks With Heterogeneous Secondary Users and Retrials