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Background: 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 conventional SG. Currently, an SG uses a static resource allocation technique to allocate resources to sensor nodes in the SG network. Static resource allocation is not efficient due to the heterogeneous nature of CRSN-based SGs. Hence, an appropriate mechanism such as dynamic radio resource allocation (RRA) is required for efficient resource allocation in CRSNs for SGs. Objective: The objective of this paper is to investigate and propose suitable dynamic RRA for efficient resource allocation in CRSNs-based SGs. This involves a proposal for appropriate strategy that will address poor throughput and excessive errors in resource allocation. Methods: In this paper, the dynamic RRA approach is used to allocate resources such as frequency, energy, channels and spectrum to the sensor nodes. This is because of the heterogeneity in a CRSN which differs for SG applications. The dynamic RRA approach is based on optimization of resource allocation criteria such as energy efficiency, throughput maximization, QoS guarantee, etc. The methods include an introduced model called “guaranteed network connectivity channel allocation for throughput maximization” (GNC-TM). Also used, is an optimal spectrum-band determination in RRA for improved throughput. Results: The results show that the model outperforms the existing protocol of channel allocation in terms of throughput and error probability. Conclusion: This study explores RRA schemes for CRSNs for SGs. The paper proposed a GNC-TM model, including demonstration of suitable spectrum band operation in CRSNs for SGs.

With the rapid development of digital communication, the demand for spectral resources is increasing and the building of cognitive radios is the right solution for that. Cognitive radio networks are designed to utilize the licensed spectrum when it is not used by the primary licensed users. We are going to propose a method for effective data transmission and streaming in cognitive radio networks. By that we can achieve energy efficiency, less power consumption and much more transmitted information. In this paper, we are going to employ the OFDM method of spectrum sensing and proposed to use Cognitive Radio MAC protocol. Further our main technique is to divide the data packets into different sizes for transmission. The simulation results reveal that there is a better improvement in the detection of idle channel in the cognitive radio network and the delay is reduced with high quality transmission.

Recently, cognitive radio sensor networks (CRSN) have evolved as a result of the introduction of cognitive capabilities to conventional wireless sensor networks. In most CRSN designs, secondary users and/or sensor nodes are permitted, under certain constraints, to use the limited resources of a primary network. One major challenge with CRSN is how to optimally appropriate and use the limited resources available in driving their communication demands. To overcome this challenge, in this paper, we develop a resource allocation (RA) model that is capable of achieving a target quality of service (QoS) demand for the heterogeneous CRSN, despite the huge resource constraints imposed on the network. The RA problem developed is a complex optimisation problem. We analyse and solve the complex RA problem using the optimisation approaches of integer linear programming, Lagrangian duality and by a heuristic. We then study the performance of the RA model for the different solution approaches investigated. The results obtained are used to establish the optimality-complexity trade-off, which is a critical criterion for QoS decision-making in practical CRSN applications.

The ever-increasing spectrum demand in wireless communication networks like Sensor Network has routed to the development of Cognitive Radio Sensor Networks (CRSN). Securing the CRSN against the Pri...

The cognitive radio (CR) opens new paradigms of opportunistic utilization of unused frequency spectrum through spectrum sharing. Dynamic spectrum access (DSA) provides an effective solution to chan...

A Cognitive Radio Sensor Network (CRSN) is a distributed network of sensor nodes, which senses event signals and collaboratively communicates over dynamically available spectrum bands in a multi-hop mode. All nodes participating in CRSN have to be cognitive of the network environment and autonomous in decision making for resolving issues related to throughput maximization, delay, and energy minimization. Clustering in CRSN is proven to tackle such issues and enlarges the network’s lifetime. However, the existing clustering algorithms designed for WSNs do not consider the CR functionalities and challenges, and CR based networks work on the assumption of unlimited energy. This paper proposes an energy and spectrum aware unequal cluster based routing (ESUCR) protocol intending to resolve the issues of clustering and routing in CRSN. In ESUCR, cluster formation is mainly performed considering the residual energy of the secondary users (SUs) and relative spectrum awareness such that the common data channels for clusters are selected based on the appearance probability of PUs. ESUCR performs energy-efficient channel sensing by deciding the channel state with the statistic previous channel states. The premature death of cluster heads (CHs) is minimized by selecting and rotating the CHs based on intra-cluster channel stability, energy, distance, and neighbor connectivity. During event detection, ESUCR performs energy-efficient data routing towards the sink node by employing hop by hop forwarding through the CHs and primary/secondary gateways. The performance of the proposed ESUCR protocol is proved through extensive simulations and compared to those of the state-of-the-art protocols under a dynamic spectrum-aware data transmission environment.

Cognitive radio (CR) is an intelligent and adaptive radio technology that automatically detects the available channels in the wireless spectrum and sometimes changes the transmission parameters to enable effective communication. Spectrum sensing in CR prevents harmful interference with the licensed users and maximizes the spectrum utilization. Thus, this paper proposes a technique for optimal channel estimation and spectrum sensing for MAC layer protocol in CR networks such that the scheduling issues are addressed. Initially, in the CR networks, spectrum sensing is done using the proposed optimal naive Bayes classifier (ONBC) based on the signal statistics, such as energy and likelihood ratio. The ONBC is developed by integrating the bat–bird swarm algorithm (BBSA) with the naive Bayes classifier, which works based on the Bayesian concept. The BBSA is newly developed by integrating the bird swarm algorithm (BSA) and bat algorithm. Finally, the channel estimation is done using the pilot-based sequential procedure and least square estimation (LSE). The analysis of the proposed method is done in the Rayleigh and Rician environments using 256 and 512 sub-carriers. From the results, it is exposed that the proposed BBSA + LSE pilot-based sequential method obtains the bit error rate, normalized energy and Probability detection (PD) of is 0.0126, 0.8446 and 0.9355, respectively.

This proposed work addresses the problem of dynamic and real-time power allocation to the cluster head (CH) in a cognitive radio sensor network (CRSN). The work is based on spectrum sensing outputs by these secondary users (SUs) for a nonergodic system. In general, a cooperative sensing network consists of multiple nodes communicating with each other about their respective spectrum sensing output, to perform the desired work. Therefore, the SU nodes of each cluster sense the spectrum all the time, which results in a continuous power consumption in CRSN. Now, the total amount of energy, which is used to allocate all the CHs at a particular place for spectrum sensing is being avoided and saved. This is performed, by the use of autocorrelation error for predicting the real-time behavior of primary users extended by the implementation of vector quantization to identify nearby active SU nodes and based on distributed artificial intelligence for power allocation. The simulation results and the mathematical derivation validate the proposed method.

In order to cope with the resource shortage problem brought by cognitive radio technology in cognitive radio sensor network (CRSN), a new CRSN called heterogeneous CRSN (HCRSN) is proposed, where cognitive nodes (CNs) and sensor nodes (SNs) are separated and undertake different functions. Different from the existing clustering algorithms for homogeneous nodes based WSN, the clustering algorithm for HCRSN needs to consider the distribution of CNs among clusters such that enough high channel detection probability of each cluster can be guaranteed by the lowest deployment cost. Therefore, this paper first proposes a heterogeneous nodes based low energy adaptive clustering hierarchy (HLEACH) algorithm. In the algorithm, the sink node first updates the global information including the optimal number of clusters and average cluster radius and then broadcast it. Each CN calculates its competition radius after receiving the broadcasting information, and then start the competition for CHs based on the proposed competition rules. The elected CHs are finally censored targeting the optimal number of clusters to optimize the distribution of final CHs. In clusters' formation stage, non-CH CNs and SNs synthetically consider the distance and the connection degree of CHs such that the distribution of CNs among clusters and the energy consumption among CHs can be energy-efficiently balanced. The simulation results show that the proposed algorithm can not only effectively balance the distribution of CNs among clusters, guaranteeing enough high channel detection probability of each cluster and network energy utilization, but also balance the energy consumption among CHs, eventually prolong the network lifetime. Finally, the optimal deployment proportion of numbers and initial energy of the two types of nodes is also theoretical derived to maximize the energy utilization efficiency (i.e. the ratio of the network lifetime to the deployment cost).

Cognitive Radio Sensor Networks (CRSNs) have become an integral portion of the new generation of smart Wireless Sensor Networks (WSNs) technology. Moreover, efficient clustering and routing could enhance the network performance by taking into account the stability and connectivity of the network that expands the network's lifetime. In this paper, we propose a scheme that aims to construct an energy-efficient clustering for CRSNs through saving the intra-communication energy between nodes into the cluster, in addition to the inter-communication energy between Cluster Head (CH) nodes to the Base Station (BS). The scheme utilizes evaluation criteria to define the CH node for each cluster by calculating a weight value for each node, and depending on the maximum weight value for nodes, the CH is picked. Moreover, to establish a route between CHs, we consider common channels between them plus the shortest distance from cluster heads to the sink. In this way, clustering and routing could enhance the network performance and extend the lifetime. To corroborate the proposed scheme, extensive simulations in MATLAB were carried out and the results of the simulation showed the superiority of the proposed technique over other algorithms in terms of the network’s lifetime.

With the rapid development of technology, the continuous progress of communication technology has brought a very serious problem - the shortage of spectrum resources. A large part of the factors that cause the shortage of spectrum resources are related to the original spectrum allocation method. In order to further optimize the benefit of wireless spectrum and network efficiency, this paper proposes an immune parallel artificial bee colony algorithm (IPABCA) to optimize the spectrum allocation efficiency, and uses the classical graph coloring method to calculate the benefit and prove the superiority of group intelligent algorithm in solving the spectrum allocation problem. It can be seen from the simulation results that IPABCA is superior to the original ant colony optimization and particle swarm optimization in terms of total network benefit.

In recent years, cognitive radio sensor networks (CRSNs) have been commonly applied in environmental monitoring and image acquisition. However, recent advances in channel allocation have led to lower network reward, lifetime, and energy utilization rate. As a basic and fundamental problem to obtain image data in CRSNs, it governs the performance of CRSNs. To further improve the reward and throughput of obtaining image, this paper proposes an improved immune hybrid bat algorithm (IIHBA) based on bat algorithm. Furthermore, we develop a simulation environment and compared the performance of IIHBA with particle swarm optimization (PSO) and genetic algorithm (GA). Last but not the least, computational experiments showed that the reward is improved 11.36%, 27.20% respectively compared with GA and PSO when the number of users is 20 and the number of channels is 5. Based on the above findings, the proposed scheme can improve the reward of system, especially in terms of higher-throughput.

Green communication with emerging future technologies like the Internet of things (IoT), Industry 4.0, and Industrial IoT (IIoT) pushes modern society towards a smarter paradigm. Thus, for its deployment and implementation in the society, the involving Wireless Sensor Networks (WSNs) and its computing technologies should be designed in an energy-efficient way. In this paper, we propose an energy-efficient cooperative technique for Cognitive Radio Sensor Networks (CRSNs) for IoT applications. In this type of applications, there is a constant need for inter and intra-cluster WSNs communication. Also, the active nodes need power based on the application rather than and uniform distributing power to all the nodes or randomly distribution (conventional). The work addresses the issue by proposing a hybrid approach of using Vector Quantization (VQ) and Hierarchical Maximum Likelihood (HML) to allocate power only for the active Secondary User (SU) nodes. The proposed work illustrates a fast computation towards cluster formation and Cluster Head (CH) communication between two clusters for IoT applications with CRSNs. The work is presented with mathematical analysis and simulation results along with the comparison of existing techniques.

A cognitive radio sensor network (CRSN) is a wireless sensor network (WSN) with cognitive radio capabilities that ensures efficient spectrum management. In general, CRSNs facilitate the combined use of unused licensed frequency spectrum and unlicensed frequency spectrum. Prolonging network lifetime is very important in the design of a medium access control (MAC) protocol in CRSNs because additional energy for spectrum sensing and dynamic spectrum access is required, in comparison to conventional WSNs. In this paper, an energy-aware robust MAC (ER-MAC) protocol for prolonging network lifetime in CRSNs is proposed, in which the network lifetime is increased by reducing collisions and adjusting sensing interval. Furthermore, the number of retransmissions is reduced by utilizing more reliable channels. Simulation results show that the proposed ER-MAC outperforms the conventional protocol in terms of network lifetime.

A cognitive radio sensor network (CRSN) is a solution that enables sensor nodes to opportunistically access licensed radio channels. Data transmitted over a network are divided into packets. In machine-to-machine communication, which is a heterogeneous nature of wireless networks, small-size packets are the common form of traffic. Due to the nature of CRSNs, small data packets will not allow a balance between optimal performance of the network and fulfilling the secondary network obligations towards the primary network in terms of interference. Either interference or channel’s underutilization would result from employing data packets of inadequate size. In this paper, the appropriate packet size for adaptive CRSN is investigated by examining the performances of small, medium, and large packet size. In contrast to the trends of exploiting small packets of sizes up to 128 bytes, this study demonstrates that medium-size packets are more appropriate to yield the best performance in CRSNs. Simulation results show that packets of size 375 bytes outperform smaller and larger packets in many CRSN protocols. The induced delay that is partially caused by interference is decreased at the same time the channels are efficiently utilized.

Cognitive radio sensor network (CRSN) is a new generation of communication systems that wants to solve the overcrowded spectrum utilization of the unlicensed bands. It has combined sensor networks and cognitive radio technology, so it has the challenges of energy restriction of sensors and also dynamic spectrum access of the cognitive radio network. On the other hand, considering both of these challenges in the routing protocol plays a basic role in network performance and we can’t apply the routing protocols that have been proposed for wireless sensor networks and cognitive radio networks, separately, in the CRSN. Therefore, this article has tried to provide a new spectrum and energy-aware routing protocol in which the source is able to choose the most stable route in the aspect of node residual energy or spectrum access probability. Not only can considering the nodal residual energy and spectrum access in the route discovery process avoid repetitive link failure, but it also can increase the network lifetime. This protocol has been compared with ESAC, SCR, ERP, and SER. The result of this comparison has shown that our protocol reduces end-to-end delay, control overhead, throughput, and lifetime in comparison to other protocols, especially in small-scale networks. ABSTRAK: Rangkaian sensor radio kognitif (CRSN) adalah generasi baru sistem telekomunikasi bagi menyelesaikan masalah kesesakan pada pemakaian band spektrum tidak berlesen. Ianya adalah kombinasi rangkaian sensor dan teknologi radio kognitif. Oleh itu, ia mempunyai cabaran sekatan tenaga pada sensor dan kemasukan spektrum secara dinamik pada rangkaian radio kognitif. Pada masa sama, dengan mengambil kira kedua-dua cabaran pada protokol rangkaian ini telah memainkan peranan asas pada prestasi rangkaian dan kami tidak boleh mengguna pakai protokol rangkaian yang telah diguna pakai pada rangkaian sensor tanpa wayar dan rangkaian radio kognitif secara asing dalam CRSN. Oleh itu, artikel ini cuba menyediakan spektrum baru dan pengawasan tenaga pada protokol rangkaian, di mana sumber boleh memilih laluan rangkaian yang stabil dengan mengambil kira pada aspek baki tenaga nod atau kebarangkalian akses spektrum. Selain itu, ianya dapat mengelakkan kegagalan laluan berulang juga menambahkan jangka hayat rangkaian. Protokol ini telah dibandingkan dengan ESAC, SCR, ERP dan SER. Perbandingan keputusan menunjukkan protokol ini mengurangkan kelewatan hujung-ke-hujung, mengawal kesesakan, mambaiki jumlah penghantaran dan menambah tempoh hayat berbanding protokol lain, khususnya pada rangkaian skala kecil.

This paper investigates the optimal path selection problem for end-to-end (e2e) outage probability minimization in clustered cognitive radio sensor networks. In order to improve outage performance of the optimal path, under a high node density regime, we consider feasibility of virtual multiple-input single-output (v-MISO) links in addition to conventional single-input single-output (SISO) links in the path. Since sensor nodes in such networks are allowed to access the spectrum of the primary network only in an opportunistic manner, the path selection problem is studied under the constraints of probabilistic interference to PU receivers and only single use of any PU channel along the path. The above problem is formulated as a joint hop-constrained routing, spectrum assignment and transmit power control problem. A convex optimization framework is used to find a closed form expression for the optimal transmit power of each transmitting node along the optimal route. Extension of the analytical result facilitates design of a novel routing algorithm, called spectrum aware-minimum outage intelligent cooperative routing (SA-MOICR) algorithm, which not only selects the minimum outage path for a given routing session, but also determines the number of nodes and the unique PU channel to be used for transmission in each hop along the path. Simulation results are found to corroborate our analytical results and quantify the significant improvement of the SA-MOICR scheme over only SISO or only v-MISO based routing solutions in terms of the achievable e2e outage probability.

In this paper, we introduce energy harvesting into a cognitive radio sensor network to power the network with renewable energy sources so as to achieve self-sustainability of energy-limited sensors. In our work, the cognitive radio technology enable sensors access to the underutilized spectrum for the purpose of coping with the spectrum-scarcity problem in the unlicensed band. Using centralized cooperative spectrum sensing, a set of cognitive sensors is chosen from candidate sensors with different received primary users’ signal powers and energy-arrival rates. After detecting the state of a primary channel, we also need to determine which cognitive sensor can get to access the primary channel as well as the power level to be used upon the transmission. The above sensing-access design problem is formulated as an infinite-horizon partially observable Markov decision process, in which the primary goal is to maximize the long-term expected throughput. Through using a value iteration approach, we propose an optimal sensing-access policy. At last, numerical results are presented to verify the superiority of our proposed policy to the existing policy.

Due to dynamic channel availability in cognitive radio sensor networks (CRSNs), clustering protocols are significantly affected. This letter proposes energy coverage efficiency (ECE) metric specifically for clustering protocols in CRSNs, in which network lifetime and effective surveillance capability are for the first time integrated. Simulation results show that ECE can provide a comprehensive evaluation for clustering protocols in CRSNs.

Several studies have focused on multimedia transmission over wireless sensor networks (WSNs). In this paper, we propose a comprehensive and robust model to transmit images over cognitive radio WSNs (CRWSNs). We estimate the spectrum sensing frequency and evaluate its impact on the peak signal‐to‐noise ratio (PSNR). To enhance the PSNR, we attempt to maximize the number of pixels delivered to the receiver. To increase the probability of successful image transmission within the maximum allowed time, we minimize the average number of packets remaining in the send buffer. We use both single‐ and multi‐channel transmissions by focusing on critical transmission events, namely hand‐off (HO), No‐HO, and timeout events. We deploy our advanced updating method, the dynamic parameter updating procedure, to guarantee the dynamic adaptation of model parameters to the events. In addition, we introduce our ranking method, named minimum remaining packet best channel selection, to enable us to rank and select the best channel to improve the system performance. Finally, we show the capability of our proposed image scrambling and filtering approach to achieve noticeable PSNR improvement.

Square lattice (SL), triangle lattice (TL), and hexagon lattice (HL) are widely used regular deployment patterns for industrial Internet of Things (IoT). However, the performance of cognitive-radio-based access with these three deployment patterns has not been explored yet. This paper first designs a transmission scheduling method named cognitive access for regular topology (CART) in cognitive radio sensor networks (CRSNs) with minimal occupation of channels and high transmission efficiency. CART consists of a timeslot-and-channel allocation scheme, a cooperative spectrum-sensing scheme, and a scheme for reporting spectrum-sensing results (SSRs) in SL, TL, and HL. This paper also analyzes CART’s cooperative spectrum-sensing performance, reception bandwidth, and transmission delay for different transmission interference range and coverage patterns. Based on numerical analysis and simulation, this paper makes comparison between CART and optimal transmission scheduling method for single channel, and that among these three deployment patterns. Results show the following insights for efficient deployment of CRSN: 1) TL yields the optimal cooperative spectrum-sensing performance among three patterns by data fusion rule of 3-out-of-7; 2) under the same conditions and area for deployment, SL provides the optimal reception bandwidth; 3) SL and TL lead to low transmission delay for critical complete coverage; 4) SL leads to the lowest transmission delay for critical multiple coverage; 5) the false alarm (FA) probability of individual node has little influence on transmission delay, while the probability of primary user’s (PU) occurrence has noticeably influence on it; and 6) increasing the ratio of transmission timeslot to spectrum-sensing timeslot may decrease transmission delay

Traffic management is a highly beneficial mechanism for satisfying quality-of-service requirements and overcoming the resource scarcity problems in...

Previous studies of real-time traffic in Cognitive Radio Sensor Network (CRSN) were done using reservation-based method and absolute priority method. These studies were done under the assumption that primary user’s traffic arrive at a low rate. However, under high arrival rate of primary user traffic, real-time secondary user traffic is starved. In this paper, we developed an analytical model of source-to-sink delay in a Cognitive radio sensor network (CRSN) that differentiates the service of users into primary and secondary and further partitions secondary user’s traffic into real-time and best effort. During service, primary traffic is given priority over real-time secondary user traffic which in turn has higher priority over best effort secondary traffic. Furthermore, a threshold is introduced on the number of primary user packets served to reduce the starvation of real-time secondary users under high arrival rate of primary user traffic. The numerical results obtained from the derived models show that real-time secondary users experience a reduction in source-to-sink delay as a result of introducing a threshold on the number of primary user packets served without over degrading the service of primary user traffic .

Collaborative Spectrum Sensing (CSS) is considered as a powerful way to improve the utilization of the spectrum resources. However, the open characteristic of Cognitive Radio Sensor Networks (CRSNs) suffer various security threats, such as Spectrum Sensing Data Falsification (SSDF) attack, the defending of this kind of attack is a popular research topic in both academia and industry due to the demanding requirements of security. To enhance the performance of sensing, the schemes based on the Dempster-Shafer (D-S) Evidence Theory have been proposed. However, if we consider that the transmitted data will be increased with the sensing nodes increasing, the energy consumption of the whole networks should be reduced. In order to reduce the total energy consumption, Sensor Selection is one efficiency way to deal with the energy limitation. In this paper, we calculate the formulas of false alarm probability and miss detection probability under the D-S Evidence Theory and propose a new algorithm to find the minimum number of sensing nodes and the optimal threshold with the given security level based on D-S Evidence Theory to against SSDF attack.

This paper proposes a fast and reliable routing protocol (FaReli) for multi-channel, multi-hop cognitive wireless sensor networks (CRSNs). Being aware of the spectrum dynamics in the CRSN, a framework is proposed to estimate the remaining duration of spectrum availability which is based on the expected idle length of channels, sensing period, and communication history on the channel. Specially, in combination with the per hop delay analysis, the routing path from sources to the sink can be established such as it provides a shorter delay and reliable communication. Simulation results are conducted to demonstrate that the proposed routing algorithm based on spectrum and delay analysis potentially improve the network performances in terms of throughput, delay.

With the increasing usage of wireless sensor network technologies, their unlicensed bands become overcrowded. To address this challenge, cognitive radio technology with the dynamic spectrum access policy has merged with Wireless Sensor Network to overcome spectrum underutilization. The Cognitive Radio Sensor Network (CRSN) has emerged as a promising solution to overcome spectrum scarcity in a resource-constrained wireless sensor network. In CRSN, TCP has to cope with a new type of packet loss due to the primary users (PU) arrival, known here as a secondary user (SU) blocking loss. In this paper SU blocking loss is modelled by a discrete-time Markov chain. The experimental results are verified using the NS2.

The distributed tree-based data communication protocol is a widely accepted efficient method in wireless sensor network-enabled Internet of Things (IoT). Due to increasing demand for IoT applications, cognitive radio sensors play a vital role in spectrum utilization. This short communication proposes a delay aware, energy-efficient data communication protocol (DEDC) for cognitive radio sensor networks (CRSN). This research proposes a first communication protocol that integrates the solution for delay and energy issues in CRSNs specific to IoT application. The respective simulation results and comparative studies with the existing models on other parameters prove the novel contribution of this research in terms of delay and energy utilization.

인지 무선 센서 네트워크 (CRSNs)에서 보조 사용자가 (SUs) 주 사용자 (PUs)에 간섭을 주지 않고 기회주의적 방식으로 라이선스 대역을 사용할 수 있다. SUs가 스펙트럼 센싱을 통해 PU의 존재 여부를 확인할 수 있다. 그리고 센싱 시간은 스펙트럼 센싱의 중요한 파라미터이다. 센싱 시간은 센싱 성능과 스루풋 간의 균형을 얻을 수 있다. 본 논문에서는 다른 관점에서 이 균형을 탐구하기를 통해 스펙트럼 센싱을 위한 새로운 기법을 제안한다: a) PU의 검출 (SSPD)과 b)스루풋(SSST)을 극대화을 위한 스펙트럼 센싱이다. 제안한 기법에서 현재 프레임의 첫 번째 센싱 결과에 따라 동적인 두 번째 스펙트럼 센싱을 수행한다. CRSNs에서 에너지 제약을 때문에 네트워크 에너지 효율도 센싱 시간의 최적화를 통해 최대화된다. 시뮬레이션 결과를 통하여 제안된 SSPD과 SSST가 각각의 에너지 효율과 스루풋의 성능을 향상시킬 수 있음을 검증하였다.

An energy-based primary signal detection framework is proposed to study the quality of detection (QoD) performance for cognitive radio sensor networks (CRSNs) under single cognitive sensor (CS) detection and multiple CS detection. We use hypothesis testing to first derive the exact solution and then the approximate solution (with less computational complexity) of the QoD metrics. Based on the approximate solution, we develop an adaptive QoD control scheme to maintain the QoD requirements. Numerical simulations are provided to validate the analysis. The proposed framework and analytic results are expected to be applied to different aspects (e.g., protocol design, network deployment) of CRSNs.

인지 무선 기술은 보조 사용자가 (SUs) 주 사용자 (PUs)에 간섭을 주지 않고 기회주의적 방식으로 라이선스 스펙트럼을 사용할 수 있는 기술이다. 인지 라디오의 핵심 기술은 스펙트럼 센싱이다. 그러나 인지 무선 센서 네트워크에서 에너지 효율적인 스펙트럼 센싱 기법에 대한 연구는 많지 않다. 본 논문에서는 클러스터 기반의 인지 무선 센서 네트워크를 위한 에너지 효율적인 협력 스펙트럼 센싱 노드 선택 기법을 제안한다. 제안하는 기법에서, 허위 경보 확률 및 에너지 소비를 최적화하기 위하여 클러스터내 스펙트럼 센싱 노드들의 수를 최소화하게 된다. 시뮬레이션 결과를 통하여 본 논문에서 제안한 최적의 스펙트럼 센싱 노드 수를 적용하므로 스펙트럼 센싱 효율성이 향상되었고, 또한 네트워크의 에너지 효율성도 보장된 것을 검증하였다.

In this paper, a clustering solution for periodic data gathering over WSNs using cognitive radio technology is proposed. The cluster heads (CHs) are selected according to the channel availability, residual energy, communication cost and node distribution parameters. Fuzzy logic and weight based techniques combines the four parameters for the CH selection. The cluster formation is based on the relative channel availability between the cluster member (CM) and CH to ensure stable cluster connectivity from link failure. To evaluate the proposed clustering algorithm, the performance of sensor networks is compared with CogLEACH, LEACH and CHEF routing protocols. The simulation results show that the proposed clustering algorithm effectively has a significant improvement with respect to the network stability without reducing the network instability and network lifetime. In addition, the proposed clustering solution also has a low and almost consistent CH energy consumption during the stability period indicating an efficient cluster formation.

With the increasing worldwide trend in population migration into urban centers, we are beginning to see the emergence of the kinds of mega-cities which were once the stuff of science fiction. It is clear to most urban planners and developers that accommodating the needs of the tens of millions of inhabitants of those megalopolises in an orderly and uninterrupted manner will require the seamless integration of and real-time monitoring and response services for public utilities and transportation systems. Part speculative look into the future of the world’s urban centers, part technical blueprint, this visionary book helps lay the groundwork for the communication networks and services on which tomorrow’s “smart cities” will run. Written by a uniquely well-qualified author team, this book provides detailed insights into the technical requirements for the wireless sensor and actuator networks required to make smart cities a reality.

In this chapter, we combine two important techniques, which are cognitive radio ( CR ) and energy harvesting ( EH ), to realize a smart city. We first study solutions to overcome the challenges for a smart city and investigate cognitive radio sensor networks ( CRSNs ), their Internet connection, and application of harvesting methods in CRSN domain. Furthermore, we reveal the motivations for spectrum‐aware and self‐sustaining communications in a smart city. In the light of these discussions, we study a networking architecture of Internet of Things ( IoT ) consisting of energy‐harvesting cognitive radios ( EH‐CRs ), i.e., Energy‐Harvesting Cognitive Internet of Things. By using this architecture, we also propose a general implementation framework for EH‐CRs for the smart city concept.

In this paper, we introduce a novel scheduling mechanism for cognitive radio sensor networks. Our approach aims at defining an optimized time slot and channel assignment that exploits spatial reuse with respect to all types of nodes communications interferences. We identify first all types of nodes conflicts, then, we solve the spatial dependencies by the mean of the Maximum Matching graph theory problem. We use after a First-price sealed-bid auction (FPSB) to perform channels and time slots assignment to completely independent cognitive sensor nodes. Our approach outperforms existing solution for scheduling in CRSN, by reducing the total length of communications and maximizing the stability factor.

In cognitive radio sensor networks (CRSNs), unused licensed frequency bands are opportunistically utilized in addition to unlicensed frequency bands. In this paper, a novel medium access control (MAC) protocol for CRSNs is proposed, which chooses a robust channel among multiple unused licensed channels via a simple selection algorithm. Our performance study shows that the proposed MAC outperforms the conventional MAC for CRSNs.

In recent year, the most emerging and growing field of research and development is “Internet of Things” (IoT). This is due to advancement in wireless sensor network (WSN) which operate in the unlicensed industrial, scientific and medical (ISM) spectrum band. However, many wireless technologies operate in the same unlicensed spectrum, make it overcrowded and hence resulted in spectrum scarcity among those bands, the performance of WSN will degrade as their popularity increases. According to FCC report, most of the licensed spectrum is underutilized, sharing of underutilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is one of the capable technology that allows sensor nodes (as a Secondary Users (SUs)) to detect and use the underutilized licensed spectrum temporarily when Primary Users (PUs) not using it. With recent advances in Cognitive Radio (CR) technology, possible to apply the Dynamic Spectrum Access (DSA) model in WSNs to get access to underutilized licensed spectrum, possibly with better propagation characteristics, but as the existing protocols and algorithms developed for CRNs and WSNs are not directly applicable to CR-based WSNs and required new protocols. In this paper, we present a survey on the novel design of CR-based MAC, identify the main advantages and challenges of using CR technology, and compare the different method of improving energy efficiency. We believe that CR-WSN is the next-generation WSN. In this paper, we also discussed the open issues to motivate new research interest in this field.

Wireless sensors equipped with cognitive radio, i.e., cognitive radio sensor networks (CRSN), can access the spectrum in an opportunistic manner and coexist with licensed users to mitigate the crowded spectrum problem and provide ubiquitous remote event monitoring and tracking for cyber-physical systems. In this paper, a novel transport layer protocol for CRSN, spectrum-aware energy-adaptive reliable transport protocol is presented to enable energy-adaptive collaborative event sensing in spectrum-scarce cyber-physical systems. To the best of our knowledge, this is the first attempt to specifically devise a reliable event transport scheme for CRSN., Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish National Academy of Sciences Distinguished Young Scientist Award Program