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Cognitive Radio Sensor Network (CRSN) provides effective utilization of spectrum, using dynamic spectrum allocation that incorporates multi-hop clustering and routing algorithms for energy-efficient data delivery. The existing clustering and routing methods modeled for Cognitive Radio Sensor Network, typically operates with the assumption of flawless spectrum sensing, disregarding incorrect alarms and miss detection techniques. These could lead to transmission failures, Primary User collisions, or restricted spectrum usage. An Adaptive Spectrum Sensing Multi-Hop Grey wolf optimization Algorithm for Primary User Emulation Attack (ASSMGA-EA) has been modeled to mitigate the effect of poor spectrum sensing which affects the sensor network performance when a Primary User Emulation Attack is present and dynamically adapts sensing approaches in response to changing network conditions and attack situations. Nodes are elected as Cluster Heads based on residual energy, high spectrum sensing capability, and accessible channel detection level functions. Accuracy-based, idle channel detection promotes effective intra-cluster and inter-cluster data transmission. Energy consumption due to control overhead is minimized by regulating cluster formation and Cluster Head selection. The malicious users also possess a spectrum sensing capability which makes spectrum access even more difficult. By distinguishing between the Primary User and Primary User Emulation Attack signals, the proposed method improves detecting accuracy while lowering the likelihood of sensing errors, and enhances throughput, and energy efficiency more than cooperative and hybrid sensing techniques. The simulations demonstrate that the proposed algorithm has clear advantages over the current clustering and routing algorithms for CRSN in terms of extending the network’s lifespan, efficient data gathering, higher residual energy, and improving the capacity of the network.

Clustering protocols and simultaneous wireless information and power transfer (SWIPT) technology can solve the issue of imbalanced energy consumption among nodes in energy harvesting-cognitive radio sensor networks (EH-CRSNs). However, dynamic energy changes caused by EH/SWIPT and dynamic spectrum availability prevent existing clustering routing protocols from fully leveraging the advantages of EH and SWIPT. Therefore, a multi-hop uneven clustering routing protocol is proposed for EH-CRSNs utilizing SWIPT technology in this paper. Specifically, an EH-based energy state function is proposed to accurately track the dynamic energy variations in nodes. Utilizing this function, dynamic spectrum availability, neighbor count, and other information are integrated to design the criteria for selecting high-quality cluster heads (CHs) and relays, thereby facilitating effective data transfer to the sink. Intra-cluster and inter-cluster SWIPT mechanisms are incorporated to allow for the immediate energy replenishment for CHs or relays with insufficient energy while transmitting data, thereby preventing data transmission failures due to energy depletion. An energy status control mechanism is introduced to avoid the energy waste caused by excessive activation of the SWIPT mechanism. Simulation results indicate that the proposed protocol markedly improves the balance of energy consumption among nodes and enhances network surveillance capabilities when compared to existing clustering routing protocols. [ABSTRACT FROM AUTHOR]

Clustering protocols and simultaneous wireless information and power transfer (SWIPT) technology can solve the issue of imbalanced energy consumption among nodes in energy harvesting-cognitive radio sensor networks (EH-CRSNs). However, dynamic energy changes caused by EH/SWIPT and dynamic spectrum availability prevent existing clustering routing protocols from fully leveraging the advantages of EH and SWIPT. Therefore, a multi-hop uneven clustering routing protocol is proposed for EH-CRSNs utilizing SWIPT technology in this paper. Specifically, an EH-based energy state function is proposed to accurately track the dynamic energy variations in nodes. Utilizing this function, dynamic spectrum availability, neighbor count, and other information are integrated to design the criteria for selecting high-quality cluster heads (CHs) and relays, thereby facilitating effective data transfer to the sink. Intra-cluster and inter-cluster SWIPT mechanisms are incorporated to allow for the immediate energy replenishment for CHs or relays with insufficient energy while transmitting data, thereby preventing data transmission failures due to energy depletion. An energy status control mechanism is introduced to avoid the energy waste caused by excessive activation of the SWIPT mechanism. Simulation results indicate that the proposed protocol markedly improves the balance of energy consumption among nodes and enhances network surveillance capabilities when compared to existing clustering routing protocols.

The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques.

In the actual wireless environment, the influence of shadow and fading will lead to different characteristics of the signals received by the sensor nodes. Therefore, some cooperative nodes in deep fading will have serious missed detection, which will affect the final result of fusion operation. To solve the above problems, this paper proposed a soft decision cooperative spectrum sensing method for cognitive wireless sensor networks(CRSN) based on entropy weight method. The method organized the sensor nodes into logical groups to improve energy efficiency and sensing performance. After receiving the soft sensing information from all member nodes, the cluster head used the equal gain soft fusion to fuse among clusters, and then forwarded the local decision to the fusion center. In the final decisionmaking process, this paper used the entropy weight method to assign the optimal weight to the corresponding clustering local decision. Simulation results show that this method is superior to the typical cooperative spectrum sensing clustering scheme in terms of detection probability and total error probability. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive-routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques. [ABSTRACT FROM AUTHOR]

In radio frequency energy harvesting-cognitive radio sensor networks (RF EH-CRSNs), clustering routing protocols can help deliver monitored data towards the sink and they are vital for network performance. However, due to the impact of dynamic channel availability, limited node transmission range, and position-dependent energy arrival, existing clustering routing protocols for nonEH-CRSNs and EH nonCRSNs cannot be applied to RF EH-CRSNs. In order to solve above challenges, an RF EH-based multihop clustering routing protocol (RFMCRP) based on nonlinear EH model is proposed in this paper. Firstly, by leveraging curve fitting tool and statistical analysis, the most reasonable nonlinear EH model is identified and it is utilized by RFMCRP to measure the harvested energy accurately. Secondly, the optimal number of clusters is theoretically derived and its value is used as benchmark to evaluate the validity of our proposal. Thirdly, energy control mechanism is introduced to manage node state, and it can help improve the stability of cluster construction. In addition, energy level function-based selection criteria are defined to select high-quality cluster heads and relays, which can help improve the energy sustainability and connectivity of the whole network. Simulation results show that RFMCRP gains obvious advantages over existing protocols in extending network lifetime and improving network monitoring capability. [ABSTRACT FROM AUTHOR]

By fully utilizing the spatial gain and exploiting the multiuser diversity, cooperative spectrum sensing can enhance the sensing accuracy. In the actual wireless environment, the effect of shadowing and fading will result in the different features of signals received by the sensing nodes with different distances from primary user. As a result, some cooperative nodes in deep fading will suffer from serious missed detection, which will affect the final results during the fusing operation. To solve the above problems, a soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks is presented. Initially, the sensor nodes will be organized into logical groups to obtain energy efficiency and improvement of sensing performance. After receiving the soft sensing information from all member nodes, the cluster heads employs the equal gain soft combination for inter-cluster fusion and then forwards the local decision to the fusion center. During the final decision, the entropy weight method is applied to assign optimal weight value to corresponding cluster local decisions. The simulation results show that the proposed method can outperform some typical clustering scheme for cooperative spectrum sensing in terms of the detection probability and the total error probability.

Incorporating cognitive radio in traditional wireless sensor networks creates a new Internet of Things paradigm, which is termed as cognitive radio sensor networks (CRSNs). This paper proposes a novel stability-aware cluster-based routing (SACR) protocol for CRSNs. The major innovation of SACR lies in the seamless integration of opportunistic forwarding and a stable clustered architecture. In the aspect of cluster formation, we propose to take into consideration of spectrum dynamics and energy consumption in the clustering process. The resulting clustered architecture is stable and thus avoids large communication overhead due to high clustering frequency. For data routing, SACR adopts an opportunistic forwarding scheme which selects a unique cluster head by accounting for its cluster size, number of available channels, and hop distance to the gateway. Last but not least, SACR is a distributed routing protocol that does not require a dedicated common control channel. Simulation results show that SACR outperforms existing routing protocols for CRSNs in terms of packet delivery ratio, delay, energy consumption and signaling overhead. [ABSTRACT FROM AUTHOR]

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 Primary User Emulation Attack (PUEA) plays a crucial role in the current research. In this paper, we propose an Opportunistic Transmission using the Hybrid Sensing method for the CRSN in the presence of smart PUEA. In Hybrid sensing method, both proactive sensing and reactive sensing techniques are effectively combined to reduce the error probability in detecting the Primary User signal. Furthermore, we have overcome the effect of PUEA by using the Opportunistic Transmission to improve the throughput and energy efficiency of the SU network. Both simulation and Software Defined Radio (SDR) based hardware test bed results show that the throughput is increased by 18%, the energy efficiency is increased by 11% and the error probability is reduced by 20% compared to the existing method. [ABSTRACT FROM AUTHOR]

Energy harvesting cognitive radio sensor networks (EH-CRSNs) are an emerging technology for low-cost, green monitoring of a wide range of environments. How to analyze the transmission capacity is a fundamental and challenging problem in EH-CRSNs due to the dynamics of spectrum and energy arrivals. In this paper, transmission capacity analysis of underlay relay-assisted EH-CRSNs is considered, where some nodes serve as decode-and-forward relays to assist the communication between one secondary source and one destination node. To characterize the end-to-end performance of underlay relay-assisted EH-CRSNs, we first assume that EH devices use harvest-store-use (HSU) mode and formulate the battery states with M/M/1/c model for the arbitrary integer value of transmission energy level threshold. Then, the closed-form expressions of transmission capacity are derived for the random, the nearest, and the farthest relay selection, respectively, based on stochastic geometry. In addition, the transmission capacities with the variable source-destination distance are also analyzed for the three kinds of relay selections. Finally, numerical simulations show that the transmission capacity of underlay relay-assisted EH-CRSNs can be influenced by large amounts of factors, including secondary access probability, source-destination distance, signal-to-interference ratio, and battery transmission energy level threshold, and the different relay selection schemes. The results also confirm that the random relay selection, compared with the nearest and farthest relay selection, is a more feasible and reasonable scheme for short range of underlay EH-CRSNs due to its low complexity implementation.

Clustering organizes nodes into groups in order to enhance the connectivity and stability of cognitive radio sensor networks. Mainly depending on the channel availability, many existing spectrum-aware clustering algorithms may not achieve the most satisfactory clustering. Taking into account the various influence factors to establish the optimal clustering is a challenge to enhance the network performance. This paper proposes a novel spectrum-aware clustering algorithm based on weighted clustering metric to obtain the optimal clustering by solving an optimization model. The new weighted clustering metric, simultaneously evaluating temporal-spatial correlation, confidence level and residual energy, is used to elect clusterheads and ally member nodes. After clustering, the clusterheads sensing spectrum instead of all member nodes greatly reduces the energy consumption of spectrum sensing and increases the opportunity of data transmission. The performance comparison between the traditional spectrum-aware clustering algorithms and our proposed algorithm has been highlighted with the experiments.

The large-scale deployment of wireless sensor networks is indispensable to the success of Internet of Things. Considering dynamic spectrum access and the limited spectrum resources in cognitive radio sensor networks, sub-Nyquist spectrum sensing based on the modulated wideband converter is introduced. Since the transmission signals are usually modulated by different carrier frequencies, the interested spectrum can be modeled as the multiband signal. Modulated wideband converter (MWC) is an attractive alternative among several sub-Nyquist sampling systems because it has been implemented in practice and the frequency support reconstruction algorithm is the most important part in MWC. However, most existing reconstruction methods require the sparse information, which is difficult to acquire in practical scenarios. In this paper, we propose a blind multiband signal reconstruction method, referred to as the statistics multiple measurement vectors (MMV) iterative algorithm to bypasses the above problem. By exploiting the jointly sparse property of MMV model, the supports can be obtained by statistical analysis for the reconstruction results. Simulation results show that, without the sparse prior, the statistics MMV iterative algorithm can accurately determine the support of the multiband signal in a wide range of signal-to-noise ratio by using various numbers of sampling channels.

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. • An optimal channel selection technique based on an intelligent channel ranking. • Energy-efficient spectrum sensing technique. • A cluster head selection and rotation technique for achieving energy balance. • A gateway and relay selection technique for performing inter-cluster data forwarding. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Energy-efficient and reliable detection of available spectrum are fundamental objectives for cooperative spectrum sensing (CSS) in cognitive radio sensor networks (CRSNs). In this paper, a novel node selection scheme for energy-efficient CSS based on Dempster–Shafer (D–S) theory is proposed. Firstly, taking into account the historical data of nodes with historical reliability and residual energy of nodes, we propose a filtering strategy to filter out ineligible nodes, in order to reduce the computation loads in later steps and the amount of sensing results to be transmitted. Secondly, taking into account energy consumption balance of the network with the distance from the remaining nodes to fusion center, we propose a representative node selection algorithm for CSS, in order to reduce energy consumption. Thirdly, we consider that some representative nodes (R-Nodes) may not work as expected. Hence, facing this problem of malicious nodes in CRSNs, we propose an evaluation method based on D–S theory which considers simultaneously the current reliability and the mutually supportive degree among different R-Nodes to derive final decision. Simulation results show that the proposed scheme can not only reduce energy consumption, but can guarantee spectrum sensing accuracy, even in the presence of malicious nodes. [ABSTRACT FROM AUTHOR]

Satisfying the quality of service (QoS) is a crucial issue in cognitive radio sensor networks (CRSNs) due to the highly variable nature of cognitive radio channels. Connection admission control (CAC) is a beneficial approach to manage the traffic to provide desired QoS. A CAC is proposed in this paper to optimize the packet loss ratio, jitter of packets and end to end delay in CRSNs. The proposed CAC decides based on the priority of data flows, network state and number of available channels. An estimation formula is proposed through a graph coloring approach to evaluate the required number of channels of network states. The proposed CAC is modeled by a semi Markov decision process (SMDP) and a sub-optimal policy is obtained by a value iteration method to achieve the maximum reward in network. Simulation results demonstrate that the proposed mechanism outperforms the recent proposed admission control mechanism in CRSNs.

Cognitive radio sensor networks (CRSNs) is the state-of-the-art communication paradigm for power constrained short range data communication. It is one of the potential technologies adopted for Internet of Things (IoT) and other futuristic machine-to-machine-based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper, we propose a heuristic exhaustive search-based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn-Tucker (KKT) condition-based Algorithm-2 to determine the OPS in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on distributed time slotted-cognitive medium access control (DTS-CMAC) and centralized common control channel-based cognitive medium access control (CC-CMAC) and their performances are compared. The simulation results reveal that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search-based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 s, while for KKT-based Algorithm-2, it is of the order of 5-10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme.

The determination of optimal packet size (OPS) for a cognitive radio-assisted sensor networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible OPS of data transmission under the simple point-to-point CRSN topology. This is contributed primarily to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a multiple-input multiple output-based CRSNs (MIMO-CRSNs) architecture for futuristic technologies, such as Internet of Things and machine-to-machine communications. A joint optimization problem is formulated, considering network constraints, such as the overall end-to-end latency, interference duration caused to the non-cognitive users, average BER, and transmit power. We propose our Algorithm 1 based on the generic exhaustive search technique to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm 2 based on solving classical Karush-Kuhn-Tucker conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are time-slotted distributed cognitive medium access control denoted as MIMO-DTS-CMAC and CSMA/CA-assisted centralized common control channel-based cognitive medium access control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO-CRSN outperforms the conventional point-to-point CRSN in terms of overall energy consumption. Moreover, the proposed Algorithm 1 and Algorithm 2 show a perfect match, and the implementation complexity of Algorithm 2 is less than Algorithm 1. Algorithm 1 takes almost 680 ms to execute and provides OPS value for a given number of users, whereas Algorithm 2 takes 4-5 ms on average to find the OPS for the proposed MIMO-CRSN framework.

In this paper, we define a cluster based scheduling algorithm for Cognitive Radio Sensor Networks. To avoid inter-clusters collision, we assign fixed channels only to nodes having one-hop neighbors out of their clusters. We denote these nodes as specific nodes. Previous studies assign distinct channels to whole neighbor clusters to avoid inter-clusters collision. Our objective is to optimize the spatial reuse and to increase the network throughput while saving sensors energy. We start by assigning channels only to the specific nodes. Once the problem of inter-clusters collision is solved, each cluster head (CH) schedules the transmissions in its cluster independently. For the cluster members that are specific nodes, the CH assigns only time slots because the channel assignment is already done. For other cluster members (CMs) (not specific nodes), the CH assigns the pair (channel, slot). Two solutions are proposed in this paper to schedule the CMs: the Frame Intra Cluster Multichannel Scheduling algorithm denoted Frame-ICMS and the Slot Intra Cluster Multichannel Scheduling algorithm denoted Slot-ICMS. We evaluate the performance of these algorithms in case of accurate PUs activity detection and in case of bad PUs activity estimation. We prove that our proposals outperform an existing one especially in terms of energy saving. [ABSTRACT FROM AUTHOR]

Technological advances have led to the emergence of wireless sensor nodes in wireless networks. Sensor nodes are usually battery powered and hence have strict energy constraints. As a result, energy conservation is very important in the wireless sensor network protocol design and the limited power resources are the biggest challenge in wireless network channels. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper we present an adaptive packet size strategy for energy efficient wireless sensor networks. The main goal is to reduce power consumption and extend the whole network life. In order to achieve this goal, the paper introduces the concept of a bounded MAB to find the optimal packet size to transfer by formulating different packet sizes for different arms under the channel condition. At the same time, in achieve fast convergence, we consider the bandwidth evaluation according to ACK. The experiment shows that the packet size is adaptive when the channel quality changes and our algorithm can obtain the optimal packet size. We observe that the MAB packet size adaptation scheme achieves the best energy efficiency across the whole simulation duration in comparison with the fixed frame size scheme, the random packet size and the extended Kalman filter (EKF).

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The cognitive radio technology enables secondary users (SUs) to occupy licensed bands when primary users (PUs) are not occupy them. Spectrum sensing is a key technology for SUs to detect PUs, and the sensing time is a critical parameter for spectrum sensing performance. Optimum sensing time tradeoffs between the spectrum sensing performance and the secondary throughput. This paper proposes a novel spectrum sensing scheme that performs spectrum sensing for either one period or two periods based on the previous sensing result. Due to the energy constraint in cognitive radio sensor networks, the energy efficiency is maximized by optimizing spectrum sensing time. In order to seek the optimal sensing time, the objective function is proven to be a concave function and the Golden Section Search method is employed. Our simulation study verifies that the proposed scheme improves the network energy efficiency, especially when PUs are more active. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simulation model for cognitive radio sensor networks (CRSNs) which is an attempt to combine the useful properties of wireless sensor networks and cognitive radio networks. The existing simulation models for cognitive radios cannot be extended for this purpose as they do not consider the strict energy constraint in wireless sensor networks. Our proposed model considers the limited energy available for wireless sensor nodes that constrain the spectrum sensing process—an unavoidable operation in cognitive radios. Our model has been thoroughly tested by performing experiments in different scenarios of CRSNs. The results generated by the model have been found accurate which can be considered for realization of CRSNs. [ABSTRACT FROM AUTHOR]

The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The cognitive radio (CR) technology allows secondary users (SUs) to occupy the licensed bands opportunistically without causing interferences to primary users (PUs). SUs perform spectrum sensing to detect whether PUs are busy or idle. Therefore, spectrum sensing directly affects the performance of the PU protection and the secondary throughput. The sensing time is a critical parameter for spectrum sensing performance, and the optimum sensing time is a tradeoff between the spectrum sensing performance and the secondary throughput. In this paper, a novel spectrum sensing scheme is proposed to maximize both sensing accuracy and network energy efficiency. In order to provide a better protection for the PU, another spectrum sensing is adaptively performed according to the first sensing result. In other words, SU will perform spectrum sensing again to confirm that the PU is indeed idle when the first sensing result indicates the PU is idle. Due to the energy constraint in CR sensor networks, this adaptive sensing interval can also be adjusted according to the varying activity of the PU to maximize the network energy efficiency. Finally, our simulation study validates that the proposed scheme improves both the spectrum sensing performance and the energy efficiency compared with other existing methods. [ABSTRACT FROM PUBLISHER]

Traffic management is a highly beneficial mechanism for satisfying quality-of-service requirements and overcoming the resource scarcity problems in networks. This paper introduces an optimal connection admission control mechanism to decrease the packet loss ratio and end-to-end delay in cognitive radio sensor networks (CRSNs). This mechanism admits data flows based on the value of information sent by the sensor nodes, the network state, and the estimated required resources of the data flows. The number of required channels of each data flow is estimated using a proposed formula that is inspired by a graph coloring approach. The proposed admission control mechanism is formulated as a semi-Markov decision process and a linear programming problem is derived to obtain the optimal admission control policy for obtaining the maximum reward. Simulation results demonstrate that the proposed mechanism outperforms a recently proposed admission control mechanism in CRSNs. [ABSTRACT FROM AUTHOR]

The spectrum allocation for cognitive radio sensor networks (CRSNs) has received considerable research attention under the assumption that the spectrum environment is static. However, in practice, the spectrum environment varies over time due to primary user/secondary user (PU/SU) activity and mobility, resulting in time-varied spectrum resources. This paper studies resource allocation for chunk-based multi-carrier CRSNs with time-varied spectrum resources. We present a novel opportunistic capacity model through a continuous time semi-Markov chain (CTSMC) to describe the time-varied spectrum resources of chunks and, based on this, a joint power and chunk allocation model by considering the opportunistically available capacity of chunks is proposed. To reduce the computational complexity, we split this model into two sub-problems and solve them via the Lagrangian dual method. Simulation results illustrate that the proposed opportunistic capacity-based resource allocation algorithm can achieve better performance compared with traditional algorithms when the spectrum environment is time-varied. [ABSTRACT FROM AUTHOR]

A spectrum handoff model and optimal channel decision method based on Extenics have been proposed in order to resolve the optimal channel decision problem of spectrum handoff in Cognitive Radio Sensor Networks. The method of matter-element Extenics is used to analyses the spectrum handoff process. The channel state through the spectrum sensing and status information of the primary users is analyzed and transformed by matter-element Extenics. Then the parameters to determine the best channel are calculated on the basis of Extenics correlation function. Finally the optimal channel is selected. The simulation results show that the proposed method can improve spectrum utilization efficiency of Cognitive Radio Sensor Networks and can reduce the interrupt probability of second users. [ABSTRACT FROM AUTHOR]

Reliability is one of the most important requirements in Smart Grid communications. Reliable detection of an emergency event enables timely response. Within the automated nature of Smart Grid, such detection and response are carried out by sensor and actuator nodes. Therefore, it is important to study the capabilities of wireless sensor actor networks. In this paper, we first present an analysis of reliability in sensor actor networks, and lay out the factors that affect reliability. We then propose a scheme, where actor nodes cooperate to reach a global estimate under interruptions due to licensed user interference, i.e., consensus. We show that consensus improves reliability compared to local estimation of event features. We further show that convergence rate depends on connectivity of actors. Our analyses are generic and can be applied to inhomogeneous licensed user activity and interference on channels. A simulation study is presented to support our analyses and demonstrate the performance of proposed scheme in achieving consensus and mitigating disagreement among actor nodes. [ABSTRACT FROM AUTHOR]

The Cognitive Radio Sensor Network (CRSN) is considered as a viable solution to enhance various aspects of the electric power grid and to realize a smart grid. However, several challenges for CRSNs are generated due to the harsh wireless environment in a smart grid. As a result, throughput and reliability become critical issues. On the other hand, the spectrum aggregation technique is expected to play an important role in CRSNs in a smart grid. By using spectrum aggregation, the throughput of CRSNs can be improved efficiently, so as to address the unique challenges of CRSNs in a smart grid. In this regard, we proposed Spectrum Aggregation Cognitive Receiver-Based MAC (SACRB-MAC), which employs the spectrum aggregation technique to improve the throughput performance of CRSNs in a smart grid. Moreover, SACRB-MAC is a receiver-based MAC protocol, which can provide a good reliability performance. Analytical and simulation results demonstrate that SACRB-MAC is a promising solution for CRSNs in a smart grid. [ABSTRACT FROM AUTHOR]

Performance evaluation of transport layer protocols in cognitive radio sensor networks (CRSNs) is useful to provide quality-of-service for real-time reliable applications. This paper develops an analytical framework to model the steady-state sending rate of collecting cognitive radio (CR) sensors in rate-based generic additive-increase multiplicative-decrease (AIMD) and additive-increase additive-decrease (AIAD) congestion control schemes. Evolution process of sending rate is modeled by a discrete time Markov chain (DTMC) in the terms of queue length. We model the queue length distribution of a CR node by a semi-Markov chain (SMC) with assuming general probability density functions (PDFs) of input rate and attainable sending rate of the node. These PDFs are derived based on the parameters of MAC and physical layers and CRSN configuration. The proposed models are verified through various simulations. [ABSTRACT FROM AUTHOR]