Cognitive Radio Wireless Sensor Networks Research Articles

Channel access mechanism for maximizing throughput with fairness in wireless sensor networks

The spectrum scarcity problem of wireless sensor networks (WSNs) is improved through amalgamation of cognitive radio networks (CRNs) into WSNs. However, spectrum allocation to secondary users (SUs) is challenging in cognitive radio wireless sensor networks (CR-WSNs) as channel is already crowded and at same time should not induce interference to primary users (PUs). In designing efficient spectrum access model for CR-WSNs recent work have adopted machine-learning game theory (GT) and statistical model. However, the major limitation of existing spectrum access model they fail to assure access fairness with maximal throughput with minimal collision. This work presents a maximizing channel access fairness model to handle the research challenges. To boost CR-WSN performance, the throughput maximization using channel access fairness (TMCAF) employs shared and non-shared channel access designs. Experiment outcome shows throughput is improved and collision in network is reduced in comparison with state-of-art channel access models.

DFPC: Dynamic Fuzzy-based Primary User Aware clustering for Cognitive Radio Wireless Sensor Networks

Clustering-based routing solutions have proven to be efficient for wireless networks such as Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), etc. Cognitive Radio WSN (CR-WSN) is a class of WSNs that consists of several resource-constrained Secondary Users (SUs), sink, and Primary Users (PUs). Compared to WSNs, there are several challenges in designing the clustering technique for CR-WSNs. As a result, one cannot directly apply the WSN clustering protocols to CR-WSNs. Developing a clustering protocol for CR-WSNs must address challenges such as ensuring PU protection, and SU connectivity, selecting the optimal Cluster Head (CH), and discovering the optimal cluster size. Present CR-WSN clustering solutions failed to resolve the trade-off among all essential clustering objectives. To address these challenges, this study presents a novel approach called Dynamic Fuzzy-based PU aware Clustering (DFPC) for CR-WSNs. DFPC uses a dynamic approach to discover the number of clusters, a fuzzy-based algorithm for optimal CH selection, and reliable multi-hop data transmission to ensure PU protection. To enhance the performance of CR-WSNs, an effective strategy was designed to define the optimal number of clusters using the network radius and live node. Fuzzy logic rules were formulated to assess the four CR-specific parameters for optimal CH selection. Finally, reliable intra- and intercluster data transmission routes are discovered to protect the PUs from malicious activities. The simulation results showed that the DFPC protocol achieved an improved average throughput of 48.04 and 46.49, a PDR of 93.36 and 84.37, and a reduced delay of 0.0271 and 0.0276 in static and dynamic topologies, respectively, which were better than those of ABCC, ATEEN, and LEACH protocols.

A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods:: A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusions: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.

Energy-Efficient Resource Allocation Algorithm for CR-WSN-Based Smart Irrigation System under Realistic Scenarios

Cognitive radio wireless sensor networks (CR-WSNs) are a type of WSNs that use cognitive radio technology to enhance the spectrum utilization and energy efficiency. This paper proposes an energy-efficient resource allocation algorithm (EERAA) to prolong the lifetime of a WSN-based smart irrigation system under realistic scenarios. In the proposed algorithm, power allocation and subcarrier assignment are performed consecutively. Considering the impact of the intercarrier interference (ICI) caused by timing offset, the problem of maximizing network-averaged capacity is formulated considering power and interference constraints in realistic scenarios. The obtained results reveal that the proposed algorithm attempts to maximize the averaged capacity of the CR-WSN subject to the total power constraint and tolerable interference. Numerically, the proposed algorithm can reduce the network energy consumption by up to 30%, compared with conventional approaches, while maintaining a high level of system performance in terms of secondary users’ (SUs) averaged capacity.

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Capacity analysis of cognitive radio wireless sensor network under optimal power allocation in imperfect channel

Capacity analysis of cognitive radio wireless sensor network under optimal power allocation in imperfect channel

Game theory based node clustering for cognitive radio wireless sensor networks

Cognitive radio wireless sensor networks (CRWSN) is a promising technology for developing bandwidth constrained applications. Future Internet of Things (IoT) applications may extensively use CRWSN. CRWSN consists of cognitive radio enabled sensor nodes which are energy constrained, in general. Hierarchical cluster based approach for overall network management is suitable for network stability and scalability. Thus node clustering is an important problem in CRWSN setup. Objective of this work is to develop a suitable node clustering algorithm for CRWSN, in which nodes are expected to be mobile. A node clustering protocol for CRWSN has been proposed in this paper. The proposed clustering protocol is based on evolutionary game theory (EGT). Initial clusters are formed through a simple partitioning approach. Eventually, initial clusters are merged to form the final clusters. After forming the clusters by the resourceful sink node, the cluster head nodes for respective clusters are determined. The sink node runs the EGT based algorithm to identify the most capable node as the cluster head. The strength of this approach is that while identifying the cluster head node, various parameters such as residual energy level, geographic location, mobility, and the probability of primary user (PU) arrival are considered. The clusters and therefore, the cluster head nodes are distributed uniformly in the geographical area. The proposed clustering protocol has been compared with LEACH, RARE and the spectrum aware clustering algorithm. The simulation results show that the proposed clustering protocol outperforms all these similar node clustering protocols. On average, the proposed protocol outperforms the benchmarks protocols by 25% in terms of number of high energy nodes selected as cluster head, by 37% in terms of uniform geographical distribution of cluster head nodes, by 23% in terms of total energy consumed during the simulation time, and by 27% in terms of network lifetime. The future scope of the work has been outlined.

In-Network Data Aggregation for Ad Hoc Clustered Cognitive Radio Wireless Sensor Network.

In cognitive radio wireless sensor networks (CRSN), the nodes act as secondary users. Therefore, they can access a channel whenever its primary user (PU) is absent. Thus, the nodes are assumed to be equipped with a spectrum sensing (SS) module to monitor the PU activity. In this manuscript, we focus on a clustered CRSN, where the cluster head (CH) performs SS, gathers the data, and sends it toward a central base station by adopting an ad hoc topology with in-network data aggregation (IDA) capability. In such networks, when the number of clusters increases, the consumed energy by the data transmission decreases, while the total consumed energy of SS increases, since more CHs need to perform SS before transmitting. The effect of IDA on CRSN performance is investigated in this manuscript. To select the best number of clusters, a study is derived aiming to extend the network lifespan, taking the SS requirements, the IDA effect, and the energy consumed by both SS and transmission into consideration. Furthermore, the collision rate between primary and secondary transmissions and the network latency are theoretically derived. Numerical results corroborate the efficiency of IDA to extend the network lifespan and minimize both the collision rate and the network latency.

An integrated antenna for cognitive radio wireless sensor networks and HD video transmission applications

This article introduces an integrated polarization divesity cognitive radio (CR) and Ultrawide Band (UWB) Multiple Inputs and Multiple Output (MIMO) antenna systems for CR wireless sensor networks (CR-WSN) and short-range HD video transmission applications. This proposed integrated antenna system's main purpose is to overcome the impediments of frequency re-configurable UWB/Narrow Band (NB) antenna system. The proposed integrated system consists of two antenna systems: the CR antenna and the UWB MIMO antenna system. The first part of the integrated antenna system is the CR antenna that comprises vertical and horizontal oriented UWB antennas to sense the spectrum and four NB antennas for communication purposes. The second part of the integrated antenna system is a 2 × 2 MIMO antenna that comprises a veritical and horizontal oriented UWB antenna with notch characteristics for MIMO applications. High isolation is achieved without using a decoupling structure by placing radiators in the horizontal and vertical planes. The proposed integrated 3D antenna system is designed with a compact size of 50 × 50 mm2. The antenna's essential parameters, such as the reflection coefficient, mutual coupling, peak gain, the radiation pattern, and radiation efficiency, are analyzed to evaluate the antenna's performance. The proposed integrated antenna system is suitable for off-device applications in industries or Laboratories. The real-time implementation of CR-WSN and short-range wireless HD video transmission applications are demonstrated using National Instruments (NI) Universal Software Radio Peripheral (USRP) and CC2538 kit.

Design of utility functions for game-based channel allocation in cognitive radio wireless sensor network

Cognitive radio enabled wireless sensor network is capable of reducing the spectrum scarcity problem of the wireless networks. Looking at the scarcity of available bandwidth, and the high growth in the number of communication devices in recent times, cognitive radio technology has proven to be a promising technology for the days to come. The application of Game Theory in cognitive radio networks has been visible in recent research works. However, only limited literature is available in which possibilities of applying the game-theory based approaches for the challenging task of channel assignment in cognitive radio wireless sensor are available in the literature. It is understood that the crux of the solution to the problem of scheming games for allocation of the channel is centered on the selection of the utility function in order to increase the efficiency of the channel allocation algorithm. Accordingly, the study regarding the influence of several utility functions on the performance of the corresponding channel allocation algorithm is important. Such a study enables designers to arrive at the optimal utility function to be used in game-theory based channel allocation algorithms, and the same is explored to the best extent, in this paper. The detailed procedure of allocating channels to all the contending nodes through game-based channel allocation has been discussed in this paper. Moreover, the performance of six different utility functions proposed which can be used for channel allocation using game theory has been evaluated for respective performances through MATLAB-based simulations.

Optimal Spectrum Sensing in MIMO-Based Cognitive Radio Wireless Sensor Network (CR-WSN) Using GLRT With Noise Uncertainty at Low SNR

Noise uncertainty can severely deteriorate a primary user (PU) detector’s sensing performance, so robustness against the noise uncertainty is of fundamental significance in cognitive radio networks. In this paper, we investigate robust detection schemes in the presence of noise uncertainty for multiple-input multiple-output (MIMO) based cognitive radio wireless sensor networks (CR-WSN) with multiple spectrum sensing scenarios. In practice, it is very seldom that accurate statistical characterization of noise is known a priori. In this context, we propose the generalized likelihood ratio test (GLRT) paradigm and estimator-correlator based optimal detectors to sense the unoccupied primary bands in the presence of noise uncertainty. First, we compute the estimator-correlator based detector (ECD) and generalized likelihood detector (GLD) for known noise uncertainty statistics. Next, a composite hypothesis based detector (CHD) is proposed using the GLRT framework for unknown noise uncertainty statistics. The proposed detection schemes provide robustness against uncertainty in the available noise power estimates using a finite number of observations (i.e., fast spectrum sensing) and particularly in critical areas of low Signal-to-Noise Ratio (SNR). Theoretical analysis is performed based on statistical theory. Closed-form expressions for detection and false-alarm probability, and analytic decision threshold are derived to demonstrate spectrum sensing performance using receiver operating characteristic (ROC) curves. Simulation results validate that proposed PU detection schemes are robust and achieve an improved sensing performance when there is noise uncertainty and corroborate our theoretical findings.

HCR-WSN: Hybrid MIMO cognitive radio system for wireless sensor network

Multi Input Multi Output (MIMO) based Cognitive Radio (CR) provisions an efficient utilization of the scarce electromagnetic spectrum. Traditional CR includes Interweave and Underlay techniques. We propose a Hybrid MIMO CR system for Wireless Sensor Network, HCR-WSN, that utilizes both underlay and interweave CR techniques and provides higher spectral efficiency. The wireless sensor network consisting of CR nodes achieves higher throughput using the proposed framework.HCR-WSN improves the spectral efficiency gain of an Uplink Hybrid Cognitive Radio MIMO system in wireless sensor network (WSN), by implementing Narrowband and Wideband (Multiple Carrier Direct Sequence Code Division Multiple Access, i.e., MC-DS-CDMA) technology adhering to the Interference Threshold limit. The WSN is consisting of nodes acting as primary users (PU) and CR secondary users (SU). The proposed Hybrid CR system ensures efficient spectral utilization under circumstances resulting in near zero throughput for SU. In the presence of PU, CR-SU transmits in either Narrowband or MC-DS-CDMA based on the proposed transmission selection algorithm. In the absence of PU, SU uses Interweave technique thereby allowing other competing SUs to transmit in Interference Alignment underlay mode in a multi-user scenario. The achievable throughput is enhanced using the proposed technique in a multi-user hybrid MIMO cognitive radio wireless sensor network.

Optimal Spectrum Allocation Based on Primary User Activity Model in Cognitive Radio Wireless Sensor Networks

In Cognitive Radio Wireless Sensor Networks, the licensed spectrum bands are highly dynamic, and their status varies overtime. With the expansion of these networks, regarding the energy constraints and the fact that reallocation of the frequency spectrum is energy-consuming, the problem of controlling the behavior of secondary users in the allocation of the spectrum is of great importance. Providing a method to reduce the number of channel reallocation, which in turn results in reducing energy consumption in such a dynamical network is essential. In this paper, considering the energy constraints, an optimal method for allocating frequency spectrum resources is presented using game theory and Nash equilibrium. By analyzing the activity model of primary users on the frequency spectrum and selecting the appropriate spectrum using the Nash equilibrium, the method reaches the network to a stationary equilibrium point. In these conditions, in addition to reducing interference between primary and secondary users, the number of channel reallocations by cognitive radio users is reduced and thus reduces overall energy consumption in the network and increases its life span.

Novel Spectrum Sensing Technique and Its Evaluation for Cognitive Radio Wireless Sensor Network

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes.

Joint Routing and Resource Allocation for Cluster Based Isolated Nodes in Cognitive Radio Wireless Sensor Networks

Clustering is an effective way to increase network lifetime but it leads to formation of isolated nodes in the wireless sensor network. These isolated sensor nodes forward data directly to sink and consume more energy which significantly reduces the network lifetime. In this article, we present how to maximize the network lifetime through joint routing and resource allocation with isolated nodes technique (JR-IN) between cluster head and isolated nodes in a cognitive based wireless sensor networks. In JR-IN technique the network area is divided into different layers and cluster size is formulated in each layer such that the size of the cluster remains unequal when it moves towards sink. Hence the cluster size is lager in the outermost layer compared to the cluster size in the inner most layer. To avoid inter cluster collision, we proposed different fixed channel to all the cluster heads in the network. For the intra cluster communication, the cluster member (sensor nodes) will lease the spectrum from the cluster head and forward data to their respective cluster head using TDMA technique. The periodical data gathering of cluster heads and forwarding the data to one hop cluster head may tend to lose energy faster and dies out quickly. We also propose in the JR-IN technique, the isolated nodes in the layer will take charge as a cluster head node and utilizes the resource allocated to the respective cluster head and forward the data to next hop cluster head. Simulation result shows that JR-IN outperforms the existing techniques, maximizes network lifetime and throughput and reduces the end to end delay.

MACBHA: Modified Adaptive Cluster-Based Heuristic Approach with Co-operative Spectrum Sensing in Wireless Sensor Networks

In this paper, a Modified Adaptive Cluster-Based Heuristic Approach (MACHBA) has been proposed for wireless sensor networks (WSNs) to perform the cooperative spectrum sensing (CSS) in the shopping mall, weather forecasting, military area and audio, video transmission applications. A Secure CSS based MACBHA has been proposed for secondary spectrum usage. Unlicensed Secondary Users (SUs) utilize parts of the spectrum, which are not used by the licensed primary users (PUs) in cognitive radio WSNs. The unused spectrum of the PUs is utilized by the secondary user cluster. The performance of the MACBHA in WSNs is evaluated using the network simulator tool NS-2.35 in Ubuntu 16.04.6 LTS (Xenial Xerus) operating system. The simulation result shows the performance improvement in network utility. Even though, the number of SUs increases, a minimum latency is achieved.

Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting.

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.

Hybrid spectrum access model using game theory approach for multi-channel heterogeneous mobile cognitive radio wireless sensor network

<p>An efficient spectrum access and medium access control (MAC) design is required to enable wireless sensor network (WSN) with cognitive Internet of Things (IoT) that enables presence of sensor network with existing wireless infrastructure. Designing spectral, energy efficient and fair spectrum access design for IoT in CR-WSN is challenging especially for heterogeneous mobile CR-WSN. Existing model induces high collision and energy overhead. As a result, they are not efficient in utilizing spectrum. To overcome the research issues, this paper proposes a hybrid spectrum access model (HSAM) for multi-channel heterogeneous mobile CR-WSN. Next it elaborates on an accurate feasible channel accessible likelihood estimation. Further, we present a game theory model for HSAM for existence of fine-grained Nash equilibrium. Experiments are conducted to evaluate the performance of HSAM-CRWSN over existing model. The outcome shows HSAM-CRWSN attains significant performance improvement over state-of-art technique in terms of energy efficiency, throughput, successful packet transmission and packet collision. The HSAM-CRWSN improves the overall spectrum efficiency of CR-WSN.</p><p><span style="font-size: small;"><br /></span></p>

An SoC architecture for energy detection based spectrum sensing using Low Latency Column Bit Compressed (LLCBC) MAC in cognitive radio wireless sensor networks

Wireless Sensor Network (WSN) requires Signal Processing and control applications in order to use technology effectively. Digital Signal Processing presents a wide range of applications in various fields with the exception of WSN due to the power constraint factor. The Spectrum holes can be effectively utilized by means of spectrum sensing technique in WSN. Multiply and Accumulate unit (MAC) is the computation Kernel in DSP Systems which in turn computes the overall speed of the system. Also, MAC is the major power consumption block due to its complex operation. Developing low power and high speed MAC and utilizing it in spectrum sensing for WSN is a challenging task. In this research, Low Latency Column Bit Compressed (LLCBC) MAC architecture is used for spectrum sensing by examining the hardware complexities. The Architecture is synthesized with 90 nm standard CMOS library using cadence SoC encounter.

Optimization issues for data rate in energy harvesting relay-enabled cognitive sensor networks

This paper looks into an energy harvesting (EH) relay-enabled cognitive radio wireless sensor network (CR-WSN) considering power splitting (PS) architecture. More specifically, a relay (RU) harvesting energy from the signals transmitted from a secondary user transmitter (ST,) and using the harvested energy to forward the resulting signals to another sensor node subsequently is being investigated. This scheme can be broken down into two components, i.e., a sensor node physically placed near the transmitter (SPNT) and a sensor node physically placed far from the transmitter (SPFT). The closed-form expressions for the successful transmission probability (STP) and the achievable data rate in both cases can be derived analytically. In order to quantify the energy consumption, the system energy efficiency (EE) is examined. Furthermore, the achievable data rate was optimized in three possible scenarios, i.e., the trade-off between the sum data rate and the sum harvested energy (R-E), the achievable data rate at RU, and the joint optimization of the power allocation and PS ratio in case of SPNT. A Monte Carlo simulation has been performed to verify the theoretical analysis obtained, and to show the impact of different parameters on system performance.