Cognitive Radio Sensor Networks

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

The adoption of cognitive radio wireless sensor networks be supposed to preserve developing, essentially in such a fields as a healthcare, logistic, scientific and a military applications. Yet, a sensor size or dimension represents a consequential constraint mainly in expressions of an energy autonomy and as a result of a life period for the batteries which is so too tiny. Thus, the reason why a concentrated research being conducted at the present time on how to control energy consumption in sensor within a network, taking communications into account as a precedence. For this intention, we propose a algorithm or method to send data from source to sink with a less energy consumption within cognitive wireless sensor networks, according to the number of nodes, data flow rate, and the distance between them. Moreover, we have succeeded in sinking energy utilization within a linear sensor network through up with nodes featuring with differing data flow rates between them.

Cognitive Radio (CR) is a novel concept that enables wireless devices to detect and adapt to their surroundings in order to enhance communication quality. The cognitive radio sensor network (CRSN) has proved to be a cost-effective solution for the spectrum constraints in wireless sensor networks (WSN). Optimizing the optimum packet size is regarded to be an essential energy constrained issue to address the practical implementation of CRSN out of all the difficulties. Small packets generate data traffic in device-to-device communication, while big packets may cause data bit corruption, requiring retransmission at a greater frequency. This will not allow access from the secondary network to the main network, since it may cause further disturbance. To maximise the WSN's energy efficiency, the optimum packet size for CRSN should be maintained while keeping the same degree of interference as the primary licenced users. The purpose of this article is to examine formulations for small, medium, and large packet sizes in order to determine the optimum packet size for adaptive CRSN. To do so, CR requires a flexible physical layer capable of carrying out the necessary tasks. This article examines the performance of CR systems that use the Orthogonal Frequency Division Multiplexing technique, which is a possible transmission technology for CR. Interference and delays are minimised and the channels are ultimately utilised effectively through Scheduling MAC protocol. This article gives the design steps to adjust the network design to get the better performance. For achieving the greatest performance on the Cognitive Radio Sensor Network (CRSN). The Jellyfish Search Optimization algorithm and the hybrid Momentum Search Algorithm are hybridized and results are achieved. This makes it possible to calculate precise packet sizes. The suggested approach decision outperforms existing methods like the Group Sparse Optimization algorithm and the Throughput Maximization Algorithm. The MATLAB/SIMULINK Platform were used to get the results.

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. CRSNs benefit from cognitive radio capabilities such as dynamic spectrum access and transmission parameters reconfigurability; but cognitive radio also brings additional challenges and leads to higher energy consumption. Motivated to improve the energy efficiency in CRSNs, we propose a robust and energy-efficient transport protocol (RETP). The novelties of RETP are two-fold: (I) it combines distributed channel sensing and channel decision with centralized schedule-based data transmission; and (II) it differentiates the types of data transmission on the basis of data content and adopts different acknowledgment methods for different transmission types. To the best of our knowledge, no transport layer protocols have yet been designed for CRSNs. Simulation results show that the proposed protocol achieves remarkably longer network lifetime and shorter event-detection delay compared to those achieved with a conventional transport protocol, while simultaneously preserving event-detection reliability.

A cognitive radio sensor network (CRSN) is a promising fusion network that combines conventional wireless sensor network (WSN) with cognitive radio (CR) functionalities. With abilities to sense for available channels and to adapt to the radio environment, a CRSN is able to be more opportunistic in accessing spectrums than a WSN, but it also has problems due to hardware constraints and energy limitations. In many studies on distributed CR MAC protocols, establishing dedicated control channels (DCCs) has been considered as an important problem because it is essentially used for coordination and negotiation of spectrum assignments. However, directly applying DCCs into CRSNs is impractical, due to its high-cost complexity in hardware and deployment. To solve it, in this paper we propose a distributed MAC protocol using virtual control channels for CRSNs, in which CR sensor nodes called secondary users are grouped into clusters, and commonly shared available channels are utilized as virtual DCCs. In addition, a channel selection scheme is developed, based on an approximated partially observable Markov decision process. Our numerical simulation results indicate that the proposed MAC protocol can not only improve the throughput rate but also reduce the energy consumption under no DCCs.

Wireless Sensor Networks (WSNs) use the unlicensed Industrial, Scientific, and Medical (ISM) band for transmissions. However, with the increasing usage of these networks for diverse applications, the currently available ISM band does not suffice for their transmissions and a new challenge appears before the WSNs' research community. One of the candidate approaches to tackle this spectrum insufficiency problem is to incorporate the opportunistic spectrum access capability of Cognitive Radio (CR) into the existing WSN, thus giving birth to Cognitive Radio Sensor Network (CRSN). Efficient spectrum utilization is another approach to overcome this challenging problem. Another challenge associated to WSN operation is the dependability of sensor nodes on battery supplied power where the batteries in general are not replaceable. Therefore, advanced and intelligent radio resource management schemes are very essential to perform dynamic and efficient spectrum allocation among multiple sensor nodes and to optimize the power consumption of each individual node in the network. Radio resource management enables the sensor nodes to efficiently utilize the available spectrum and power, which in turn ensures QoS transmissions, maximizes the network lifetime, and reduces the inter-node and inter-network interferences. In this chapter, the authors present a comprehensive overview of the recent advances in radio resource management for CRSN. Radio resource management in CRSN has been reviewed in various scenarios (i.e., centralized, cluster-based, and distributed). The related issues and challenges are discussed, and future research directions are highlighted.

Spectrum sharing fairness is an important topic in cognitive radio ad hoc networks (CRAHNs) and cognitive radio sensor networks (CRSNs). Consensus-based protocols can provide light-weight and efficient solutions for CRAHNs and CRSNs but the theoretical ground needs to be investigated for spectrum sharing fairness. In this paper, we investigate the convergence condition when applying a consensus-based protocol to spectrum sharing while ensuring spectrum sharing fairness. Based on the local observation and local control scheme using spectrum-related information, an individual cognitive node can effectively perform the spectrum sharing. Then we propose a consensus-based protocol for spectrum sharing. Supported with computer simulation results, we show the effectiveness of using the proposed consensus-based protocol to solve the spectrum sharing problems in CRAHNs and CRSNs.

Channel bonding (CB) is a proven technique to increase bandwidth and reduce delays in wireless networks. It has been applied in traditional wireless networks such as cellular networks and wireless local area networks along with the emerging cognitive radio networks. This paper first focuses on providing a survey of CB schemes for traditional wireless networks such as cellular networks, wireless local area networks, and wireless sensor networks, and then provides a detailed discussion on the CB schemes proposed for cognitive radio networks. Finally, we highlight a number of issues and challenges regarding CB in cognitive radio sensor networks and also provide some guidelines on using CB schemes in these futuristic networks.

Traditional wireless sensor networks (WSNs) working in the license-free spectrum suffer from uncontrolled interference as the license-free spectrum becomes increasingly crowded. A WSN based on cognitive radios can potentially provide data transmissions with quality of service, while avoiding the high cost for accessing the licensed spectrum and uncontrolled interference in the license-free spectrum. Our previous works have studied delay performance of constant bit rate traffic in a cognitive radio sensor network (CRSN). In this paper we study the delay performance of random traffic with burst arrivals in the network. We derive the average delay for transmitting the random traffic using different channel sensing mechanisms, and verify the results using computer simulation. Our results show that satisfactory latency performance can be provided in the CRSN.

The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, calledS-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer.

CRSN (Cognitive Radio Sensor Networks) have recently gained huge interest as an area of research. Naturally, due to energy limitations in WSN (Wireless Sensor Networks), grouping sensor nodes into clusters has been widely adopted by the research community to overcome the issue of limited energy budget and generally achieve high energy efficiency as well as prolonging network lifetime in large-scale WSN environments. Combining clustering of WSN with MIMO (Multiple Input Multiple Output) is a promising technique used in literature of this research area leading to what is called cooperative MIMO clustered WSN. Compared with existing work, our contributions are: i) Extension of CR (Cognitive Radio) technique to cooperative MIMO clustered WSN in a spectrum sharing sense as well as demonstrating that SISO setting isn't always the best setting for transmission even at small distances. The shift to other settings is more favorable to guarantee not disturbing the primary user while transmitting on the same channel; and ii) Proposing a selection criterion for cooperative MIMO CRSN that chooses the minimum energy setting (SISO/SIMO/MISO/MIMO) to guarantee a proper communication for the secondary CR system under some interference constraint to the primary user as well as satisfying a specific BER requirement for CR secondary system.

A cognitive radio sensor network (CRSN) is a wireless sensor network in which sensor nodes are equipped with cognitive radio. In this paper, we propose an energy-efficient game-theory-based spectrum decision (EGSD) scheme for CRSNs to prolong the network lifetime. Note that energy efficiency is the most important design consideration in CRSNs because it determines the network lifetime. The central part of the EGSD scheme consists of two spectrum selection algorithms: random selection and game-theory-based selection. The EGSD scheme also includes a clustering algorithm, spectrum characterization with a Markov chain, and cluster member coordination. Our performance study shows that EGSD outperforms the existing popular framework in terms of network lifetime and coordination overhead.

This paper investigates the design issues of spectrum sensing in the cognitive radio (CR) networks of opportunistic unlicensed spectrum access. The cognitive radios can perform a communication using the incumbent user spectrum band without the interference caused by the cognitive radio users. In this case, the cognitive radios must know the real-time radio environments of the incumbent user spectrum band using the spectrum sensing, beacon signal, and geo-location database access. Then in this paper, we are going to provide spectrum sensing issues which include the sensing techniques, the regulatory requirements, the analysis of DTV detection threshold, and main considerations associated with the spectrum sensing design in cognitive radio systems. Also, this paper introduces design trade-offs in order to optimize the sensing parameters such as sensing time and sensing complexity.

One of the drawbacks of LEACH protocol is the uncontrolled selection of cluster heads which, in some rounds, leads to the concentration of them in a limited area due to the randomness of the selection procedure. LEACH-C is a variant of LEACH that uses a centralized clustering algorithm and forms good clusters through sink control. According to experimental results, the IEEE 802.15.4 packets are damaged by WLAN interferences in ISM band. It seems that, sensor nodes equipped with cognitive radio capabilities can overcome this problem. In cognitive radio sensor networks (CRSN), routing must be accompanied by channel allocation. This requires spectrum management which can be devolved to cluster heads. For this networks, new duty cycle mechanisms must be designed that jointly consider neighbor discovery, and spectrum sensing/allocation. Cluster-based network architecture is a good choice for effective dynamic spectrum management. In such architecture, cluster heads have a proper spatial distribution and are optimally located all over the network. In this paper, using the physical layer information and preserving the feature of random cluster head selection in LEACH, it has been tried to both move the position of cluster heads to appropriate locations and make their quantity optimal. The simulation results show that the transferal of cluster heads to appropriate locations increases the network lifetime significantly though this comes at the price of early instability appearance. By considering the energy level in cluster head election algorithm, one can overcome the network stability issues too. However, this will move the cluster heads away from their appropriate locations.

An extensive area implementation of fully observed greenhouses motivates on research, especially in remote greenhouses. However, implementation of wireless sensor networks (WSNs) is still needed for investigation. Cognitive radio sensor networks (CRSNs) took advantage of using the cognitive radio (CR) concept to which allowed wireless sensor networks to dynamically access into white space channels which is unused channels. In this paper, we adopted the Generalized Implicit-OR as CRSN sensing protocol to reduce the energy consumption and increase the network lifetime in multiple numbers of greenhouses. Our results showed that enhanced energy consumption and improved network lifetime compared to ordinary WSN.