Cognitive Radio Wireless Sensor Networks Research Articles

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.

Performance Evaluation of Relay Selection Schemes in Beacon-Assisted Dual-Hop Cognitive Radio Wireless Sensor Networks under Impact of Hardware Noises.

To solve the problem of energy constraints and spectrum scarcity for cognitive radio wireless sensor networks (CR-WSNs), an underlay decode-and-forward relaying scheme is considered, where the energy constrained secondary source and relay nodes are capable of harvesting energy from a multi-antenna power beacon (PB) and using that harvested energy to forward the source information to the destination. Based on the time switching receiver architecture, three relaying protocols, namely, hybrid partial relay selection (H-PRS), conventional opportunistic relay selection (C-ORS), and best opportunistic relay selection (B-ORS) protocols are considered to enhance the end-to-end performance under the joint impact of maximal interference constraint and transceiver hardware impairments. For performance evaluation and comparison, we derive the exact and asymptotic closed-form expressions of outage probability (OP) and throughput (TP) to provide significant insights into the impact of our proposed protocols on the system performance over Rayleigh fading channel. Finally, simulation results validate the theoretical results.

An anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks

In wireless sensor networks, time synchronization is an important issue for all nodes to have a unified time. The wireless sensor network nodes should cooperatively adjust their local time according to certain distributed synchronization algorithms to achieve global time synchronization. Conventionally, it is assumed that all nodes in the network are cooperative and well-functioned in the synchronization process. However, in cognitive radio wireless sensor networks, the global time synchronization process among secondary users is prone to fail because the communication process for exchanging synchronization reference may be frequently interrupted by the primary users. The anomaly nodes that failed to synchronize will significantly affect the global convergence performance of the synchronization algorithm. This article proposes an anomaly node detection method for distributed time synchronization algorithm in cognitive radio sensor networks. The proposed method adopts the statistical linear correlation analysis approach to detect anomaly nodes through the historical time synchronization information stored in local nodes. Simulation results show that the proposed method can effectively improve the robustness of the synchronization algorithm in distributed cognitive radio sensor networks.

A Learning Method for Secondary Users to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks State of the art in CR-WSN security

A Learning Method for Secondary Users to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks State of the art in CR-WSN security

Exploring the Effect of Various Cluster Structures on Energy Consumption and End-to-End Delay in Cognitive Radio Wireless Sensor Networks

The growing demand and wide deployment of cost effective wireless communication networks, and the challenges related to its technical design and operations have necessitated the work on the more efficient use of the very limited radio frequency spectrum, limited energy resources and reduction of the end-to-end delay as an integral part of the next generation smart wireless networks. In this paper, we explore three different cognitive radio wireless sensor networks cluster structures; modified single-hop structure, multi-hop cluster structure, and hybrid cluster structure. We study the effect of the three structures in multiple setups in regards to varying the selected area. The evaluation results of the suggested three structures are compared with the single-hop cluster. Extensive simulation results carried out using MATLAB package showed that the end-to-end delay is minimum for the hybrid algorithm on the expense of a slight increase in energy consumption compared with the single-hop and the multi-hop cluster structures. On the other hand, the multi-hop cluster structure is more energy consuming than the single-hop, but it achieves a wider coverage area.

Optimal Operational Parameters for 5G Energy Harvesting Cognitive Wireless Sensor Networks

ABSTRACTDense communication networks have been investigated recently to enable communications of low power wireless devices such as Internet-of-things (IoT) applications in the fifth cellular generation (5G). The wireless sensor network (WSN) is one of the principal technologies of IoT as it plays a critical role in numerous industries such as agriculture, healthcare, and environmental applications. Despite the advantages of the WSN, it is yet difficult to be deployed due to the scarcity of the radio spectrum with the increasing popularity of wireless applications. Therefore, merging of two technologies WSN and cognitive radio network (CRN), as cognitive radio wireless sensor network (CR-WSN), became essential for IoT applications. Another major challenge in such systems is the power constrain and delay sensitivity in such numerous wireless devices. Radio-frequency energy harvesting (EH) capability is supposed to merge with such systems in order to efficiently power and enhance the overall system energy. Thus, this paper discusses a CR-WSN model based on EH in a non-ordinary M/M/1 Markovian battery model with the proposal of a frame structure of the wireless node's charging and sensing time. The contribution of the power obtained from harvesting is derived with the proposal of a realistic power required/harvested model in RF EH CRN. Moreover, the power efficiency of the CR-WSN model is calculated and the derivation of the transmission delay is introduced in the same model. Furthermore, combined optimization of the required power, probability of packet loss and the transmission delay is proposed to validate the overall system performance with the recommended system operational parameters.

A Nonlinearity Mitigation Method for a Broadband RF Front-End in a Sensor Based on Best Delay Searching.

The cognitive radio wireless sensor network (CR-WSN) is experiencing more and more attention for its capacity to automatically extract broadband instantaneous radio environment information. Obtaining sufficient linearity and spurious-free dynamic range (SFDR) is a significant premise of guaranteeing sensing performance which, however, usually suffers from the nonlinear distortion coming from the broadband radio frequency (RF) front-end in the sensor node. Moreover, unlike other existing methods, the joint effect of non-constant group delay distortion and nonlinear distortion is discussed, and its corresponding solution is provided in this paper. After that, the nonlinearity mitigation architecture based on best delay searching is proposed. Finally, verification experiments, both on simulation signals and signals from real-world measurement, are conducted and discussed. The achieved results demonstrate that with best delay searching, nonlinear distortion can be alleviated significantly and, in this way, spectrum sensing performance is more reliable and accurate.

Joint Channel Assignment and Routing Protocol for Cognitive Radio Wireless Sensor Networks

Multi-hop transmissions over a single channel that is shared by all the nodes in a network limit the throughput of conventional multi-hop wireless sensor networks. To improve the performance of these networks, the use of multiple channels has been proposed. Channel assignment is an important problem in multi-channel wireless sensor networks; it asks the question of how channels should be assigned so that the network performance in maximized. Many existing channel assignment scheme seek to improve network performance by trying to reduce interference. However, it is not easy to measure the actual amount of interference in a network. We tackled the channel assignment problem from a novel approach. Instead of distributing the nodes, links or network interfaces to different channels to reduce interference, we try to assign as many channels as possible to the links. A new distributed scheme is proposed and we show that is outperforms over comparable schemes.

An Opportunistic Spectrum Access Scheme for Multicarrier Cognitive Sensor Networks

In this paper, we consider a broadband cognitive radio wireless sensor network operating in a crowded spectrum scenario, where a sensor transmitter (i.e., a sensor node that needs to transmit information to another sensor node or to a sink) opportunistically uses the channel of a primary system adopting an underlay paradigm. Both the primary and sensor systems employ an orthogonal frequency-division multiplexing (OFDM) modulation scheme. The proposed approach is reminiscent of additive superposition coding: specifically, the sensor transmitter behaves as an amplify-and-forward relay for the primary system, by sending a linearly weighted combination of the received primary signal and its own block of symbols. Such a superposition is performed by the sensor transmitter on the fly, in a single OFDM symbol of the primary system. Moreover, the proposed scheme does not require spectrum sensing at the sensor nodes and does not demand any cooperation between primary and sensor systems, thereby accounting for the inherent resource constraints of sensor nodes. With reference to both primary and sensor communications, we address the problem of power allocation by considering different amounts of channel state information and develop a worst case analysis of the achievable information rates. The developed algorithms and the derived formulas, which are corroborated by numerical Monte Carlo simulations, allow one to recognize and discuss interesting tradeoffs between the main parameters of the cognitive radio sensor transmission.

Distributed cooperative spectrum sensing with wireless sensor network cluster architecture for smart grid communications

Smart grid (SG) is widely considered as the next generation power grid. Recently, wireless sensor networks (WSNs) have shown its advantages for SG by achieving low-power transmissions. However, SG communication technologies are subjects to ineffective spectrum allocation issues. Cognitive radio wireless sensor network (CRWSN) has been identified as a new technique for SG to face these challenges. CRWSN is a new research paradigm where WSN and cognitive radio are combined. In this paper, WSN with cluster tree topology is proposed as a compatible choice for SG design. We introduce CRWSN in to SG using cluster tree topology to improve the sensing performance and solve the rarity of spectrum by opening the underused licensed bands to secondary users. We adopt cluster distributed cooperative sensing as a method of spectrum sensing. We later evaluate the performance of this method under two distributed policies; OR and AND rules based on energy detection.

A self-organised routing algorithm for cognitive radio-based wireless sensor networks using biologically-inspired method

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.

Mitigating RF Front-End Nonlinearity of Sensor Nodes to Enhance Spectrum Sensing.

The cognitive radio wireless sensor network (CR-WSN) has gained worldwide attention in recent years for its potential applications. Reliable spectrum sensing is the premise for opportunistic access to sensor nodes. However, as a result of the radio frequency (RF) front-end nonlinearity of sensor nodes, distortion products can easily degrade the spectrum sensing performance by causing false alarms and degrading the detection probability. Given the limitations of the widely-used adaptive interference cancellation (AIC) algorithm, this paper develops several details to avoid these limitations and form a new mitigation architecture to alleviate nonlinear distortions. To demonstrate the efficiency of the proposed algorithm, verification tests for both simulations and actual RF front-end measurements are presented and discussed. The obtained results show that distortions can be suppressed significantly, thus improving the reliability of spectrum sensing. Moreover, compared to AIC, the proposed algorithm clearly shows better performance, especially at the band edges of the interferer signal.

A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks.

Cognitive radio wireless sensor networks (CR-WSNs) have attracted a great deal of attention recently due to the emerging spectrum scarcity issue. This work attempts to provide a detailed analysis of the role of node clustering in CR-WSNs. We outline the objectives, requirements, and advantages of node clustering in CR-WSNs. We describe how a CR-WSN with node clustering differs from conventional wireless sensor networks, and we discuss its characteristics, architecture, and topologies. We survey the existing clustering algorithms and compare their objectives and features. We suggest how clustering issues and challenges can be handled.

An energy efficient Reinforcement Learning based Cooperative Channel Sensing for Cognitive Radio Sensor Networks

In Cognitive Radio (CR), the conventional narrow band spectrum sensing requires either random channel sensing order or predefined channel sensing sequence to sense all channels in a specified spectrum band in order to detect vacant channels. This may be inefficient in energy constraint devices networks such as Cognitive Radio Wireless Sensor Network (CR-WSN). In this paper, we propose a Reinforcement Learning based clustered Cooperative Channel Sensing (RL-CCS) that learns channels’ dynamic behaviors in terms of channel availability, sensing energy cost, and channel impairment to achieve optimal sensing sequence and optimal set of channels. The problem of selecting optimal policy is formulated as a Markov Decision Problem (MDP) to determine optimal solutions that minimize sensing energy while improving Primary User (PU) detection and channel utilization in CR-WSN. Simulation results show convergence and adaptability of the algorithm to dynamic environment in achieving optimal solutions. The results also indicate that with optimal channel sensing sequence and optimal sets of channels, sensing energy cost savings of 15.14% per channel sensing cycle can be achieved while improving PU detection accuracy and channel utilization compared to the sensing sequence based on Greedy search approach. Performance comparison of the proposed algorithm with other benchmark schemes indicates viability of our proposed approach over the other schemes in minimizing sensing energy and improving PU detection performance.

Large-Scale Multi-Cluster MIMO Approach for Cognitive Radio Sensor Networks

This paper proposes a large-scale cooperative multiple-input multiple-output (CMIMO) beamforming scheme for uplink (UL) access in broadband cognitive radio wireless sensor networks (CR-WSNs) sharing the same spectrum with a primary network and employing orthogonal frequency-division multiplexing. The CR-WSN is divided into clusters each consisting of cooperative nodes that form a virtual antenna array. Using particle swarm optimization (PSO), each cluster seeks the optimal transmit weight vectors that maximize the UL channel capacity of each cluster, while controlling the interference levels to the primary network. Under the assumption of very large number of sensor nodes at each cluster, semi-analytic expressions for the symbol error rate and the ergodic channel capacity of the CMIMO-based CR-WSN are derived and validated with Monte-Carlo simulation. The PSO-based capacity-aware (PSO-CA) scheme is compared with the one based on the traditional gradient search scheme (GS-CA) and the results show that PSO-CA requires considerably less computational complexity while achieving essentially the same level of performance as the GS-CA.

Adaptive Window Size-Based Medium Access Control Protocol for Cognitive Radio Wireless Sensor Networks

Many existing medium access control protocols for cognitive radio wireless sensor networks waste bandwidth and suffer from delay because of the fixed channel negotiation period in the common control channel. In this paper, we propose a medium access control protocol for cognitive radio wireless sensor networks that adjusts the channel negotiation period based on network density. We simulate the proposed approach and compare it to the performance of the fixed channel negotiation period based approaches. The results show that the proposed approach efficiently utilizes white spaces and increases the aggregated goodput.

Event-driven hybrid MAC protocol for a two-tier cognitive wireless sensor network: design and implementation

In this paper, an integrated hardware and software system for a cognitive-radio wireless sensor network CR-WSN is designed and implemented for event detection and monitoring applications. The proposed CR-WSN is a pre-deployed two-level cluster-based network. The lower level consists of several clusters of sensor nodes, each with its own cognitive-enabled cluster-head CR-CH. The upper-level consists of a network of CR-CHs. The data collected by each sensor node is sent to the associated CR-CH. The CR-CH opportunistically transmits the sensed information over one of the available primary radio PR channels to a monitoring node sink to take the appropriate action. To connect between the various elements and nodes in the system, we develop efficient communication protocols for the CR-WSN. Specifically, we present a novel event-driven hybrid MAC design called CRS-MAC for the CR-WSN such that reliable data delivery is achieved. CRS-MAC divides the MAC-layer functionality into two types: CSMA-based single-channel intra-cluster communications and cognitive multi-channel CSMA-based inter-cluster communications. The developed protocol is implemented and tested to provide a flexible software platform that enables efficient and reliable operation for the CR-WSN. Experimental and simulation results demonstrate a robust and scalable performance of the proposed CR-WSN system.

A MAC Protocol for CR-WSN without a Dedicated Common Control Channel

We present a decentralized medium access control protocol for cognitive radio wireless sensor networks. The proposed protocol allows secondary wireless sensors nodes to recognize spectrum opportunities and transmits data based on the licensed users’ arrival prediction on the channel. It estimates the number of active cognitive wireless sensor nodes and it also adjusts the sleep cycle in order to conserve energy. The proposed protocol does not need a dedicated common control channel to negotiate for the data channels. We evaluate delay, energy consumption, and goodput, which are the three important qualities of service parameters through simulation. The simulation results show that the proposed approach achieves higher energy conservation with a small cost of delay and adequate aggregated goodput.

Distributed Cooperative Spectrum Sensing with Wireless Sensor Network Cluster Architecture for Smart Grid Communications

Smart grid (SG) is widely considered as the next generation power grid. Recently, wireless sensor networks (WSNs) have shown its advantages for SG by achieving low-power transmissions. However, SG communication technologies are subjects to ineffective spectrum allocation issues. Cognitive radio wireless sensor network (CRWSN) has been identified as a new technique for SG to face these challenges. CRWSN is a new research paradigm where WSN and cognitive radio are combined. In this paper, WSN with cluster tree topology is proposed as a compatible choice for SG design. We introduce CRWSN in to SG using cluster tree topology to improve the sensing performance and solve the rarity of spectrum by opening the underused licensed bands to secondary users. We adopt cluster distributed cooperative sensing as a method of spectrum sensing. We later evaluate the performance of this method under two distributed policies; OR and AND rules based on energy detection.

Exploiting Multiple Channels for Low Latency and Semireliable Broadcasting in Cognitive Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), disaster management is a crucial issue that focuses on disaster relief and recovery. Mobile sensor nodes support disaster relief and recovery by means of real-time bidirectional communication. For its high data rate requirement, IEEE 802.11 specification can be used for the radio interface of sensor nodes, and the nodes can be equipped with multiple 802.11 radios to utilize multiple channels and link data rates. Channel assignment algorithms can be applied in cognitive radio enabled networks which performs dynamic channel configuration for utilizing multiple channels. For efficient and semireliable broadcast in cognitive radio WSNs, we focus on reducing broadcast latency and achieving 100% delivery percentage. To realize these goals, in this study, we present our design for a novelChannel Assignment Algorithm for a Collision-Reduced Broadcast Tree(CA-CBT). Fundamentally, CA-CBT builds a broadcast tree and then uses several heuristic procedures to allocate collision-free channels to links on the tree. If CA-CBT fails to allocate collision-free channels due to a limited number of available channels, it allocates non-collision-free channels with the smallest number of interfering nodes. Through extensive simulations, we demonstrated that CA-CBT supports lower broadcast latency and higher delivery percentages compared with existing broadcast algorithms.