Cognitive Nodes Research Articles

Supporting fast and fair rendezvous for cognitive radio networks

A cognitive node in a cognitive radio network (CRN) temporarily and opportunistically uses a channel not being occupied by any licensed user. It is challenging to provide a rendezvous between any pair of nodes in CRNs. Enabling nodes to hop among channels is a dominant mechanism to solve the rendezvous problem without using a dedicated common control channel. Existing channel hopping solutions may suffer from poor or uneven rendezvous for different pairs of nodes. In this paper, we propose a novel distributed channel hopping protocol (HHCH) which efficiently provides rendezvous guarantee and fair rendezvous opportunity. The balanced rendezvous among nodes is provided by utilizing a Hadamard matrix while fair rendezvous opportunity is achieved by using a hash function. In addition to rendezvous guarantee and fairness, the HHCH protocol also provides high and even channel utilization. Analytical and simulation results verify that HHCH performs better in terms of time to rendezvous and network throughput when compared to existing representative protocols Jump-Stay, ACH, SYNC-ETCH, QLCH, and RQL.

A network steganographic approach to overlay cognitive radio systems utilizing systematic coding

Network steganography is an information hiding technique that utilizes network protocols to facilitate hidden communication. The aim is to embed secret information bits into regular network traffic (as a carrier) so that confidential data can be transmitted covertly between two communicating parties. In this paper, the concept of network steganography is extended for overlay cognitive radio networks utilizing cooperative relaying protocols with systematic channel codes. In the considered model, the cognitive node relays the primary data according to a time-slotted decode-and-forward cooperative protocol with maximum-ratio combining (MRC) at the primary destination. We propose a steganographic approach in which the cognitive node embeds confidential cognitive data at the wavelet transform domain into primary data code words. In this approach, the embedding operation is designed to preserve the statistical properties of the cover data in terms of decoded BER after MRC decoding of code words at the destination node which includes both the effect of wireless channel errors and errors due to embedding distortion. From this point of view, the proposed scheme may be seen as a lossless steganography scheme that embeds secret data into coded data strings. We provided implementation examples using both systematic BCH and turbo channel coding. The results show that the proposed scheme can provide diversity gain using stego-based MRC at the primary receiver and covert confidential cognitive data communications simultaneously.

Joint spectrum access and transmission power management for energy harvesting cognitive radio sensor networks

In this paper, we investigate the joint optimisation of spectrum access and the transmission power for an energy harvesting cognitive sensor node, which operates in time-slotted fashion with causal knowledge of channel conditions along with the energy harvesting states. Allowing for the sensing imperfection, we formulate this joint optimisation problem as an infinite-horizon discretetime Markov decision process (MDP), in which the cognitive sensor aims at maximising the long-term expected throughput. An optimal policy which specifies the spectrum access decision as well as the power level is proposed. It is indicated that the optimal long-term expected throughput is nondecreasing with the battery available energy. Moreover, we introduce a low-complexity policy and prove that the optimal low-complexity policy has a threshold structure with respect to the battery available energy. An efficient algorithm for deriving the optimal low-complexity policy is introduced. Finally, numerical results are presented to confirm the superiority of proposed policies.

Optimized Hexagon-Based Deployment for Large-Scale Ubiquitous Sensor Networks

Ubiquitous Sensor Network describes an application platform comprised of intelligently networked sensors deployed over a large area, supporting multiple application scenarios. On one hand, at the user-end, storing and managing the large amount of heterogeneous data generated by the network is a daunting task. On the other hand, at the network-end, ensuring network connectivity and longevity in a dynamically changing network environment, while trying to provide context-aware application data to the end-users are very challenging for the resource constrained sensor network. While cloud computing offers a cost-effective solution for storage of the large volume of data generated by the underlying heterogeneous network, an equally elegant solution does not exist on the network interface to provide application-aware data. In this paper, we propose the use of cognitive nodes (CNs) in the underlying sensor network to provide intelligent information processing and knowledge-based services to the end-users. We identify tools and techniques to implement the cognitive functionality and formulate a strategy for the deployment of CNs in the underlying sensor network to ensure a high probability of successful data reception among communicating nodes. From Matlab simulations, we were able to verify that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to deliver application-aware data that satisfies the end-user’s quality of information requirements, even at high application payloads.

Quantifying Trust for Robust Fusion While Spectrum Sharing in Distributed DSA Networks

In this paper, we quantify the trustworthiness of secondary nodes that share spectrum sensing reports in a distributed dynamic spectrum access network. We propose a spatio-spectral anomaly monitoring technique that effectively captures anomalies in the spectrum sensing reports shared by individual cognitive radio nodes. Based on this, we propose an optimistic trust model for a system with a normal risk attitude and using approximation to the Beta distribution. For a more conservative and risk averse system, we propose a multinomial Dirichlet distribution-based conservative trust framework. Using a machine learning approach, we classify malicious nodes with a high degree of certainty regardless of their aggressiveness of attacks or variations introduced by the wireless environment. Subsequently, we propose two instantaneous fusion models: 1) optimistic trust-based fusion and 2) conservative trust-based fusion, which exclude untrustworthy sensing reports from participating nodes during spectrum data fusion. Our work considers random, deterministic, and preferential (ON–OFF) attack models to demonstrate the utility of our proposed model under varied attack scenarios. Through extensive simulation experiments, we show that the trust values help identify malicious nodes with a high degree of certainty.

Cognitive-Node Architecture and a Deployment Strategy for the Future WSNs

The advent of sensing and communication technologies represents the next step in the evolution of wireless sensor networks (WSNs) and future applications. Future WSNs systems demand that connected devices could be able to work autonomously, while surfing on-line generated data and process them for self-decision making. Accordingly, we propose a cognitive Information-Centric Sensor Network (ICSN) framework. The fundamentals of cognition in ICSN can be recognized as a promising direction in addressing opportunities and challenges posed by the needs of WSNs. Nevertheless, these fundamental concepts could be difficult to implement due to the wide spectrum of tasks to be covered from the perspective of each device in the WSN. Consequently, we propose the use of Cognitive Nodes (CNs) in typical sensor networks to provide intelligent information processing and knowledge-based services to the end-users. The CNs act on queries from the end-user, containing information about the nature of the request, without the need to modify and/or change any component at the end nodes from where data is collected. We present the detailed architecture and deployment strategy of the cognitive inspired framework that learns and exploits an expanded network information from relays and clustered sensors. Extensive simulation results show that in a network with randomly deployed sensor nodes, CNs can be strategically deployed at pre-determined positions, to ensure that end-user’s Quality of Information (QoI) requirements are met, even under heavy traffic conditions and extensive packets’ payloads. This shows the potential use of ICSN framework in producing the future cognitive applications, while catering to user-desired information quality.

Cluster-Based Collaborative Spectrum Sensing for Energy Harvesting Cognitive Wireless Communication Network

We consider a cluster-based collaborative spectrum sensing (CSS) scheme in energy harvesting cognitive wireless communication network (EH-CWCN), where cognitive nodes (CNs) are clustered based on their received power levels for enhancing sensing performance. In CSS scheme, time resource is limited and shared by energy harvesting, spectrum sensing, and data transmission. The purpose of this paper is to maximize the average throughput of EH-CWCN by identifying the optimal parameter set, including the durations of energy harvesting and spectrum sensing, local detection threshold, and the number of CNs. Moreover, it is hard to confirm the optimal local detection threshold at CNs with different received power levels. By constructing a fictitious cognitive node (FCN), which is assumed to have the same sensing performance as the sink node, the process for finding the optimal local detection threshold can be converted into the process for searching the optimal received signal-to-noise ratio of the FCN. Then, we formulate the general optimization problem under the collision constraint and energy constraint. Then, the existence and uniqueness of time fractions for energy harvesting and spectrum sensing are proved in this paper. The optimal parameter set is achieved by using the bisection method and a simplified linear search method. Finally, the theoretical analysis and the impact of the optimized parameters on the system performance are verified and shown through numerical simulations.

A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks

Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

A Secure and Efficient Authentication Mechanism Applied to Cognitive Radio Networks

Cognitive radio (CR) has been introduced to accommodate the steady increment in the spectrum demand. Wireless security in CR network (CRN) is a challenging technical area due to the dynamic and unique characteristics of CRNs. As a cognitive node can dynamically join or leave the spectrum, providing secure communication becomes problematic and requires more investigation. Authentication is a primary security property in wireless networks, wherein the identity of a cognitive node is verified before providing access to available resources. In this paper, a two-level authentication scheme for communication in a CRN is proposed. Before joining the network, a CR node is validated by obtaining security credentials from an authorized point. The proposed scheme relies on public- and symmetric-key cryptography, instead of using a digital signature-based approach. It encrypts data between the communicating nodes in order to improve network security in terms of resource availability and accessibility. This mitigates attacks such as reflection attack , denial of service attack , and man-in-the-middle attack . The scheme has been evaluated and verified in terms of security functionality, its correctness, and the performance, which shows less computation and communication requirements.

Packet-Size Optimization for Multiple-Input Multiple-Output Cognitive Radio Sensor Networks-Aided Internet of Things

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.

Underlay Interference Analysis of Power Control and Receiver Association Schemes

In this paper, we present a precise comprehensive analysis of the aggregate interference $I$ generated from an underlay network of cognitive radio (CR) nodes employing several transmit power control and receiver association schemes. Importantly, we consider spatial randomness by modeling CR transmitter nodes and receiver nodes as two independent Poisson point processes (PPPs). For the cognitive nodes, we investigate receiver association based on the distance or the instantaneous received power and power control based on the maximum possible transmitter–receiver distance, fixed or location-dependent cutoff power levels, feedback from the primary system, or the maximum number of available receivers. For each of these schemes, the exact moment generating function (mgf) and mean of aggregate $I$ power are derived for links with Rayleigh fading and exponential path loss. The resulting primary outage and the probability of secondary transmitter cutoff are also derived. Numerical results show that the secondary power thresholds and node densities significantly affect the aggregate $I$ , the primary receiver (PR) outage, and the secondary transmitter cutoff arising from the different schemes.

Learning Data Delivery Paths in QoI-Aware Information-Centric Sensor Networks

In this paper, we envision future sensor networks to be operating as information-gathering networks in large-scale Internet-of-Things applications such as smart cities, which serve multiple users with diverse quality-of-information (QoI) requirements on the data delivered by the network. To learn data delivery paths that dynamically adapt to changing user requirements in this information-centric sensor network (ICSN) environment, we make use of cognitive nodes that implement both learning and reasoning in the network. In this paper, we focus on the learning strategies and propose two techniques, namely learning data delivery A* (LDDA*) and cumulative-heuristic accelerated learning (CHAL) that use heuristics to improve the success rate of data delivered to the sink in the cognitive ICSN. While LDDA* updates a single heuristic function to choose paths that can deliver data with good QoI to the sink, CHAL accumulates heuristic values from multiple observations from the environment to choose data delivery paths that are more resource aware and considerate toward the energy consumption of the network. Extensive simulations have shown improvement of about 40% in the average rate of successful data delivery to the sink with the use of heuristic learning, when compared with a network that did not implement any learning.

Mobility Prediction Based Joint Stable Routing and Channel Assignment for Mobile Ad Hoc Cognitive Networks

Link instability and channel interference cause significant performance degradation to mobile ad hoc cognitive networks (MACNets). Existing work designs routing and assigns channels separately or does not consider mobility prediction and channel vacation to primary nodes. In this paper, we investigate how to jointly optimize route setup and channel assignment. Firstly, we propose an integrated data transmission cost (IDTC) to quantitatively measure the communication quality of links. This novel routing metric IDTC integratively considers 1) node mobility, 2) co-channel interference among primary and cognitive nodes, 3) relay workload on a specified channel, and 4) distance between the relay and the destination node. We, then, design channel assignment algorithms that completely avoid the interference with primary nodes and minimize the conflict to cognitive nodes. Finally, we propose a joint stable routing and channel assignment (J-SRCA) protocol based on mobility prediction for the network throughput maximization. In our J-SRCA, each link selected hop by hop is simultaneously assigned an interference-avoiding channel during a route setup. NS2-based simulation results demonstrate that our J-SRCA significantly improves various network performance, and the higher interference degree cognitive networks experience, the more improvement our J-SRCA will bring to the networks.

Opportunistic Channel Selection by Cognitive Wireless Nodes Under Imperfect Observations and Limited Memory: A Repeated Game Model

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a two-state machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

A Collaborative Approach towards Securing Spectrum Sensing in Cognitive Radio Networks

Cognitive radio (CR) is introduced to accommodate the steady increment in the spectrum demand. Spectrum sensing is used to detect the unexploited sub-bands in the radio environment. In order to improve the accuracy of the spectrum sensing, the cooperative spectrum sensing method is assumed to be the best method to be used. However, misbehaving sensing nodes might falsify the spectrum sensing data to prevent legitimate nodes from utilizing the spectrum. Hence, wireless security has become an important issue in cognitive radio networks to ensure a reliable spectrum sensing and a fair resource allocation and management. A node misbehaves during the spectrum sensing phase by sending false sensing data to other cooperative sensing nodes. In this paper we propose a novel collaborative approach during spectrum sensing phase to monitor sensing nodes’ behavior and identify the misbehaving sensing nodes. The proposed approach measures the node's reliability through a value called belief level (BL). The clustering method is used to divide all the sensing nodes in a specific number of clusters. Each cluster has a cluster head (CH) which is responsible for collecting sensing reports from different cognitive nodes in the same cluster about each other and identifying the misbehaving sensing nodes. Simulation results show the added value and the effectiveness of the proposed approach.

Energy-Efficiency-Based Optimal Relay Selection Scheme With a BER Constraint in Cooperative Cognitive Radio Networks

Due to the energy constraint of each cognitive user and potential secondary transmission errors, energy efficiency is very important for cognitive nodes in spectrum sensing and cooperative transmissions. In this paper, an energy awareness optimal relay selection (EAORS) scheme is proposed, in which the number of optimal relays is determined by a weighted objective function considering energy consumption in both spectrum sensing and the cooperative transmission phases, under the constraints of the global misdetection requirement and secondary cooperative transmission bit error rate (BER). In the proposed EAORS scheme, the authors investigate the tradeoff between detection accuracy and energy efficiency and construct the objective function as a weighted convex function of relay numbers with the consideration of global missing detection probability and cooperative transmission BER. This nonlinear convex problem is solved by applying numerical analysis to obtain the optimal relay numbers. Energy consumption analysis is conducted to show the effectiveness of the proposed EAORS scheme. Numerical results show that the proposed scheme outperforms a compressed sensing (CS)-based energy-efficient cooperative detection scheme and provides a tradeoff between detection accuracy and energy efficiency.

A Robust and Energy Efficient Cooperative Spectrum Sensing Scheme in Cognitive Wireless Sensor Networks

Cooperative spectrum sensing (CSS) is able to effectively solve the hidden terminal, depth attenuation, multipath shadows and other issues which are not addressed by the single-user sensing. Therefore, it has attracted a large amount of interest and several CSS algorithms have been proposed. However, they are not specifically tailored for cognitive wireless sensor networks (CWSNs) where transmission reliability, power management and interference avoidance are critical issues. In this paper, we propose a robust and energy efficient CSS scheme in CWSNs. Firstly, taking into account the limited energy of sensor node, especially the mobile node, we introduce the nodes of the network into multiple clusters for the CSS in order to save energy consumed in reporting results and exchanging information and extend the lifetime of the network. Secondly, we consider that some cognitive nodes may not work as expected. Hence, facing the problem of faulty nodes in clusters, we propose an evaluation method which considers simultaneously the node reliability and the mutually supportive degree among different nodes to support adapted decisions. Finally, after removing the node of low credibility, the energy efficiency and reliability of each cluster are improved significantly. Simulation results allow to validate that the proposed method outperforms the state of the art in energy efficiency and detection reliability, even in presence of faulty nodes.

Frequency Hopping CR MAC Protocol in Cognitive Radio Ad Hoc Network

To insure the fairness for the Cognitive Radio (CR) nodes with various available channels, a frequency hopping CR MAC protocol based dynamic (named DCR-MAC) is proposed. In the case of uneven distribution of frequency spectrum in cognitive Ad Hoc network, based on the numbers of workable channels of each cognitive node, DCR-MAC protocol adjusts access time dynamically, ensuring the node priority access channel number, in order to improve the overall performance of the network. Meanwhile, against the problem of the multi-channel hidden terminal which appears in cognitive network of single wireless interface configuration, through introducing the segment mechanism, and informing present channels occupied condition of adjacent node via receiving node, DCR-MAC protocol broadcast of channel occupation in transmission channel, and prohibit possible data transmission of adjacent node, in order to solve this problem on this account. Finally, proving that DCR-MAC protocol is obviously excelled other FH CR MAC protocols in through put capacity, packet access delay, accessing fairness which through network simulation.

A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor Networks.

In a cognitive sensor network (CSN), the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs) becomes very large. In this paper, a novel wireless power transfer (WPT)-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF) energy of the primary node (PN) to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.

Spectrum Bargaining: A Model for Competitive Sharing of Unlicensed Radio Spectrum

In this paper, we address the problem of dynamic channel access by a set of cognitive radio-enabled nodes, where each node acting in a selfish manner tries to access and use as many channels as possible, subject to interference constraints. We model the dynamic channel access problem as a modified Rubinstein–Stahl bargaining game. In our model, each node (player) negotiates with the other nodes in the network reduce in a distributed manner to obtain an agreeable sharing rule of the available channels such that no two interfering nodes use the same channel. We solve the bargaining game by finding subgame perfect Nash equilibrium (SPNE) strategies of the game. First, we consider finite horizon version of the bargaining game and investigate its SPNE strategies, which allow each node to maximize its utility against the other nodes (opponents). We then extend these results to the infinite horizon bargaining game. Furthermore, we identify Pareto optimal equilibria of the game, which help enhance network throughput. We conduct simulations to study how the “self-gain” maximizing strategy of the players impact systemwide performance.