Collaborative Spectrum Research Articles

Retracted: Research and Application of Computer Collaborative Spectrum Sensing Guided by Radio Technology

Retracted: Research and Application of Computer Collaborative Spectrum Sensing Guided by Radio Technology

Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

Research and Application of Computer Collaborative Spectrum Sensing Guided by Radio Technology

Currently, due to the proliferation of smart appliances and the rapid development of wireless communication technologies, there is a huge shortage of spectrum resources. Cognitive radio utilizes technologies that dramatically increase the utilization of free frequency bands, detecting the environment in real time and maximizing the reallocation of spectrum resources by sensing and reallocating the licensed free frequency bands. The frequency and accuracy required to determine the free bands is an important basis for determining the spectrum awareness in all aspects of a sensing radio system. Therefore, for the more complex network environment in reality and the intrusion of malicious users, this paper uses Fast K-medoids and Mean-shift methods for information fusion and an iterative approach to overcome malicious use to fuse data and finally obtain realistic feature statistics. The results show that the collaborative identification strategy proposed in this paper has good results.

Hierarchical Cat and Mouse based ensemble extreme learning machine for spectrum sensing data falsification attack detection in cognitive radio network

While cognitive radio networks (CRNs) provide a promising solution to mitigate the scarcity of the radio spectrum, they are still susceptible to several security threats. Until now, only a few researchers considered the usage of intrusion detection systems (IDSs) to combat the threads against CRNs. In CRN, spectrum sensing is considered as a significant function and collaborative spectrum sensing (CSS) has known to result in better sensing accuracy. However, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack wherein a node maliciously falsifies the sensing report prior to sending it to the Fusion Centre (FC), with the aim of disrupting the spectrum sensing process. Thus, a new novel machine learning model is significant for detecting of attacks in a CRN. In this paper, a Hierarchical Cat and Mouse Based Ensemble Extreme Learning Machine (HCM-EELM) model has proposed to analyse the security threats in CRN for enhancing the network performance by minimizing the attacks. Also, it is attempted at investigating the viability of machine learning classification for detecting SSDF attack in a binary reporting CRN. History of the sensing reports accumulated at the FC are applied to acquire the temporal characteristics of SU, and thereby establishing the training and testing data-sets. Here, three attacks namely Random Yes (RY) attack, Random No (RN) attack, and Random False (RF) attack have considered. Moreover, the proposed model is implemented in MATLAB software and the stimulation results are compared with existing models such as Extreme Learning Machine (ELM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM). The result has determined in terms of performance metrics such as accuracy, precision, recall, F1-score and FAR. As a result, better accuracy for 99.7% has achieved than the existing models by detecting the attacks efficiently.

Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework

Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.

Development of levels of buyer-supplier collaboration: a Delphi study

The world is witnessing a stupendous increase in collaborative buyer-supplier practices in sustainable supply chains due to large-scale globalisation and mounting stakeholder pressure. There is an increasing need for classifying collaborative parameters into stages and levels of progression. With various already existing indicators of buyer supplier collaboration, classifying these collaborative parameters into stages and levels of progression is required to understand the development of these practices. This paper attempts to identify various significant indicators of such collaborations in Indian markets and to establish the different levels of collaborative spectrum Indian buyer's and supplier's perspective. This study uses a Delphi method to identify, rank and categorise the significant factors in levels of buyer-supplier collaboration using multi stakeholder viewpoints. A total of 66 experts comprising of academicians, NGOs and representatives of buyers and suppliers were selected to participate in the three rounds of the Delphi study. The study establishes seven levels of buyer-supplier collaboration that represent consensus-based indicators and display stages of locally accepted assessment of sustainable supply chain collaboration.

Cooperative Spectrum Sensing Algorithm Based on Evolutionary Game Theory

To address the inert behavior of cognitive users who do not sense to increase their throughput in the collaborative spectrum sensing process, a spectrum sensing model with a contribution reward and punishment mechanism is proposed to increase the motivation of spectrum sensing. The whole spectrum sensing process is normalized to the number of transmitted bits, and the energy consumption in the sensing phase is considered comprehensively and a reward and punishment function with a reward and punishment strength of 0.25 is introduced in the transmission phase to create an evolutionary game model for spectrum sensing, further select an evolutionarily stable strategy, and use a distributed algorithm to deal with the convergence problem for a given initial participation rate. The simulation results show that the stability of Dual-User cooperative spectrum sensing is the game strategy obtained by the fixed small penalty factor (perception, perception), and the game strategy obtained by the fixed large penalty factor (perception, nonperception) and (nonperception, perception). The superiority of the present reward mechanism is verified in the multi-user case, which can significantly improve the spectrum-aware participation rate and increase spectrum-aware motivation.

A Neuro Fuzzy with Improved GA for Collaborative Spectrum Sensing in CRN

Cognitive Radio Networks (CRN) have recently emerged as an important solution for addressing spectrum constraint and meeting the stringent criteria of future wireless communication. Collaborative spectrum sensing is incorporated in CRNs for proper channel selection since spectrum sensing is a critical capability of CRNs. According to this viewpoint, this study introduces a new Adaptive Neuro Fuzzy logic with Improved Genetic Algorithm based Channel Selection (ANFIGA-CS) technique for collaborative spectrum sensing in CRN. The suggested method’s purpose is to find the best transmission channel. To reduce spectrum sensing error, the suggested ANFIGA-CS model employs a clustering technique. The Adaptive Neuro Fuzzy Logic (ANFL) technique is then used to calculate the channel weight value and the channel with the highest weight is selected for transmission. To compute the channel weight, the proposed ANFIGA-CS model uses three fuzzy input parameters: Primary User (PU) utilization, Cognitive Radio (CR) count and channel capacity. To improve the channel selection process in CRN, the rules in the ANFL scheme are optimized using an updated genetic algorithm to increase overall efficiency. The suggested ANFIGA-CS model is simulated using the NS2 simulator and the results are investigated in terms of average interference ratio, spectrum opportunity utilization, average throughput, Packet Delivery Ratio (PDR) and End to End (ETE) delay in a network with a variable number of CRs.

Retribution or Reconciliation? Post‐Conflict Attitudes toward Enemy Collaborators

AbstractArmed groups seeking to govern territory require the cooperation of many civilians, who are widely perceived as enemy collaborators after conflict ends. The empirical literature on attitudes toward transitional justice focuses heavily on fighters, overlooking more nuanced understandings of proportional justice for civilian collaborators. Through a survey experiment conducted in an Iraqi city that was controlled by the Islamic State, we find that variations in the type of collaboration an actor engages in strongly determine preferences for punishment and forgiveness. While exposure to violence is associated with a greater desire for revenge, perceived volition behind an act—a relatively unstudied factor—is much more important. This research provides unique empirical data on the microfoundations of enemy collaborator culpability. By widening our analytical lens to consider a more realistically broad spectrum of enemy collaboration, we avoid affirming a false dichotomy between victims and perpetrators that is commonly adopted in postwar settings.

Hierarchical game-based secure data collection with trust and reputation management in the cognitive radio network

In cognitive radio networks, collaborative spectrum sensing as a promising approach is widely investigated for better spectrum management in many emergent applications suffering from spectrum scarcity such as 5G, IoT, Vanet, etc. However, such cooperation is vulnerable to being threatened by malicious users. To this end, we introduce a hierarchical multi-game-based scheme from the perspective of trust and reputation management to secure the data collection process that begins with the spectrum sensing phase and ends with the data routing phase. It is an intelligent multi-dimensional security paradigm where each game layer aims to secure one of the cognitive features. In this composite multi-game model, a penalty mechanism is introduced to contain the malicious behavior as a new security safeguard layer in cognitive radio networks.Extensive simulation results show that the proposed model has better stability and resistance in a hostile environment. Importantly, our model achieves better performance in terms of the correct decision, throughput ratio, and residual energy compared with benchmark models.

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

Analysis of Throughput in Narrowband Cognitive Radio Networks over Fading Channels: A Collaborative Spectrum Sensing Approach

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Improved Collaborative Spectrum Sensing Scheme for Maritime Cognitive Radio

Expeditious growth in wireless networks for numerous wireless services and applications lead to the increase in demand for radio spectrum in both terrestrial and marine wireless communications. Radio spectrum is scarce as the available spectrum is already been allocated to various applications. Cognitive radio technology is an optimistic solution for the spectral scarcity. In Cognitive Radio Networks (CRN), the unused licensed bands are dynamically accessed by the unlicensed secondary users for data transmission. Spectrum Sensing (SS) is the key technique to detect the presence or absence of the primary users. SS for terrestrial wireless communication have been studied vastly. This paper is aimed to study SS for Maritime Cognitive Radio Networks (MCRN) which is daunting as SS in MCRN depends on the sea state. Existing work on SS in MCRN deals with Classical Energy Detection (CED) which is a straight forward procedure with low complexity and can be applied generally to any signal irrespective of its format. Here we intend to perform SS in MCRN using Improved Energy Detection (IED) which surpasses the performance of CED without ruining its general attributes. Evaluations and analysis are carried out using detection probability performance metric for both CED and IED, simulated and compared for different sea states.

Throughput Maximization of Collaborative Spectrum Sensing Under SSDF Attacks

In cognitive radio networks (CRNs), secondary users (SUs) can opportunistically access the licensed bands through spectrum sensing while primary users (PUs) are inactive. While collaborative spectrum sensing (CSS) exploits the benefits of cooperation, the spectrum sensing data falsification (SSDF) attackers are subsequently introduced, which aim at undermining the performance of CRNs. We thus are motivated to maximize the throughput of the CRNs under SSDF attacks. First, to better discern SSDF attackers, we designate a double reputation based algorithm to differentiate the SSDF attackers and honest secondary users (HSUs). Then the trade off problem is formulated to find the optimal network parameters including, sensing duration, global decision threshold, energy detection threshold, reputation thresholds for maximizing the throughput of the secondary users network (SUN) subject to limited interference with the PUs. To address the non-convexity of the optimization problem, dummy variables are introduced and the alternative optimization method is adopted. Numerical results show that the proposed algorithm is effective in increasing throughput of the SUN with sufficient protection to the PUs.

Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies

This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum-sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and that the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software-defined radios.

Gini Index Test Metric Based Collaborative Sensing of Spectrum for Cognitive Networks

The key confront for a cognitive radio network is to identify the presence of primary users consistently so as to curtail the intrusion to license approved communications. Consequently, sensing of spectrum is a main significant requisite of a cognitive network. Nevertheless, because of the ambiguity in the channel, local interpretations don’t provide a trustworthy solution and therefore collaboration is needed between the users. Collaborative spectrum sensing is a competent scheme to enhance the spectrum utility in wireless networks, which utilizes cooperation between multiple nodes to prevail over the inadequacy of single-node to improve the performance of detection. In this paper, a detailed investigation, simulation and comparative analysis of collaborative scheme for sensing the spectrum of cognitive networks based on Gini Index test metrics is done and the results are compared with various other detecting schemes. The simulation is accomplished by means of Matlab R2014 software.

On effective rate and energy detection based spectrum sensing over cascaded FTR fading channel

The detrimental effects of multipath fading and multi-hope/keyhole propagation of millimetre–wave (mm) on the effective rate (ER) and the detection performance of energy detector (ED) have been investigated in this article. Cascaded fluctuating two ray fading model have been used to characterize the mm-wave propagation environment. Utilizing probability density function-based approach, novel closed form mathematical expressions for ER, average detection probability (ADP) and average area under the receiver operating characteristics (AUC) for the single and multi-antennae receiver-based system have been derived. To this end, novel mathematical expression for instantaneous signal to noise ratio (SNR) at the output of maximal ratio combiner (MRC) is also obtained. Subsequently obtained results are employed to compute performance metrics for the collaborative spectrum sensing where each individual receiver consists of multiple antennae. Obtained expressions are useful to evaluate ER and ED performance over different fading scenarios impaired due to cascading effects, and also helps to maintain ER versus ADP trade-off by exploiting time-bandwidth product relation. Analysis shows a considerable degradation in ER and ADP as the channel cascading effect increases and employing diversity reception is useful to overcome the degraded performance. In the end, Monte-Carlo simulations are provided to validate the numerical accuracy of theoretically derived expressions.

Research on the scheme of radio cooperative spectrum sensing for offshore ships based on trust degree in Ad Hoc network mode

Radio communication is an important part of maritime communication. As the number of ships in the offshore area continues to increase, spectrum resources appear to be particularly tight. In this work, a trust-based cooperative spectrum sensing scheme in Ad Hoc network is proposed to improve the accuracy and reliability of maritime radio communications. This scheme first divides the ship nodes into different subnets according to the Ad Hoc network protocol. And after the malicious ship nodes are selected based on actual conditions, a trust-based collaborative spectrum sensing fusion method will be given, that is, a smaller trust weight is given to malicious nodes, the method will be more trustworthy to receive the spectrum occupancy information provided by ship nodes with a higher degree of trust. Simulations have been performed to illustrate the high detection probability of the proposed spectrum sensing scheme. Therefore, it is feasible and beneficial to apply the proposed spectrum sensing scheme to improve the trustworthiness of maritime radio communication.

Collaborative spectrum sensing for cognitive visible light communications.

Cognitive visible light communication (VLC) has attracted increasing attention. By sharing underutilized VLC spectrum resources of primary users (PUs) with secondary users (SUs) opportunistically, improved spectrum utilization can be achieved without interfering with PUs. As an essential component in cognitive VLC, reliable spectrum sensing is crucial to ensure accurate cognition of PU's signal. However, due to limiting factors such as low signal-to-noise ratio (SNR) and link blocking in VLC systems, it would be difficult for a single SU to identify the status of PUs accurately and rapidly. To tackle this issue, we propose a new collaborative sensing (CS) scheme which can enhance sensing accuracy effectively by coordinating multiple SUs to participate in spectrum sensing. To evaluate the performance of the proposed CS scheme, we first develop an analytical model for the scenario of a single SU, subject to various factors such as indoor reflections and signal sampling size. Next, based on the single-SU evaluation, we further analyze the performance of the CS scheme by extending the single-SU analytical models to the multi-SU scenario. It is found that the analytical models can accurately predict the performance of the proposed CS scheme and match the results obtained by simulations. Moreover, the proposed CS scheme is effective in improving the sensing accuracy by about 40% and 10% compared with the local-sensing and the conventional CS schemes, respectively.

Spectrum Sensing for Optical Wireless Scattering Communications Over Málaga Fading—A Cooperative Approach With Hard Decision Fusion

This paper presents collaborative spectrum sensing for optical wireless scattering communications. The received signal is characterized by the conditional Poisson distribution, given the mean photon count following Málaga distributed correlated fading. We consider multi-scattered non-coplanar links and use the stochastic analytical method to model the channel. A system model is developed, utilizing the cooperative diversity drawn from the fact that the users are randomly distributed. A novel centralized cooperative spectrum sensing technique is developed that all the raw data available at each collaborative user are combined at the fusion center to make a decision. Another novel decentralized technique is also proposed where only one-bit information is required to be sent to the fusion center. For this decentralized technique, we propose a unique voting rule that leverages the location flexibility of randomly distributed users with each user experiencing correlated fading. Results demonstrate their effectiveness and provide insights into the proposed cooperative spectrum sensing system. Interestingly, it is also found that the use of the proposed voting rule requires fewer than the total number of collaborative users while satisfying a given error rate, and yields the Bernoulli distributed output of the fusion center.