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Copyright of National Security Review / Nemzetbiztonsági Szemle is the property of National University of Public Service and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In an era characterized by vast data streams and complex socioeconomic dynamics, the fusion and precise analysis of multi‐sourced intelligence has emerged as a pivotal challenge. To address this, the study constructs a sophisticated intelligence fusion network (IFN) architecture leveraging the potential of Artificial General Intelligence (AGI) and the security tenets of blockchain technology. Drawing from diverse fields including informatics, computer science, data analytics, and network security, the research adopts an integrative methodology comprising both a comprehensive literature review and systems analysis. Key findings highlight the prowess of AGI‐driven IFNs in enhancing governmental early warning systems for crisis management. These networks underscore a paradigm shift from reactive postevent measures to proactive pre‐event forecasting, thus bolstering the efficacy of governmental responses. Moreover, the decentralized nature of blockchain technology ensures data integrity, fostering trust in interdepartmental data sharing—an essential for efficient crisis management in hierarchical administrative structures. This study accentuates the need for redefining crisis management strategies, emphasizing data‐driven decision‐making and seamless intelligence sharing to ensure optimal outcomes. [ABSTRACT FROM AUTHOR]

Cognitive radio network is an intelligent and adaptive communication technology used to effectively utilize the radio spectrum to provide better communication. It enables the secondary (unlicensed) users to communicate over vacant channels without disrupting the communication of the primary (licensed) users. This paper proposes a novel contention-free MAC protocol for full-duplex and half-duplex cognitive radio networks. A novel concept of a fusion center-based channel allocation scheme is proposed to provide better channel allocation and reduced energy consumption. The proposed technique is compared with the existing techniques in terms of the average number of channels being sensed by each user, average wait time, collisions, throughput, and average energy consumption. The number of collisions increases as the maximum allotted time of communication increases in existing approaches. The experiment is performed with 20, 40, 60, and 80 s. The existing approaches show for 20 s, the number of collisions is 65, 61, 68, and 62; for 40 s, the number of collisions is 93, 96, 95, and 92; for 60 s, the number of collisions is 122, 126, 133, and 130; for 80 s, the number of collisions is 176, 170, 173, and 175. The proposed method approaches the number of collisions will become zero as the maximum allotted time of communication increases. The experimental results show that the proposed approach performs better than existing approaches. Moreover, it can be observed that the average waiting time of secondary users when a collision occurs is reduced with the application of a fusion center-based channel allocation scheme. [ABSTRACT FROM AUTHOR]

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.

A central problem in analog wireless sensor networks is to design the gain or phase-shifts of the sensor nodes (i.e. the relaying configuration) in order to achieve an accurate estimation of some parameter of interest at a fusion center, or more generally, at each node by employing a distributed parameter estimation scheme. In this paper, by using an over-parametrization of the original design problem, we devise a cyclic optimization approach that can handle tuning both gains and phase-shifts of the sensor nodes, even in intricate scenarios involving sensor selection or discrete phase-shifts. Each iteration of the proposed design framework consists of a combination of the Gram-Schmidt process and power method-like iterations, and as a result, enjoys a low computational cost. Along with formulating the design problem for a fusion center, we further present a consensus-based framework for decentralized estimation of deterministic parameters in a distributed network, which results in a similar sensor gain design problem. The numerical results confirm the computational advantage of the suggested approach in comparison with the state-of-the-art methods-an advantage that becomes more pronounced when the sensor network grows large.

Copyright of National Security Review / Nemzetbiztonsági Szemle is the property of National University of Public Service and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: The current work investigates the performance of multi‐slot‐based dynamic clustering in a cognitive radio network. For the spectrum sensing, every secondary user utilizes the multi‐slots of sensing time frame and combines the decisions of every multi‐slot, using the OR logic scheme, to get the sub‐local decision. The channel state information of the connections among the secondary users and access points present on the network's access points has been used to cluster the secondary users. To form the clusters, the secondary users with higher quality reporting channels for a specific access point have been chosen. Therefore, the clusters formed, in each iteration, with their respective access points, are dynamic in nature. Every secondary user in a cluster detects the availability of a primary user, makes a decision, and forwards it to its corresponding access point, via the reporting‐channel. Each access point employs the OR rule to merge the decisions of secondary users in the respective clusters, to make a local decision. Finally, the local decisions of all access points have been directed to fusion center, making the final decision about primary user's accessibility. The performance of multi‐slot‐based dynamic clustering has been studied relative to the different parameters, like detection probability, number of clusters, number of multi‐slots, secondary users, and access points. Further, the performance evaluation and comparisons of a multi‐slot‐based cognitive radio network with a single slot, cluster‐based cognitive radio network with non‐clustered, and multi‐slot‐based dynamic cluster with individual multi‐slots as well as dynamic clustering, have been done. [ABSTRACT FROM AUTHOR]

Abstract Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by $$13\%$$ 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

Recently, the cognitive radio (CR), which is a technology to effectively use the radio band, has been introduced to allow unlicensed users to use the spectrum efficiently. The key problem of the CR system is the overhead issue in control channel as the number of secondary users (SUs) is very high in the cooperative spectrum sensing (CSS) scenario. To alleviate this problem, the Cluster-based CSS solution is useful to prevent crowding on the control channel. Therefore, this paper uses a neural network (NN) approach based on a supervised learning algorithm at the fusion center (FC) for the suggested clustering technique. Scheme suggests a combination of hard decisions, using NN approach to increase the efficiency of CRNs with multiple clusters and to decrease the overhead issue between the FC to cluster head (CH). Also, under the Rayleigh fading channel, the proposed scheme is examined and simulated to test their detection efficiency and compare their performance with conventional schemes. The simulation results prove that proposed clustering approach enhances the accuracy of the system. Also, it is more robust than other conventional schemes.

Abstract The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

Cognitive radio (CR) is a smart innovation in wireless industry, to resolve the issues of spectrum scarcity and underutilization. In this article, detection performance of majority rule is improved further by modifying the minimum number of justifiable local decisions required. A weight based improved hard fusion technique is proposed to enhance the detection performance. A weight factor is provided to the sensed results of the secondary users (SUs) based on their position from primary user (PU) and signal‐to‐noise ratio (SNR) value. A pipelined frame structure is proposed for combining sensing and reporting durations of users, nearer to PU. This increases duration of data transmission. Finally, a relay assistance method is proposed. Users with low residual energy are provided relay assistance with relay users (RUs), to prevent from running out of energy. With improved Majority fusion, 86.93% increase in detection is obtained at SNR of (−10 to −1) dB for false alarm of 0.2. There is about 24.30%, 38.10%, and 79.56% increase in detection for weight based AND, OR, and majority fusion respectively at false alarm of 0.2. In addition to detection performance, energy consumption, and throughput analysis of the proposed methods are performed to determine the achieved energy efficiency (EE). [ABSTRACT FROM AUTHOR]

Over the past few years, dynamic spectrum access has been gaining an increasing attention as a solution to the spectrum scarcity problem. In this paper, a primary user detection technique based on Maximum A Posteriori estimation is proposed for dynamic spectrum access networks. In the proposed technique, a set of secondary users acting as sensing nodes send their individual decisions about the existence of the primary user to a central fusion center. The fusion center uses the received data to form a codeword then, applies the maximum a posteriori estimation rule to make a final decision regarding the presence of the primary user. The proposed technique takes into consideration the accuracy of the local decisions provided by the secondary users when making a final decision. In this paper, we analyze the performance of the proposed scheme and derive closed-form expressions for the upper bounds of the false alarm and misdetection probabilities. The results show that the proposed technique outperforms other combining techniques in terms of its ability to detect the primary user and, accordingly, minimizes the harmful interference to the licensed network. Moreover, the proposed technique achieves better performance at a lower number of reporting secondary users which compensates for the complexity of the maximum a posteriori estimation.

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the "n-out-of-k" rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)]. [ABSTRACT FROM AUTHOR]

The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability. [ABSTRACT FROM AUTHOR]

In cluster-based cooperative cognitive radio networks (CCRNs), spectrum sensing and decision making processes to determine whether the primary user (PU) signal is present or absent in the network are very important and vital issues to the utilisation of the idle spectrum. The reporting time delay is a very important matter to make quick and effective global decisions for the fusion center (FC) in a cluster-based CCRNs. In this paper, we propose the concept of multiple reporting channels (MRC) for cluster-based CCRNs to better utilize the reporting time slot by extending the sensing time of secondary users (SUs). A multiple reporting channels concept is proposed based on frequency division multiple access to enhance the spectrum sensing performance and reduce the reporting time delay of all cluster heads (CHs). In this approach, we assign an individual reporting channel to each cluster for reporting purposes. All the SUs in each cluster sequentially pass their sensing results to the corresponding cluster head (CH) via the assigned single reporting channel, which extends the sensing time duration of SUs. Each CH uses the dedicated reporting channel to forward the cluster decision to the FC that makes a final decision by using the "K-out-of-N" rule to identify the presence of the PU signal. This approach significantly enhances the sensing time for all SUs than the non-sequential as well as minimize the reporting time delay of all CHs than sequential single channel reporting approach. These two features of our proposed approach increase the decision accuracy of the FC more than the conventional approach. Simulation results prove that our proposed approach significantly enhances the sensing accuracy and mitigate the reporting time delay of CH compared to the conventional approach. [ABSTRACT FROM AUTHOR]

Spectrum scarcity and the number of mobile users accessing the spectrum are increasing radically, as a complement to this, under utilization of the licensed spectrum also subsist day by day. Hence, in this paper, a solution has been devised to efficiently access the unused spectrum of the licensed user to eradicate the crowded spectrum shortage. A fusion center based authenticated framework has been established to accommodate licensed Primary Users (PUs) and secondary users. The licensed Primary User (PU), updates its information to the Fusion Center (FC1) and the updated information is encrypted and uploaded to the cloud environment. Subsequently, Fusion Center (FC2) communicating with the cloud environment, downloads the PU information. The authenticated secondary users connected with FC2 were given access to the spectrum based on priority and their signal conditioning. This proposed methodology also evades the Primary user emulation attack by adopting encryption algorithms. Further, Confidentiality, Integrity, and Authentication were also guaranteed while accessing the cloud framework. Various communication and security metrices were estimated to validate the proposed system. [ABSTRACT FROM AUTHOR]

A hybrid form of the genetic algorithm and the modified K-Means cluster algorithm forms as a Gencluster to detect a spectrum hole among n-number of primary users (PUs) is present in the cooperative spectrum sensing model. The fusion center (FC), applies the genetic algorithm to identify the best chromosome, which contains many PUs cluster centers and by applying the modified K-Means cluster algorithm identifies the cluster with the PU vacant spectrum showing high accuracy, and maximum probability of detection with minimum false alarm rates are achieved. The graphical representation of the performance metric of the system model shows 95% accuracy with 98.63% detection probability and 1.36% false alarm probability. [ABSTRACT FROM AUTHOR]

We address the problem of distributed detection in multiple sensor systems in the context of underwater target detection. A number of sensor nodes are employed to detect the presence or absence of the signal. The objective is to introduce a method for improving the detection performance with the help of conventional beamforming technique. The sensors are assumed to be independent and observe a common phenomenon. The local data from all the sensors will be collected by a fusion center (FC), which makes a final global decision. The problem of distributed detection is considered with the assumption that the sensor quantized data are independent in the distributed system. The performance matrices considered in our work are probability of detection and false alarm probabilities. We apply decisions rules at the single sensor and beamformer stage and conduct performance analysis of detection using Receiver Operating Characteristic (ROC) curves. The local decisions from sensors will be provided to a fusion center, where fusion rules are used to combine the decisions and provide a global inference. We show that the detection performance increases after each stage of distributed detection, with the aid of beamforming. We have mathematically modeled the scenario and derived the equations for related parameters and verified it with simulation results. [ABSTRACT FROM AUTHOR]