Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

Mustafa, Ali; Islam, Muhammad; Ahmed, Salman

doi:10.1109/access.2021.3053254

Cited by 14 publications

(9 citation statements)

References 71 publications

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“…Different connectivity in a graph can be ensured by modifying the value of the transmission radius. As shown in the literature [22,34,45,46], this graph type is often applied to modeling real-world systems such as wireless sensor networks, ad-hoc IoT networks, social networks, etc. The algorithm used to generate RGGs applied in our research can be described as follows: in the beginning, we have a blank working area formed by 150 points on both the x-and the y-axis.…”

Section: Mathematical Model Of Multi-agent Systemsmentioning

confidence: 99%

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Kenyeres

2021

Future Internet

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Determining the network size is a critical process in numerous areas (e.g., computer science, logistic, epidemiology, social networking services, mathematical modeling, demography, etc.). However, many modern real-world systems are so extensive that measuring their size poses a serious challenge. Therefore, the algorithms for determining/estimating this parameter in an effective manner have been gaining popularity over the past decades. In the paper, we analyze five frequently applied distributed consensus gossip-based algorithms for network size estimation in multi-agent systems (namely, the Randomized gossip algorithm, the Geographic gossip algorithm, the Broadcast gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol). We examine the performance of the mentioned algorithms with bounded execution over random geometric graphs by applying two metrics: the number of sent messages required for consensus achievement and the estimation precision quantified as the median deviation from the real value of the network size. The experimental part consists of two scenarios—the consensus achievement is conditioned by either the values of the inner states or the network size estimates—and, in both scenarios, either the best-connected or the worst-connected agent is chosen as the leader. The goal of this paper is to identify whether all the examined algorithms are applicable to estimating the network size, which algorithm provides the best performance, how the leader selection can affect the performance of the algorithms, and how to most effectively configure the applied stopping criterion.

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Section: Mathematical Model Of Multi-agent Systemsmentioning

confidence: 99%

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Kenyeres

2021

Future Internet

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“…Compared to the centralized approach, these schemes are characterized by decreased communication and computation requirements, higher scalability, higher robustness to potential lapses, etc., thereby finding the usage in a wide range of applications over recent years [25][26][27][28]. As seen in the literature [29][30][31], distributed consensus-based algorithms for data aggregation have been gaining in popularity over the past decades.…”

Section: Data Aggregation In Wireless Sensor Networkmentioning

confidence: 99%

Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs

Kenyeres

2021

Mathematics

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In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In the presented article, we address the average consensus algorithm over bipartite regular graphs, where the application of the maximum-degree weights causes the divergence of the algorithm. We provide a spectral analysis of the algorithm, propose a distributed mechanism to detect whether a graph is bipartite regular, and identify how to reconfigure the algorithm so that the convergence of the average consensus algorithm is guaranteed over bipartite regular graphs. More specifically, we identify in the article that only the largest and the smallest eigenvalues of the weight matrix are located on the unit circle; the sum of all the inner states is preserved at each iteration despite the algorithm divergence; and the inner states oscillate between two values close to the arithmetic means determined by the initial inner states from each disjoint subset. The proposed mechanism utilizes the first-order forward and backward finite-difference of the inner states (more specifically, five conditions are proposed) to detect whether a graph is bipartite regular or not. Subsequently, the mixing parameter of the algorithm can be reconfigured the way it is identified in this study whereby the convergence of the algorithm is ensured in bipartite regular graphs. In the experimental part, we tested our mechanism over randomly generated bipartite regular graphs, random graphs, and random geometric graphs with various parameters, thereby identifying its very high detection rate and proving that the algorithm can estimate the arithmetic mean with high precision (like in error-free scenarios) after the suggested reconfiguration.

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“…represents the vector of the probabilities that the primary channels are inactive (i.e., free) in superframe τ . The values of these probabilities are updated by using equations (8)(9)(10) at the end of each superframe according to the result of each action taken by the GW (i.e., the GW successfully/unsuccessfully receives the assigned packets on the primary channels). Thereby, the GW can estimate the belief of the primary channels to decide the channel allocation for the devices in the next superframe.…”

Section: A Markov Decision Processmentioning

confidence: 99%

“…CR is a form of wireless communication in which a radio can sense the surrounding environment and automatically alter its characteristics such as power, frequency, modulation, and other operating parameters to dynamically reuse whatever spectrum is available. On the other hand, CR is regarded as a promising technology to improve the spectrum utilization of wireless users via heterogeneous wireless sensor networks by enabling secondary users to share the spectrum with primary users [9]. Variant functionalities of CR including spectrum sensing, spectrum management, spectrum sharing, and spectrum mobility, have been well investigated in literature [10].…”

Section: Introductionmentioning

confidence: 99%

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

et al. 2021

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The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, the opportunistic spectrum access capability of cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast network, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the successful transmission probability of the packets among field devices by opportunistically allocating the primary channels to the devices for data transmissions. In this paper, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process by considering the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe to maximize the received packets at the GW. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.INDEX TERMS wirelessHART, cognitive radio, markov decision process, industrial scientific medical

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Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

Cited by 14 publications

References 71 publications

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Comparative Study of Distributed Consensus Gossip Algorithms for Network Size Estimation in Multi-Agent Systems

Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs

Packet Delivery Maximization Using Deep Reinforcement Learning-Based Transmission Scheduling for Industrial Cognitive Radio Systems

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