BOHR Journal of Computational Intelligence and Communication Network

Linear solution of the passive localization problem with combined single-station direction finding and two-station time difference information

2024-05-02T10:02:22+00:00

Based on the main station’s direction measurement value and the time difference measurement value of the two stations, two definite solution equations can be listed in the polar coordinate system by directly utilizing the path difference equation and the cosine theorem, and the analytical solution can be obtained by solving the two definite solution equations jointly. Further comparison of the ranging errors shows that the ranging accuracy of the two-station directional and time-difference positioning system is only slightly worse than that of the three-station time-difference positioning system. The new results provide a better basis for the engineering design of two-station hybrid localization.

Performance evaluation of proactive, reactive, and hybrid routing protocols for small, medium, and large mobile ad hoc networks

2024-05-02T10:22:01+00:00

The popularity of wireless ad hoc networks is increasing daily. Examples of such networks are MANETs, VANETs, and Sensor networks. These types of wireless networks are dynamic and have different working features in common, and it is not always obvious to know which network type should be used given the needs of users. Determining the best protocols for a particular sort of wireless network’s problems is frequently another challenge. We evaluate the above-mentioned networks to fill this gap and ultimately demonstrate that MANET is the best option since it can be easily deployed anywhere, at any time, to meet the needs of the majority of users. For evaluation purposes, we used prominent network evaluation parameter metrics, i.e., End-to-end Delay, Network Throughput, and Packet delivery ratio. We compare three different MANET routing protocol types—PROACTIVE, REACTIVE, and HYBRID—one from each type; DSDV [Proactive], TORA [Hybrid], and AODV [Reactive]. The simulations and their results for both small and large systems were done on the OPNET simulator. According to MANET, DSDV outperforms other protocols in networks with high node densities in terms of the same metrics, while TORA performs better in networks with low node densities in terms of packet delivery ratio, end-to-end delay, and throughput.

Harvesting cognitive radio networks using artificial intelligence

2024-05-03T04:44:34+00:00

The utilization of Artificial Intelligence (AI) in leveraging Cognitive Radio Networks (CRNs) represents an emerging field of study. This surge is primarily driven by operational expenses, concerns over traditional power sources, and limitations inherent in current CRN technologies. Furthermore, integrating AI into CRN operations significantly enhances efficiency and maximizes the application of the electromagnetic spectrum. To enable real-time processing, Cognitive Radio (CR) is paired with AI methodologies, fostering adaptive and intelligent resource allocation. This research paper outlines CRNs: their objectives, available resources, and constraints. It subsequently introduces AI techniques, emphasizing the profound influence of learning within CR contexts. The application of model methods such as Markov Model, fuzzy logic, and Neural Network is explored. AI technology is employed in critical CR tasks like spectrum sharing, spectrum sensing, resource allocation, optimization of spectrum mobility, decision-making processes, and more. The overarching goal is to showcase how AI can assist researchers in harnessing and implementing diverse CR designs effectively.