Prediction-Based Channel Selection Prediction in Mobile Cognitive Radio Network

The emerging 5G wireless communications enabled diverse multimedia applications and smart devices in the network. It promises very high mobile traffic data rates, quality of service as in very low latency and improvement in user’s perceived quality of experience compared to current 4G wireless network. This encourages the increasing demand of significant bandwidth which results a significant urge of efficient spectrum utilization. In this paper, modelling, performance analysis and optimization of future channel selection for cognitive radio network by jointly exploiting both CR mobility and primary user activity to provide efficient spectrum access is studied.  The modelling and prediction method is implemented by using Hidden Markov Model algorithm. The movement of CR in wireless network yields location-varying spectrum opportunities. The current approaches in most literatures which only depend on reactive selection spectrum opportunities result of inefficient channel usages. Moreover, conventional random selection method tends to observe a higher handoff and operation delays in network performance.  This inefficiency can cause continuous transmission interruptions leading to the degradation of advance wireless services. This work goal is to improve the performance of CR in terms number of handoffs and operation delays. We perform simulation on our prediction strategy with a commonly used random sensing method with and without location. Through simulations, it is shown that the proposed prediction and learning strategy can obtain significant improvements in number of handoffs and operation delays performance parameters. It is also shown that future CR location is beneficial in increasing mobile CR performance. This study also shows that the number of primary user in the network and the PU protection range affect the performance of mobile CR channel selection for all methods

Location Assisted Proactive Channel in Heterogeneous Cognitive Radio Network

Cognitive Radio Network is an emerging technology to increase spectrum efficiency by intelligently accessing the spectrum in an opportunistic manner. The secondary user must sense every spectrum band available in order to prevent harmful interference to primary user. However, in heterogeneous environment, spectrum opportunity varies when the secondary user is mobile according to its’ geographical location. There is a certain transmission region surrounding the primary users where their transmission ranges will not exceed which therefore provides a platform for secondary user to define new policies to capture spectrum opportunities. Therefore, in this paper, we explored and proposed a proactive based spectrum decision framework based on secondary users mobility to capture more spectrum opportunities. The results showed significant improvements in throughput and switching performance when localization is inherited in the cognitive radio system

Integration of Heterogeneous Spatial Databases for Disaster Management

International audienceThe response phase in a disaster case is often considered to be the most critical in terms of saving lives and dealing with irreversible damage. The timely provision of geospatial information is crucial in the decision-making process. Thus, there is a need for the integration of heterogeneous spatial databases which are inherently distributed and created under different projects by various organizations. The integration of all relevant data for timely decision making is a challenging task due to syntactic, schematic and semantic heterogeneity. This paper aims to propose a framework for the integration of heterogeneous spatial databases using novel approaches, such as web services and ontologies. We focus on providing solutions for the three levels of heterogeneity, in order to be able to interrogate the content of the different databases conveniently. Based on the proposed framework, we implemented a use case using heterogeneous data belonging to La Rochelle city in France

Location assisted proactive channel in heterogeneous Cognitive Radio Network

Cognitive Radio Network is an emerging technology to increase spectrum efficiency by intelligently accessing the spectrum in an opportunistic manner. The secondary user must sense every spectrum band available in order to prevent harmful interference to primary user. However, in heterogeneous environment, spectrum opportunity varies when the secondary user is mobile according to its’ geographical location. There is a certain transmission region surrounding the primary users where their transmission ranges will not exceed which therefore provides a platform for secondary user to define new policies to capture spectrum opportunities. Therefore, in this paper, we explored and proposed a proactive based spectrum decision framework based on secondary users mobility to capture more spectrum opportunities. The results showed significant improvements in throughput and switching performance when localization is inherited in the cognitive radio system

A Delphi Study to Validate a New Model and Instruments for Assessment of Data Utilization of Flood Management

Since there are no methods for determining the extent to which data is used, it is currently difficult for the Malaysian government to identify potential improvements required for successful flood management. In the light of this situation, the development of a new model and instruments to assess data utilization of flood management in Malaysia using a performance measurement approach has been proposed. Therefore, validation of the assessment model and instruments is required to determine acceptability for successful data utilization assessment implementation. The initial model and instruments went through two Delphi rounds with nine validation panelists. Consensus was reached among all panelists, indicating relatively high acceptance and it is quite evident that they have accepted the proposed data utilization assessment model and instrument for this study

A Deep Dive into Robot Vision - An Integrative Systematic Literature Review Methodologies and Research Endeavor Practices

Novel technological swarm and industry 4.0 mold the recent Robot vision research into innovative discovery. To enhance technological paradigm Deep Learning offers remarkable pace to move towards diversified advancement. This research considers the most topical, recent, related and state-of-the-art research reviews that revolve around Robot vision, and shapes the research into Systematic Literature Survey SLR. The SLR considers a combination of more than 100 reviews and empirical studies to perform a critical categorical study and shapes findings against research questions. The research study contribution spans over multiple categories of Robot vision and is tinted along with technical limitations and future research endeavors. Previously multiple research studies have been observed to leverage Robotic vision techniques. Yet, there is none like SLR summarizing recent vision techniques for all targeted Robotic fields. This research SLR could be a precious milestone in Robot vision for each glimpse of Robotic