CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit ...CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.展开更多
The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almo...The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almost every industry, employeeperformance appraisal is done manually which may lead to favoritisms. This paperproposes a framework to perform automatic employee performance appraisal based ondata sensed from IoT. The framework classifies raw IoT data into three activities (Positive,Negative, Neutral), co-locates employee and activity in order to calculate employeeimplication and then performs cognitive decision making using fuzzy logic. From theexperiments carried out it is observed that automatic system has improved performance ofemployees. Also, the impact of the proposed system leads to motivation among employees.The simulation results show how fuzzy approach can be exploited to reward or penalizeemployees based on their performance.展开更多
基金The authors would like to thank for the support from Taif University Researchers Supporting Project Number(TURSP-2020/239),Taif University,Taif,Saudi Arabia。
文摘CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.
文摘The ubiquitous Internet of Things (IoT) through RFIDs, GPS, NFC and otherwireless devices is capable of sensing the activities being carried around Industrialenvironment so as to automate industrial processes. In almost every industry, employeeperformance appraisal is done manually which may lead to favoritisms. This paperproposes a framework to perform automatic employee performance appraisal based ondata sensed from IoT. The framework classifies raw IoT data into three activities (Positive,Negative, Neutral), co-locates employee and activity in order to calculate employeeimplication and then performs cognitive decision making using fuzzy logic. From theexperiments carried out it is observed that automatic system has improved performance ofemployees. Also, the impact of the proposed system leads to motivation among employees.The simulation results show how fuzzy approach can be exploited to reward or penalizeemployees based on their performance.
