<span>the increased usage of smart phones has led to increase usage an internet based application services. These application requires different quality of service (QoS) and bandwidth requirement. WiMAX is an efficient network to provision high bandwidth connectivity and coverage to end user. To meet QoS requirement the exiting model used adaptive model selection scheme. However, these model induce bandwidth wastage as it does not considers any feedback information for scheduling. This work present an Adaptive Uplink Scheduling (AUS) by optimizing MAC layer using </span><span>Multi-Objective Genetic Algorithm</span><span> (MOGA). </span><span>The MAC scheduler use feedback information from both physical layer and application layer. Further, to meet QoS requirement of application and utilize bandwidth efficiently this paper presented an adaptive modulation selection scheme based on user application requirement using MOGA. Our model provides </span><span>application level based QoS provisioning for WiMAX network. Experiment are conducted to evaluate performance of AUS over exiting model. The overall result attained shows AUS model attain good performance in term of throughput, successful packet transmission and packet collision.</span>
Abstract-In this era of digital world, Internet of Things (IoT) plays a vital role almost in every field of engineering. Now a days, almost every system has adopted this technology due to it's ease in access, design and development. However the technology still suffers from the issues of available resources for computing of huge amount of IoT data. In order to solve these issues, it is necessary to adopt trustworthy cloud based architecture. The trust level calculation of these cloud services is a challenging task. In this paper, we have developed a triple integrated assessment for the trust evaluation of a cloud network. This assessment has been carried out using the three major parameters i.e. security, privacy and reputation. Security assessment of the cloud service has been carried out using the security metrics like security controls deliverable. The privacy assessment is evaluated using the Privacy Impact Assessment(PIA) tool. Finally the reputation assessment of the cloud network is carried out using the reputation of it's cloud services. Experiments are carried out on different real -world web service datasets which shows that the proposed assessment model works efficiently than all other assessment models.
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