HMF Based QoS aware Recommended Resource Allocation System in Mobile Edge Computing for IoT

Internet of Things (IoT) is facing the problem of continuously increasing number of IoT devices, and they generates a huge data transmission load to the cloud data centers. This also decreases the data transmission rate along with the Quality of Services (QoS). Selection of a specific user from a group of users those are demanding for the same services is quite difficult. As a solution we have proposed a QoS aware resources allocation policy using user rating implicit feedback in Mobile Edge Computing (MEC) for IoT to overcome the delay of the service. The proposed selection procedure will select the user depending upon their previous purchase preferences and implicit feedback from the cluster, which is develop using similarity calculation. We have recommend resource to eligible users according to implicit feedback of the previously served users, by applying time-based collaborating filter. The selected user will get the resource according to the minimum distance between user and resource. Precision, recall and F-Measure are used for the accuracy checking purpose. We achieve 80-92% accuracy using proposed method.