IoT Resource Estimation Challenges and Modeling in Fog

Abstract

Internet of Things (IoT) is transitioning from theory to practice. As IoT-based services evolve and the means of connectivity progress, a multitude of devices and objects will become part of it. As a result of which a lot of data will be generated and management of it is going to be a big challenge. In order to build upon realistic and more useful services, better resource management is required at the data perception layer. In this regard, fog computing plays a very vital role. Prevailing Wireless Sensor Networks (WSNs), healthcare, crowdsensing, and smart living related services have made it difficult to handle all the data in an efficient and effective way and create more useful services. Different devices generate different types of data with different frequencies, which cannot be handled by a standalone IoT. Therefore, consolidation of cloud computing with IoT, termed as Cloud of Things (CoT), has recently been under discussion. CoT provides ease of management for the growing media content and other data. Besides this, features like ubiquitous access, service creation, service discovery, and resource provisioning play a significant role which comes with CoT. Emergency, healthcare, and latency sensitive services require real-time response. With the advent of Vehicular Ad hoc Networks (VANETs) and remote healthcare and monitoring, quick response time and latency minimization are required. Fog resides between the underlying IoTs—multiple IoT networks—and the cloud datacenter in a CoT scenario. Its purpose is to manage resources, perform data filtration, preprocess, and take required security measures. To achieve this, fog requires an effective and efficient resource management framework, which we propose in this chapter as an extension of our previous work. Fog has to deal with mobile nodes and IoTs, which involve objects and devices of different types having a fluctuating connectivity behavior. All such types of service customers have an unpredictable service abortion pattern (relinquish probability), since any object or device can stop using resources at any moment. Fog, a localized cloud placed close to the underlying IoTs, provides the means to cater such issues by analyzing the behavior of the nodes and estimating resources accordingly. Similarly, Service Level Agreement (SLA) management and meeting the Quality of Service (QoS) requirements also become issues. QoS directly effects the Quality of Experience (QoE), which plays a key role in influencing the loyalty of the customer. This chapter focuses on estimation of resources for IoT nodes on the basis of their Relinquish Rate (RR) and QoS. This helps in creating a dynamic and rational way of estimating resources according to the requirements with loyalty of customers paying for itself. The devised algorithms are implemented using Java and simulated through CloudSim simulation toolkit to get the evaluation results.