Virtualization Based Efficient Service Matching and Discovery in Internet of Things

Abstract

Internet of Things (IoT) is getting more popular day by day, which triggers its adoption for solving domain specific problems. Cities are becoming smart by gathering the context knowledge through sensors and controlling specific parameters through actuators. Dynamically discovering and integrating different data streams from different sensors is a major challenge these days. In this paper, a service matchmaking algorithm is presented for service discovery utilizing IoT devices and services in a particular geographic area. It helps us to identify services based on a variety of parameters (location, query size and processing time, etc.). Customization of service selection and discovery are also explored. The conceptual framework is provided for the proposed model along with a matchmaking algorithm based on IoT devices virtualization. The simulation results elaborate the increased complexity of processing time with respect to the increasing pool of available services. The average processing time varies as the number of conditions are multiplied. Query size and complexity increases with additional number of filters and conditions which results in the reduction of the number of matching services. Moreover, upon decreasing the radius of geographic search area, the number of candidate services decreases for service matching algorithm. This is based on the assumption that IoT devices and services are evenly distributed in a given geographic area. Similarly, the remaining energy of IoT devices is also assumed to be uniformly distributed and, therefore, if we are interested in IoT devices or services with more residual energy, then a limited number of IoT devices or services will fulfill this criterion.

Effective resource virtualization for dynamic IoT devices in road network

“…Applications NFV model for long term road network [2] Reduce the resource cost Sensor virtualization module (SVM) [3] Enabled application development DEVMAN methodology [5] IoT device monitoring and management in physical network Virtsense [6] Provide the Sensing and Controlling Policy in IoT environments Service and virtual objects management (SVOM) [7] Monitor the physical resources using virtual objects and services Dynamic connectivity in network devices [8] Dynamic end to end connectivity in IoT devices Effective resource utilization in IoT platform [9] Docker, Kubernetes and Apache Kafka prototype used for resource sharing and utilization SDN and NFV [11] Energy reduced using SDN and NFV Virtualization based service matching [12] Identified the services based on the geographic area RADV [13] IoT devices identification and virtualization Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752 …”

“…The method described for service and connectivity based on the location. Also using this method, customer service selection, matching scenarios and device virtualization are performed [12]. IoT devices identification and virtualization processed by randomized and asynchronous distributed virtualiation (RADV) methods [13].…”

Ripple-Induced Whale Optimization Algorithm for Independent Tasks Scheduling on Fog Computing

“…With the flourishing of digital technologies in recent decades, cloud computing established its roots to serve users with various kinds of resources on a pay-per-use basis. Virtualization lies at the core of cloud computing, providing resources such as computation, storage, bandwidth, and application development [1]. The global adoption of cloud computing contributed to various business sectors like education, banking, and e-commerce.…”

Parallel Enhanced Whale Optimization Algorithm for Independent Tasks Scheduling on Cloud Computing

“…mining the goal of executing workloads swiftly in addition to achieving optimal resource utilization [4]. Virtualization [5] of hardware resources is the core technology for cloud resource sharing where multiple virtual machines are created on a single computing node to allow running multiple tasks from multiple users as shown in Figure 1. Cloud data centers receive hundreds of thousands of tasks to run on the virtual machines on a daily basis.…”