Ph.D Guidance in Edge Computing

As the increased number of endpoints in the Internet of Things (IoT) technology, consolidating and processing the data in a single data center is critical. Edge computing brings the computing platform close to the data source, which improves the performance and speed of data without sending the data to the centralized systems. It is an extension of the self-healing network, remote cloud services, distributed data, and peer-to-peer networking. Edge computing pushes the communication capabilities, processing power, and intelligence of an edge gateway into the devices.

With the increasing quantity of data generated from the IoT devices, transmitting the data at rapid speed becomes the bottleneck in the cloud computing environment. Edge computing needs to satisfy the requirements of high resilience and low latency. For example, in the smart transportation system, edge computing responds through the wireless Internet rather than waiting for the response from the cloud. Moreover, edge computing plays a vital role in adjusting the suitable resolution of the images or videos at the edge before uploading to the remote server.

Nowadays, different applications use the edge computing technology to speed up the processing. Augmented and virtual reality, self-driving cars, and smart logistics, video surveillance, smart manufacturing, smart environment monitoring, smart healthcare, and smart homes are few potential applications of the edge computing.

  • Research Proposal in Edge Computing
  • Problem Identification in Edge Computing
  • Research Methodology
  • Mathematical Model / Formulation
  • Literature Survey in Edge Computing
  • System Design and Implementation
  • Performance Analysis and Results
  • Writing Services: Journal Papers, Synopsis and Thesis


  • Ph.D – Computer Science and Engineering, Computer Science, Computer Application, Information Technology and Computer Networks.
  • M.E/M.Tech/M.S – Computer Science and Engineering, Computer Science, Computer Application, Information Technology and Computer Networks.

Research Topics

  • Design of Edge computing architecture
  • Real-time data analytics in Edge gateway
  • Edge Computation offloading
  • Resource allocation in edge computing
  • Virtualization in edge computing
  • Deep learning for edge computing
  • Design of edge intelligence
  • Cost and performance-efficient edge analytics
  • Energy-efficient decision-making
  • Context-aware stream data management
  • Artificial Intelligence (AI) based decision making at the edge
  • Agricultural monitoring and control
  • Environmental and climate change monitoring
  • Big data analytics

Tools and Technologies

  • Java/CloudSim
  • Netbeans 7.0
  • Eclipse 4.3
  • Tomcat 7.0
  • Glass Fish 4.0
  • My-SQL 5.5
  • Apache Axis2
  • ArgoUML
  • Java Database Connectivity (JDBC)
  • Java Server Pages (JSP)
  • Servlets
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