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Self-Healing in Mobile Networks with Big Data

The data that self-healing uses as input and justifies its classification as big data

Self-Healing in Mobile Networks with Big Data


Mobile networks have rapidly evolved in recent years due to the increase in multimedia traffic and offered services. This has led to a growth in the volume of control data and measurements that are used by self-healing systems. To maintain a certain quality of service, self-healing systems must complete their tasks in a reasonable time. The conjunction of a big volume of data and the limitation of time requires a big data approach to the problem of self-healing. This article reviews the data that self-healing uses as input and justifies its classification as big data. Big data techniques applied to mobile networks are examined, and some use cases along with their big data solutions are surveyed.

Architecture Diagram:

Proposed System:

The main objective of self-healing is to automate the troubleshooting task described earlier by

using programs that replicate the manual processes. Commercial requirements (the demand

for QoS) create the need for a fast and reliable troubleshooting system that minimizes downtime.

Therefore, automation in troubleshooting is required. Self-healing algorithms are usually implemented using knowledge-based systems (KBSs) that imitate the process of human experts in order to accomplish a task. KBSs are algorithms composed of two parts:

Knowledge base (KB): a codified representation of the field knowledge, that is, the knowledge that the experts need in order to complete the task. To generate and improve

the KB, a continuous data mining (DM) process is run in the batch layer.

2. Inference engine (IE): the procedures that use the KB in order to complete the task.

The IE conforms to the speed layer. Some KBSs that have been previously used in troubleshooting are fuzzy logic or Bayesian networks. It is important to follow the guidelines

of big data when designing these implementations (i.e., creating parallelizable algorithms).

For an algorithm to be parallelizable, its design must guarantee that the final result is the same

when it is run as a single process and when the task is divided among multiple instances.


Communication has enhanced to convey the information quickly to the consumers.

Working professionals can work and access Internet anywhere and anytime without carrying cables or wires wherever they go. This also helps to complete the work anywhere on time and improves the productivity.

 Doctors, workers and other professionals working in remote areas can be in touch with medical centres through wireless communication.

4.   Urgent situation can be alerted through wireless communication. The affected regions can         be provided help and support with the help of these alerts through wireless communication.

5. Wireless networks are cheaper to install and maintain.

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