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Summary Background Smart vision of the world is the latest challenge believing that 5G networks could feed all of our internet device needs. The exchange of information (data) needs not to go through backhaul because lots of capabilities will be available close to where it is needed. Presently, the majority of communications are based on single‐mode transmission. Communications will be more responsive and interactive due to 5G technology. The implementation of 5G faces various problems. It is necessary to improve the network capacity by densification, increasing spectrum, and measuring the spectral efficiency. And it also needs to reduce the latency, and it must be energy efficient. Purpose It is fit for inter‐access service network (ASN) (asynchronous) communications due to the incapability to maintain vertical handoff communications. The need for vertical handovers hinges on various factors that need to be analyzed concurrently with the signal strength in heterogeneous wireless environment. Methodology Thus, it proposed to utilize a vertical handover decision algorithm centered on the priority and speed of the subscriber. Implementation confirms the claim of the proposed method to achieve better channel utilization and maintains the QoS requirements of the mobile users. Findings Time latency calculation, evaluation of energy utilization between different nodes, and performance analysis packet loss are evaluated. Conclusion Better efficiency is attained for data transmission among the users that is one mobile node to other with commercial event management and appropriate processing in each node.

Heterogeneous network is considered as key technologies involved in significant utilization of available unused spectrum. Through the implementation of heterogeneous network energy utilization is achieved through effective spectral efficiency with higher throughput. However, this heterogeneous network environment is subjected to the challenge of latency. To derive the complete potential of heterogeneous networks machine learning algorithms need to be adopted for a dynamic environment. In the case of a practical scenario, it is difficult to reduce the network latency due to the complex network nature. To overcome those limitations, this paper proposed a Q-learning Reinforcement learning (QleaRL) for reducing latency. The proposed QleaRL utilizes Cooperative Q - learning based on consideration of state, action, and reward. Through optimal policy, reinforcement learning is computed based on Q -values. The performance of the proposed QleaRL is evaluated for latency. Simulation of proposed QleaRL is examined in terms of numerical analysis. The performance of the proposed QleaRL exhibits superior performance than the fixed power allocation (FPA) and tabular Q learning.

Abstract--This paper describes a new way of providing security for objects; the object can either be a file or an automotive like car, etc. The method used for providing security to objects is by creating a virtual fence around the object in such a way that whenever the object is moved out of the fence it is considered as an event and the event is notified to the user. Encryption is one of the techniques for providing security to objects, and the key used for encryption plays major role in providing security. This paper explains a new way of key generation which makes the file to be decrypted at the same location and by the same person (who knows the password) where it is encrypted, and the decrypted file is deleted whenever the fence is exited. This paper also explains a method for providing security to automobile by creating a fence around the vehicle. The engine automatically locks whenever the fence is exited and when the vehicle is used by an unauthorized person.