Samir Causevic
University of Sarajevo
16 Papers
19 Citations
Samir Causevic is an academic researcher from University of Sarajevo. The author has contributed to research in topics: Software-defined networking & Multiprotocol Label Switching. The author has an hindex of 3, co-authored 16 publications.
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Papers
Benefits of using 5G Network Slicing to implement Vehicle-to-Everything (V2X) technology
Irena Seremet,Samir Causevic +1 more
- 20 Mar 2019
TL;DR: The aim of this paper is to describe advantages of using 5G network slicing V2X in relation to 4GNetwork slicing V1X, and to suggest ways to improve the quality of network slicing for 5G networks.
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Cost and Performance Optimisation in the Technological Phase of Parcel Delivery – A Literature Review
TL;DR: The present review paper provides a systematic insight into the most recent research in the field of technology, innovations and outsourcing models with the aim of reducing the cost and enhancing the productivity and quality in parcel delivery.
Potentials and advantages of applying geographic information systems in various fields of traffic engineering
Samir Causevic,Abidin Deljanin,Muhamed Begovic,Emir Deljanin +3 more
- 01 Jan 2018
TL;DR: Using the power of GIS systems, it is possible to improve and facilitate operations in all segments of traffic engineering, from planning to traffic management by performing spatial analyzes with appropriate visualization, and to integrate spatial data with other types of information into a single application for complex analysis.
Advancing Multiprotocol Label Switching Traffic Engineering with Segment Routing in Software Defined Network environment
Irena Seremet,Samir Causevic +1 more
- 18 Mar 2020
TL;DR: Benefits of using Segment Routing Traffic Engineering (SR-TE) in Software Defined Network (SDN) environment in order to overcome several MPLS TE problems and challenges presented in this paper.
8
AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT
TL;DR: A seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT is proposed and the results proved that the proposed AIaided SD- IoT model provides superior QoS performance compared to previous approaches.