TY - JOUR
T1 - Integration of OMNI channels and machine learning with smart technologies
AU - Qureshi, Faisal Fayyaz
AU - Iqbal, Rahat
AU - Qasim, Mohammad
AU - Doctor, Faiyaz
AU - Chang, Victor
N1 - Publisher Copyright:
© 2017 Springer-Verlag GmbH Germany, part of Springer Nature
PY - 2017/12/29
Y1 - 2017/12/29
N2 - Fast evolution of the Internet, mobile technologies and energy efficient communication protocols has given a new momentum to e-businesses and world become a global village. Due to increase in the usage of internet and number of mobile users, many companies use these channels to make their products and brands visible to their customers all over the world. Although, some progress has been made towards this direction but further exploration is required, particularly it is still a challenge to enhance in-flight passengers’ shopping experience through efficient and reliable communication protocols. In this paper, we proposed a framework for omni-channel which is based on cognitive radio and machine learning. The proposed cognitive radio communication protocols provide seamless connectivity to in-flight passengers through energy efficient mode like machine learning (ML). Here, machine learning helps to develop user profile, based on relevance feedback that address the problem of catalogue and information overload. In this paper, we also discuss various challenges and opportunities associated with the proposed omni-channel business model. Moreover, the role and impact of emerging technologies such as cognitive radio and 5G in realizing omni-channel businesses is discussed in this paper. Our results explain the seamless communication between aircraft users and merchandise, through reliable and efficient connectivity when the aircraft passes over different geographic areas i.e. urban/rural land or sea at different altitudes and geographic locations. Here, backup data channel is introduced which further enhance the reliability of connection especially when primary users turns ON during the communication. Furthermore, the proposed model helps to reduce communication time and consume less energy to transmit with high throughput as compared to the benchmark cognitive radio protocols.
AB - Fast evolution of the Internet, mobile technologies and energy efficient communication protocols has given a new momentum to e-businesses and world become a global village. Due to increase in the usage of internet and number of mobile users, many companies use these channels to make their products and brands visible to their customers all over the world. Although, some progress has been made towards this direction but further exploration is required, particularly it is still a challenge to enhance in-flight passengers’ shopping experience through efficient and reliable communication protocols. In this paper, we proposed a framework for omni-channel which is based on cognitive radio and machine learning. The proposed cognitive radio communication protocols provide seamless connectivity to in-flight passengers through energy efficient mode like machine learning (ML). Here, machine learning helps to develop user profile, based on relevance feedback that address the problem of catalogue and information overload. In this paper, we also discuss various challenges and opportunities associated with the proposed omni-channel business model. Moreover, the role and impact of emerging technologies such as cognitive radio and 5G in realizing omni-channel businesses is discussed in this paper. Our results explain the seamless communication between aircraft users and merchandise, through reliable and efficient connectivity when the aircraft passes over different geographic areas i.e. urban/rural land or sea at different altitudes and geographic locations. Here, backup data channel is introduced which further enhance the reliability of connection especially when primary users turns ON during the communication. Furthermore, the proposed model helps to reduce communication time and consume less energy to transmit with high throughput as compared to the benchmark cognitive radio protocols.
KW - Airline industry
KW - Cognitive radio
KW - Communication time
KW - Smart technology
UR - http://www.scopus.com/inward/record.url?scp=85049603643&partnerID=8YFLogxK
U2 - 10.1007/s12652-017-0646-6
DO - 10.1007/s12652-017-0646-6
M3 - Article
AN - SCOPUS:85049603643
SN - 1868-5137
SP - 1
EP - 17
JO - Journal of Ambient Intelligence and Humanized Computing
JF - Journal of Ambient Intelligence and Humanized Computing
ER -