Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process

Energy-efficient vertical handover parameters, classification and solutions over wireless heterogeneous networks: a comprehensive survey

In the last few decades, the popularity of wireless networks has been growing dramatically for both home and business networking. Nowadays, smart mobile devices equipped with various wireless networking interfaces are used to access the Internet, communicate, socialize and handle short or long-term businesses. As these devices rely on their limited batteries, energy-efficiency has become one of the major issues in both academia and industry. Due to terminal mobility, the variety of radio access technologies and the necessity of connecting to the Internet anytime and anywhere, energy-efficient handover process within the wireless heterogeneous networks has sparked remarkable attention in recent years. In this context, this paper first addresses the impact of specific information (local, network-assisted, QoS-related, user preferences, etc.) received remotely or locally on the energy efficiency as well as the impact of vertical handover phases, and methods. It presents energy-centric state-of-the-art vertical handover approaches and their impact on energy efficiency. The paper also discusses the recommendations on possible energy gains at different stages of the vertical handover process

Prediction Model for Offloading in Vehicular Wi-Fi Network

It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi) as well as fourth generation long term evolution advanced (4G LTE-A) network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement and driver profile to predict the next available AP.  The proposed model is compared with the existing models to evaluate its practicability

Intelligent Reward based Data Offloading in Next Generation Vehicular Networks

A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module

セルラーネットワークにおける無線LANオフロードのためのネットワーク選択方式に関する研究

早大学位記番号:新7456早稲田大

Context-aware multi-attribute decision multi - attribute decision making for radio access technology selection in ultra dense network

Ultra Dense Network (UDN) is the extreme densification of heterogeneous Radio Access Technology (RAT) that is deployed closely in coordinated or uncoordinated manner. The densification of RAT forms an overlapping zone of signal coverage leading to the frequent service handovers among the RAT, thus degrading overall system performance. The current RAT selection approach is biased towards network-centric criteria pertaining to signal strength. However, the paradigm shift from network-centric to user-centric approach necessitates a multi-criteria selection process, with methodology relating to both network and user preferences in the context of future generation networks. Hence, an effective selection approach is required to avoid unnecessary handovers in RAT. The main aim of this study is to propose the Context-aware Multiattribute decision making for RAT (CMRAT) selection for investigating the need to choose a new RAT and further determine the best amongst the available methods. The CMRAT consists of two mechanisms, namely the Context-aware Analytical Hierarchy Process (CAHP) and Context-aware Technique for Order Preference by Similarity to an Ideal Solution (CTOPSIS). The CAHP mechanism measures the need to switch from the current RAT, while CTOPSIS aids in decision making to choose the best target RAT. A series of experimental studies were conducted to validate the effectiveness of CMRAT for achieving improved system performance. The investigation utilises shopping mall and urban dense network scenarios to evaluate the performance of RAT selection through simulation. The findings demonstrated that the CMRAT approach reduces delay and the number of handovers leading to an improvement of throughput and packet delivery ratio when compared to that of the commonly used A2A4-RSRQ approach. The CMRAT approach is effective in the RAT selection within UDN environment, thus supporting heterogeneous RAT deployment in future 5G networks. With context-aware selection, the user-centric feature is also emphasized

SDN-assisted efficient LTE-WiFi aggregation in next generation IoT networks

Currently, the increasing demands of user terminals has surged drastically and pulling up the global data traffic along. According to 3GPP, offloading is one of the most beneficial and advantageous options to handle this critical traffic bottleneck, however, both Long Term Evolution (LTE) and Wireless Local Area Network (WLAN) are loosely coupled. To mitigate the User Equipment (UE) from latency issues during offloading and for tighter integration of LTE and WLAN radio networks, LTE-WLAN Aggregation (LWA) was introduced by 3GPP which is apparently suitable for Internet of Things (IoT) devices. However, LWA is not suitable for high mobility scenarios as UEs’ information need to be updated for every new environment because of the frequent aggregation triggers which are mostly non-optimal and demands for a high-level controller. To resolve the disadvantage of non-optimal aggregation triggers, in this paper, we proposed Software Defined Networking (SDN) based approach for LWA, named as LWA under SDN Assistance (LWA-SA). In this approach, SDN initiates aggregation appropriately between LTE and an optimal WLAN Access Point (AP) which avoids frequent reconnections and deprived services. As multiple parameters are required for selection of an optimal WLAN AP, so we use Genetic Algorithm (GA) that considers each parameter as fitness value for the selection of optimal WLAN AP. This maximizes the throughput of UE and reduces the traffic pressure over licensed spectrum. Further, mathematical model is formulated that uses Karush-Kuhn-Tucker (KKT) to find the maximum attainable throughput of a UE. Using NS-3, we compared our approach with offloading scenarios and LWA. The simulation results clearly depict that LWA-SA outperforms existing schemes and achieves higher throughput