The emerging 5G mobile network technology is envisioned to provide an efficient platform to interconnect machines, objects, and devices in addition to interconnecting people. Equipped with peak data rates, low latency, and massive capacity, 5G technology will empower new user experiences such as virtual reality and augmented reality and provide new service areas such as connecting massive IoT. Dual connectivity is an important feature where 5G systems are overlaid on the existing 4G core network. In this paper, we propose an MM (mobility management) algorithm to efficiently perform handovers between 4G and 5G RATs (radio access technologies). Our proposed MM algorithm utilizes the strength of DC (dual connectivity) for MM as DC inherently has lesser amount of handover interruption as compared to conventional hard handover. Our MM scheme suggests appropriate data split mechanism between 4G and 5G RATs based on application-specific strategy. We provide a framework based on probabilistic model checking that leverages DC and suggests strategy-based data split mechanism for a mobile user for a variety of market verticals. We model the system as MDP (Markov decision process) where a controller breaks all the nondeterminism in the MDP based on reward calculations. The proposed framework is implemented in a well known model checker and various scenarios are used to assess its applicability. INDEX TERMS 5G, 4G, mobility management, dual connectivity, split architecture, probabilistic model checking.

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This study is carried out for indigenous development of an ITS (Intelligent Transportation System) device aimed particularly for public transport buses in the developing countries with poor infrastructure and unaccountable transportation services. The developed system uses IoT technology, extendable hardware and open source software in order to achieve commercial scalability by government and transportation authorities in such countries. The proposed device essentially collects and store data from the bus fleet and communicates the information to a central server from which software applications for passengers and transport operators are developed accordingly. The device functionality is divided into three categories which are Environment Sensing, Vehicle Management and Safety. The acquired dataset can be used to produce useful insights that can benefit the social and commercial sector of the city. Analytics such as fuel consumption, vehicle health, predicting driver behaviour, forecasting of rush-hours and vehicle maintenance requirements can be estimated. The device is packaged into a box with proper considerations of reliability in terms of power, weather proofing, storage and cabling. The proposed device will allow the passengers, especially in developing cities, to plan their journeys ahead of time and provide a holistic decision support system to the transport authorities.

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