The implementation of intelligent transportation systems, or what is otherwise referred to as IoT and big data analytics in transportation system are affected by several factors. Considering the overall impact of a definite and comprehensive plan that involves business, infrastructure, and organizational administration, improved customers satisfaction can better be achieved when all these antecedent factors are investigated, and their elements are determined. There is no recorded study on multidimensional implementation model where more than one of them is considered. This paper, therefore, stems from the general background of implementation success factors but aims to discover the generality of the definite plan and procedure that must be executed for a successful implementation of big data analytics and IoT-oriented transportation system. This paper employs a systematic literature review and an in-depth interview to address the obvious limitation. This paper also discussed the qualitative approach that was used to gather data on the successful implementation factors in the transportation system. The deliverable from this research will be the implementation success factors for the big data analytics and IoT-oriented transportation system.

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The growing relevance of printed and digitalized hand-written characters has necessitated the need for convalescent automatic recognition of characters in Optical Character Recognition (OCR). Among the handwritten characters, Arabic is one of those with special attention due to its distinctive nature, and the inherent challenges in its recognition systems. This distinctiveness of Arabic characters, with the difference in personal writing styles and proficiency, are complicating the effectiveness of its online handwritten recognition systems. This research, based on limitations and scope of previous related studies, studied the recognition of Arabic isolated characters through the identification of its features and dots in view of producing an efficient online Arabic handwriting isolated character recognition system. It proposes a hybrid of decision tree and Artificial Neural Network (ANN), as against being combined with other algorithms as found in previous studies. The proposed recognition process has four main steps with associated sub-steps. The results showed that the proposed method achieved the highest performance at 96.7%, whereas the benchmark methods which are EDMS and Naeimizaghiani had 68.88% and 78.5 % respectively. Based on this, ANN has the best performance recognition rate at 98.8%, while the best rate for decision tree was obtained at 97.2%.

Proxy Mobile IPv6 (PMIPv6) was proposed by the Internet Engineering Task Force (IETF) as a new network-based mobility protocol which does not require the involvement of MN’s in any form of mobility management. MN can handover relatively faster in PMIPv6 than in host-based mobility protocols (e.g. Mobile IPv6 (MIPv6)) because it actively uses link-layer attachment information which reduces the movement detection time, and eliminates duplicate address detection procedures. However, the current PMIPv6 cannot provide continuous mobility support for MN when roaming between different PMIPv6 domains; we introduce a novel inter-domain PMIPv6 scheme to support seamless handover for vehicle in motion to support continuous and seamless connection while roaming in the new PMIPv6 domain. In this paper we analytically evaluate our proposed scheme to support inter-domain mobility for vehicle roaming between two PMIPv6 domains by using Media Independent Handover (MIH) and Fully Qualified Domain Name (FQDN) to support the handover in addition to a continuous connection.