FLINT: Flows for the Internet of Things

New protocols and technologies are continuously competing in the Internet of Things. This has resulted in a fragmented landscape that complicates the integration of different solutions. Standardization efforts try to avoid this problem, however within a certain ecosystem, multiple standards still require integration to enable trans-sector innovation. Moreover, existing devices require transformations to fit in an ecosystem. In this paper, we discuss several integration problems in the field of Low Power Wide Area Networks in the context of the Port of the Future and propose a new distributed platform architecture, called FLINT. FLINT is a framework to program flexible and configurable flows on a per device basis. A flow is constructed from fine-grained components, called adapters. Due to the modularity of an adapter, users can easily integrate existing software. We evaluated FLINT based on five levels of interoperability and show that FLINT can be used to interconnect non-interoperable systems and protocols on every level. We have also implemented FLINT in a container based environment and demonstrated that a basic configuration has a 99% forwarding rate of 17.500 513-byte packets per second, showing that the architecture can deliver good performance.

Security-Driven Distributed Platforms for Intellectual Property Resource Provision - A Case Study of TSITE IP

In the information age, with the development of big data intelligence, Intellectual Property (IP) related data is growing in a geometric progression, so the demand for data storage space is also growing, and the distributed platform of intellectual property data based on cloud storage is also emerging. Cloud computing platform has huge storage space and powerful computing power, and the distributed platform of intellectual property data based on cloud storage has emerged one after another. With this, the privacy and security issues of cloud platform also get more attention. Because the biggest feature of cloud storage is that storage is a service, it puts forward higher requirements for intellectual property services. Firstly, this paper introduces the domestic IP cloud platform services from three perspectives of government support, state-owned enterprises and private enterprises. Secondly, four typical distributed platforms provided by business resources are selected to introduce their operation modes respectively, and the problems faced by domestic IP service modes are summarized emphatically. Then, it compares and discusses the current situation of domestic IP distributed platforms. In view of the current domestic intellectual property service mode, taking TSITE IP as an example, the paper puts forward the design and construction strategy of intellectual property protection, intellectual property operation service distributed platform and operation service mode under the background of information age.

Preceptors’ perceptions of assessing clinical associate students at district hospital sites

Background: Preceptors are key stakeholders in distributed health professions’ education. They supervise students in the clinical setting to enable them to have a practical experience with patients, and they assess students’ skills at the highest tier of clinical assessment. The university where this study was done conducts a distributed Bachelor of Clinical Medical Practice course on a distributed platform which is dependent on preceptors at the training sites. Understanding the perceptions of preceptors, as major stakeholders, regarding the student assessment they do will assist the faculty to provide better support and development that might be needed and assist in maximising the benefits of distributed training.Aim: The aim of this study was to explore the perceptions of preceptors regarding assessing clinical associate students at district hospitals in the Bachelor of Clinical Medical Practice programme.Setting: The study was conducted at a rural university in the Eastern Cape province of South Africa.Methods: This was a qualitative study involving nine preceptors who were purposively selected from three district hospital training sites based on their involvement in assessing clinical associate students. Semi-structured interviews were conducted, recorded, transcribed and thematically analysed.Results: Four themes emerged from thematic analysis: assessment issues, preceptor issues, student issues and university support issues. Preceptors are committed and enthusiastic in training and assessing the clinical associate students but require input from the university in terms of training and ongoing support.Conclusion: Lack of training threatens the validity of preceptor assessment. Academic institutions should train and support preceptors to enable them better to fulfil their roles.

SLAQA

Continuous demands for higher performance and reliability within stringent resource budgets is driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as Directed-acyclic Task Graph (DTG) due to the complex interactions between their functional components that are often distributed in nature. In this article, we consider the problem of scheduling a real-time application modelled as a single DTG, where tasks may have multiple implementations designated as quality-levels, with higher quality-levels producing more accurate results and contributing to higher rewards/Quality-of-Service for the system. First, we introduce an optimal solution using Integer Linear Programming (ILP) for a DTG with multiple quality-levels, to be executed on a heterogeneous distributed platform . However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose two low-overhead heuristic algorithms called Global Slack Aware Quality-level Allocator ( G-SLAQA ) and Total Slack Aware Quality-level Allocator ( T-SLAQA ), which are able to produce satisfactorily efficient as well as fast solutions within a reasonable time. G-SLAQA , the baseline heuristic, is greedier and faster than its counter-part T-SLAQA , whose performance is at least as efficient as G-SLAQA . The efficiency of all the proposed schemes have been extensively evaluated through simulation-based experiments using benchmark and randomly generated DTGs. Through the case study of a real-world automotive traction controller , we generate schedules using our proposed schemes to demonstrate their practical applicability.

E-Commerce Security Research in Big Data Environment

Rapid development of e-commerce and mobile communication opens a new era of big data. In this article, the authors put big data and e-commerce security together. They construct electronic commerce security system from these aspects: the creation of database, the security of information storage, the mining of information based on big data environment thoroughly. The second-generation product distributed platform- Apache Hadoop which is more popular and instant has been brought in. what's more, this article expounds the structure and working process. On the base of this platform, this article analyses the certainty and security of e-commerce transactions data developed on the condition of big data. It puts forward a construction view that people should guide and monitor the behavior of e-commerce, and improve the security system of electronic commerce on the base of data.

Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system

<abstract> <p>With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.</p> </abstract>

E-Commerce Security Research in Big Data Environment

Rapid development of e-commerce and mobile communication opens a new era of big data. In this article, the authors put big data and e-commerce security together. They construct electronic commerce security system from these aspects: the creation of database, the security of information storage, the mining of information based on big data environment thoroughly. The second-generation product distributed platform- Apache Hadoop which is more popular and instant has been brought in. what's more, this article expounds the structure and working process. On the base of this platform, this article analyses the certainty and security of e-commerce transactions data developed on the condition of big data. It puts forward a construction view that people should guide and monitor the behavior of e-commerce, and improve the security system of electronic commerce on the base of data.