IoHT-MBA: An Internet of Healthcare Things (IoHT) platform based on microservice and brokerless architecture

Internet of Thing (IoT), currently, is one of the technology trends that are most interested. IoT can be divided into five main areas including: Health-care, Environmental, Smart city, Commercial and Industrial. The IoHT-MBA Platform is considered the backbone of every IoT architecture, so the optimal design of the IoHT-MBA Platform is essential issue, which should be carefully considered in the different aspects. Although, IoT is applied in multiple domains, however, there are still three main features that are challenge to improve: i) data collection, ii) users, devices management, and iii) remote device control. Today's medical IoT systems, often too focused on the big data or access control aspects of participants, but not focused on collecting data accurately, quickly, and efficiently; power redundancy and system expansion. This is very important for the medical sector - which always prioritizes the availability of data for therapeutic purposes over other aspects. In this paper, we introduce the IoHT Platform for Healthcare environment which is designed by microservice and brokerless architecture, focusing strongly on the three aforementioned characteristics. In addition, our IoHT Platform considers the five other issues including (1) the limited processing capacity of the devices, (2) energy saving for the device, (3) speed and accurate of the data collection, (4) security mechanisms and (5) scalability of the system. Also, in order for the IoHT Platform to be suitable for the field of health monitoring, we also add realtime alerts for the medical team. In the evaluation section, moreover, we describe the evaluation to prove the effectiveness of the proposed IoHT Platform (i.e. the proof-of-concept) in the performance, non-error, and non affected by geographical distance. Finally, a complete code solution is publicized on the authors' GitHub repository to engage further reproducibility and improvement.Web of Science12760159

Crowdsourcing Metadata For Library And Museum Collections Using A Taxonomy Of Flickr User Behavior

Library and museum staff members are faced with having to create descriptions for large numbers of items found within collections. Immense collections and a shortage of staff time prevent the description of collections using metadata at the item level. Large collections of photographs may contain great scholarly and research value, but this information may only be found if items are described in detail. Without detailed descriptions, the items are much harder to find using standard web search techniques, which have become the norm for searching library and museum collection catalogs. To assist with metadata creation, institutions can attempt to reach out to the public and crowdsource descriptions. An example of crowdsourced description generation is the website, Flickr, where the entire user community can comment and add metadata information in the forms of tags to other users' images. This paper discusses some of the problems with metadata creation and provides insight on ways in which crowdsourcing can benefit institutions. Through an analysis of tags and comments found on Flickr, behaviors are categorized to show a taxonomy of users. This information is used in conjunction with survey data in an effort to show if certain types of users have characteristics that are most beneficial to enhancing metadata in existing library and museum collections

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

Minding the Gap: Computing Ethics and the Political Economy of Big Tech

In 1988 Michael Mahoney wrote that “[w]hat is truly revolutionary about the computer will become clear only when computing acquires a proper history, one that ties it to other technologies and thus uncovers the precedents that make its innovations significant” (Mahoney, 1988). Today, over thirty years after this quote was written, we are living right in the middle of the information age and computing technology is constantly transforming modern living in revolutionary ways and in such a high degree that is giving rise to many ethical considerations, dilemmas, and social disruption. To explore the myriad of issues associated with the ethical challenges of computers using the lens of political economy it is important to explore the history and development of computer technology