IoT Smart City Architectures: An Analytical Evaluation

© 2018 IEEE. while several IoT architectures have been proposed for enabling smart city visions, not much work has been done to assess and compare these architectures. By applying our proposed evaluation framework that incorporates a variety of 33 criteria, this paper presents a comparative analysis of nine existing well-known IoT architectures. The results of the analysis highlight the strengths and weaknesses of these architectures and give insight to city leaders, architects, and developers aiming at selecting the most appropriate architecture or their combination that may fit their own specific smart city development scenario

A study of influential factors in designing self-reconfigurable robots for green manufacturing

© 2018 ACIS2018.org. All rights reserved. There is incremental growth in adopting self-reconfigurable robots in automating manufacturing conventional product lines. Using this class of robots adapting themselves with ever-changing environmental conditions has been acclaimed as a promising way of reducing energy consumption and environmental impact and thus enabling green manufacturing. Whilst the majority of existing research focuses on highlighting the efficacy of self-reconfigurable robots in energy reduction with technical driven solutions, the research on exploring the salient factors in design and development self-reconfigurable robots that directly enable or hinder green manufacturing is non-extant. This interdisciplinary research contributes to the nascent body of the knowledge by empirical investigation of design-time, run-time, and hardware aspects which should be contingently balanced when developing green-aware self-reconfigurable robots

Big data analytics architecture design—An application in manufacturing systems

© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. Next, it combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture

Reusing empirical knowledge during cloud computing adoption

© 2017 Elsevier Inc. Moving existing legacy systems to cloud platforms is an ever popular option. But, such endeavour may not be hazard-free and demands a proper understanding of requirements and risks involved prior to taking any action. The time is indeed ripe to undertake a realistic view of what migrating legacy systems to cloud may offer, an understanding of exceptional situations causing system quality goal failure in such a transition, and insights on countermeasures. The cloud migration body of knowledge, although is useful, is dispersed over the current literature. It is hard for busy practitioners to digest, synthesize, and harness this body of knowledge into practice when integrating legacy systems with cloud services. We address this issue by creating an innovative synergy between the approaches evidence-based software engineering and goal-oriented modelling. We develop an evidential repository of commonly occurred obstacles and platform agnostic resolution tactics related to cloud enablement of legacy systems. The repository is further utilized during systematic goal-obstacle elaboration of given cloud migration scenarios. The applicability of our proposed framework is also demonstrated

Experiential probabilistic assessment of cloud services

© 2019 Elsevier Inc. Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can also mitigate risks against system quality goals. Towards this, we propose an interactive goal reasoning approach which is supported by a probabilistic layer for the precise analysis of cloud migration risks to improve the reliability of risk control. The approach is illustrated using a commercial scenario of integrating a digital document processing system to Microsoft Azure cloud platform

Communications in Computer and Information Science

Research around cloud computing has largely been dedicated to addressing technical aspects associated with utilizing cloud services, surveying critical success factors for the cloud adoption, and opinions about its impact on IT functions. Nevertheless, the aspect of process models for the cloud migration has been slow in pace. Several methodologies have been proposed by both academia and industry for moving legacy applications to the cloud. This paper presents a criteria-based appraisal of such existing methodologies. The results of the analysis highlight the strengths and weaknesses of these methodologies and can be used by cloud service consumers for comparing and selecting the most appropriate ones that fit specific migration scenarios. The paper also suggests research opportunities to improve the status quo

A generic cloud migration process model

© 2018, © Operational Research Society 2018. The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios

An Overview of Ontologies and Tool Support for COVID-19 Analytics

Context: The outbreak of the SARS-CoV-2 pandemic of the new COVID-19 disease (COVID-19 for short) demands empowering existing medical, economic, and social emergency backend systems with data analytics capabilities. An impediment in taking advantages of data analytics in these systems is the lack of a unified framework or reference model. Ontologies are highlighted as a promising solution to bridge this gap by providing a formal representation of COVID-19 concepts such as symptoms, infections rate, contact tracing, and drug modelling. Ontology-based solutions enable the integration of diverse data sources that leads to a better understanding of pandemic data, management of smart lockdowns by identifying pandemic hotspots, and knowledge-driven inference, reasoning, and recommendations to tackle surrounding issues.Objective: This study aims to investigate COVID-19 related challenges that can benefit from ontology-based solutions, analyse available tool support, and identify emerging challenges that impact research and development of ontologies for COVID-19. Moreover, reference architecture models are presented to facilitate the design and development of innovative solutions that rely on ontology-based solutions and relevant tool support to address a multitude of challenges related to COVID-19.Method: We followed the formal guidelines of systematic mapping studies and systematic reviews to identify a total of 56 solutions - published research on ontology models for COVID-19 - and qualitatively selected 10 of them for the review.Results: Thematic analysis of the investigated solutions pinpoints five research themes including telehealth, health monitoring, disease modelling, data intelligence, and drug modelling. Each theme is supported by tool(s) enabling automation and user-decision support. Furthermore, we present four reference architectures that can address recurring challenges towards the development of the next generation of ontology-based solutions for COVID-19 analytics

Software engineering for internet of underwater things to analyze oceanic data

Internet of Things (IoTs) represents a networked collection of heterogeneous sensors – enabling seamless integration between systems, humans, devices, etc. – to support pervasive computing for smart systems. IoTs unify hardware (embedded sensors), software (algorithms to manipulate sensors), and wireless network (protocols that transmit sensor data) to develop and operationalize a wide range of smart systems and services. The Internet of Underwater Things (IoUTs for short) is a specific genre of IoTs in which data about ocean ecosystems is continuously ingested via underwater sensors. IoUTs referred to as context-sensing eyes and ears under the sea operationalize a diverse range of scenarios ranging from exploring marine life to analyzing water pollution and mining oceanic data. This paper proposes a layered architecture that (i) ingests oceanic data as a sensing layer, (ii) computes the correlation between the data as an analytics layer, and (iii) visualizes data for human decision support via the interface layer. We unify the concepts of software engineering (SE) and IoTs to exploit software architecture, underlying algorithms, and tool support to develop and operationalize IoUTs. A case study-based approach is used to demonstrate the sensors’ throughput, query response time, and algorithmic execution efficiency. We collected IoUT sensor data, involving 6 distinct sensors from two locations including the Arabian Sea, and the Red Sea for 60 days. Evaluation results indicate (i) sensors’ throughput (daily average: 10000–20000 KB data transmission), (ii) query response time (under 30 ms), (iii) and query execution performance (CPU utilization between 60%–80%). The solution exploits SE principles and practices for pattern-based architecting and validation of emerging and next-generation IoUTs in the context of smart oceans