A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

Absorptive Capacity and Smart Library

The current competitive environment is significantly modifying the libraries' learning processes due to an information explosion, allowing this to be transformed into knowledge. This opportunity has been exploited in the past by the tools of “business intelligence,” but integrating it into libraries is still a daunting task. Absorptive capacity was applied to smart libraries from Schöpel's multidimensional model's perspective. Literature was thoroughly reviewed from credible sources such as ISI Web of Knowledge and Scopus. The contribution to the literature is smart library development through absorptive capacity. This approach aims to create a library intelligence model that aims to explain the absorptive capacity process that leads to smart services, people, place, and governance. This chapter presents a unique integration of various concepts: the concept of absorptive capacity and smart library. This allows the development of better library practices by obtaining benefits from these investments and facilitating intelligence creation inside libraries.

Making a Building Smart with a Co-Created and Continuously Evolving Enjoyable Service Entity—Insights from a Collaborative Study

Buildings shape cities as those cities grow from and nurture people living and working within the built environment. Thus, the conceptualization of smart building should be brought closer to the smart city initiatives that particularly target ensuring and enhancing the sustainability and quality of urban life. In this paper, we propose that a smart building should be interlinked with a smart city surrounding it; it should provide good experiences to its various occupants and it should be in an ongoing state of evolving as an ecosystem, wherein different stakeholders can join to co-produce, co-provide and co-consume services. Smart buildings require a versatile set of smart services based on digital solutions, solutions in the built environment and human activities. We conducted a multiphase collaborative study on new service opportunities guided by a Design Thinking approach. The approach brought people, technology, and business perspectives together and resulted in key service opportunities that have the potential to make the buildings smart and provide enjoyable experience to the occupants who support their living and working activities in smart cities. This paper provides the resulting practical implications as well as proposes future avenues for research.

FOLIO and Smart Libraries: Meeting the future needs of Chinese Libraries

The vision and concept of FOLIO have gained the attention and expectation from libraries. Its open and flexible service platform provides an approach to the realization of the Smart Libraries in the future. On the basis of summarising the needs of Smart Libraries in China, the paper expounds the architecture and ideas of providing smart services based on FOLIO. By analyzing the problems and challenges in the implementation and localization of FOLIO in China, the author points out that the“Chinese Alliance for Library Service Platform”(also called YunHan Alliance) should play a very important role in running the Chinese FOLIO community. It should steward the direction of the community, carry out the minimum open source software application suite, cultivate an open platform ecology, so as to support the new business models for the prosperity of Chinese libraries in the future.

How to Improve the Quality of Living in Smart Cities: Recommendations for a Citizen-centric Strategic Approach for Msheireb?

Various countries across the globe have implemented smart city initiatives to enhance the quality of living (QoL) for their citizens and other frequent users by offering smart services and innovative solutions. However, in many cases, end-users are often neglected during the planning, designing, and implementation phases of smart cities, which can impact the success of locally-based initiatives because their needs are not always fully taken into consideration. This research aims at exploring the approach of citizen-centricity in smart cities, define their key components, investigate the gap and challenges as perceived by citizens, users, and developers, and propose strategic recommendations for ways to enhance the QoL in Msheireb Smart City (MSC). The primary research was conducted to investigate citizens’ satisfaction, experience, usage of services, and to collect their feedback on areas of improvements in MSC. The results show that MSC’s users had low level of awareness about the smart services offered in the city, which resulted in less frequent use of the services. Based on this research, the strategic recommendations advise that smart city decision-makers should recognize citizens’ needs and preferences by implementing a customer relationship management strategy, promote citizens’ inclusion and engagement by establishing a citizen-centric inclusion strategy, and improve their awareness and usage of services by implementing a marketing campaign strategy.

Smart cities: potential and challenges

This paper aims to discuss a few fundamental questions related to the smart city paradigm, such as “what is actually a smart city?”, “what can we expect from a smart city?”,and “which problems have to be addressed and solved in order to turn a standard (dumb) city into a smart one?” Starting from a discussion of the Smart City concept, we will illustrate some of the most popular smart services using the results of proof-of-concept experiments carried out in different cities around the world. Successively, we will describe the fundamental functions required to build a smart service and the corresponding enabling technologies. We will then describe the main research challenges that need to be addressed in order to fulfill the Smart City vision, and we will conclude with some final remarks and considerations about the possible evolution of the Smart City concept.

Building an IoT Platform Based on Service Containerisation

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.