Technology for Smart Futures

This book chapter is an extended version of (Kor et al., SMART-ITEM: IoT-Enabled Living. Proceedings of IEEE Future Technologies Conference 2016, San Francisco, 6–8 December, 2016). The main goal of this proposed project is to harness the emerging IoT technology to empower elderly population to self-manage their own health and stay active, healthy, and independent as long as possible within a smart and secured living environment. An integrated open-sourced IoT ecosystem will be developed. It will encompass the entire data life cycle which involves the following processes: data acquisition and data transportation; data integration, processing, manipulation, and computation; visualization; data intelligence and exploitation; data sharing; and data storage. This innovative cloud-based IoT ecosystem will provide a one-stop shop for integrated smart IoT-enabled services to support older people (greater or equal to 65 years old) who live alone at home (or care homes). Another innovation of this system is the design and implementation of an integrated IoT gateway for well-being wearable and home automation system sensors with varying communication protocols. The SMART-ITEM system and services will appropriately address the following: (i) smart health and care, (ii) smart quality of life, and (iii) SMART-ITEM social community. The development of the system will be based on the user-centered design methodology so as to ensure active user engagement throughout the entire project life cycle and necessary standards as well as compliances will be adhered to (e.g., security, trust, and privacy) in order to enhance user acceptance.

Cloud computing has emerged to address the needs of businesses and to improve the quantity and quality of data that we can collect and analyse from multiple sources and devices. Cloud computing has also revolutionised the software paradigm by changing into a service-oriented paradigm where cloud resources and software are offered as a service. This service archetype has changed the way we have been thinking when producing a cloud service. This chapter provides an outline of the underpinning definition, principles and concepts which currently lack in the literature. This chapter will also outline the foundations of cloud computing and then endeavours to draft the emerging trends and evolution of cloud applications. The emerging trends will include new services, federations of cloud paradigm, smart cities, big data, IoT and mobile cloud.

Recent advances in the Internet of things (IoT) have led to renewed interests in the integration of wireless sensors, ubiquitous connectivity, and ambient intelligence technologies for developing contextually intelligent smart homes for the elderly. While tremendous results have emerged out of these efforts, the question of how to support the elderly to interact meaningfully and intuitively with intelligent devices in an IoT-enabled smart home environment remains underexplored. The different shades of physical and cognitive impairments that come with aging mean that the elderly require a uniquely different personalization approach for engaging with digital technologies. As a result, designing effective models of interaction for smart home applications for the elderly is a challenging task and will require approaches that are based on clearly defined models of context and system software architecture, as well as a systematic application of user-centered design principles. This chapter proposes a cognitive middleware for managing an elderly person’s interaction with intelligent devices in a smart home. A key contribution of the chapter is the introduction of a cognitive mechanism – that learns the habits, lifestyles, and preferences of an elderly person – into the traditional middleware. It provides a homogeneous interface involving interactive context management capabilities for adapting application behaviors to the changing environments and interaction requirements of an elderly person in a smart home.

The path planning of an autonomous surface vehicle in a lake for environmental monitoring has been modeled as a particular case of the traveling salesman problem, in which the ASV should visit a ring of beacons deployed at the shore of the lake for delivering the collected data. For achieving a complete representation of the lake, the distance traveled should be maximized instead of minimized as in the classic traveling salesman problem. The evolutionary technique known as genetic algorithm is applied for finding the optimal solution. The simulations show promising results even in the case that some constrains are included in the problem.

Paralleling the state of human progress, developments in healthcare reflect a deeply entrained drive to improve the parameters governing our own existence, including both those which threaten to disrupt our biological functions and followed by those which limit our ability to improve the effectiveness of the former [1, 2]. The technology of the past has allowed us to improve the conditions of our environment and to undertake limited medical interventions in the absence of a direct understanding of disease-causing mechanisms [2]. It is the arrival of the modern era that has opened unprecedented understanding of biological systems and disease mechanisms [3–9], yet such depth of knowledge has also brought a wider realisation of the full complexity and scale of the systems responsible for the biological processes underpinning our existence [10–13]. It is clear that in order to rise to the unprecedented challenges presented by such novel domains, the methods at our disposal must be advanced accordingly to support the changing nature of our task frameworks. The idea that representable forms of information processing may underpin familiar (and novel) forms of intelligence, such as the human brain, raises the possibility that intelligence itself may be practically simulated in alternative settings, for example, via computation, providing a capacity to sustainably address problems of arbitrary complexity. The field of intelligent systems, a research direction within the wider field of artificial intelligence (AI), is concerned with enabling the computational resources of today for the construction of systems that may respond to problems through intelligent abstractions, whose parameters differ from human cognition. Through the combination of computational infrastructure and the patterns of intelligence in this way, it is conceivable that the frontiers of healthcare and medicine may be sustainably advanced to address the broad challenges that underpin the current era.

Global demographic trends clearly point out that the world population is aging due to a combination of dropping mortality rates and increasing life expectancy. The global community is seeking solutions to address the pressing societal challenge of providing effective and efficient healthcare to the elderly. It is difficult to achieve satisfactory results merely by relying on scaling up conventional healthcare infrastructures. These techniques will not be sufficient to independently assist the elderly to live alone in a house mainly if they are suffering from chronic diseases, thus require continuous health monitoring. It is imperative to exploit the advances in emerging technologies such as biosensors, mobile devices, and communication networks to provide remote health monitoring services along with the physical infrastructural facilities. Remote and continuous monitoring of patients with chronic diseases is being considered as an efficient and cost-effective solution, which will reduce the burden on the elderly and his/her families, as well as on the health government’s expenses. While considerable research and development is being undertaken in this field, most of the current state of the art reflects a lack of a concerted and cohesive approach to develop an integrated remote health monitoring system. This chapter surveys existing pervasive healthcare systems and classifies them as academia based or industrial based, and then it develops a set of criteria to compare these solutions. It discusses some drawbacks of existing solutions and proposes future directions in pervasive healthcare, which are predicted to shape future pervasive healthcare systems. Finally, it proposes a novel healthcare monitoring framework based on an integrated and scalable architecture, which provides flexibility and enables interoperability between myriads of healthcare monitoring devices. The proposed framework relies on the analytics of both evidenced data collected from sensors as well as the massive data collected from social networks. A prototype of the framework has been developed to evaluate the applicability and the efficiency of monitoring and analytics practices.

Stakeholders have often failed to foresee, identify and address implementation barriers when introducing new technologies into health and social care settings. As a result, many technologies have not been adopted by organisations or have failed to be used to their full potential. This chapter is written from the perspective of a small-sized technology enterprise in the UK and critically discusses the experiences of deploying a novel ‘software as a service system’ in the UK health and social care sectors. The chapter will specifically discuss the barriers that have been faced throughout implementations and how they were addressed in a series of case studies. We propose that the lessons learned from these implementations will be just as relevant in the future as they were at the time of writing.

This book chapter is an extension of (Bazarhanova et al., Belief rule-based environmental responsibility assessment for small and medium-sized enterprises (note: this includes a comparison with fuzzy logic), Proceedings of 2016 IEEE Future Technologies Conference, 6–7 December, San Francisco, US, 2016; Bazarhanova et al., A Belief Rule-Based Environmental Responsibility Assessment System for Small and Medium-Sized Enterprises (note: without comparison with fuzzy logic), International SEEDS Conference, 14–15th September, 2016, Leeds. (Won Highly Commended Award for Green Infrastructure Category, 2016)) and adaptation from (Bazarhanova, A Belief Rule-Based Environmental Responsibility Assessment System for Small and Medium-Sized Enterprises, An unpublished Masters Degree Dissertation, Leeds Beckett University. URL: https://​www.​doria.​fi/​bitstream/​handle/​10024/​124773/​Thesis%20​Bazarhanova.​pdf?​sequence=​2, 2016). This chapter proposes the use of belief rule-based (BRB) inference engine for Environmental Responsibility assessment in small- and medium-sized enterprises. Such a context-adapted approach is believed to generate well-balanced, sensible, and Green ICT readiness-adapted results, to help enterprises focus on making improvement on more sustainable business operations.

Machine-to-Machine communication is rapidly growing and becoming a significant part of the current 4G and future 5G mobile network data traffic, which is intended to provide coverage support and lower costs for mobile network providers. The 5G mobile network represents a promising technology to support the future of Machine-to-Machine communications. In recent years, smart devices, such as smartphones and traffic monitoring systems, have experienced exponential growth over mobile networks with different radio resource usage. This has caused massive challenges as a result of simultaneous access data traffic as well as a large number of devices sending small-sized data. This chapter proposes a novel data traffic aggregation and slicing model with algorithms in 5G uplink, based on classifying and measuring the data traffic to achieve quality of service for smart systems. Moreover, 5G radio resources are efficiently shared by several smart devices in a relay node by aggregating incoming data traffic based on quality of service.

In recent years, a number of major improvements were introduced in the area of computer networks, energy-efficient network protocols, and network management systems. Software-defined networking (SDN) as a new paradigm for managing complex networks brings a significant opportunity to reduce the energy consumption among ICT. In this paper, we are tackling improvements in the process of monitoring the states of the networking devices and optimizing the existing solutions. We are monitoring the energy consumption of a network architecture and augment the retrieved raw power data to detect changes in the state of the devices. The goal is to benchmark the difference between the power data fetched from the real measures and the data extracted from the power models, translated as the expected behavior of the devices. An application is designed to monitor and analyze the retrieved power data of a simulated ICT infrastructure composed of Cisco switches and routers, Dell Precision stations, and Raritan PDUs. Moreover, smart algorithms are developed which outcome results with detection of changes in the state of the devices, as well as with detection and isolation of possible anomalies and their impact on the energy consumption. The application is envisioned to be an extension to the existing SDN controllers for monitoring and reporting the changes in the state of the ICT devices.

The energy consumption by information and communication technology (ICT) equipment is rapidly increasing which causes a significant economic and environmental problem. At present, the network infrastructure is becoming a large portion of the energy footprint in ICT. Thus, the concept of energy-efficient or green networking has been introduced. Now one of the main concerns of network industry is to minimize energy consumption of network infrastructure because of the potential economic benefits, ethical responsibility, and its environmental impact. In this paper, the energy management strategies to reduce the energy consumed by network switches in local area network (LAN) have been developed. According to the life-cycle assessment of network switches, the highest amount of energy is consumed during usage phase. The study considers bandwidth, link load, and traffic matrices as input parameters which have the highest contribution to energy footprints of network switches during usage phase and energy consumption as output. Then with the objective of reducing energy usage of network infrastructure, the feasibility of putting Ethernet switches hibernate or sleep mode was investigated. After that, the network topology was reorganized using clustering method based on the spectral approach for putting network switches to hibernate or switched off mode considering the time and communications among them. Experimental results show the interest in this approach in terms of energy consumption.

While there has been considerable academic, political and economic attention given to the development of smart futures, little attention has been paid to the impact of smart technologies on childhood. The chapters in this section focus on the impact of emerging technologies in society, and our intention here is to consider some of the challenges which arise in safeguarding children in digital environments. Children, previously ignored in sociological analysis, have increasingly become the centre of social science attention with the growth of childhood studies (see [35]) and framed, arguably often inappropriately, as digital natives [67]. Many of these analyses remain, as Holloway and Valentine argued in [31] either ‘cybertopian’ or ‘cybercritical’, yet adopting such polarised standpoints is unhelpful in understanding the challenges that smart technologies bring. This chapter sets out to consider some of the emerging trends, developments and challenges associated with the design and applications of future technologies and smart environments in safeguarding children online.

The risk assessment of any network or security systems has a high level of uncertainties because usually probability and statistics were used to evaluate the security of different cybersecurity systems. In this book chapter, we will use Shannon entropy to represent the uncertainty of information utilised to calculate systems risk and entropy weight method since the weight of the object index is normally used and points to the significant components of the index. We evaluate the risk of security systems in terms of different vulnerabilities and protections existing in each host. A new methodology was developed to present an attack graph with a dynamic cost metric based on a Dynamic Vulnerability Scoring System (DVSS), and also a novel methodology to estimate and represent the cost-centric approach for each host’s states was followed up.

A framework is carried out on a test network, using Shannon entropy with the Nessus scanner to detect known vulnerabilities, to implement these results and to build and represent the dynamic cost-centric attack graph. We used the results to represent possible risks as a matrix. At the next stage, the proposed risk’s matrix was normalised to calculate the entropy and the entropy weight. Finally, the weight and the path will be used to evaluate and calculate the total risk in the system and suggest to the system administrator a clear guidance on the vulnerable security entities. We try to develop a novel approach to suggest the cybersecurity approach that is suitable for the majority of cyber systems by introducing the term security entities.

Global demographics and the rising number of people living with long-term conditions have placed increasing pressure on existing healthcare delivery. Technological innovation in the form of telehealth and telecare has been posited as one possible solution, offering individuals with chronic conditions a set of tools to monitor and better manage their health, reducing pressure on existing services and making possible cost savings through fewer admissions to hospital [30]. These advances are challenging and reshaping existing models of care, revolutionizing how and where healthcare is provided.

The Comprehensive Digital Forensic Investigation Process Model (the CDFIPM), presented in Montasari (IJESDF 8(4)285–301, 2016), provides guidelines for carrying out digital investigations in the UK jurisdiction in a forensically sound manner. The CDFIPM is comprehensive in that it covers the entire digital forensic investigative process; it is generic such that it can be applied in the three fields of law enforcement, incident response and commerce. The model is also formal in that it synthesises, harmonises and extends the existing digital forensic investigation process models. The CDFIPM also needs to be subjected to an evaluation process in order to determine ‘whether the model has been built right’ and ‘whether the right model has been built’. To this end, the CDFIPM is applied to a case study in this paper to determine whether the model meets the two components of ‘utility’ and ‘usability’.

Knowledge management (KM) is considered to be one of the key players essential for improvement and achievement of performance excellence. KM systems accumulate data and information in order to provide better opportunities in a journey toward sustainability in the knowledge economy. The present research conducts a systematic literature review on the application of KM systems and processes to the field of green ICT and ICT for greening. The standard systematic literature review method was used in order to employ a manual search of journals and conference proceedings. More than 40 articles and book chapters were chosen for an in-depth analysis, which shows increasing interest in the application of KM to sustainable development. The review covers essential knowledge management tools and technologies identified within the body of literature and provides a summary of their implication to sustainable development. The paper illustrates that KM systems are essential in order to foster green ICT and ICT for greening. This study concludes by proposing suggestions for future research.

Nuclear power plants (NPPs) are extremely complex systems that are operated and monitored by human operators. Maximum care is exercised to keep the likelihood of potential risks to a very low value. However, in the event of an unlikely abnormal occurrence, the operator has to take necessary actions relatively fast, which involves complex judgments, making trade-offs between partly incompatible demands, and requires expertise to take proper decision. Over the years, several intelligent systems have evolved to assist the operator for decision-making; however they are highly computationally intensive and may not be suitable for real-time online monitoring or may require large amounts of data. In this paper, an efficient artificial neural network (ANN) model has been developed based on principal component analysis (PCA) for identification of large break loss-of-coolant accident (LOCA) in NPPs. A large database of reactor process parameters is generated through various thermal hydraulic codes, and PCA was performed for 32 break scenarios of LOCA in inlet and outlet reactor headers with and without the availability of emergency core cooling system (ECCS). The PCA was used to optimize the inputs of ANNs. The results of comparison between the classical and PCA-based ANN have been presented in this paper. The simplified ANN model based on PCA is relatively in good agreement with the classical ANN model. It can be said that the PCA-based ANN gives a great computational advantage, due to an important factor when the input parameter dimension is substantially optimized and is usually a case in NPPs. However, there is a scope of improvement in the PCA-based ANN in terms of reduction of error, and this could be achieved by incorporating more of variance during dimension reduction by PCA and also applying different architectures of ANN.