Hissam Tawfik

Path planning in construction sites: performance evaluation of the Dijkstra, A∗, and GA search algorithms

Abstract This paper presents the application of path planning in construction sites according to multiple objectives. It quantitatively evaluates the performance of three optimisation algorithms namely: Dijkstra, A∗, and Genetic algorithms that are used to find multi-criteria paths in construction sites based on transportation and safety-related cost. During a construction project, site planners need to select paths for site operatives and vehicles, which are characterised by short distance, low risks and high visibility. These path evaluation criteria are combined using a multi-objective approach. The criteria can be optimised to present site planners with the shortest path, the safest path, the most visible path or a path that reflects a combination of short distance, low risk and high visibility. The accuracy of the path solutions and the time complexities of the optimisation algorithms are compared and critically analysed.

GreeDi: An energy efficient routing algorithm for big data on cloud

Abstract The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches are used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology.

A simulation environment for construction site planning

This paper presents the design of a simulation environment for the modelling, visualisation and optimisation of construction site layouts. This construction site workspace application aims to support the site planning task by analysing space and risk on the site and generating automated site layouts which satisfy a combination of cost, efficiency and safety criteria. The construction site simulation environment forms part of an EU funded project called DIVERCITY (Distributed Virtual Workspace for enhancing Communication within the Construction Industry). This is concerned with the development of virtual simulation prototypes to support the client briefing, design review, and construction planning stages of the construction process. The site analysis module comprises a safety analysis component, a space analysis component, and an optimisation component. The safety analysis component is a generic model for the graphical and numerical representation of risk/hazard spaces. The space analysis component represents and classifies the various spaces on the construction site according to their relative importance in terms of accessibility and visibility. The optimisation component takes safety and space analysis information, and performs site layout optimisation according to travelling distance minimisation, risk minimisation, and space use maximisation criteria.

An energy-aware service composition algorithm for multiple cloud-based IoT applications

There has been a shift in research towards the convergence of the Internet-of-Things (IoT) and cloud computing paradigms motivated by the need for IoT applications to leverage the unique characteristics of the cloud. IoT acts as an enabler to interconnect intelligent and self-configurable nodes things to establish an efficient and dynamic platform for communication and collaboration. IoT is becoming a major source of big data, contributing huge amounts of streamed information from a large number of interconnected nodes, which have to be stored, processed, and presented in an efficient, and easily interpretable form. Cloud computing can enable IoT to have the privilege of a virtual resources utilization infrastructure, which integrates storage devices, visualization platforms, resource monitoring, analytical tools, and client delivery. Given the number of things connected and the amount of data generated, a key challenge is the energy efficient composition and interoperability of heterogeneous things integrated with cloud resources and scattered across the globe, in order to create an on-demand energy efficient cloud based IoT application. In many cases, when a single service is not enough to complete the business requirement; a composition of web services is carried out. These composed web services are expected to collaborate towards a common goal with large amount of data exchange and various other operations. Massive data sets have to be exchanged between several geographically distributed and scattered services. The movement of mass data between services influences the whole application process in terms of energy consumption. One way to significantly reduce this massive data exchange is to use fewer services for a composition, which need to be created to complete a business requirement. Integrating fewer services can result in a reduction in data interchange, which in return helps in reducing the energy consumption and carbon footprint.This paper develops a novel multi-cloud IoT service composition algorithm called (E2C2) that aims at creating an energy-aware composition plan by searching for and integrating the least possible number of IoT services, in order to fulfil user requirements. A formal user requirements translation and transformation modelling and analysis is adopted for the proposed algorithm. The algorithm was evaluated against four established service composition algorithms in multiple cloud environments (All clouds, Base cloud, Smart cloud, and COM2), with the results demonstrating the superior performance of our approach.

Context-aware knowledge modelling for decision support in e-health

In the context of e-health, professionals and healthcare service providers in various organisational and geographical locations are to work together, using information and communication systems, for the purpose of providing better patient-centred and technology-supported healthcare services at anytime and from anywhere. However, various organisations and geographies have varying contexts of work, which are dependent on their local work culture, available expertise, available technologies, peoples perspectives and attitudes and organisational and regional agendas. As a result, there is the need to ensure that a suggestion - information and knowledge - provided by a professional to support decision making in a different, and often distant, organisation and geography takes into cognizance the context of the local work setting in which the suggestion is to be used. To meet this challenge, we propose a framework for context-aware knowledge modelling in e-health, which we refer to as ContextMorph. ContextMorph combines the commonKADS knowledge modelling methodology with the concept of activity landscape and context-aware modelling techniques in order to morph, i.e. enrich and optimise, a knowledge resource to support decision making across various contexts of work. The goal is to integrate explicit information and tacit expert experiences across various work domains into a knowledge resource adequate for supporting the operational context of the work setting in which it is to be used.

Understanding Clinical Work Practices for Cross-Boundary Decision Support in e-Health

One of the major concerns of research in integrated healthcare information systems is to enable decision support among clinicians across boundaries of organizations and regional workgroups. A necessary precursor, however, is to facilitate the construction of appropriate awareness of local clinical practices, including a clinicians actual cognitive capabilities, peculiar workplace circumstances, and specific patient-centered needs based on real-world clinical contexts across work settings. In this paper, a user-centered study aimed to investigate clinical practices across three different geographical areas-the U.K., the UAE and Nigeria-is presented. The findings indicate that differences in clinical practices among clinicians are associated with differences in local work contexts across work settings, but are moderated by adherence to best practice guidelines and the need for patient-centered care. The study further reveals that an awareness especially of the ontological, stereotypical, and situated practices plays a crucial role in adapting knowledge for cross-boundary decision support. The paper then outlines a set of design guidelines for the development of enterprise information systems for e-health. Based on the guidelines, the paper proposes the conceptual design of CaDHealth, a practice-centered framework for making sense of clinical practices across work settings for effective cross-boundary e-health decision support.

The application of ridge polynomial neural network to multi-step ahead financial time series prediction

Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. The RPNN is constructed from a number of increasing orders of Pi–Sigma units, which are used to capture the underlying patterns in financial time series signals and to predict future trends in the financial market. In particular, this paper systematically investigates a method of pre-processing the financial signals in order to reduce the influence of their trends. The performance of the networks is benchmarked against the performance of MLPs, functional link neural networks (FLNN), and Pi–Sigma neural networks (PSNN). Simulation results clearly demonstrate that RPNNs generate higher profit returns with fast convergence on various noisy financial signals.

e-HTAM: A Technology Acceptance Model for electronic health

Serving citizens through an integrated e-Health system requires an understanding of the behaviour of the population as well as the factors that influence their acceptance and usage of technology, such as technology design and sociocultural factors. This has been called e- Health acceptance.

On unified quality of service resource allocation scheme with fair and scalable traffic management for multiclass internet services

This study concerns the problem of controlling multiclass (elastic, inelastic and unresponsive) Internet traffic without sacrificing quality of service (QoS) by adopting a unified `resource allocation and traffic management` approach. The aim is to minimise the need for relying on dedicated QoS traffic control mechanisms in order to avoid spiralling complicatedness that, in practice, leads to `robust yet fragile` Internet. In order to address this challenge, the authors first introduce an end-to-end non-convex network utility maximisation-based resource allocation algorithm to guarantee enhanced QoS to elastic and inelastic flows. Then, a pricing-based fair and scalable traffic management scheme, called Purge, is introduced to protect transmission control protocol-friendly traffic from unfairness attacks by unresponsive flows. Finally, the main contribution of this work, the unified algorithm, is developed by adapting Purge to complement link-control of the proposed resource allocation algorithm to enable it to enforce fairness while maintaining a scalable network core. The unified approach thus delivers QoS guarantees for multiclass traffic.

A Conceptual Design of an Adaptive and Collaborative E-Work Environment

Emerging work models increasingly take the form of loosely structured, often self-organising networks of nimble and virtual knowledge work teams within and between organisations. To model such work patterns requires a different approach from that of traditional workflow management systems. This paper presents the conceptual design of a prototype adaptive and collaborative e-Work environment - e-Workbench, which we are currently developing to enable future collaborative workspaces to adapt to emerging knowledge work models. We argue that with appropriate knowledge of tasks, workspaces will be able to adapt to work, and automatically retrieve contextually relevant knowledge elements from the Web in order to contribute creatively to problem solving and semantically manage shared information among collaborating workers. Our goal is to enable e-Workbench to become, not only a working environment but also, a collaborator and a co-worker as a result of its knowledge of work and creative participation in problem solving