Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

On the interplay of Internet of Things and Cloud Computing: A systematic mapping study

With the advancements in Science and Technology, capabilities for handling complex problems increase through optimal usage and management of computing power. Resources being distributed are reality, identifying and accumulating required resources for solving problem is a difficult task. In order to overcome this burden the recent trend is to effectively use grid computing, suppor1ting resource haring. The goal is to aggregate idle network, processing resources such as CPU cycles, and storage spaces to facilitate optimal utilization and computing. For the above, we need an effective method for identifying service providers based on all needs of any job for successful execution. The assignments and scheduling of the jobs should result in efficient way to solve the problem and promote optimal use of resource .This project proposes to address the above problem using Multi Agent Brokering Approach for the identification of service providers in a grid environment. The Multi Agent Approach can ensure that the agents can specialize in identification of service provider for scheduling jobs. Firefly algorithm used for reducing the make span time by its status table, records the behavior in detail as well. Also the proposed algorithm makes weak ones to jump to new positions to attain high probability. This leads to increase the performance better in finding the optimal solution. The experimental result shows that the Firefly mechanism is more efficient than standard firefly algorithm and other heuristic methods.

PurposeBuilt asset management processes require a long transition period to collect, edit and update asset conditions information from existing data sets. This paper aims to explore and explain whether and how digital technologies, including asset information model (AIM), Internet of Things (IoT) and blockchain, can enhance asset conditions assessment and lead to better asset management.Design/methodology/approachMixed methods are applied to achieve the research objective with a focus in universities. The questionnaire aims to test whether the integration of AIM, IoT and blockchain can enhance asset condition assessment (ACA). Descriptive statistical analysis was applied to the quantitative data. The mean, median, mode, standard deviation, variance, skewness and range of the data group were calculated. Semi-structured interviews were designed to answer how the integration of AIM, IoT and blockchain can enhance the ACA. Quantitative data was analysed to define and explain the essential factors for each sub-hypothesis. Meanwhile, to strengthen the evaluation of the research hypothesis, the researcher also obtained secondary data from the literature review.FindingsThe research shows that the integration of AIM, IoT and blockchain strongly influences asset conditions assessment. The integration of AIM, IoT and blockchain can improve the asset monitoring and diagnostics through its life cycle and in different aspects, including financial, physical, functional and sustainability. Moreover, the integration of AIM, IoT and blockchain can enhance cross-functional collaboration to avoid misunderstandings, various barriers and enhance trust, communication and collaboration between the team members. Finally, costs and risk could be reduced, and performance could be increased during the ACA.Practical implicationsThe contribution of this study indicated that the integration of AIM, IoT and blockchain application in asset assessment could increase the efficiency, accuracy, stability and flexibility of asset assessment to ensure the reliability of assets and lead to a high-efficiency working environment. More importantly, a key performance indicator for ACA based on the asset information, technology and people experience could be developed gradually.Originality/valueThis study can break the gap between transdisciplinary knowledge to improve the integration of people, technology (AIM, IoT and blockchain) and process value-based ACA in built asset management within universities.

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

In 1998, Ian Foster and Carl Kesselman, together with thirty distinguished experts in high-performance computing and networking, laid the foundations of a new computing model called GRID. Their vision was introduced in the book, The GRID: Blueprint for a New Computing Infrastructure (see review, SCPE Vol. 3, No. 3 ). The editors wrote: … A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities… allowing new classes of applications to emerge… as yet, we have only a preliminary understanding of what these new applications will look like. Over the next five years, Grid computing became the hottest R&D field. Academia and national labs built experimental infrastructures, new startups were established to develop Grid technologies, and the HPC giant vendors joined the party. Each one pushed in a different direction based on what and how he believed Grid computing should look. It was only a matter of time until people started to ask what the Grid is all about. A survey of ten Grid experts produced ten different answers. The question remains without a clear answer, leaving many people confused. Ian Foster picked up the gloves. In his article, What is the GRID? A three point checklist (GRIDtoday, July 22, 2002: Vol. 1, No. 6), he redefines the Grid model to reflect the trades and changes in the field during the past five years. According to Foster, a Grid is a system that: Coordinates resources that are not subject to centralized control. Uses standard, open, general-purpose protocols and interfaces. Delivers a nontrivial quality of service. But Foster's new definition could not encompass all of the Grid's approaches. Moreover, Foster explained by example what the Grid is not: A cluster management system such as Sun's Sun Grid Engine, Platform's Load Sharing Facility, or Viridian's Portable Batch System can, when installed on a parallel computer or local area network, deliver quality of service guarantees and thus constitutes a powerful Grid resource. However, such a system is not a Grid itself, due to its centralized control of the hosts that it manages Within a week the responses started to come in. The first one was from Wolfgang Gentzsch, Director of Grid computing at Sun Microsystems (GRIDtoday, August 5, 2002: Vol. 1, No. 8). Gentzsch wrote: I very much like Ian's two earlier definitions. Combined, they may provide a very generic Grid definition. The new checklist, however, reduces the potential of a wide and rich variety of Grids, with different architectures, operational models, and applications, and may miss the chance to becoming widely accepted as a standard definition of the Grid. Why do we need a definition for the Grid? Can the Grid be defined? Is a definition is necessary to the Grid's development? The Grid is an open vision and thus cannot be defined. An attempt to define the Grid can only reach the foreseen technology horizon, and this horizon is only one to two years ahead. Grid computing is a paradigm in shift. New architectures and standards are changing the shape and direction of the Grid's development. In the past year one such prominent architecture was Open Grid Services Architecture (OGSA). The OGSA is the first attempt to bridge the gap between two cyberspace worlds: the Internet and the Grid. The Internet and the Grid are two different computing models. There are people who believe that the Grid is the next generation of the Internet and the World Wide Web, saying that the Great Global Grid is still many years away. Others state that the Grid will complement, not replace, the Internet as we know it. In 1998, when the Grid blueprint was published, a new Internet language appeared called eXtensible Markup Language (XML). XML is a development tool for a new Internet computing model known as Web Services. At that time, nobody understood that there is a relationship between Web Services and the Grid. Three years past before Steve Tuecke from Argonne National Laboratory started to write a spec for Grid Services. Later, people from IBM joined the mission and together they created OGSA. The OGSA specification outlines interfaces to grid computing software that comply with Web Services standards. If adopted, Grid services such as job scheduling, authentication, failure-detection, staging of applications and data, and migration of applications and data, will all be accessed through standard Web Services architecture. OGSA is basically where Grid meets Web Services and maybe the first step towards full integration of the Internet and the Grid. The Internet appeared a decade ago. In the beginning, the Net offered primitive applications: a mailing system, ftp services, and a web of home pages. Nobody asked, What is the Internet? The years passed and new emerging Internet technologies were developed. Today we can talk on the phone over the Net, chat with friends, and video conference with colleagues on the other side of the world. New computing and business models were developed: e-commerce, e-learning, e-science, and e-you-name-it. And nobody asks what the Internet is. The Grid is a super-model, a bag of networking models associated with many types of architectures, software packages, applications, and standards. Today, when people talk about the Grid they mention Data Grids, Science Grids, and Campus Grids. And at the same time they mention Farm computing, Peer-to-peer computing, and Utility computing, among others. Ten years from now many of today's technologies will disappear and new ones will be developed. I don't know how Grid computing will look a decade from now, but I know that we will have g-commerce, g-learning, g-science, and g-chat. Nobody will ask what the Grid is. It will be obvious, just as the Internet is today. Ami Marowka The Computer Aided Design Laboratory School of Engineering and Computer Science Hebrew University of Jerusalem

This chapter gives overview of Internet of Things (IoT), various issues in IoT and describes data management in IoT. IoT is emerging technology which interconnects things through the Internet. Things present in the surrounding are communicated and control the objects without human intervention. IoT helps in performing two way communications among various heterogeneous devices by using cloud storage and cloud computing. IoT mainly concentrates on communication, so the vast amount of data generated from plenty of devices is to be managed as it consumes lot of memory. Data management includes data processing techniques such as data filtering, aggregation, compression, data archiving. Various processing techniques eliminate the irrelevant data, reduce communication overhead and enhance bandwidth, storage space and Quality of service.

Internet of Things (IoT) technology in Electric Vehicles (EVs) has the potential to enhance performance, connectivity, and the overall user experience. This connection improves EV efficiency, battery life, user interaction, charging infrastructure, and traffic management systems. Dependable communication networks, system compatibility, and data security are all essential. Several concerns must be solved before manufacturers can use the IoT in EVs. Internet of Things-based Accurate Estimation Monitoring Analysis (IoT-AEMA) is presented in this paper as a solution to address these problems. Intending to enhance energy management, safety, and predictive maintenance, the IoT-AEMA has taken the initiative. Electric vehicle (EV) performance can be monitored comprehensively and in real-time with the help of IoT-AEMA, which utilizes IoT technology. This technology makes monitoring metrics like energy use and battery health more accurate. Proactive maintenance is made possible, and communication with smart infrastructure is improved. Improving electric vehicle (EV) connection and efficiency has never been easier than with this scalable solution that prioritizes sustainability. This objective will be accomplished by providing extensive analysis and monitoring of vehicle parameters in real-time. These applications use this technology to enhance data accuracy, the decision-making process for drivers and manufacturers, and the development of intelligent transportation networks. The effectiveness of IoT-AEMA has been demonstrated through simulation studies in various circumstances. By giving accurate insights and encouraging collaboration, this research implies that the electric vehicle industry is on the verge of experiencing a paradigm change. According to the information presented in this article, the IoT and advanced energy management have the potential to make EVs more dependable, efficient, and integrated into the infrastructure of smart cities. The proposed method increases the Energy Management Optimization ratio by 97.6%, Data Accuracy ratio by 90.2%, Predictive Maintenance ratio by 95.7%, System Compatibility ratio by 93.4% and Reliability Analysis ratio by 98.4% compared to other existing methods.

Research on the Internet of Things (IoT) has been a hot spot in recent years. Resource authentication and management in IoT is a most important and challenging problem and is far to be solved. In this paper, we propose an effective distributed hierarchical IoT resource authentication and management model based on the key technologies of distributed hash table, Chord Ring and public key infrastructure. By using the exceptional advantage of search and Research on the Internet of Things (IoT) has been a hot spot in recent years. Resource authentication and management in IoT is a most important and challenging problem and is far to be solved. In this paper, we propose an effective distributed hierarchical IoT resource authentication and management model based on the key technologies of distributed hash table, Chord Ring and public key infrastructure. By using the exceptional advantage of search and query ability of the above technologies, the proposed model owns enhanced security and improved efficiency. Simulation results show that the proposed model can mutually authenticate the IoT end entities each other in more short time compared to other existing approaches. query ability of the above technologies, the proposed model owns enhanced security and improved efficiency. Simulation results show that the proposed model can mutually authenticate the IoT end entities each other in more short time compared to other existing approaches.

The Internet of Things (IoT), which seamlessly interconnects heterogeneous devices with diverse functionalities, is an attractive choice for the large-scale petrochemical industry to develop an integrated system. With the industrial revolution, efforts have mainly been focused on factory automation, transportation security, and surveillance. The IoT plays a major role in factories with its new methods of data management and data collection. However, the IoT is still at an early stage in large-scale petrochemical plants due to the multiple coexisting heterogeneous networks in harsh and complex large-scale industrial networks. This paper presents a comprehensive survey on the IoT in large-scale petrochemical plants as well as recent activities in communication standards for the IoT in industries. This paper addresses the key enabling middleware approaches, e.g., an industrial intelligent sensing ecosystem (IISE), which allows rapid deployment and integration of heterogeneous wireless sensor networks with advancements in crowdsensing based services. In addition, this survey highlights the research issues of data management in the IoT for large-scale petrochemical plants.

Integrating Internet of Things (IoT) with Artificial Intelligence (AI) is one of the catalysts for improving traditional healthcare services. This integration has created many opportunities that have led to healthcare shifting towards enabling home care, the concept that harnesses the technologies advanced potential such as the IoT and deep learning for intelligent monitor and manage chronic diseases. As population growth increases, restrictions on traditional healthcare services increase. Some diseases, such as chronic diseases, require innovative solutions that go beyond the boundaries of traditional healthcare settings due to their impact on individuals’ health for example traditional healthcare systems have little capacity to provide high-quality and real-time services. Empowering home care services using deep learning and internet of things technology is promising. It enables continuous monitoring through interconnected devices and deep learning, which provides intelligent insights from massive data sets. This brief explores the key components of enabling home care, including continuous patient health monitoring, predictive analytics, medication management, and remote patient support by healthcare providers, and provides friendly interfaces for end-users. The conjunction between the IoT and deep learning in home-care signals a shift toward precision medicine, enhancing patient outcomes and creating a sustainable model for chronic disease management in the era of decentralized healthcare. This review article aims to discuss the following aspects: presenting the latest technologies in home care systems, showing the merit of combining the Internet of Medical Things (IoMT) and deep learning and its role in monitoring patient conditions and managing chronic disease to improve patient health status accurately, in real-time, and cost-effective, and lastly, debating future studies and providing recommendations for the ongoing development of home care remote monitoring applications.

With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.

In the Internet of Things (IoT) paradigm, data is gathered for treatment from locals, machines, vehicles, etc. Cloud computing is providing suitable hardware and software for data processing. Thus, the integration of IoT with cloud capabilities offers several benefits for many applications. However, challenges persist for some use-cases like delay-sensitive services due to the huge amount of information collected by IoT devices and to be processed by cloud servers. Fog computing is expected to overcome several limits and challenges in cloud computing concerning the Quality of Service (QoS) requirements like latency, bandwidth and location awareness. Nevertheless, researchers still have to deal with several issues namely the architectural level and the energetic aspect. In this paper, we investigate fog system architectures and energy consumption in literature, while considering QoS requirements in the synthesis. A system model is then introduced with a potential solution for QoS management for fog computing environment.

In these days, a single search on Google Scholar using the expression Internet of Things (IoT) brings almost 3 520 000 results. Since 1999, when Kevin Ashton used the expression Internet of Things for the first time as the title of a presentation he made at Procter & Gamble, it has received more and more attention from academics, IT experts, the popular press, and the companies in general. All this attention is due to the fact that the IoT technologies are transforming the way in which companies work and do business. Two of the promising domains of the IoT technology are intelligent building and workplace management. In these days, the workstation became the second companies’ largest expense just after the payroll. Therefore, the development and implementation of IoT technology contribute to the optimization of buildings (energy saving, temperature, and air conditioning control, etc.) and workplaces (occupation rates, etc.), and has the potential to change the major patterns of the corporate real estate. As a result, integrating IoT technology in the workplace management is a strategic challenge in order to create value for the company through supporting strategic decisions, investments and workers’ productivity. The purpose of this work is to review the literature dedicated to IoT and workplace management, in order to identify relevant issues and knowledge concerning the relationship between the IoT technologies (e.g. RFID, sensor, actuator, smart item) and a building on the optimization of the workplace and the productivity of its occupants. The literature review is the best approach to the appropriation of a subject once it allows using the ideas developed in the literature to justify a particular approach to the topic, the selection of methods and demonstration of the relevance and possible contributions (Hart, 1998). This work will also present a discussion of future research directions and the principal challenges related to the IoT and workplace productivity.Purpose: A paper review of well-known journals publishing research on the subject “IoT and workspace management” from the last 10 years.Design/methodology/approach: We will follow the approach proposed by Levy and Ellis (2006) to conduct an effective literature review. We also will lean on the work of Mishra et al. (2016) that identified the most relevant authors on the IoT literature in order to identify those studies/authors relating IoT technology and workspace productivity.

As the complexity of Internet of Things (IoT) systems increases, a large variety of tools and technologies for IoT management are making their way into both research setups and the market. IoT management solutions must consider the resource restrictions of embedded devices, as well as their heterogeneity and network dynamics. With these in mind, the Internet Engineering Task Force developed several standards targeting the integration and interoperation of heterogeneous devices, such as the Representational State Transfer Configuration Protocol (RESTCONF) or the Constrained Application Protocol (CoAP) Management Interface. Concurrently, the Open Mobile Alliance developed the Lightweight Machine-to-Machine protocol, for IoT device management. This paper provides a comprehensive, up-to-date overview of IoT management technologies, frameworks and protocols. Also, it proposes a taxonomy for IoT devices management. In addition to presenting the various solutions, the paper provides comparative views, standardization timeline, and market analysis. The presented analysis ranges from traditional network management protocols, such as Simple Network Management Protocol, to the newest IoT management and configuration protocols, such as CoAP Management Interface and Lightweight Machine-to-Machine protocols. Moreover, this survey identifies remaining challenges and solutions offered by recent management protocols, not covered by previous surveys.