Internet of Things and Their Coming Perspectives: A Real Options Approach

Internet of things is developing at a dizzying rate, and companies are forced to implement it in order to maintain their operational efficiency. The high flexibility inherent to these technologies makes it necessary to apply an appropriate measure, which properly assesses risks and rewards. Real options methodology is available as a tool which fits the conditions, both economic and strategic, under which investment in internet of things technologies is developed. The contribution of this paper is twofold. On the one hand, it offers an adequate tool to assess the strategic value of investment in internet of things technologies. On the other hand, it tries to raise awareness among managers of internet of things technologies because of their potential to contribute to economic and social progress. The results of the research described in this paper highlight the importance of taking action as quickly as possible if companies want to obtain the best possible performance. In order to enhance the understanding of internet of things technologies investment, this paper provides a methodology to assess the implementation of internet of things technologies by using the real options approach; in particular, the option to expand has been proposed for use in the decision-making process

An Analytic Real-Time Framework for IoT based Home Automation System

The Internet of Things or IoT means the ability to connect billions of physical devices around the world that are now linking to the internet for collecting and sharing data. Internet of Things (IoT) technologies is used to sense the real-time primordial manufacture data and included the energy alliance data and the provision circumstance data. The Internet of Things (IoT) will approve any contents to be sensed or monitored remotely wherever there any remaining network infrastructure, making amenities for the integration of the actual world into computer-based systems. Real-time narrates the path of flowing media is processing. In the real-time procedure, anyone can entrance information barring to narrate for it and save our time. In our proposed system, we build a system where we calculate real-time. To compute real-time, we need an IoT based automated system. And here we use an IoT based home automation system. We are passing data through the system for collecting data with the help of Cisco Packet Tracer simulator. For calculating real-time performance, we use six performance metrics to evaluate the event detection system performance. They are sensitivity, specificity, precision, accuracy, F1-score, and G-mean

Did Class 1 and Class 2 Aminoacyl Trna Synthetases Descend from Genetically Complimentary, Catalytically Active ATP-Binding Motifs?

The Internet of Things is widely regarded as one of the most disruptive technologies as it inte-grates smart physical objects into the networked society and digital value networks. Today, the Internet of Things has the potential to transform business-to-customer interactions enabled by smart things. Remote patient monitoring, predictive maintenance, and automatic car repair are just a few examples of evolving business-to-thing (B2T) interactions. However, the Internet of Things remains a space low on theoretical investigations. Complementing the tech-nical/engineering focus on the Internet of Things, we developed and evaluated a taxonomy of B2T interaction patterns, building on sociomateriality as justificatory knowledge. We also demonstrated the taxonomy’s applicability and usefulness based on simple and complex real-life objects (e.g., Nest, RelayRides, Uber). Our taxonomy contributes to the descriptive knowledge related to the Internet of Things, as it enables the classification of B2T interactions, providing the basis for sense-making research and early theory-led design. When combining the weak and the strong form of sociomateriality as justificatory knowledge, we also found that the Internet of Things enables and requires a new perspective on material agency, treating smart things as independent actors

A Federated Filtering Framework for Internet of Medical Things

Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities. The IoMT devices are continuously pushing their readings to the remote cloud servers for real-time data analytics, that causes faster drainage of the device battery. Moreover, other demerits of continuous centralizing of data include exposed privacy and high latency. This paper presents a novel Federated Filtering Framework for IoMT devices which is based on the prediction of data at the central fog server using shared models provided by the local IoMT devices. The fog server performs model averaging to predict the aggregated data matrix and also computes filter parameters for local IoMT devices. Two significant theoretical contributions of this paper are the global tolerable perturbation error (${To{l_F}}$) and the local filtering parameter ($\delta$); where the former controls the decision-making accuracy due to eigenvalue perturbation and the later balances the tradeoff between the communication overhead and perturbation error of the aggregated data matrix (predicted matrix) at the fog server. Experimental evaluation based on real healthcare data demonstrates that the proposed scheme saves upto 95\% of the communication cost while maintaining reasonable data privacy and low latency.Comment: 6 pages, 6 Figures, accepted for oral presentation in IEEE ICC 2019, Internet of Things, Federated Learning and Perturbation theor

Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.Comment: 8 pages, 9 figure

Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.Comment: 14 pages, 4 figure