The phenomenal rise of Internet of Things(IoT) has driven the ascend of fog computing as a distributed model to reduce delays in networks. Generally, fog devices are resource-restricted while IoT applications are becoming computationally demanding which require certain QoS to be accomplished within hard time limits. Therefore, it is preferable for IoT jobs to finish their processing within deadline limits by producing approximate results than producing accurate result late. The placement of IoT jobs on fog and cloud resources for execution is a widely-recognized NP-hard problem. We study the placement of real-time IoT workflows in a fog and cloud infrastructure by applying approximate computations and TOPSIS. Our methodology aims to place complete as well as partial tasks in the available idle schedule holes in the schedules of fog as well as cloud resources. The proposed technique is verified through simulation experiments and is contrasted with state-of-the-art techniques on different performance measures. The experimental findings establish that the proposed technique is able to render better performance compared to its alternatives in terms of performance metrics like SLA violation ratio, response time and energy consumption at an insignificant loss of 0.15% of result precision for all experimental scenarios that are taken into consideration.

The advent of internet of things (IoT) and wearable technologies have created immense possibilities which are fundamentally altering the way healthcare services are provided. Lately, these advancements have garnered substantial attention due to their ability to support multiple healthcare scenarios. Wearables are a significant component of a healthcare IoT network which are embedded with sensing devices and analytical techniques that enable monitoring and evaluation of vital activities. Although applications of wearable technology and the IoT in healthcare have been reviewed extensively in literature, a healthcare application specific review is missing. Therefore, the chapter offers a comprehensive overview of the IoT healthcare applications and relevant wearable technology including the types of sensors used and data collected for different dimensions of healthcare. The chapter further examines the challenges hindering the adoption of IoT based healthcare devices and outlines the future research directions.

Fog integrated Cloud Computing is a distributed computing paradigm where near-user end devices known as fog nodes cooperate with cloud resources hosted at distant datacentres for providing computational and storage services to end user applications. One of the most challenging issues in fog integrated cloud based system is task scheduling. Most of the existing scheduling approaches involve centralized decision making which fail to exploit the advantages that may be achieved by a decentralized approach, that directly maps with the distributed architecture of fog based systems. This work proposes a decentralized heuristic algorithm for scheduling real-time IoT applications bounded by tolerable latency as the Quality of Service (QoS) constraint. The proposed technique aims to take into consideration the resource constraints of the fog resources to yield a schedule that not only meets the QoS requirements defined in terms of tolerable latency but also improves the response time of applications hosted on a fog-cloud infrastructure. Performance evaluation on different IoT applications indicate that the presented algorithm delivers better performance by reducing response time by 11% on an average in comparison to the other state-of-the-art policies.

The proliferation of the Internet of Things (IoT) has generated immense possibilities in the healthcare domain. IoT has the potential to transform the healthcare sector by changing its current focus from curative approach to ensuring complete wellness of an individual. However, this domain is still in infancy, and a number of aspects must be examined before its full potential can be realized. In order to assist the researchers, this chapter provides a 360-degree view of IoT around the healthcare domain. The chapter also presents a distant view as well as in-depth study of the five-layered IoT architecture with reference to the healthcare domain; compares a number of communication protocols for IoT-based applications on the basis of their data rate, range coverage, energy requirements, cost, and other notable parameters; discusses the role of cloud as well as fog for processing a large amount of data collected through an IoT ecosystem and their associated security aspects; discusses the applicability of IoT in preventive and curative healthcare with respect to various operational areas including disease monitoring, age-based monitoring, physical abnormality monitoring, and profile-based monitoring; and presents open research questions, as well as future research directions in relation to the use of IoT in healthcare.

The advent of Internet of Things (IoT) has created immense possibilities which are fundamentally altering the way healthcare services are provided around the world. In addition to improving accessibility of healthcare services and patient safety, this has also reduced healthcare expenditures and increased operational efficiency in the healthcare sector. In this regard, Cloud resources are widely used to support near real-time IoT based healthcare applications by performing efficient algorithms on the huge amount of data produced by medical sensor devices. However, response time along with data privacy and security still represent major concerns that inhibit Internet of Things (IoT) medical devices and frameworks from being entrusted as an efficient solution to attain the goal. Of late, there is a burgeoning interestedness towards development of fog and edge based frameworks as a way to counteract the shortcomings of the cloud. The paper offers a review of healthcare related developments from a variety of perspectives, including outbreak control, chronic disease management, neonatal and pediatric treatment, elderly care, and disability management. The paper further examines the challenges hindering the adoption of IoT based healthcare devices and outlines the future research directions.

Internet of Things (IoT) being a powerful integration of radio-frequency identification (RFID), sensor and wireless devices, has given a challenging yet powerful opportunity to shape the existing systems thereby making them intelligent. Abounding applications are developed in the recent years. Millions of physical objects are expected to be connected to form a system creating wide distribution network inferencing meaningful deductions from raw data.

This paper introduces a new contrast enhancement strategy based on Cuckoo Search and Dual Tree Complex Wavelet Transform-Singular Value Decomposition (DTCWT-SVD) applied on low contrast remotely sensed images. The local details of the image are first enhanced by using CLAHE and then DTCWT is applied which divides the image into various sub-bands and after that Cuckoo Search is applied which optimizes the sub-bands and lastly SVD is used which produces the singular value matrix of the decomposed image as any alteration to singular values changes the illumination of the input image. Then, we get an enhanced and optimized image which is sharper and brighter than before. The experimental analysis depicts the supremacy of the proposed method over conventional and state-of-the-art techniques in terms of Standard deviation, Mean, PSNR, MSE, RMSE and BER.