It has become widely accepted that the Internet of Things (IoT) devices and technologies are the key enablers for many emerging applications including remote health monitoring. Various physiological sensing devices have been designed and equipped with different radio technologies. The choice of radio hardware plays an important role on the overall performance of the system since it imposes some limitations on the delivered quality of service. Hence, it is critical to properly evaluate the embedded radio technology based on the application requirements. In this paper, we perform extensive experiments on Shimmer physiological sensors that is one of the leading providers of wearable wireless sensor products powered by Bluetooth classic radio. Shimmer sensors are designed and used for monitoring various human health information such as temperature, heart rate, movement, etc. We review and investigate different scenarios in which Shimmer devices are used by medical practitioners to monitor the ECG signal and the movement of a human. This study shows that the Shimmer device can provide reliable data delivery by using a specific configuration. For instance, employing a maximum number of seven Shimmer devices attached on a body at home environment within the range of at most 5 m and with the sampling rate of 512 Hz would result in a reasonable quality of service, while varying these parameters may degrade the overall performance. Mobility of human body, noisy environment, and higher packet transmission rates are some examples that will reduce the system quality. © Copyright 2019 IADIS Press All rights reserved.

Recent advances in Internet of Things (IoT) connectivity have made IoT devices prone to Cyber attacks. Moreover, vendors are eager to provide autonomous and open source device, which in turn adds more security threat to the system. In this paper, we consider network traffic attack, and provide a Fog-assisted solution, dubbed as FIRE- WORK, that reduces risk of security attacks by periodically monitor- ing network traffic, and applying traffic isolation techniques to overcome network congestion and performance degradation.

Mobility is becoming a challenging issue in upcoming IoT applications, where it is crucial to employ mobile entities. Patients with sensors attached to their body in health monitoring application, AGVs in industrial monitoring and factory automation applications, cars with several sensing devices in vehicular applications are a few examples of use cases with the need for mobile nodes. In parallel, Fog computing has revolutionized network architecture, while enabling local processing of measurements, and reducing bandwidth overhead, which results in a more reliable system and real-time support. However, mobility management is a missing framework within the mobile IoT networks with Fog computing architecture. This paper provides a simple and generic seamless handoff model, dubbed as MobiFog, where it addresses the handoff mechanism with zero delay, while providing high reliability.

Fog computing offers a wide range of service levels including low bandwidth usage, low response time, support of heterogeneous applications, and high energy efficiency. Therefore, real-time embedded applications could potentially benefit from Fog infrastructure. However, providing high system utilization is an important challenge of Fog computing especially for processing embedded applications. In addition, although Fog computing extends cloud computing by providing more energy efficiency, it still suffers from remarkable energy consumption, which is a limitation for embedded systems. To overcome the above limitations, in this paper, we propose SoFA, a Spark-oriented Fog architecture that leverages Spark functionalities to provide higher system utilization, energy efficiency, and scalability. Compared to the common Fog computing platforms where edge devices are only responsible for processing data received from their IoT nodes, SoFA leverages the remaining processing capacity of all other edge devices. To attain this purpose, SoFA provides a distributed processing paradigm by the help of Spark to utilize the whole processing capacity of all the available edge devices leading to increase energy efficiency and system utilization. In other words, SoFA proposes a near- sensor processing solution in which the edge devices act as the Fog nodes. In addition, SoFA provides scalability by taking advantage of Spark functionalities. According to the experimental results, SoFA is a power-efficient and scalable solution desirable for embedded platforms by providing up to 3.1x energy efficiency for the Word-Count benchmark compared to the common Fog processing platform.

A heterogeneous Internet of Things (IoT) architecture for remote health monitoring (RHM) is proposed, that employs Bluetooth and IEEE 802.15.4 wireless connectivity. The RHM system encompasses Shimmer physiological sensors with Bluetooth radio, and OpenMote environmental sensors with IEEE 802.15.4 radio. This system architecture collects measurements in a relational database in a local server to implement a Fog node for fast data analysis as well as in a remote server in the Cloud. 

Nowadays, the Industrial Internet of Things (IIoT) has the potential to be implemented in factories and supply chains to improve manufacturing efficiency. It is becoming more common to use mobile robots in such factories for further improvements. Adding data collection capability to the mobile robots would realize the mobile sink deployment in future factories. As it is important to reduce the deployment cost, we are aiming at a network with minimum number of mobile sinks while ensuring network reliability and timeliness. In this paper, we analytically model a given trajectory for the motion of mobile sinks and the routing of mobile sinks along the trajectory in an IIoT system. We introduce an optimization problem in the form of Integer Linear Programming (ILP) to specify the minimum number of required mobile sinks to reduce deployment cost of an IIoT system, and also to identify the routing of multiple mobile sinks along a given trajectory. The proposed ILP model can be solved by several existing off-the-shelf ILP-solvers. 

Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time-critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software-defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run-time. It is of paramount importance to select a proper number of these devices (i.e., software-defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time-critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA-MSCP, an algorithm hybridizing a parallel version of the max-min ant system with simulated annealing for multiple-sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well-known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.