Fog computing (FC) has emerged as a promising platform for processing delay-sensitive Internet of Things (IoT) tasks. Fog nodes (FNs) are prone to failure, which necessitates the introduction of a mechanism to increase FC dependability. Leveraging a failure-aware task scheduling approach is one way to make FC a dependable platform for IoT task execution. However, the dynamicity, heterogeneity, and execution uncertainty inherent in FC make designing such a system difficult. To this end, we propose a learning-based dynamic fault-tolerant scheduling approach that considers the three intrinsic characteristics of the IoT-Fog environment to improve fog service reliability for time-constrained IoT tasks. The proposed approach provides a hybrid fault-tolerance solution based on proactive, reactive and replication failure handling approaches. It also integrates a dynamic task runtime and energy-awareness in the decision-making to address the dynamicity, uncertainties, and energy consumption challenges in the FC environment. To validate the performance of the proposed fault-tolerant task scheduling approach, we carried out extensive experimental analysis and compared it with several baseline approaches. The proposed task scheduling approach improves fog service reliability, energy efficiency, and resource utilisation compared to baseline methods.

Fog computing has become a platform of choice for executing emerging applications with low latency requirements. Since the devices in fog computing tend to be resource constraint and highly distributed, how fog computing resources can be effectively utilized for executing delay-sensitive tasks is a fundamental challenge. To address this problem, we propose and evaluate a new task scheduling algorithm with the aim of reducing the total system makespan and energy consumption for fog computing platform. The proposed approach consists of two key components: 1) a new bio-inspired optimization approach called Ant Mating Optimization (AMO) and 2) optimized distribution of a set of tasks among the fog nodes within proximity. The objective is to find an optimal trade-off between the system makespan and the consumed energy required by the fog computing services, established by end-user devices. Our empirical performance evaluation results demonstrate that the proposed approach outperforms the bee life algorithm, traditional particle swarm optimization and genetic algorithm in terms of makespan and consumed energy.

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to a serious consequences including patient death. Therefore, an approach for congestion estimation and control is imperative to enhance the network quality of service (QoS). In this paper, we propose a new fuzzy based congestion detection and control protocol for a WBAN application that monitors patient ECG signals remotely. The proposed system is able to detect link congestion by considering local information such as available bandwidth (BW) and end to end delay. The proposed protocol changes the rate of transferred ECG information and assigns priorities to patients based on their level of urgency. As a result, the proposed approach provides a better QoS for transmitting highly important ECG signs.

Due to recent technological advancements in wireless communications and low-power sensor devices, wireless body area networks (WBANs) has become increasingly popular in pervasive healthcare monitoring. However, continuously collecting patients' physiological signs will result in large amounts of monitored data that require a scalable architecture for storage and analysis. This fact motivates the integration of WBANs with Cloud technology to manage and store humongous data effectively. In this paper, we propose a Cloud-based WBAN framework for real-time health monitoring of patients. The significance of the proposed framework is the use of mobile technology and Cloud computing to provide services for the ill and elderly people in their independent living. This paper describes the general design of the proposed approach as well as a case study and implementation setup for the real-time monitoring and analysis process of Electromyography (EMG) healthcare system.

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Congestion in wireless sensor networks (WSNs) is a crucial issue. That is due to the relatively high node density and source-to-sink communication pattern. Congestion not only causes packet loss, but also leads to excessive energy consumption as well as delay. Therefore, in order to prolong network lifetime and improve fairness and provide better quality of service, developing a novel solution for congestion estimation and control is important to be considered. To address this problem, we propose a type-2 fuzzy logic based algorithm to detect and control congestion level in WSNs. The proposed algorithm considers local information such as packet loss rate and delay to control congestion in the network. Simulation results show that our protocol performs better than a recently developed protocol in prolonging network lifetime as well as decreasing packet loss.