Fog Computing: A Taxonomy, Survey and Future Directions

In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named "Fog computing" has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/ sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features.We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research

Empirical analysis of polarization division multiplexing-dense wavelength division multiplexing hybrid multiplexing techniques for channel capacity enhancement

This paper exemplifies dense wavelength division multiplexing combined with polarization division multiplexing with C-band frequency range-based single-mode fiber. In the proposed link, 32 independent channels with 16 individual wavelengths are multiplexed with two different angles of polarization. Each carrying 130 Gbps dual-polarization data with 200 GHz channel spacing claiming a net transmission rate of 4.16 Tbits/s with spectral efficiency of 69% with 20% side-mode-suppression-ratio (SMSR) and optical signal to noise ratio (OSNR) 40.7. The performance of the proposed techniques has been analyzed using optimized system parameters securing a minimum bit error rate (BER) 10-9 at a transmission distance up to 50 km

A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of Things; Engineering Cyber Physical Human Systems" journal on January 9, 202

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient’s heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.</p

Software Defined Network-based Scalable Resource Discovery for Internet of Things

Geo-distributed and heterogeneous Internet of Things (IoT) devices can generate huge amount of data. Inefficient management of IoT-data promotes network congestion and increases computational overhead on the data-processing entities. Traditional networking architecture, that is lack of functional abstraction and monitoring capabilities, often fails to meet the dynamics of IoT. Software Define Network (SDN) can be a viable alternative of the traditional networking architecture while dealing with IoT. In SDN, management, monitoring and context sensing of the connected componen ts are sim plifie and can be customized. In this paper, SDN-sensed contextual inf orma tion of different components (computational entities, network, IoT devices) are combined together to facilitate scalable resource discovery in IoT. The proposed policy targets balanced processing and congestion-less forwarding of IoT-da ta. Through sim ula tion studies, it has been demonstrated that the SDN-based resource discov ery in IoT outperf orms the traditional networking based approaches in terms of resource discovery time and Quality of Service (QoS) satisfaction rate

Phenology-based classification of Sentinel-2 data to detect coastal mangroves

Precise categorization of mangrove forests with medium spatial resolution satellite data is challenging and occasionally yields mixed outcomes. The available methods to estimate mangrove vegetation cover using moderately high-resolution images lack differentiation between mangrove and homestead vegetation. Mangrove vegetation displays a range of responses across the phenological cycle at different wavelengths of an optical sensor. Taking advantage of this principle, this study utilized some mangrove and non-mangrove vegetation indices (VIs) as predictor variables sourced from monthly Sentinel-2 data into the random forest algorithm to derive a phenology-based classification outcome. It also ascertained a suitable month for thresholding mangroves across different VIs. Results indicated that phenology-based classification with three classes was more accurate (95% overall accuracy) than threshold-based or WorldCover v100 classifications. MI and MVI layers from December image performed better in discerning mangroves. Findings have important implications in separating mangroves from other coastal vegetations