A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays

Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks

These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, ETrain and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, roundtrip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.publishedVersio

Dynamic application partitioning and task-scheduling secure schemes for biosensor healthcare workload in mobile edge cloud

Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.publishedVersio

Quality of service optimization in IoT driven intelligent transportation system

High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction

Decentralized energy efficient model for data transmission in IoT-based healthcare system

The growing world population is facing challenges such as increased chronic diseases and medical expenses. Integrate the latest modern technology into healthcare system can diminish these issues. Internet of medical things (IoMT) is the vision to provide the better healthcare system. The IoMT comprises of different sensor nodes connected together. The IoMT system incorporated with medical devices (sensors) for given the healthcare facilities to the patient and physician can have capability to monitor the patients very efficiently. The main challenge for IoMT is the energy consumption, battery charge consumption and limited battery lifetime in sensor based medical devices. During charging the charges that are stored in battery and these charges are not fully utilized due to nonlinearity of discharging process. The short time period needed to restore these unused charges is referred as recovery effect. An algorithm exploiting recovery effect to extend the battery lifetime that leads to low consumption of energy. This paper provides the proposed adaptive Energy efficient (EEA) algorithm that adopts this effect for enhancing energy efficiency, battery lifetime and throughput. The results have been simulated on MATLAB by considering the Li-ion battery. The proposed adaptive Energy efficient (EEA) algorithm is also compared with other state of the art existing method named, BRLE. The Proposed algorithm increased the lifetime of battery, energy consumption and provides the improved performance as compared to BRLE algorithm. It consumes low energy and supports continuous connectivity of devices without any loss/interruptions

Towards convergence of AI and IoT for energy efficient communication in smart homes

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) promotes the energy efficient communication in smart homes. Quality of Service (QoS) optimization during video streaming through wireless micro medical devices (WMMD) in smart healthcare homes is the main purpose of this research. This paper contributes in four distinct ways. First, to propose a novel Lazy Video Transmission Algorithm (LVTA). Second, a novel Video Transmission Rate Control Algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission) and standard deviation is investigated while comparing the LVTA, VTRCA, and Baseline approaches. Experimental results demonstrate that the reduction in encoding (32%, 35.4%) and transmission (37%, 39%) energy drains, PMR (5, 4), and standard deviation (3dB, 4dB) for VTRCA and LVTA, respectively, is greater than that obtained by Baseline during video streaming through WMMD

A survey on 802.11 MAC industrial standards, architecture, security & supporting emergency traffic: Future directions

The IEEE 802.11-based Wireless Local Area Network (WLAN) has become a ubiquitous networking technology deployed around the world. IEEE 802.11 WLAN are now widely used for real-time multimedia applications (e.g. voice and video streaming) and distributed emergency services such as telemedicine, healthcare, and disaster recovery. Both time-sensitive applications and emergency traffic are not only characterized by their high bandwidth requirements, but also impose severe restrictions on end-to-end packet delays (i.e. response time), jitter (i.e. delay variance) and packet losses. In other words, time-sensitive applications and emergency services require a strict Quality of Service (QoS) guarantee. Medium Access Control (MAC) protocol is one of the key factors that influence the performance of WLANs. The IEEE 802.11e working group enhanced the 802.11 MAC to provide QoS support in WLANs. However, recent studies have shown that 802.11e Enhanced Distributed Channel Access (EDCA) standard has limitations and it neither supports strict QoS guarantee nor emergency traffic. Providing a strict QoS guarantee as well as supporting emergency traffic under high traffic loads is really a challenging task in WLANs. A thorough review of literature on QoS MAC protocols reveals that most QoS schemes have focused on either network throughput enhancement or service differentiation by adjusting Contention Window (CW) or Inter-Frame Spaces (IFS). Therefore, a research on developing techniques to provide a strict QoS guarantee as well as support for emergency traffic is required in such systems. To achieve this objective, a general understanding of WLANs is required. This paper aims introduce various key concepts of WLANs that are necessary for design, model and develop such framework. Our main contribution in this paper is the QoS for IEEE 802.11 WLAN and MAC protocols for supporting industrial emergency traffic over network and future directions

A survey on the architecture, application, and security of software defined networking: challenges and open issues

Software Defined Networking (SDN) is a new technology that makes computer networks farther programmable. SDN is currently attracting significant consideration from both academia and industry. SDN is simplifying organisations to implement applications and assist flexible delivery, offering the capability of scaling network resources in lockstep with application and data. This technology allows the user to manage the network easily by permitting the user to control the applications and operating system. SDN not only introduces new ways of interaction within network devices, but it also gives more flexibility for the existing and future networking designs and operations. SDN is an innovative approach to design, implement, and manage networks that separate the network control (control plane) and the forwarding process (data plane) for a better user experience. The main differentiation between SDN and Traditional Networking is that SDN removes the decision-making part from the routers and it provides, logically, a centralised Control-Plane that creates a network view for the control and management applications. Through the establishment of SDN, many new network capabilities and services have been enabled, such as Software Engineering, Traffic Engineering, Network Virtualisation and Automation, and Orchestration for Cloud Applications. This paper surveys the state-of-the-art contribution such as a comparison between SDN and traditional networking. Also, comparison with other survey works on SDN, new information about controller, details about OpenFlow architecture, configuration, comprehensive contribution about SDN security threat and countermeasures, SDN applications, benefit of SDN, and Emulation & Tested for SDN. In addition, some existing and representative SDN tools from both industry and academia are explained. Moreover, future direction of SDN security solutions is discussed in detail