Energy Conservation Solutions for Fog-Edge Computing Paradigms

Internet of Things (IoT) has been developed as a heterogeneous environment that contains network devices with limited resources. The application of IoT principles in the smart city domain creates new opportunities and requires diligent implementation mechanisms for optimal resource utilization. With time, the IoT applications tend to generate and forward a huge amount of data in the smart cities and require a real-time response from the servers. Due to this, the traditional cloud computing architecture is unable to handle the latency-sensitive applications efficiently, and hence, the FoG architecture has been widely implemented with IoT devices to efficiently retrieve or forward the data. For the comprehensive utilization of the resources in the FoG systems-based smart cities, various energy-aware resource allocation schemes have been discussed in this chapter. The schemes suggest different mechanisms to access the required contents with minimal energy consumptions for the applications that are used in smart cities.

The next generation Internet of Things (IoT) applications are offering multiple services and run in a distributed heterogeneous environment. In such applications, Quality of Service (QoS) requirements are in jeopardy when the computing operations are only outsourced to the public cloud. For IoT applications, a comprehensive framework that supports QoS-aware service placement in a fog computing environment is highly required. It is a challenging task to orchestrate the time critical IoT applications in the fog environment. To alleviate this problem, this paper proposes a novel multitier fog computing architecture called Deadline-oriented Service Placement (DoSP) that provides the services both in fog and cloud nodes. This research work proposed a methodology to utilize low-cost fog resources while ensuring that the response time satisfies a given time constraint. It uses the Genetic Algorithm (GA) to dynamically determine the service placement in the fog environment. In this work, we used the iFogSim simulator to model DoSP and measured the impact of the service placement technique in terms of service deadline. It has been observed that through the proposed solution, there is a reduction in service execution delay, i.e., approximately 10.19% of the overall response time to the EdgeWard and 2.58% to the Cloud Only.

Fog computing is gaining rapid acceptance as a distributed computing paradigm that brings cloud-like services near the end devices. It enhances the computation capabilities of mobile nodes and IoT (Internet of Things) devices by providing compute and storage capabilities similar to the cloud but at a lower latency and using lesser bandwidth. Additional advantages of fog computing include its support for node mobility, context awareness, reliability and scalability. Due to its multiple benefits, fog computing is used for offloading tasks from applications executing on end devices. This allows faster execution of applications using the capabilities of fog nodes. However, the task offloading problem in the fog environment is challenging due to the dynamic nature of fog environment and multiple QoS (Quality of Service) parameters dependent on the application being executed. Therefore, the chapter proposes a QoS-aware task offloading strategy using a novel nature-inspired optimization algorithm, known as the Smart Flower Optimization Algorithm (SFOA). The proposed strategy takes into account the QoS parameters such as the task deadlines and budget constraints in selection of appropriate fog nodes where computation tasks can be offloaded. The proposed strategy has been simulated and the results have verified the efficacy of the strategy.

In this chapter, authors analyzes how fog computing can be efficiently utilized to improve the productivity in the industry 4.0 and smart city applications. The main aim of industry 4.0 applications is to improve the efficiency of manufacturing process through the incorporation of latest technologies. This environment can be improved by incorporating the fog computing paradigm. High energy consumption and abundance of data to be processed at the data nodes are some of the challenges that need to be addressed in industry 4.0 and smart city implementations. A fog computing-enabled architecture helps to reduce some of these challenges by working as a low complexity computational layer between cloud and internet of things (IoT) layers. By introducing this fog layer computationally intensive data processing tasks can be moved from the cloud layer to the fog layer and this fog layer can also act a gateway to the other upper layers. In smart city applications also fog computing can be effectively utilized. In the fog computing environment the data analytics tasks can be pushed to the edge of the network which leads to better efficiency. In fog computing paradigm major functionalities are moved near to the local nodes. Since most of the computations are happening locally the need of transferring data to the cloud servers is significantly reduced. The benefits of this architecture can be utilized to improve the efficiency of smart city and industry 4.0 implementations. In this chapters, authors analyze how fog computing can be effectively utilized to improve industry 4.0 and smart city applications.

Computing Paradigms on the Cloud-platform has become an integral service of computing devices that uses internet and big data as the core platform, Computation of big-data under the cloud structure has an emerging need to support the time-sensitive applications over the existing intelligence services. Increase in volume of load leads to increased latency time and reduces the overall efficiency of the services due to access delay. Hence a systematic architecture is proposed for time-line based emergency services to bridge the two platforms namely cloud-server and the edge devices by supporting computational task through fog-cloning over the multiple Cloud Servers and their by ensuring optimal utilization of energy with improved residual sources retained. A novel taxonomy is proposed as a part of minimizing energy consumed through perceptual cloning which aids in identifying time-sensitive data using the location-awareness in the areas not limiting to Military applications, Health Monitoring equipments, Accident Detection Process, etc., thus the latency time of network bandwidth is efficiently reduced with increased resource availability and ensuring improved the quality of service.

The uses of Internet of Things (IoT) devices and sensors have been increasing day by day. In order to provide storage and computational needs for time-sensitive applications low powered end devices which use IoT devices and sensors, a new computing paradigm “Fog Computing” has come into the picture. Virtual machines (VMs) inside the fog nodes are responsible for immediate processing and analyzing the IoT workloads. One of the open research problems is to efficiently scalable the fog centers so that a minimum number of client requests can be renege from the fog system. The service providers are intended to retain the client requests in the fog system by providing efficient services. In this chapter, a multi-server queuing system having reneging with retention policy is modeled to measure the several performance measures of the fog system. The profit and revenue of the system are analyzed. Further, an efficient greedy-based VM placement scheme GVMP is proposed to optimize the energy consumption of the fog centers. The efficiency of the algorithm GVMP is compared with the state of art algorithms such as FFD, BFD, RR and MBFD.

Various IoT gadgets are ceaselessly increasing day by day and producing an enormous volume of raw data. All of this produced information is passed to cloud servers for processing. As this process adds delay to processing, so it is not suitable for certain applications as some applications require a speedy response. To overcome this condition, fog computing comes in existent, which is an extension to cloud computing. Also, in the present time, fog is the most popular technology due to the vast demand for IoT devices. The fog nodes are placed between IoT devices and the cloud servers. As the execution of the request is performed at the fog layer so it can work with a limited number of resources i.e., less bandwidth, cost, and time as the processing is pushed closer to the end clients. The most challenging task in a fog environment is to appropriately distribute workload among computing nodes during the execution of IoT applications as it is one of the important factors which affect resource efficiency. The performance of any computing paradigm is directly proportional to the load balancing handling mechanism; poor mechanism reduces the overall performance of any computing environment. Realizing the challenge of load balancing among the computing nodes in the fog environment, various mechanisms and methods have been proposed so far and various experiments have also been conducted by the researchers to check the effectiveness of the mechanism. The appropriate load balancing mechanism will increase the effectiveness of the fog system due to better resource utilization. The chapter presents a framework (OLBA) for Load Balancing in Fog computing environments to balance the load between fog devices and improves QoS parameters i.e., Turnaround time resource utilization, response time, and delay parameter. This approach is based on Particle Swarm Optimization (PSO) technique to find the local best and then to compare all the local best to find the ultimate global best solution. An analysis and comparison with the traditional techniques, i.e., FCFS, SJF, Max_Min is also performed for a better understanding of load balancing mechanism in Fog Computing.

Expeditious technical developments have remodeled the industrial sector. These developments vary from mechanization of industrial tasks to autonomous industrial processes in which no human intervention is needed for regular working. An advanced concept i.e. Industrial Internet of Things (IIoT) evolved with the appliance of Internet of Things (IoT) in industrial processes; gave a new dimension to the technological advancements in the industrial sector by facilitating industrial processes with the support of Internet. Impeding the interpretation of IIoT to the production process supported another sub-domain of IoT, recognized as Industry 4.0. The concept of Industry 4.0 is realized using sensor networks, automated business processes, robots, smart equipment and machines, actuators, and people. Consequently, a huge volume of disparate data is initialized for analysis and processing. In industry, most of the processes are real-time. To avoid communication delays and ensure data security, the majority of the processes are completed locally and only necessary data is transferred over the Internet for cloud storage. To fulfill this objective, there is always a high requirement of a middleware amidst industrial processes/tools and cloud. In this connection, Fog is the most workable solution for distinct industrial scenarios. In the manufacturing industry, it can facilitate local processing along with tolerable communication delay to robots and actuators. Data gathered from various industrial processes is usually disorganized which needs pre-processing for refinement using Fog locally then communicated to the cloud. So, fog computing plays a vital role in various Industry 4.0 applications by resolving various issues. But the deployment of Fog computing in Industry 4.0 also faces a lot many challenges of different kinds related to programmability, security, heterogeneity, and interoperability. In this book chapter, we present an overview of Fog computing along with the architectural framework of Industry 4.0. We discussed the various applications of Fog computing in industry 4.0 in detail. Different problems faced in the implementation of fog computing in Industry 4.0 will be discussed. We have also introduced various research challenges to be dealt with for the efficient deployment of fog in Industry 4.0.

Fog computing is an extension to cloud computing, offering benefits such as minimal latency, wide geographical distribution, and location awareness by providing flexible services at the edge of the network. The onset of fog computing has catered solutions to many applications, Smart City projects being one of them. Fog computing has the potential to deliver an impact in smart city projects, as the former application involves economic and social aspects along with the technical aspect. The increase in city urbanization demands smart solutions that tackle critical problems such as healthcare, mobility, infrastructure, parking space availability, waste management, and energy consumption. Industry 4.0 conceptualizes that, Internet of Things (IoT) along with fog computing would be used for the development of a network of devices. These devices function independently in real-time and provide the required infrastructure for a smart city. This research study presents a comprehensive literature survey on the deployment architectures of fog computing in smart city applications such as Smart Waste Management and Smart Parking. An emphasis is laid more on the integration of Industry 4.0’s core concepts and fog computing while also taking into consideration the deployment aspects. With the proposed architectures and mentioned approaches, improvements would be seen in terms of resource utilization, processing overhead, and latency. In the latter part of the research survey, the potential merits of the proposed approaches and future work directions are discussed.

The evolution of IoT-based Energy Internet (EI) applications has been discussed in a detailed study of modern IoT (Internet of Things) technologies for smart grids and smart urban ecosystems (SUE), such as smart cities, smart buildings, smart metering, and energy storage infrastructures. Fog computing is a revolutionary networking architecture that enables edge nodes to share, compute and store data resources. One of the most compelling scenarios in IoT is energy consumption while communicating information through a wireless sensor network (WSN). In this scenario, sensors, actuators, and smart devices communicate and consume a large amount of energy. Since Fog nodes are widely spread and mostly operate on batteries, effective energy storage should be considered to extend the network lifespan. In this chapter, we proposed a design model for the integration of a wireless sensor network for the hospital environment by enabling IoT-based energy harvesting (EHIoT) methods. This approach combines collecting healthcare-related data from the hospital environment and monitoring them through the IoT/Fog-based system. We applied EHIoT methodologies for harvesting energy and managing them in the proposed WSN. Ambient-based and human-based energy harvesting methods were used to design the proposed EHIoT system.

The prime research goal of the study is to address the security problems in WSN communication in order to safeguard information exchange over various potential critical applications integrated with different radio-frequency (RF) channels. It has also evaluated the background of security limitations in WSN by investigating significant scientific literatures and its impact on other performance aspects from energy viewpoint. The design limitations associated with the existing secure routing approaches shows that most of the working principles of routing only limited toward privacy protection of data and the importance of energy consumption minimization are to some extent ignored and overlooked. Thereby, the research goal defined in this study is to minimize the security loopholes in WSN with higher degree of intrusion detection and prevention. Thereby, the study formulates a novel tree-based strategy which is exclusively meant for secure and energy-efficient hierarchical routing in WSN. The system modeling of the formulated approach consists of three different solution spaces such as (i) a framework for energy-efficient secure routing (FEESR), which incorporates a novel tree-based approach is introduced in this study, followed by two more approaches such as (ii) delay sensitive protocol for intelligent routing (DSP-IR) and an optimized and roust sandboxing approach (ORSA). All the solution approaches are designed in a way where these all jointly addresses the security problems of WSN without compromising the energy aspects. The study introduces a novel optimization policy to balance the trade-off between energy and security aspects. The experimental simulation of the proposed analytical modelings is carried out in a numerical computing platform with respect to mathematical computation principle. The performance validation of the models FEESR, DSP-IR, and ORSA are done with respect to different significant parameters where the comparison has been done with the most significant energy-efficient and secure routing baselines. The Simulation outcome obtained for all the analytical models shows that the study outperforms the conventional baselines from both energy and security viewpoint.

Wireless communication networks and technologies have transformed each and every aspect of our lives. The computer-intensive applications and the Internet of Things (IoT) are the primary driving force behind the unprecedented and exponential growth of network traffic in recent times. It has also generated an enormous demand for computational requirements to meet the ever-growing and anticipated evolution of 5G networks. However, it is pertinent to highlight that the end-users, despite all this, continue to struggle with small storage-capacities and limited processing speed. To address this issue, Mobile Edge Computing (MEC) is the way forward, as it is the technology supporting the evolution of the 5G communication networks. Since it can host intensive computing applications, it creates efficient optimization of mobile resources. Before transmitting it to the cloud, it can also process extensive data and provide mobile users near proximity to cloud computing capabilities within the Radio Access Network (RAN). It offers context-aware services through the deployment of RAN knowledge. MEC, therefore, makes a wide range of applications where real-time response is appropriate, e.g. driverless cars, virtual reality, robotics and live media. Non-orthogonal multiple access (NOMA), dense heterogeneous networks (HetNets), cloud radio access networks (C-RAN), unmanned aerial vehicles (UAV), IoT, wireless power transfer (WPT) and energy harvesting (EH) and machine learning (ML) are all main technologies for 5G. The MEC summary, including the fundamental characteristics, challenges and market factors, is presented in this chapter. In addition to the integration of MEC with the upcoming 5G and beyond technologies including NOMA, WPT EH, UAV, IoT and heterogeneous CRAN and ML, we also address the role of MEC in the 5G network architecture. In doing so, we address the state of the art research activities and describe the potential of MEC’s future course.

In health centers, hospital services are a useful source of information that cannot be interrupted, as they provide necessary clinical information such as results of clinical examinations, results of radiological examinations, search results for available drugs, among other necessary information. In the absence of commercial electrical energy, mechanisms should be considered to ensure the continuity of information services through the use of battery banks, this methodology presents a technique based on IoT techniques, through which the consumption of the package is monitored. of batteries found in the data centers of hospital establishments, to be able to analyze the level of charge and the state of the batteries at all times when the commercial electrical power is active, when the commercial power is cut, the battery bank power, so your tracking on the consumption of the battery Charging is of vital importance to be able to activate the alternative and energy mechanisms such as electric power generating motors, but these motors have an ignition and stabilization time, it is at this time that it is necessary to monitor the use and discharge of energy from the power bank batteries found in data centers, the mechanism to be used for the connection through IoT is based on an ATECC608A development device, which has an ATWINC1510 Wi-Fi connection with an interface developed in LabView, The results demonstrate the use and practicality as most power outages occur at night so data center workers are not at their workplace and remote monitoring of battery banks is very useful in these situations based on IoT technology.