Mobile Edge Computing

Fifth generation mobile networks aim to use multi-tier heterogeneous cellular networks integrated with cloud computing to provide users with low latency and energy-aware service. However, for high bandwidth and low latency services, edge/fog computing comes into the scenario. In edge/fog computing, the intermediate devices between end users and cloud participate in processing and storage of data as well as execution of applications. Mobile edge computing provides cloud computing services at the edge of mobile network, which facilitates the developers, service providers as well as the users. Internet of Things (IoT) has become a principle component to design smart technological solutions for our daily life. For low latency and high bandwidth services, edge computing assisted IoT has become the pillar for the development of smart home, smart health etc. This chapter will discuss the overview of mobile edge computing along with its real time applications.

Mobile devices and their corresponding services have become ubiquitous and vital components of almost every aspect of social and business life. Mobile services enhance collaboration, communication, monitoring, tracking, streaming, and many other applications. This intense and continuous engagement presents significant challenges due to mobile devices’ limited computation power, dependence on batteries, and sensitivity to transmission network capacity and availability. A common technique for resolving mobile shortcomings is to migrate (offload) complex computations to more powerful resources such as edges, clouds, mobile clouds or integration. However, the huge variety in mobile applications complicates alignment of the unique characteristics and user quality of service (QoS) requirements for each application to a convenient offloading plan. The availability of powerful resources at different computing layers is another challenge for offloading techniques. This chapter was designed to generate insights into ways the mobile communications industry could realise cost savings and high-quality data-aware offloading solutions by adopting new technologies such as edge computing and region-based local networks. To demonstrate these insights, this chapter provides an experimental work on how to select the best mobile-aware computing environment based on parameters including application type, data size and network bandwidth quality. Moreover, this chapter provides a comprehensive analysis that highlights the experiment results and provides recommendations for scheduling the execution of data-intensive applications on mobile-aware computation systems.

Real-time geospatial applications are ever-increasing with modern Information and Communication Technology. Latency and Quality of Service-aware these applications are required to process at the edge of the networks, not at the central cloud servers. Edge and fog nodes of the networks are capable enough for caching the frequently accessed small volume geospatial data, processing with lightweight tools and libraries. Finally, display the image of the processed geospatial data at the edge devices according to the user’s Point of Interest. Several kinds of research are going on edge and fog computing, especially in the geospatial aspects. Health monitoring, weather prediction, emergency communication, disaster management, disease expansion are examples of geospatial real-time applications. In this chapter, we have investigated the existing work in the edge and fog computing with the geospatial paradigm. We propose a taxonomy on related works. At the end of this chapter, we discuss the limitations and future direction of the geospatial edge and fog computing.

Recently, there has been a lot of innovative work on cloud-based mobile networks. While distributed computing gives immense chances, it likewise forces a few difficulties. One of the difficulties that current information system administrators and future Fifth Generation (5G) wireless communication are predicting is a gigantic increment in data traffic. It is anticipated based on the vision of Internet of Things (IoT) that the growing 5G wireless communication will meet an extraordinary increment in congestion of calculating and processing of data as IoT incorporated exaggerated applications. A fundamental innovation in the escalating age of 5G is Mobile Edge Computing (MEC). Before sending the data to the cloud server, MEC can upgrade mobile devices by facilitating inventory intensified applications, process huge information and give the distributed computing platform within the radio access network (RAN). Hence, MEC empowers a wide range of utilizations. Without a doubt, the worldview is moving to the future generation network which could turn into a reality with the coming of new mechanical ideas. The actual response of MEC is still in its early stages and requests for steady endeavors from both scholarly and industry networks. With the ever-developing energy utilization for data and wireless communication innovation, the communication nodes and infrastructure undertake a significant job in worldwide greenhouse substance releases. Thus, the improvement of green 5G has become a significant task for the structure and execution of future remote communication. As MEC is a key segment of 5G, the energy efficiency has become a standard worry for the construction of the MEC component. In this chapter, we initially give an all-encompassing outline of MEC, its energy efficient innovation, potentials, needs, and applications. We further sum up exercises gained from energy efficient resource allocation and task offloading. We also talk about difficulties and expected future headings for MEC research.

The development of mobile user equipment progresses cooperatively with the advancement of the latest mobile applications. Still, the limited battery capacity prevents users from running computationally intensive applications on their gadgets. This one stimulated the evolution of Mobile cloud computing (MCC). Instead of its ample data storage and processing capability, MCC suffers from high latency. To deal with the latency problem a novel promising concept known as mobile edge computing has been introduced. Mobile edge computing (MEC) and wireless sensor networks (WSN) are two ever-promising research domains of the wireless network. The integration of MEC with WSN has given birth to Sensor Mobile Edge Computing (SMEC). However, sensor mobile edge computing is an emerging field, and energy-efficiency is one of the major challenges of this field. In MEC, services are provided at the edge of the mobile network for reducing the latency that in turn can improve the quality of user experience. Previously MEC focused on the use of base stations for offloading computations from mobile devices. However, after the arrival of fog computing, the definition of edge devices becomes broader. SMEC is a fusion of mobile edge computing and wireless sensor network. SMEC is an architecture where the sensor nodes capture the status of environmental objects and the collected data are sent to the cloud through the edge devices which participate in data processing also. This chapter discusses sensor mobile edge computing, its architecture, and its applications. The future scopes and challenges of SMEC are also addressed in this chapter.

Internet of Things (IoT) as a backbone of future customer value enables ubiquitously available digital services. However, providing smart digital services in an IoT ecosystem that billions of devices are connected to the network, needs high processing power and high capacity as well as low latency communications. In this regard, the emergence of MultiAccess Edge Computing (MEC) technology offers cloud computing capabilities to the network edge to meet IoT-based application requirements by providing real-time, high-bandwidth, low-latency access to the network resources. In this chapter, the most important topics related to IoT integrated with MEC have been presented. After introduction, the role of MEC in providing IoT services by using real-time analysis, caching and computing mechanisms are explained. By considering the importance of the integration in service delivery and platform in the next-generation networks (e.g. 5G), the MEC API section is presented. It discusses about the interaction of devices, third-parties and service providers with MEC platform through API as a common language. Then, the mobility management in IoT ecosystem related to service delivery and QoS using MEC has been studied. Finally, after presenting a benchmark for deployed IoT use cases by famous operators, challenges and future direction have been surveyed.

The development of Internet of Things (IoT) technology enables the rapid growth of connected smart devices and mobile applications. However, due to the constrained resources and limited battery capacity, there are bottlenecks when utilizing the smart devices. Mobile edge computing (MEC) offers an attractive paradigm to handle this challenge. In this work, we concentrate on the MEC application for IoT and deal with the energy saving objective via offloading workloads between cloud and edge. In this regard, we firstly identify the energy-related challenges in MEC. Then we present a green-aware framework for MEC to address the energy-related challenges, and provide a generic model formulation for the green MEC. We also discuss some state-of-the-art workloads offloading approaches to achieve green IoT and compare them in comprehensive perspectives. Finally, some future research directions related to energy efficiency in MEC are given.

Developments in the Industrial Internet of Things (IIoT) have enabled large-scale sensing and data collection, leading to predictive maintenance and the Industry 4.0 revolution. Predictive maintenance minimizes machine maintenance downtime, while simultaneously minimizing the risk of failures. Prescriptive maintenance aims to improve on that by directly optimizing the maintenance decisions. We present a prescriptive maintenance method for a distributed factory environment using the Partially Observable Markov Decision Process (POMDP) framework. To allow for continual learning, a particle filter algorithm enables online estimation of POMDP models, allowing unique adaptation to each machine. Performance evaluations of the POMDP model with respect to several other models show significant improvements in revenue and reduced downtime. The POMDP and particle filter computations are implemented on GPU-accelerated edge computing devices which achieve speed-ups of 4 to 20 times compared to the CPU-only versions.

Software Defined Networking (SDN) has emerged as a programmable approach for provisioning and managing network resources by defining a clear separation between the control and data forwarding planes. Nowadays SDN has gained significant attention in the military domain. Its use in the battlefield communication facilitates the end-to-end interactions and assists the exploitation of edge computing resources for processing data in the proximity. However, there are still various challenges related to the security and interoperability among several heterogeneous, dynamic, intermittent, and data packet technologies like multi-bearer network (MBN) that need to be addressed to leverage the benefits of SDN in tactical environments. In this chapter, we explicitly analyse these challenges and review the current research initiatives in SDN-enabled tactical networks. We also present a taxonomy on SDN-based tactical network orchestration according to the identified challenges and map the existing works to the taxonomy aiming at determining the research gaps and suggesting future directions.

With the pervasiveness of IoT devices, smart-phones and improvement of location-tracking technologies, huge volume of heterogeneous geo-tagged (location specific) data is generated facilitating several location-aware services. The analytics with this spatio-temporal (having location and time dimensions) datasets provide varied important services such as, smart transportation, emergency services (health-care, national defence or urban planning). While cloud paradigm is suitable for the capability of storage and computation, the major bottleneck is network connectivity loss. In time-critical application, where real-time response is required for emergency service-provisioning, such connectivity issues increases the latency and thus affects the overall quality of system (QoS). To overcome the issue, fog/edge topology is emerged, where partial computation is carried out in the edge of the network to reduce the delay in communication. Such fog/edge based system complements the cloud technology and extends the features of the system. This chapter discusses cloud-fog-edge based hierarchical collaborative framework, where several components are deployed to improve the QoS. On the other side mobility is another critical factor to enhance the efficacy of such location-aware service provisioning. Therefore, this chapter discusses the concerns and challenges associated with mobility-driven cloud-fog-edge based framework to provide several location-aware services to the end-users efficiently.

Fog and Edge related computing paradigms promise to deliver exciting services in the Internet of Things (IoT) networks. The devices in such paradigms are highly dynamic and mobile, which presents several challenges to ensure service delivery with the utmost level of quality and guarantee. Achieving effective resource allocation and provisioning in such computing environments is a difficult task. Resource allocation and provisioning are one of the well-studied domains in the Cloud and other distributed paradigms. Lately, there have been several studies that have tried to explore the mobility of end devices in-depth and address the associated challenges in Fog and Edge related computing paradigms. But, the research domain is yet to be explored in detail. As such, this chapter reflects the current state-of-the-art of the methods and technologies used to manage the resources to support mobility in Fog and Edge environments. The chapter also highlights future research directions to efficiently deliver smart services in real-time environments.

One of the core elements for the upcoming generation of wireless cellular networks is the availability of network service access continuity in addition to high-speed internet and low latency. The forthcoming fifth generation (5G) greatly improves users’ demand in terms of faster download rates, exceptional system availability, superb end to end coverage with exceptionally low latency and ultra reliability. One of the solutions to provide end to end low latency is the utilization of Mobile Edge Computing (MEC) in the network. MEC provides cloud advantages to users by setting up a small cloud server in the edge node (i.e. close to the end-user), which decreases the amount of latency in network connections, in this regard, service migration has required as users migrate to the new location. Optimal migration decisions are challenging because they depend on the cloud environment, or edge nodes belong to different orchestrators, and security issues in the migration process must also be resolved in order to prevent unreliable requests. This study provides different approaches to address these challenges by identifying the security implications of migration methods based on the blockchain integration.

The new era of the Internet of Everything (IoE) applications demands low latency along with security into the networks. The cloud-based architecture alone cannot provide low response time to the users or mobile devices (like phone, laptop, sensors device, etc.). Therefore between mobile devices and cloud, edge devices (known as Fog device) are introduced as middleware device. From the edge devices, users can get information from local devices without interacting with the cloud via the Internet or radio. In such complicated networks, security preservation in communications becomes a challenging task. The security protocols for critical communication in such applications (e-medical, e-banking) are based on the architecture of the networks which can be centralized or distributed or hybrid (a mixture of centralized and distributed). This book chapter discusses the different security protocols in communications for the aforementioned architectures which can be designed for Mobile Edge Computing (MEC) based IoE applications. Moreover, this chapter covers (a) architectures and their security threats, (b) necessity of security model in such applications, (c) different secure communication protocols for those applications, (d) challenges to design security protocols to reduce response time, and latency (e) the future direction of this research domain which can be explored more.

Mobile edge computing (MEC) and next-generation mobile networks are set to disrupt the way intelligent and autonomous systems are interconnected. This will have an effect on a wide range of domains, from the Internet of Things to autonomous mobile robots. The integration of such a variety of MEC services in an inherently distributed architecture requires a robust system for managing hardware resources, balancing the network load and securing the distributed applications. Blockchain technology has emerged a solution for managing MEC services, with consensus protocols and data integrity checks that enable transparent and efficient distributed decision-making. In addition to transparency, the benefits from a security point of view are evident. Nonetheless, blockchain technology faces significant challenges in terms of scalability. In this chapter, we review existing consensus protocols and scalability techniques in both well-established and next-generation blockchain architectures. From this, we evaluate the most suitable solutions for managing MEC services and discuss the benefits and drawbacks of the available alternatives.

With the advent of 5th Generation (5G) of mobile networks, a diverse range of new computer networking technologies are being devised to meet the stringent demands of applications that require ultra-low latency, high bandwidth and geolocation-based services. Mobile Edge Computing (MEC) is a prominent example of such an emerging technology, which provides cloud computing services at the edge of the network using mobile base stations. This architectural shift of services from centralised cloud data centers to the network edge, helps reduce bandwidth usage and improve response time, meeting the ultra-low latency requirements laid out for 5G. However, MEC also inherits some of the vulnerabilities affecting traditional networks and cloud computing, such as coordinated attacks. Previous works have proposed the use of Intrusion Detection Systems (IDS), specifically Collaborative Intrusion Detection Systems (CIDS), which have proven to be effective in identifying distributed attacks. However, identifying the right CIDS model is not straightforward due to the tradeoff between different factors such as detection accuracy, network overhead, computation and memory overhead. In this chapter, we outline some of the characteristics relevant for evaluating CIDS deployment models and survey existing CIDS architectures in the context of MEC, while presenting novel strategies and architectures of our own.

Rapid advancement of the technology makes the system more reliable and the outcome from the system produces in a timely fashion. In this work, a conceptual framework for biomedical image analysis is considered which is based on wireless sensor networks. Here, Z-Wave based wireless biomedical image analysis system is analyzed that can be implemented to provide a concrete WSN based health care system. This work can serve as a foundation to the real-life remote health care system based on Z-Wave. Periodic study of different patients is possible from their own home which can help the physicians to take appropriate decisions in stipulated time that will certainly accelerate the physical and mental improvement. This paper studies the concepts of wireless biomedical image monitoring systems along with their features. In this context mobile edge computing can play a vital role because biomedical image monitoring systems needs to deal with huge amount of data. In general, image data consists of large volume of information. Storage and processing of such a huge amount of data is really a headache. Technologies based on mobile edge computing allows us to save valuable resources in the processing nodes and suitable to handle the resource-hungry applications. Various aspects of the WSN healthcare systems are analyzed and future directions are reported and analyzed in a comprehensive way so that this work will be beneficial for the society and can be extended towards real life implementation.

In the modern era of Information Technology, a combined solution framework integrating Wireless Sensor Network (WSN), Internet of Things (IoT), cloud and edge computing, data analytics, and other related technologies are explored and the newest proposals for its probable implementation in the arena of farming is stated in this chapter. Briefing Mobile edge computing (MEC) is an up-coming framework in which the cloud computing services are stretched to the boundary of mobile end-nodes. Further, to boost up the productivity of the crops and working efficacy in the agriculture area, the practice of IoT, edge computing data analytics, etc., are introduced. In this chapter, we surveyed the crucial propositions, the contemporary research efforts, the recent innovations in technologies and research topics, and those explicit edge-cloud integrated IoT solutions that have direct application to agriculture. We aim to design a complete image of both enduring research efforts and upcoming research possibilities through comprehensive and elaborated deliberations. The chapter presents a study of more than a hundred papers, which constitute the most significant work in the relevant field along with research challenges and future open issues and which are also identified and discussed thoroughly.

The success of Artificial Intelligence (AI) through Deep Learning (DL) and Computer Vision has inspired many researchers to work on many real-life and human-centered tasks. These current AI systems are in use to augment the intelligence of IoT. IoT devices are equipped with very low computing and fewer storage resources. In the case of visual computing, a massive number of images or video data are needed to be processed, which seems to be not feasible for an IoT device. Therefore, those data are needed to transfer to a cloud machine for computation. However, in this case, bandwidth scarcity is a huge problem. Real-time computation and security and privacy of data are also very challenging issues. To handle this problem, Mobile Edge Computing (MEC) is used in IoT to perform the real-time computation locally. Combining state-of-the-art computer vision algorithms such as DL, especially Deep Convolutional Neural Network (CNN) based algorithms and MEC, can be a smart solution for onsite visual computing. This chapter scholarly discussed how deep CNN through MEC could be a potential technique for IoT based solutions. It also discuss how a deep transfer learning procedure can be applied in this method. This chapter proposes how different layers of deep CNN can be split up among Edge devices, Fog gateway, and Cloud servers to do visual computing at IoTs. Relevant technical backgrounds, current state-of-the-art, and future scopes are also emphasized in this chapter.

By performing latency-sensitive computations at the edge and the remaining computations on a backend server, edge computing systems can effectively handle the processing of data in a timely manner. This chapter focuses on an edge computing framework that partitions the processing of sensor data at a mobile node placed at the edge and backend computations at a powerful server. The primary application of the framework is in the area of processing of complex events each of which may correspond to the simultaneous occurrence of multiple raw events generated by sensors that are monitoring the phenomena of interest. Application of such complex event processing techniques spans smart buildings, smart machinery as well as smart healthcare systems. This chapter focuses on using the proposed framework and techniques to a smart phone based remote patient monitoring system and by using prototyping and measurement presents a rigorous performance analysis of the system.

The edge cloud is attractive to provide low latency services to mobile users. It overcomes computation, storage, and energy limitations of mobile devices through computation offloading. It also avoids long delays in migration of big data from the point of their generation by IoT devices to the centralized data centers. Context-aware edge cloud design provides mobile users with more personalized and customized services that improve their over-all experience. It manages the cloud infrastructure for resource provisioning, scheduling, and load balancing. The latency constraints of MEC applications need light-weight container service in the edge cloud. Kubernetes container orchestration is popular in the industry that is supported by all major edge cloud platforms. Container migration is important for ensuring low latency to new mobile applications of connected vehicles and drones. In this chapter we present the current state of research and development in the application design and service provisioning for edge cloud.

Fog computing is a novel distributed computing paradigm that provides cloud-like services at the edge of the network. It emerges as an efficient paradigm to process the enormous amount of Internet of Things (IoT) data and can address the limitations of cloud-centric IoT models in terms of large end-to-end delays, and huge network bandwidth consumption. Recently, fog computing and IoT have been employed in several domains, including transportation, education, healthcare, and manufacturing industry. To imitate different complex application scenarios for these domains, a notable number of fog computing-based simulators has already been developed. Among them, iFogSim has attained significant attention because of its simplified interface and low complexity. In this article, we present a tutorial on how to use iFogSim toolkit to simulate four real-time case studies for (1) smart car parking, (2) smart waste management system, (3) smart coal mining industry, and (4) sensing as a service. This article is expected to assist the researchers in understanding and implementing various aspects of fog computing using the iFogSim toolkit.