Edge Computing for IoT Concepts, Buzzwords & Differences

Edge Computing for IoT: The fast development of smartphones and Internet of Things applications have caused severe technological challenges with the use of cloud technology. These challenges include high latency, low Spectrum Efficiency (SE), and non-adaptive communication protocols. Edge computing is the new technology that is driving a trend that shifts the function of centralized cloud computing to edge devices. Several edge computing technologies have evolved from different backgrounds to address these challenges. Three edge computing technologies, namely mobile edge computing; cloudlet – internet edge and fog computing, are popular.

Internet of Things refers to the interaction and communication between billions of devices that produce and exchange data related to real-world objects. IoT’s features three significant aspects 1) ultra-largescale network of things, device, 2) network heterogeneity, and 3) large numbers of events generated by these things. This complexity makes the development of diverse applications and services a very challenging task. These requirements are becoming difficult to accomplish in the IoT interacting with the cloud. IoT applications generate enormous amounts of data by using IoT sensors. Big data are subsequently analyzed to determine reactions to events or to extract analytics or statistics. However, sending all the data to the cloud requires high network bandwidth.

For IoT, Edge computing can provide data services with faster response and higher quality, in comparison with cloud computing. Hence Edge computing is more contextual to be integrated with IoT to provide efficient and secure services for a large number of end-users, and edge computing-based architecture severs as the future IoT infrastructure.

Three significant concepts have evolved in Edge computing with a group of companies supporting each of them:
1) Cloudlets: Open Edge Computing (OEC) was set up to accelerate the development of the IoT Edge ecosystem based on the concept called cloudlets. Open Edge Computing (OEC) initiative was launched in June 2015 by Vodafone, Intel, and Huawei companies in partnership with Carnegie Mellon University.
2) Mobile Edge Computing: An Industry Specification Group (ISG) was to allow the creation of industry specifications for Mobile Edge Computing (MEC). This initiative is supported by Huawei, IBM, Intel, Nokia Networks, NTT DoCoMo, Vodafone, and other companies.
3) Fog computing: Another technology similar to edge computing is known as fog computing, which was initiated by Cisco. To speed the adoption of fog computing, the OpenFog Consortium has been founded by ARM, Cisco, Dell, Intel, Microsoft, and Princeton University.

Cloudlet: The edge of the internet

One of the primary purposes of the cloudlet is to support computer resource-intensive mobile applications by providing powerful computing resources to applications running on mobile devices with lower latency.

There are few but essential differentiators between cloud and cloudlet:
1) A cloudlet, when compared to the cloud, needs to be much more agile in its provisioning because the association with mobile devices is highly dynamic with considerable churn due to user mobility.
2) Primarily to support user mobility, a VM handoff technology needs to be used to seamlessly migrate the offloaded services on the first cloudlet to the second cloudlet as a user moves away from the currently associated cloudlet.
3) Cloudlets are small DCs distributed geographically. A mobile device first has to discover, select, and associate with the appropriate cloudlet among multiple candidates before it starts provisioning.

Mobile Edge Computing (MEC):

MEC is a crucial concept in IoT for mission-critical, vertical solutions, and is also recognized as one of the key architectural element. ETSI defined the concept of MEC as a new technology that provides an IT environment and cloud-computing at the edge of the mobile network using a wireless protocol.

MEC has many advanced features that offer low latency, proximity, high bandwidth, and real-time insight into radio network information and location awareness. Hence it enables a large number of new types of applications and services for multiple sectors, such as consumer, enterprise, and health. In particular, MEC is considered to be a promising solution for handling video streaming services in the context of smart cities. The IoT also generates additional messaging on telecommunication networks like 3G/4G/5G and requires gateways to aggregate the messages and ensure low latency and high security.

Fog computing: Edge works closely with cloud

The OpenFog Consortium was founded to drive industry and academic leadership in fog computing architecture. It provides a testbed development, and a variety of inter-operability deliverables that can leverage the cloud and edge solutions to enable end-to-end IoT scenarios.
The OpenFog Consortium definition of fog computing is a system-level horizontal architecture that distributes resources and services of computing, storage, control, and networking anywhere along the continuum from the cloud to things. Fog computing is different from edge computing and provides tools for distributing, orchestrating, managing and securing resources and services across networks and between devices that reside at the edge.

An open architecture based on fog computing enables interoperability in IoT, 5G, AI, tactile internet, virtual reality, and other complex data and network intensive applications. Based on the IoT situation on data, fog nodes are used to carry out data mining and data analysis on a large volume of multi-modal and heterogeneous data from various sensor devices and other IoT devices to achieve real-time and fast processing for decision making.

Edge computing challenges & solutions:

1) Edge computing has to be data-aware and reconfigured to adapt the massive types of packet traffic and the time-varied data channel. Big data is described by volume, variety, velocity, and value. There is fast development in the field of big data mining. Hence it is feasible to utilize big data technology to extract exciting patterns or knowledge to enhance the self-organizing capabilities in edge computing.

2) To address all types of use cases and business models for the emerging applications of mobile internet and IoT, both revolutionary wireless network architectures and advanced technologies are anticipated. As a result, network slicing is proposed recently to provide SDN in a cost-efficient way flexible. In the concept of network slicing, the network entity is sliced into multiple isolated network slice instances, and each slice instance has appropriate network functions


These are state-of-the-art edge computing technologies. This article divides the concepts into cloudlet, mobile edge computing, and fog computing. However, given the relative infancy of the Edge computing field, there are still many outstanding problems that require further investigation from the perspective of analytical techniques and advanced solutions.


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