A New Edge Computing Architecture for IoT and Multimedia Data Management

Abstract

The Internet of Things and multimedia devices generate a tremendous amount of data. The transfer of this data to the cloud is a challenging problem because of the congestion at the network level, and therefore processing time could be too long when we use a pure cloud computing strategy. On the other hand, new applications requiring the processing of large amounts of data in real time have gradually emerged, such as virtual reality and augmented reality. These new applications have gradually won over users and developed a demand for near real-time interaction of their applications, which has completely called into question the way we process and store data. To address these two problems of congestion and computing time, edge architecture has emerged with the goal of processing data as close as possible to users, and to ensure privacy protection and responsiveness in real-time. With the continuous increase in computing power, amounts of memory and data storage at the level of smartphone and connected objects, it is now possible to process data as close as possible to sensors or directly on users devices. The coupling of these two types of processing as close as possible to the data and to the user opens up new perspectives in terms of services. In this paper, we present a new distributed edge architecture aiming to process and store Internet of Things and multimedia data close to the data producer, offering fast response time (closer to real time) in order to meet the demands of modern applications. To do this, the processing at the level of the producers of data collaborate with the processing ready for the users, establishing a new paradigm of short supply circuit for data transmission inspired of short supply chains in agriculture. The removing of unnecessary intermediaries between the producer and the consumer of the data improves efficiency. We named this new paradigm the Short Supply Circuit Internet of Things (SSCIoT).

Wireless Sensor Network Architecture Based on Mobile Edge Computing

“…MEC computing approaches lack improved interoperability between future 5G wireless sensor networks (WSN) and current wireless sensor networks (MEC). With the 5G-MEC paradigm, the author proposes an interoperable architecture that combines a wireless sensor network with a functional but outdated wireless sensor network [12]. As a result, MEC and end-users will take advantage of low latency and high transmission speeds.…”

“…e authors propose a new paradigm to improve data processing efficiency at the sensor, network, and end-user device levels by making better local resources [12].…”

Edge Computing in Centralized Data Server Deployment for Network Qos and Latency Improvement for Virtualization Environment

“…Giving and additionally dealing with the sources at the edge would empower the end gadget to save resources (e.g., put away energy in batteries) and accelerate calculation, and permit utilizing resources it doesn't have. Also, keeping information near where it was produced empowers better control, particularly for protection related issues [7]. At last, being found near the client, EC makes it conceivable to build the nature of offered types of assistance using profiling inside a neighborhood setting, without compromising the protection or taking care of countless clients.…”

Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT Systems

“…), and can be considered a main preprocessing stage (see Figure 18 ). We stress that an application of the proposed DAT representation of the image provides the following: Resource-efficient EC and training at the edge, which is of particular importance, for example, in intelligent transportation systems [ 57 ]; Easy implementation in fog EC and mobile EC architectures [ 18 ], as well as in “non-classic” ones, for instance, short supply circuit IoT [ 58 ]; Data protection, in particular, satisfies the so-called zero-trust principle, which belongs to the set of top trends [ 40 ]; a high level of protection and confidentiality is ensured by the great variety of settings, in particular, the atomic function applied in DAT and a structure of this core procedure, as well as several ways to encode quantized DAT coefficients; despite this, its comparison to other methods, for example, biometric security through visual encryption [ 59 ] and lightweight cryptographic algorithm [ 60 ], must be carried out; Ability to construct artificial intelligence of things or AIoT systems [ 20 ], as well as that one which provides distributed learning, edge learning, and mobile intelligence [ 44 ]. …”

“…Easy implementation in fog EC and mobile EC architectures [ 18 ], as well as in “non-classic” ones, for instance, short supply circuit IoT [ 58 ];…”