Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

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On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

Elements of Information Theory (2nd ed.). Thomas M. Cover and Joy A. Thomas

Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data.We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs.

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On the interplay between routing and signal representation for Compressive Sensing in wireless sensor networks

Motivated by streaming applications with stringent delay constraints, we consider the design of online network coding algorithms with timely delivery guarantees. Assuming that the sender is providing the same data to multiple receivers over independent packet erasure channels, we focus on the case of perfect feedback and heterogeneous erasure probabilities. Based on a general analytical framework for evaluating the decoding delay, we show that existing ARQ schemes fail to ensure that receivers with weak channels are able to recover from packet losses within reasonable time. To overcome this problem, we re-define the encoding rules in order to break the chains of linear combinations that cannot be decoded after one of the packets is lost. Our results show that sending uncoded packets at key times ensures that all the receivers are able to meet specific delay requirements with very high probability.

Effective Delay Control in Online Network Coding

In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

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Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

The automotive and telco industries have taken an investment bet on the connected car market, pushing for the digital transformation of the sector by exploiting recent information and communications technology (ICT) progress. As ICT developments continue, it is expected that the technology advancements will be able to fulfill the sophisticated requirements for vehicular use cases, such as: low latency and reliable communications for safety; increased computing power to process large amounts of sensed data; and increased bandwidth for onboard infotainment. The aforementioned requirements have received significant focus during the ongoing definition of the 3GPP 5G mobile standards, where there has been a drive to facilitate vertical segments such as automotive, in addition to providing the core aspects of the communication infrastructure. Among the technology enablers for 5G, multi-access edge computing (MEC) can be considered essential. That is, a cloud environment located at the edge of the network, in proximity of the end users and coupled with the service provider's network infrastructure. Even before 5G is rolled out, current mobile networks can already target support for these challenging use cases using MEC technology. This is because MEC is able to fulfill low latency and high bandwidth requirements, and, in addition, lends itself to be deployed at vertical industrial sector premises such as road infrastructure, air/seaports, and smart factories, thus bringing computing power where it is needed most. This work showcases the automotive use cases that are relevant for MEC, providing insights into the technologies specified and investigated by the ETSI Industry Specification Group MEC, who were the pioneers in creating a standardized computing platform for advanced mobile networks with regard to network edge related use cases.

Multi-Access Edge Computing: The Driver Behind the Wheel of 5G-Connected Cars

Network coding is a highly efficient data dissemination mechanism for wireless networks. Since network coded information can only be recovered after delivering a sufficient number of coded packets, the resulting decoding delay can become problematic for delay-sensitive applications such as real-time media streaming. Motivated by this observation, we consider several algorithms that minimize the decoding delay and analyze their performance by means of simulation. The algorithms differ both in the required information about the state of the neighborspsila buffers and in the way this knowledge is used to decide which packets to combine through coding operations. Our results show that a greedy algorithm, whose encodings maximize the number of nodes at which a coded packet is immediately decodable significantly outperforms existing network coding protocols.

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Daniele Munaretto

Papers

On the interplay between routing and signal representation for Compressive Sensing in wireless sensor networks

Effective Delay Control in Online Network Coding

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Multi-Access Edge Computing: The Driver Behind the Wheel of 5G-Connected Cars

Informed network coding for minimum decoding delay