The amount of data generated by sensors, actuators, and other devices in the Internet of Things (IoT) has substantially increased in the last few years IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers As a consequence, network bandwidth and communication latency become serious bottlenecks This paper advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming To this end, we consider a resource-intensive 3-D application as a paradigmatic example and evaluate the response delay in different deployment scenarios Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications

/pdf/edge-computing-for-the-internet-of-things-a-case-study-1bmf94u6bt.pdf

Edge Computing for the Internet of Things: A Case Study

Many cloud-based applications employ a data centers as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

/pdf/challenges-and-opportunities-in-edge-computing-1ldas05v70.pdf

Challenges and Opportunities in Edge Computing

Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency-related issues, its deployment engenders new challenges. In this article, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing-enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions.

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey

Internet of Things (IoT) is an emerging technology that makes people’s lives smart by conquering a plethora of diverse application and service areas. In near future, the fifth-generation (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e., slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning (AI/ML) in network slicing and IoT integration.

https://researchrepository.ucd.ie/bitstream/10197/12083/2/IEEE_COMST_Slicing___IoT_survey__Final_Submission__%20%281%29.pdf

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Edge computing for Internet of Things: A survey, e-healthcare case study and future direction

Cyber-Physical Systems (CPSs) are complex systems comprising computation, physical, and networking assets. Used in various domains such as manufacturing, agriculture, vehicles, etc., they blend the control of the virtual and physical worlds. Smart homes are a peculiar type of CPS where the local networking fundamentals have seen little evolution in the past decades, while the context in which home networks operate has drastically evolved. With the advent of the Internet of Things (IoT), the number and diversity of devices connected to our home networks are exploding. Some of those devices are poorly secured and put users' data privacy and security at risk. At the same time, administrating a home network has remained a tedious chore, requiring skills from un-savvy users. We present Future Spaces, an end-to-end hardware-software prototype providing fine-grained control over IoT connectivity to enable easy and secure management of smart homes. Relying on Software-Defined Networking-enabled home gateways and the virtualization of network functions in the cloud, we achieve advanced networking security and automation through the definition of isolated, usage-oriented slices. This disrupts how users discover, control and share their connected assets across multiple domains, smoothly adapting to various usage contexts.

Future Spaces: Reinventing the Home Network for Better Security and Automation in the IoT Era.

We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogical reasoning. We apply word embedding techniques from natural language processing (NLP) to train embeddings for library packages ("library vectors"). Library vectors represent libraries by similar context of use as determined by import statements present in source code. Experimental results obtained from training such embeddings on three large open source software corpora reveals that library vectors capture semantically meaningful relationships among software libraries, such as the relationship between frameworks and their plug-ins and libraries commonly used together within ecosystems such as big data infrastructure projects (in Java), front-end and back-end web development frameworks (in JavaScript) and data science toolkits (in Python).

Import2vec learning embeddings for software libraries

This paper introduces a new paradigm for service oriented networking being developed in the FUSION project(1). Despite recent proposals in the area of information centric networking, a similar treatment of services - where networked software functions, rather than content, are dynamically deployed, replicated and invoked - has received little attention by the network research community to date. Our approach provides the mechanisms required to deploy a replicated service instance in the network and to route client requests to the closest instance in an efficient manner. We address the main issues that such a paradigm raises including load balancing, resource registration, domain monitoring and inter-domain orchestration. We also present preliminary evaluation results of current work.

/pdf/service-oriented-networking-1v1ciuundm.pdf

Service oriented networking

Today's centralized cloud-computing infrastructures have not been designed with geo-localized, personalized, bandwidth/processing-intensive, real-time applications in mind. High network delay and low throughput can have a significant impact on the user experience. Instead, such services could be deployed in distributed service nodes at the edge of the network, closer to the user. In this paper we focus on composite services of which the components are running in different service nodes. We present a two-layer framework that provides service orchestration and instance selection. We present the orchestration mechanisms to enable the flexible re-use of components across different composite services. For the resolution layer of our framework, we present two modes of operation that combine network and service availability information for efficient per-request instance selection among a multitude of service replicas.

/pdf/challenges-for-orchestration-and-instance-selection-of-9h08ffrwcq.pdf

Challenges for orchestration and instance selection of composite services in distributed edge clouds

We consider the problem of developing suitable learning representations (embeddings) for library packages that capture semantic similarity among libraries. Such representations are known to improve the performance of downstream learning tasks (e.g. classification) or applications such as contextual search and analogical reasoning. 
We apply word embedding techniques from natural language processing (NLP) to train embeddings for library packages ("library vectors"). Library vectors represent libraries by similar context of use as determined by import statements present in source code. Experimental results obtained from training such embeddings on three large open source software corpora reveals that library vectors capture semantically meaningful relationships among software libraries, such as the relationship between frameworks and their plug-ins and libraries commonly used together within ecosystems such as big data infrastructure projects (in Java), front-end and back-end web development frameworks (in JavaScript) and data science toolkits (in Python).

Frederik Vandeputte

Papers

Future Spaces: Reinventing the Home Network for Better Security and Automation in the IoT Era.

Import2vec learning embeddings for software libraries

Service oriented networking

Challenges for orchestration and instance selection of composite services in distributed edge clouds

Import2vec - Learning Embeddings for Software Libraries