Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Effect of IoT capabilities and energy consumption behavior on green supply chain integration

The Internet of Things (IoT) is the next generation of internet-connected information communication technologies (ICT). IoT typically integrates supply chain activities to enhance green supply chain performance (GSCP). Since every organization has different IoT capabilities in comparison with other organizations, GSCP can enable supply chain integration activities for enhanced performance. The implementation of an IoT system can reduce the consumption of organizational resources like energy, electricity, and time and can increase the operational speed to gain better logistics and, ultimately, improved supply chain performance. This study has developed and empirically tested the relationship between IoT capabilities, energy consumption behavior (ECB), supply chain integration, green training (GT), and supply chain practices. Such a multidisciplinary relationship has not previously been established in the literature. The proposed study can fulfill the literature gap and opens new horizons for interdisciplinary research. Data used in this study are collected through offline and online survey methods. A total number of 250 out of 400 respondents participated in the survey. Data has been analyzed through partial least square-structure equation modeling (PLS-SEM) technique. The results of this study empirically test the developed model. IoT has a positive effect on supplier integration (SI), and customer integration (CI). Furthermore, SI and CI have a mediating role between IoT and GSCP, and GT has a positive impact on GSCP. It is concluded that the implementation of IoT can integrate CI and SI to increase GSCP. GT and ECB can ultimately improve GSCP. Additionally, the use of technology and GT can motivate employees to save energy and protect the environment to increase GSCP

Edge/Fog Computing Technologies for IoT Infrastructure

The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure

The railway industry plays a pivotal role in the socioeconomic landscape of many countries. However, its operation poses considerable challenges in terms of safety, environmental impact, and the intricacies of intertwined technical and social structures. Addressing these challenges necessitates the adoption of innovative approaches and advanced technologies. This doctoral research delves into the potential of the Internet of Things (IoT) as an enabler for railway infrastructure monitoring and predictive maintenance, aiming to enhance reliability, efficiency, and safety within the industry. Rooted in a pragmatic modelist philosophical stance, this thesis employs an exploratory sequential mixed-method approach incorporating qualitative and quantitative methodologies. The research process involves engaging with key stakeholders to gain insights into the challenges faced in railway maintenance and the opportunities presented by IoT implementation. Following this, an IoT system is developed, and a comprehensive value-creation framework is proposed for its effective implementation within the railway sector. The findings of this investigation underscore the transformative potential of IoT integration in railway infrastructure monitoring, yielding significant improvements in maintenance processes, safety, and operational efficiency. Furthermore, this doctoral research provides a foundation for future innovation and adaptation in the railway industry, contributing to its ongoing evolution and resilience in an ever-changing technological landscape

Hybrid SDN Evolution: A Comprehensive Survey of the State-of-the-Art

Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises can not afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives

South American Expert Roundtable : increasing adaptive governance capacity for coping with unintended side effects of digital transformation

This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization

Software Engineering Methods for the Internet of Things: A Comparative Review

Accessing different physical objects at any time from anywhere through wireless network heavily impacts the living style of societies worldwide nowadays. Thus, the Internet of Things has now become a hot emerging paradigm in computing environments. Issues like interoperability, software reusability, and platform independence of those physical objects are considered the main current challenges. This raises the need for appropriate software engineering approaches to develop effective and efficient IoT applications software. This paper studies the state of the art of design and development methodologies for IoT software. The aim is to study how proposed approaches have been solved issues of interoperability, reusability, and independence of the platform. A comparative study is presented for the different software engineering methods used for the Internet of Things. Finally, the key research gaps and open issues are highlighted as future directions