Delay-Tolerant ICN and Its Application to LoRa

Connecting long-range wireless networks to the Internet imposes challenges due to vastly longer round-trip-times (RTTs). In this paper, we present an ICN protocol framework that enables robust and efficient delay-tolerant communication to edge networks. Our approach provides ICN-idiomatic communication between networks with vastly different RTTs. We applied this framework to LoRa, enabling end-to-end consumer-to-LoRa-producer interaction over an ICN-Internet and asynchronous data production in the LoRa edge. Instead of using LoRaWAN, we implemented an IEEE 802.15.4e DSME MAC layer on top of the LoRa PHY and ICN protocol mechanisms in RIOT OS. Executed on off-the-shelf IoT hardware, we provide a comparative evaluation for basic NDN-style ICN [60], RICE [31]-like pulling, and reflexive forwarding [46]. This is the first practical evaluation of ICN over LoRa using a reliable MAC. Our results show that periodic polling in NDN works inefficiently when facing long and differing RTTs. RICE reduces polling overhead and exploits gateway knowledge, without violating ICN principles. Reflexive forwarding reflects sporadic data generation naturally. Combined with a local data push, it operates efficiently and enables lifetimes of >1 year for battery powered LoRa-ICN nodes.Comment: 12 pages, 7 figures, 2 table

Application of genetic algorithm to load balancing in networks with a homogeneous traffic flow

The concept of extended cloud requires efficient network infrastructure to support ecosystems reaching form the edge to the cloud(s). Standard approaches to network load balancing deliver static solutions that are insufficient for the extended clouds, where network loads change often. To address this issue, a genetic algorithm based load optimizer is proposed and implemented. Next, its performance is experimentally evaluated and it is shown that it outperforms other existing solutions.Comment: Accepted for the conference -- The International Conference on Computational Science ICCS202

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

WHY WE SHOULD TALK? THE POTENTIALS OF COMMUNITY DIALOG IN GROUNDING AN INTEGRATED RURAL DEVELOPMENT

Rural development is a social process. It involves local community in all stages of development. Community dialog is a means for facilitating community involvement in determining a development direction, potential development plan and development sus-tainability in the future. Frequently, local community is considered as the development target. This position puts them just being development watchers, spectators, silent and passive recipients. Moreover, these silent roles make them remain unempowered since they do not know how to determine their future, how to take part in collective decision and feel being neglected. This study examines potentials of community involvement in dialog. A qualitative research paradigm is adopted. The data are collected byrecording, transcribing and analyzing community dialog at Klagen, Nganjuk, Jawa Timur.  The study finds that community dialog offers considerable potentials. The first potential of community dialog is generating local community commitment, awareness, sense of belongingness and supportive character to build their own homeland. These positive development psychological states,characters and ethos are soft human dimensions which can be critical drivers in rural development. The second is creation of local knowledge and scientific knowledge joint enabling innovation and collective learning process. This joint-knowledge allows the combination of local wisdom and scientific insight. The third is building shared or collective development vision and plan. This plan and vision allow the development prioritizing process and development of rural strength, potential competitive advantage and resource building. The fourth is expanding rural networking and exercising rural people capacity to build wider internal and external social relationship. 

Can we exploit machine learning to predict congestion over mmWave 5G channels?

It is well known that transport protocol performance is severely hindered by wireless channel impairments. We study the applicability of Machine Learning (ML) techniques to predict congestion status of 5G access networks, in particular mmWave links. We use realistic traces, using the 3GPP channel models, without being affected using legacy congestion-control solutions. We start by identifying the metrics that might be exploited from the transport layer to learn the congestion state: delay and inter-arrival time. We formally study their correlation with the perceived congestion, which we ascertain based on buffer length variation. Then, we conduct an extensive analysis of various unsupervised and supervised solutions, which are used as a benchmark. The results yield that unsupervised ML solutions can detect a large percentage of congestion situations and they could thus bring interesting possibilities when designing congestion-control solutions for next-generation transport protocols.This work was supported by the Spanish Government (MINECO) by means of the project FIERCE “Future Internet Enabled Resilient smart CitiEs” under Grant Agreement No. RTI2018-093475-A-I00

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

Leveraging and Fusing Civil and Military Sensors to support Disaster Relief Operations in Smart Environments

Natural disasters occur unpredictably and can range in severity from something locally manageable to large scale events that require external intervention. In particular, when large scale disasters occur, they can cause widespread damage and overwhelm the ability of local governments and authorities to respond. In such situations, Civil-Military Cooperation (CIMIC) is essential for a rapid and robust Humanitarian Assistance and Disaster Relief (HADR) operation. These type of operations bring to bear the Command and Control (C2) and Logistics capabilities of the military to rapidly deploy assets to help with the disaster relief activities. Smart Cities and Smart Environments, embedded with IoT, introduce multiple sensing modalities that typically provide wide coverage over the deployed area. Given that the military does not own or control these assets, they are sometimes referred to as gray assets, which are not as trustworthy as blue assets, owned by the military. However, leveraging these gray assets can significantly improve the ability for the military to quickly obtain Situational Awareness (SA) about the disaster and optimize the planning of rescue operations and allocation of resources to achieve the best possible effects. Fusing the information from the civilian IoT sensors with the custom military sensors could help validate and improve trust in the information from the gray assets. The focus of this paper is to further examine this challenge of achieving Civil-Military cooperation for HADR operations by leveraging and fusing information from gray and blue assets

From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Knowledge Management, Trust and Communication in the Era of Social Media

The article entitled "Selected Aspects of Evaluating Knowledge Management Quality in Contemporary Enterprises" broadens the understanding of knowledge management and estimates select aspects of knowledge management quality evaluations in modern enterprises from theoretical and practical perspectives. The seventh article aims to present the results of pilot studies on the four largest Information Communication Technology (ICT) companies' involvement in promoting the Sustainable Development Goals (SDGs) through social media. Studies examine which communication strategy is used by companies in social media. The primary purpose of the eighth article is to present the relationship between trust and knowledge sharing, taking into account the importance of this issue in the efficiency of doing business. The results showed that trust is vital in sharing knowledge and essential in achieving a high-performance efficiency level. The ninth article presents the impact of social media on consumer choices in tourism and tourist products' specificity. The study's main purpose was to indicate the most commonly used social media in selecting a tourist destination and implementing Generation Y's journey. The 10th article aims to identify the most critical purposes of using social media by responding to women's attitudes according to age and their respective countries' economic development. The research was done through an online survey in 2017–2018, followed by an analysis of eight countries' results. The article entitled "Integrated Question-Answering System for Natural Disaster Domains Based on Social Media Messages Posted at the Time of Disaster" presents the framework of a question-answering system that was developed using a Twitter dataset containing more than 9 million tweets compiled during the Osaka North Earthquake that occurred on 18 June 2018. The authors also study the structure of the questions posed and develop methods for classifying them into particular categories to find answers from the dataset using an ontology, word similarity, keyword frequency, and natural language processing. The book provides a theoretical and practical background related to trust, knowledge management, and communication in the era of social media. The editor believes that the collection of articles can be relevant to professionals, researchers, and students' needs. The authors try to diagnose the situation and show the new challenges and future directions in this area