Resource Management for Edge Computing in Internet of Things (IoT)

Die große Anzahl an Geräten im Internet der Dinge (IoT) und deren kontinuierliche Datensammlungen führen zu einem rapiden Wachstum der gesammelten Datenmenge. Die Daten komplett mittels zentraler Cloud Server zu verarbeiten ist ineffizient und zum Teil sogar unmöglich oder unnötig. Darum wird die Datenverarbeitung an den Rand des Netzwerks verschoben, was zu den Konzepten des Edge Computings geführt hat. Informationsverarbeitung nahe an der Datenquelle (z.B. auf Gateways und Edge Geräten) reduziert nicht nur die hohe Arbeitslast zentraler Server und Netzwerke, sondern verringer auch die Latenz für Echtzeitanwendungen, da die potentiell unzuverlässige Kommunikation zu Cloud Servern mit ihrer unvorhersehbaren Netzwerklatenz vermieden wird. Aktuelle IoT Architekturen verwenden Gateways, um anwendungsspezifische Verbindungen zu IoT Geräten herzustellen. In typischen Konfigurationen teilen sich mehrere IoT Edge Geräte ein IoT Gateway. Wegen der begrenzten verfügbaren Bandbreite und Rechenkapazität eines IoT Gateways muss die Servicequalität (SQ) der verbundenen IoT Edge Geräte über die Zeit angepasst werden. Nicht nur um die Anforderungen der einzelnen Nutzer der IoT Geräte zu erfüllen, sondern auch um die SQBedürfnisse der anderen IoT Edge Geräte desselben Gateways zu tolerieren. Diese Arbeit untersucht zuerst essentielle Technologien für IoT und existierende Trends. Dabei werden charakteristische Eigenschaften von IoT für die Embedded Domäne, sowie eine umfassende IoT Perspektive für Eingebettete Systeme vorgestellt. Mehrere Anwendungen aus dem Gesundheitsbereich werden untersucht und implementiert, um ein Model für deren Datenverarbeitungssoftware abzuleiten. Dieses Anwendungsmodell hilft bei der Identifikation verschiedener Betriebsmodi. IoT Systeme erwarten von den Edge Geräten, dass sie mehrere Betriebsmodi unterstützen, um sich während des Betriebs an wechselnde Szenarien anpassen zu können. Z.B. Energiesparmodi bei geringen Batteriereserven trotz gleichzeitiger Aufrechterhaltung der kritischen Funktionalität oder einen Modus, um die Servicequalität auf Wunsch des Nutzers zu erhöhen etc. Diese Modi verwenden entweder verschiedene Auslagerungsschemata (z.B. die übertragung von Rohdaten, von partiell bearbeiteten Daten, oder nur des finalen Ergebnisses) oder verschiedene Servicequalitäten. Betriebsmodi unterscheiden sich in ihren Ressourcenanforderungen sowohl auf dem Gerät (z.B. Energieverbrauch), wie auch auf dem Gateway (z.B. Kommunikationsbandbreite, Rechenleistung, Speicher etc.). Die Auswahl des besten Betriebsmodus für Edge Geräte ist eine Herausforderung in Anbetracht der begrenzten Ressourcen am Rand des Netzwerks (z.B. Bandbreite und Rechenleistung des gemeinsamen Gateways), diverser Randbedingungen der IoT Edge Geräte (z.B. Batterielaufzeit, Servicequalität etc.) und der Laufzeitvariabilität am Rand der IoT Infrastruktur. In dieser Arbeit werden schnelle und effiziente Auswahltechniken für Betriebsmodi entwickelt und präsentiert. Wenn sich IoT Geräte in der Reichweite mehrerer Gateways befinden, ist die Verwaltung der gemeinsamen Ressourcen und die Auswahl der Betriebsmodi für die IoT Geräte sogar noch komplexer. In dieser Arbeit wird ein verteilter handelsorientierter Geräteverwaltungsmechanismus für IoT Systeme mit mehreren Gateways präsentiert. Dieser Mechanismus zielt auf das kombinierte Problem des Bindens (d.h. ein Gateway für jedes IoT Gerät bestimmen) und der Allokation (d.h. die zugewiesenen Ressourcen für jedes Gerät bestimmen) ab. Beginnend mit einer initialen Konfiguration verhandeln und kommunizieren die Gateways miteinander und migrieren IoT Geräte zwischen den Gateways, wenn es den Nutzen für das Gesamtsystem erhöht. In dieser Arbeit werden auch anwendungsspezifische Optimierungen für IoT Geräte vorgestellt. Drei Anwendungen für den Gesundheitsbereich wurden realisiert und für tragbare IoT Geräte untersucht. Es wird auch eine neuartige Kompressionsmethode vorgestellt, die speziell für IoT Anwendungen geeignet ist, die Bio-Signale für Gesundheitsüberwachungen verarbeiten. Diese Technik reduziert die zu übertragende Datenmenge des IoT Gerätes, wodurch die Ressourcenauslastung auf dem Gerät und dem gemeinsamen Gateway reduziert wird. Um die vorgeschlagenen Techniken und Mechanismen zu evaluieren, wurden einige Anwendungen auf IoT Plattformen untersucht, um ihre Parameter, wie die Ausführungszeit und Ressourcennutzung, zu bestimmen. Diese Parameter wurden dann in einem Rahmenwerk verwendet, welches das IoT Netzwerk modelliert, die Interaktion zwischen Geräten und Gateway erfasst und den Kommunikationsoverhead sowie die erreichte Batterielebenszeit und Servicequalität der Geräte misst. Die Algorithmen zur Auswahl der Betriebsmodi wurden zusätzlich auf IoT Plattformen implementiert, um ihre Overheads bzgl. Ausführungszeit und Speicherverbrauch zu messen

Building an Improved Internet of Things Smart Sensor Network Based on a Three-Phase Methodology

© 2013 IEEE. In recent years, the Internet of Things (IoT) has allowed the easy, intelligent, and efficient connection of many devices used in daily life by means of numerous smart sensors which communicate with each other using wireless signals. The rapid development of the IoT has been a result of recent advances in sensing technology. This paper proposes a three-phase methodology to improve the quality of experience for IoT system technologies. The proposed method employs the concepts of simple routing and two well-known multi-criteria decision-making method (MCDM) techniques: The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, all simple routings are obtained using the proposed depth-first search technology (DFS). AHP is applied to analyze the structure of the problem and to obtain weights for various selected criteria in the second phase. In the third phase, TOPSIS is utilized to rank the simple routings, which are simple paths. A case study example is provided to demonstrate the proposed three-phase methodology. The results from the numerical experiments show that the proposed methodology can successfully achieve the aim of this paper

Low-Power Embedded System for Gait Classification Using Neural Networks

Abnormal foot postures can be measured during the march by plantar pressures in both dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine learning classifier inside a low-power embedded system in order to obtain information from the user’s gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power consumption and the model effectiveness. The machine learning classifier is trained using an acquired dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over 99% and the power consumption tests obtains a battery autonomy over 25 days

Low-power wide-area networks : design goals, architecture, suitability to use cases and research challenges

Previous survey articles on Low-Powered Wide-Area Networks (LPWANs) lack a systematic analysis of the design goals of LPWAN and the design decisions adopted by various commercially available and emerging LPWAN technologies, and no study has analysed how their design decisions impact their ability to meet design goals. Assessing a technology's ability to meet design goals is essential in determining suitable technologies for a given application. To address these gaps, we have analysed six prominent design goals and identified the design decisions used to meet each goal in the eight LPWAN technologies, ranging from technical consideration to business model, and determined which specific technique in a design decision will help meet each goal to the greatest extent. System architecture and specifications are presented for those LPWAN solutions, and their ability to meet each design goal is evaluated. We outline seventeen use cases across twelve domains that require large low power network infrastructure and prioritise each design goal's importance to those applications as Low, Moderate, or High. Using these priorities and each technology's suitability for meeting design goals, we suggest appropriate LPWAN technologies for each use case. Finally, a number of research challenges are presented for current and future technologies. © 2013 IEEE

Strategies for Integrating the Internet of Things in Educational Institutions

The introduction of the Internet of Things (IoT) into educational institutions has necessitated the integration of IoT devices in the information technology (IT) infrastructural environment of educational institutions. Many IT leaders at educational institutions, however, lack strategies for integrating and deploying IoT devices in their institutions, which has resulted in numerous security breaches. The purpose of this study was to explore security strategies adopted by IT administrators to prevent data breaches resulting from the integration of IoT devices in their educational institutions. The diffusion of innovations theory served as the conceptual framework for this qualitative multiple case study. Eleven IT leaders in 11 public K–12 educational institutions, who had successfully integrated IoT in their educational institutions in the United States Midwest region, were interviewed. Thematic analysis was the data analysis strategy. The 3 major themes that emerged were (a) organizational breach prevention, (b) infrastructure management—external to IT, and (c) policy management—internal to IT. A key recommendation is for IT leaders to develop strategies to harness the efficiencies and stabilities that exist during the integration of IoT devices in their educational institutions. The implications for social change include the potential for securely transforming the delivery of education to students and ensuring the safety of academic personnel by identifying strategies that IT leaders can use to securely integrate IoT devices in educational settings

Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future

Given the exponential expansion of the internet, the possibilities of security attacks and cybercrimes have increased accordingly. However, poorly implemented security mechanisms in the Internet of Things (IoT) devices make them susceptible to cyberattacks, which can directly affect users. IoT forensics is thus needed for investigating and mitigating such attacks. While many works have examined IoT applications and challenges, only a few have focused on both the forensic and security issues in IoT. Therefore, this paper reviews forensic and security issues associated with IoT in different fields. Future prospects and challenges in IoT research and development are also highlighted. As demonstrated in the literature, most IoT devices are vulnerable to attacks due to a lack of standardized security measures. Unauthorized users could get access, compromise data, and even benefit from control of critical infrastructure. To fulfil the security-conscious needs of consumers, IoT can be used to develop a smart home system by designing a FLIP-based system that is highly scalable and adaptable. Utilizing a blockchain-based authentication mechanism with a multi-chain structure can provide additional security protection between different trust domains. Deep learning can be utilized to develop a network forensics framework with a high-performing system for detecting and tracking cyberattack incidents. Moreover, researchers should consider limiting the amount of data created and delivered when using big data to develop IoT-based smart systems. The findings of this review will stimulate academics to seek potential solutions for the identified issues, thereby advancing the IoT field.Comment: 77 pages, 5 figures, 5 table

An Anthology of Next-Generation WSNs and Transformative IoT Use-Cases

The Internet of Things (IoT) paradigm brought an ever-increasing dependence on low-power devices to collect sensor data and transmit that information to the cloud, placing greater demand on connectivity and lifespan. In response, rapid worldwide innovation demonstrates the trade-offs in processing, communication, and energy consumption with diverse approaches to low-power components, duty-cycle schemes, cost, and many other critical constraints for complex use-cases, such as track-and-trace (T&T). This work explores the central theme of low-power wireless sensor networks (WSNs) in the IoT and Industrial IoT (IIoT). A collection of publications evolves through the theme, from an IoT literature review to enabling densely-scalable WSNs for logistics & asset management (LAM). Next, this research enhances the WSN design by leveraging wake-up radio (WUR) and energy harvesting (EH) to achieve battery-free operation. Lastly, this work presents WSNs to improve visibility and control of airflow/microclimate management in potentially transformative IIoT use-cases, such as data centers and agriculture

Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

The Extended Cloud:Review and Analysis of Mobile Edge Computing and Fog from a Security and Resilience Perspective

Mobile Edge Computing (MEC) and Fog are emerging computing models that extend the cloud and its services to the edge of the network. The emergence of both MEC and fog introduce new requirements, which mean their supported deployment models must be investigated. In this paper, we point out the influence and strong impact of the extended cloud (i.e., the MEC and fog) on existing communication and networking service models of the cloud. Although the relation between them is fairly evident, there are important properties, notably those of security and resilience, that we study in relation to the newly posed requirements from the MEC and fog. Although security and resilience have been already investigated in the context of the cloud - to a certain extent - existing solutions may not be applicable in the context of the extended cloud. Our approach includes the examination of models and architectures that underpin the extended cloud, and we provide a contemporary discussion on the most evident characteristics associated with them. We examine the technologies that implement these models and architectures, and analyse them with respect to security and resilience requirements. Furthermore, approaches to security and resilience-related mechanisms are examined in the cloud (specifically, anomaly detection and policy based resilience management), and we argue that these can also be applied in order to improve security and achieve resilience in the extended cloud environment