The Extreme Edge at the Bottom of the Internet of Things: A Review

Portilla, Jorge; Mujica, Gabriel; Lee, Jin‐Shyan; Riesgo, Teresa

doi:10.1109/jsen.2019.2891911

Cited by 77 publications

(42 citation statements)

References 48 publications

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“…Portilla et al [50] structured the research in the area of the Internet of Things and the extreme of the network. The authors presented a general review of the following concepts: The Internet of Things, the cloud, fog, edge, and extreme edge.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding IoT systems, Portilla et al [50] described and analyzed the structure of IoT systems according to the four computing paradigms: Cloud, Fog, Edge, and Extreme Edge. They focused especially on the extreme edge concept, outlining its implementation requirements and options, which vary depending on the environment in which they are implemented and the type of devices that make it up.…”

Section: Related Workmentioning

confidence: 99%

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A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

View full text Add to dashboard Cite

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“…The traditional hardware solutions that were used in Wireless Sensor Networks mostly relied on de-facto standard sensor motes, such as Micas and TelosB [1], or similar approaches where 8-bit or 16-bit-based microcontrollers were integrated as the core of the wireless devices, to perform simple yet energy-efficient tasks for the target application. During the last decade tens of hardware platforms for the Extreme Edge [2] of the IoT have appeared with different elements and focusing on aspects such as low power consumption, high processing capabilities or open HW philosophy, among others [3]. Although these hardware platforms have been valid for many WSN application contexts, the ongoing revolution of IoT is pushing the hardware implementation towards the integration of more complex capabilities that allow tackling the challenges of smart and highly dynamic scenarios [4], particularly concerning the arising end-to-end IoT security issues with such an amount of expected Edge devices in place.…”

Section: Introductionmentioning

confidence: 99%

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

et al. 2020

Self Cite

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The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

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“…IoT transforms regular objects and sensors in Internet nodes, allowing them to interact with human beings and other machines to carry out their tasks. As a consequence, the perspective of having ubiquitous and pervasive computing devices supporting our daily activities without significant human intervention has been increasingly consolidated [6,7].…”

Section: Introductionmentioning

confidence: 99%

IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices

Bezerra

Costa

Barbon

et al. 2019

Sensors

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Internet of Things (IoT) devices have become increasingly widespread. Despite their potential of improving multiple application domains, these devices have poor security, which can be explored by attackers to build large-scale botnets. In this work, we propose a host-based approach to detect botnets in IoT devices, named IoTDS (Internet of Things Detection System). It relies on one-class classifiers, which model only the legitimate device behaviour for further detection of deviations, avoiding the manual labelling process. The proposed solution is underpinned by a novel agent-manager architecture based on HTTPS, which prevents the IoT device from being overloaded by the training activities. To analyse the device’s behaviour, the approach extracts features from the device’s CPU utilisation and temperature, memory consumption, and number of running tasks, meaning that it does not make use of network traffic data. To test our approach, we used an experimental IoT setup containing a device compromised by bot malware. Multiple scenarios were made, including three different IoT device profiles and seven botnets. Four one-class algorithms (Elliptic Envelope, Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine) were evaluated. The results show the proposed system has a good predictive performance for different botnets, achieving a mean F1-score of 94% for the best performing algorithm, the Local Outlier Factor. The system also presented a low impact on the device’s energy consumption, and CPU and memory utilisation.

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The Extreme Edge at the Bottom of the Internet of Things: A Review

Cited by 77 publications

References 48 publications

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices

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