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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 25323

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Dr. Raffaele Montella

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Guest Editor

Department of Science and Technology, University of Naples “Parthenope”, CDN Isola C4, 80143 Napoli, Italy
Interests: HPC, Cloud, and GPU computing; environmental predictions and simulations (weather, climate, atmosphere, ocean); mobile computing and embedded systems; Internet of Things; GPGPU virtualization; middleware for computational environmental science (i.e. Workflows, model coupling, multidimensional data provisioning); data science

Dr. José Luis González Compeán

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Guest Editor

Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Victoria, Tamaulipas, México
Interests: cloud computing; software architecture; big data; IoT data; information security; distributed systems; virtualization; storage

Dr. Sokol Kosta

E-Mail Website
Guest Editor

Department of Electronic Systems, Aalborg University, Copenhagen, Denmark
Interests: mobile computing; edge computing; Internet of Things cloud computing; dstributed systems; networking; security of IoT and wireless systems; ubiquitous computing

Special Issue Information

Dear Colleagues,

At the beginning of the last decade, the Internet of Things and the Cloud Computer worlds were both rising. The first dealt with tiny low-power devices while the latter focused on elasticity, virtualization, and the pay-per-use model, promising the democratic distribution of computer power and storage. In recent years, the two technologies have become intimately related to each other, leveraging on a new paradigm that could be defined as the high-performance Internet of Things. This evolutionary thread has pushed sensor technologies and their related applications in a skyrocketing scenario with the massive rise of distributed low-cost sensor networks and the data crowdsourcing model. At the same time, the computation at the edge and the fog computing paradigms has enabled the design and development of a new class of cloud-native applications in which the IoT acts as a bridge between sensing technologies and cloud computing hosted applications.

This Special Issue looks for novel contributions related to the application of IoT and the sensing system to Cloud Computing powered infrastructures. The main topics of interest include but are not limited to the following:

GPU, FPGA, and heterogeneous processing algorithms;
IoT microservice applications;
Urban informatics and cloud applications;
Novel protocols for fast, secure, reliable, and resilient data transfer;
Workflows and orchestration systems involving IoT, sensing, and cloud resources;
CPU, GPU, and FPGA offloading at the edge;
Cloud-native pattern recognition algorithms for sensors and IoT produced data;
Osmotic computing and other edge computing paradigms;
Security and reliability for IoT data;
Cloud computing data distribution and provisioning;
Artificial Intelligence for IoT and sensors in the cloud;
Computational intelligence and machine learning for IoT and cloud-based smart systems for sensor networks;
Federated learning;
Scheduling of IoT sensing hybrid/cloud applications;
IoT solutions for coastal and ocean monitoring;
Long-range systems for ocean vessel-to-cloud data transfer;
Sensors and IoT data mining on the cloud.

Dr. Raffaele Montella
Dr. José Luis González Compeán
Dr. Sokol Kosta
Guest Editors

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Related Special Issue

Internet of Things, Sensing and Cloud Computing—2nd Edition in Sensors

Published Papers (10 papers)

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Research

21 pages, 8236 KiB

Open AccessArticle

Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning

by Homayun Kabir, Mau-Luen Tham, Yoong Choon Chang, Chee-Onn Chow and Yasunori Owada

Sensors 2023, 23(14), 6448; https://0-doi-org.brum.beds.ac.uk/10.3390/s23146448 - 17 Jul 2023

Viewed by 1044

Abstract

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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26 pages, 7038 KiB

Open AccessArticle

FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment

by Yi Jie Wong, Mau-Luen Tham, Ban-Hoe Kwan and Yasunori Owada

Sensors 2023, 23(5), 2494; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052494 - 23 Feb 2023

Cited by 1 | Viewed by 2114

Abstract

Federated learning (FL) is a technique that allows multiple clients to collaboratively train a global model without sharing their sensitive and bandwidth-hungry data. This paper presents a joint early client termination and local epoch adjustment for FL. We consider the challenges of heterogeneous Internet of Things (IoT) environments including non-independent and identically distributed (non-IID) data as well as diverse computing and communication capabilities. The goal is to strike the best tradeoff among three conflicting objectives, namely global model accuracy, training latency and communication cost. We first leverage the balanced-MixUp technique to mitigate the influence of non-IID data on the FL convergence rate. A weighted sum optimization problem is then formulated and solved via our proposed FL double deep reinforcement learning (FedDdrl) framework, which outputs a dual action. The former indicates whether a participating FL client is dropped, whereas the latter specifies how long each remaining client needs to complete its local training task. Simulation results show that FedDdrl outperforms the existing FL scheme in terms of overall tradeoff. Specifically, FedDdrl achieves higher model accuracy by about 4% while incurring 30% less latency and communication costs. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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21 pages, 2525 KiB

Open AccessArticle

Parallel Processing of Sensor Data in a Distributed Rules Engine Environment through Clustering and Data Flow Reconfiguration

by Adrian Alexandrescu

Sensors 2023, 23(3), 1543; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031543 - 31 Jan 2023

Cited by 3 | Viewed by 1354

Abstract

An emerging reality is the development of smart buildings and cities, which improve residents’ comfort. These environments employ multiple sensor networks, whose data must be acquired and processed in real time by multiple rule engines, which trigger events that enable specific actuators. The problem is how to handle those data in a scalable manner by using multiple processing instances to maximize the system throughput. This paper considers the types of sensors that are used in these scenarios and proposes a model for abstracting the information flow as a weighted dependency graph. Two parallel computing methods are then proposed for obtaining an efficient data flow: a variation of the parallel k-means clustering algorithm and a custom genetic algorithm. Simulation results show that the two proposed flow reconfiguration algorithms reduce the rule processing times and provide an efficient solution for increasing the scalability of the considered environment. Another aspect being discussed is using an open-source cloud solution to manage the system and how to use the two algorithms to increase efficiency. These methods allow for a seamless increase in the number of sensors in the environment by making smart use of the available resources. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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18 pages, 1498 KiB

Open AccessArticle

Moving-Target Defense in Depth: Pervasive Self- and Situation-Aware VM Mobilization across Federated Clouds in Presence of Active Attacks

by Yousra Magdy, Mohamed Azab, Amal Hamada, Mohamed R. M. Rizk and Nayera Sadek

Sensors 2022, 22(23), 9548; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239548 - 06 Dec 2022

Cited by 2 | Viewed by 2352

Abstract

Federated clouds are interconnected cooperative cloud infrastructures offering vast hosting capabilities, smooth workload migration and enhanced reliability. However, recent devastating attacks on such clouds have shown that such features come with serious security challenges. The oblivious heterogeneous construction, management, and policies employed in federated clouds open the door for attackers to induce conflicts to facilitate pervasive coordinated attacks. In this paper, we present a novel proactive defense that aims to increase attacker uncertainty and complicate target tracking, a critical step for successful coordinated attacks. The presented systemic approach acts as a VM management platform with an intrinsic multidimensional hierarchical attack representation model (HARM) guiding a dynamic, self and situation-aware VM live-migration for moving-target defense (MtD). The proposed system managed to achieve the proposed goals in a resource-, energy-, and cost-efficient manner. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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15 pages, 1881 KiB

Open AccessArticle

Data Collection in IoT Using UAV Based on Multi-Objective Spotted Hyena Optimizer

by Hamza Mohammed Ridha Al-Khafaji

Sensors 2022, 22(22), 8896; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228896 - 17 Nov 2022

Cited by 3 | Viewed by 1566

Abstract

Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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17 pages, 981 KiB

Open AccessArticle

On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments

by Alfredo Barron, Dante D. Sanchez-Gallegos, Diana Carrizales-Espinoza, J. L. Gonzalez-Compean and Miguel Morales-Sandoval

Sensors 2022, 22(18), 7016; https://0-doi-org.brum.beds.ac.uk/10.3390/s22187016 - 16 Sep 2022

Cited by 3 | Viewed by 1575

Abstract

Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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24 pages, 2286 KiB

Open AccessArticle

A Novel Automate Python Edge-to-Edge: From Automated Generation on Cloud to User Application Deployment on Edge of Deep Neural Networks for Low Power IoT Systems FPGA-Based Acceleration

by Tarek Belabed, Vitor Ramos Gomes da Silva, Alexandre Quenon, Carlos Valderamma and Chokri Souani

Sensors 2021, 21(18), 6050; https://0-doi-org.brum.beds.ac.uk/10.3390/s21186050 - 09 Sep 2021

Cited by 2 | Viewed by 2205

Abstract

Deep Neural Networks (DNNs) deployment for IoT Edge applications requires strong skills in hardware and software. In this paper, a novel design framework fully automated for Edge applications is proposed to perform such a deployment on System-on-Chips. Based on a high-level Python interface that mimics the leading Deep Learning software frameworks, it offers an easy way to implement a hardware-accelerated DNN on an FPGA. To do this, our design methodology covers the three main phases: (a) customization: where the user specifies the optimizations needed on each DNN layer, (b) generation: the framework generates on the Cloud the necessary binaries for both FPGA and software parts, and (c) deployment: the SoC on the Edge receives the resulting files serving to program the FPGA and related Python libraries for user applications. Among the study cases, an optimized DNN for the MNIST database can speed up more than 60× a software version on the ZYNQ 7020 SoC and still consume less than

0.43

W

. A comparison with the state-of-the-art frameworks demonstrates that our methodology offers the best trade-off between throughput, power consumption, and system cost. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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20 pages, 2233 KiB

Open AccessArticle

A WoT-Based Method for Creating Digital Sentinel Twins of IoT Devices

by Ivan Lopez-Arevalo, Jose Luis Gonzalez-Compean, Mariana Hinojosa-Tijerina, Cristhian Martinez-Rendon, Raffaele Montella and Jose L. Martinez-Rodriguez

Sensors 2021, 21(16), 5531; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165531 - 17 Aug 2021

Cited by 5 | Viewed by 2762

Abstract

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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26 pages, 2494 KiB

Open AccessArticle

An Indoor Navigation Methodology for Mobile Devices by Integrating Augmented Reality and Semantic Web

by Jesus Ivan Rubio-Sandoval, Jose L. Martinez-Rodriguez, Ivan Lopez-Arevalo, Ana B. Rios-Alvarado, Adolfo Josue Rodriguez-Rodriguez and David Tomas Vargas-Requena

Sensors 2021, 21(16), 5435; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165435 - 12 Aug 2021

Cited by 10 | Viewed by 4300

Abstract

Indoor navigation systems incorporating augmented reality allow users to locate places within buildings and acquire more knowledge about their environment. However, although diverse works have been introduced with varied technologies, infrastructure, and functionalities, a standardization of the procedures for elaborating these systems has not been reached. Moreover, while systems usually handle contextual information of places in proprietary formats, a platform-independent model is desirable, which would encourage its access, updating, and management. This paper proposes a methodology for developing indoor navigation systems based on the integration of Augmented Reality and Semantic Web technologies to present navigation instructions and contextual information about the environment. It comprises four modules to define a spatial model, data management (supported by an ontology), positioning and navigation, and content visualization. A mobile application system was developed for testing the proposal in academic environments, modeling the structure, routes, and places of two buildings from independent institutions. The experiments cover distinct navigation tasks by participants in both scenarios, recording data such as navigation time, position tracking, system functionality, feedback (answering a survey), and a navigation comparison when the system is not used. The results demonstrate the system’s feasibility, where the participants show a positive interest in its functionalities. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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18 pages, 896 KiB

Open AccessArticle

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

by Marco Lapegna, Walter Balzano, Norbert Meyer and Diego Romano

Sensors 2021, 21(16), 5395; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165395 - 10 Aug 2021

Cited by 23 | Viewed by 2767

Abstract

The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices. Full article

(This article belongs to the Special Issue Internet of Things, Sensing and Cloud Computing)

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