Edge-Based Missing Data Imputation in Large-Scale Environments

Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices. Numerous experiments have been carried out on real data to confirm the validity of the proposed method.

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An IoT-Based Services Infrastructure for Utility-Scale Distributed Solar Farms

Energies ◽

10.3390/en15020440 ◽

2022 ◽

Vol 15 (2) ◽

pp. 440

Author(s):

Salsabeel Shapsough ◽

Imran Zualkernan

Keyword(s):

Large Scale ◽

Smart Cities ◽

Low Cost ◽

Data Transport ◽

Resource Monitoring ◽

Efficient Resource ◽

Networking Technologies ◽

Edge Based ◽

Utility Scale

Internet of Things (IoT) provides large-scale solutions for efficient resource monitoring and management. As such, the technology has been heavily integrated into domains such as manufacturing, healthcare, agriculture, and utilities, which led to the emergence of sustainable smart cities. The success of smart cities depends on the availability of data, as well as the quality of the data management infrastructure. IoT introduced numerous new software, hardware, and networking technologies designed for efficient and low-cost data transport, storage, and processing. However, proper selection and integration of the correct technologies is crucial to ensuring a positive return on investment for such systems. This paper presents a novel end-to-end infrastructure for solar energy analysis and prediction via edge-based analytics.

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Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

Journal of Sensor and Actuator Networks ◽

10.3390/jsan10010013 ◽

2021 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Claudia Campolo ◽

Giacomo Genovese ◽

Antonio Iera ◽

Antonella Molinaro

Keyword(s):

Resource Constraints ◽

Smart Cities ◽

Traditional Approach ◽

Low Cost ◽

Iot Applications ◽

Software And Hardware ◽

Iot Devices ◽

Consumer Devices ◽

Hardware Platforms ◽

Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

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Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽

10.1093/genetics/165.4.2269 ◽

2003 ◽

Vol 165 (4) ◽

pp. 2269-2282

Author(s):

D Mester ◽

Y Ronin ◽

D Minkov ◽

E Nevo ◽

A Korol

Keyword(s):

Discrete Optimization ◽

High Performance ◽

Large Scale ◽

Simulated Data ◽

Real Data ◽

Genetic Maps ◽

Chromosome 1 ◽

Evolutionary Strategy ◽

Group A ◽

The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽

10.1155/2018/7698274 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Yiwen Zhang ◽

Yuanyuan Zhou ◽

Xing Guo ◽

Jintao Wu ◽

Qiang He ◽

...

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

Real Data ◽

Second Phase ◽

Data Sets ◽

Number Of Clusters ◽

Large Scale Data ◽

Long Time ◽

Two Phases ◽

Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

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LoRaWAN Mesh Networks: A Review and Classification of Multihop Communication

Sensors ◽

10.3390/s20154273 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4273

Author(s):

Jeferson Rodrigues Cotrim ◽

João Henrique Kleinschmidt

Keyword(s):

Large Scale ◽

Simulation Analysis ◽

Mesh Networks ◽

Smart Cities ◽

Low Cost ◽

Wide Area ◽

Area Network ◽

Full Potential ◽

Wide Area Network ◽

Coverage Area

The growth of the Internet of Things (IoT) led to the deployment of many applications that use wireless networks, like smart cities and smart agriculture. Low Power Wide Area Networks (LPWANs) meet many requirements of IoT, such as energy efficiency, low cost, large coverage area, and large-scale deployment. Long Range Wide Area Network (LoRaWAN) networks are one of the most studied and implemented LPWAN technologies, due to the facility to build private networks with an open standard. Typical LoRaWAN networks are single-hop in a star topology, composed of end-devices that transmit data directly to gateways. Recently, several studies proposed multihop LoRaWAN networks, thus forming wireless mesh networks. This article provides a review of the state-of-the-art multihop proposals for LoRaWAN. In addition, we carried out a comparative analysis and classification, considering technical characteristics, intermediate devices function, and network topologies. This paper also discusses open issues and future directions to realize the full potential of multihop networking. We hope to encourage other researchers to work on improving the performance of LoRaWAN mesh networks, with more theoretical and simulation analysis, as well as practical deployments.

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Lightweight MIPv6 with IPSec Support

Mobile Information Systems ◽

10.1155/2014/563274 ◽

2014 ◽

Vol 10 (1) ◽

pp. 37-77 ◽

Cited By ~ 8

Author(s):

Antonio J. Jara ◽

David Fernandez ◽

Pablo Lopez ◽

Miguel A. Zamora ◽

Antonio F. Skarmeta

Keyword(s):

Smart Cities ◽

Mobile Ipv6 ◽

Security And Privacy ◽

Mobility Support ◽

Memory Processing ◽

Constrained Environments ◽

Iot Devices ◽

The One ◽

New Generation ◽

Constrained Devices

Mobility management is a desired feature for the emerging Internet of Things (IoT). Mobility aware solutions increase the connectivity and enhance adaptability to changes of the location and infrastructure. IoT is enabling a new generation of dynamic ecosystems in environments such as smart cities and hospitals. Dynamic ecosystems require ubiquitous access to Internet, seamless handover, flexible roaming policies, and an interoperable mobility protocol with existing Internet infrastructure. These features are challenges for IoT devices, which are usually constrained devices with low memory, processing, communication and energy capabilities. This work presents an analysis of the requirements and desirable features for the mobility support in the IoT, and proposes an efficient solution for constrained environments based on Mobile IPv6 and IPSec. Compatibility with IPv6-existing protocols has been considered a major requirement in order to offer scalable and inter-domain solutions that were not limited to specific application domains in order to enable a new generation of application and services over Internet-enabled dynamic ecosystems, and security support based on IPSec has been also considered, since dynamic ecosystems present several challenges in terms of security and privacy. This work has, on the one hand, analysed suitability of Mobile IPv6 and IPSec for constrained devices, and on the other hand, analysed, designed, developed and evaluated a lightweight version of Mobile IPv6 and IPSec. The proposed solution of lightweight Mobile IPv6 with IPSec is aware of the requirements of the IoT and presents the best solution for dynamic ecosystems in terms of efficiency and security adapted to IoT-devices capabilities. This presents concerns in terms of higher overhead and memory requirements. But, it is proofed and concluded that even when higher memory is required and major overhead is presented, the integration of Mobile IPv6 and IPSec for constrained devices is feasible.

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Smart City as a Model for Sustainable Development of Territories

SHS Web of Conferences ◽

10.1051/shsconf/202111005003 ◽

2021 ◽

Vol 110 ◽

pp. 05003

Author(s):

Konstantin Semyachkov

Keyword(s):

Sustainable Development ◽

Urban Environment ◽

Smart City ◽

Large Scale ◽

Smart Cities ◽

Digital Technologies ◽

Economic Systems ◽

The Sustainable Development ◽

City Model ◽

The Impact

The article examines the impact of digital technologies on the sustainable development of ecological and economic systems. The main aspects that make the development of digital technologies especially relevant for environmental modernization and sustainable development are analyzed. It is shown that the large-scale use of digital technologies contributes to the development of new tools, models and methods of urban management. One of the promising areas for the development of the urban environment in these conditions is the concept of a smart city. Based on the analysis of research on the topic of smart cities, the effects of the use of the smart city model for the formation of the foundations of sustainable development of territories are noted.

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Modern Subsampling Methods for Large-Scale Least Squares Regression

International Journal of Cyber-Physical Systems ◽

10.4018/ijcps.2020070101 ◽

2020 ◽

Vol 2 (2) ◽

pp. 1-28

Author(s):

Tao Li ◽

Cheng Meng

Keyword(s):

Least Squares ◽

Large Scale ◽

Computing Time ◽

Real Data ◽

Optimality Criteria ◽

Estimation Accuracy ◽

Least Squares Regression ◽

Large Scale Data ◽

Coefficient Estimation ◽

Sampling Probability

Subsampling methods aim to select a subsample as a surrogate for the observed sample. As a powerful technique for large-scale data analysis, various subsampling methods are developed for more effective coefficient estimation and model prediction. This review presents some cutting-edge subsampling methods based on the large-scale least squares estimation. Two major families of subsampling methods are introduced: the randomized subsampling approach and the optimal subsampling approach. The former aims to develop a more effective data-dependent sampling probability while the latter aims to select a deterministic subsample in accordance with certain optimality criteria. Real data examples are provided to compare these methods empirically, respecting both the estimation accuracy and the computing time.

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Adaptive Load Balanced Routing in IOT Networks: A Distributed Learning Approach

10.24271/psr.19 ◽

2019 ◽

Vol 3 (1) ◽

pp. 1-5

Author(s):

Rzgar Sirwan ◽

Muzhir Ani

Keyword(s):

Large Scale ◽

Low Cost ◽

Distributed Learning ◽

Low Complexity ◽

Learning Approach ◽

Traffic Efficiency ◽

Data Channel ◽

Iot Devices ◽

Balancing Efficiency ◽

Networked Society

Facilitating large-scale load-efﬁcient Internet of things (IoT) connectivity is a vital step toward realizing the networked society. Although legacy wide-area wireless systems are heavily based on network-side coordination, such centralized methods will become infeasible in the future, by the unbalanced signaling level and the expected increment in the number of IoT devices. In the present work, this problem is represented through self-coordinating for IoT networks and learning from past communications. In this regard, ﬁrst, we assessed low-complexity distributed learning methods that can be applied to IoT communications. We presented a learning solution then, for adapting devices’ communication parameters to the environment to maximize the reliability and load balancing efﬁciency in data transmissions. Moreover, we used leveraging instruments from stochastic geometry to assess the behavior of the presented distributed learning solution against centralized coordinations. Ultimately, we analyzed the interplay amongst traffic efﬁciency, communications’ reliability against interference and noise over data channel, as well as reliability versus adversarial interference over feedback and data channels. The presented learning approach enhanced both reliability and traffic efﬁciency within IoT communications considerably. By such promising findings obtained via lightweight learning, our solution becomes promising in numerous low-power low-cost IoT uses.

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GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

Applied Sciences ◽

10.3390/app112210793 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10793

Author(s):

Azin Moradbeikie ◽

Ahmad Keshavarz ◽

Habib Rostami ◽

Sara Paiva ◽

Sérgio Ivan Lopes

Keyword(s):

Large Scale ◽

Smart Cities ◽

Low Cost ◽

Security And Privacy ◽

Smart Manufacturing ◽

Localization Accuracy ◽

Smart Transportation ◽

Smart Healthcare ◽

Definition Of ◽

Outdoor Localization

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

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