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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 58059

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Special Issue Editors

Dr. Dhananjay Singh

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Guest Editor

ReSESNE Labs, Department of Electronics Engineering, Hankuk (Korea) University of Foreign Studies (HUFS), Seoul 02450, Republic of Korea
Interests: AI; IoT; smart city; e-healthcare; blockchain; connected vehicles; wireless communication
Special Issues, Collections and Topics in MDPI journals

Dr. Mario Divan

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Guest Editor

Department of Information System, National Technological University, Córdoba, Argentina
Interests: Information system; Data Streaming; Measurement and Evaluation; IoT; Data Management; blockchain; Data Communication; Artificial Intelligence; Big Data; High-Performance and Grid Computing
Special Issues, Collections and Topics in MDPI journals

Dr. Madhusudan Singh

E-Mail Website
Guest Editor

Endicott College of International Studies, Woosong University, Daejeon 300-718, Korea
Interests: cybersecurity; Autonomous vehicles; Industry 4.0; future internet/5G; IoT; WMN; blockchain; mobile display; data communication; artificial intelligence; data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The decision-making process is fully supported by sensors data and avoids mere intuition. This represents a context in which information and knowledge are disregarded, while sensors data represent captured facts. Information is associated with those sensors data that simultaneously satisfy the conditions of interest, consistency, reliability, and opportunity. On the other hand, knowledge is characterized by information able to be applied and produce a quantifiable result. The IoT has provided the possibility of easily deploying tiny, cheap, available, and durable devices able to collect IoT data in real time, with continuous supply. IoT markets are desperately in need of solutions that can improve community services and the security of IoT devices. The idea is to utilize and share real-time sensors data to provide an intelligent service to the authorities. In this Special Issue, we invite papers on IoT technology solutions for community safety in personal and public scenarios. Therefore, we invite cutting-edge scientific researchers to draw a picture of the use of sensors data for decision-making aimed at providing intelligent community services. Topics of interest include but are not limited to:

IoT technology
IoT markets
Internet of vehicles
Blockchain tech for communities
IoT big Data cloud and fog computing
IoT and Decision-Making processes
IoT and machine learning
IoT and measurement frameworks
IoT sensors data streaming
IoT sensors supervised models
IoT sensors non-supervised models
IoT data processing architectures

Dr. Dhananjay Singh
Dr. Mario Divan
Dr. Madhusudan Singh
Guest Editor

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Published Papers (11 papers)

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Editorial

Jump to: Research, Review

6 pages, 183 KiB

Open AccessEditorial

Internet of Things for Smart Community Solutions

by Dhananjay Singh, Mario Divan and Madhusudan Singh

Sensors 2022, 22(2), 640; https://doi.org/10.3390/s22020640 - 14 Jan 2022

Cited by 9 | Viewed by 2537

Abstract

The term IoT (Internet of Things) constitutes the quickly developing advanced gadgets with highest computing power with in a constrained VLSI design space [...] Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

Research

Jump to: Editorial, Review

13 pages, 550 KiB

Open AccessCommunication

Blockchain Technologies: Probability of Double-Spend Attack on a Proof-of-Stake Consensus

by Mikolaj Karpinski, Lyudmila Kovalchuk, Roman Kochan, Roman Oliynykov, Mariia Rodinko and Lukasz Wieclaw

Sensors 2021, 21(19), 6408; https://doi.org/10.3390/s21196408 - 25 Sep 2021

Cited by 10 | Viewed by 3813

Abstract

Two double-spend attack strategies on a proof-of-stake consensus are considered. For each strategy, the probability of its success is obtained, which depends on the network parameters and the number of confirmation blocks. These results can be used to define how many confirmation blocks [...] Read more.

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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20 pages, 5065 KiB

Open AccessArticle

SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles

by Rayan Nouh, Madhusudan Singh and Dhananjay Singh

Sensors 2021, 21(11), 3893; https://doi.org/10.3390/s21113893 - 4 Jun 2021

Cited by 17 | Viewed by 4657

Abstract

The Internet of vehicles (IoV) is a rapidly emerging technological evolution of Intelligent Transportation System (ITS). This paper proposes SafeDrive, a dynamic driver profile (DDP) using a hybrid recommendation system. DDP is a set of functional modules, to analyses individual driver’s behaviors, using prior violation and accident records, to identify driving risk patterns. In this paper, we have considered three synthetic data-sets for 1500 drivers based on their profile information, risk parameters information, and risk likelihood. In addition, we have also considered the driver’s historical violation/accident data-set records based on four risk-score levels such as high-risk, medium-risk, low-risk, and no-risk to predict current and future driver risk scores. Several error calculation methods have been applied in this study to analyze our proposed hybrid recommendation systems’ performance to classify the driver’s data with higher accuracy based on various criteria. The evaluated results help to improve the driving behavior and broadcast early warning alarm to the other vehicles in IoV environment for the overall road safety. Moreover, the propoed model helps to provide a safe and predicted environment for vehicles, pedestrians, and road objects, with the help of regular monitoring of vehicle motion, driver behavior, and road conditions. It also enables accurate prediction of accidents beforehand, and also minimizes the complexity of on-road vehicles and latency due to fog/cloud computing servers. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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16 pages, 634 KiB

Open AccessArticle

Distributed Group Location Update Algorithm for Massive Machine Type Communication

by Mincheol Paik and Haneul Ko

Sensors 2020, 20(24), 7336; https://doi.org/10.3390/s20247336 - 21 Dec 2020

Cited by 2 | Viewed by 2281

Abstract

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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17 pages, 21697 KiB

Open AccessArticle

Pre-Emption of Affliction Severity Using HRV Measurements from a Smart Wearable; Case-Study on SARS-Cov-2 Symptoms

by Gatha Varma, Ritu Chauhan, Madhusudan Singh and Dhananjay Singh

Sensors 2020, 20(24), 7068; https://doi.org/10.3390/s20247068 - 10 Dec 2020

Cited by 16 | Viewed by 5502

Abstract

Smart wristbands and watches have become an important accessory to fitness, but their application to healthcare is still in a fledgling state. Their long-term wear facilitates extensive data collection and evolving sensitivity of smart wristbands allows them to read various body vitals. In this paper, we hypothesized the use of heart rate variability (HRV) measurements to drive an algorithm that can pre-empt the onset or worsening of an affliction. Due to its significance during the time of the study, SARS-Cov-2 was taken as the case study, and a hidden Markov model (HMM) was trained over its observed symptoms. The data used for the analysis was the outcome of a study hosted by Welltory. It involved the collection of SAR-Cov-2 symptoms and reading of body vitals using Apple Watch, Fitbit, and Garmin smart bands. The internal states of the HMM were made up of the absence and presence of a consistent decline in standard deviation of NN intervals (SSDN), the root mean square of the successive differences (rMSSD) in R-R intervals, and low frequency (LF), high frequency (HF), and very low frequency (VLF) components of the HRV measurements. The emission probabilities of the trained HMM instance confirmed that the onset or worsening of the symptoms had a higher probability if the HRV components displayed a consistent decline state. The results were further confirmed through the generation of probable hidden states sequences using the Viterbi algorithm. The ability to pre-empt the exigent state of an affliction would not only lower the chances of complications and mortality but may also help in curbing its spread through intelligence-backed decisions. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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23 pages, 1931 KiB

Open AccessArticle

Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication

by Muhidul Islam Khan, Luca Reggiani, Muhammad Mahtab Alam, Yannick Le Moullec, Navuday Sharma, Elias Yaacoub and Maurizio Magarini

Sensors 2020, 20(22), 6692; https://doi.org/10.3390/s20226692 - 23 Nov 2020

Cited by 5 | Viewed by 2683

Abstract

In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately

67 %

in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately

30 %

. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately

50 %

w.r.t. the baseline. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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42 pages, 12872 KiB

Open AccessArticle

Lightweight Proof of Game (LPoG): A Proof of Work (PoW)’s Extended Lightweight Consensus Algorithm for Wearable Kidneys

by Adarsh Kumar, Deepak Kumar Sharma, Anand Nayyar, Saurabh Singh and Byungun Yoon

Sensors 2020, 20(10), 2868; https://doi.org/10.3390/s20102868 - 19 May 2020

Cited by 18 | Viewed by 5468

Abstract

In healthcare, interoperability is widely adopted in the case of cross-departmental or specialization cases. As the human body demands multiple specialized and cross-disciplined medical experiments, interoperability of business entities like different departments, different specializations, the involvement of legal and government monitoring issues etc. are not sufficient to reduce the active medical cases. A patient-centric system with high capability to collect, retrieve, store or exchange data is the demand for present and future times. Such data-centric health processes would bring automated patient medication, or patient self-driven trusted and high satisfaction capabilities. However, data-centric processes are having a huge set of challenges such as security, technology, governance, adoption, deployment, integration etc. This work has explored the feasibility to integrate resource-constrained devices-based wearable kidney systems in the Industry 4.0 network and facilitates data collection, liquidity, storage, retrieval and exchange systems. Thereafter, a Healthcare 4.0 processes-based wearable kidney system is proposed that is having the blockchain technology advantages. Further, game theory-based consensus algorithms are proposed for resource-constrained devices in the kidney system. The overall system design would bring an example for the transition from the specialization or departmental-centric approach to data and patient-centric approach that would bring more transparency, trust and healthy practices in the healthcare sector. Results show a variation of 0.10 million GH/s to 0.18 million GH/s hash rate for the proposed approach. The chances of a majority attack in the proposed scheme are statistically proved to be minimum. Further Average Packet Delivery Rate (ADPR) lies between 95% to 97%, approximately, without the presence of outliers. In the presence of outliers, network performance decreases below 80% APDR (to a minimum of 41.3%) and this indicates that there are outliers present in the network. Simulation results show that the Average Throughput (AT) value lies between 120 Kbps to 250 Kbps. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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26 pages, 1768 KiB

Open AccessArticle

An Energy-Efficient Evolutionary Clustering Technique for Disaster Management in IoT Networks

by Morteza Biabani, Hossein Fotouhi and Nasser Yazdani

Sensors 2020, 20(9), 2647; https://doi.org/10.3390/s20092647 - 6 May 2020

Cited by 35 | Viewed by 4684

Abstract

Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure system availability by increasing the lifetime of WSNs. Clustering is one of the routing techniques that benefits energy efficiency in WSNs. This paper provides an evolutionary clustering and routing method which is capable of managing the energy consumption of nodes while considering the characteristics of a disaster area. The proposed method consists of two phases. First, we present a model with improved hybrid Particle Swarm Optimization (PSO) and Harmony Search Algorithm (HSA) for cluster head (CH) selection. Second, we design a PSO-based multi-hop routing system with enhanced tree encoding and a modified data packet format. The simulation results for disaster scenarios prove the efficiency of the proposed method in comparison with the state-of-the-art approaches in terms of the overall residual energy, number of live nodes, network coverage, and the packet delivery ratio. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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19 pages, 1020 KiB

Open AccessArticle

LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things

by Jin Wang, Yangning Tang, Shiming He, Changqing Zhao, Pradip Kumar Sharma, Osama Alfarraj and Amr Tolba

Sensors 2020, 20(9), 2451; https://doi.org/10.3390/s20092451 - 26 Apr 2020

Cited by 81 | Viewed by 12116

Abstract

Log anomaly detection is an efficient method to manage modern large-scale Internet of Things (IoT) systems. More and more works start to apply natural language processing (NLP) methods, and in particular word2vec, in the log feature extraction. Word2vec can extract the relevance between words and vectorize the words. However, the computing cost of training word2vec is high. Anomalies in logs are dependent on not only an individual log message but also on the log message sequence. Therefore, the vector of words from word2vec can not be used directly, which needs to be transformed into the vector of log events and further transformed into the vector of log sequences. To reduce computational cost and avoid multiple transformations, in this paper, we propose an offline feature extraction model, named LogEvent2vec, which takes the log event as input of word2vec to extract the relevance between log events and vectorize log events directly. LogEvent2vec can work with any coordinate transformation methods and anomaly detection models. After getting the log event vector, we transform log event vector to log sequence vector by bary or tf-idf and three kinds of supervised models (Random Forests, Naive Bayes, and Neural Networks) are trained to detect the anomalies. We have conducted extensive experiments on a real public log dataset from BlueGene/L (BGL). The experimental results demonstrate that LogEvent2vec can significantly reduce computational time by 30 times and improve accuracy, comparing with word2vec. LogEvent2vec with bary and Random Forest can achieve the best F1-score and LogEvent2vec with tf-idf and Naive Bayes needs the least computational time. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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Review

Jump to: Editorial, Research

44 pages, 4806 KiB

Open AccessReview

A Survey on Applications of Artificial Intelligence for Pre-Parametric Project Cost and Soil Shear-Strength Estimation in Construction and Geotechnical Engineering

by Sparsh Sharma, Suhaib Ahmed, Mohd Naseem, Waleed S. Alnumay, Saurabh Singh and Gi Hwan Cho

Sensors 2021, 21(2), 463; https://doi.org/10.3390/s21020463 - 11 Jan 2021

Cited by 47 | Viewed by 7626

Abstract

Ensuring soil strength, as well as preliminary construction cost and duration prediction, is a very crucial and preliminary aspect of any construction project. Similarly, building strong structures is very important in geotechnical engineering to ensure the bearing capability of structures against external forces. Hence, in this first-of-its-kind state-of-the-art review, the capability of various artificial intelligence (AI)-based models toward accurate prediction and estimation of preliminary construction cost, duration, and shear strength is explored. Initially, background regarding the revolutionary AI technology along with its different models suited for geotechnical and construction engineering is presented. Various existing works in the literature on the usage of AI-based models for the abovementioned applications of construction and maintenance are presented along with their advantages, limitations, and future work. Through analysis, various crucial input parameters with great impact on the estimation of preliminary construction cost, duration, and soil shear strength are enumerated and presented. Lastly, various challenges in using AI-based models for accurate predictions in these applications, as well as factors contributing to the cost-overrun issues, are presented. This study can, thus, greatly assist civil engineers in efficiently using the capabilities of AI for solving complex and risk-sensitive tasks, and it can also be used in Internet of things (IoT) environments for automated applications such as smart structural health-monitoring systems. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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19 pages, 481 KiB

Open AccessReview

Selfishness in Vehicular Delay-Tolerant Networks: A Review

by Ghani-Ur Rehman, Anwar Ghani, Shad Muhammad, Madhusudan Singh and Dhananjay Singh

Sensors 2020, 20(10), 3000; https://doi.org/10.3390/s20103000 - 25 May 2020

Cited by 25 | Viewed by 4009

Abstract

Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future. Full article

(This article belongs to the Special Issue Internet of Things for Smart Community Solutions)

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