Falls are the leading cause of injuries and death in elderly individuals who live alone at home. The core service of assistive living technology is to monitor elders’ activities through wearable devices, ambient sensors, and vision systems. Vision systems are among the best solutions, as their implementation and maintenance costs are the lowest. However, current vision systems are limited in their ability to handle cluttered environments, occlusion, illumination changes throughout the day, and monitoring without illumination. To overcome these issues, this paper proposes a 24/7 monitoring system for elders that uses retroreflective tape fabricated as part of conventional clothing, monitored through low-cost infrared (IR) cameras fixed in the living environment. IR camera records video even when there are changes in illumination or zero luminance. For classification among clutter and occlusion, the tape is considered as a blob instead of a human silhouette; the orientation angle, fitted through ellipse modeling, of the blob in each frame allows classification that detects falls without pretrained data. System performance was tested using subjects in various age groups and “fall” or “non-fall” were detected with 99.01% accuracy.
{"title":"Real time fall detection using infrared cameras and reflective tapes under day/night luminance","authors":"E. Ramanujam, S. Padmavathi","doi":"10.3233/AIS-210605","DOIUrl":"https://doi.org/10.3233/AIS-210605","url":null,"abstract":"Falls are the leading cause of injuries and death in elderly individuals who live alone at home. The core service of assistive living technology is to monitor elders’ activities through wearable devices, ambient sensors, and vision systems. Vision systems are among the best solutions, as their implementation and maintenance costs are the lowest. However, current vision systems are limited in their ability to handle cluttered environments, occlusion, illumination changes throughout the day, and monitoring without illumination. To overcome these issues, this paper proposes a 24/7 monitoring system for elders that uses retroreflective tape fabricated as part of conventional clothing, monitored through low-cost infrared (IR) cameras fixed in the living environment. IR camera records video even when there are changes in illumination or zero luminance. For classification among clutter and occlusion, the tape is considered as a blob instead of a human silhouette; the orientation angle, fitted through ellipse modeling, of the blob in each frame allows classification that detects falls without pretrained data. System performance was tested using subjects in various age groups and “fall” or “non-fall” were detected with 99.01% accuracy.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"12 1","pages":"285-300"},"PeriodicalIF":1.7,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81151995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Marufuzzaman, Teresa Tumbraegel, L. F. Rahman, L. Sidek
A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities.
{"title":"A machine learning approach to predict the activity of smart home inhabitant","authors":"M. Marufuzzaman, Teresa Tumbraegel, L. F. Rahman, L. Sidek","doi":"10.3233/AIS-210604","DOIUrl":"https://doi.org/10.3233/AIS-210604","url":null,"abstract":"A smart home inhabitant performs a unique pattern or sequence of tasks repeatedly. Thus, a machine learning approach will be required to build an intelligent network of home appliances, and the algorithm should respond quickly to execute the decision. This study proposes a decision tree-based machine learning approach for predicting the activities using different appliances such as state, locations and time. A noise filter is employed to remove unwanted data and generate task sequences, and dual state properties of a home appliance are utilized to extract episodes from the sequence. An incremental decision tree approach was taken to reduce execution time. The algorithm was tested using a well-known smart home dataset from MavLab. The experimental results showed that the algorithm successfully extracted 689 predictions and their location at 90% accuracy, and the total execution time was 94 s, which is less than that of existing methods. A hardware prototype was designed using Raspberry Pi 2 B to validate the proposed prediction system. The general-purpose input-output (GPIO) interfaces of Raspberry Pi 2 B were used to communicate with the prototype testbed and showed that the algorithm successfully predicted the next activities.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"41 1","pages":"271-283"},"PeriodicalIF":1.7,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88950299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. W. L. Tam, H. Aghajan, J. Augusto, Andrés Muñoz
Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c and Andrés Muñoz d a Department of Electrical and Electronic Engineering, The University of Hong Kong, China b imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium c Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK d Polytechnic School, Universidad Católica de Murcia, Spain
Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c和andrs Muñoz da中国香港大学电气与电子工程系b c, IPI，比利时根特大学电信与信息处理系c c英国米德尔塞克斯大学计算机科学系和智能环境发展研究小组d西班牙穆西亚大学理工学院Católica
{"title":"Preface to JAISE 13(4)","authors":"V. W. L. Tam, H. Aghajan, J. Augusto, Andrés Muñoz","doi":"10.3233/AIS-210603","DOIUrl":"https://doi.org/10.3233/AIS-210603","url":null,"abstract":"Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c and Andrés Muñoz d a Department of Electrical and Electronic Engineering, The University of Hong Kong, China b imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium c Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK d Polytechnic School, Universidad Católica de Murcia, Spain","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"68 1","pages":"269-270"},"PeriodicalIF":1.7,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83188224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrés Muñoz, J. Augusto, V. W. L. Tam, H. Aghajan
Andrés Muñoz a, Juan Carlos Augusto b, Vincent Tam c and Hamid Aghajan d a Polytechnic School, Catholic University of Murcia, Spain b Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK c Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, China d imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium
andr Muñoz a、Juan Carlos Augusto b、Vincent Tam c及Hamid Aghajan d a西班牙天主教大学穆西亚理工学院b英国米德尔塞克斯大学计算机科学系与智能环境发展研究小组c中国香港大学工程学院电气与电子工程系d c比利时根特大学电信与信息处理系IPI
{"title":"Preface to JAISE 13(3)","authors":"Andrés Muñoz, J. Augusto, V. W. L. Tam, H. Aghajan","doi":"10.3233/AIS-210596","DOIUrl":"https://doi.org/10.3233/AIS-210596","url":null,"abstract":"Andrés Muñoz a, Juan Carlos Augusto b, Vincent Tam c and Hamid Aghajan d a Polytechnic School, Catholic University of Murcia, Spain b Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK c Department of Electrical and Electronic Engineering, Faculty of Engineering, The University of Hong Kong, China d imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"16 1","pages":"181"},"PeriodicalIF":1.7,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90819853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, many daily web/app services (e.g. Facebook, Twitter, and Foursquare) generate data and traces that are often transparently annotated with location and contextual information. Many core challenges are involved to fully exploit geo-labeled data. The main challenge is to combine ideas and techniques from various research communities, such as recommender systems, data management, geographic information systems, social network analytics, and text mining. We aim to provide a platform to discuss indepth and collecting feedback about challenges, opportunities, novel techniques, and applications. Finally, we have four papers for this special issue. A summary of these papers is outlined below. In the paper entitled “Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation”, Minsung Hong and Jason J. Jung propose a multi-criteria tensor model combining spatial and temporal information in the recommender systems. Specifically, the five-order tensor model consists of users, items, multiple ratings, spatial and temporal data, which keeps the latent structure of the interrelations between multi-criteria and spatial/temporal information. Experimental results with a TripAdvisor dataset show that the proposed model outperforms other baselines. In the paper entitled “A mobile services recommendation system fuses implicit and explicit user trust relationships”, Pengcheng Luo, Jilin Zhang,
近年来，许多日常网络/应用程序服务(如Facebook, Twitter和Foursquare)生成的数据和痕迹通常带有位置和上下文信息的透明注释。充分利用地理标记数据涉及许多核心挑战。主要的挑战是结合来自不同研究团体的想法和技术，如推荐系统、数据管理、地理信息系统、社会网络分析和文本挖掘。我们的目标是提供一个平台来深入讨论和收集关于挑战、机遇、新技术和应用的反馈。最后，我们这期特刊有四篇论文。下面概述了这些论文的摘要。Minsung Hong和Jason J. Jung在《旅游推荐的时空信息整合多准则张量模型》一文中提出了一种结合时空信息的推荐系统多准则张量模型。具体而言，五阶张量模型由用户、项目、多重评分、时空数据组成，保留了多准则与时空信息之间相互关系的潜在结构。TripAdvisor数据集的实验结果表明，该模型优于其他基线。罗鹏程、张吉林在论文《融合隐式和显式用户信任关系的移动服务推荐系统》中，
{"title":"Location-aware computing to mobile services recommendation: Theory and practice","authors":"Honghao Gao, Andrés Muñoz, Wenbing Zhao, Yuyu Yin","doi":"10.3233/ais-200588","DOIUrl":"https://doi.org/10.3233/ais-200588","url":null,"abstract":"In recent years, many daily web/app services (e.g. Facebook, Twitter, and Foursquare) generate data and traces that are often transparently annotated with location and contextual information. Many core challenges are involved to fully exploit geo-labeled data. The main challenge is to combine ideas and techniques from various research communities, such as recommender systems, data management, geographic information systems, social network analytics, and text mining. We aim to provide a platform to discuss indepth and collecting feedback about challenges, opportunities, novel techniques, and applications. Finally, we have four papers for this special issue. A summary of these papers is outlined below. In the paper entitled “Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation”, Minsung Hong and Jason J. Jung propose a multi-criteria tensor model combining spatial and temporal information in the recommender systems. Specifically, the five-order tensor model consists of users, items, multiple ratings, spatial and temporal data, which keeps the latent structure of the interrelations between multi-criteria and spatial/temporal information. Experimental results with a TripAdvisor dataset show that the proposed model outperforms other baselines. In the paper entitled “A mobile services recommendation system fuses implicit and explicit user trust relationships”, Pengcheng Luo, Jilin Zhang,","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"344 1","pages":"3-4"},"PeriodicalIF":1.7,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75095144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.
{"title":"A mobile services recommendation system fuses implicit and explicit user trust relationships","authors":"Pengcheng Luo, Jilin Zhang, Jian Wan, Nailiang Zhao, Zujie Ren, Li Zhou, Jing Shen","doi":"10.3233/AIS-200585","DOIUrl":"https://doi.org/10.3233/AIS-200585","url":null,"abstract":"In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"31 1","pages":"21-35"},"PeriodicalIF":1.7,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85232464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.
{"title":"Accuracy analysis of BLE beacon-based localization in smart buildings","authors":"R. Ivanov","doi":"10.3233/AIS-210607","DOIUrl":"https://doi.org/10.3233/AIS-210607","url":null,"abstract":"The majority of services that deliver personalized content in smart buildings require accurate localization of their clients. This article presents an analysis of the localization accuracy using Bluetooth Low Energy (BLE) beacons. The aim is to present an approach to create accurate Indoor Positioning Systems (IPS) using algorithms that can be implemented in real time on platforms with low computing power. Parameters on which the localization accuracy mostly depends are analyzed: localization algorithm, beacons’ density, deployment strategy, and noise in the BLE channels. An adaptive algorithm for pre-processing the signals from the beacons is proposed, which aims to reduce noise in beacon’s data and to capture visitor’s dynamics. The accuracy of five range-based localization algorithms in different use case scenarios is analyzed. Three of these algorithms are specially designed to be less sensitive to noise in radio channels and require little computing power. Experiments conducted in a simulated and real environment show that using proposed algorithms the localization accuracy less than 1 m can be obtained.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"12 1","pages":"325-344"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87670872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. W. L. Tam, H. Aghajan, J. Augusto, Andrés Muñoz
Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c and Andrés Muñoz d a Department of Electrical and Electronic Engineering, The University of Hong Kong, China b imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium c Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK d Polytechnic School, Universidad Católica de Murcia, Spain
Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c和andrs Muñoz da中国香港大学电气与电子工程系b c, IPI，比利时根特大学电信与信息处理系c c英国米德尔塞克斯大学计算机科学系和智能环境发展研究小组d西班牙穆西亚大学理工学院Católica
{"title":"Preface to JAISE 13(2)","authors":"V. W. L. Tam, H. Aghajan, J. Augusto, Andrés Muñoz","doi":"10.3233/AIS-210595","DOIUrl":"https://doi.org/10.3233/AIS-210595","url":null,"abstract":"Vincent Tam a, Hamid Aghajan b, Juan Carlos Augusto c and Andrés Muñoz d a Department of Electrical and Electronic Engineering, The University of Hong Kong, China b imec, IPI, Department of Telecommunications and Information Processing, Gent University, Belgium c Department of Computer Science and Research Group on Development of Intelligent Environments, Middlesex University, UK d Polytechnic School, Universidad Católica de Murcia, Spain","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"13 1","pages":"75-76"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73603132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, smart applications are increasing day by day to improve the standard of living in smart cities. A modern-day smart city is characterized by the presence of numerous smart Information and Communication Technology (ICT)-enabled services such as automated healthcare, automatic building monitoring, home automation, smart parking, traffic management, data security, among others. Such cities employ multitudes of Internet of Things (IoT) devices to collect and share data between trusted users by means of a centralized intermediary for monitoring and control of the myriad automatic activities. However, a centralized intermediary is plagued by issues such as single point of failure, risk of data loss, man-in-the-middle attack, and so forth. Blockchain-based smart contracts for automated control in smart cities provide a decentralized and secure alternative. In this paper, an Ethereum based system design for decentralized applications in smart cities has been proposed that enables systems to share data without an intermediary between trusted and non-trusted stakeholders using Ethereum based self-executing contracts. Such contracts allow automated multi-step workflows for smart applications. Two use cases, have been considered namely smart healthcare and smart building monitoring, as proof of stake of the proposed Ethereum based contract. The performance of the proposed scheme for these use cases has been presented with Keccack 256 transaction hash, the total number of transactions, gas consumed by each contract. Such an attempt is a worthwhile addition to state of the art as evident from the results presented herein. The modeling simulation and analysis of hashing power shows that for hashing power greater than 55% the probability of double spending attack reaches to 42% maximum. So it is concluded that the probability of double spending increases with the increase of transaction values.
{"title":"Smart contracts for automated control system in Blockchain based smart cities","authors":"N. Pradhan, A. Singh","doi":"10.3233/AIS-210601","DOIUrl":"https://doi.org/10.3233/AIS-210601","url":null,"abstract":"Nowadays, smart applications are increasing day by day to improve the standard of living in smart cities. A modern-day smart city is characterized by the presence of numerous smart Information and Communication Technology (ICT)-enabled services such as automated healthcare, automatic building monitoring, home automation, smart parking, traffic management, data security, among others. Such cities employ multitudes of Internet of Things (IoT) devices to collect and share data between trusted users by means of a centralized intermediary for monitoring and control of the myriad automatic activities. However, a centralized intermediary is plagued by issues such as single point of failure, risk of data loss, man-in-the-middle attack, and so forth. Blockchain-based smart contracts for automated control in smart cities provide a decentralized and secure alternative. In this paper, an Ethereum based system design for decentralized applications in smart cities has been proposed that enables systems to share data without an intermediary between trusted and non-trusted stakeholders using Ethereum based self-executing contracts. Such contracts allow automated multi-step workflows for smart applications. Two use cases, have been considered namely smart healthcare and smart building monitoring, as proof of stake of the proposed Ethereum based contract. The performance of the proposed scheme for these use cases has been presented with Keccack 256 transaction hash, the total number of transactions, gas consumed by each contract. Such an attempt is a worthwhile addition to state of the art as evident from the results presented herein. The modeling simulation and analysis of hashing power shows that for hashing power greater than 55% the probability of double spending attack reaches to 42% maximum. So it is concluded that the probability of double spending increases with the increase of transaction values.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"2 2 1","pages":"253-267"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85586043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the current health care scenario, security is the major concern in IoT-WSN with more devices or nodes. Attack or anomaly detection in the IoT infrastructure is increasing distress in the field of medical IoT. With the enormous usage of IoT infrastructure in every province, threats and attacks in these infrastructures are also mounting commensurately. This paper intends to develop a security mechanism to detect and prevent the black hole and selective forwarding attack from medical IoT-WSN. The proposed secure strategy is developed in five stages: First is selecting the cluster heads, second is generating k-routing paths, third is security against black hole attack, fourth is security against the selective forwarding attack, and the last is optimal shortest route path selection. Initially, a topology is developed for finding the cluster heads and discovering the best route. In the next phase, the black hole attacks are detected and prevented by the bait process. For detecting the selective forwarding attacks, the packet validation is done by checking the transmitted packet and the received packet. For promoting the packet security, Elliptic Curve Cryptography (ECC)-based hashing function is deployed. As the main contribution of this paper, optimal shortest route path is determined by the proposed hybrid algorithm with the integration of Deer Hunting Optimization Algorithm (DHOA), and DragonFly Algorithm (DA) termed Dragonfly-based DHOA (D-DHOA) by concerting the parameters like trust, distance, delay or latency and packet loss ratio in the objective model. Hence, the entire phases will be very active in detecting and preventing the two fundamental attacks like a black hole and selective forwarding from IoT-WSN in the health care sector.
{"title":"Black Hole and Selective Forwarding Attack Detection and Prevention in IoT in Health Care Sector: Hybrid meta-heuristic-based shortest path routing","authors":"T. Srinivas, S. Manivannan","doi":"10.3233/AIS-210591","DOIUrl":"https://doi.org/10.3233/AIS-210591","url":null,"abstract":"In the current health care scenario, security is the major concern in IoT-WSN with more devices or nodes. Attack or anomaly detection in the IoT infrastructure is increasing distress in the field of medical IoT. With the enormous usage of IoT infrastructure in every province, threats and attacks in these infrastructures are also mounting commensurately. This paper intends to develop a security mechanism to detect and prevent the black hole and selective forwarding attack from medical IoT-WSN. The proposed secure strategy is developed in five stages: First is selecting the cluster heads, second is generating k-routing paths, third is security against black hole attack, fourth is security against the selective forwarding attack, and the last is optimal shortest route path selection. Initially, a topology is developed for finding the cluster heads and discovering the best route. In the next phase, the black hole attacks are detected and prevented by the bait process. For detecting the selective forwarding attacks, the packet validation is done by checking the transmitted packet and the received packet. For promoting the packet security, Elliptic Curve Cryptography (ECC)-based hashing function is deployed. As the main contribution of this paper, optimal shortest route path is determined by the proposed hybrid algorithm with the integration of Deer Hunting Optimization Algorithm (DHOA), and DragonFly Algorithm (DA) termed Dragonfly-based DHOA (D-DHOA) by concerting the parameters like trust, distance, delay or latency and packet loss ratio in the objective model. Hence, the entire phases will be very active in detecting and preventing the two fundamental attacks like a black hole and selective forwarding from IoT-WSN in the health care sector.","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"69 1","pages":"133-156"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83333010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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