The rapid growth of the connected "Things" to the Internet create new technology known as the Internet of Things (IoT). The complex connection between the "things" in the IoT environment bring about security challenges. This paper analyzes some of these security challenges and provides some countermeasures for a four layers IoT architecture.
{"title":"Cybersecurity Issues in Internet of Things and Countermeasures","authors":"Hoda Ghadeer","doi":"10.1109/ICII.2018.00037","DOIUrl":"https://doi.org/10.1109/ICII.2018.00037","url":null,"abstract":"The rapid growth of the connected \"Things\" to the Internet create new technology known as the Internet of Things (IoT). The complex connection between the \"things\" in the IoT environment bring about security challenges. This paper analyzes some of these security challenges and provides some countermeasures for a four layers IoT architecture.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126270866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ryan Brummet, Dolvara Gunatilaka, Dhruv Vyas, O. Chipara, Chenyang Lu
Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.
{"title":"A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks","authors":"Ryan Brummet, Dolvara Gunatilaka, Dhruv Vyas, O. Chipara, Chenyang Lu","doi":"10.1109/ICII.2018.00017","DOIUrl":"https://doi.org/10.1109/ICII.2018.00017","url":null,"abstract":"Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121805002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Message from the ICII 2018 Program Co-Chairs","authors":"","doi":"10.1109/icii.2018.00006","DOIUrl":"https://doi.org/10.1109/icii.2018.00006","url":null,"abstract":"","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134241885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The focused crawler is the key technology of the focused search engine. The current focused crawler is prone to poor adaptability and low search accuracy in the process of crawling the webpage. For these reasons, we proposes a focused crawler model (VRPSOFC) based on mutation improving particle swarm optimization. First, get three seed pages based on the click rate of the topic-related page. Then, get the four weighted documents of the seed pages. Finally, using the focused crawler model based on mutation improving particle swarm optimization algorithm to crawl the webpage, the results of the analysis show that the focused crawler model has a significant improvement in the precision of optimization.
{"title":"A Focused Crawler Model Based on Mutation Improving Particle Swarm Optimization Algorithm","authors":"Guangxia Xu, Peng Jiang, Chuang Ma, M. Daneshmand","doi":"10.1109/ICII.2018.00031","DOIUrl":"https://doi.org/10.1109/ICII.2018.00031","url":null,"abstract":"The focused crawler is the key technology of the focused search engine. The current focused crawler is prone to poor adaptability and low search accuracy in the process of crawling the webpage. For these reasons, we proposes a focused crawler model (VRPSOFC) based on mutation improving particle swarm optimization. First, get three seed pages based on the click rate of the topic-related page. Then, get the four weighted documents of the seed pages. Finally, using the focused crawler model based on mutation improving particle swarm optimization algorithm to crawl the webpage, the results of the analysis show that the focused crawler model has a significant improvement in the precision of optimization.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chanhyung Lee, Nak-Myoung Sung, L. Nkenyereye, Jaeseung Song
The Internet of things(IoT) is predicted to connect millions of devices. Therefore, standardized IoT technologies play a crucial role connecting massive IoT devices with interoperability. Various Standards Development Organizations (SDO) are working towards a horizontal solution that would fit several vertical platforms including oneM2M global IoT service layer standards initiative. Blockchain is a distributed ledger technology which is being applied in diverse applications such as crypto currency and smart contract. In this demo, we develop a blockchain-enabled IoT service layer platform based on oneM2M IoT standards and an blockchain hybrid application. We use a blockchain system named Logchain that is suitable for IoT due to its consensus algorithm. The hybrid application and oneM2M optional attribute added to the IoT platform enables the IoT users to either store their data in a conventional database or distributed ledger database.
{"title":"Blockchain Enabled Internet-of-Things Service Platform for Industrial Domain","authors":"Chanhyung Lee, Nak-Myoung Sung, L. Nkenyereye, Jaeseung Song","doi":"10.1109/ICII.2018.00033","DOIUrl":"https://doi.org/10.1109/ICII.2018.00033","url":null,"abstract":"The Internet of things(IoT) is predicted to connect millions of devices. Therefore, standardized IoT technologies play a crucial role connecting massive IoT devices with interoperability. Various Standards Development Organizations (SDO) are working towards a horizontal solution that would fit several vertical platforms including oneM2M global IoT service layer standards initiative. Blockchain is a distributed ledger technology which is being applied in diverse applications such as crypto currency and smart contract. In this demo, we develop a blockchain-enabled IoT service layer platform based on oneM2M IoT standards and an blockchain hybrid application. We use a blockchain system named Logchain that is suitable for IoT due to its consensus algorithm. The hybrid application and oneM2M optional attribute added to the IoT platform enables the IoT users to either store their data in a conventional database or distributed ledger database.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130982289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This abstract demonstrates an IoT based positioning service platform for future autonomous vehicles. A decision tree is developed which selects the best available positioning algorithm depending on several criteria. The decision tree is then integrated in EURECOM IoT Platform as a web service. We also present an Android Auto application which acts as a vehicular Cloudlet in this context.
{"title":"IoT Based Positioning Service Platform","authors":"S. K. Datta, J. Haerri, C. Bonnet","doi":"10.1109/ICII.2018.00027","DOIUrl":"https://doi.org/10.1109/ICII.2018.00027","url":null,"abstract":"This abstract demonstrates an IoT based positioning service platform for future autonomous vehicles. A decision tree is developed which selects the best available positioning algorithm depending on several criteria. The decision tree is then integrated in EURECOM IoT Platform as a web service. We also present an Android Auto application which acts as a vehicular Cloudlet in this context.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131271866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ICII 2018 Organizing Committee","authors":"","doi":"10.1109/icii.2018.00007","DOIUrl":"https://doi.org/10.1109/icii.2018.00007","url":null,"abstract":"","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124514063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For predictive maintenance of equipment with Industrial Internet of Things (IIoT) technologies, existing IoT Cloud systems provide strong monitoring and data analysis capabilities for detecting and predicting status of equipment. However, we need to support complex interactions among different software components and human activities to provide an integrated analytics, as software algorithms alone cannot deal with the complexity and scale of data collection and analysis and the diversity of equipment, due to the difficulties of capturing and modeling uncertainties and domain knowledge in predictive maintenance. In this paper, we describe how we design and augment complex IoT big data cloud systems for integrated analytics of IIoT predictive maintenance. Our approach is to identify various complex interactions for solving system incidents together with relevant critical analytics results about equipment. We incorporate humans into various parts of complex IoT Cloud systems to enable situational data collection, services management, and data analytics. We leverage serverless functions, cloud services, and domain knowledge to support dynamic interactions between human and software for maintaining equipment. We use a real-world maintenance of Base Transceiver Stations to illustrate our engineering approach which we have prototyped with state-of-the art cloud and IoT technologies, such as Apache Nifi, Hadoop, Spark and Google Cloud Functions.
{"title":"Integrated Analytics for IIoT Predictive Maintenance Using IoT Big Data Cloud Systems","authors":"Hong Linh Truong","doi":"10.1109/ICII.2018.00020","DOIUrl":"https://doi.org/10.1109/ICII.2018.00020","url":null,"abstract":"For predictive maintenance of equipment with Industrial Internet of Things (IIoT) technologies, existing IoT Cloud systems provide strong monitoring and data analysis capabilities for detecting and predicting status of equipment. However, we need to support complex interactions among different software components and human activities to provide an integrated analytics, as software algorithms alone cannot deal with the complexity and scale of data collection and analysis and the diversity of equipment, due to the difficulties of capturing and modeling uncertainties and domain knowledge in predictive maintenance. In this paper, we describe how we design and augment complex IoT big data cloud systems for integrated analytics of IIoT predictive maintenance. Our approach is to identify various complex interactions for solving system incidents together with relevant critical analytics results about equipment. We incorporate humans into various parts of complex IoT Cloud systems to enable situational data collection, services management, and data analytics. We leverage serverless functions, cloud services, and domain knowledge to support dynamic interactions between human and software for maintaining equipment. We use a real-world maintenance of Base Transceiver Stations to illustrate our engineering approach which we have prototyped with state-of-the art cloud and IoT technologies, such as Apache Nifi, Hadoop, Spark and Google Cloud Functions.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhouzhou Li, Hua Fang, Honggang Wang, Shaoen Wu, M. Daneshmand
Securely growing or de-growing nodes is a mandatory requirement to manage Wireless Body Area Networks (WBANs). This requirement raises significant challenges in node authentication, backward node authentication, initial node configuration, and node de-growth. Unlike the traditional approaches using pre-stored secrets or relying on special authentication hardware, we explore the characteristics of WBAN and wireless signal to develop an efficient scheme for adding/removing WBAN node securely and effectively. The major idea of the proposed scheme is to construct a 'virtual' dual-antennae proximity detection system by fully utilizing the existing legitimate nodes and the behavior of human body. We built a system prototype on wireless devices and verified our scheme through experiments. In addition, a data mining (clustering) algorithm is also applied to successfully detect newly joined legitimate node and identify potential attackers.
{"title":"A New Efficient Scheme for Securely Growing WBAN Nodes","authors":"Zhouzhou Li, Hua Fang, Honggang Wang, Shaoen Wu, M. Daneshmand","doi":"10.1109/ICII.2018.00026","DOIUrl":"https://doi.org/10.1109/ICII.2018.00026","url":null,"abstract":"Securely growing or de-growing nodes is a mandatory requirement to manage Wireless Body Area Networks (WBANs). This requirement raises significant challenges in node authentication, backward node authentication, initial node configuration, and node de-growth. Unlike the traditional approaches using pre-stored secrets or relying on special authentication hardware, we explore the characteristics of WBAN and wireless signal to develop an efficient scheme for adding/removing WBAN node securely and effectively. The major idea of the proposed scheme is to construct a 'virtual' dual-antennae proximity detection system by fully utilizing the existing legitimate nodes and the behavior of human body. We built a system prototype on wireless devices and verified our scheme through experiments. In addition, a data mining (clustering) algorithm is also applied to successfully detect newly joined legitimate node and identify potential attackers.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122407355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yutao Huang, Yuhe Lu, Feng Wang, Xiaoyi Fan, Jiangchuan Liu, Victor C. M. Leung
Due to the ever-growing demands in modern cities, unreliable and inefficient power transportation becomes one critical issue in nowadays power grid. This makes power grid monitoring one of the key modules in power grid system and play an important role in preventing severe safety accidents. However, the traditional manual inspection cannot efficiently achieve this goal due to its low efficiency and high cost. Smart grid as a new generation of the power grid, sheds new light to construct an intelligent, reliable and efficient power grid with advanced information technology. In smart grid, automated monitoring can be realized by applying advanced deep learning algorithms on powerful cloud computing platform together with such IoT (Internet of Things) devices as smart cameras. The performance of cloud monitoring, however, can still be unsatisfactory since a large amount of data transmission over the Internet will lead to high delay and low frame rate. In this paper, we note that the edge computing paradigm can well complement the cloud and significantly reduce the delay to improve the overall performance. To this end, we propose an edge computing framework for real-time monitoring, which moves the computation away from the centralized cloud to the near-device edge servers. To maximize the benefits, we formulate a scheduling problem to further optimize the framework and propose an efficient heuristic algorithm based on the simulated annealing strategy. Both real-world experiments and simulation results show that our framework can increase the monitoring frame rate up to 10 times and reduce the detection delay up to 85% comparing to the cloud monitoring solution.
{"title":"An Edge Computing Framework for Real-Time Monitoring in Smart Grid","authors":"Yutao Huang, Yuhe Lu, Feng Wang, Xiaoyi Fan, Jiangchuan Liu, Victor C. M. Leung","doi":"10.1109/ICII.2018.00019","DOIUrl":"https://doi.org/10.1109/ICII.2018.00019","url":null,"abstract":"Due to the ever-growing demands in modern cities, unreliable and inefficient power transportation becomes one critical issue in nowadays power grid. This makes power grid monitoring one of the key modules in power grid system and play an important role in preventing severe safety accidents. However, the traditional manual inspection cannot efficiently achieve this goal due to its low efficiency and high cost. Smart grid as a new generation of the power grid, sheds new light to construct an intelligent, reliable and efficient power grid with advanced information technology. In smart grid, automated monitoring can be realized by applying advanced deep learning algorithms on powerful cloud computing platform together with such IoT (Internet of Things) devices as smart cameras. The performance of cloud monitoring, however, can still be unsatisfactory since a large amount of data transmission over the Internet will lead to high delay and low frame rate. In this paper, we note that the edge computing paradigm can well complement the cloud and significantly reduce the delay to improve the overall performance. To this end, we propose an edge computing framework for real-time monitoring, which moves the computation away from the centralized cloud to the near-device edge servers. To maximize the benefits, we formulate a scheduling problem to further optimize the framework and propose an efficient heuristic algorithm based on the simulated annealing strategy. Both real-world experiments and simulation results show that our framework can increase the monitoring frame rate up to 10 times and reduce the detection delay up to 85% comparing to the cloud monitoring solution.","PeriodicalId":330919,"journal":{"name":"2018 IEEE International Conference on Industrial Internet (ICII)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122797954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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