Pub Date : 2018-07-23DOI: 10.1109/WAINA.2018.00149
Michele Bottone, F. Raimondi, G. Primiero
In the past ten years distributed ledgers such as Bitcoin and smart contracts that can run code autonomously have seen an exponential growth both in terms of research interest and in terms of industrial and financial applications. These find a natural application in the area of Sensor Networks and Cyber-Physical Systems. However, the incentive architecture of blockchains requires massive computational resources for mining, delays in the confirmation of transactions and, more importantly, continuously growing transaction fees, which are ill-suited to systems in which services may be provided by resource-limited devices and confirmation times and transaction costs should be kept minimal, ideally absent. We focus on a new block-less, fee-less paradigm for distributed ledgers suitable for the WSN, IoT and CPS in which transactions are nodes of a directed acyclic graph, that overcomes the limitations of blockchains for these applications, and where e.g. sensors can be at the same time issuers of transactions and validators of previous transactions. In particular, we present and release open-source a simulation environment that can be easily extended and analysed, and confirms the available results on the performance of the network.
{"title":"Multi-agent Based Simulations of Block-Free Distributed Ledgers","authors":"Michele Bottone, F. Raimondi, G. Primiero","doi":"10.1109/WAINA.2018.00149","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00149","url":null,"abstract":"In the past ten years distributed ledgers such as Bitcoin and smart contracts that can run code autonomously have seen an exponential growth both in terms of research interest and in terms of industrial and financial applications. These find a natural application in the area of Sensor Networks and Cyber-Physical Systems. However, the incentive architecture of blockchains requires massive computational resources for mining, delays in the confirmation of transactions and, more importantly, continuously growing transaction fees, which are ill-suited to systems in which services may be provided by resource-limited devices and confirmation times and transaction costs should be kept minimal, ideally absent. We focus on a new block-less, fee-less paradigm for distributed ledgers suitable for the WSN, IoT and CPS in which transactions are nodes of a directed acyclic graph, that overcomes the limitations of blockchains for these applications, and where e.g. sensors can be at the same time issuers of transactions and validators of previous transactions. In particular, we present and release open-source a simulation environment that can be easily extended and analysed, and confirms the available results on the performance of the network.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944351","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}
Pub Date : 2018-07-23DOI: 10.1109/WAINA.2018.00097
A. Alfoudi, M. Dighriri, Abayomi Otebolaku, R. Pereira, G. Lee
Network slicing is an architectural solution that enables the future 5G network to offer a high data traffic capacity and efficient network connectivity. Moreover, software defined network (SDN) and network functions virtualization (NFV) empower this architecture to visualize the physical network resources. The network slicing identified as a multiple logical network, where each network slice dedicates as an end-to-end network and works independently with other slices on a common physical network resources. Most user devices have more than one smart wireless interfaces to connect to different radio access technologies (RATs) such as WiFi and LTE, thereby network operators utilize this facility to offload mobile data traffic. Therefore, it is important to enable a network slicing to manage different RATs on the same logical network as a way to mitigate the spectrum scarcity problem and enables a slice to control its users' mobility across different access networks. In this paper, we propose a mobility management architecture based network slicing where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In this architecture, each slice has a different mobility demands and these demands are governed by a network slice configuration and service characteristics. Therefore, our mobility management architecture follows a modular approach where each slice has individual module to handle the mobility demands and enforce the slice policy for mobility management. The advantages of applying our proposed architecture include: i) Sharing network resources between different network slices; ii) creating logical platform to unify different RATs resources and allowing all slices to share them; iii) satisfying slice mobility demands.
{"title":"Mobility Management Architecture in Different RATs Based Network Slicing","authors":"A. Alfoudi, M. Dighriri, Abayomi Otebolaku, R. Pereira, G. Lee","doi":"10.1109/WAINA.2018.00097","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00097","url":null,"abstract":"Network slicing is an architectural solution that enables the future 5G network to offer a high data traffic capacity and efficient network connectivity. Moreover, software defined network (SDN) and network functions virtualization (NFV) empower this architecture to visualize the physical network resources. The network slicing identified as a multiple logical network, where each network slice dedicates as an end-to-end network and works independently with other slices on a common physical network resources. Most user devices have more than one smart wireless interfaces to connect to different radio access technologies (RATs) such as WiFi and LTE, thereby network operators utilize this facility to offload mobile data traffic. Therefore, it is important to enable a network slicing to manage different RATs on the same logical network as a way to mitigate the spectrum scarcity problem and enables a slice to control its users' mobility across different access networks. In this paper, we propose a mobility management architecture based network slicing where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In this architecture, each slice has a different mobility demands and these demands are governed by a network slice configuration and service characteristics. Therefore, our mobility management architecture follows a modular approach where each slice has individual module to handle the mobility demands and enforce the slice policy for mobility management. The advantages of applying our proposed architecture include: i) Sharing network resources between different network slices; ii) creating logical platform to unify different RATs resources and allowing all slices to share them; iii) satisfying slice mobility demands.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128271212","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}
Pub Date : 2018-07-20DOI: 10.1109/WAINA.2018.00159
Chi-Pan Hwang, Mu-Song Chen, Chih-Min Shih, Hsing-Yu Chen, Wen-Kai Liu
The long term accumulated driving information can effectively summarize the specific driver behavior by statistical analysis. In order to widely and chronically collect driving information of drivers, the cloud computing platform is the most suitable mechanism to log the dynamic vehicle information stream from OBD port to build up Big Data for data mining about driver behavior, currently. The research of this paper has focused on the application layer in the cloud computing platform, Python has been adopted to as the main development tool accompanying with the packages of numpy, pandas, and scipy to calculate the kurtosis and skewness in statistics of each driving route, then decision tree classification technique was applied to generate the analyzing knowledge for driver behavior analysis. Finally the driver behavior are summarized from the completed decision tree classifier to defensive, weak defensive, weak aggressive, and aggressive to complete the overall operations.
{"title":"Apply Scikit-Learn in Python to Analyze Driver Behavior Based on OBD Data","authors":"Chi-Pan Hwang, Mu-Song Chen, Chih-Min Shih, Hsing-Yu Chen, Wen-Kai Liu","doi":"10.1109/WAINA.2018.00159","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00159","url":null,"abstract":"The long term accumulated driving information can effectively summarize the specific driver behavior by statistical analysis. In order to widely and chronically collect driving information of drivers, the cloud computing platform is the most suitable mechanism to log the dynamic vehicle information stream from OBD port to build up Big Data for data mining about driver behavior, currently. The research of this paper has focused on the application layer in the cloud computing platform, Python has been adopted to as the main development tool accompanying with the packages of numpy, pandas, and scipy to calculate the kurtosis and skewness in statistics of each driving route, then decision tree classification technique was applied to generate the analyzing knowledge for driver behavior analysis. Finally the driver behavior are summarized from the completed decision tree classifier to defensive, weak defensive, weak aggressive, and aggressive to complete the overall operations.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655316","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00065
Stathis Maneas, Bianca Schroeder
Frameworks for large-scale distributed data processing, such as the Hadoop ecosystem, are at the core of the big data revolution we have experienced over the last decade. In this paper, we conduct an extensive study of the Hadoop Distributed File System (HDFS)'s code evolution. Our study is based on the reports and patch files (patches) available from the official Apache issue tracker (JIRA) and our goal was to make complete use of the entire history of HDFS at the time and the richness of the available data. The purpose of our study is to assist developers in improving the design of similar systems and implementing more solid systems in general. In contrast to prior work, our study covers all reports that have been submitted over HDFS's lifetime, rather than a sampled subset. Additionally, we include all associated patch files that have been verified by the developers of the system and classify the root causes of issues at a finer granularity than prior work, by manually inspecting all 3302 reports over the first nine years, based on a two-level classification scheme that we developed. This allows us to present a different perspective of HDFS, including a focus on the system's evolution over time, as well as a detailed analysis of characteristics that have not been previously studied in detail. These include, for example, the scope and complexity of issues in terms of the size of the patch that fixes it and number of files it affects, the time it takes before an issue is exposed, the time it takes to resolve an issue and how these vary over time. Our results indicate that bug reports constitute the most dominant type, having a continuously increasing rate over time. Moreover, the overall scope and complexity of reports and patch files remain surprisingly stable throughout HDFS' lifetime, despite the significant growth the code base experiences over time. Finally, as part of our work, we created a detailed database that includes all reports and patches, along with the key characteristics we extracted.
{"title":"The Evolution of the Hadoop Distributed File System","authors":"Stathis Maneas, Bianca Schroeder","doi":"10.1109/WAINA.2018.00065","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00065","url":null,"abstract":"Frameworks for large-scale distributed data processing, such as the Hadoop ecosystem, are at the core of the big data revolution we have experienced over the last decade. In this paper, we conduct an extensive study of the Hadoop Distributed File System (HDFS)'s code evolution. Our study is based on the reports and patch files (patches) available from the official Apache issue tracker (JIRA) and our goal was to make complete use of the entire history of HDFS at the time and the richness of the available data. The purpose of our study is to assist developers in improving the design of similar systems and implementing more solid systems in general. In contrast to prior work, our study covers all reports that have been submitted over HDFS's lifetime, rather than a sampled subset. Additionally, we include all associated patch files that have been verified by the developers of the system and classify the root causes of issues at a finer granularity than prior work, by manually inspecting all 3302 reports over the first nine years, based on a two-level classification scheme that we developed. This allows us to present a different perspective of HDFS, including a focus on the system's evolution over time, as well as a detailed analysis of characteristics that have not been previously studied in detail. These include, for example, the scope and complexity of issues in terms of the size of the patch that fixes it and number of files it affects, the time it takes before an issue is exposed, the time it takes to resolve an issue and how these vary over time. Our results indicate that bug reports constitute the most dominant type, having a continuously increasing rate over time. Moreover, the overall scope and complexity of reports and patch files remain surprisingly stable throughout HDFS' lifetime, despite the significant growth the code base experiences over time. Finally, as part of our work, we created a detailed database that includes all reports and patches, along with the key characteristics we extracted.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124416547","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00119
B. D. Martino, Salvatore D'Angelo, A. Esposito, Riccardo Cappuzzo, Anderson Santana de Oliveira
We present the benefits of applying the code once deploy everywhere approach to clustering of categorical data over large datasets. The paper brings two main contributions: an step-by step application of the code based approach and an enhancement for the ROCK algorithm for clustering categorical data.
{"title":"ROCK Algorithm Parallelization with TOREADOR Primitives","authors":"B. D. Martino, Salvatore D'Angelo, A. Esposito, Riccardo Cappuzzo, Anderson Santana de Oliveira","doi":"10.1109/WAINA.2018.00119","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00119","url":null,"abstract":"We present the benefits of applying the code once deploy everywhere approach to clustering of categorical data over large datasets. The paper brings two main contributions: an step-by step application of the code based approach and an enhancement for the ROCK algorithm for clustering categorical data.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125095577","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00155
Cheng-Fang Peng, J. Hsieh, S. Leu, Chi-Hung Chuang
This paper presents a drone-based method for vacant parking space detection using aerial images. Due to the limited field of view, it is better to use a camera mounted on a drone to monitor a huge parking lot. However, a drone-based camera is not fixed to the ground. Thus, there are many challenges to detect parking spaces and classify their status. To detect parking spaces, the RANSAC scheme is first used to estimate the homography relation between the current captured image and the reference parking space. Then, three novel features are extracted from each park space for occupancy condition judgment, i.e., vehicle color feature, local gray-scale variant feature, and corner feature. A deep NN is then trained to determine the occupancy status of each parking space based on the above three features. The performance of our system is evaluated on varies parking lots under different lighting and weather conditions. The average accuracy can be achieved up to 97%.
{"title":"Drone-Based Vacant Parking Space Detection","authors":"Cheng-Fang Peng, J. Hsieh, S. Leu, Chi-Hung Chuang","doi":"10.1109/WAINA.2018.00155","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00155","url":null,"abstract":"This paper presents a drone-based method for vacant parking space detection using aerial images. Due to the limited field of view, it is better to use a camera mounted on a drone to monitor a huge parking lot. However, a drone-based camera is not fixed to the ground. Thus, there are many challenges to detect parking spaces and classify their status. To detect parking spaces, the RANSAC scheme is first used to estimate the homography relation between the current captured image and the reference parking space. Then, three novel features are extracted from each park space for occupancy condition judgment, i.e., vehicle color feature, local gray-scale variant feature, and corner feature. A deep NN is then trained to determine the occupancy status of each parking space based on the above three features. The performance of our system is evaluated on varies parking lots under different lighting and weather conditions. The average accuracy can be achieved up to 97%.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128701019","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00143
A. Khan, N. Javaid, Z. Iqbal, N. Anwar, A. Saboor, Inzimam ul-Haq, U. Qasim
With the advent of Smart Grid (SG), it provides the consumers with the opportunity to schedule their power consumption load efficiently in such a way that it reduces their energy cost while also minimizing their Peak to Average Ratio (PAR) in the process. We in this paper target the appliances to schedule in such a way that it increases User Comfort (UC) and decreases electricity consumption load which benefits both consumer and utility. In this paper, we proposed hybrid of Bacterial Forging Algorithm (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI) to schedule appliances while balancing User Comfort which is the main objective of the Demand Side Management (DSM). This paper tries to reduce both waiting time and electricity cost simultaneously in the new hybrid Bacterial Foraging Tabu Search (BFTS) technique. Real time pricing (RTP) scheme was used to get the total cost of electricity consumed. We compared the results of proposed hybrid scheme with Bacterial Forging (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI). The result shows effectiveness of using hybrid Bacterial Foraging Tabu Search (BFTS) technique for Demand Side Management (DSM).
{"title":"A Hybrid Bacterial Foraging Tabu Search Heuristic Optimization for Demand Side Management in Smart Grid","authors":"A. Khan, N. Javaid, Z. Iqbal, N. Anwar, A. Saboor, Inzimam ul-Haq, U. Qasim","doi":"10.1109/WAINA.2018.00143","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00143","url":null,"abstract":"With the advent of Smart Grid (SG), it provides the consumers with the opportunity to schedule their power consumption load efficiently in such a way that it reduces their energy cost while also minimizing their Peak to Average Ratio (PAR) in the process. We in this paper target the appliances to schedule in such a way that it increases User Comfort (UC) and decreases electricity consumption load which benefits both consumer and utility. In this paper, we proposed hybrid of Bacterial Forging Algorithm (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI) to schedule appliances while balancing User Comfort which is the main objective of the Demand Side Management (DSM). This paper tries to reduce both waiting time and electricity cost simultaneously in the new hybrid Bacterial Foraging Tabu Search (BFTS) technique. Real time pricing (RTP) scheme was used to get the total cost of electricity consumed. We compared the results of proposed hybrid scheme with Bacterial Forging (BFA) and Tabu Search (TS) Algorithm using different Operational time Interval (OTI). The result shows effectiveness of using hybrid Bacterial Foraging Tabu Search (BFTS) technique for Demand Side Management (DSM).","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129916011","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00103
Yosra Ben Dhief, Y. Djemaiel, S. Rekhis, N. Boudriga
Within the emergence of smart cities over the world, there is a great need for a set of services that ensure the protection of citizens and cities assets. Several supervisory systems have been deployed everywhere making use of a set of fixed and mobile sensors. Facing the new threats, these systems are unable to ensure the monitoring of large areas that are under the control of different entities which prevent an efficient protection against the malicious activities targeting cities. In this context, this paper proposes a novel cloud-based monitoring system that provides global monitoring services using a set of sensors that are deployed by different supervisory systems, enabling the reconfiguration of sensors according to an efficient scheduling scheme. The efficiency of the proposed model is illustrated through a case study for a camera-based smart city surveillance.
{"title":"Cloud-Based Global Monitoring System for Smart Cities","authors":"Yosra Ben Dhief, Y. Djemaiel, S. Rekhis, N. Boudriga","doi":"10.1109/WAINA.2018.00103","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00103","url":null,"abstract":"Within the emergence of smart cities over the world, there is a great need for a set of services that ensure the protection of citizens and cities assets. Several supervisory systems have been deployed everywhere making use of a set of fixed and mobile sensors. Facing the new threats, these systems are unable to ensure the monitoring of large areas that are under the control of different entities which prevent an efficient protection against the malicious activities targeting cities. In this context, this paper proposes a novel cloud-based monitoring system that provides global monitoring services using a set of sensors that are deployed by different supervisory systems, enabling the reconfiguration of sensors according to an efficient scheduling scheme. The efficiency of the proposed model is illustrated through a case study for a camera-based smart city surveillance.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123593240","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00171
Omid Ameri Sianaki, A. Yousefi, A. R. Tabesh, M. Mahdavi
The Internet of Things (IoT) is recognized as one of the major key areas of future technology and is gaining vast attention from an extensive range of industries. The sensors and devices are generating massive amounts of high-dimensional and heterogeneous data that need to be stored and processed. Machine learning encompasses the widespread techniques of artificial intelligence that can deduce patterns and relationships from unstructured data. Big data analytics are advanced statistical and predictive analytic methods which are capable of manipulating data in a range of Exabytes and more. This paper presents a review of the applications of the Internet of Everything and the machine learning techniques in the fields of health, smart electrical grid, and supply chain management. A review of the literature has been conducted to demonstrate the future issues and challenges that researchers will face.
{"title":"Internet of Everything and Machine Learning Applications: Issues and Challenges","authors":"Omid Ameri Sianaki, A. Yousefi, A. R. Tabesh, M. Mahdavi","doi":"10.1109/WAINA.2018.00171","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00171","url":null,"abstract":"The Internet of Things (IoT) is recognized as one of the major key areas of future technology and is gaining vast attention from an extensive range of industries. The sensors and devices are generating massive amounts of high-dimensional and heterogeneous data that need to be stored and processed. Machine learning encompasses the widespread techniques of artificial intelligence that can deduce patterns and relationships from unstructured data. Big data analytics are advanced statistical and predictive analytic methods which are capable of manipulating data in a range of Exabytes and more. This paper presents a review of the applications of the Internet of Everything and the machine learning techniques in the fields of health, smart electrical grid, and supply chain management. A review of the literature has been conducted to demonstrate the future issues and challenges that researchers will face.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129411542","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}
Pub Date : 2018-05-16DOI: 10.1109/WAINA.2018.00091
Sundas Shafiq, Sikandar Asif, Iqra Fatima, K. Yousaf, Wajiha Safat, N. Javaid
Smart grid is an emerging technology which is successfully implemented by the use of different communication methods. Demand side management (DSM) plays a significant role in the management of load and energy consumption in order to reduce cost in the smart grids. Smart buildings and smart homes are usually considered important for reducing the electricity consumption by home energy management controllers (HEMC). In this paper, an energy management controller (EMC) is proposed with an objective to minimize the energy consumption, curtail load demand, reduce electricity usage and minimize peak to average (PAR) ratio with increased user comfort level. For this purpose, three evolutionary and iterative techniques were proposed as backtracking search optimization algorithm (BSOA), tabu search (TS) and their hybrid as tabu backtracking search optimization (TBSO) algorithm. Simulations were performed in MATLAB by using real time pricing (RTP) tariff for bill calculation. 9 household appliances were scheduled and results proved that our proposed hybrid technique outperforms the remaining two in terms of cost minimization.
{"title":"An Approach Towards Efficient Scheduling of Home Energy Management System Using Backtracking Search Optimization and Tabu Search","authors":"Sundas Shafiq, Sikandar Asif, Iqra Fatima, K. Yousaf, Wajiha Safat, N. Javaid","doi":"10.1109/WAINA.2018.00091","DOIUrl":"https://doi.org/10.1109/WAINA.2018.00091","url":null,"abstract":"Smart grid is an emerging technology which is successfully implemented by the use of different communication methods. Demand side management (DSM) plays a significant role in the management of load and energy consumption in order to reduce cost in the smart grids. Smart buildings and smart homes are usually considered important for reducing the electricity consumption by home energy management controllers (HEMC). In this paper, an energy management controller (EMC) is proposed with an objective to minimize the energy consumption, curtail load demand, reduce electricity usage and minimize peak to average (PAR) ratio with increased user comfort level. For this purpose, three evolutionary and iterative techniques were proposed as backtracking search optimization algorithm (BSOA), tabu search (TS) and their hybrid as tabu backtracking search optimization (TBSO) algorithm. Simulations were performed in MATLAB by using real time pricing (RTP) tariff for bill calculation. 9 household appliances were scheduled and results proved that our proposed hybrid technique outperforms the remaining two in terms of cost minimization.","PeriodicalId":296466,"journal":{"name":"2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127452225","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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