Pub Date : 2020-12-22DOI: 10.1109/IKT51791.2020.9345637
Hanieh Kashfi, F. S. Aliee
Business process is a set of activities that provides outputs to achieve a specific and predefined goal. Accordingly, Business Process Improvement is one of the most important factors in achieving the goals of enterprises. Unfortunately, despite the exorbitant costs in enterprises to improve business processes, activities in this area often fail. The reason for this, is the existence of numerous challenges on the path to improvement, which are often overlooked by researchers and practitioners in the field. Following this issue, this paper tries to identify the main challenges in this direction. While creating a comprehensive set of business process improvement challenges, a high-level abstraction cycle is presented for business process improvement activities. Finally, the identified challenges are categorized according to the steps in the proposed cycle.
{"title":"Business Process Improvement Challenges: A Systematic Literature Review","authors":"Hanieh Kashfi, F. S. Aliee","doi":"10.1109/IKT51791.2020.9345637","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345637","url":null,"abstract":"Business process is a set of activities that provides outputs to achieve a specific and predefined goal. Accordingly, Business Process Improvement is one of the most important factors in achieving the goals of enterprises. Unfortunately, despite the exorbitant costs in enterprises to improve business processes, activities in this area often fail. The reason for this, is the existence of numerous challenges on the path to improvement, which are often overlooked by researchers and practitioners in the field. Following this issue, this paper tries to identify the main challenges in this direction. While creating a comprehensive set of business process improvement challenges, a high-level abstraction cycle is presented for business process improvement activities. Finally, the identified challenges are categorized according to the steps in the proposed cycle.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115143342","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345616
Yosra Bahrani, O. Fatemi
Learner's Knowledge gap detection is one of the important issues in learner's knowledge assessment. The knowledge gap is the gap between actual knowledge of the educational concepts and that received by the learner from them. This paper presents a new method to calculate the knowledge gap of each concept of instructional videos based on the learner click behavior. Many studies have analyzed learner behavior based on click behavior., but one of the main issues in event analysis is to identify the amount of knowledge learned by the learner and communicated between the actual concept and that perceived by the learner. One of the main goals of knowledge gap extraction is to detect students at risk and help them to be on the right path of the learning process. In this paper., rules are proposed based on click behavior of learners using Apriori Algorithm. Furthermore., the knowledge gap for each group of learners is calculated based on the behavioral classification. The test project is done on 52 students in the microprocessor course at the e-learning center., University of Tehran. The proposed method is evaluated and then a number of rules are extracted in this study.
{"title":"Knowledge gap extraction based on the learner click behavior in interaction with videos using the association rule algorithm","authors":"Yosra Bahrani, O. Fatemi","doi":"10.1109/IKT51791.2020.9345616","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345616","url":null,"abstract":"Learner's Knowledge gap detection is one of the important issues in learner's knowledge assessment. The knowledge gap is the gap between actual knowledge of the educational concepts and that received by the learner from them. This paper presents a new method to calculate the knowledge gap of each concept of instructional videos based on the learner click behavior. Many studies have analyzed learner behavior based on click behavior., but one of the main issues in event analysis is to identify the amount of knowledge learned by the learner and communicated between the actual concept and that perceived by the learner. One of the main goals of knowledge gap extraction is to detect students at risk and help them to be on the right path of the learning process. In this paper., rules are proposed based on click behavior of learners using Apriori Algorithm. Furthermore., the knowledge gap for each group of learners is calculated based on the behavioral classification. The test project is done on 52 students in the microprocessor course at the e-learning center., University of Tehran. The proposed method is evaluated and then a number of rules are extracted in this study.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116642772","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345623
S. Doostali, S. M. Babamir, Mohammad Shiralizadeh Dezfoli, Behzad Soleimani Neysiani
Environmental pollution and urban dissatisfaction with traffic are the biggest challenges in metropolitan cities. Nevertheless, the inevitability of allocating roads for temporary and sometimes long periods to specific issues such as meetings, conferences, accidents, etc. could cause traffic on the surrounding roads. One of the solutions to reduce traffic on these roads is to make some roads one-way or two-way depending on their properties, where urban communication is not disrupted. In this paper, we presented an approach to employ the Internet of Things (IoT) to detect traffic information and create weighted dependency graphs to minimize the amount of free road traffic. To model the urban roads, we consider a directed graph in which each edge represents the stream. Then the optimal directed graph obtained using the genetic algorithm represented the traffic model of the vehicles. According to the car's location and destination, the optimal path was announced to the driver via the car internet. This method improved the average waiting time and queue length.
{"title":"IoT-Based Model in Smart Urban Traffic Control: Graph theory and Genetic Algorithm","authors":"S. Doostali, S. M. Babamir, Mohammad Shiralizadeh Dezfoli, Behzad Soleimani Neysiani","doi":"10.1109/IKT51791.2020.9345623","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345623","url":null,"abstract":"Environmental pollution and urban dissatisfaction with traffic are the biggest challenges in metropolitan cities. Nevertheless, the inevitability of allocating roads for temporary and sometimes long periods to specific issues such as meetings, conferences, accidents, etc. could cause traffic on the surrounding roads. One of the solutions to reduce traffic on these roads is to make some roads one-way or two-way depending on their properties, where urban communication is not disrupted. In this paper, we presented an approach to employ the Internet of Things (IoT) to detect traffic information and create weighted dependency graphs to minimize the amount of free road traffic. To model the urban roads, we consider a directed graph in which each edge represents the stream. Then the optimal directed graph obtained using the genetic algorithm represented the traffic model of the vehicles. According to the car's location and destination, the optimal path was announced to the driver via the car internet. This method improved the average waiting time and queue length.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126002651","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345609
Mustafa Mohammadi Garasuie, M. Shabankhah, A. Kamandi
Graph Neural Networks (GNNs) are models that use the structure of graphs to better exploit the bilateral relationship between neighboring nodes. Some problems, however, require that we consider a more general relationship which involve not only two nodes but rather a group of nodes. This is the approach adopted in Hypergraph Convolution and Hypergraph Attention Networks (HGAT) [1]. In this paper, we first propose to incorporate a weight matrix into these networks which, as our experimentations show, can improve the performance of the models in question. The other novelty in our work is the introduction of self-loops in the graphs which again leads to slight improvements in the accuracy of our architecture(named iHGAN).
图神经网络(gnn)是利用图的结构来更好地利用相邻节点之间的双边关系的模型。然而，有些问题要求我们考虑一种更一般的关系，这种关系不仅涉及两个节点，而且涉及一组节点。这就是超图卷积和超图注意网络(Hypergraph Convolution and Hypergraph Attention Networks, HGAT)[1]所采用的方法。在本文中，我们首先提出在这些网络中加入一个权重矩阵，正如我们的实验所表明的那样，可以提高所讨论模型的性能。我们工作中的另一个新颖之处是在图中引入了自循环，这再次导致我们的架构(名为iHGAN)的准确性略有提高。
{"title":"Improving Hypergraph Attention and Hypergraph Convolution Networks","authors":"Mustafa Mohammadi Garasuie, M. Shabankhah, A. Kamandi","doi":"10.1109/IKT51791.2020.9345609","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345609","url":null,"abstract":"Graph Neural Networks (GNNs) are models that use the structure of graphs to better exploit the bilateral relationship between neighboring nodes. Some problems, however, require that we consider a more general relationship which involve not only two nodes but rather a group of nodes. This is the approach adopted in Hypergraph Convolution and Hypergraph Attention Networks (HGAT) [1]. In this paper, we first propose to incorporate a weight matrix into these networks which, as our experimentations show, can improve the performance of the models in question. The other novelty in our work is the introduction of self-loops in the graphs which again leads to slight improvements in the accuracy of our architecture(named iHGAN).","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127868734","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345639
Zeinab Borhanifard, Hossein Basafa, S. Z. Razavi, Heshaam Faili
Natural language understanding is a critical module in task-oriented dialogue systems. Recently, state-of-the-art approaches use deep learning methods and transformers to improve the performance of dialogue systems. In this work, we propose a natural language understanding model with a specific-shopping named entity recognizer using a joint learning-based BERT transformer for task-oriented dialogue systems in the Persian Language. Since there is no published available dataset for Persian online shopping dialogue systems, to tackle the lack of data, we propose two methods for generating training data: fully-simulated and semi-simulated method. We created a simulated dataset with a hybrid of rule-based and template-based generation methods and a semi-simulated dataset where the language generation part is done by a human to increase the quality of the dataset. Our experiments with the natural language understanding module show that a combination of the datasets can improve results. These dataset generation methods can apply in other domains for low-resource languages in task-oriented dialogue systems too to solve the cold start problem of datasets.
{"title":"Persian Language Understanding in Task-Oriented Dialogue System for Online Shopping","authors":"Zeinab Borhanifard, Hossein Basafa, S. Z. Razavi, Heshaam Faili","doi":"10.1109/IKT51791.2020.9345639","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345639","url":null,"abstract":"Natural language understanding is a critical module in task-oriented dialogue systems. Recently, state-of-the-art approaches use deep learning methods and transformers to improve the performance of dialogue systems. In this work, we propose a natural language understanding model with a specific-shopping named entity recognizer using a joint learning-based BERT transformer for task-oriented dialogue systems in the Persian Language. Since there is no published available dataset for Persian online shopping dialogue systems, to tackle the lack of data, we propose two methods for generating training data: fully-simulated and semi-simulated method. We created a simulated dataset with a hybrid of rule-based and template-based generation methods and a semi-simulated dataset where the language generation part is done by a human to increase the quality of the dataset. Our experiments with the natural language understanding module show that a combination of the datasets can improve results. These dataset generation methods can apply in other domains for low-resource languages in task-oriented dialogue systems too to solve the cold start problem of datasets.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"6 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120995195","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345618
Melika Golestani, S. Z. Razavi, Heshaam Faili
Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of sentence ordering is proposed to learn succession of events with applications in AI tasks. The performance of previous works employing statistical methods is poor, while the neural networks-based approaches are in serious need of large corpora for model learning. In this paper, we propose a method for sentence ordering which does not need a training phase and consequently a large corpus for learning. To this end, we generate sentence embedding using BERT pre-trained model and measure sentence similarity using cosine similarity score. We suggest this score as an indicator of sequential events' level of coherence. We finally sort the sentences through brute-force search to maximize overall similarities of the sequenced sentences. Our proposed method outperformed other baselines on ROCStories, a corpus of 5-sentence human-made stories. The method is specifically more efficient than neural network-based methods when no huge corpus is available. Among other advantages of this method are its interpretability and needlessness to linguistic knowledge.
{"title":"A New Sentence Ordering Method using BERT Pretrained Model","authors":"Melika Golestani, S. Z. Razavi, Heshaam Faili","doi":"10.1109/IKT51791.2020.9345618","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345618","url":null,"abstract":"Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of sentence ordering is proposed to learn succession of events with applications in AI tasks. The performance of previous works employing statistical methods is poor, while the neural networks-based approaches are in serious need of large corpora for model learning. In this paper, we propose a method for sentence ordering which does not need a training phase and consequently a large corpus for learning. To this end, we generate sentence embedding using BERT pre-trained model and measure sentence similarity using cosine similarity score. We suggest this score as an indicator of sequential events' level of coherence. We finally sort the sentences through brute-force search to maximize overall similarities of the sequenced sentences. Our proposed method outperformed other baselines on ROCStories, a corpus of 5-sentence human-made stories. The method is specifically more efficient than neural network-based methods when no huge corpus is available. Among other advantages of this method are its interpretability and needlessness to linguistic knowledge.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114084783","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345641
Z. Kashani, A. Masoudi-Nejad, A. Nowzari-Dalini
Biological networks have recently gathered much attraction in finding their motifs. Motifs can be considered as subgraphs that occur in a particular network at significantly higher frequencies than random networks. The importance of this problem causes attention of improving the existing algorithms. As the runtime of an algorithm is an important aspect, applying parallel techniques is appropriate for better improvement. In this paper a parallel algorithm (ParaKavosh) for finding network motifs is presented. Our algorithm is tested on E. coli, S. cerevisiae, Homo sapiens and Rattus norvegicus networks. The cost optimality of the algorithm is also shown by analyzing the obtained results with an efficient sequential algorithm. The results show that the algorithm performs much better in terms of runtime.
{"title":"ParaKavosh: A Parallel Algorithm for Finding Biological Network Motifs","authors":"Z. Kashani, A. Masoudi-Nejad, A. Nowzari-Dalini","doi":"10.1109/IKT51791.2020.9345641","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345641","url":null,"abstract":"Biological networks have recently gathered much attraction in finding their motifs. Motifs can be considered as subgraphs that occur in a particular network at significantly higher frequencies than random networks. The importance of this problem causes attention of improving the existing algorithms. As the runtime of an algorithm is an important aspect, applying parallel techniques is appropriate for better improvement. In this paper a parallel algorithm (ParaKavosh) for finding network motifs is presented. Our algorithm is tested on E. coli, S. cerevisiae, Homo sapiens and Rattus norvegicus networks. The cost optimality of the algorithm is also shown by analyzing the obtained results with an efficient sequential algorithm. The results show that the algorithm performs much better in terms of runtime.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126779373","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345640
Arman Sepehr, H. Beigy, Mohammadzaman Zamani, Shabnam Behzad
Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.
{"title":"Revert Propagation: Who are responsible for a contagion initialization in a Diffusion Network?","authors":"Arman Sepehr, H. Beigy, Mohammadzaman Zamani, Shabnam Behzad","doi":"10.1109/IKT51791.2020.9345640","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345640","url":null,"abstract":"Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628473","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345615
M. Aliabadi, H. Haghighi, M. V. Asl, R. Meybodi
Test oracle problem is considered as a major challenge in software testing. Specification mining techniques are shown to be effective to tackle test oracle problem in software systems. In addition, modern systems such as Cyber-Physical Systems (CPSes) have special constraints that should be satisfied when deriving the test oracles for these systems. However, comparing different specification mining techniques for CPS applications is challenging, because no common ground to assess the effectiveness of such techniques has been established yet. In this survey, our contribution is two folded: First, we analyze the CPS constraints from the test oracle point of view, and present a framework of requirements representing six essential criteria for evaluating to which extent specification miners satisfy CPS constraints. Secondly, we review the literature for the specification mining techniques, and use our framework to compare the effectiveness of various static and dynamic analysis-based specification mining techniques, and to discuss their respective advantages and disadvantages,
{"title":"Challenges of Specification Mining-Based Test Oracle for Cyber-Physical Systems","authors":"M. Aliabadi, H. Haghighi, M. V. Asl, R. Meybodi","doi":"10.1109/IKT51791.2020.9345615","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345615","url":null,"abstract":"Test oracle problem is considered as a major challenge in software testing. Specification mining techniques are shown to be effective to tackle test oracle problem in software systems. In addition, modern systems such as Cyber-Physical Systems (CPSes) have special constraints that should be satisfied when deriving the test oracles for these systems. However, comparing different specification mining techniques for CPS applications is challenging, because no common ground to assess the effectiveness of such techniques has been established yet. In this survey, our contribution is two folded: First, we analyze the CPS constraints from the test oracle point of view, and present a framework of requirements representing six essential criteria for evaluating to which extent specification miners satisfy CPS constraints. Secondly, we review the literature for the specification mining techniques, and use our framework to compare the effectiveness of various static and dynamic analysis-based specification mining techniques, and to discuss their respective advantages and disadvantages,","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194274","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 : 2020-12-22DOI: 10.1109/IKT51791.2020.9345607
Behnam Khazael, H. Malazi
Publish-subscribe architecture is one of the dominating architectural styles in designing the Internet of things (IoT) monitoring applications. Establishment of a brokerless publish-subscribe system leads to exchanging a high number of control messages including advertise, subscribe and update messages. Additionally, the in-efficiency in forwarding publish messages in a resource-constrained IoT environment results in network congestion, communication delay, and energy loss. The recent approaches in brokerless publish-subscribe systems for IoT domain introduced new communication protocols, maintaining routing tables of IoT nodes, and clustering the network. However, the efficiency in exchanging control messages does not receive much attention. In this paper, we propose an enhanced communication protocol to establish a brokerless publish-subscribe IoT system. The protocol is based on beaconing for packet dissemination. We applied a new structure that provides metadata in the packet header that facilitates the receivers regarding their next possible actions. The simulation results demonstrate that the proposed method reduces the network traffic by 28% on average and decreases the energy consumption of nodes up to 33%. The results also reveal that the number of publish messages is reduced by 10% in comparison to the baseline method.
{"title":"Distributed coordination protocol for event data exchange in IoT monitoring applications","authors":"Behnam Khazael, H. Malazi","doi":"10.1109/IKT51791.2020.9345607","DOIUrl":"https://doi.org/10.1109/IKT51791.2020.9345607","url":null,"abstract":"Publish-subscribe architecture is one of the dominating architectural styles in designing the Internet of things (IoT) monitoring applications. Establishment of a brokerless publish-subscribe system leads to exchanging a high number of control messages including advertise, subscribe and update messages. Additionally, the in-efficiency in forwarding publish messages in a resource-constrained IoT environment results in network congestion, communication delay, and energy loss. The recent approaches in brokerless publish-subscribe systems for IoT domain introduced new communication protocols, maintaining routing tables of IoT nodes, and clustering the network. However, the efficiency in exchanging control messages does not receive much attention. In this paper, we propose an enhanced communication protocol to establish a brokerless publish-subscribe IoT system. The protocol is based on beaconing for packet dissemination. We applied a new structure that provides metadata in the packet header that facilitates the receivers regarding their next possible actions. The simulation results demonstrate that the proposed method reduces the network traffic by 28% on average and decreases the energy consumption of nodes up to 33%. The results also reveal that the number of publish messages is reduced by 10% in comparison to the baseline method.","PeriodicalId":382725,"journal":{"name":"2020 11th International Conference on Information and Knowledge Technology (IKT)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826430","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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