Jean Bertin Nkamla Penka, Saïd Mahmoudi, Olivier Debauche
Agriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots, and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use case. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances
{"title":"An Optimized Kappa Architecture for IoT Data Management in Smart Farming","authors":"Jean Bertin Nkamla Penka, Saïd Mahmoudi, Olivier Debauche","doi":"10.5383/juspn.17.02.002","DOIUrl":"https://doi.org/10.5383/juspn.17.02.002","url":null,"abstract":"Agriculture 4.0 is a domain of IoT in full growth which produces large amounts of data from machines, robots, and sensors networks. This data must be processed very quickly, especially for the systems that need to make real-time decisions. The Kappa architecture provides a way to process Agriculture 4.0 data at high speed in the cloud, and thus meets processing requirements. This paper presents an optimized version of the Kappa architecture allowing fast and efficient data management in Agriculture. The goal of this optimized version of the classical Kappa architecture is to improve memory management and processing speed. the Kappa architecture parameters are fine tuned in order to process data from a concrete use case. The results of this work have shown the impact of parameters tweaking on the speed of treatment. We have also proven that the combination of Apache Samza with Apache Druid offers the better performances","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121314913","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 recent floods have shown that the classic monitoring systems for watercourses are no longer adapted because other phenomena such as the insufficient capacity and/or obstruction of drainage networks, the modification of cultivation practices and rotations, the increase in the size of plots linked to the reparcelling, the urbanization of floodable areas, etc. The combination of all these causes, plus the modification of the water regime, implies an increase in the risk of flooding and an adapted monitoring that is no longer limited to watercourses in order to give early warning of the risk of flooding by runoff. The Internet of Things (IoT) and the availability of microcontrollers and sensors with low data rates and long ranges, as well as low-power wide area networks (LPWANs), allow for much more advanced monitoring systems.
{"title":"Towards Low-Cost IoT and LPWAN-Based Flood Forecast and Monitoring System","authors":"Nassima Tadrist, Olivier Debauche, Saïd Mahmoudi, Adriano Guttadauria","doi":"10.5383/juspn.17.01.006","DOIUrl":"https://doi.org/10.5383/juspn.17.01.006","url":null,"abstract":"The recent floods have shown that the classic monitoring systems for watercourses are no longer adapted because other phenomena such as the insufficient capacity and/or obstruction of drainage networks, the modification of cultivation practices and rotations, the increase in the size of plots linked to the reparcelling, the urbanization of floodable areas, etc. The combination of all these causes, plus the modification of the water regime, implies an increase in the risk of flooding and an adapted monitoring that is no longer limited to watercourses in order to give early warning of the risk of flooding by runoff. The Internet of Things (IoT) and the availability of microcontrollers and sensors with low data rates and long ranges, as well as low-power wide area networks (LPWANs), allow for much more advanced monitoring systems.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114767892","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}
Hackers are increasingly launching phishing attacks via SMS and social media. Games and dating apps introduce yet another attack vector. However, current deep learning-based phishing detection applications do not apply to mobile devices due to the computational burden. We propose a lightweight phishing detection algorithm that distinguishes phishing from legitimate websites solely from URLs to be used in mobile devices. As a baseline performance, we apply Artificial Neural Networks (ANNs) to URL-based and HTML-based website features. A model search results in 15 ANN models with accuracies >96%, comparable to state-of-the-art approaches. Next, we test the performance of deep ANNs on URLbased features only; however, all models perform poorly with the highest accuracy of 86.2%, indicating that URL-based features alone are not adequate to detect phishing websites even with deep ANNs. Since language transformers learn to represent context-dependent text sequences, we hypothesize that they will be able to learn directly from the text in URLs to distinguish between legitimate and malicious websites. We apply three state-of-the-art deep transformers (BERT, ELECTRA, and RoBERTa) for phishing detection. Testing custom and standard vocabularies, we find that pre-trained transformers available for immediate use (with fine-tuning) outperform the model trained with the custom URL-based vocabulary. In addition, we test a thinner BERT transformer which is suitable for lightweight devices like mobiles, called MobileBERT. Our results emphasize that evaluation metrics of this model are competitive to other models in this study, yet the testing time is significantly less, making this model a choice for embedding phishing detection algorithms in mobile phones. Using pre-trained transformers to predict phishing websites from only URLs has five advantages: 1) requires little training time (230 to 320 s), 2) is more easily updatable than feature-based approaches because no pre-processing of URLs is required, 3) is safer to use because phishing websites can be predicted without physically visiting the malicious sites, 4) is easily deployable for real-time detection and is applicable to run on mobile devices, and 5) using a mobile specific transformer yields comparable performance and predicts 3 times faster than the other transformer models in this study.
{"title":"Towards Performance of NLP Transformers on URL-Based Phishing Detection for Mobile Devices","authors":"H. Shirazi, K. Haynes, I. Ray","doi":"10.5383/juspn.17.01.005","DOIUrl":"https://doi.org/10.5383/juspn.17.01.005","url":null,"abstract":"Hackers are increasingly launching phishing attacks via SMS and social media. Games and dating apps introduce yet another attack vector. However, current deep learning-based phishing detection applications do not apply to mobile devices due to the computational burden. We propose a lightweight phishing detection algorithm that distinguishes phishing from legitimate websites solely from URLs to be used in mobile devices. As a baseline performance, we apply Artificial Neural Networks (ANNs) to URL-based and HTML-based website features. A model search results in 15 ANN models with accuracies >96%, comparable to state-of-the-art approaches. Next, we test the performance of deep ANNs on URLbased features only; however, all models perform poorly with the highest accuracy of 86.2%, indicating that URL-based features alone are not adequate to detect phishing websites even with deep ANNs. Since language transformers learn to represent context-dependent text sequences, we hypothesize that they will be able to learn directly from the text in URLs to distinguish between legitimate and malicious websites. We apply three state-of-the-art deep transformers (BERT, ELECTRA, and RoBERTa) for phishing detection. Testing custom and standard vocabularies, we find that pre-trained transformers available for immediate use (with fine-tuning) outperform the model trained with the custom URL-based vocabulary. In addition, we test a thinner BERT transformer which is suitable for lightweight devices like mobiles, called MobileBERT. Our results emphasize that evaluation metrics of this model are competitive to other models in this study, yet the testing time is significantly less, making this model a choice for embedding phishing detection algorithms in mobile phones. Using pre-trained transformers to predict phishing websites from only URLs has five advantages: 1) requires little training time (230 to 320 s), 2) is more easily updatable than feature-based approaches because no pre-processing of URLs is required, 3) is safer to use because phishing websites can be predicted without physically visiting the malicious sites, 4) is easily deployable for real-time detection and is applicable to run on mobile devices, and 5) using a mobile specific transformer yields comparable performance and predicts 3 times faster than the other transformer models in this study.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"95 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120825185","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}
Simon André Scherr, Mher Ter-Tovmasyan, Frauke Neugebauer, Steffen Hupp, Frank Elberzhager
Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool.
{"title":"The way it made me feel - Creating and evaluating an in-app feedback tool for mobile apps","authors":"Simon André Scherr, Mher Ter-Tovmasyan, Frauke Neugebauer, Steffen Hupp, Frank Elberzhager","doi":"10.5383/juspn.17.01.004","DOIUrl":"https://doi.org/10.5383/juspn.17.01.004","url":null,"abstract":"Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122356381","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}
Road safety is a subject of concern the world over and many studies have looked into how to improve safe travel. Motorcycles, including motorcycle taxis, are particularly vulnerable. This paper reports the outcome of a study conducted on motorcycle taxi safety problems using a system dynamics method. Qualitative data was obtained from the field and analysed using qualitative analysis methods. The outcome of the qualitative analysis led to the formulation of a dynamic hypothesis for a system dynamics approach, whose first step was to develop and analyse a causal loop diagram [CLD]. This CLD demonstrates how deterrence, a behavioural pattern that can be produced by the appropriate application of sanctions, is both strengthened and weakened within the system. The paper uses this analysis to provide insights about the behavioural patterns of motorcycle taxi operation in Nigeria. These insights include the possibility of maintaining the system at equilibrium for a desired level of deterrence as well as the possibility of breaking undesirable cycles of bribery and jumping arrest loops. These insights can also be useful in other countries of the world where motorcycle taxis operate.
{"title":"Understanding the Operation of Motorcycle Taxi Drivers in Nigeria Using Causal Loop Diagram","authors":"O. Aluko, A. Gühnemann, P. Timms","doi":"10.5383/juspn.16.01.001","DOIUrl":"https://doi.org/10.5383/juspn.16.01.001","url":null,"abstract":"Road safety is a subject of concern the world over and many studies have looked into how to improve safe travel. Motorcycles, including motorcycle taxis, are particularly vulnerable. This paper reports the outcome of a study conducted on motorcycle taxi safety problems using a system dynamics method. Qualitative data was obtained from the field and analysed using qualitative analysis methods. The outcome of the qualitative analysis led to the formulation of a dynamic hypothesis for a system dynamics approach, whose first step was to develop and analyse a causal loop diagram [CLD]. This CLD demonstrates how deterrence, a behavioural pattern that can be produced by the appropriate application of sanctions, is both strengthened and weakened within the system. The paper uses this analysis to provide insights about the behavioural patterns of motorcycle taxi operation in Nigeria. These insights include the possibility of maintaining the system at equilibrium for a desired level of deterrence as well as the possibility of breaking undesirable cycles of bribery and jumping arrest loops. These insights can also be useful in other countries of the world where motorcycle taxis operate.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574826","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}
R. Strand, Sindre Stokkenes, L. Kristensen, T. Log
Dry and cold winter seasons result in very dry indoor conditions and have historically contributed to severe fires in the high and dense representation of wooden homes in Norway. The fire in Lærdalsøyri, January 2014, is a devastating reminder of town fires still posing a threat to a modern society. In order to reduce conflagration probability and consequences, it is necessary to have an accurate estimate of the current and near future fire risk to take proper planning precautions. Cloud computing services providing access to weather data in the form of measurements and forecasts, combined with recent developments in fire risk modelling, may enable smart and fine-grained fire risk prediction services. The main contribution of this study is implementation and experimental validation of a wooden home predictive fire risk indication model, as well as outlining a wooden home fire risk concept. The wooden home fire risk model focuses on the first house catching fire (indoors) in a potential conflagration event. Such a fire would be critical to intervene prior to the fire developing exterior flames and embers post flashover, and thus high likelihood of fire spread. The implemented model exploits cloud-provided weather measurements and forecasts, to predict the current- and near future fire risk at given geographical locations. It computes the indoor wooden fuel moisture content of houses that may catch fire, using measured and forecasted outdoor temperature and relative humidity, and estimates the time to flashover. The latter is found through an empirical relation with the fuel moisture content, and can be used as an indication of the fire risk, beyond the modelled single house. The model implementation was integrated into a micro-service based software system and experimentally validated at selected geographical locations, relying on weather data provided by the RESTful API’s of the Norwegian Meteorological Institute. The validation took place by applying the model to predefined cases, with an outcome known from observations or theory. The first part is a general evaluation of the outputs, considering three historical fires. Then, seasonal changes and natural climate variations were considered. Our evaluation demonstrates the ability to provide trustworthy and accurate fire risk indications using a combination of recorded weather data and forecasts. Further, our cloud- and micro-service based software system implementation is efficient with respect to data storage and computation time. Finally, the novel fire risk concept is demonstrated for a selected city, based on model output. It successfully depicts the implications following reduced indoor humidity by utilizing location specific fire risk contours.
{"title":"Fire Risk Prediction Using Cloud-based Weather Data Services","authors":"R. Strand, Sindre Stokkenes, L. Kristensen, T. Log","doi":"10.5383/juspn.16.01.005","DOIUrl":"https://doi.org/10.5383/juspn.16.01.005","url":null,"abstract":"Dry and cold winter seasons result in very dry indoor conditions and have historically contributed to severe fires in the high and dense representation of wooden homes in Norway. The fire in Lærdalsøyri, January 2014, is a devastating reminder of town fires still posing a threat to a modern society. In order to reduce conflagration probability and consequences, it is necessary to have an accurate estimate of the current and near future fire risk to take proper planning precautions. Cloud computing services providing access to weather data in the form of measurements and forecasts, combined with recent developments in fire risk modelling, may enable smart and fine-grained fire risk prediction services. The main contribution of this study is implementation and experimental validation of a wooden home predictive fire risk indication model, as well as outlining a wooden home fire risk concept. The wooden home fire risk model focuses on the first house catching fire (indoors) in a potential conflagration event. Such a fire would be critical to intervene prior to the fire developing exterior flames and embers post flashover, and thus high likelihood of fire spread. The implemented model exploits cloud-provided weather measurements and forecasts, to predict the current- and near future fire risk at given geographical locations. It computes the indoor wooden fuel moisture content of houses that may catch fire, using measured and forecasted outdoor temperature and relative humidity, and estimates the time to flashover. The latter is found through an empirical relation with the fuel moisture content, and can be used as an indication of the fire risk, beyond the modelled single house. The model implementation was integrated into a micro-service based software system and experimentally validated at selected geographical locations, relying on weather data provided by the RESTful API’s of the Norwegian Meteorological Institute. The validation took place by applying the model to predefined cases, with an outcome known from observations or theory. The first part is a general evaluation of the outputs, considering three historical fires. Then, seasonal changes and natural climate variations were considered. Our evaluation demonstrates the ability to provide trustworthy and accurate fire risk indications using a combination of recorded weather data and forecasts. Further, our cloud- and micro-service based software system implementation is efficient with respect to data storage and computation time. Finally, the novel fire risk concept is demonstrated for a selected city, based on model output. It successfully depicts the implications following reduced indoor humidity by utilizing location specific fire risk contours.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116068776","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}
Cloud providers offer storage as a service to the data owners to store emails and files on the cloud server. However, sensitive data should be encrypted before storing on the cloud server to avoid privacy concerns. With the encryption of documents, it is not feasible for data owners to retrieve documents based on keyword search as they can do with plain text documents. Hence, it is desirable to perform a multi-keyword search on encrypted data. To achieve this goal, we present a fast privacy-preserving model for keyword search on encrypted outsourced data in this paper. Specifically, the model first performs a keyword search on encrypted data and checks its support for dynamic operations. Based on keyword search results, it then sorts all the relevant data documents using the number of keywords matched for a given query. To evaluate its performance of our model, we applied the standard metrics like precision and recall. The results show the effectiveness of our privacy-preserving keyword search on encrypted outsourced data.
{"title":"An Effective and Efficient Framework for Fast Privacy-Preserving Keyword Search on Encrypted Outsourced Cloud Data","authors":"A. Cuzzocrea, C. Leung, S. Sourav, Bryan H. Wodi","doi":"10.5383/juspn.15.02.006","DOIUrl":"https://doi.org/10.5383/juspn.15.02.006","url":null,"abstract":"Cloud providers offer storage as a service to the data owners to store emails and files on the cloud server. However, sensitive data should be encrypted before storing on the cloud server to avoid privacy concerns. With the encryption of documents, it is not feasible for data owners to retrieve documents based on keyword search as they can do with plain text documents. Hence, it is desirable to perform a multi-keyword search on encrypted data. To achieve this goal, we present a fast privacy-preserving model for keyword search on encrypted outsourced data in this paper. Specifically, the model first performs a keyword search on encrypted data and checks its support for dynamic operations. Based on keyword search results, it then sorts all the relevant data documents using the number of keywords matched for a given query. To evaluate its performance of our model, we applied the standard metrics like precision and recall. The results show the effectiveness of our privacy-preserving keyword search on encrypted outsourced data.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132219938","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}
Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. For Arabic Text Compression, a sequential encoding technique was suggested that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques. In this research, a multistage compression process was implemented on Turkish and Arabic languages, by using the new encoding technique, in addition to the 7-Zip application, which has shown a significant file size reduction.
{"title":"Multistage Arabic and Turkish Text Compression via Characters Encoding and 7-Zip","authors":"Tariq Abu Hilal, H. A. Hilal, Ala Abu Hilal","doi":"10.5383/JUSPN.15.01.002","DOIUrl":"https://doi.org/10.5383/JUSPN.15.01.002","url":null,"abstract":"Turkish lossless text compression was proposed by converting the character’s from UTF-8 to ANSI system for space-preserving. Likewise, we present a decoding method that transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, some of the Turkish alphabets are being stored in 2 bytes size. All that space comes at a price. The developed sequential encoding technique will reduce the size of the text file up to 9%. Moreover, the Turkish encoded text will retain its original form after decoding. According to our proposal, it is considered as a lossless text compression, where it’s a common concern today. Thus, many parties have become interested in Unicode compression. Basically, our algorithm is mapping Unicode Turkish characters into ANSI, by using the available 8-bit legacy. For Arabic Text Compression, a sequential encoding technique was suggested that efficiently converts Arabic characters string from UTF-8 to ANSI characters coding. The encoding algorithm presented in this paper significantly reduces the file size. The decoding method transforms the encoded ANSI string back to its original format. Unlike the one-byte ANSI characters, Arabic alphabets are currently being stored in 2 bytes size which leads to inefficient space utilization. The newly developed sequential encoding technique reduces the space required for storage up to fifty percent. In addition, the proposed technique will retain the Arabic encoded text to its original form after decoding, which is leading to a lossless text compression. Thus, addressing the common concern of the currently available Arabic characters compression techniques. In this research, a multistage compression process was implemented on Turkish and Arabic languages, by using the new encoding technique, in addition to the 7-Zip application, which has shown a significant file size reduction.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"67 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128540593","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}
Reinforcement learning and recurrent networks are two emerging machine-learning paradigms. The first learns the best actions an agent needs to perform to maximize its rewards in a particular environment and the second has the specificity to use an internal state to remember previous analysis results and consider them for the current one. Research into RL and recurrent network has been proven to have made a real contribution to the protection of ubiquitous systems and pervasive networks against intrusions and malwares. In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research in network security was complete.
{"title":"AI'S Contribution to Ubiquitous Systems and Pervasive Networks Security - Reinforcement Learning vs Recurrent Networks","authors":"C. Feltus","doi":"10.5383/JUSPN.15.02.001","DOIUrl":"https://doi.org/10.5383/JUSPN.15.02.001","url":null,"abstract":"Reinforcement learning and recurrent networks are two emerging machine-learning paradigms. The first learns the best actions an agent needs to perform to maximize its rewards in a particular environment and the second has the specificity to use an internal state to remember previous analysis results and consider them for the current one. Research into RL and recurrent network has been proven to have made a real contribution to the protection of ubiquitous systems and pervasive networks against intrusions and malwares. In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research in network security was complete.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134027879","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}
In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.
{"title":"Unbounded Spatial Data Stream Query Processing using Spatial Semijoins","authors":"W. Osborn","doi":"10.5383/JUSPN.15.02.005","DOIUrl":"https://doi.org/10.5383/JUSPN.15.02.005","url":null,"abstract":"In this paper, the problem of query processing in spatial data streams is explored, with a focus on the spatial join operation. Although the spatial join has been utilized in many proposed centralized and distributed query processing strategies, for its application to spatial data streams the spatial join operation has received very little attention. One identified limitation with existing strategies is that a bounded region of space (i.e., spatial extent) from which the spatial objects are generated needs to be known in advance. However, this information may not be available. Therefore, two strategies for spatial data stream join processing are proposed where the spatial extent of the spatial object stream is not required to be known in advance. Both strategies estimate the common region that is shared by two or more spatial data streams in order to process the spatial join. An evaluation of both strategies includes a comparison with a recently proposed approach in which the spatial extent of the data set is known. Experimental results show that one of the strategies performs very well at estimating the common region of space using only incoming objects on the spatial data streams. Other limitations of this work are also identified.","PeriodicalId":376249,"journal":{"name":"J. Ubiquitous Syst. Pervasive Networks","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115375246","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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