Pub Date : 2016-11-01DOI: 10.1109/AICCSA.2016.7945815
N. E. D. Ferreyra, Johanna Schäwel, M. Heisel, Christian Meske
Nowadays the information flowing across the different Social Network Sites (SNSs) like Facebook is highly diverse and rich in its content. It is precisely the diversity of the users' contributions to SNSs that makes these platforms attractive and interesting to engage with. Nevertheless, there is a high amount of private and sensitive information being disclosed permanently by these users in order to take full advantage of the services offered by such sites. Current privacy-protection approaches (like the one provided by Facebook) allow users to restrict the audience of their contributions and hide particular pieces of information; however, they are still far from being widely adopted and put proactively into practice. For this reason, we propose to analyze and address different aspects of online self-disclosure in Social Media from a pedagogical and self-adaptive perspective. In this work we introduce the architecture of an Instructional Awareness System (IAS) based on the MAPE-K blueprint for autonomic systems, and provide a definition of its feedback mechanism using principles of Constraint-Based Modeling (CBM).
{"title":"Addressing self-disclosure in social media: An instructional awareness approach","authors":"N. E. D. Ferreyra, Johanna Schäwel, M. Heisel, Christian Meske","doi":"10.1109/AICCSA.2016.7945815","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945815","url":null,"abstract":"Nowadays the information flowing across the different Social Network Sites (SNSs) like Facebook is highly diverse and rich in its content. It is precisely the diversity of the users' contributions to SNSs that makes these platforms attractive and interesting to engage with. Nevertheless, there is a high amount of private and sensitive information being disclosed permanently by these users in order to take full advantage of the services offered by such sites. Current privacy-protection approaches (like the one provided by Facebook) allow users to restrict the audience of their contributions and hide particular pieces of information; however, they are still far from being widely adopted and put proactively into practice. For this reason, we propose to analyze and address different aspects of online self-disclosure in Social Media from a pedagogical and self-adaptive perspective. In this work we introduce the architecture of an Instructional Awareness System (IAS) based on the MAPE-K blueprint for autonomic systems, and provide a definition of its feedback mechanism using principles of Constraint-Based Modeling (CBM).","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125697659","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945679
Abdellatif Serhani, N. Naja, A. Jamali
Mobile Ad-hoc Networks are highly reconfigurable networks of mobile nodes which communicate by wireless links. The main issues in MANETs include the mobility of the network nodes, energy limitations and bandwidth. Thus, routing protocols should explicitly consider network changes into the algorithm design. In order to support service requirements of multimedia and real-time applications, the routing protocol must provide Quality of Service (QoS) in terms of packets loss and average End-to-End Delay (ETED). This work proposes a Q-Learning based Adaptive Routing model (QLAR), developed via Reinforcement Learning (RL) techniques, which has the ability to detect the level of mobility at different points of time so that each individual node can update routing metric accordingly. The proposed protocol introduces: (i) new model, developed via Q-Learning technique, to detect the level of mobility at each node in the network; (ii) a new metric, called Qmetric, which account for the static and dynamic routing metrics, and which are combined and updated to the changing network topologies. The proposed metric and routing model in this paper are deployed on the Optimized Link State Routing (OLSR) protocol. Extensive simulations validate the effectiveness of the proposed model, through comparisons with the standard OLSR protocols.
移动自组织网络是由移动节点组成的高度可重构网络，通过无线链路进行通信。manet的主要问题包括网络节点的移动性、能量限制和带宽。因此，路由协议在算法设计中应明确考虑网络变化。为了支持多媒体和实时应用的业务需求，路由协议必须提供QoS (Quality of service)，即丢包率和平均端到端时延。这项工作提出了一种基于q学习的自适应路由模型(QLAR)，该模型通过强化学习(RL)技术开发，能够检测不同时间点的移动水平，以便每个单独的节点可以相应地更新路由度量。提出的协议引入:(i)通过Q-Learning技术开发的新模型来检测网络中每个节点的移动水平;(ii)一种新的度量，称为Qmetric，它考虑静态和动态路由度量，并根据不断变化的网络拓扑进行组合和更新。本文提出的度量和路由模型部署在优化链路状态路由(OLSR)协议上。通过与标准OLSR协议的比较，广泛的仿真验证了所提出模型的有效性。
{"title":"QLAR: A Q-learning based adaptive routing for MANETs","authors":"Abdellatif Serhani, N. Naja, A. Jamali","doi":"10.1109/AICCSA.2016.7945679","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945679","url":null,"abstract":"Mobile Ad-hoc Networks are highly reconfigurable networks of mobile nodes which communicate by wireless links. The main issues in MANETs include the mobility of the network nodes, energy limitations and bandwidth. Thus, routing protocols should explicitly consider network changes into the algorithm design. In order to support service requirements of multimedia and real-time applications, the routing protocol must provide Quality of Service (QoS) in terms of packets loss and average End-to-End Delay (ETED). This work proposes a Q-Learning based Adaptive Routing model (QLAR), developed via Reinforcement Learning (RL) techniques, which has the ability to detect the level of mobility at different points of time so that each individual node can update routing metric accordingly. The proposed protocol introduces: (i) new model, developed via Q-Learning technique, to detect the level of mobility at each node in the network; (ii) a new metric, called Qmetric, which account for the static and dynamic routing metrics, and which are combined and updated to the changing network topologies. The proposed metric and routing model in this paper are deployed on the Optimized Link State Routing (OLSR) protocol. Extensive simulations validate the effectiveness of the proposed model, through comparisons with the standard OLSR protocols.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114866942","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945810
Fatimah A. Al-alem, M. Alsmirat, M. Al-Ayyoub
Since security vulnerabilities represent one of the grand challenges of the Internet of Things (IoT), researchers have proposed what is known as the Internet of Biometric Things (IoBT), which mixes traditional biometric technologies with context-aware authentication techniques. One of the most famous biometric technologies is electronic fingerprint recognition, which acquire fingerprints using different technologies, some of which are more suitable for IoBT than others. Moreover, different fingerprint databases have been built to study the impact of different factors on the accuracy of different fingerprint recognition algorithms and systems. In this paper, we survey the available fingerprint acquisition technologies and the available fingerprint databases. We also identify the advantages and disadvantages of each technology and database.
{"title":"On the road to the Internet of Biometric Things: A survey of fingerprint acquisition technologies and fingerprint databases","authors":"Fatimah A. Al-alem, M. Alsmirat, M. Al-Ayyoub","doi":"10.1109/AICCSA.2016.7945810","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945810","url":null,"abstract":"Since security vulnerabilities represent one of the grand challenges of the Internet of Things (IoT), researchers have proposed what is known as the Internet of Biometric Things (IoBT), which mixes traditional biometric technologies with context-aware authentication techniques. One of the most famous biometric technologies is electronic fingerprint recognition, which acquire fingerprints using different technologies, some of which are more suitable for IoBT than others. Moreover, different fingerprint databases have been built to study the impact of different factors on the accuracy of different fingerprint recognition algorithms and systems. In this paper, we survey the available fingerprint acquisition technologies and the available fingerprint databases. We also identify the advantages and disadvantages of each technology and database.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114782104","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945702
Bassim Chabibi, A. Anwar, M. Nassar
Models have always been adopted in trades implemented in Systems Engineering (SE). Those models go from concrete representations, such as reduced plans or models, to abstract ones like equations systems. In this context, SysML became an SE standard because of its capabilities of supporting the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. However, SysML descriptive models are insufficient to perform system behavior verifications. This lack can be handled by simulation process that allows performing experiments on models to eliminate poor design alternatives, and ensures that a preferred alternative meets the stakeholders' objectives. As design process efficiency is considerably reduced by the fact that both system modeling and simulation tools are often used separately, several research works aim combining and integrating both approaches in a common framework. This paper proposes a study of common constructs, semantics and modeling methodologies of simulation tools on the basis of whom we define a modeling language that we name: Simulation Modeling Language. This latter is aimed to bridge the gap between SysML modeling and various simulation tools.
{"title":"Metamodeling approach for creating an abstract representation of simulation tools concepts","authors":"Bassim Chabibi, A. Anwar, M. Nassar","doi":"10.1109/AICCSA.2016.7945702","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945702","url":null,"abstract":"Models have always been adopted in trades implemented in Systems Engineering (SE). Those models go from concrete representations, such as reduced plans or models, to abstract ones like equations systems. In this context, SysML became an SE standard because of its capabilities of supporting the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. However, SysML descriptive models are insufficient to perform system behavior verifications. This lack can be handled by simulation process that allows performing experiments on models to eliminate poor design alternatives, and ensures that a preferred alternative meets the stakeholders' objectives. As design process efficiency is considerably reduced by the fact that both system modeling and simulation tools are often used separately, several research works aim combining and integrating both approaches in a common framework. This paper proposes a study of common constructs, semantics and modeling methodologies of simulation tools on the basis of whom we define a modeling language that we name: Simulation Modeling Language. This latter is aimed to bridge the gap between SysML modeling and various simulation tools.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128457525","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945755
M. Gofman, S. Mitra, Nicholas Smith
Although biometrics have forayed into the mobile world, most current approaches rely on a single biometric modality. This limits their recognition accuracy in uncontrolled conditions. For example, performance of face and voice recognition systems may suffer in poorly lit and noisy settings, respectively. Integration of identifying information from multiple biometric modalities can help solve this problem; high-quality identifying information in one modality can compensate for the absence of such information in a modality affected by uncontrolled conditions. In this paper, we present a novel multimodal biometric scheme that uses Hidden Markov Models to consolidate data from face and voice biometrics at the feature level. An implementation on the Samsung Galaxy S5 (SG5) phone using a dataset of face and voice samples captured using SG5 in real-world operating conditions, yielded 4.18% and 9.71% higher recognition accuracy than face and voice single-modality systems, respectively.
{"title":"Hidden Markov Models for feature-level fusion of biometrics on mobile devices","authors":"M. Gofman, S. Mitra, Nicholas Smith","doi":"10.1109/AICCSA.2016.7945755","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945755","url":null,"abstract":"Although biometrics have forayed into the mobile world, most current approaches rely on a single biometric modality. This limits their recognition accuracy in uncontrolled conditions. For example, performance of face and voice recognition systems may suffer in poorly lit and noisy settings, respectively. Integration of identifying information from multiple biometric modalities can help solve this problem; high-quality identifying information in one modality can compensate for the absence of such information in a modality affected by uncontrolled conditions. In this paper, we present a novel multimodal biometric scheme that uses Hidden Markov Models to consolidate data from face and voice biometrics at the feature level. An implementation on the Samsung Galaxy S5 (SG5) phone using a dataset of face and voice samples captured using SG5 in real-world operating conditions, yielded 4.18% and 9.71% higher recognition accuracy than face and voice single-modality systems, respectively.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128487898","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945670
Fatih Saglam, H. Sever, Burkay Genç
Internet is a very rich resource of documents that need to be analysed to extract their sentimental values. Sentiment Analysis which is a subfield of Natural Language Processing discipline focuses on this issue. The existence of sentiment lexicons in their own language is a very important resource for scientists studying in sentiment analysis field. Since many studies of sentiment analysis have been conducted on text written in English language, developed methods and resources for English may not produce the desired results in other languages. In Turkish, a rich sentiment lexicon does not exists, such as SentiWordNet for English. In this study, we aimed to develop Turkish sentiment lexicon, and we enhanced an existing lexicon which has 27K Turkish words to 37K words. For quantifying the performance of this enhanced lexicon, we tested both lexicons on domain independent news texts. The accuracy of determining the polarity of news written in Turkish has been increased from 60.6% to 72.2%.
{"title":"Developing Turkish sentiment lexicon for sentiment analysis using online news media","authors":"Fatih Saglam, H. Sever, Burkay Genç","doi":"10.1109/AICCSA.2016.7945670","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945670","url":null,"abstract":"Internet is a very rich resource of documents that need to be analysed to extract their sentimental values. Sentiment Analysis which is a subfield of Natural Language Processing discipline focuses on this issue. The existence of sentiment lexicons in their own language is a very important resource for scientists studying in sentiment analysis field. Since many studies of sentiment analysis have been conducted on text written in English language, developed methods and resources for English may not produce the desired results in other languages. In Turkish, a rich sentiment lexicon does not exists, such as SentiWordNet for English. In this study, we aimed to develop Turkish sentiment lexicon, and we enhanced an existing lexicon which has 27K Turkish words to 37K words. For quantifying the performance of this enhanced lexicon, we tested both lexicons on domain independent news texts. The accuracy of determining the polarity of news written in Turkish has been increased from 60.6% to 72.2%.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100552","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945693
Loukmen Regainia, S. Salva, Cedric Ecuhcurs
Security patterns are generic solutions that can be applied since early stages of software life to overcome recurrent security weaknesses. Their generic nature and growing number make their choice difficult, even for experts in system design. To help them on the pattern choice, this paper proposes a semi-automatic methodology of classification and the classification itself, which exposes relationships among software weaknesses, security principles and security patterns. It expresses which patterns remove a given weakness with respect to the security principles that have to be addressed to fix the weakness. The methodology is based on seven steps, which anatomize patterns and weaknesses into set of more precise sub-properties that are associated through a hierarchical organization of security principles. These steps provide the detailed justifications of the resulting classification and allow its upgrade. Without loss of generality, this classification has been established for Web applications and covers 185 software weaknesses, 26 security patterns and 66 security principles. Research supported by the industrial chair on Digital Confidence (http://confiance-numerique.clermont-universite.fr/index-en.html).
{"title":"A classification methodology for security patterns to help fix software weaknesses","authors":"Loukmen Regainia, S. Salva, Cedric Ecuhcurs","doi":"10.1109/AICCSA.2016.7945693","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945693","url":null,"abstract":"Security patterns are generic solutions that can be applied since early stages of software life to overcome recurrent security weaknesses. Their generic nature and growing number make their choice difficult, even for experts in system design. To help them on the pattern choice, this paper proposes a semi-automatic methodology of classification and the classification itself, which exposes relationships among software weaknesses, security principles and security patterns. It expresses which patterns remove a given weakness with respect to the security principles that have to be addressed to fix the weakness. The methodology is based on seven steps, which anatomize patterns and weaknesses into set of more precise sub-properties that are associated through a hierarchical organization of security principles. These steps provide the detailed justifications of the resulting classification and allow its upgrade. Without loss of generality, this classification has been established for Web applications and covers 185 software weaknesses, 26 security patterns and 66 security principles. Research supported by the industrial chair on Digital Confidence (http://confiance-numerique.clermont-universite.fr/index-en.html).","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130513259","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945639
A. Erradi, H. Kholidy
The prediction of cloud consumer resource needs is a vital step for several cloud deployment applications such as capacity planning, workload management, and dynamic allocation of cloud resources. In this paper, we develop a new prediction model for predicting cloud consumer resource needs. The new model uses a new hybrid prediction approach that combines the Multiple Support Vector Regression (MSVR) model and the Autoregressive Integrated Moving Average (ARIMA) model to predict with higher accuracy the resource needs of a cloud consumer in terms of CPU, memory, and disk storage utilization. The new model is also able to predict the response time and throughput which in turn enable the cloud consumers to make a better scaling decision. The new model elucidated a better prediction accuracy than the current prediction models. In terms of CPU utilization prediction, it outperforms the accuracy of the existing cloud consumer prediction models that uses Linear Regression, Neural Network, and Support Vector Machines approaches by 72.66%, 44.24%, and 56.78% respectively according to MAPE and 56.95%, 80.42%, and 63.86% according to RMSE. The analysis, architecture, and experiment results of the new model are discussed in details in this paper.
{"title":"An efficient hybrid prediction approach for predicting cloud consumer resource needs","authors":"A. Erradi, H. Kholidy","doi":"10.1109/AICCSA.2016.7945639","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945639","url":null,"abstract":"The prediction of cloud consumer resource needs is a vital step for several cloud deployment applications such as capacity planning, workload management, and dynamic allocation of cloud resources. In this paper, we develop a new prediction model for predicting cloud consumer resource needs. The new model uses a new hybrid prediction approach that combines the Multiple Support Vector Regression (MSVR) model and the Autoregressive Integrated Moving Average (ARIMA) model to predict with higher accuracy the resource needs of a cloud consumer in terms of CPU, memory, and disk storage utilization. The new model is also able to predict the response time and throughput which in turn enable the cloud consumers to make a better scaling decision. The new model elucidated a better prediction accuracy than the current prediction models. In terms of CPU utilization prediction, it outperforms the accuracy of the existing cloud consumer prediction models that uses Linear Regression, Neural Network, and Support Vector Machines approaches by 72.66%, 44.24%, and 56.78% respectively according to MAPE and 56.95%, 80.42%, and 63.86% according to RMSE. The analysis, architecture, and experiment results of the new model are discussed in details in this paper.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121766949","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945812
Majed AlOtaibi, L. Tawalbeh, Y. Jararweh
The advances in IT sector, cloud computing, the wide usage of sensors and mobile devices, and the Internet-of-Things (IoT) made our world looks like a small town. These rapid developments keep us connected all the day and seven days a week. Also, the IoT enables the connection of the devices around us (including different sensors) to the internet via different wireless and wired communication technologies. These networked sensors can be used to collect different types of data from different applications (healthcare, agriculture, civil and social life) and send it for processing and extraction of appropriate decisions. The mobile cloud computing technology is an efficient solution to process different types of collected data and respond with the required answer in real time situations where the quick response is very important. In this paper, we build a multipurpose integrated sensors system. This integrated system consists of networked sensors for different purposes and applications. For example, the sensors can be health sensors to measure the pulse and blood pressure of patients, or it can be sensors to measure the temperature to indicate a fire accident. The networked sensors will transfer the sensed data through wireless technologies to a Cloud for processing and notifying the listed users to take the proper action.
{"title":"Integrated sensors system based on IoT and mobile cloud computing","authors":"Majed AlOtaibi, L. Tawalbeh, Y. Jararweh","doi":"10.1109/AICCSA.2016.7945812","DOIUrl":"https://doi.org/10.1109/AICCSA.2016.7945812","url":null,"abstract":"The advances in IT sector, cloud computing, the wide usage of sensors and mobile devices, and the Internet-of-Things (IoT) made our world looks like a small town. These rapid developments keep us connected all the day and seven days a week. Also, the IoT enables the connection of the devices around us (including different sensors) to the internet via different wireless and wired communication technologies. These networked sensors can be used to collect different types of data from different applications (healthcare, agriculture, civil and social life) and send it for processing and extraction of appropriate decisions. The mobile cloud computing technology is an efficient solution to process different types of collected data and respond with the required answer in real time situations where the quick response is very important. In this paper, we build a multipurpose integrated sensors system. This integrated system consists of networked sensors for different purposes and applications. For example, the sensors can be health sensors to measure the pulse and blood pressure of patients, or it can be sensors to measure the temperature to indicate a fire accident. The networked sensors will transfer the sensed data through wireless technologies to a Cloud for processing and notifying the listed users to take the proper action.","PeriodicalId":448329,"journal":{"name":"2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121773117","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 : 2016-11-01DOI: 10.1109/AICCSA.2016.7945688
Fatma Somaa, I. Korbi, L. Saïdane
The interest for wireless sensor networks (WSNs) is continuously growing especially with the emergence of applications such as smart grids, smart cities, e-health, etc. where billions of objets (sensors) will be permanently connected to the Internet. In this context, the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) is placed as the routing standard for the next generation multi-hop WSNs. The RPL scheme fits the sensor nodes characteristics since the protocol was originally designed for energy efficiency. Nevertheless, in the case of sensor nodes' movement, RPL poorly adapts to such scenarios which rapidly alters the network performance. In this paper, we investigate the problem of mobility support over RPL. Therefore, we propose a new Sensor Nodes' Speed Classifier (SNSC) to predict the sensor nodes' movement in the network. Then, we exploit the speed values-predicted by the SNSC model-to enhance the behavior of the native RPL in the mobility context. Our modified RPL scheme is called Mobility Prediction based RPL (MP-RPL). To evaluate the performance of MP-RPL, we first validate the SNSC model using the Cooja/Contiki simulation environment. Then, we compare MP-RPL to the native RPL in terms of Packet Loss Rate (PLR) and Packet Delivery Delay (PDD).
对无线传感器网络(wsn)的兴趣不断增长，特别是随着智能电网、智能城市、电子医疗等应用的出现，数十亿物体(传感器)将永久连接到互联网。在这种情况下，IPv6 RPL (Routing Protocol for Low power and Lossy Networks)被作为下一代多跳wsn的路由标准。RPL方案符合传感器节点的特性，因为该协议最初是为了提高能效而设计的。然而，在传感器节点移动的情况下，RPL对这种快速改变网络性能的场景的适应能力较差。本文研究了RPL上的机动性支持问题。因此，我们提出了一种新的传感器节点速度分类器(SNSC)来预测网络中传感器节点的运动。然后，我们利用SNSC模型预测的速度值来增强原生RPL在移动环境中的行为。我们改进的RPL方案被称为基于迁移预测的RPL (MP-RPL)。为了评估MP-RPL的性能，我们首先使用Cooja/Contiki仿真环境验证SNSC模型。然后，我们将MP-RPL与本地RPL在丢包率(PLR)和包传递延迟(PDD)方面进行比较。
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