In 2020 more than50 billions devices will be connected over the Internet. Every device will be connected to
anything, anyone, anytime and anywhere in the world of Internet of Thing or IoT. This network will
generate tremendous unstructured or semi structured data that should be shared between different
devices/machines for advanced and automated service delivery in the benefits of the user’s daily life. Thus,
mechanisms for data interoperability and automatic service discovery and delivery should be offered.
Although many approaches have been suggested in the state of art, none of these researches provide a fully
interoperable, light, flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and service
management through service oriented paradigm. It proposes sensors/actuators and scenarios independent
flexible context aware and distributed architecture for IoT systems, in particular smart home systems.
Study on Fog Computing and Data Concurrency in IoT. Includes an analysis of different data concurrency techniques, their principle and some recent developments in the area. Also covers the topic of Fog Computing and its development and application in IoT.
Internet of Things (IoT) integrates billions of the heterogeneous IoT things with the Internet in which the embedded systems such as sensors and actuators linked together to improve quality of life, and becomes the future of technologies in any field of human daily life. These IoT devices cooperate with each other and generate useful information to provide better services and applications to the governments and the society. Also, there is a need to store these data on Cloud for monitoring. This paper, surveys IoT applications, new challenges and issues arise in different fields and provides IoT architecture, focuses on explanation of IoT protocols and their operations and functionalities, presents different microcontroller types used by researchers. With the huge amount of data generated from IoT devices, the integrating Cloud and IoT may helpful, Therefore, a survey on open issues faced when these two concepts integrating together is discussed. The objective of this paper is to provide a survey for everything related to IoT and direct it to all beginners in this filed or academic researchers.
Hardware/Software Interoperability and Single Point Vulnerability Problems of...BRNSS Publication Hub
As reiterated by many authors, internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect and exchange data, creating opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improvements, economic benefits, and reduced human exertions. This is made possible by the communications models with the enabling technologies which make communications possible among IoT connected devices, although, with drawbacks. These drawbacks are the major reasons for adoption problems of IoT services by the society. This paper carried out an investigative study on previous works on the societal applications and adoption problems of IoT, IoT communications models, and pros and cons of IoT. Through the study, it was revealed that for IoT devices and services to be widely adopted with no or minimal problems, future IoT technology will not only address the known drawbacks but also will require hardware and software components that are highly interoperable, dependable, reconfigurable, and, in many applications, certifiable.
WIRELESS SENSORS INTEGRATION INTO INTERNET OF THINGS AND THE SECURITY PRIMITIVESIJCNCJournal
The common vision of smart systems today, is by and large associated with one single concept, the internet of things (IoT), where the whole physical infrastructure is linked with intelligent monitoring and communication technologies through the use of wireless sensors. In such an intelligent vibrant system, sensors are connected to send useful information and control instructions via distributed sensor networks. Wireless sensors have an easy deployment and better flexibility of devices contrary to wired setup. With the rapid technological development of sensors, wireless sensor networks (WSNs) will become the key technology for IoT and an invaluable resource for realizing the vision of Internet of things (IoT) paradigm.
It is also important to consider whether the sensors of a WSN should be completely integrated into IoT or not. New security challenges arise when heterogeneous sensors are integrated into the IoT. Security needs to be considered at a global perspective, not just at a local scale. This paper gives an overview of sensor integration into IoT, some major security challenges and also a number of security primitives that can be taken to protect their data over the internet.
Study on Fog Computing and Data Concurrency in IoT. Includes an analysis of different data concurrency techniques, their principle and some recent developments in the area. Also covers the topic of Fog Computing and its development and application in IoT.
Internet of Things (IoT) integrates billions of the heterogeneous IoT things with the Internet in which the embedded systems such as sensors and actuators linked together to improve quality of life, and becomes the future of technologies in any field of human daily life. These IoT devices cooperate with each other and generate useful information to provide better services and applications to the governments and the society. Also, there is a need to store these data on Cloud for monitoring. This paper, surveys IoT applications, new challenges and issues arise in different fields and provides IoT architecture, focuses on explanation of IoT protocols and their operations and functionalities, presents different microcontroller types used by researchers. With the huge amount of data generated from IoT devices, the integrating Cloud and IoT may helpful, Therefore, a survey on open issues faced when these two concepts integrating together is discussed. The objective of this paper is to provide a survey for everything related to IoT and direct it to all beginners in this filed or academic researchers.
Hardware/Software Interoperability and Single Point Vulnerability Problems of...BRNSS Publication Hub
As reiterated by many authors, internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables these things to connect and exchange data, creating opportunities for more direct integration of the physical world into computer-based systems, resulting in efficiency improvements, economic benefits, and reduced human exertions. This is made possible by the communications models with the enabling technologies which make communications possible among IoT connected devices, although, with drawbacks. These drawbacks are the major reasons for adoption problems of IoT services by the society. This paper carried out an investigative study on previous works on the societal applications and adoption problems of IoT, IoT communications models, and pros and cons of IoT. Through the study, it was revealed that for IoT devices and services to be widely adopted with no or minimal problems, future IoT technology will not only address the known drawbacks but also will require hardware and software components that are highly interoperable, dependable, reconfigurable, and, in many applications, certifiable.
WIRELESS SENSORS INTEGRATION INTO INTERNET OF THINGS AND THE SECURITY PRIMITIVESIJCNCJournal
The common vision of smart systems today, is by and large associated with one single concept, the internet of things (IoT), where the whole physical infrastructure is linked with intelligent monitoring and communication technologies through the use of wireless sensors. In such an intelligent vibrant system, sensors are connected to send useful information and control instructions via distributed sensor networks. Wireless sensors have an easy deployment and better flexibility of devices contrary to wired setup. With the rapid technological development of sensors, wireless sensor networks (WSNs) will become the key technology for IoT and an invaluable resource for realizing the vision of Internet of things (IoT) paradigm.
It is also important to consider whether the sensors of a WSN should be completely integrated into IoT or not. New security challenges arise when heterogeneous sensors are integrated into the IoT. Security needs to be considered at a global perspective, not just at a local scale. This paper gives an overview of sensor integration into IoT, some major security challenges and also a number of security primitives that can be taken to protect their data over the internet.
The Internet of Things (IoT), also referred to as the Internet of Objects, will change everything—including ourselves. This may seem like a bold statement, but consider the impact the Internet already had on education, science, communication, business, government, and humanity. Clearly, the Internet is one of the most important and a powerful creation in all of human history. This paper discussesIOT architecture, IOT applications and limitations of IOT.
IoT: Ongoing challenges and opportunities in Mobile TechnologyAI Publications
Mobile technology opens the door for a new kind of learning called here and now learning that occurs when learners have access to information anytime and anywhere to perform authentic activities in the context of their learning. Mobile devices, applications and services have become integrated into people's daily lives on a personal and professional level. The purpose of this study was to investigate challenges &opportunities of IoT in mobile technology. The paper is divided in 5 sections and the content of the paper covers the history, elements, challenges and opportunities salong with future of IoT specific to Indian Mobile arena.
MULTI-ACCESS EDGE COMPUTING ARCHITECTURE AND SMART AGRICULTURE APPLICATION IN...ijmnct
The Ubiquitous Power Internet of Things (UPIoT) is a deep integration of the interconnected power
network and communication network, enabling full perception of the system status and business operations
for power production, transmission, and consumption. To address the challenges of real-time perception,
rapid response, and privacy protection, UPIoT can benefit from the use of edge computing technology.
Edge computing is a new and innovative computing architecture that enables quick and efficient
processing of data close to the source, bypassing network latency and bandwidth issues. By shifting
computing power to the edge of the network, edge computing reduces the strain on cloud computing
centers and decreases input response time for users. However, access latency can still be a bottleneck,
which may overshadow the benefits of edge computing, particularly for data-intensive services. While edge
computing offers promising solutions for the IoT network, there are still some issues to address, such as
security, incomplete data, and investment and maintenance costs. In this paper, researcher conducts a
comprehensive survey of edge computing and how edge device placement can improve performance in IoT
networks. The paper includes a comparative use case of smart agriculture edge computing
implementations and discusses the various challenges faced in implementing edge computing in the UPIoT
context. The results also aim to inspire new edge-based IoT security designs by providing a complete
review of IoT security solutions at the edge layer in UPIoT
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It
extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates
opportunities in numerous domains. However, this increase in connectivity creates many prominent
challenges. This paper provides a survey of some of the major issues challenging the widespread adoption
of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the
IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.
WIRELESS SENSORS INTEGRATION INTO INTERNET OF THINGS AND THE SECURITY PRIMITIVEScsandit
The common vision of smart systems today, is by and large associated with one single concept,
the internet of things (IoT), where the whole physical infrastructure is linked with intelligent
monitoring and communication technologies through the use of wireless sensors. In such an
intelligent vibrant system, sensors are connected to send useful information and control
instructions via distributed sensor networks. Wireless sensors have an easy deployment and
better flexibility of devices contrary to wired setup. With the rapid technological development of
sensors, wireless sensor networks (WSNs) will become the key technology for IoT and an
invaluable resource for realizing the vision of Internet of things (IoT) paradigm. It is also
important to consider whether the sensors of a WSN should be completely integrated into IoT or
not. New security challenges arise when heterogeneous sensors are integrated into the IoT. Security needs to be considered at a global perspective, not just at a local scale. This paper gives an overview of sensor integration into IoT, some major security challenges and also a
number of security primitives that can be taken to protect their data over the internet.
A SOLUTION FRAMEWORK FOR MANAGING INTERNET OF THINGS (IOT)IJCNCJournal
Internet of Things (IoT) refers to heterogeneous systems and devices (often referred to as smart objects) that connect to the internet, and is an emerging and active area of research with tremendous technological,
social, and economical value for a hyper-connected world. In this paper, we will discuss how billions of these internet connected devices and machines will change the future in which we shall live, communicate and do the business. The devices, which would be connected to the internet, could vary from simple systems on chip (SOC) without any Operating System (OS) to highly powerful processor with intelligent OS with widely varying processing capability and diverse protocol support. Many of these devices can also communicate with each other directly in a dynamic manner. A key challenge is: how to manage such a diverse set of devices of such massive scale in a secured and effective manner without breaching privacy. In this paper, we will discuss various management issues and challenges related to different communication
protocol support and models, device management, security, privacy, scalability, availability and analytic support, etc., in managing IoT. The key contribution of this paper is proposal of a reference management system architecture based on cloud technology in addressing various issues related to anagement of IoThaving billions of smart objects.
IoT Challenges: Technological, Business and Social aspectsRoberto Minerva
Internet of Things is promising to be a set of technologies able to have a high impact on how people live, produce, modify and interact with the environment. Such a transformation is driven by increasing technologies capabilities of sensors/actuators, communications, general-purpose hardware, availability of software and programmability of devices. The integration of so different technologies is a problem in itself and IoT is also trying to solve cogent issues of specific problem domains, such as e-health, transportation, manufacturing, and so on. Large IoT systems (e.g., smart cities) stand on their own because the smartness requires integration of different technologies, processes and different administrative domains creating the needs to deal with a complex system. In addition to technological and problem domain specific challenges, there exist further challenges that fall in business, social and regulation realms. They can greatly impact the deployment and the success of IoT deployment. The speech aims at providing a view on some major technologies challenges of IoT and to cover a few critical business and social issues that could hamper the large deployment of IoT systems by providing some examples of implementation.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-...Istabraq M. Al-Joboury
Internet of Things (IoT) evolves very rapidly over time, since everything such as sensors/actuators linked together from around the world with use of evolution of ubiquitous computing through the Internet. These devices have a unique IP address in order to communicate with each other and transmit data with features of wireless technologies. Fog computing or so called edge computing brings all Cloud features to embedded devices at edge network and adds more features to servers like pre-store data of Cloud, fast response, and generate overhasty users reporting. Fog mediates between Cloud and IoT devices and thus enables new types of computing and services. The future applications take the advantage of combing the two concepts Fog and Cloud in order to provide low delay Fog-based and high capacity of storage Cloud-based. This paper proposes an IoT architecture for healthcare network based on Fog to Cloud and Data in Motion (F2CDM). The proposed architecture is designed and implemented over three sites: Site 1 contains the embedded devices layer, Site 2 consists of the Fog network layer, while Site 3 consists of the Cloud network. The Fog layer is represented by a middleware server in Al-Nahrain University with temporary storage such that the data lives inside for 30 min. During this time, the selection of up-normality in behavior is send to the Cloud while the rest of the data is wiped out. On the other hand, the Cloud stores all the incoming data from Fog permanently. The F2CDM works using Message Queue Telemetry Transport (MQTT) for fast response. The results show that all data can be monitored from the Fog in real time while the critical data can be monitored from Cloud. In addition, the response time is evaluated using traffic generator called Tsung. It has been found that the proposed architecture reduces traffic on Cloud network and provides better data analysis.
A MIDDLEWARE FOR THE INTERNET OF THINGSIJCNCJournal
The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as “things” to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things; or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on
the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.
Insight into IoT Applications and Common Practice Challengesijtsrd
IoT caused a revolution in the technological world. Not only is the IoT related to computers, people or cell phones but also to various sensors, actuators, vehicles, and other modern appliances. There are around 14 billion interconnected digital devices across the globe i.e. almost 2 devices per human being on earth. The IoT serves as a medium to connect non living things to the internet to transfer information from one point to another in their community network which automates processes and ultimately makes the life of human beings convenient. The subsequent result of amalgamating internet connectivity with powerful data analysis is a complete change in the way we humans work and live. The most vital characteristics of IoT include connectivity, active engagement, sensors, artificial intelligence, and small device use. All of this creates many challenges that need to be solved to keep this technology to continue expanding. In this paper, we have identified various applications of IoT based on recent technological and business trends and highlighted the existing challenges faced by IoT which need to be addressed considering the exponential acceptance of the concept globally and the way those challenges had been addressed in the past. We have also made a few comments on the way such challenges are being attempted to be resolved now. This paper presents the current status Internet of Things IoT in terms of technical details, and applications. Also, this paper opens a window for future work on the historical approach to study and address IoT challenges. Lubna Alazzawi | Jamal Alotaibi "Insight into IoT Applications and Common Practice Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30286.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/30286/insight-into-iot-applications-and-common-practice-challenges/lubna-alazzawi
Ubiquitous computing will surround users
with a comfortable and convenient information environment and a smart
space that merges physical and computational infrastructures into an integrated
habitat. This habitat will feature a proliferation of hundreds or thousands of
computing devices and sensors that will provide new functionality, offer specialized
services, and boost productivity
and interaction among the devices and
the
users.
The Internet of Things (IoT), also referred to as the Internet of Objects, will change everything—including ourselves. This may seem like a bold statement, but consider the impact the Internet already had on education, science, communication, business, government, and humanity. Clearly, the Internet is one of the most important and a powerful creation in all of human history. This paper discussesIOT architecture, IOT applications and limitations of IOT.
IoT: Ongoing challenges and opportunities in Mobile TechnologyAI Publications
Mobile technology opens the door for a new kind of learning called here and now learning that occurs when learners have access to information anytime and anywhere to perform authentic activities in the context of their learning. Mobile devices, applications and services have become integrated into people's daily lives on a personal and professional level. The purpose of this study was to investigate challenges &opportunities of IoT in mobile technology. The paper is divided in 5 sections and the content of the paper covers the history, elements, challenges and opportunities salong with future of IoT specific to Indian Mobile arena.
MULTI-ACCESS EDGE COMPUTING ARCHITECTURE AND SMART AGRICULTURE APPLICATION IN...ijmnct
The Ubiquitous Power Internet of Things (UPIoT) is a deep integration of the interconnected power
network and communication network, enabling full perception of the system status and business operations
for power production, transmission, and consumption. To address the challenges of real-time perception,
rapid response, and privacy protection, UPIoT can benefit from the use of edge computing technology.
Edge computing is a new and innovative computing architecture that enables quick and efficient
processing of data close to the source, bypassing network latency and bandwidth issues. By shifting
computing power to the edge of the network, edge computing reduces the strain on cloud computing
centers and decreases input response time for users. However, access latency can still be a bottleneck,
which may overshadow the benefits of edge computing, particularly for data-intensive services. While edge
computing offers promising solutions for the IoT network, there are still some issues to address, such as
security, incomplete data, and investment and maintenance costs. In this paper, researcher conducts a
comprehensive survey of edge computing and how edge device placement can improve performance in IoT
networks. The paper includes a comparative use case of smart agriculture edge computing
implementations and discusses the various challenges faced in implementing edge computing in the UPIoT
context. The results also aim to inspire new edge-based IoT security designs by providing a complete
review of IoT security solutions at the edge layer in UPIoT
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It
extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates
opportunities in numerous domains. However, this increase in connectivity creates many prominent
challenges. This paper provides a survey of some of the major issues challenging the widespread adoption
of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the
IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security,
management, and privacy.
THE INTERNET OF THINGS: NEW INTEROPERABILITY, MANAGEMENT AND SECURITY CHALLENGESIJNSA Journal
The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.
WIRELESS SENSORS INTEGRATION INTO INTERNET OF THINGS AND THE SECURITY PRIMITIVEScsandit
The common vision of smart systems today, is by and large associated with one single concept,
the internet of things (IoT), where the whole physical infrastructure is linked with intelligent
monitoring and communication technologies through the use of wireless sensors. In such an
intelligent vibrant system, sensors are connected to send useful information and control
instructions via distributed sensor networks. Wireless sensors have an easy deployment and
better flexibility of devices contrary to wired setup. With the rapid technological development of
sensors, wireless sensor networks (WSNs) will become the key technology for IoT and an
invaluable resource for realizing the vision of Internet of things (IoT) paradigm. It is also
important to consider whether the sensors of a WSN should be completely integrated into IoT or
not. New security challenges arise when heterogeneous sensors are integrated into the IoT. Security needs to be considered at a global perspective, not just at a local scale. This paper gives an overview of sensor integration into IoT, some major security challenges and also a
number of security primitives that can be taken to protect their data over the internet.
A SOLUTION FRAMEWORK FOR MANAGING INTERNET OF THINGS (IOT)IJCNCJournal
Internet of Things (IoT) refers to heterogeneous systems and devices (often referred to as smart objects) that connect to the internet, and is an emerging and active area of research with tremendous technological,
social, and economical value for a hyper-connected world. In this paper, we will discuss how billions of these internet connected devices and machines will change the future in which we shall live, communicate and do the business. The devices, which would be connected to the internet, could vary from simple systems on chip (SOC) without any Operating System (OS) to highly powerful processor with intelligent OS with widely varying processing capability and diverse protocol support. Many of these devices can also communicate with each other directly in a dynamic manner. A key challenge is: how to manage such a diverse set of devices of such massive scale in a secured and effective manner without breaching privacy. In this paper, we will discuss various management issues and challenges related to different communication
protocol support and models, device management, security, privacy, scalability, availability and analytic support, etc., in managing IoT. The key contribution of this paper is proposal of a reference management system architecture based on cloud technology in addressing various issues related to anagement of IoThaving billions of smart objects.
IoT Challenges: Technological, Business and Social aspectsRoberto Minerva
Internet of Things is promising to be a set of technologies able to have a high impact on how people live, produce, modify and interact with the environment. Such a transformation is driven by increasing technologies capabilities of sensors/actuators, communications, general-purpose hardware, availability of software and programmability of devices. The integration of so different technologies is a problem in itself and IoT is also trying to solve cogent issues of specific problem domains, such as e-health, transportation, manufacturing, and so on. Large IoT systems (e.g., smart cities) stand on their own because the smartness requires integration of different technologies, processes and different administrative domains creating the needs to deal with a complex system. In addition to technological and problem domain specific challenges, there exist further challenges that fall in business, social and regulation realms. They can greatly impact the deployment and the success of IoT deployment. The speech aims at providing a view on some major technologies challenges of IoT and to cover a few critical business and social issues that could hamper the large deployment of IoT systems by providing some examples of implementation.
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
F2CDM: Internet of Things for Healthcare Network Based Fog-to-Cloud and Data-...Istabraq M. Al-Joboury
Internet of Things (IoT) evolves very rapidly over time, since everything such as sensors/actuators linked together from around the world with use of evolution of ubiquitous computing through the Internet. These devices have a unique IP address in order to communicate with each other and transmit data with features of wireless technologies. Fog computing or so called edge computing brings all Cloud features to embedded devices at edge network and adds more features to servers like pre-store data of Cloud, fast response, and generate overhasty users reporting. Fog mediates between Cloud and IoT devices and thus enables new types of computing and services. The future applications take the advantage of combing the two concepts Fog and Cloud in order to provide low delay Fog-based and high capacity of storage Cloud-based. This paper proposes an IoT architecture for healthcare network based on Fog to Cloud and Data in Motion (F2CDM). The proposed architecture is designed and implemented over three sites: Site 1 contains the embedded devices layer, Site 2 consists of the Fog network layer, while Site 3 consists of the Cloud network. The Fog layer is represented by a middleware server in Al-Nahrain University with temporary storage such that the data lives inside for 30 min. During this time, the selection of up-normality in behavior is send to the Cloud while the rest of the data is wiped out. On the other hand, the Cloud stores all the incoming data from Fog permanently. The F2CDM works using Message Queue Telemetry Transport (MQTT) for fast response. The results show that all data can be monitored from the Fog in real time while the critical data can be monitored from Cloud. In addition, the response time is evaluated using traffic generator called Tsung. It has been found that the proposed architecture reduces traffic on Cloud network and provides better data analysis.
A MIDDLEWARE FOR THE INTERNET OF THINGSIJCNCJournal
The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as “things” to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things; or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on
the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.
Insight into IoT Applications and Common Practice Challengesijtsrd
IoT caused a revolution in the technological world. Not only is the IoT related to computers, people or cell phones but also to various sensors, actuators, vehicles, and other modern appliances. There are around 14 billion interconnected digital devices across the globe i.e. almost 2 devices per human being on earth. The IoT serves as a medium to connect non living things to the internet to transfer information from one point to another in their community network which automates processes and ultimately makes the life of human beings convenient. The subsequent result of amalgamating internet connectivity with powerful data analysis is a complete change in the way we humans work and live. The most vital characteristics of IoT include connectivity, active engagement, sensors, artificial intelligence, and small device use. All of this creates many challenges that need to be solved to keep this technology to continue expanding. In this paper, we have identified various applications of IoT based on recent technological and business trends and highlighted the existing challenges faced by IoT which need to be addressed considering the exponential acceptance of the concept globally and the way those challenges had been addressed in the past. We have also made a few comments on the way such challenges are being attempted to be resolved now. This paper presents the current status Internet of Things IoT in terms of technical details, and applications. Also, this paper opens a window for future work on the historical approach to study and address IoT challenges. Lubna Alazzawi | Jamal Alotaibi "Insight into IoT Applications and Common Practice Challenges" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30286.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/30286/insight-into-iot-applications-and-common-practice-challenges/lubna-alazzawi
Ubiquitous computing will surround users
with a comfortable and convenient information environment and a smart
space that merges physical and computational infrastructures into an integrated
habitat. This habitat will feature a proliferation of hundreds or thousands of
computing devices and sensors that will provide new functionality, offer specialized
services, and boost productivity
and interaction among the devices and
the
users.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
SEMANTIC TECHNIQUES FOR IOT DATA AND SERVICE MANAGEMENT: ONTOSMART SYSTEM
1. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
DOI: 10.5121/ijwmn.2016.8403 43
SEMANTIC TECHNIQUES FOR IOT DATA
AND SERVICE MANAGEMENT:
ONTOSMART SYSTEM
L. Nachabe 3
, M. Girod-Genet 1
and B. ElHassan 2
1
Department of Réseaux et Services Multimédia Mobiles, Telecom SudParis University,
Evry, France
2
Department of Electricity and Electronics, Faculty of Engineering, Branch 1,
Lebanese University, Tripoli, Lebanon.
3
American University of Culture & Education, Faculty of Science,
Beirut, Lebanon.
ABSTRACT
In 2020 more than50 billions devices will be connected over the Internet. Every device will be connected to
anything, anyone, anytime and anywhere in the world of Internet of Thing or IoT. This network will
generate tremendous unstructured or semi structured data that should be shared between different
devices/machines for advanced and automated service delivery in the benefits of the user’s daily life. Thus,
mechanisms for data interoperability and automatic service discovery and delivery should be offered.
Although many approaches have been suggested in the state of art, none of these researches provide a fully
interoperable, light, flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and service
management through service oriented paradigm. It proposes sensors/actuators and scenarios independent
flexible context aware and distributed architecture for IoT systems, in particular smart home systems.
KEYWORDS
Smart-Home, IoT, Multi-Agent, distributed systems, WSN, semantic, Ontology, sensing as a service,
interoperability, semantic interoperability.
1. INTRODUCTION
The continuing rise of mobile internet access, especially the emergence of 5G technology, in
addition to the significant increase of smartphone users and the integration of mass scale cloud
computing, are now transforming our society to what is called Smart Society. C. Levy et al.
defined Smart Society as “the potential of digital technology and connected devices and the use
of digital networks to improve people’s lives[1]. Smart Society, in particular smart cities and
smart homes, are formed of wireless sensor networks capable of sensing, communicating,
computing and potentially actuating. These wireless networks can play the role of decision
makers to facilitate the life of users. For example, when temperature exceeds a threshold the
2. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
44
window is opened automatically. The solutions are really endless. In fact, the Smart Society
affects all aspects of our life including health services, smart home and automated/remote
controlling and monitoring services, smart utility consumption (energy, water) and smart grid,
smart mobility and Vehicle Area Networks, smart public services (parking, safety), Intelligent
Transportation Systems, smart entertainment, etc.[2]. All of these solutions are providing remote
monitoring and control capabilities (in particular through 5G technologies) using machine to
machine communication (M2M) for intelligent data interpretation, new data and knowledge
creation and fast intervention. Fortunately, it will pave the way to the development of new
business and services opportunities. Industry analysts predict that 50 billion devices will be
connected to mobile networks by 2020 [3]. University of Edinburgh is involved in a project call
Smart Society which objective is “to move towards a hybrid system where people and machines
tightly work together to build a smarter society”. They stressed on the importance of bridging the
semantic gap between low-level machine and high-level human interpretation of data in order to
collaborate for conflict resolution goals both at individual and societal levels [4].
5G Era is empowering the idea of stay connected, which means that everything is connected to
anything, anyone, anytime and anywhere. This what has been dubbed the Internet of Thing (IoT)
[5]. IoT is formed of extremely heterogeneous nodes in terms of roles (sensor, actuator, relay,
etc.), manufacturer, communication interface, data rate, data type, etc. Moreover, these nodes can
have limited resource capabilities (memory, battery lifetime and CPU), where tremendous data is
generated. That is why, in most cases, these generated data should be collected and treated in an
external node as local/web server or cloud server [2]. Cloud solutions have been integrated in
these systems because of its unlimited capacity of storage, processing power, virtual resources
creation and service delivery.
X. Sheng et al. [6] and A. Zaslavsky et al. [5] introduced the idea of sensing as service. With the
growth of cloud computing the idea of offering everything as service has been integrated in
almost every business model. Sensing as a service (SeaaS) reveals the idea of transforming
physical word (sensors/actuators) to a service that can be discovered and invoked by users on
demand. To develop SaaS solutions, developers should procure on-demand services, data
portability and interoperability between different data providers, context and situation awareness
as well as mechanisms dedicated for security and privacy.
In this paper, we present a new general architectural for data collection, processing and
management of IoT systems. Furthermore, this architecture paves the way towards SaaS
paradigm where users can request any discovered service regardless the underneath mechanisms
(data collection and sensor configurations). The architecture relies on semantic techniques to
tackle the problem of heterogeneity within the same system, and between different data systems.
Furthermore, it uses the idea of web semantic web servers to provide SaaS solutions. In addition,
it is based on multi-agent architecture in order to distribute the processing among different
components. This leads to flexible, embedded devices compatible and more power consumption
efficient solution. All these aforementioned ideas will be presented in the next sections.
The paper is organized as follow. In Section II IoT challenges are described in order to explain
the reasons behind using ontologies and multi-Agent architectures. Overview of the retained
modular ontologies is also presented in this section. Section III describes the general architecture
3. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
45
of our proposed system called OntoSmart system describing the role of different agents. Section
IV details our OntoSmart based applications called OntoSmartHome for data monitoring and
management. Moreover, tests and analysis are given in this section. Finally, Section V concludes
the paper.
2. BACKGROUND
2.1. IoT Challenges
M. Truck defined the IoT “the transformation of any physical object into a digital data product”
[7]. In other words, using your smartphone as pedometer in order to calculate your burned
calories, and publishing these data on social media is part of IoT. In fact, we are now part of IoT
which is becoming a real booming topic. The miniaturization of sensors, the constant evolution of
wearable devices, the connectivity anywhere at any time and the use of smartphone are
conducting us to be part of the IoT. Although IoT paradigm is fundamentally controlling huge
amount of our daily activities (calendar notification, social media, health applications, cloud
storage applications, weather broadcasting, search engines, etc.), but still have paramount
challenges.
The European Research Cluster on IoT (IERC) defined five challenges [8]:
•Develop reference architecture to enable cross industry technology cooperation and resolve the
problem of interoperability.
•Provide complex services and composed solutions.
•Integrate heterogeneous technology at various levels.
•Combine various business models.
•Procure secure, private and reliable solutions.
Zaslavsky et al. insisted on the importance of providing a flexible, easy and distributed
architecture for IoT where data is provided as service. Moreover reasoning and decision making
should be integrated in the architecture [5].
Texas Instruments [9] presented the main challenges of IoT as follow:
•Sensing a complex (Heterogeneous) environment.
•Wide variety of wireless/wired communication protocols.
•Critical power consumption.
•Publishing data to the cloud.
•Security concerns.
Texas instruments summarized the IoT into three levels: the physical devices, the IoT
gateway/relay and the cloud level.
Where billions of devices have been connected, IoT is characterized by Big Data[5]. Volume,
velocity and variety are at the core of big data. Thus, the main challenges of IoT are how to
process this high volume of processing with low power devices. Moreover, the data should be
4. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
46
analyzed in real time (especially for health services where immediate intervention should be
done) and enriched with social media options (people nowadays are familiar with social media).
Although data can be shared, security and privacy concerns are still considered the main question
in IoT.
Table 1 summarizes the points that should be respected when designing IoT solution. Moreover,
Table 1 depicts how our proposed IoT architecture addresses each of these points. In the next
sections, all these points are detailed.
Table 1- IoT Challenges and our proposed solutions
2.2 .Reason Behind Using Ontologies
As IoT encompasses different systems, applications, and WSNs, different terminologies are used
to describe the same properties or object. Moreover, various ranges of sensors/actuators (ambient
sensors, electrical devices, biomedical sensors, etc.) from wide range of vendors supporting
different wireless technologies (Bluetooth LE, LoRa, ZigBee, etc.) are used [7]. This variety
caused the problem of heterogeneity management and interoperability. In general, these systems
are monolithically deployed. The integration of a new device or a new service needs the
reconfiguration of the overall system, and some time the deployment of a new system [8]. For
that reason, new techniques that deal with the data meaning and enhance the automatic node and
service discovery are needed. These techniques are named Semantic techniques where an entity
presents an aspect of the real domain described by metadata (data about data). Thus, by using
semantic techniques within IoT, the raw data can be annotated by semantic data respecting a
predefined semantic model to unify its description [9]. In that way, the problem of
interoperability between different systems can be resolved. Ontologies are the most powerful
semantic techniques. Using the formal definition, ontologies are “tools for specifying the
semantics of terminology system in a well-defined and unambiguous manner” [10]. W3C
recommended the use of OWL language to describe ontologies [11]. In fact, the most powerful
point behind using OWL is the capability of reasoning in order to infer more significant data.
5. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
47
Many researches have introduced the usage of ontologies in IoT. The IoT-A project presents a
high level abstraction of the IoT domain. It basically consists of a Domain Model, Information
model and Communication Model [10]. The Domain Model includes a user who can be a Human
or an Active Digital Entity. The user interacts with a physical entity and invokes at least one
service. A virtual Entity can be associated with a service or/and a resource. The service accesses
at least one resource. The resource can be storage, network, or on-device resources (which hosts a
device). A device can be an actuator, a sensor or a tag device. The authors in [11] extended the
works done in the IoT-A project by adding semantic description and linking the models to
existing domain specific ontologies. The core of the IoT, Information model, consists of Entity,
Resource, Service and Device.
Almost all IoT researches suggested including the idea of sensing as service. Thus, these
researches use OWL-S service ontology as upper service layer. OWL-S [12], the semantic mark-
up for web services, developed by DARPA DAML program using OWL languages aims to
describe semantic web services. It permits the automatic web service: discovery, invocation,
composition, interoperation, and execution monitoring. The OWL-S ontology includes mainly
three sub-ontologies: the service profile ontology that describes what the service does; the process
model ontology that describes how the service is used; and the grounding ontology that describes
how to interact with the service. Nambi et al. [13] proposed a unified semantic knowledge for IoT
where resources are readable, recognizable, locatable, addressable and/or controllable via the
Internet. The Resource Ontology describes the sensors, actuators, physical objects and composite
objects. It is reused from the SSN ontology [14]. The Location Ontology adds the geospatial
information to IoT information. It is reused from the GeoNames ontology [15]. The Domain
ontology models a specific or a generic domain such as E-health, smart home, etc. Any domain
ontology can be imported depending on the IoT application. The Context Domain enables
context-awareness and contextual interoperability during service discovery and composition.
Aspect-Scale-Context (ASC) is used to model conceptual information. The Policy Ontology
defines how to accomplish a service requested by a user. There are three different policy levels:
high level policy defining abstract service, concrete policies for specific services, and low level
policies defining the execution plan of the services. The Service Ontology is built upon the
concept of OWL-S.
Wang et al. [16] introduced a new Semantic Model for IoT architecture composed of seven main
modules. The IoT resources are defined by extending the SSN ontology to include actuators,
servers and gateways. The Observation & Measurement Ontology is reused from the SSN
Ontology. The entity of Interest and physical locations represent the object of the physical world.
It is rigorous that none of these attempts combine the fully description of the sensors/actuators,
data, process, and services. While reliability and service consistency are primordial in such
systems, the QoS and computational resources as well as remaining battery level are needed for
service modeling. In addition, IoT involves wide range of data/service accessing protocols
(HTTP, CoAP, MQTT, SOAP, etc.). Thus grounding methods for service accessing will facilitate
the service discovery.
Therefore, the contribution of this work is OntoSmart system that relies on both MyOntoSens
Ontology [17] (summarized in Figure1) and MyOntoService Ontology (depicted in Figure 3) to
model IoT data and services. These ontologies are provided with associated management
6. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
48
enablers, within our OntoSmart system, and will enable the handling of all the aforementioned
IoT challenges cited in Table 1.
3. ONTOSMART SYSTEM ONTOLOGIES
IoT can be seen as a WSN (Wireless Sensor Network) encompassing different nodes (sensors,
actuators, equipment, servers and gateways). Each WSN (modeling a home, building, or network)
can have one or more owner (e.g. the patient to be monitored at home) and contact persons
(medical staff or relatives). Each node is used for certain process (temperature, humidity, etc.)
and each actuator performs certain action (turn ON/OFF, shuttle Up/Down, etc.). That is why we
used MyOntoSens ontology detailed in [17] and extended in [18] to describe the components of
our proposed system since this ontology already takes into account all the required attributes that
model WSN (network description, process, data, constraints, measurements, sensors, actuators,
etc.) . MyOntoSens has been designed as a modular ontology and is divided into three parts:
MyOntoSensWSN, MyOntoSensNode and MyOntoSensProcess. Figure 1 summarizes the main
classes of MyOntoSens ontology, while Figure 2 depicts the main classes and attributes of
MyOntoSensProcess part.
Figure 1-MyOntoSens Ontology
7. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
49
Moreover, the IoT should provide services to the users (monitoring, notification, reminder, etc.).
We are modeling these services by introducing original service ontology, called MyOntoService
•MyOntoServiceProfile that describes the service (QoS, QoI, service constraints and ontology
and depicted in Figure 3. Our new upper service ontology adopts the idea of OWL-S ontology
[18]. It is composed of three main sub-ontologies (as depicted in Figure 3):service type) and
enables their auto-discovery,
•MyOntoServiceProcess that describes how the service is realized based on the Input, Output
Precondition, and Effect (IOPE) mechanism,
•And finally MyOntoServiceGrounding that describes how the service can be invoked (or
accessed in IoT e.g. HTTP, CoAP, etc.)
To ensure automatic senor/service discovery, the ontologies are described by SWRL [18] rules
and the Pellet [19] reasoner is used to infer explicit data. MyOntoService ontology will enable the
use of Sensing as Service paradigm due to the use of the semantic rule (Rule 1). When a sensor is
used for a process, this process will be automatically inferred as atomic service enabling the
automatic sensor discovery. The user can enable/disable the service by adding the object property
“enable”.
Rule 1: Process(?P) -> Service(?P); Sensor(?S), Atomic(?a), usedFor(?S, ?a) ->
hasProducer(?a, ?S)
4. ONTOSMART MULTI-AGENT ARCHITECTURE
The main aim of our proposed system is modularity, scalability, flexibility and distributed
computing. Thus, this architecture should logically be divided in standalone modules offering
certain services, capable of interacting with other modules and that can be instantiated when
required. This is exactly what a software agent can do. An agent is a piece of software that can
run autonomy to perform certain behavior. The composition of many agents is called Multiple
Agents System (MAS) [20]. The agents interact by passing predefined messages. The MAS
architecture describes how different agents are interconnecting. MAS are widely used in complex
system where abstract definition of certain task is required. Figure 4 depicts this multilayered
architecture. In our proposed system basic functionalities are offered by agents capable of
invoking other agents. This is in particular the case for our data collection and information
management/sharing/retrieval functionalities. For example, we can cite the data collector agent,
query agent, notification agent, writer agent, etc.
8. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
50
Figure 1-MyOntoSensProcess Classes Hierarchy
Figure 2-MyOntoService Ontology
Moreover, using expressive ontologies without defining a general mechanism for data annotation
is pointless [5]. That is why we defined three main agents in our overall system:
9. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
51
•Scanners: for each communication protocol (Bluetooth LE, WIFI, ZigBee) there is a data
scanner that read raw data and call the adequate wrapper for semantic annotation. Moreover, for
each grounding protocol (CoAP, HTTP, etc.) there is a service scanner that reads the request from
external user and calls the adequate service.
•Writers: To send data from the system to external users, the adequate writer encapsulates the
sent data based on the requested protocol.
•Wrappers: To add semantic description of the raw data based on MyOntoSens and
MyOntoService ontologies.
Detailed description of how to use these components is depicted in section 4.
Figure 3- OntoSmart General Architecture
4. IMPLEMENTATION OF ONTO SMART SYSTEM ONTOLOGY
The increasing number of older persons living independently, in parallel with the considerable
number of individuals with chronic diseases or disabilities pushed the researchers to work on the
idea of smart home systems [18]. These systems help to keep individuals safe, to assist them in
10. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
52
controlling home appliances, and to inform their relatives and the medical staff about their status.
Thus, to test our OntoSmart architecture we have chosen the case of a smart home dedicated for
elderly monitoring and assisting. This use case is offering sensors/actuators and scenarios
independent flexible context aware and distributed solution based on standardized WSN and
service ontologies as well as multi-agent architecture described in sections 2 and 3. Figure 5
depicts this smart home.
Figure 4- OnoSmart Home Clusters
4.1 WBAN Cluster
The main aim of the WBAN Cluster is to collect patient’s vital signals in order to send it to the
local server. Here comes the role of the Data Collector Agent (DCA), also called the Cluster Hub.
In our scenario, the WBAN Cluster is formed of the heart rate sensor, acceleration sensor and the
smart phone of the user which is playing the role of the Cluster Hub. To capture the heart rate, the
H7Polar sensor is used. This senor sends the heart rate via its Bluetooth LE interface.
Therefore, a Bluetooth LE Scanner (BLES) agent is installed on the patient’s smart phone in
order to:
•identify any Bluetooth LE device,
11. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
53
•and recognize the offered service due to the use of UUID defined in standard GATT Profiles
[21].
This scanner gives the first step toward an automatic sensor discovery.
To determine the posture of the patient, the smart phone built-in three acceleration sensor is used.
The detail of the posture calculation is given in [18].
After identifying the node’s characteristics, these latter should be sent to the local server for
semantic annotation and further treatments. JSON [22] message format was adopted due to its
lightness and simplicity. Therefore, a JSON Writer (JS) agent is installed on the smart phone in
order to encapsulate data and send it to the local server.
If other types of sensors are used (like ZigBee), the only think that should be done is to install the
convenient scanner (like ZigBee Scanner). Likewise, if data from the smartphone is encapsulated
using a message format other than the JSON messages, the suitable writer agent should be
compelled. In fact, the use of these data agents and writers enable the automatic discovery of any
added nodes and enhance the flexibility and scalability of the system where any nodes can be
added without the need to rebuild the overall system. What is really required is to ensure that the
communication protocol used by the device has the suitable data scanner agent.
4.2 .WSN Cluster
The WSN Cluster is formed of the sensors dedicated for indoor localization, the ambient
parameters sensors, and the local server (DCA or cluster Hub). In our case, we use the
RadBeacon Bluetooth LE sensors to identify the location of the patient. Moreover, the ambient
parameters (light intensity, humidity and temperature) are retrieved from the built-in sensors in
the patient’s smartphone in order to be sent to the remote server using JSON messages. We
installed, on the local server, the following agents:
•BLES agent: to identify Bluetooth LE sensors and retrieve the required information in order to
invoke the adequate wrapper.
•JSON Scanner (JS) agent: to retrieve data sent from the smartphone and invoke the adequate
wrapper.
•Node Wrapper (NW) agent: to add semantic annotation about the sensors/hubs based on
MyOntoSensNode ontology.
•Process Wrapper (PW) agent: To add semantic annotation about the process used by the sensors
based on MyOntoSensProcess.
In that way, the local server will contain all the semantic description of the home devices.
Because more than one patient can use the same WSN sensors (e.g. indoor localization sensors
can be used to locate more than one patient in the same home), the object property
“measuredFor” has been added between the sensors measurements (Measurement class of
12. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
54
MyOntoSensProcess ontology) and the patient (Patient Class of MyOntoSensWSN ontology). To
this point, all nodes are automatically discovered. Regarding the service discover, we added
SWRL [12] rules to infer basic services offered by the system. All processes are considered as
service (e.g. heart rate, temperature, etc.). This inference is insured by using Rule 1. Composed
services are deduced from additional rules. For example, using the heart rate sensor implies that
the maximum heart rate service can be used for notification purposes (Rule 2).
(Rule 2) Process (HR) -> Service (MaxHR), hasEffect (MaxHR, Notification).
Moreover, the object property “enable” between patient individual and service individual is used
for allowing the patient to enable/disable any service offered by the system. Only enabled
services are retrieved when invoking the query dedicated for service discovery.
4.3 Remote Semantic Storage and Management Server
The main aim of the remote semantic server is to save the fully semantic description of the smart
home. It plays the role of semantic registry. While intelligence treatment for each home is located
on its local server, the intelligent cooperation between different TSHCSs, and advanced data
analysis for statistical purposes can be conducted on this remote server. In our scenario, the
remote server encompasses the following agents:
•SPARQL Agent: to query semantic information saved on the remote/local server in order to
retrieve new semantic information.
•CoAP Agent: to publish the data in order to be used by external users for remote monitoring
(e.g. patient’s relatives and medical staff). The CoAP [23] is a Rest-full protocol used for
constraint networks. It uses the UDP protocol as transport layer and the reliability mechanism is
driven by the application layer. We used the CoAP Confirmable messages to ensure the required
reliability in our proposed TSCHS system. In that way, the Remote server will play the Role of
CoAP monitoring server allowing remote users to access the data by using CoAP GET requests.
•Notification Agent: to notify external users about abnormal patient’s attitude and dangerous
measured values. In our case, we notify the user if the patient is falling or if his/her heart rate
exceeds the normal value. It is worthy to note that the security mechanism is not within the scope
of this study. That’s why we based our test-bed authentication mechanisms on Android GCM due
to its implementation simplicity, and by assuming that all our system users have a Gmail account.
Let’s precise here that for a commercial application, stronger authentication mechanisms will
obviously have to be designed and implemented. Figure 6 depicts all the aforementioned agents
and building blocks location and interactions.
13. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
Figure 5-Implemented Agents and interactions
The remote server will invoke the WSN Wrapper in order to add the new WSN and Patient into
its semantic registry. The PatientID (identifier generated by the remote server) is now returned to
the smartphone and saved in its
used when needed (for patient identification). The patient can now launch its OntoSmartHome
application and log into the OntoSmart system using his/her Gmail account.
5. ONTOSMARTHOME
Two applications have been developed for OntoSmart system: the first application (detailed in
Section A) is dedicated for the patient in order to initialize the system and display the monitoring
values; the second application (detailed in Section 5.2
patient’s monitoring. The same monitoring interface (depicted in Figure 7) is retained for the
patient and relative applications.
5.1. Patient’s OntoSmartHome Application
We have provided our OntoSmart system with a p
applicative agents (see Figure 6). This application, depicted in Figure 7 and Figure 8, is integrated
within the Smartphone of the patient. The details of the patient’s mobile application can be found
in [18]. Note that a compromise between system accuracy and battery consumption is mandatory
in such systems. That’s why, after practical testing, we have chosen to acquire 50 samples/sec
from the sensors. In addition, these modified values will be sent to the loc
server via the JW in order to be updated on the CoAP server and registered in the remote
ontology repository.
International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
Implemented Agents and interactions as deployed in OntoSmart System
The remote server will invoke the WSN Wrapper in order to add the new WSN and Patient into
its semantic registry. The PatientID (identifier generated by the remote server) is now returned to
the smartphone and saved in its shared Preference (local database on the Smartphone) for being
used when needed (for patient identification). The patient can now launch its OntoSmartHome
application and log into the OntoSmart system using his/her Gmail account.
ONTOSMARTHOME APPLICATIONS
Two applications have been developed for OntoSmart system: the first application (detailed in
Section A) is dedicated for the patient in order to initialize the system and display the monitoring
values; the second application (detailed in Section 5.2) is dedicated for relatives for remote
patient’s monitoring. The same monitoring interface (depicted in Figure 7) is retained for the
Patient’s OntoSmartHome Application
We have provided our OntoSmart system with a patient-dedicated application and corresponding
applicative agents (see Figure 6). This application, depicted in Figure 7 and Figure 8, is integrated
within the Smartphone of the patient. The details of the patient’s mobile application can be found
Note that a compromise between system accuracy and battery consumption is mandatory
in such systems. That’s why, after practical testing, we have chosen to acquire 50 samples/sec
from the sensors. In addition, these modified values will be sent to the local server and the remote
server via the JW in order to be updated on the CoAP server and registered in the remote
International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
55
as deployed in OntoSmart System
The remote server will invoke the WSN Wrapper in order to add the new WSN and Patient into
its semantic registry. The PatientID (identifier generated by the remote server) is now returned to
shared Preference (local database on the Smartphone) for being
used when needed (for patient identification). The patient can now launch its OntoSmartHome
Two applications have been developed for OntoSmart system: the first application (detailed in
Section A) is dedicated for the patient in order to initialize the system and display the monitoring
) is dedicated for relatives for remote
patient’s monitoring. The same monitoring interface (depicted in Figure 7) is retained for the
dedicated application and corresponding
applicative agents (see Figure 6). This application, depicted in Figure 7 and Figure 8, is integrated
within the Smartphone of the patient. The details of the patient’s mobile application can be found
Note that a compromise between system accuracy and battery consumption is mandatory
in such systems. That’s why, after practical testing, we have chosen to acquire 50 samples/sec
al server and the remote
server via the JW in order to be updated on the CoAP server and registered in the remote
14. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
Figure 6- Monitoring Interface of OntoSmartHome Application
Figure 7
5.2. Relatives OntoSmartHome Application
The first time the application is installed, the relative should successfully log in using his/her
Gmail account. Afterward, the relative is asked to enter the patient’s email. The patient and
relative emails are sent via the JSON Writer (JW) to the remote server where a SPARQL query is
invoked to verify the relative permissions. Figure 8 depicts the overall authentication process for
the relative’s mobile application.
If the relative is able to monitor the patient, CoAP GET re
CoAP remote server in order to retrieve the values.
International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
Monitoring Interface of OntoSmartHome Application
Figure 7-Patient's OntoSmartHome application
ves OntoSmartHome Application
The first time the application is installed, the relative should successfully log in using his/her
Gmail account. Afterward, the relative is asked to enter the patient’s email. The patient and
JSON Writer (JW) to the remote server where a SPARQL query is
invoked to verify the relative permissions. Figure 8 depicts the overall authentication process for
the relative’s mobile application.
If the relative is able to monitor the patient, CoAP GET requests are sent every minute to the
CoAP remote server in order to retrieve the values.
International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
56
The first time the application is installed, the relative should successfully log in using his/her
Gmail account. Afterward, the relative is asked to enter the patient’s email. The patient and
JSON Writer (JW) to the remote server where a SPARQL query is
invoked to verify the relative permissions. Figure 8 depicts the overall authentication process for
quests are sent every minute to the
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57
Our OntoSmart system provides the discovery of any used BLE sensor due to the use of BLE
Scanner which discovers the node, the node wrapper which adds the node’s semantic description
to the semantic registry, and the SPARQL agent that executes the node and service discovery
queries. Moreover, publishing the monitoring patient’s data through a CoAP server permits the
relatives to instantly monitor these data by issuing CoAP GET requests. Further analysis and
advanced diagnostics can be conducted on the remote server by domain experts in order to help
the patient. Moreover, due to the use of semantic registry on the remote server, new domain
expert ontologies can be imported to infer severe situations related, for example, to heredity
problems or environmental diseases.
5.3. Testing & Results
In fact our testing process focused on two main principles: the requirements (as highlighted in
Section I) of IoT systems and the mobile application itself. For that purposes, the applications
were tested on Samsung GALAXY S4 (Android 4.4.2 KITKAT-API 19 and Android 5.0.1-API
21 Lollipop after upgrade) and SONY XPERIA SL (Android 4.1.2 Jelly Bean- API 16). We first
considered one patient per WSN. We installed the application on two different patients’ mobile.
For each patient, one relative was added. We first insure that the ontology is well classified. Due
to the use of incremental reasoner, the services were discovered sequentially. For example, once
the H7 Polar is paired, the heart rate service was added on the server. Moreover, the user was able
to enable/disable the services by his/her own without the intervention of experts. At certain stage,
the Patient 1 decided to disable the indoor localization services. Immediately, on the relative
smart phone, the location field was replaced by “Unknown”. This proves the ability of our
proposed system to offer services based on the occupants needs and requirements.
Then, we considered the scenario where two patients are sharing the same WSN, i.e. sharing the
same Rad Beacons for the indoor localization. The distance between rooms was about 20 m and
three Rad beacons were carried out and setup in three different zones at the patient’s home. For
testing purposes only, the remote server and the local server were on the same machine, but
working on different port numbers. The ontology was well classified without any ambiguity. This
insures the reusability of the proposed system by more than one occupant.
The privacy concerns were addressed by the use of email accounts for application installation and
the authentication mechanism when a relative requests to monitor a patient. It is evident that this
authentication mechanisms, as well as security concerns, could be enhanced by implementing
advanced protocols. However, this enhancement does not affect the overall architecture, but only
require the adoption of a new security/authentication agent invoked when needed.
After insuring the well functionality of the system, we focused on measuring the complexity of
the system on the server. For both cases, the tests were conducted for about 8 hours. Table 2
depicts the time needed to load and classify the ontology, as well as the CPU and memory usage.
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Table 2- System Evaluation on the Server
Case 1 Case 2
Ontology
Load 24 sec 35 sec
Classify 90 sec 75 sec
Model Size 1403 1403
Inferred
Model size
1019 1000
CPU
Max. CPU 76% 60%
Avg. CPU 35% 27%
Memory 425 692 K 415 728 K
We can note that the ontology is classified within 2 minutes. The size of the inferred model
reflects the importance of SWRL rules used in the proposed system. As few data is required to be
sent for the mobile application to the semantic server, lowest bandwidth will be consumed. The
potential of the semantic rules is inferring all needed data to discover the properties of a service.
For example, all HR services will be inferred by just sending that the heart process is offered by
H7 Polar sensor (see Rule 1 and Rule 2). We are actually working on studying the performance of
the server on large scale (more than 10 patients registered in the application) to estimate the
probability of crashing.
We passed than to evaluate the performance of the patient’s mobile application. The time needed
for node/service discovery was maximum 1 second. The patient’s application was tested for
about 8 hours; the battery consumption was maximum 12 % and minimum 5%, while the average
CPU usage was 8%. Table 2 depicts the processing, network and battery usage on the patient
smart phone in initiation mode (done only when the application is loaded for the first time) and
the normal mode where the measurements are taken periodically.
This evaluation ensures that our proposed application is not dramatically affecting the resources
on the smart phone. In fact, these consumptions can be decreased by the use of ambient sensors or
biomedical accelerometer sensors instead of the built-in sensors. In that way, the data will be
received directly on the server for semantic annotation and treatments. Aiming to determine the
falling detection, we tried to test to detect a falling after standing, from bed or from a chair. These
fallings were successfully detected. We can note that the posture and the falling status are not
100% accurate because we were using the Smartphone embedded sensors for the measurements
(for economy purposes). These measurements can be enhanced by using dedicated accelerometer
sensors capable of detecting more precisely the posture of the patient.
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It is now the time to evaluate the relatives application. We first tested the authentication process.
Only authenticated relatives were able to monitor the patients and receive notifications. We tested
the network and battery consumption (depicted in Table 3) of the relatives for about 8 hours.
Table 3- Resources Consumption for the Patient Smart Phone
Initialization
Phase
Main Activity
Max Average Max Average
CPU % 5% 2% 11% 8%
Network Usage 16 Kb 12 Kb 40 Kb 36 Kb
Battery Usage 7% 5% 12% 10%
On the relatives’ side, the measured values and patient’s status and alarms were displayed in real
time. The CPU usage on the relative’s application attempts as average 0.56 % and as maximum 1
%. These resources consumption are considered insignificant due the use of CoAP messages
(1280 bytes). The values were displayed in real time and the notification when abnormal values
are detected was received within 1 second.
Table 4- Resources Consumption on the Relative Mobile Application
Average CPU % 0.56%
Average Battery
Usage
0.40%
6. CONCLUSION
In summary, although our OntoSmart system does not actually provide the best solution for fall
detection (only because Smartphone embedded sensors were used instead of dedicated sensors), it
offers the flexibility to enhance this detection, whenever needed, by just adding the necessary
sensors. Our OntoSmart system is the first step toward a Sensing as Service where users can
easily add sensors and actuators. Service developers will only focus on ads-on services and
applications without the need to deal with sensor integration and configuration. In addition to the
passive intervention devices, active intervention devices for reminder systems or medical
assistance can be added to OntoSmart System. This system proved the efficiency in solving the
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challenges in IoT system (see Table 1). Due to the use of Service scanner and MyOntoService
ontology, the users just ask for a service and it will be automatically discovered and served. The
data scanner and node wrappers permit the discovery of any node regardless the physical
interface. Multi agent usage, in combination with the proposed modular ontology allow to
distribute the processing in different IoT nodes taking into the consideration the node’s
capabilities (processing, memory, battery lifetime). The use of semantic rules and SPARQL agent
enhance intelligent decision making by allowing the expert to define domain based rules.
It is worthy to note that, as aforementioned, security mechanisms are not within the scope of this
study. That’s why we based our test-bed authentication mechanisms on Android GCM due to its
implementation simplicity, and by assuming that all our system users have a Gmail account.
Stronger authentication mechanisms will obviously have to be designed and implemented for a
commercial version of our proposed framework. More broadly, security and privacy by design
mechanisms will have to be addressed, specified and carried out within our proposed OntoSmart
system for in particular critical use cases handling and personal data privacy protection.
Nevertheless, we have already investigated few potential security/trust/privacy solutions that
could probably be carried out in our IoT compliant and semantic based system (OntoSmart) for
that purposes. These envisioned potential solutions are summarized below. It is mostly probable
that, for critical e-Health uses cases, a private IoT solution would be preferred to a public one.
For security, the raw data exchanged within the patient’s WSN clusters could be encrypted at the
sensor/actuator level prior to its capture/relay. This could be achieved by e.g. carrying out a
simplified distributed key management infrastructure, provided with distributed Certifications
Authorities, within our OntoSmart system. These distributed certifications authorities could e.g.
reside both in the distributed control and monitoring servers of the patient-associated caregivers
and in the local servers of the patient (e.g. the local semantic server and/or the hub presented in
Figure 4). The simplified keys and certificates provided within such architecture would be used
within OntoSmart for: exchanged data encryption, communication channel security and
authentication purposes (patients and nodes). Furthermore, the keys could also be used by the
Semantic Wrapper Agent of OntoSmart ‘(see Figure 4) to:
•Decrypt the raw data received from Data Scanner Agents,
•Encrypt the corresponding semantic information based on our OntoSmart system ontologies.
For hiding the identity of the users, all the IDs used inside the data/ontologies would probably
have to be replaced by Virtual IDs (VIDs). Only authorized entities (e.g. the patients, their
relatives and their authorized caregivers) would be able (have the right) to link a VID with its
corresponding real ID.
Our OntoSmart system is fully based on MyOntoSens ontology that already embeds a ‘trust level’
attribute within its ‘Node’ building block. This attribute could be in particular associated to a trust
label that a certification authority could delivered based on some level of security
properties/mechanisms carried out within the node. This trust label could be verifiable through
e.g. a certified web-based distributed repository.
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Concerning data access control and privacy, mechanisms based on data integrated privacy related
attributes and rules (by design within the semantic data model and ontology) associated with
semantic data annotation agents (i.e. data tattooing like agents) and rule engine agents (see Figure
4), could be envisioned. Indeed, our OntoSmart system is fully based on MyOntoSens ontology
that already embeds the following attributes within its ‘Proccess and Measurements’ building
block:
•Data ownership and data owner(s) ID(s) (or VID(s)),
•Data access rights and authorized data reader/writer ID(s) (or VID(s)).
In that way, the data concentrators (e.g. the semantic local/remote servers and the hubs in our
OntoSmart system, see Figure 4) could decide to relay or not the information based on real time
semantic annotation of data, data embedded privacy-related rules that could be added to the
MyOntoSens WSN ontology, and destination node thrust level.
More agents are under construction for example, remind patients to take his/her medication or
alert him/her if a stove burner is left on. Surely, actuators can be added to the system. All these
concerns do not need to re-invent the wheel, but just upgrade the system by adding adequate
agents. More tests are being conducted to measure the efficiency of the remote semantic storage
and management server when dealing with large amount of TSHCSs. Additional scanners and
writers are also under development, especially ZigBee and HL7 scanners/writers. Finally, more
security and privacy concerns are under investigations.
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Authors
Lina Nachabe received her engineering Diploma in 2007 from the faculty of
engineering of the Lebanese university, Tripoli Lebanon. She got her PHD degree
from the University of Télécom SudParis, RS2M department in 2015. From 2009 she
started to teach database and networking courses in Lebanese universities. She is
actually a Telecom Lab Assistant in the Lebanese university, faculty of engineering.
She supervised engineering project in MUT University. Lina Nachabe contributed in
the ETSI technical committee TC SmartBAN, Work Item 1 "Heterogeneity
management, data representation and transfer" as part of her PHD study. She is
interested in wireless sensor networks and ontology researches.
Associate Professor Dr. Marc Girod-Genet received his engineering degree with
distinction (top in one's year) in ‘telecommunications and advanced techniques’ from
EPITA engineering school, Paris, France, in September 1994. He received the MS
degree in Computer Science from Stevens Institute of Technology, Hoboken, New
Jersey, USA, in Mai 1995. He finally received a Ph.D. degree with highest honors in
Computer Science from University of Versailles/Paris VI, France, in July 2000. He
joined Télécom SudParis first as a research project manager in September 2000 and
then was confirmed as a full Associate Professor in January 2005. He teaches courses in
personal communications, short range wireless technologies, service and context
management architectures, optimization/modeling and machine learning. He is also an associate research at
CNRS (network intelligence field) and works / has worked in several national and European research
projects. Dr. Marc Girod-Genet is involved in the ETSI technical committee TC SmartBAN where he
serves as Reporter of Work Item 1 "Heterogeneity management, data representation and transfer". He is /
21. International Journal of Wireless & Mobile Networks (IJWMN) Vol. 8, No. 4, August 2016
63
has been a Technical Program Committee member of IEEE conferences, as well as a reviewer for
conferences and journals such as Communications Magazine and Transactions on Wireless
Communications. He has been involved in organizing international conferences symposia and events such
as Globecom Wireless Communication symposium and ASWN workshop. In 2010, Dr. Marc Girod-Genet
received 2 awards: the ITEA2 Achievement Award 2010 Silver Medal for the CAM4Home EU project and
the ADEME Special Award 'Prix de la Croissance Verte Numérique' (digital green growth) for his joint
work on Smart Grids with two of his colleagues.
Professor Dr. Bachar A. ElHassan got his engineering Diploma in 1991 from the
faculty of engineering of the Lebanese university, Tripoli Lebanon, his MS in signal
processing in 1992 from the National polytechnic institute (INPG) of Grenoble,
France and his PhD in electronics in 1995 from the INPG-France in collaboration with
France Telecom. Since 1996 till now he is working at the faculty of engineering of the
Lebanese university Tripoli Lebanon where he is now an associate professor. He
served as chairman of the electrical engineering department for six years. He also
founded the Telecommunications and Networking research team at the laboratory of
Electronics Systems, Telecommunication and Networking (LaSTRe) of the Doctoral
School of Sciences and Technologies (EDST) of the Lebanese University. His research interest’s concern
digital communication and wireless sensor networks. He is the chair of IEEE ComSoc Lebanon chapter
since Jan 2013.