The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fift... more The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the ML-assisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration. INDEX TERMS 5G, massive MIMO, millimeter-wave transmission, machine learning, adaptive beamforming.
2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Data gathering and critical events detection are two essential functionalities for Wireless Senso... more Data gathering and critical events detection are two essential functionalities for Wireless Sensor Network (WSN). In this paper, we propose double mobile sinks network architecture, where two mobile sink nodes visit the Cluster Heads (CHs) to collect the captured data, which is very energy effective in terms of energy transmission efficiency and reliable compared with the case of having one static sink node. Moreover, the proposed architecture provides a capable scheme for supporting critical and non-critical data, which assures a timely delivery for any critical event to the remote monitoring and decision-making center with minimal interference to the non-critical data. Our proposed architecture shows a superior performance in terms of packets transmission delay, and requires low buffer occupancy for the CHs nodes when compared to related work in the literature. Finally, the paper provides a preliminary hardware design and implementation for the proposed architecture.
Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficu... more Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model finetuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease's spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases. INDEX TERMS CBIR, CNN, deep convolutional features, deep learning, global features, image retrieval, LoRaWAN, local features.
2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2019
A number of sensor network applications are envisioned to be applied to industry settings where t... more A number of sensor network applications are envisioned to be applied to industry settings where the existence of mobile nodes (MN) is required. In critical applications, the realtime monitoring of a MN must always be available, something that requires the existence of a suitable mobility protocol to control the handoff procedure. In this paper, we use an industrial WSN setting to perform a comprehensive performance evaluation of different mobility handling solutions based on single-and multiple-metric options. The results show that Fuzzy Logic-based Mobility Controller (FLMC), the multiple-metric approach we used (based on Fuzzy Logic), performs better compared to any single metric-based approach under a varying set of conditions. More specifically, we demonstrate that the Fuzzy Logic-based approach can efficiently control the handoff triggering procedure and provide high reliability (low packet loss) under different mobility models, different radio propagation models, and different topologies.
2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2021
Wireless Sensor Networks (WSNs) consist of remote sensors used to monitor and record physical con... more Wireless Sensor Networks (WSNs) consist of remote sensors used to monitor and record physical conditions of the environment and analyze the collected data at a central location for further processing and decision making. WSNs measure environmental conditions such as temperature, pressure, pollution levels, humidity, etc. Long Range (LoRa) wireless technology is a good candidate for providing long-range, low data rate, low energy consumption for WSNs. In this paper, a methodology for selecting the best LoRa parameters, namely, the Spread Factor (SF), Bandwidth (BW), Coding Rate (CR), that achieve the highest bit rate, lowest packet loss, best receiver sensitivity has been proposed using the idea of Radar diagram, which should help researchers and applications' developers choose the best LoRa parameters according to their application requirements. Furthermore, a set of experimental tests has been conducted to study experimentally the LoRa parameters' impact on the transmission quality measured using the packet loss rate (PLR) and the Receiving Signal Strength Indicator (RSSI).
2018 Innovations in Intelligent Systems and Applications (INISTA), 2018
Applying Wireless Sensor Networks to industry settings requests the dynamic reaction of mobile no... more Applying Wireless Sensor Networks to industry settings requests the dynamic reaction of mobile nodes. In critical applications, such as, e-health monitoring of workers, the existence of a suitable protocol to efficiently handle the hand-off procedure is essential for the real-time monitoring of mobile nodes. The main contribution of this paper is a comprehensive performance evaluation considering different single and multiple metric options. The results confirm that the implementation of a fuzzy method to control the triggering procedure outperforms solutions that are based only on single metric approaches.
The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80... more The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80% of the global population is expected to be living in urban areas. To become smart, a city should develop an approach of services that will focus mainly on citizens to be the primary beneficiaries of the services offered by a Smart City. In this work, we present through a survey of 545 participants, the citizens’ perception about the smart city concept and reveal the Greek and Cypriot citizens’ level of knowledge regards to a Smart City’s actions, applications, and elements. The final results of this study revealed several interesting outcomes. Firstly, this study showed that Cypriot citizens seem to know better what a “Smart City” is compared to Greek citizens, secondly, the study revealed that a large number of participants do not believe that any efforts have been made in their city in order to become “smart” and finally, regards to the most important challenges for the development o...
2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)
The COVID-19 pandemic is stress-testing existing health information exchange systems. There exist... more The COVID-19 pandemic is stress-testing existing health information exchange systems. There exists an increasing demand for sharing patient information and efficiently responding to patient medial data requests. Current health information technologies lack data fluidity, especially for remotely sharing medical data beyond their protected, local data storage. This paper presents a blockchain-based data-sharing framework that leverages the properties of immutability and decentralization to ensure a secure, user-centric approach for accessing and controlling access to sensitive medical data. The proposed framework builds its foundations on a peer-to-peer network fueled by the distributed InterPlanetary File System combined with on-chain tagging, and on the use of cryptographic generation techniques for enabling a secure way of sharing medical data. The flow of information is orchestrated by a smart-contract deployed on a blockchain-based protocol to ensure traceability and data integrity. The effectiveness of the framework is demonstrated with the implementation of the framework over a pilot study.
2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)
Artificial Intelligence (AI) has been flourishing recently as a viable solution for many applicat... more Artificial Intelligence (AI) has been flourishing recently as a viable solution for many applications and scenarios, including smart irrigation and weather forecasting systems. In these systems, it is crucial to have an accurate prediction for the weather and soil conditions to optimize the irrigation process such that the minimal amount of water is provided. In this paper, a smart irrigation system utilizing Artificial Intelligence (AI) and Long Range Wide Area Network (LoRaWAN) communication link is proposed. The system is composed of sensors that are used to measure soil moisture, atmosphere temperature, and humidity. This information is sent via LoRaWAN communication link to a remote center that gathers, analyzes the captured information, quantifies the appropriate amount of water for irrigation, and then sends the decision back to the irrigation system. Furthermore, the collected information will be stored in the cloud for wider accessibility. This paper describes the technical implementation of the smart irrigation system and focuses on the weather forecasting process, which is performed using the Wind Driven Optimization - Least Square Support Vector Machine (WDO-LS-SVM) algorithm. The obtained results show a better performance when compared to the LS-SVM, which verifies the effectiveness of jointly utilizing the WDO with the LS-SVM.
The remarkable evolution of the IoT raised the need for an efficient way to update the device’s f... more The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway.
Our epoch is continuously disrupted by the rapid technological advances in various scientific dom... more Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final r...
Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attemp... more Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers' vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms' increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its' pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.
Technology is composed of the words "Techne" and "Logos" that refer to the artistic/ creative and... more Technology is composed of the words "Techne" and "Logos" that refer to the artistic/ creative and the logical/scientific aspects of its dualism. And so inherent this Promethean concept lie the concepts of the Schumpeterian creative destruction and also the promise and potential for humanity's better tomorrows. We live in an era of artificial intelligence-driven as well as viral disruptions that challenge the mind as well as the body. At the same time, the impact of our pursuit of prosperity at any cost on the environment triggers displaced people floods and viral pandemics undermining the standard of living and more importantly the foundations of trust in institutions and in a better tomorrow feeding populist movements and autocratic trends in democracies as well as emboldening dictators. This work discusses the concepts of Risk Management 5.0, Industry 4.0, Industry 5.0, Society 5.0, Digital Transformation, Blockchain, and the role of AI via the Internet of Things architectures that could enable "smarter as well as more humane solutions to our challenges."
Hardware complexity reduction is a key concept towards the design and implementation of next gene... more Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed systemlevel simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks - PM2HW2N '12, 2012
In recent years, sensor networks characteristics have led to incremental utilization in different... more In recent years, sensor networks characteristics have led to incremental utilization in different types of applications. Several techniques have been proposed to evaluate the performance of WSNs; the two most popular being mathematical analysis and simulations. An important drawback of these techniques is that they provide evaluation results that usually are not similar to those of real deployments. One reason for this is the fact that both techniques introduce physical layer modeling assumptions, which do not usually corresponded to real-life environments. In this paper, we used measurements from an industrial environment to develop a new radio propagation model for use in simulators and mathematical tools. The proposed radio model was implemented in the COOJA simulator and validated against real-life results obtained from a testbed inside a running oil refinery, which were found not to conform to any legacy radio propagation model. The proposed model has been shown to successfully match the refinery testbed behavior.
The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fift... more The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the ML-assisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration. INDEX TERMS 5G, massive MIMO, millimeter-wave transmission, machine learning, adaptive beamforming.
2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Data gathering and critical events detection are two essential functionalities for Wireless Senso... more Data gathering and critical events detection are two essential functionalities for Wireless Sensor Network (WSN). In this paper, we propose double mobile sinks network architecture, where two mobile sink nodes visit the Cluster Heads (CHs) to collect the captured data, which is very energy effective in terms of energy transmission efficiency and reliable compared with the case of having one static sink node. Moreover, the proposed architecture provides a capable scheme for supporting critical and non-critical data, which assures a timely delivery for any critical event to the remote monitoring and decision-making center with minimal interference to the non-critical data. Our proposed architecture shows a superior performance in terms of packets transmission delay, and requires low buffer occupancy for the CHs nodes when compared to related work in the literature. Finally, the paper provides a preliminary hardware design and implementation for the proposed architecture.
Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficu... more Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model finetuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease's spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases. INDEX TERMS CBIR, CNN, deep convolutional features, deep learning, global features, image retrieval, LoRaWAN, local features.
2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), 2019
A number of sensor network applications are envisioned to be applied to industry settings where t... more A number of sensor network applications are envisioned to be applied to industry settings where the existence of mobile nodes (MN) is required. In critical applications, the realtime monitoring of a MN must always be available, something that requires the existence of a suitable mobility protocol to control the handoff procedure. In this paper, we use an industrial WSN setting to perform a comprehensive performance evaluation of different mobility handling solutions based on single-and multiple-metric options. The results show that Fuzzy Logic-based Mobility Controller (FLMC), the multiple-metric approach we used (based on Fuzzy Logic), performs better compared to any single metric-based approach under a varying set of conditions. More specifically, we demonstrate that the Fuzzy Logic-based approach can efficiently control the handoff triggering procedure and provide high reliability (low packet loss) under different mobility models, different radio propagation models, and different topologies.
2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), 2021
Wireless Sensor Networks (WSNs) consist of remote sensors used to monitor and record physical con... more Wireless Sensor Networks (WSNs) consist of remote sensors used to monitor and record physical conditions of the environment and analyze the collected data at a central location for further processing and decision making. WSNs measure environmental conditions such as temperature, pressure, pollution levels, humidity, etc. Long Range (LoRa) wireless technology is a good candidate for providing long-range, low data rate, low energy consumption for WSNs. In this paper, a methodology for selecting the best LoRa parameters, namely, the Spread Factor (SF), Bandwidth (BW), Coding Rate (CR), that achieve the highest bit rate, lowest packet loss, best receiver sensitivity has been proposed using the idea of Radar diagram, which should help researchers and applications' developers choose the best LoRa parameters according to their application requirements. Furthermore, a set of experimental tests has been conducted to study experimentally the LoRa parameters' impact on the transmission quality measured using the packet loss rate (PLR) and the Receiving Signal Strength Indicator (RSSI).
2018 Innovations in Intelligent Systems and Applications (INISTA), 2018
Applying Wireless Sensor Networks to industry settings requests the dynamic reaction of mobile no... more Applying Wireless Sensor Networks to industry settings requests the dynamic reaction of mobile nodes. In critical applications, such as, e-health monitoring of workers, the existence of a suitable protocol to efficiently handle the hand-off procedure is essential for the real-time monitoring of mobile nodes. The main contribution of this paper is a comprehensive performance evaluation considering different single and multiple metric options. The results confirm that the implementation of a fuzzy method to control the triggering procedure outperforms solutions that are based only on single metric approaches.
The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80... more The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80% of the global population is expected to be living in urban areas. To become smart, a city should develop an approach of services that will focus mainly on citizens to be the primary beneficiaries of the services offered by a Smart City. In this work, we present through a survey of 545 participants, the citizens’ perception about the smart city concept and reveal the Greek and Cypriot citizens’ level of knowledge regards to a Smart City’s actions, applications, and elements. The final results of this study revealed several interesting outcomes. Firstly, this study showed that Cypriot citizens seem to know better what a “Smart City” is compared to Greek citizens, secondly, the study revealed that a large number of participants do not believe that any efforts have been made in their city in order to become “smart” and finally, regards to the most important challenges for the development o...
2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS)
The COVID-19 pandemic is stress-testing existing health information exchange systems. There exist... more The COVID-19 pandemic is stress-testing existing health information exchange systems. There exists an increasing demand for sharing patient information and efficiently responding to patient medial data requests. Current health information technologies lack data fluidity, especially for remotely sharing medical data beyond their protected, local data storage. This paper presents a blockchain-based data-sharing framework that leverages the properties of immutability and decentralization to ensure a secure, user-centric approach for accessing and controlling access to sensitive medical data. The proposed framework builds its foundations on a peer-to-peer network fueled by the distributed InterPlanetary File System combined with on-chain tagging, and on the use of cryptographic generation techniques for enabling a secure way of sharing medical data. The flow of information is orchestrated by a smart-contract deployed on a blockchain-based protocol to ensure traceability and data integrity. The effectiveness of the framework is demonstrated with the implementation of the framework over a pilot study.
2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)
Artificial Intelligence (AI) has been flourishing recently as a viable solution for many applicat... more Artificial Intelligence (AI) has been flourishing recently as a viable solution for many applications and scenarios, including smart irrigation and weather forecasting systems. In these systems, it is crucial to have an accurate prediction for the weather and soil conditions to optimize the irrigation process such that the minimal amount of water is provided. In this paper, a smart irrigation system utilizing Artificial Intelligence (AI) and Long Range Wide Area Network (LoRaWAN) communication link is proposed. The system is composed of sensors that are used to measure soil moisture, atmosphere temperature, and humidity. This information is sent via LoRaWAN communication link to a remote center that gathers, analyzes the captured information, quantifies the appropriate amount of water for irrigation, and then sends the decision back to the irrigation system. Furthermore, the collected information will be stored in the cloud for wider accessibility. This paper describes the technical implementation of the smart irrigation system and focuses on the weather forecasting process, which is performed using the Wind Driven Optimization - Least Square Support Vector Machine (WDO-LS-SVM) algorithm. The obtained results show a better performance when compared to the LS-SVM, which verifies the effectiveness of jointly utilizing the WDO with the LS-SVM.
The remarkable evolution of the IoT raised the need for an efficient way to update the device’s f... more The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway.
Our epoch is continuously disrupted by the rapid technological advances in various scientific dom... more Our epoch is continuously disrupted by the rapid technological advances in various scientific domains that aim to drive forward the Fourth Industrial Revolution. This disruption resulted in the introduction of fields that present advanced ways to train students as well as ways to secure the exchange of data and guarantee the integrity of those data. In this paper, a decentralized application (dApp), namely skillsChain, is introduced that utilizes Blockchain in educational robotics to securely track the development of students’ skills so as to be transferable beyond the confines of the academic world. This work outlines a state-of-the-art architecture in which educational robotics can directly execute transactions on a public ledger when certain requirements are met without the need of educators. In addition, it allows students to safely exchange their skills’ records with third parties. The proposed application was designed and deployed on a public distributed ledger and the final r...
Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attemp... more Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers' vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms' increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its' pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.
Technology is composed of the words "Techne" and "Logos" that refer to the artistic/ creative and... more Technology is composed of the words "Techne" and "Logos" that refer to the artistic/ creative and the logical/scientific aspects of its dualism. And so inherent this Promethean concept lie the concepts of the Schumpeterian creative destruction and also the promise and potential for humanity's better tomorrows. We live in an era of artificial intelligence-driven as well as viral disruptions that challenge the mind as well as the body. At the same time, the impact of our pursuit of prosperity at any cost on the environment triggers displaced people floods and viral pandemics undermining the standard of living and more importantly the foundations of trust in institutions and in a better tomorrow feeding populist movements and autocratic trends in democracies as well as emboldening dictators. This work discusses the concepts of Risk Management 5.0, Industry 4.0, Industry 5.0, Society 5.0, Digital Transformation, Blockchain, and the role of AI via the Internet of Things architectures that could enable "smarter as well as more humane solutions to our challenges."
Hardware complexity reduction is a key concept towards the design and implementation of next gene... more Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed systemlevel simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).
Proceedings of the 7th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks - PM2HW2N '12, 2012
In recent years, sensor networks characteristics have led to incremental utilization in different... more In recent years, sensor networks characteristics have led to incremental utilization in different types of applications. Several techniques have been proposed to evaluate the performance of WSNs; the two most popular being mathematical analysis and simulations. An important drawback of these techniques is that they provide evaluation results that usually are not similar to those of real deployments. One reason for this is the fact that both techniques introduce physical layer modeling assumptions, which do not usually corresponded to real-life environments. In this paper, we used measurements from an industrial environment to develop a new radio propagation model for use in simulators and mathematical tools. The proposed radio model was implemented in the COOJA simulator and validated against real-life results obtained from a testbed inside a running oil refinery, which were found not to conform to any legacy radio propagation model. The proposed model has been shown to successfully match the refinery testbed behavior.
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