Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975942
Mahmoud M. Salim, H. Elsayed, Mohamed S. Abdalzaher, M. Fouda
Radio frequency (RF) Energy Harvesting (EH) plays an important role to deal with the energy efficiency (EE) shortage for wireless networks. On the other hand, relay nodes (RNs) can participate in device-to-device (D2D) communications to play a significant role in enhancing their performance. Also, they can exploit the RF EH while assisting the relay-aided D2D networks. This paper investigates the two-way relaying (TWR) D2D communication underlaying conventional cellular communication assuming the RF EH capabilities of the relays based on the power splitting (PS) protocol. Accordingly, the paper contributions are divided into two folds. First, it presents a power allocation (PA) model such that the TWR D2D link rate is maximized. The second and more important contribution is that the paper answers the contentious question of whether using RF EH in TWR D2D communication is worthwhile. The results depict the consistency of the PA model according to various parameters as well as the RF EH dependency for the participating relays.
{"title":"RF Energy Harvesting Dependency for Power Optimized Two-Way Relaying D2D Communication","authors":"Mahmoud M. Salim, H. Elsayed, Mohamed S. Abdalzaher, M. Fouda","doi":"10.1109/IoTaIS56727.2022.9975942","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975942","url":null,"abstract":"Radio frequency (RF) Energy Harvesting (EH) plays an important role to deal with the energy efficiency (EE) shortage for wireless networks. On the other hand, relay nodes (RNs) can participate in device-to-device (D2D) communications to play a significant role in enhancing their performance. Also, they can exploit the RF EH while assisting the relay-aided D2D networks. This paper investigates the two-way relaying (TWR) D2D communication underlaying conventional cellular communication assuming the RF EH capabilities of the relays based on the power splitting (PS) protocol. Accordingly, the paper contributions are divided into two folds. First, it presents a power allocation (PA) model such that the TWR D2D link rate is maximized. The second and more important contribution is that the paper answers the contentious question of whether using RF EH in TWR D2D communication is worthwhile. The results depict the consistency of the PA model according to various parameters as well as the RF EH dependency for the participating relays.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128431276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975972
M. M. Daud, Z. Kadim, H. W. Hon
The palm oil industry can be considered one of the crucial industries, especially in Asian countries like Malaysia and Indonesia. The production flow is depending on the efficiency of the palm oil harvest management. The management during harvest time includes collecting and counting the fresh fruit bunch (FFB) and loose fruitlet (LF) production. Thus, in this paper, we proposed a method that able to detect and count the FFB and LF production automatically. The as-proposed method consisted of two parts: (a) fruitlet detection using an image processing prior to harvest and (b) palm oil tree, FFB, and grabber detection using the Faster R-CNN algorithm during harvest time. During the pre-harvest, the number of fruitlets can be determined through the status of the tree either by ready-to-harvest (RTH) or not ready-to-harvest (NRTH). If the status was RTH, the system performed tree detection for tagging purposes. When they started to harvest, the detection system would detect the FFB and grabber. It would then track the grabber until overlaps with the FFB location. Then, the system would add the FFB to the count module. It means the FFB had been taken to the truck. The as-proposed system achieved the detection accuracy of 96.5% for FFB, 99.2% for grabber, and 97.2% for tree.
{"title":"Loose Fruitlet and Fresh Fruit Bunch Detection for Palm Oil Harvest Management","authors":"M. M. Daud, Z. Kadim, H. W. Hon","doi":"10.1109/IoTaIS56727.2022.9975972","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975972","url":null,"abstract":"The palm oil industry can be considered one of the crucial industries, especially in Asian countries like Malaysia and Indonesia. The production flow is depending on the efficiency of the palm oil harvest management. The management during harvest time includes collecting and counting the fresh fruit bunch (FFB) and loose fruitlet (LF) production. Thus, in this paper, we proposed a method that able to detect and count the FFB and LF production automatically. The as-proposed method consisted of two parts: (a) fruitlet detection using an image processing prior to harvest and (b) palm oil tree, FFB, and grabber detection using the Faster R-CNN algorithm during harvest time. During the pre-harvest, the number of fruitlets can be determined through the status of the tree either by ready-to-harvest (RTH) or not ready-to-harvest (NRTH). If the status was RTH, the system performed tree detection for tagging purposes. When they started to harvest, the detection system would detect the FFB and grabber. It would then track the grabber until overlaps with the FFB location. Then, the system would add the FFB to the count module. It means the FFB had been taken to the truck. The as-proposed system achieved the detection accuracy of 96.5% for FFB, 99.2% for grabber, and 97.2% for tree.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127367376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975858
Surjandy, Meyliana, A. Condrobimo, H. A. Widjaja, Cadelina Cassandra
A smart contract is a business process agreed upon to run. With a smart contract, all business processes can run automatically after the smart contract receives a trigger and cannot be canceled or changed. Previous research on smart boxes is generally only used for health storage of smart drugs. In this study, facilitated off-chain blockchain model MSCWR SmartBox will focus on shipping for logistics, such as for the use of shipping goods such as valuables. This research focuses on creating an off-chain blockchain smart contract model that is written in pseudocode to support the operations of the MSCWR SmartBox. The creation of off-chain blockchain smart contracts is very important in supporting the MSCWR business process. Making off-chain blockchain Smart Contracts is very important considering the business processes that create follow the Multichain process (Blockchain applications for cryptocurrencies without changing the multichain application). The research method used is user-centered design (UCD). In this study, we use an application prototype approach, use multichain as a blockchain platform and use the environment at BeeBlock Laboratories. The validation and testing of the equipment used with proof of logs from the server show that the smart contract created can be used to support the business processes of the MSCWR SmartBox.
{"title":"The Development Off-Chain Blockchain Smart Contract Model on MSCWR SmartBox","authors":"Surjandy, Meyliana, A. Condrobimo, H. A. Widjaja, Cadelina Cassandra","doi":"10.1109/IoTaIS56727.2022.9975858","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975858","url":null,"abstract":"A smart contract is a business process agreed upon to run. With a smart contract, all business processes can run automatically after the smart contract receives a trigger and cannot be canceled or changed. Previous research on smart boxes is generally only used for health storage of smart drugs. In this study, facilitated off-chain blockchain model MSCWR SmartBox will focus on shipping for logistics, such as for the use of shipping goods such as valuables. This research focuses on creating an off-chain blockchain smart contract model that is written in pseudocode to support the operations of the MSCWR SmartBox. The creation of off-chain blockchain smart contracts is very important in supporting the MSCWR business process. Making off-chain blockchain Smart Contracts is very important considering the business processes that create follow the Multichain process (Blockchain applications for cryptocurrencies without changing the multichain application). The research method used is user-centered design (UCD). In this study, we use an application prototype approach, use multichain as a blockchain platform and use the environment at BeeBlock Laboratories. The validation and testing of the equipment used with proof of logs from the server show that the smart contract created can be used to support the business processes of the MSCWR SmartBox.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130874851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975951
Hanem I. Hegazy, Adly S. Tag Eldien, M. M. Tantawy, M. Fouda, Heba A. Tageldien
The smart grid is a multi-dimensional data-generating cyber-physical system. Distributed architectures and the heterogeneous nature of the Internet-of-Things (IoT) sensors make it more prone to various cyber-attacks. False data injection attacks (FDIAs) have recently emerged as significant threats to smart grid state estimation. As a result, real-time locational detection of stealthy FDIAs is critical for smart grid security and reliability. In this paper, we introduce a comparative analysis of various deep-learning approaches to test their effectiveness in the location-based detection of FDIA. Also, a deep learning approach is developed by constructing a multi-feature architecture based on a convolution neural network and long short-term memory network (MCNN-LSTM). Extensive testing on IEEE test cases has demonstrated that the proposed approach outperforms the existing deep learning approaches in locating FDIAs for small and large systems under different attack scenarios. We evaluate the performance of each model in terms of presence and location-based detection accuracy, model complexity, and prediction time. Extensive results in the IEEE 14 and IEEE 118-bus systems show that the suggested architecture has a locational detection accuracy of more than 94% and 95%, respectively. From the results, we can conclude the proposed approach is more robust, scalable, and faster in detecting the locations of compromised measurements than the other deep learning models.
{"title":"Online Location-based Detection of False Data Injection Attacks in Smart Grid Using Deep Learning","authors":"Hanem I. Hegazy, Adly S. Tag Eldien, M. M. Tantawy, M. Fouda, Heba A. Tageldien","doi":"10.1109/IoTaIS56727.2022.9975951","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975951","url":null,"abstract":"The smart grid is a multi-dimensional data-generating cyber-physical system. Distributed architectures and the heterogeneous nature of the Internet-of-Things (IoT) sensors make it more prone to various cyber-attacks. False data injection attacks (FDIAs) have recently emerged as significant threats to smart grid state estimation. As a result, real-time locational detection of stealthy FDIAs is critical for smart grid security and reliability. In this paper, we introduce a comparative analysis of various deep-learning approaches to test their effectiveness in the location-based detection of FDIA. Also, a deep learning approach is developed by constructing a multi-feature architecture based on a convolution neural network and long short-term memory network (MCNN-LSTM). Extensive testing on IEEE test cases has demonstrated that the proposed approach outperforms the existing deep learning approaches in locating FDIAs for small and large systems under different attack scenarios. We evaluate the performance of each model in terms of presence and location-based detection accuracy, model complexity, and prediction time. Extensive results in the IEEE 14 and IEEE 118-bus systems show that the suggested architecture has a locational detection accuracy of more than 94% and 95%, respectively. From the results, we can conclude the proposed approach is more robust, scalable, and faster in detecting the locations of compromised measurements than the other deep learning models.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133108086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975928
Haesik Kim
The 5G for HEalth, AquacultuRe and Transport (5G-HEART) validation trials project validates 5G vertical trials on top of all three ICT-17 facilities and two national 5G test platforms with use cases from three different vertical domains. The key selected verticals for 5G-HEART trials are healthcare, transport, and aquaculture that have been identified as priority vertical sectors for Europe. All three vertical use cases include multiple scenarios, providing a diverse set of requirements for the project. The project consortium contains full value chains for the each vertical and enables the validation of both vertical specific business Key Performance Indicators (KPIs) and technical 5G network KPIs during the trials. In this paper, key use case scenarios of 5G-HEART are introduced and also the selected trials results are presented.
{"title":"5G Vertical Trials, Use Cases and Scenarios","authors":"Haesik Kim","doi":"10.1109/IoTaIS56727.2022.9975928","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975928","url":null,"abstract":"The 5G for HEalth, AquacultuRe and Transport (5G-HEART) validation trials project validates 5G vertical trials on top of all three ICT-17 facilities and two national 5G test platforms with use cases from three different vertical domains. The key selected verticals for 5G-HEART trials are healthcare, transport, and aquaculture that have been identified as priority vertical sectors for Europe. All three vertical use cases include multiple scenarios, providing a diverse set of requirements for the project. The project consortium contains full value chains for the each vertical and enables the validation of both vertical specific business Key Performance Indicators (KPIs) and technical 5G network KPIs during the trials. In this paper, key use case scenarios of 5G-HEART are introduced and also the selected trials results are presented.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975938
Siswandi Agung Hidayat, Wahyu Juniardi, A. Khatami, Riri Fitri Sari
In today’s technological developments, the need for a system to guarantee security, transparency, and transaction speed is the underlying point in creating new technologies. Blockchain is a digital data recording and storage technology that is interconnected between one device and another and cannot be changed by anyone due to the implementation of cryptography. The consensus algorithm is a mechanism used by computers and blockchain systems in approving the addition of new data. In Blockchain, no one authority oversees the activities where the entire system is made in a decentralized manner so that decision making, verification, and authentication are on the blockchain must involve all users in it. Therefore, the consensus is needed by the blockchain in forming an efficient, fair, reliable, and secure mechanism so that all parties involved in it can have a vote. In this paper, we evaluate the performance of several consensus algorithms, such as Paxos, Raft, and PBFT, by simulating the time to reach consensus using the NS3 network simulator. We chose Paxos because this algorithm is the forerunner of the consensus algorithm, while Raft and PBFT are algorithms that have evolved from Paxos, which are still widely implemented in blockchain technology until now. Finally, based on the evaluation results, it was found that the PBFT algorithm has a speed five times faster than Raft and six times faster than Paxos to reach consensus. So we consider the PBFT algorithm to have the best speed and scalability. We hope that this research can be used as a reference for implementing the consensus algorithm in the development of blockchain technology.
{"title":"Performance Comparison and Analysis of Paxos, Raft and PBFT Using NS3","authors":"Siswandi Agung Hidayat, Wahyu Juniardi, A. Khatami, Riri Fitri Sari","doi":"10.1109/IoTaIS56727.2022.9975938","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975938","url":null,"abstract":"In today’s technological developments, the need for a system to guarantee security, transparency, and transaction speed is the underlying point in creating new technologies. Blockchain is a digital data recording and storage technology that is interconnected between one device and another and cannot be changed by anyone due to the implementation of cryptography. The consensus algorithm is a mechanism used by computers and blockchain systems in approving the addition of new data. In Blockchain, no one authority oversees the activities where the entire system is made in a decentralized manner so that decision making, verification, and authentication are on the blockchain must involve all users in it. Therefore, the consensus is needed by the blockchain in forming an efficient, fair, reliable, and secure mechanism so that all parties involved in it can have a vote. In this paper, we evaluate the performance of several consensus algorithms, such as Paxos, Raft, and PBFT, by simulating the time to reach consensus using the NS3 network simulator. We chose Paxos because this algorithm is the forerunner of the consensus algorithm, while Raft and PBFT are algorithms that have evolved from Paxos, which are still widely implemented in blockchain technology until now. Finally, based on the evaluation results, it was found that the PBFT algorithm has a speed five times faster than Raft and six times faster than Paxos to reach consensus. So we consider the PBFT algorithm to have the best speed and scalability. We hope that this research can be used as a reference for implementing the consensus algorithm in the development of blockchain technology.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"7 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133076594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975979
R. Avanzato, F. Beritelli, Valerio Francesco Puglisi
The application of automated monitoring systems for precision breeding has seen a great increase in recent years. In particular, several studies have addressed the possibility of recognizing cow behavior using computer vision, as well as the opportunity of uniquely identifying and locating individual cows within the barn. In this study, the authors propose a system for recognizing cow behavior within the barn, using a particular type of Convolutional Neural Network (CNN), YOLOv5, and estimation of cattle position via Multi-object recognition. The recordings are obtained from multiple cameras placed inside the barn, a mixed and vast dataset containing several “Cow” objects was obtained and then labeled in two classes “Cow_Standing” and “Cow_Lying.” After the training phase, testing of the network was carried out. The results obtained using this Deep Learning (DL) model, show 94% accuracy, 96% precision and 92% recall in the training phase. In the inference phase, accuracy and recall of 88% and 91% were obtained, respectively.
{"title":"Dairy Cow Behavior Recognition Using Computer Vision Techniques and CNN Networks","authors":"R. Avanzato, F. Beritelli, Valerio Francesco Puglisi","doi":"10.1109/IoTaIS56727.2022.9975979","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975979","url":null,"abstract":"The application of automated monitoring systems for precision breeding has seen a great increase in recent years. In particular, several studies have addressed the possibility of recognizing cow behavior using computer vision, as well as the opportunity of uniquely identifying and locating individual cows within the barn. In this study, the authors propose a system for recognizing cow behavior within the barn, using a particular type of Convolutional Neural Network (CNN), YOLOv5, and estimation of cattle position via Multi-object recognition. The recordings are obtained from multiple cameras placed inside the barn, a mixed and vast dataset containing several “Cow” objects was obtained and then labeled in two classes “Cow_Standing” and “Cow_Lying.” After the training phase, testing of the network was carried out. The results obtained using this Deep Learning (DL) model, show 94% accuracy, 96% precision and 92% recall in the training phase. In the inference phase, accuracy and recall of 88% and 91% were obtained, respectively.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975871
Muhammad Habib Ullah, G. Gelli, F. Verde
Sensor systems for electronic health (e-Health) applications typically rely on radio-frequency (RF) wireless components, which are bulky and energy-demanding. Moreover, their electromagnetic radiation may cause interference to medical devices and side effects on living organisms. In such applications, an intriguing alternative to RF-based transmission is visible light communications (VLC), which leverage light-emitting diodes (LEDs) for data transmission, in addition to illumination purposes. In many cases, e-Health applications demand bi-directional communication among LEDs and sensor tags. In this paper, we explore the feasibility of potentially using optical backscattering to perform indoors bi-directional VLC for e-Health applications in hospital environments. Specifically, we develop a mathematical model of the visible light backscattering link, which allows one to accurately predict the amount of light required to ensure an acceptable received power. Moreover, we analytically show the impact of the relevant system parameters on the achievable bit-error-rate performance of the information transfer process. Finally, we verify our analytical findings regarding system performance via numerical simulations.
{"title":"Visible light backscattering communications in healthcare scenarios: link modeling and performance analysis","authors":"Muhammad Habib Ullah, G. Gelli, F. Verde","doi":"10.1109/IoTaIS56727.2022.9975871","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975871","url":null,"abstract":"Sensor systems for electronic health (e-Health) applications typically rely on radio-frequency (RF) wireless components, which are bulky and energy-demanding. Moreover, their electromagnetic radiation may cause interference to medical devices and side effects on living organisms. In such applications, an intriguing alternative to RF-based transmission is visible light communications (VLC), which leverage light-emitting diodes (LEDs) for data transmission, in addition to illumination purposes. In many cases, e-Health applications demand bi-directional communication among LEDs and sensor tags. In this paper, we explore the feasibility of potentially using optical backscattering to perform indoors bi-directional VLC for e-Health applications in hospital environments. Specifically, we develop a mathematical model of the visible light backscattering link, which allows one to accurately predict the amount of light required to ensure an acceptable received power. Moreover, we analytically show the impact of the relevant system parameters on the achievable bit-error-rate performance of the information transfer process. Finally, we verify our analytical findings regarding system performance via numerical simulations.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127992170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975849
Evangelia Konstantopoulou, N. Sklavos
With the advent of 5G and 6G networks and the anticipated expansion of the Internet of Things (IoT), novel applications are developed to address the need for low latency, capacity, higher data rate, and QoS for an unprecedentedly large number of devices. Demand for lightweight, fast, and efficient cryptographic algorithms is emerging, as an increasing number of systems that are used daily are becoming time-critical and often constrained in resources. One such algorithm that has been proposed is stream cipher Espresso, developed to simultaneously improve both hardware size and performance. At the same time, NIST states that any proposed lightweight cryptographic algorithm must fulfill the standards outlined in the Hardware API for Lightweight Cryptography specification, in order to ensure fair benchmarking. In this paper, a Lightweight Cryptographic Module compliant with these requirements is suggested. The crypto core employs an optimized implementation of the Espresso algorithm, both in comparison to other stream ciphers and to other Espresso implementations in the literature. The system is built on the Spartan-7 series xc7s100fgga676-2 Field Programmable Gate Array (FPGA) and works at a maximum frequency of 687 MHz.
{"title":"Design and Implementation of a Lightweight Cryptographic Module, for Wireless 5G Communications and Beyond","authors":"Evangelia Konstantopoulou, N. Sklavos","doi":"10.1109/IoTaIS56727.2022.9975849","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975849","url":null,"abstract":"With the advent of 5G and 6G networks and the anticipated expansion of the Internet of Things (IoT), novel applications are developed to address the need for low latency, capacity, higher data rate, and QoS for an unprecedentedly large number of devices. Demand for lightweight, fast, and efficient cryptographic algorithms is emerging, as an increasing number of systems that are used daily are becoming time-critical and often constrained in resources. One such algorithm that has been proposed is stream cipher Espresso, developed to simultaneously improve both hardware size and performance. At the same time, NIST states that any proposed lightweight cryptographic algorithm must fulfill the standards outlined in the Hardware API for Lightweight Cryptography specification, in order to ensure fair benchmarking. In this paper, a Lightweight Cryptographic Module compliant with these requirements is suggested. The crypto core employs an optimized implementation of the Espresso algorithm, both in comparison to other stream ciphers and to other Espresso implementations in the literature. The system is built on the Spartan-7 series xc7s100fgga676-2 Field Programmable Gate Array (FPGA) and works at a maximum frequency of 687 MHz.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128013303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1109/IoTaIS56727.2022.9975981
Yangyang Chang, G. Sobelman
This paper proposes the class algorithm, a new type of evolutionary algorithm. The methodology is inspired by the concepts of division of labor and specialization. Individuals form subpopulations of different classes, where each class has its own characteristics. The entire population evolves through influences among individuals within and between the different subpopulations. The proposed approach can be applied in both continuous and discrete problem domains. The performance of the class algorithm surpasses other evolutionary algorithms for many test functions of single-objective continuous optimization benchmark problems. The class algorithm also shows a competent ability to solve the large-scale discrete optimization problems.
{"title":"The Class Algorithm: Evolution Based on Division of Labor and Specialization","authors":"Yangyang Chang, G. Sobelman","doi":"10.1109/IoTaIS56727.2022.9975981","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975981","url":null,"abstract":"This paper proposes the class algorithm, a new type of evolutionary algorithm. The methodology is inspired by the concepts of division of labor and specialization. Individuals form subpopulations of different classes, where each class has its own characteristics. The entire population evolves through influences among individuals within and between the different subpopulations. The proposed approach can be applied in both continuous and discrete problem domains. The performance of the class algorithm surpasses other evolutionary algorithms for many test functions of single-objective continuous optimization benchmark problems. The class algorithm also shows a competent ability to solve the large-scale discrete optimization problems.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130282565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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