Kamal Kumar, Vinod Kumar, Seema, M. Sharma, Akber Ali Khan, M. J. Idrisi
Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.
{"title":"A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems","authors":"Kamal Kumar, Vinod Kumar, Seema, M. Sharma, Akber Ali Khan, M. J. Idrisi","doi":"10.1155/2024/9979371","DOIUrl":"https://doi.org/10.1155/2024/9979371","url":null,"abstract":"Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788653","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}
Kamal Kumar, Vinod Kumar, Seema, M. Sharma, Akber Ali Khan, M. J. Idrisi
Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.
{"title":"A Systematic Review of Blockchain Technology Assisted with Artificial Intelligence Technology for Networks and Communication Systems","authors":"Kamal Kumar, Vinod Kumar, Seema, M. Sharma, Akber Ali Khan, M. J. Idrisi","doi":"10.1155/2024/9979371","DOIUrl":"https://doi.org/10.1155/2024/9979371","url":null,"abstract":"Blockchain is a very secure, authentic, and distributed technology and is very prominent in areas such as edge computation, cloud computation, and Internet-of-things. Artificial intelligence assists in the completion of activities efficiently and effectively by providing intelligence, analytics, and predicting capabilities. There is an obvious convergence between the two technologies. Artificial intelligence systems can utilize blockchain to establish trust in communication channels, ensuring that messages are securely transmitted and received without the need for a centralized intermediary. By leveraging blockchain, artificial intelligence systems can maintain an immutable record of communications, ensuring transparency and preventing unauthorized modifications. The integration of blockchain and artificial intelligence technologies can enhance the security, transparency, and privacy of communication systems. By leveraging blockchain’s decentralized nature and artificial intelligence’s analytical capabilities, secure and trustworthy communication channels can be established, benefiting various domains such as finance, healthcare, and supply chain. Overall, the integration of blockchain and artificial intelligence has the potential to offer several benefits, and as these technologies continue to evolve, new and innovative applications will continue to emerge.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848400","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}
Murk Marvi, Adnan Aijaz, Anam Qureshi, Muhammad Khurram
Data offloading is considered as a potential candidate for alleviating congestion on wireless networks and for improving user experience. However, due to the stochastic nature of the wireless networks, it is important to take optimal actions under different conditions such that the user experience is enhanced and congestion on heavy-loaded radio access technologies (RATs) is reduced by offloading data through lower loaded RATs. Since artificial intelligence (AI)-based techniques can learn optimal actions and adapt to different conditions, in this work, we develop an AI-enabled Q-agent for making data offloading decisions in a multi-RAT wireless network. We employ a model-free Q-learning algorithm for training of the Q-agent. We use stochastic geometry as a tool for estimating the average data rate offered by the network in a given region by considering the effect of interference. We use the Markov process for modeling users’ mobility, that is, estimating the probability that a user is currently located in a region given its previous location. The user equipment (UE) plays the role of a Q-agent responsible for taking sequence of actions such that the long-term discounted cost for using network service is minimized. Q-agent performance has been evaluated and compared with the existing data offloading policies. The results suggest that the existing policies offer the best performance under specific situations. However, the Q-agent has learned to take near-optimal actions under different conditions. Thus, the Q-agent offers performance which is close to the best under different conditions.
数据卸载被认为是缓解无线网络拥塞和改善用户体验的潜在方法。然而，由于无线网络具有随机性，因此必须在不同条件下采取最佳行动，以便通过较低负载的 RAT 卸载数据来提高用户体验并减少重负载无线接入技术（RAT）的拥塞。由于基于人工智能（AI）的技术可以学习最佳行动并适应不同条件，因此在这项工作中，我们开发了一个支持人工智能的 Q-agent，用于在多 RAT 无线网络中做出数据卸载决策。我们采用无模型 Q 学习算法来训练 Q 代理。我们使用随机几何作为工具，通过考虑干扰的影响来估算网络在给定区域内提供的平均数据传输速率。我们使用马尔可夫过程来模拟用户的移动性，即根据用户之前的位置来估算其当前位于某一区域的概率。用户设备（UE）扮演 Q 代理的角色，负责采取一系列行动，使使用网络服务的长期贴现成本最小化。对 Q 代理的性能进行了评估，并与现有的数据卸载策略进行了比较。结果表明，在特定情况下，现有策略能提供最佳性能。然而，Q 代理学会了在不同条件下采取接近最优的行动。因此，Q 代理在不同条件下都能提供接近最佳的性能。
{"title":"Development of an AI-Enabled Q-Agent for Making Data Offloading Decisions in a Multi-RAT Wireless Network","authors":"Murk Marvi, Adnan Aijaz, Anam Qureshi, Muhammad Khurram","doi":"10.1155/2024/9571987","DOIUrl":"https://doi.org/10.1155/2024/9571987","url":null,"abstract":"Data offloading is considered as a potential candidate for alleviating congestion on wireless networks and for improving user experience. However, due to the stochastic nature of the wireless networks, it is important to take optimal actions under different conditions such that the user experience is enhanced and congestion on heavy-loaded radio access technologies (RATs) is reduced by offloading data through lower loaded RATs. Since artificial intelligence (AI)-based techniques can learn optimal actions and adapt to different conditions, in this work, we develop an AI-enabled Q-agent for making data offloading decisions in a multi-RAT wireless network. We employ a model-free Q-learning algorithm for training of the Q-agent. We use stochastic geometry as a tool for estimating the average data rate offered by the network in a given region by considering the effect of interference. We use the Markov process for modeling users’ mobility, that is, estimating the probability that a user is currently located in a region given its previous location. The user equipment (UE) plays the role of a Q-agent responsible for taking sequence of actions such that the long-term discounted cost for using network service is minimized. Q-agent performance has been evaluated and compared with the existing data offloading policies. The results suggest that the existing policies offer the best performance under specific situations. However, the Q-agent has learned to take near-optimal actions under different conditions. Thus, the Q-agent offers performance which is close to the best under different conditions.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139599089","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}
Xiaorong Qiu, Ye Xu, Yingzhong Shi, S. K. Deepa, S. Balakumar
Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user’s accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases.
{"title":"Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method","authors":"Xiaorong Qiu, Ye Xu, Yingzhong Shi, S. K. Deepa, S. Balakumar","doi":"10.1155/2023/8840168","DOIUrl":"https://doi.org/10.1155/2023/8840168","url":null,"abstract":"Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user’s accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139132505","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}
Juan Benedicto L. Aceron, Marc Elizette R. Teves, Wilson M. Tan
Internet of Things (IoT) for home systems enables new functionalities and results in significant conveniences. However, their reliance on a stable, continuous Internet connectivity reduces their overall reliability. Losing connectivity to the Internet, for many of these devices, translates to the cessation of even the most basic of functionalities (e.g., being able to turn on the light, even from within the house). A possible solution to this problem is to shift some functionalities done by cloud-based servers to the edge (e.g. the home router), but doing so conveniently would necessitate the ability to dynamically identify pairs of IoT devices (usually sensor-actuator pairs) that communicate (their associations), and the rewriting/rerouting of packets or messages between those devices. The first problem is also known as the association detection problem, and is where this paper makes a contribution. We describe a solution to the association detection problem using a modified Apriori algorithm and a method to create its input from network traffic, and then revise the solution to respond to fluctuating network conditions. The final design accurately discovers sensor-actuator pairs using a simple approach with low computational complexity, and with only the hardware addresses of monitored IoT devices as its starting knowledge.
{"title":"Detecting Application-Level Associations Between IoT Devices Using a Modified Apriori Algorithm","authors":"Juan Benedicto L. Aceron, Marc Elizette R. Teves, Wilson M. Tan","doi":"10.37256/cnc.1220233263","DOIUrl":"https://doi.org/10.37256/cnc.1220233263","url":null,"abstract":"Internet of Things (IoT) for home systems enables new functionalities and results in significant conveniences. However, their reliance on a stable, continuous Internet connectivity reduces their overall reliability. Losing connectivity to the Internet, for many of these devices, translates to the cessation of even the most basic of functionalities (e.g., being able to turn on the light, even from within the house). A possible solution to this problem is to shift some functionalities done by cloud-based servers to the edge (e.g. the home router), but doing so conveniently would necessitate the ability to dynamically identify pairs of IoT devices (usually sensor-actuator pairs) that communicate (their associations), and the rewriting/rerouting of packets or messages between those devices. The first problem is also known as the association detection problem, and is where this paper makes a contribution. We describe a solution to the association detection problem using a modified Apriori algorithm and a method to create its input from network traffic, and then revise the solution to respond to fluctuating network conditions. The final design accurately discovers sensor-actuator pairs using a simple approach with low computational complexity, and with only the hardware addresses of monitored IoT devices as its starting knowledge.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992826","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}
Relying on the flexible-access interconnects to the scalable storage and compute resources, data centers deliver critical communications connectivity among numerous servers to support the housed applications and services. To provide the high-speeds and long-distance communications, the data centers have turned to fiber interconnections. With the stringently increased traffic volume, the data centers are then expected to further deploy the optical switches into the systems infrastructure to implement the full optical switching. This paper first summarizes the topologies and traffic characteristics in data centers and analyzes the reasons and importance of moving to optical switching. Recent techniques related to the optical switching, and main challenges limiting the practical deployments of optical switches in data centers are also summarized and reported.
{"title":"Optical Switching Data Center Networks: Understanding Techniques and Challenges","authors":"Yisong Zhao, Xuwei Xue, Xiongfei Ren, Wenzhe Li, Yuanzhi Guo, Changsheng Yang, Daohang Dang, Shicheng Zhang, Bingli Guo, Shanguo Huang","doi":"10.37256/cnc.1220233159","DOIUrl":"https://doi.org/10.37256/cnc.1220233159","url":null,"abstract":"Relying on the flexible-access interconnects to the scalable storage and compute resources, data centers deliver critical communications connectivity among numerous servers to support the housed applications and services. To provide the high-speeds and long-distance communications, the data centers have turned to fiber interconnections. With the stringently increased traffic volume, the data centers are then expected to further deploy the optical switches into the systems infrastructure to implement the full optical switching. This paper first summarizes the topologies and traffic characteristics in data centers and analyzes the reasons and importance of moving to optical switching. Recent techniques related to the optical switching, and main challenges limiting the practical deployments of optical switches in data centers are also summarized and reported.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135402473","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}
Heterogeneous network (HetNet) is considered to be the most promising approach for increasing communication capacity. However, HetNet control problems are difficult due to their intertier interference. Recently, the enhanced intercell interference coordination (eICIC) technology is introduced to offer several benefits, including a more equitable traffic load distribution across the macro and embedded small cells. In this paper, we design a new resource allocation scheme for the eICIC-based HetNet. Our proposed scheme is formulated as a joint cooperative game to handle conflicting requirements. By adopting the ideas of Kalai and Smorodinsky solution (KSS), multicriteria Kalai and Smorodinsky solution (MCKSS), and sequential Raiffa solution (SRS), we develop a hybrid control algorithm for an adaptive resource sharing between different base stations. To effectively adjust the eICIC fraction rates, the concepts of MCKSS and SRS are applied in an interactive manner. For mobile devices in the HetNet, the assigned resource is distributed by using the idea of KSS. The key insight of our algorithm is to translate the originally competitive problem into a hierarchical cooperative problem to reach a socially optimal outcome. The main novelty of our approach is its flexibility to reach a reciprocal consensus under dynamic HetNet environments. Exhaustive system simulations illustrate the performance gains along different dimensions, such as system throughput, device payoff, and fairness among devices. The superiority of our proposed scheme is fully demonstrated in comparison with three other existing eICIC control protocols.
{"title":"Cooperative Game-Based Resource Allocation Scheme for Heterogeneous Networks with eICIC Technology","authors":"Sungwook Kim","doi":"10.1155/2023/5526441","DOIUrl":"https://doi.org/10.1155/2023/5526441","url":null,"abstract":"Heterogeneous network (HetNet) is considered to be the most promising approach for increasing communication capacity. However, HetNet control problems are difficult due to their intertier interference. Recently, the enhanced intercell interference coordination (eICIC) technology is introduced to offer several benefits, including a more equitable traffic load distribution across the macro and embedded small cells. In this paper, we design a new resource allocation scheme for the eICIC-based HetNet. Our proposed scheme is formulated as a joint cooperative game to handle conflicting requirements. By adopting the ideas of Kalai and Smorodinsky solution (KSS), multicriteria Kalai and Smorodinsky solution (MCKSS), and sequential Raiffa solution (SRS), we develop a hybrid control algorithm for an adaptive resource sharing between different base stations. To effectively adjust the eICIC fraction rates, the concepts of MCKSS and SRS are applied in an interactive manner. For mobile devices in the HetNet, the assigned resource is distributed by using the idea of KSS. The key insight of our algorithm is to translate the originally competitive problem into a hierarchical cooperative problem to reach a socially optimal outcome. The main novelty of our approach is its flexibility to reach a reciprocal consensus under dynamic HetNet environments. Exhaustive system simulations illustrate the performance gains along different dimensions, such as system throughput, device payoff, and fairness among devices. The superiority of our proposed scheme is fully demonstrated in comparison with three other existing eICIC control protocols.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41924262","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}
Chalima Dimitra Nassar Kyriakidou, Athanasia Maria Papathanasiou, G.C. Polyzos
Decentralized Identity (dID) has brought to the forefront the advantages and importance of total user control over identity. Previous solutions delegate identity management to the responsibility of third-party applications or services, which may raise multiple privacy and security concerns regarding users' personal data. In this paper, we highlight the significance of dID and in particular Self-Sovereign Identity (SSI) for a rapidly evolving ecosystem with a plethora of interconnected devices with different characteristics, such as the Internet of Things (IoT). Specifically, we analyze the benefits of incorporating SSI principles and technologies in IoT environments, while also discussing the challenges that may be introduced when combining the complexity of SSI concepts with the diverse and large-scale IoT environment. In addition, we present a thorough overview of existing systems that integrate SSI components into IoT environments, in order to address the challenges of authentication, authorization, and access control even for constrained IoT devices. Finally, we provide a comprehensive analysis regarding the contributions of Decentralized Identifiers and Verifiable Credentials, the two main pillars of SSI, for enhanced privacy and security for the Internet at large and for the IoT in particular.
{"title":"Decentralized Identity With Applications to Security and Privacy for the Internet of Things","authors":"Chalima Dimitra Nassar Kyriakidou, Athanasia Maria Papathanasiou, G.C. Polyzos","doi":"10.37256/cnc.1220233048","DOIUrl":"https://doi.org/10.37256/cnc.1220233048","url":null,"abstract":"Decentralized Identity (dID) has brought to the forefront the advantages and importance of total user control over identity. Previous solutions delegate identity management to the responsibility of third-party applications or services, which may raise multiple privacy and security concerns regarding users' personal data. In this paper, we highlight the significance of dID and in particular Self-Sovereign Identity (SSI) for a rapidly evolving ecosystem with a plethora of interconnected devices with different characteristics, such as the Internet of Things (IoT). Specifically, we analyze the benefits of incorporating SSI principles and technologies in IoT environments, while also discussing the challenges that may be introduced when combining the complexity of SSI concepts with the diverse and large-scale IoT environment. In addition, we present a thorough overview of existing systems that integrate SSI components into IoT environments, in order to address the challenges of authentication, authorization, and access control even for constrained IoT devices. Finally, we provide a comprehensive analysis regarding the contributions of Decentralized Identifiers and Verifiable Credentials, the two main pillars of SSI, for enhanced privacy and security for the Internet at large and for the IoT in particular.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76246938","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}
While most Internet of Things (IoT) solutions involve sensing, some of them also introduce actuation mechanisms. Specifically, devices interact with assets in the environment and transmit sensor readouts to applications that perform analytics. These applications typically reside on the network core and, in turn, process the readouts that trigger the transmission of actuation commands to the device. One important issue in these schemes is the nature of the communication channels. Most devices are wireless and therefore they are affected by the effects of signal multipath fading that results in application loss. More importantly, these impairments may cause actuation commands to be lost or to be critically delayed. In this context, several standard mechanisms have been proposed for the transmission of traffic from the application to the devices. They fall under two main architectural categories: (1) Representational State Transfer (REST) and (2) Event Driven Architecture (EDA). In this paper, we analyze two protocols associated with each of these two architectures by comparing them in order to assess their efficiency in IoT actuation solutions. This analysis leads to the development of a mathematical model that enables the dynamic selection of the right technology based on network impairments.
{"title":"Dynamic Session Layer Selection in IoT Actuation","authors":"Rolando Herrero","doi":"10.37256/cnc.1220232874","DOIUrl":"https://doi.org/10.37256/cnc.1220232874","url":null,"abstract":"While most Internet of Things (IoT) solutions involve sensing, some of them also introduce actuation mechanisms. Specifically, devices interact with assets in the environment and transmit sensor readouts to applications that perform analytics. These applications typically reside on the network core and, in turn, process the readouts that trigger the transmission of actuation commands to the device. One important issue in these schemes is the nature of the communication channels. Most devices are wireless and therefore they are affected by the effects of signal multipath fading that results in application loss. More importantly, these impairments may cause actuation commands to be lost or to be critically delayed. In this context, several standard mechanisms have been proposed for the transmission of traffic from the application to the devices. They fall under two main architectural categories: (1) Representational State Transfer (REST) and (2) Event Driven Architecture (EDA). In this paper, we analyze two protocols associated with each of these two architectures by comparing them in order to assess their efficiency in IoT actuation solutions. This analysis leads to the development of a mathematical model that enables the dynamic selection of the right technology based on network impairments.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78779145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The constant desire for faster data rates, lower latency, improved reliability, global device integration, and pervasiveness are some of the factors driving the development of next-generation communication systems. Sixth-generation (6G) networks have received a lot of attention from the industry and academics as fifth-generation (5G) communications are being rolled out globally. With the proliferation of smart devices and the Internet of Things (IoT), 6G networks will require ultra-reliable and low-latency communication. Routing protocols have a significant role in improving the performance of a network. Traditional routing techniques will have difficulty coping with the highly complex and dynamic 6G environments. Recently, machine learning (ML), a key component of artificial intelligence, is emerging as the key to managing complex and dynamic networks efficiently. However, there are still several significant challenges that need to be addressed. In this paper, we provide an overview of current machine-learning techniques used in network routing. Lastly, we highlight open research problems that need to be addressed and prospects for future research.
{"title":"Review on Machine Learning for Intelligent Routing, Key Requirement and Challenges Towards 6G","authors":"Bidyarani Langpoklakpam, Lithungo K Murry","doi":"10.37256/cnc.1220233039","DOIUrl":"https://doi.org/10.37256/cnc.1220233039","url":null,"abstract":"The constant desire for faster data rates, lower latency, improved reliability, global device integration, and pervasiveness are some of the factors driving the development of next-generation communication systems. Sixth-generation (6G) networks have received a lot of attention from the industry and academics as fifth-generation (5G) communications are being rolled out globally. With the proliferation of smart devices and the Internet of Things (IoT), 6G networks will require ultra-reliable and low-latency communication. Routing protocols have a significant role in improving the performance of a network. Traditional routing techniques will have difficulty coping with the highly complex and dynamic 6G environments. Recently, machine learning (ML), a key component of artificial intelligence, is emerging as the key to managing complex and dynamic networks efficiently. However, there are still several significant challenges that need to be addressed. In this paper, we provide an overview of current machine-learning techniques used in network routing. Lastly, we highlight open research problems that need to be addressed and prospects for future research.","PeriodicalId":45621,"journal":{"name":"Journal of Computer Networks and Communications","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90956550","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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