SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.
{"title":"NOMA‐based precoded quadrature spatial modulation in multiuser MIMO downlink transmission over correlated channel","authors":"Shekhar Pratap Singh, Pyari Mohan Pradhan","doi":"10.1002/dac.5931","DOIUrl":"https://doi.org/10.1002/dac.5931","url":null,"abstract":"SummaryIn this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Umar Farooq, Anjaneyulu Lokam, Sandhya Mallavarapu
SummaryThis work presents a compact two‐element multi‐input‐multi‐output (MIMO) antenna for 5G‐enabled IoT devices. The antenna operates over a wide frequency range of 24.6 to 31.4 GHz (28‐GHz band) and 57.6 to 60.2 GHz (60‐GHz band). Each MIMO element consists of an inverted L‐shaped slotted radiator with a partial ground plane. The antenna offers a peak gain of 5.45 and 5.56 dBi across two operating bands. The minimum isolation between the two ports is −26.5 dB, reaching a maximum value of over −45 dB. The investigation of MIMO metrics like “envelope correlation coefficient (ECC),” “diversity gain (DG),” “mean effective gain (MEG),” “channel capacity loss (CCL),” and “total active reflection coefficient (TARC)” also show favorable characteristics. The antenna is fabricated on a 10 × 22 × 0.503 mm3 Rogers 5880 substrate. The experimental results are in close agreement with that of the simulation results. The distinguishing features of the proposed antenna such as its compact design, simple geometrical configuration, wide operating bandwidth, low ECC, and high isolation make it a strong candidate for 5G‐enabled IoT devices.
{"title":"Compact planar 28/60‐GHz wideband MIMO antenna for 5G‐enabled IoT devices","authors":"Umar Farooq, Anjaneyulu Lokam, Sandhya Mallavarapu","doi":"10.1002/dac.5932","DOIUrl":"https://doi.org/10.1002/dac.5932","url":null,"abstract":"SummaryThis work presents a compact two‐element multi‐input‐multi‐output (MIMO) antenna for 5G‐enabled IoT devices. The antenna operates over a wide frequency range of 24.6 to 31.4 GHz (28‐GHz band) and 57.6 to 60.2 GHz (60‐GHz band). Each MIMO element consists of an inverted L‐shaped slotted radiator with a partial ground plane. The antenna offers a peak gain of 5.45 and 5.56 dBi across two operating bands. The minimum isolation between the two ports is −26.5 dB, reaching a maximum value of over −45 dB. The investigation of MIMO metrics like “envelope correlation coefficient (ECC),” “diversity gain (DG),” “mean effective gain (MEG),” “channel capacity loss (CCL),” and “total active reflection coefficient (TARC)” also show favorable characteristics. The antenna is fabricated on a 10 × 22 × 0.503 mm<jats:sup>3</jats:sup> Rogers 5880 substrate. The experimental results are in close agreement with that of the simulation results. The distinguishing features of the proposed antenna such as its compact design, simple geometrical configuration, wide operating bandwidth, low ECC, and high isolation make it a strong candidate for 5G‐enabled IoT devices.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryBy controlling the network, the Internet of Things (IoT)‐connected software‐defined network (SDN) limits the scalability of IoT devices. Since SDN depends on a centralized controller that attackers can easily affect, it is incredibly susceptible to attacks. Secure access control to the SDN controller was the focus of the prior methods for controller scalability and restricted trust management. A framework called Safeguard Authentication Dynamic Access Control (SANDMAC) is suggested to safeguard and offer useful services to enterprises. Authentication confirms legitimacy after all users and applications have been registered. To improve network security, policies let users grant access to account attributes, legal activities, and temporal components. The administrator lessens conflicts between the methods by validating and saving the policies in the database. The services are provided to dependable customers using the forensic‐based investigation algorithm, depending on the quality of service and software level agreements requirements, decreasing reaction times and maximizing resource usage. Performance comparisons between the new and previous efforts are validated using a variety of parameters, and the proposed work is validated using the iFogSim application. According to the findings, SANDMAC significantly raises key performance indicators. SANDMAC specifically keeps false positives at 3.5% and accomplishes a low response time of 60 ms for roughly 800 authorized accesses. SANDMAC is a better option because of these enhancements, which result in longer network lifetimes and more dependable data transmission.
摘要 通过控制网络，与物联网（IoT）相连的软件定义网络（SDN）限制了物联网设备的可扩展性。由于 SDN 依赖于攻击者可以轻易影响的集中式控制器，因此极易受到攻击。对 SDN 控制器的安全访问控制是先前控制器可扩展性和受限信任管理方法的重点。建议采用一种名为 "安全认证动态访问控制"（SANDMAC）的框架，以保障安全并为企业提供有用的服务。所有用户和应用程序注册后，身份验证会确认其合法性。为提高网络安全性，策略允许用户授予账户属性、合法活动和时间组件的访问权限。管理员通过在数据库中验证和保存策略来减少方法之间的冲突。根据服务质量和软件级别协议的要求，使用基于取证的调查算法向可靠的客户提供服务，缩短反应时间，最大限度地提高资源利用率。使用各种参数对新的工作和以前的工作进行了性能比较，并使用 iFogSim 应用程序对拟议的工作进行了验证。根据研究结果，SANDMAC 显著提高了关键性能指标。具体而言，SANDMAC 将误报率控制在 3.5%，并在大约 800 次授权访问中实现了 60 毫秒的低响应时间。由于这些改进，SANDMAC 成为更好的选择，从而延长了网络寿命，提高了数据传输的可靠性。
{"title":"Bilevel access control and constraint‐aware response provisioning in edge‐enabled software defined network‐internet of things network using the safeguard authentication dynamic access control model","authors":"Sahana D S, Brahmananda S H","doi":"10.1002/dac.5946","DOIUrl":"https://doi.org/10.1002/dac.5946","url":null,"abstract":"SummaryBy controlling the network, the Internet of Things (IoT)‐connected software‐defined network (SDN) limits the scalability of IoT devices. Since SDN depends on a centralized controller that attackers can easily affect, it is incredibly susceptible to attacks. Secure access control to the SDN controller was the focus of the prior methods for controller scalability and restricted trust management. A framework called Safeguard Authentication Dynamic Access Control (SANDMAC) is suggested to safeguard and offer useful services to enterprises. Authentication confirms legitimacy after all users and applications have been registered. To improve network security, policies let users grant access to account attributes, legal activities, and temporal components. The administrator lessens conflicts between the methods by validating and saving the policies in the database. The services are provided to dependable customers using the forensic‐based investigation algorithm, depending on the quality of service and software level agreements requirements, decreasing reaction times and maximizing resource usage. Performance comparisons between the new and previous efforts are validated using a variety of parameters, and the proposed work is validated using the iFogSim application. According to the findings, SANDMAC significantly raises key performance indicators. SANDMAC specifically keeps false positives at 3.5% and accomplishes a low response time of 60 ms for roughly 800 authorized accesses. SANDMAC is a better option because of these enhancements, which result in longer network lifetimes and more dependable data transmission.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Thiyagarajan, N. Nagabhooshanam, K.D.V. Prasad, P. Poojitha
SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.
{"title":"A novel approach for missing data recovery and fault nodes detection in wireless sensor networks","authors":"R. Thiyagarajan, N. Nagabhooshanam, K.D.V. Prasad, P. Poojitha","doi":"10.1002/dac.5924","DOIUrl":"https://doi.org/10.1002/dac.5924","url":null,"abstract":"SummaryEnsuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryIn recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches.
{"title":"Metaheuristic optimization‐based clustering with routing protocol in wireless sensor networks","authors":"Chinnarao Kurangi, Kiran Kumar Paidipati, A. Siva Krishna Reddy, Jayasankar Uthayakumar, Ganesan Kadiravan, Shabana Parveen","doi":"10.1002/dac.5914","DOIUrl":"https://doi.org/10.1002/dac.5914","url":null,"abstract":"SummaryIn recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.
{"title":"Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach","authors":"Muhammad Asim Ejaz, Guowei Wu, Tahir Iqbal","doi":"10.1002/dac.5916","DOIUrl":"https://doi.org/10.1002/dac.5916","url":null,"abstract":"SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76<jats:italic>%</jats:italic>, 33<jats:italic>%</jats:italic>, and 23<jats:italic>%</jats:italic>, respectively.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless body area network (WBAN) is a potential low-cost technology for privacy-sensitive telemedicine and e-health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency-aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.
{"title":"Performance evaluation of the congestion severity aware rate regulation (CSRR) algorithm in wireless body area networks","authors":"Vamsi kiran Mekatohti, Nithya B","doi":"10.1002/dac.5892","DOIUrl":"10.1002/dac.5892","url":null,"abstract":"<div>\u0000 \u0000 <p>Wireless body area network (WBAN) is a potential low-cost technology for privacy-sensitive telemedicine and e-health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency-aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the \u0000<span></span><math>\u0000 <msup>\u0000 <mi>N</mi>\u0000 <mi>th</mi>\u0000 </msup></math> primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.</p>\u0000 </div>","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryThis paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs.
摘要 本文提出了一种新方法，利用流路由（SER）算法优化多接入网络（MANET）中的流量管理。SER 算法用于确定哪条路由路径最节省时间和资源。所提出的方法以与 SER 方法一致的方式利用了多路径路由。通过结合流量、队列和链路的状态，基于图神经网络（GNN）的模型试图打破这些函数所描述的循环依赖关系。仿真设置的联合参数包括剩余能量、数据包交付率（PDR）和端到端延迟。实验结果表明，与使用一些专为城域网设计的基线协议相比，所提出的协议在网络效率方面有显著提高。
{"title":"Graph neural networks based queuing model for optimal load balancing in mobile ad hoc network","authors":"G. Rajiv Suresh Kumar, G. Arul Geetha","doi":"10.1002/dac.5922","DOIUrl":"https://doi.org/10.1002/dac.5922","url":null,"abstract":"SummaryThis paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thomas Mejstrik, Taulant Berisha, Sebastian Woblistin
SummaryUnmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately , compared to for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss.
{"title":"Fast computation of radio wave diffraction effects","authors":"Thomas Mejstrik, Taulant Berisha, Sebastian Woblistin","doi":"10.1002/dac.5930","DOIUrl":"https://doi.org/10.1002/dac.5930","url":null,"abstract":"SummaryUnmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately , compared to for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141872915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.
{"title":"Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol","authors":"Arvind Kumar, Sangeeta Kumari","doi":"10.1002/dac.5929","DOIUrl":"https://doi.org/10.1002/dac.5929","url":null,"abstract":"SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.","PeriodicalId":13946,"journal":{"name":"International Journal of Communication Systems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：