The Experts below are selected from a list of 156 Experts worldwide ranked by ideXlab platform
Evelio M G Fernandez - One of the best experts on this subject based on the ideXlab platform.
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dynamic control of Beacon Transmission rate and power with position error constraint in cooperative vehicular networks
ACM Symposium on Applied Computing, 2018Co-Authors: Sandy Bolufe, Samuel Montejosanchez, Cesar A Azurdiameza, Sandra Cespedes, Richard Demo Souza, Evelio M G FernandezAbstract:Cooperative vehicular networks require the continuous exchange of Beacon messages between neighboring vehicles to support cooperative awareness applications. The position error computed by surrounding vehicles impacts on applications capability to detect and mitigate potentially dangerous traffic situations in real-time. A challenge in cooperative safety systems is to maintain a high position accuracy, while controlling the communication channel load. In this paper, we propose a novel joint power/rate control distributed algorithm to meet the position accuracy requirements of cooperative safety applications. The Beacon Transmission rate is adjusted dynamically as a function of the vehicle movement status, to constrain position error computed by surrounding vehicles. Then, the transmit power is adjusted according to the channel load and the preset Beacon Transmission rate, to decrease packet collisions. This algorithm has been evaluated in a realistic simulation framework, considering different traffic densities for an urban scenario. The simulation results show that the proposed algorithm outperforms other basic Beaconing algorithms addressed in this paper, in terms of trade-off between position accuracy, packet collisions, and warning range.
Takeo Fujii - One of the best experts on this subject based on the ideXlab platform.
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receiver Beacon Transmission interval design using q learning focused on packet delivery rate for multi stage wireless sensor networks
International Conference on Artificial Intelligence, 2020Co-Authors: Yuki Hatada, Takeo FujiiAbstract:U-Bus Air is a protocol that can transmit packets by multi-hop communication, and is adopted in high-rise buildings and so on. Since U-Bus Air is time asynchronous, the communication quality deteriorates due to packet collision as the number of nodes increases. In previous research, centralized control that collects topology information in a Fusion Center (FC), calculates the communication interval, and then performs feedback. However, since information collection and feedback packet communication are required, communication overhead occurs, leading to degradation of communication quality. In addition, the authors have proposed a machine learning method that focuses on packet holding time, but local solutions have not been considered. In this paper, we propose a method for controlling the autonomously distributed receiver Beacon Transmission interval considering local solution by using machine learning. In this protocol, each node calculates its own PDR and transmits it to surrounding nodes with link table exchange. After a certain period of time has passed, the communication interval is determined using machine learning based on the packet delivery rate (PDR) of the surrounding nodes on the link table. Using computer simulation, we show improvements in PDR, throughput and power consumption compared to the previous research.
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receiver Beacon Transmission interval design using machine learning for multi stage wireless sensor networks
International Conference on Ubiquitous and Future Networks, 2019Co-Authors: Yuki Hatada, Takeo FujiiAbstract:U-Bus Air is a protocol that can transmit packets by multi-hop communication, and is utilized in tall buildings and apartments. While U-Bus Air is a time asynchronous wireless sensor network, packet collisions cause degradation of communication quality as the number of terminals increases. A previous research has been proposed, an appropriate Transmission cycle control algorithm based on the estimated collision probability by using collected topology information at a Fusion Center (FC). By using this algorithm, the communication performance is improved by feedback from FC to each terminal. However, its communication overhead becomes large due to topology information and feedback packets, and communication efficiency is degraded. In order to solve this problem, we propose a method to autonomously control receiver Beacon Transmission interval by leveraging decentralized Q-Learning. In this method, each terminal transmits information of a packet holding time at the same time as responding to the Beacon. Finally, each terminal utilizes Q-Learning to derive an appropriate Transmission interval. In computer simulation, we show an improvement of packet delivery rate (PDR), throughput and power consumption compared with the previous method.
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Intermittent Interval Feedback Design for Multi-Stage Wireless Sensor Networks
Mobile Networks and Applications, 2017Co-Authors: Tomokazu Moriyama, Taiki Nakayama, Takeo FujiiAbstract:Smart meters are needed for realizing energy savings and automatic meter reading. As smart meters used for the gas or water supply infrastructure cannot always receive supplied power, a low-power-consumption communication protocol for battery-powered smart meters is required. The U-Bus Air protocol is standardized for gas meters in mid- and high-rise buildings. U-Bus Air has the features of low power consumption using time asynchronous communication and multi-hop communication among nodes. However, the interference power increases as the number of nodes increases. In order to solve the problem, intermittent receiver-driven data Transmission media access control (IRDT-MAC) was proposed; it finds the appropriate Transmission cycle (intermittent interval); the communication performance is improved using the derivation of the collision probability. On the other hand, IRDT-MAC cannot correspond to a change of link topology. In this paper, we propose a protocol for controlling the receiver Beacon Transmission interval using the fusion center (FC) for solving the above problem. In this protocol, the link topology of each smart meter is sent to the FC and the FC calculates the suitable Transmission cycle for this specific topology. Finally, the suitable Transmission cycle is fed back to each node from the FC. Using computer simulations, improvements of the packet delivery ratio (PDR), the throughput, and the power consumption are confirmed in two topologies. Specifically, the improvements are confirmed in the case of two stages or more in the building-wide square topology.
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receiver Beacon Transmission interval design for multi stage wireless sensor networks
International Conference on Ubiquitous and Future Networks, 2016Co-Authors: Tomokazu Moriyama, Taiki Nakayama, Takeo FujiiAbstract:U-Bus Air is the protocol that can transmit packets via multi-stages, and is introduced to the smart meter in the medium-high rise apartment house. While it has the feature of asynchrony and power-saving, the interference power increases as the number of nodes increases. In a prior study, Intermittent Receiver-Driven data Transmission (IRDT) MAC has been proposed with finding the appropriate Transmission cycle and the communication performance is improved by the derivation of the collision probability. In this paper, we introduce a collision probability analysis of IRDT-MAC to U-Bus Air. However there is a problem that it cannot correspond to a change of link topology. We propose a protocol for controlling receiver Beacon Transmission interval by the Fusion Center (FC). In this protocol, the link topology of each smart meter is sent to the FC and the FC calculates the suitable Transmission cycle from this individual topology. Finally, this suitable Transmission cycle is fed back to each node from the FC. By using the computer simulations, the improvement of packet delivery ratio (PDR), the throughput and the reduction of power consumption are confirmed.
Mirjalili Seyedali - One of the best experts on this subject based on the ideXlab platform.
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Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information
'Elsevier BV', 2018Co-Authors: Sadiq, Ali Safa, Khan Suleman, Ghafoor, Kayhan Zrar, Guizani Mohsen, Mirjalili SeyedaliAbstract:As part of the new era the Internet of Things, an evolved form of Vehicle Ad-hoc Networks has recently emerged as the Internet of Vehicles (IoV). IoV has obtained a lot of attention among smart vehicle manufactures and illustrations due to its promising potential, but there are still some problems and challenges that need to be addressed. Transmission error occurs when an emergency message is disseminated to provide traffic awareness, and vehicles have to increase their channel Transmission power to ensure further coverage and mitigate possible accidents. This might cause channel congestion and unnecessary power consumption due to an inaccurate Transmission power setup. A promising solution could be achieved via periodically and predictively evaluating channel and GEO information that is transmitted over piggybacked Beacons. Thus, in this paper we propose a Transmission Power Adaptation (TPA) scheme for obtaining better power tuning, which senses and examines the probability of channel congestion. Afterwards, it proactively predicts upcoming channel statuses using developed evaluation-weighted matrix, which observes correlations between coefficients of variance for estimated metrics. Considering Beacon Transmission error rate, crowding inter-vehicle distance, and channel delay, the matrix is periodically constructed and proavtively weighted for each metric based on a predefined threshold value. Eventually, predicted channel status is used as an indicator to adjust Transmission power. This leads to decreased channel congestion and better awareness in IoV. The performance of the proposed TPA scheme is evaluated using OMNeT++ simulation tools. The simulation results show that our proposed TPA scheme performs better than existing method in terms of overall throughput, average Beacon congestion rate, Beacon recipient rate probabilities, channel-busy time, Transmission power over distance, and accident probabilities
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Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information
Elsevier BV * North-Holland, 2018Co-Authors: Sadiq Ali, Khan Suleman, Guizani Mohsen, Ghafoor Kayhan, Mirjalili SeyedaliAbstract:As part of the new era the Internet of Things, an evolved form of Vehicle Ad-hoc Networks has recently emerged as the Internet of Vehicles (IoV). IoV has obtained a lot of attention among smart vehicle manufactures and illustrations due to its promising potential, but there are still some problems and challenges that need to be addressed. Transmission error occurs when an emergency message is disseminated to provide traffic awareness, and vehicles have to increase their channel Transmission power to ensure further coverage and mitigate possible accidents. This might cause channel congestion and unnecessary power consumption due to an inaccurate Transmission power setup. A promising solution could be achieved via periodically and predictively evaluating channel and GEO information that is transmitted over piggybacked Beacons. Thus, in this paper we propose a Transmission Power Adaptation (TPA) scheme for obtaining better power tuning, which senses and examines the probability of channel congestion. Afterwards, it proactively predicts upcoming channel statuses using developed evaluation-weighted matrix, which observes correlations between coefficients of variance for estimated metrics. Considering Beacon Transmission error rate, crowding inter-vehicle distance, and channel delay, the matrix is periodically constructed and proavtively weighted for each metric based on a predefined threshold value. Eventually, predicted channel status is used as an indicator to adjust Transmission power. This leads to decreased channel congestion and better awareness in IoV. The performance of the proposed TPA scheme is evaluated using OMNeT++ simulation tools. The simulation results show that our proposed TPA scheme performs better than existing method in terms of overall throughput, average Beacon congestion rate, Beacon recipient rate probabilities, channel-busy time, Transmission power over distance, and accident probabilities.Full Tex
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Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information
'Elsevier BV', 2018Co-Authors: Sadiq, Ali Safa, Khan Suleman, Ghafoor, Kayhan Zrar, Guizani Mohsen, Mirjalili SeyedaliAbstract:This is an accepted manuscript of an article published by Elsevier in Computer Networks on 04/06/2018, available online: https://doi.org/10.1016/j.comnet.2018.03.019 The accepted version of the publication may differ from the final published version.© 2018 Elsevier B.V. As part of the new era the Internet of Things, an evolved form of Vehicle Ad-hoc Networks has recently emerged as the Internet of Vehicles (IoV). IoV has obtained a lot of attention among smart vehicle manufactures and illustrations due to its promising potential, but there are still some problems and challenges that need to be addressed. Transmission error occurs when an emergency message is disseminated to provide traffic awareness, and vehicles have to increase their channel Transmission power to ensure further coverage and mitigate possible accidents. This might cause channel congestion and unnecessary power consumption due to an inaccurate Transmission power setup. A promising solution could be achieved via periodically and predictively evaluating channel and GEO information that is transmitted over piggybacked Beacons. Thus, in this paper we propose a Transmission Power Adaptation (TPA) scheme for obtaining better power tuning, which senses and examines the probability of channel congestion. Afterwards, it proactively predicts upcoming channel statuses using developed evaluation-weighted matrix, which observes correlations between coefficients of variance for estimated metrics. Considering Beacon Transmission error rate, crowding inter-vehicle distance, and channel delay, the matrix is periodically constructed and proavtively weighted for each metric based on a predefined threshold value. Eventually, predicted channel status is used as an indicator to adjust Transmission power. This leads to decreased channel congestion and better awareness in IoV. The performance of the proposed TPA scheme is evaluated using OMNeT++ simulation tools. The simulation results show that our proposed TPA scheme performs better than existing method in terms of overall throughput, average Beacon congestion rate, Beacon recipient rate probabilities, channel-busy time, Transmission power over distance, and accident probabilities.Published versio
Sandy Bolufe - One of the best experts on this subject based on the ideXlab platform.
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dynamic control of Beacon Transmission rate and power with position error constraint in cooperative vehicular networks
ACM Symposium on Applied Computing, 2018Co-Authors: Sandy Bolufe, Samuel Montejosanchez, Cesar A Azurdiameza, Sandra Cespedes, Richard Demo Souza, Evelio M G FernandezAbstract:Cooperative vehicular networks require the continuous exchange of Beacon messages between neighboring vehicles to support cooperative awareness applications. The position error computed by surrounding vehicles impacts on applications capability to detect and mitigate potentially dangerous traffic situations in real-time. A challenge in cooperative safety systems is to maintain a high position accuracy, while controlling the communication channel load. In this paper, we propose a novel joint power/rate control distributed algorithm to meet the position accuracy requirements of cooperative safety applications. The Beacon Transmission rate is adjusted dynamically as a function of the vehicle movement status, to constrain position error computed by surrounding vehicles. Then, the transmit power is adjusted according to the channel load and the preset Beacon Transmission rate, to decrease packet collisions. This algorithm has been evaluated in a realistic simulation framework, considering different traffic densities for an urban scenario. The simulation results show that the proposed algorithm outperforms other basic Beaconing algorithms addressed in this paper, in terms of trade-off between position accuracy, packet collisions, and warning range.
Jeroen Famaey - One of the best experts on this subject based on the ideXlab platform.
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Real-time station grouping under dynamic traffic for ieee 802.11ah
Sensors (Switzerland), 2017Co-Authors: Le Tian, Steven Latre, Evgeny Khorov, Jeroen FamaeyAbstract:IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the Transmission frequency over time. The TAROA algorithm is executed at each target Beacon Transmission time (TBTT), and it first estimates the packet Transmission interval of each station only based on packet Transmission information obtained by access point (AP) during the last Beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated Transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.