The Experts below are selected from a list of 45006 Experts worldwide ranked by ideXlab platform

Chuang Lin - One of the best experts on this subject based on the ideXlab platform.

  • traffic aware dynamic routing to alleviate Congestion in wireless sensor networks
    IEEE Transactions on Parallel and Distributed Systems, 2011
    Co-Authors: Fengyuan Ren, Sajal K Das, Chuang Lin
    Abstract:

    The Congestion Problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current Congestion control algorithms try to alleviate the Congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate Congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the Congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by Congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.

Fengyuan Ren - One of the best experts on this subject based on the ideXlab platform.

  • traffic aware dynamic routing to alleviate Congestion in wireless sensor networks
    IEEE Transactions on Parallel and Distributed Systems, 2011
    Co-Authors: Fengyuan Ren, Sajal K Das, Chuang Lin
    Abstract:

    The Congestion Problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current Congestion control algorithms try to alleviate the Congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate Congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the Congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by Congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.

Sajal K Das - One of the best experts on this subject based on the ideXlab platform.

  • traffic aware dynamic routing to alleviate Congestion in wireless sensor networks
    IEEE Transactions on Parallel and Distributed Systems, 2011
    Co-Authors: Fengyuan Ren, Sajal K Das, Chuang Lin
    Abstract:

    The Congestion Problem in Wireless Sensor Networks (WSNs) is quite different from that in traditional networks. Most current Congestion control algorithms try to alleviate the Congestion by reducing the rate at which the source nodes inject packets into the network. However, this traffic control scheme always decreases the throughput so as to violate fidelity level required by the applications. In this paper, we present a solution that sufficiently exerts the idle or underloaded nodes to alleviate Congestion and improve the overall throughput in WSNs. To achieve this goal, a traffic-aware dynamic routing (TADR) algorithm is proposed to route packets around the Congestion areas and scatter the excessive packets along multiple paths consisting of idle and underloaded nodes. Utilizing the concept of potential in classical physics, our TADR algorithm is designed through constructing a hybrid virtual potential field using depth and normalized queue length to force the packets to steer clear of obstacles created by Congestion and eventually move toward the sink. The simulation results show that the proposed solution improves the overall throughput by around 370 percent as compared to MintRoute, which is one of benchmark routing protocols. Furthermore, TADR scheme has low overhead suitable for large-scale, dense sensor networks.

Huihui Wang - One of the best experts on this subject based on the ideXlab platform.

  • urban traffic control in software defined internet of things via a multi agent deep reinforcement learning approach
    IEEE Transactions on Intelligent Transportation Systems, 2021
    Co-Authors: Jiachen Yang, Jipeng Zhang, Huihui Wang
    Abstract:

    As the growth of vehicles and the acceleration of urbanization, the urban traffic Congestion Problem becomes a burning issue in our society. Constructing a software defined Internet of things(SD-IoT) with a proper traffic control scheme is a promising solution for this issue. However, existing traffic control schemes do not make the best of the advances of the multi-agent deep reinforcement learning area. Furthermore, existing traffic Congestion solutions based on deep reinforcement learning(DRL) only focus on controlling the signal of traffic lights, while ignore controlling vehicles to cooperate traffic lights. So the effect of urban traffic control is not comprehensive enough. In this article, we propose Modified Proximal Policy Optimization (Modified PPO) algorithm. This algorithm is ideally suited as the traffic control scheme of SD-IoT. We adaptively adjust the clip hyperparameter to limit the bound of the distance between the next policy and the current policy. What’s more, based on the collected data of SD-IoT, the proposed algorithm controls traffic lights and vehicles in a global view to advance the performance of urban traffic control. Experimental results under different vehicle numbers show that the proposed method is more competitive and stable than the original algorithm. Our proposed method improves the performance of SD-IoT to relieve traffic Congestion.

Yanhua Zhang - One of the best experts on this subject based on the ideXlab platform.

  • DaVe: Offloading Delay-Tolerant Data Traffic to Connected Vehicle Networks
    IEEE Transactions on Vehicular Technology, 2016
    Co-Authors: Pengbo Si, Haipeng Yao, Ruizhe Yang, Yu He, Yanhua Zhang
    Abstract:

    The promising connected vehicle technologies will enable a huge network of roadside units (RSUs) and vehicles equipped with communication, computing, storage, and positioning devices. Current research on connected vehicle networks focuses on delivering the data generated from or required by the vehicle networks themselves, of which the data traffic is light; thus, the vehicle-network resource utilization efficiency is low. On the other hand, a large amount of delay-tolerant traffic in other data networks consumes significant communication resources. In this paper, we introduce a new architecture of DaVe to utilize efficiently the potential resource from connected vehicles and to mitigate the Congestion Problem in other data networks. Delay-tolerant data traffic is offloaded from the data networks to the connected vehicle networks without extra infrastructure/hardware deployment. An optimal distributed data hopping mechanism is also proposed to enable delay-tolerant data routing over connected vehicle networks. We formulate the next-hop decision optimization Problem as a partially observable Markov decision process (POMDP) and propose a heuristic algorithm to reduce computational complexity. Extensive simulation results are also presented to demonstrate the significant performance improvement of the proposed scheme.