In this paper, a probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange to improve the traffic flow on the surface streets and highways. The signal phasing, green-time exten...
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In this paper, a probabilistic fuzzy logic control is developed for the signalized control of a diamond interchange to improve the traffic flow on the surface streets and highways. The signal phasing, green-time extension, and ramp metering are decided in response to real-time traffic conditions. The probabilistic fuzzy logic for diamond interchange (PFLDI) includes the following three modules: probabilistic fuzzy phase timing that controls the green-time extension process of the current running phase, phase-selection logic that decides the next phase based on the presetup phase logic by the local transport authority, and probabilistic fuzzy ramp metering that determines the on-ramp-metering rate based on the traffic conditions of the arterial streets and highways. The advanced interactive microscopic simulator for urban and nonurban network software was used to model the diamond interchange and measure the effectiveness of the proposed PFLDI algorithm. The performance of the PFLDI was compared with that of an actuated diamond interchange (ADI) control based on the asservissement lineaire d'entre autoroutiere (ALINEA) algorithm and a conventional fuzzy logic control for a diamond interchange (FLDI) algorithm. Simulation results show that the PFLDI lowers system total travel time and average delay, improves the downstream average speed, and lowers the downstream average delay compared with ADI and FLDI.
Revealing the structural features of social networks is vitally important to both scientific research and practice, and the explosive growth of online social networks in recent years has brought us dramatic advances t...
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Revealing the structural features of social networks is vitally important to both scientific research and practice, and the explosive growth of online social networks in recent years has brought us dramatic advances to understand social structures. Here we proposed a community detection approach based on user interaction behavior in weighted dynamic online social networks. We researched interaction behaviors in online social networks and built a directed and unweighted network model in terms of the Weibo following relationships between social individuals at the very beginning. In order to refine the interaction behavior, level one fuzzy comprehensive evaluation model was employed to describe how closely individuals are connected to each other. According to this intimate degree description, weights are tagged to the prior unweighted model we built. Secondly, a heuristic community detection algorithm for dynamic network was provided based on the improved version of modularity called module density. As for the heuristic rule, we chose greedy strategy and merely fed the algorithms with the changed parts within neighboring time slice. Experimental results show that the proposed algorithm can obtain high accuracy and simultaneously get comparatively lower time complexity than some typical algorithms. More importantly, our algorithm needs no a priori conditions.
In order to overcome the energy hole problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with mobile sink nodes for wireless sensor networks (LOA MSN) is proposed. In LOA MSN, hybrid posit...
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In order to overcome the energy hole problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with mobile sink nodes for wireless sensor networks (LOA MSN) is proposed. In LOA MSN, hybrid positioning algorithm of satellite positioning and RSSI positioning is proposed to save energy. Based on location information, movement path constraints, flow constraint, energy consumption constraint, link transmission constraint, and other constraints are analyzed. Network optimization model is established and decomposed into movement path selection model and lifetime optimization model with known grid movement paths. Finally, the two models are solved by distributed method. Sink nodes gather data of sensor nodes along the calculated paths. Sensor nodes select father nodes and transmit data to corresponding sink node according to local information. Simulation results show that LOA MSN makes full use of node energy to prolong network lifetime. LOA MSN with multiple sink nodes also reduces node energy consumption and data gathering latency. Under certain conditions, it outperforms MCP, subgradient algorithm, EASR, and GRAND.
This paper presents a tag localization algorithm based on the time-difference-of-arrival (TDOA) of mobile tag signal for asynchronous wireless sensor network (WSN) with N anchors (nodes with known locations) and a lar...
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This paper presents a tag localization algorithm based on the time-difference-of-arrival (TDOA) of mobile tag signal for asynchronous wireless sensor network (WSN) with N anchors (nodes with known locations) and a large number of mobile tags. To obtain time synchronization, all anchors broadcast signals periodically;relative clock offsets and skews of anchor pairs are estimated by the least-square (LS) method using the times-of-arrival (TOAs) of broadcast signals at anchors. When a tag transmits signal, the TOA of tag signal at each anchor is stamped and errors in original TDOAs of tag signal due to relative clock offsets and skews of anchor pairs are eliminated. Based on Gaussian noisemodel, maximum likelihood estimation (MLE) for the tag position is obtained. Performance issues are addressed by evaluating the Cramer-Rao lower bound of synchronization and localization algorithms. Since the tag can be located via a single transmission, least power consumption of tag is required, and large number of tags can be served in WSN. The proposed algorithm is simple and effective, with performance close to that of synchronous TDOA algorithm.
To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. Thi...
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To extend the lifetime of a wireless sensor network and improve the energy efficiency of its nodes, it is necessary to use node collaborative sleep algorithm to reduce the number of redundant nodes in the network. This paper proposes a particle swarm optimization sleep scheduling mechanism for use in wireless sensor networks based on sleep scheduling algorithm. The mechanism adopts the approach of density control and finds the redundant nodes based on the computation results of the network coverage. Experimental results show that the proposed algorithm can ensure adequate coverage under the premise of the ability to close off the redundant nodes, while reducing the total energy consumption of the network.
We analyze Range-free localization algorithms of WSN and propose a new localization algorithm based on amendatory simulation curve fitting theory. Firstly, we present the current research status of localization techno...
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We analyze Range-free localization algorithms of WSN and propose a new localization algorithm based on amendatory simulation curve fitting theory. Firstly, we present the current research status of localization technology and some improved Range-free localization algorithms based on the modification of hop distance and selection of anchors. Secondly, we analyze the disadvantages of those algorithms. Thirdly, we propose a new algorithm based on amendatory simulation curve fitting (ASCF) through selecting more accurate reference distance and anchors. The new algorithm can improve the localization accuracy. At last, simulation experiments are conducted, and the experimental results indicate that the new algorithm can enhance the localization accuracy efficiently.
In wireless sensor networks (WSNs), energy efficiency is critical for increasing network lifetime. WSNs consist of low-cost nodes with constrained energy. Present study demonstrates that data aggregation is an effecti...
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In wireless sensor networks (WSNs), energy efficiency is critical for increasing network lifetime. WSNs consist of low-cost nodes with constrained energy. Present study demonstrates that data aggregation is an effective approach to reduce energy consumption in WSNs. Current data aggregation algorithms, such as cluster-based, tree-based, and chain-based data aggregation algorithm, incur high overhead to maintain structures and in dynamic scenarios incur much more cost to continuously reconstruct aggregate route. In this paper, we propose a dynamic virtual force-based algorithm (VFE) for data aggregation which can adapt to different scenario changes. Inspired by the concept of cost field and virtual force, VFE constructs dynamic routing without structure overhead, which makes data aggregation more efficient. The simulations confirm that VFE achieves significant energy saving and prolongs network lifetime.
People immediately want to access the required data in vehicular ad hoc networks (VANETs). They provide the requests to road side unit (RSU) while travelling. The job of RSU is to handle various requests in such a way...
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People immediately want to access the required data in vehicular ad hoc networks (VANETs). They provide the requests to road side unit (RSU) while travelling. The job of RSU is to handle various requests in such a way so that the service ratio and the quality of service (QoS) is optimized. In this paper we present a mechanism to achieve this goal. Our proposed algorithm considers both data size and deadline and optimizes uploading/downloading based on number of requests to be handled. It also deals with the impact of missed upload operations to achieve maximum optimization. The proposed scheme categorizes data into two classes to assign weight according to their effect on QoS and assigns priorities to upload and download requests while maintaining common queue for both upload and download requests. Simulation results corroborate that the proposed algorithm provides better service ratio and QoS than existing techniques.
Models of three-dimensional space filling based on growth of two-dimensional sheets are proposed. Beginning from planar Eden-style growth of sheets, additional growth modes are introduced. These enable the sheets to f...
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Models of three-dimensional space filling based on growth of two-dimensional sheets are proposed. Beginning from planar Eden-style growth of sheets, additional growth modes are introduced. These enable the sheets to form layered or disordered structures. The growth modes can also be combined. An off-lattice kinetic Monte Carlo–based computer algorithm is presented and used to study the kinetics of the new models and the resulting structures. It is possible to study space filling by two-dimensional growth in a three-dimensional domain with arbitrarily oriented sheets; the results agree with previously published models where the sheets are only able to grow in a limited set of directions. The introduction of a bifurcation mechanism gives rise to complex disordered structures that are of interest as model structures for the mesostructure of calcium silicate hydrate in hardened cement paste.
We propose an algorithm for multitarget tracking by particle filtering in wireless sensor networks based on received signal strength (RSS) measurement where we also localize a newly appearing target whose location and...
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We propose an algorithm for multitarget tracking by particle filtering in wireless sensor networks based on received signal strength (RSS) measurement where we also localize a newly appearing target whose location and reference power are unknown. Therefore, the number, the reference power, and the initial locations of targets are unknown in this problem. At the initial localization step, we apply approximate least squares (LS) method to roughly estimate the target location. After the initial location is estimated, we estimate the reference power. This is possible because we can use multiple number of measurements for estimating multiparameters. The proposed approach is particularly emphasized on the initialization step that completes the whole multitarget tracking system by particle filtering in a challenging scenario. The proposed approach is validated by computer simulations for its effectiveness.
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