Wireless sensor networks (WSNs) for industrial manufacturing nowadays are demanding faster delivery of important data than ordinary data. Thus Medium Access Control (MAC) protocols are required to provide low delay me...
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ISBN:
(纸本)9781728152103
Wireless sensor networks (WSNs) for industrial manufacturing nowadays are demanding faster delivery of important data than ordinary data. Thus Medium Access Control (MAC) protocols are required to provide low delay media access for traffic of the important data. Successive Interference Cancellation (SIC), which enables multiple-packet reception, gives an opportunity to decrease access delay. Nevertheless, existing MAC protocls are either differetiate access delay for various traffic types without using SIC, or only exploit SIC for unique traffic type. To cover this gap, we propose a distributed MAC protocol that employ SIC to lower access delay for different traffic types in industrial WSNs. By analyzing performance of this protocol, we find a heuristic method to improve adaptability of the proposed protocol and prove the convergence of this heuristic approach. The major contributions of our work are: first, a twostage contention process is adopted in our protocol, which allows multiple transmitters to access edia simutaneously. Second, we analyze performance of the proposed protocol and find a heuristic method to improve it. With the heuristic method, our protocol is available in networks where status of traffic types is unkown. We also prove the convergence of this heuristic method. Simulation results reveal that our protocols performs better on access delay and packet loss rate than the existing good performing priority based distributed MAC protocols.
Due to the rapid increase in the use of and advancement of social media platforms, the amount of data available on the internet is increasing. The available information on the internet can be used to gain insights abo...
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ISBN:
(纸本)9781728152103
Due to the rapid increase in the use of and advancement of social media platforms, the amount of data available on the internet is increasing. The available information on the internet can be used to gain insights about searching trends and public interests. The advancements in machine learning and deep learning techniques drastically improved data analytics and processing solutions for social media and infotainment industry. It is no doubt that the majority of regular commuter encounters traffic congestion daily. There is a growing number of population in Metropolitans. This leads to higher population density rates and traditional methods for collecting traffic information using physical sensors are expensive, however, by using social media tools information regarding traffic jam, road and traffic congestion can be improved. In this paper, we analyze traffic congestion using Twitter data (tweets) in real-time. The proposed model extracts traffic-related tweets from Twitter and classifies the extracted information for traffic commute estimation road. In this study, tweets from Los Angeles, USA are taken into consideration as an analysis example. A machine learning classifier and a deep learning classier are used to classify traffic information. The model was trained to collect tweets containing the word 'traffic' in a real-time environment.
In this paper, we consider the problem of processing aggregate nearest neighbor (ANN) queries in time-dependent road networks (TDANN) taking the service time constraints of points of interest (POIs) into account. Give...
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ISBN:
(纸本)9781728152103
In this paper, we consider the problem of processing aggregate nearest neighbor (ANN) queries in time-dependent road networks (TDANN) taking the service time constraints of points of interest (POIs) into account. Given a time-dependent road network where travel time along each edge is a function of the departure time, TDANN query is to find the nearest POI which minimizes the time-dependent aggregate travel time from each query point to the POI and the waiting time to be served. The existed Time-Dependent Aggregate Nearest Neighbor Hub Label( TD-ANNHL) algorithm for answering TDANN query assumes that each POI is served for 24 hours and have no waiting time, and the algorithm is inefficient when POIs have service time constraints due to the lack of consideration of the waiting time. To solve the TDANN query under service time constraints, an improved TD-ANNHL algorithm based on service time heuristic values (TD-ANN-STC) was proposed. In the process of generating the candidate set phase, TD-ANN-STC will search the nearest POI in the lower bound time-dependent road network, and those POIs who cannot provide service in time based on the arriving time will be filtered out which could reduce the online expansion area. In the verification phase, A* algorithm is utilized to compute the estimation of time-dependent travel time from each query point to the candidate POIs. To find the best POI, both the estimation of travel time and waiting time are considered by the heuristic function to avoid expanding the network to POI that can be reached quickly but cannot be served immediately. The experimental results show that the number of expanded nodes of TD-ANN-STC is 54.52% lower than the TD-ANNHL algorithm, and the processing time of TD-ANN-STC is 73.61% lower than TD-ANNHL.
This article first provides a brief introduction to the numerical reservoir simulation and a parallel numerical reservoir integrated simulation platform from RDCPS(Research & Development Center for parallel Softwa...
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This article first provides a brief introduction to the numerical reservoir simulation and a parallel numerical reservoir integrated simulation platform from RDCPS(Research & Development Center for parallel Software,Institute of Software,Chinese Academy of Sciences),including Pre-processing,Simulator(for a Three-Dimensional & Three-Phase Black Oil models),Post processing,seamlessly integrated with parallel *** then present key technologies of the simulator,such as the nonlinear and linear solvers,communications among processors,parallel I/O,etc., and corresponding ***,some results with the platform to solve one million grid blocks cases from Chinese oil fields will be given in the article,which can show that the simulator has a very robust portability,high-speed for deadline and good scalability for the tested *** application software,our object is always focusing on meeting deadlines from oil ***,for one million grid blocks' case with 20～30 years production,its elapsed time with 16 processors is less than 12 hours on parallel computers based on Myrinet or QsNet,namely"to submit a case just before off-duty and get its result just before on-duty".A decreasing line of elapsed time appears for a one million grid blocks *** developing trace of the simulator along with parallel computers can be also inferred.
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