Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
Localization is crucial for the monitoring applications of cities, such as road monitoring,\nenvironment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are\noften sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot\ncommunicate with each other via ranging hardware or one-hop connectivity, rendering the existing\nlocalization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights\nSchedule-based localization algorithm (TLS), which is built on the fact that vehicles move through\nthe intersection with a known traffic light schedule. We can first obtain the law by binary vehicle\ndetection time stamps and describe the law as a matrix, called a detection matrix. At the same time,\nwe can also use the known traffic light information to construct the matrices, which can be formed as\na collection called a known matrix collection. The detection matrix is then matched in the known\nmatrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm\nby extensive simulation. The results show that the localization accuracy of intersection sensors can\nreach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it\nhas a wider operational region....
The advances in wireless communication schemes, mobile cloud and fog computing, and context-aware services boost a growing\ninterest in the design, development, and deployment of driver behavior models for emerging applications. Despite the progressive\nadvancements in various aspects of driver behavior modeling (DBM), only limited work can be found that reviews the growing\nbody of literature, which only targets a subset of DBM processes. Thus a more general review of the diverse aspects of DBM,\nwith an emphasis on the most recent developments, is needed. In this paper, we provide an overview of advances of in-vehicle\nand smartphone sensing capabilities and communication and recent applications and services of DBM and emphasize research\nchallenges and key future directions....
Traffic management at road intersections is a complex requirement that has been an important topic of research and\ndiscussion. Solutions have been primarily focused on using vehicular ad hoc networks (VANETs). Key issues in VANETs\nare high mobility, restriction of road setup, frequent topology variations, failed network links, and timely\ncommunication of data, which make the routing of packets to a particular destination problematic. To address these\nissues, a new dependable routing algorithm is proposed, which utilizes a wireless communication system between\nvehicles in urban vehicular networks. This routing is position-based, known as the maximum distance on-demand\nrouting algorithm (MDORA). It aims to find an optimal route on a hop-by-hop basis based on the maximum distance\ntoward the destination from the sender and sufficient communication lifetime, which guarantee the completion of the\ndata transmission process. Moreover, communication overhead is minimized by finding the next hop and forwarding\nthe packet directly to it without the need to discover the whole route first. A comparison is performed between\nMDORA and ad hoc on-demand distance vector (AODV) protocol in terms of throughput, packet delivery ratio, delay,\nand communication overhead. The outcome of the proposed algorithm is better than that of AODV....
The article proposes a novel two-stage network traffic anomaly detection method for the railway transportation\ncritical infrastructure monitored using wireless sensor networks (WSN). The first step of the proposed solution is to\nfind and eliminate any outlying observations in the analyzed parameters of the WSN traffic using a simple and fast\none-dimensional quartile criterion. In the second step, the remaining data is used to estimate autoregressive fractional\nintegrated moving average (ARFIMA) statistical models describing variability of the tested WSN parameters. The paper\nalso introduces an effective method for the ARFIMA model parameters estimation and identification using Haslett and\nRaftery estimator and Hyndman and Khandakar technique. The choice of the ââ?¬Å?economicallyââ?¬Â parameterized form of the\nmodel was based on the compromise between the conciseness of representation and the estimation of the error size.\nTo detect anomalous behavior, i.e., a potential network attack, the proposed detection method uses statistical relations\nbetween the estimated traffic model and its actual variability. The obtained experimental results prove the effectiveness\nof the presented approach and aptness of selection of the statistical models....
According to the parking features of electric vehicles (EVs) and load of production unit, a power supply system including EVs\ncharging station was established, and an orderly discharging strategy for EVs was proposed as well to reduce the basic tariff of\nproducer and improve the total benefits of EV discharging. Based on the target of maximizing the annual income of producer,\nconsidering the total benefits of EV discharging, the electric vehicle aggregator (EVA) and time-of-use (TOU) price were introduced\nto establish the optimization scheduling model of EVs discharging. Furthermore, an improved artificial fish swarm algorithm\n(IAFSA) combined with the penalty function methods was applied to solve the model. It can be shown from the simulation results\nthat the optimal solution obtained by IAFSA is regarded as the orderly discharging strategy for EVs, which could reduce the basic\ntariff of producer and improve the total benefits of EV discharging....
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