MaPIT: An Enhanced Pending Interest Table for NDN With Mapping Bloom Filter

2017

Self Cite

Map-matching is the process of matching the GPS locus to the road network on the digital map. However, due to the most existing map-matching algorithms that are based on high sampling rate, when the sampling interval is increased, the correct rate of the algorithm will be greatly reduced. Based on this, this paper proposed a new algorithm of map-matching for low sampling rate GPS trajectories. The algorithm gave full consideration to the road network of the geometric structure and topological structure and the mutual influence between adjacent points (time, speed information) by calculating the probability of each trajectory point of candidate points to determine matching results. At the end of this paper, we use the data of Beijing UCAR Inc.'s car in a case study. This case demonstrates: For low sampling rate matching track points in the complex road, the algorithm has a good uptime, and an exact match was found.

Section: Application Conditions Of the Algorithmmentioning

confidence: 99%

A novel algorithm of low sampling rate GPS trajectories on map-matching

2017

Self Cite

“…In this section, we illustrate the data structure called MBF which is proposed in [12]. The MBF, which is an improvement of BF, supports querying and mapping the set elements in the memory and lowers on-chip memory (summary) consumption.…”

Section: Mapping Bloom Filtermentioning

confidence: 99%

“…Meanwhile, the use of Mapping Bloom filter (MBF) [12] and multi-level hardware memory further improve the lookup speed in MaFIB, reduces the frequency of access to memory and realizes significant reduction in memory consumption. Our evaluations indicate that MaFIB can minimize the on-chip memory consumption to 4.194 MB and can accelerate the lookup process in the memory.…”

Section: Introductionmentioning

confidence: 99%

Hybrid wireless networks with FIB-based Named Data Networking

et al. 2017

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In Named Data Networking (NDN) architecture, packets carry data names rather than source or destination addresses. To realize this paradigm, each NDN router maintains three data structures CS, PIT, FIB in the forwarding plane. Designing a quick enough forwarding plane with high capacity is a major challenge within the overall NDN research area. In this paper, we present a novel implementation of FIB called MaFIB. The evaluations indicate that the excellent data structure and the efficient lookup algorithm of MaFIB can effectively reduce the memory cost and can accelerate the lookup process. Its outstanding performance in probability of false positive also makes MaFIB meet current network requirements.

“…Direction-of-arrival (DOA) estimation is a major research issue in array signal-processing applications such as radar, wireless communication, and sonar [1][2][3][4][5]. Among different DOA estimation methods, 2-D DOA estimation of coherent source signals [6][7][8][9][10] has drawn increasing attentions.…”

Section: Introductionmentioning

confidence: 99%

Efficient method of two-dimensional DOA estimation for coherent signals

Shi

et al. 2017

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An effective decoherence method called the fourth-order cumulants-based improved Toeplitz matrices reconstruction (FOC-ITMR) is addressed for two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals. To avoid the loss of the array's physical aperture, the FOC-ITMR method fully utilizes the information of received data from the whole two parallel uniform linear arrays (ULAs) and the changing reference element based on FOC. Compared with previous works, the proposed method can offer excellent decoherence performance in both white noise and color noise environments. In addition, the proposed algorithm can achieve automatic pair-matching without additional computation. The theoretical analysis and simulation results confirm the effectiveness of the proposed algorithm.