Optimizing the Performance and Robustness of Type-2 Fuzzy Group Nearest-Neighbor Queries

Given two positive parameters k and r, a constrained k-nearest neighbor (CkNN) query returns the k closest objects within a network distance r of the query location in road networks. In terms of the scalability of monitoring these CkNN queries, existing solutions based on central processing at a server suffer from a sudden and sharp rise in server load as well as messaging cost as the number of queries increases. In this paper, we propose a distributed and scalable scheme called DAEMON for the continuous monitoring of CkNN queries in road networks. Our query processing is distributed among clients (query objects) and server. Specifically, the server evaluates CkNN queries issued at intersections of road segments, retrieves the objects on the road segments between neighboring intersections, and sends responses to the query objects. Finally, each client makes its own query result using this server response. As a result, our distributed scheme achieves close-to-optimal communication costs and scales well to large numbers of monitoring queries. Exhaustive experimental results demonstrate that our scheme substantially outperforms its competitor in terms of query processing time and messaging cost.

Section: Related Workmentioning

confidence: 99%

A Distributed Approach to Continuous Monitoring of Constrained k-Nearest Neighbor Queries in Road Networks

Cho

Choe

Chung

2012

“…Most existing works based on network coding are designed for robust video transmission system and focus on the application of random linear network coding (RLNC) [14][15][16]. Nguyen et al [14] proposed a scheme on multipath transmit with network coding to meet the demand of high bandwidth for video transmission, but the best strategy proposed does not take into account optimization [17] and the variable quality of service. In order to reduce transmission delay caused by network coding for video streaming [18], proposed an effective allocation algorithm for nodes to perform network coding in an overlay network, which minimize the transmission delay for video streaming.…”

Section: Existing Work With Network Coding For Video Transmissionmentioning

confidence: 99%

Joint Redundant and Random Network Coding for Robust Video Transmission over Lossy Networks

Zhang

Jing

Tang

et al. 2012

Abstract. In this paper a novel unequal packet loss protection scheme R2NC based on low-triangular global coding matrix with ladder-shaped partition is presented, which combines redundant and random network coding for robust H.264/SVC video transmission. Firstly, the error-correcting capabilities of redundant network coding make our scheme resilient to loss. Secondly, the implementation of random network coding at the intermediate nodes with multiple input links can reduce the cost of network bandwidth, thus reducing the end-to-end delay for video transmission. Thirdly, the low-triangular global coding matrix with ladder-shaped partition is maintained throughout the R2NC processes to reduce the impact of global coding matrix's rank deficiency on video transmission and provide unequal erasure protection for H.264/SVC priority layers. The redundant network coding avoids the retransmission of lost packets and improves error-correcting capabilities of lost packets. Based only on the knowledge of the packet loss rates on the output links, the source node and intermediate nodes can make decisions for redundant network coding and random network coding (i.e., how much redundancy to add at this node). However, the redundancy caused by redundant network coding makes the network load increases. In order to improve network throughput, we performed random network coding at the intermediate nodes. Our approach is grounded on the overall distortion of reconstructed video minimization by optimizing the amount of redundancy assigned to each layer. The convex optimization model is constructed under the constraint of network coding and scalable video coding. Experimental results are shown to demonstrate the significant improvement of H.264/SVC video reconstruction quality with R2NC over packet lossy networks.

“…The points of interest (POIs; e.g., accommodation, restaurants, and gas stations) marked on web mapping services such as Google Maps, Bing Maps, and Yahoo Maps are located in a road network and their proximity is measured as their shortest path distances [7,8,13,14]. A k nearest neighbor (kNN) query is one of the most important query types in location-based services [2,16], where a user submits a kNN query to the service provider for the k-nearest objects (i.e., POIs) to his/her current location.…”

Section: Introductionmentioning

confidence: 99%

A Safe Exit Algorithm for Continuous Nearest Neighbor Monitoring in Road Networks

Cho

Kwon

Chung

2013

Query processing in road networks has been studied extensively in recent years. However, the processing of moving queries in road networks has received little attention. In this paper, we introduce a new algorithm called the Safe Exit Algorithm (SEA), which can efficiently compute the safe exit points of a moving nearest neighbor (NN) query on road networks. The safe region of a query is an area where the query result remains unchanged, provided that the query remains inside the safe region At each safe exit point, the safe region of a query and its non-safe region meet so that a set of safe exit points represents the border of the safe region. Before reaching a safe exit point, the client (query object) does not have to request the server to re-evaluate the query This significantly reduces the server processing costs and the communication costs between the server and moving clients. Extensive experimental results show that SEA outperforms a conventional algorithm by up to two orders of magnitude in terms of communication costs and computation costs.