On the efficient use of multiple channels by single‐receiver clients in wireless data broadcasting

Recent development of wireless technology and smart mobile devices has spurred intense research efforts to address spatial queries. In particular, an increasing interest for tackling spatial query processing in broadcast environments has been observed. To the best of our knowledge, most of the existing work on this problem has assumed an Euclidean space. However, for real applications, query clients move within a road sensor network, where the distance between a data object and a query is determined by the connectivity of the road sensor network. This paper explores the problem of spatial query processing in road sensor networks by means of wireless data broadcast. We present an efficient method to partition the recordkeeping information about the underlying road sensor network and its associated objects, by which we develop a fully distributed air index, called integrated exponential index, based on an extended version of the Hilbert curve. We also propose efficient client-side algorithms to facilitate the processing of several kinds of spatial queries, including kNN query, CkNN query, and range query. Finally, extensive simulation experiments have been conducted to demonstrate the strengths of our proposed techniques. access for relatively heavily loaded systems. A year and a half ago, soccer fans from around the world arrived in Brasilia to watch the 2014 FIFA World Cup. After a game, many people would like to locate good nearby restaurants. Because there could be tons of people doing the search at the same time, an ideal solution in this case would be to provide spatial query processing using data broadcast.Earlier approaches to designing broadcast-based spatial query processing methods [2-7] had assumed that MCs and searched objects were located in an Euclidean space. In real-life situations, however, MCs normally move along a road sensor network, and the Euclidean distance may not properly reflect real road sensor network distance [8]. Thus, the existing spatial query processing methods proposed for Euclidean space are inapplicable to process spatial queries in road sensor networks. Recently, researchers have begun to zero in on the problem. Li et al. [9] addressed the issue of processing CNN queries in road networks on the air and proposed an network Voronoi diagrams (NVDs)-based distributed air index to support query processing. One key limitation of the proposed method is that it processes CkNN queries with k D 1. Sun et al. [10] presented an air index called network partition index (NPI) to support spatial query processing in road networks on the air and proposed efficient algorithms to process different spatial queries such as kNN query, range query, and CNN query. NPI is, however, not a fully distributed structure, and thus, each query client will have to wait for the arrival of the index part followed by the whole index before starting its query processing, which could lead to long access latency and tuning time. In this paper, we aim to overcome these limitations and propose a fully distributed air ...

Section: Cknn Query Algorithmmentioning

confidence: 99%

Section: Algorithm 4: Obtainhcs -Obtain a List Of Hc Values Of The Grmentioning

confidence: 99%

A novel distributed air index for efficient spatial query processing in road sensor networks on the air

Shu

Zhu

et al. 2016

“…In the recent years, LA have arisen to different network applications such as wireless sensor networks [44], WiMAX networks [45], network security [46], wireless mesh networks [47], mobile video surveillance [48], vehicular environment [49,50], Peer-to-Peer networks [51], wireless data broadcasting systems [52][53][54], smart grid systems [55], grid computing [56], and cloud computing [57], to mention a few. In the recent years, LA have arisen to different network applications such as wireless sensor networks [44], WiMAX networks [45], network security [46], wireless mesh networks [47], mobile video surveillance [48], vehicular environment [49,50], Peer-to-Peer networks [51], wireless data broadcasting systems [52][53][54], smart grid systems [55], grid computing [56], and cloud computing [57], to mention a few.…”

Section: Refsmentioning

confidence: 99%

“…The LA as a simple and effective technique has found successfully applications in complex and dynamical environments where a large amount of uncertainty or lack of information about the environment exists. In the recent years, LA have arisen to different network applications such as wireless sensor networks [44], WiMAX networks [45], network security [46], wireless mesh networks [47], mobile video surveillance [48], vehicular environment [49,50], Peer-to-Peer networks [51], wireless data broadcasting systems [52][53][54], smart grid systems [55], grid computing [56], and cloud computing [57], to mention a few.…”

Section: Refsmentioning

confidence: 99%

A new learning automata‐based sampling algorithm for social networks

Rezvanian

Meybodi

2015

Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known sampling algorithms in terms of relative error and Kolmogorov-Smirnov test. It is shown that eDLA-based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA-based sampling algorithm in comparison with the DLA-based sampling algorithm has a 26.93% improvement for the average of Kolmogorov-Smirnov value for degree distribution taken over all test networks. Therefore, researchers try to study and analyze real networks such as communication, technological, information and OSNs via some metrics (i.e., centrality measures) or some techniques (i.e., network sampling methods) to estimate the characterization of these networks. Besides, some hidden important properties of networks (e.g., user age distribution, user activities, user connectivity, and many more in OSN) are discovered by studying on small sampled data. It is noted that sometimes sampling methods applied on the networks subject to preserve vital properties of the networks to reach a suitable visualization.A social network can be represented as a graph with a set of nodes such as users and a certain type of relationship between users as edges of graph. Let G = hV, Ei be a given social network graph, where V is the set of nodes and E is the set of edges. Sampled network G s = hV s , E s i is the induced subgraph of G by sampling rate ϕ, where V s ⊆ V, E s ⊆ E and 0 < ϕ < 1. It is noted that the main goal of sampling algorithms is to achieve a smaller subgraph similar to the original graph G. Sampling algorithms [8-10] play an important role in preprocessing, characterizing, studying, and analyzing the real networks. Network sampling can be applied for characterization of a small portion of a network subject to preserve main properties of the original network. For example, due to privacy restrictions of some OSN services, accessibility to the whole network information is not practical, therefore, the network is studied and characterized by estimated small samples from an important part of the original network. Taking into account large size, complexity, and dynamic properties of such real networks, most classic...

“…Learning automata have found applications in many areas, such as sensor networks [27][28][29][30], wireless data broadcasting systems [31][32][33], cognitive networks [34][35][36], mesh networks [37], peer-to-peer networks [38][39][40][41][42], channel assignment [43], image processing [44], neural networks engineering [45,46] and evolutionary computing [47,48], to mention a few.…”

Section: Learning Automatamentioning

confidence: 99%

A distributed adaptive landmark clustering algorithm based on mOverlay and learning automata for topology mismatch problem in unstructured peer‐to‐peer networks

Saghiri

Meybodi

2015

Peer-to-peer networks are overlay networks that are built on top of communication networks that are called underlay networks. In these networks, peers are unaware of the underlying networks, so the peers choose their neighbors without considering the underlay positions, and therefore, the resultant overlay network may have mismatches with its underlying network, causing redundant end-to-end delay. Landmark clustering algorithms, such as mOverlay, are used to solve topology mismatch problem. In the mOverlay algorithm, the overlay network is formed by clusters in which each cluster has a landmark peer. One of the drawbacks of mOverlay is that the selected landmark peer for each cluster is fixed during the operation of the network. Because of the dynamic nature of peer-to-peer networks, using a non-adaptive landmark selection algorithm may not be appropriate. In this paper, an adaptive landmark clustering algorithm obtained from the combination of mOverlay and learning automata is proposed. Learning automata are used to adaptively select appropriate landmark peers for the clusters in such a way that the total communication delay will be minimized. Simulation results have shown that the proposed algorithm outperforms the existing algorithms with respect to communication delay and average round-trip time between peers within clusters. peers in the overlay network. This graph is called as overlay graph. The topology of the underlay network can be represented by a graph G′ = (V′, E′) in which V′ = {position 1 , position 2 , …, position n } is a set of positions in the underlay network and E′ ⊆ V′ × V′ is a set of links connecting the positions in the underlay network. In peer-to-peer networks, each peer in V is mapped to a position in V′ according to a one-to-one function H : V → V′. In landmark clustering algorithms, the overlay graph is formed by clusters in which each cluster has a landmark peer. The peers that are not selected as landmark peers are called ordinary peers. The communication between ordinary peers should be handled by landmark peers. Let C i (where i ∈ {1, 2, 3, 4, …, p}) represent the set of p clusters in the network and L i ∈ V denote the landmark peer of cluster C i . The topology of the landmark peers network can be represented by a graph G l = (V l , E l ) in which V l ⊆ V is a set of landmark peers and E l ⊆ V × V is a set of links connecting the landmark peers in the overlay network. This graph is called landmark graph. In the peer-to-peer network that is constructed based on the landmark clustering algorithms, each landmark peer in V l is mapped to several ordinary peers in V according to a one-to-many function H′ : V l → V. Each landmark peer in V l is mapped to several positions in V′ using H′ and H. Figure 1 shows an example of a peer-to-peer network that uses two clusters to manage the overlay topology. In this example, peer m and peer j are two landmark peers.A primary goal of landmark clustering algorithms is to improve the performance of the overlay network in terms of communication dela...