Browsing by Subject "Location information"
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Item Open Access Distributed and location-based multicast routing algorithms for wireless sensor networks(SpringerOpen, 2009-01) Korpeoglu, I.; Bagci, H.Multicast routing protocols in wireless sensor networks are required for sending the same message to multiple different destinations. In this paper, we propose two different distributed algorithms for multicast routing in wireless sensor networks which make use of location information of sensor nodes. Our first algorithm groups the destination nodes according to their angular positions and forwards the multicast message toward each group in order to reduce the number of total branches in multicast tree which also reduces the number of messages transmitted. Our second algorithm calculates an Euclidean minimum spanning tree at the source node by using the positions of the destination nodes. The multicast message is forwarded to destination nodes according to the calculated MST. This helps in reducing the total energy consumed for delivering the message to all destinations by decreasing the number of total transmissions. Evaluation results show that the algorithms we propose are scalable and energy efficient, so they are good candidates to be used for multicasting in wireless sensor networks. Copyright © 2009 H. Bagci and I. Korpeoglu.Item Open Access Integrating social features into mobile local search(Elsevier Inc., 2016) Kahveci, B.; Altıngövde, İ. S.; Ulusoy, ÖzgürAs availability of Internet access on mobile devices develops year after year, users have been able to make use of search services while on the go. Location information on these devices has enabled mobile users to use local search services to access various types of location-related information easily. Mobile local search is inherently different from general web search. Namely, it focuses on local businesses and points of interest instead of general web pages, and finds relevant search results by evaluating different ranking features. It also strongly depends on several contextual factors, such as time, weather, location etc. In previous studies, rankings and mobile user context have been investigated with a small set of features. We developed a mobile local search application, Gezinio, and collected a data set of local search queries with novice social features. We also built ranking models to re-rank search results. We reveal that social features can improve performance of the machine-learned ranking models with respect to a baseline that solely ranks the results based on their distance to user. Furthermore, we find out that a feature that is important for ranking results of a certain query category may not be so useful for other categories.Item Open Access PETAL: a fully distributed location service for wireless ad hoc networks(Academic Press, 2017) Ilkhechi, A. R.; Korpeoglu, I.; Güdükbay, Uğur; Ulusoy, ÖzgürLocation service is an essential prerequisite for mobile wireless ad hoc networks (MANETs) in which the underlying routing protocol leverages physical location information of sender and receiver nodes. Fulfillment of this requirement is challenging partly due to the mobility and unpredictability of nodes in MANETs. Moreover, scalability and location information availability under various circumstances are also substantial factors in designing an effective location service paradigm. By and large, utilizing centralized or distributed location servers responsible for storing the location information of all, or a subset of participant mobile devices, is a method employed in a significant portion of location service schemes. However, from the fairness point of view, it is more suitable to employ a location service scheme that treats participant nodes fairly, without mandating an unlucky subset to undertake the responsibility of serving as location server(s). In this work, we propose a scalable and fully decentralized location service scheme (PETAL) in which the burden of location update and inquiry tasks is almost evenly distributed among the nodes, resulting in an improvement in resilience against individual node failures. PETAL does not require hashing which results in more complexity, it is resilient against swarm mobility pattern, it requires minimal periodic location update messages when nodes do not move, and finally it does not require too many parameter configurations on all nodes. Our simulation results reveal that PETAL performs efficiently, particularly in environments densely populated by wireless devices. © 2017 Elsevier LtdItem Open Access SLIM: A scalable location-sensitive information monitoring service(IEEE, 2013) Bamba, B.; Wu, K.-L.; Gedik, Buğra; Liu L.Location-sensitive information monitoring services are a centerpiece of the technology for disseminating content-rich information from massive data streams to mobile users. The key challenges for such monitoring services are characterized by the combination of spatial and non-spatial attributes being monitored and the wide spectrum of update rates. A typical example of such services is "alert me when the gas price at a gas station within 5 miles of my current location drops to $4 per gallon". Such a service needs to monitor the gas price changes in conjunction with the highly dynamic nature of location information. Scalability of such location sensitive and content rich information monitoring services in the presence of different update rates and monitoring thresholds poses a big technical challenge. In this paper, we present SLIM, a scalable location sensitive information monitoring service framework with two unique features. First, we make intelligent use of the correlation between spatial and non-spatial attributes involved in the information monitoring service requests to devise a highly scalable distributed spatial trigger evaluation engine. Second, we introduce single and multi-dimensional safe value containment techniques to efficiently perform selective distributed processing of spatial triggers to reduce the amount of unnecessary trigger evaluations. Through extensive experiments, we show that SLIM offers high scalability for location-sensitive, content-rich information monitoring services in terms of the number of information sources being monitored, number of users and monitoring requests. © 2013 IEEE.