Data dissemination in mobile peer-to-peer networks

Repantis, Thomas; Kalogeraki, Vana

doi:10.1145/1071246.1071279

Cited by 44 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mobile Peer-to-Peer/MANET search can be roughly classified into: i) Blind Search [18,31,51], where mobile peers propagate the query using an unsophisticated (e.g., random, ttl property) approach to as many nodes in the network as possible, and ii) Informed Search [8,23,24,33,39,46], where mobile peers use semantic or location information to forward queries to specific nodes in the network. The proposed search approach presented in this paper belongs to the latter class with the difference that we utilize a centralized approach where mobile peers (i.e., smartphone devices) subscribe to a centralized registry.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The proposed search approach presented in this paper belongs to the latter class with the difference that we utilize a centralized approach where mobile peers (i.e., smartphone devices) subscribe to a centralized registry. Similar to [39], we utilize a content summary mechanism (i.e., profile) for discovering mobile peers that will participate in a query Q by the centralized node. However, in our setting, the content summary of each mobile peer is stored at the centralized node upon its registration thus allowing multiple query users to use this information without requiring the retransmission of the content summary to each mobile peer.…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent search in social communities of smartphone users

Konstantinidis

Zeinalipour-Yazti

Andreou

et al. 2012

Distrib Parallel Databases

View full text Add to dashboard Cite

Abstract. Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain, however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft's GeoLife project, DBLP and Pics 'n' Trails but also using our real Android SmartP2P 3 system deployed over our SmartLab 4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Intelligent search in social communities of smartphone users

Konstantinidis

Zeinalipour-Yazti

Andreou

et al. 2012

Distrib Parallel Databases

View full text Add to dashboard Cite

show abstract

“…In [19], [20], [21] and [22] the authors propose that each peer stores two types of filters, a local filter (LF) computed from attributes of local objects and remote filters (RF) for attributes of objects stored in remote peers. When a peer receives a query for a particular set of attributes in the form of a bit-vector, it searches its LF then the RF filters in order to decide whether the requested information is stored locally or the request should be further routed.…”

Section: A Bloom Filtersmentioning

confidence: 99%

Distributed Information Storage and Collection for WSNs

Jardak

Osipov

Mähönen

2007

2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems

View full text Add to dashboard Cite

Abstract-Distributed data storage is an important component of wireless sensor networks, which protects the mission critical information from unexpected node failures or malicious destruction of parts of the network. In this paper we present DISC, a protocol for distributed information storage and collection. The two major mechanisms in DISC which make our solution distinct from the related approaches are probabilistic choice of storing nodes and a search engine based on the usage of Bloom filters. In comparison to the deterministic choice of the backup node, the random selection strategy makes it virtually impossible for an attacker to determine and destroy the exact node keeping a particular piece of information. The usage of Bloom filters in the information search engine makes the navigation to a specific data fast and efficient. We show that with DISC the amount of recovered information is more than two times higher than that in deterministic storage schemes.

show abstract

“…In mobile p2p networks, communication overhead, a network centric metric has been well addressed [4]- [8]. However, we believe that search overhead at local nodes is also important.…”

Section: Introductionmentioning

confidence: 99%

A Multi-tiered Architecture for Content Retrieval in Mobile Peer-to-Peer Networks

Dutta

Kotikalapudi

Saxena

et al. 2011

2011 IEEE 12th International Conference on Mobile Data Management

View full text Add to dashboard Cite

Abstract-In this paper, we address content retrieval in Mobile Peer-to-Peer (P2P) Networks. We design a multi-tiered architecture for content retrieval, where at Tier 1, we design a protocol for content similarity governed by a parameter α that trades accuracy with search overhead. At Tier 2, we introduce a novel concept called Chained Bloom Filters and design a protocol where popular search items are linked with popular content at each node in an efficient manner for subsequent retrieval. Extensive analysis and numerical simulations demonstrate the effectiveness of our techniques.

show abstract

Data dissemination in mobile peer-to-peer networks

Cited by 44 publications

References 24 publications

Intelligent search in social communities of smartphone users

Intelligent search in social communities of smartphone users

Distributed Information Storage and Collection for WSNs

A Multi-tiered Architecture for Content Retrieval in Mobile Peer-to-Peer Networks

Contact Info

Product

Resources

About