11,540 research outputs found
Fundamental Limits of Caching in Wireless D2D Networks
We consider a wireless Device-to-Device (D2D) network where communication is
restricted to be single-hop. Users make arbitrary requests from a finite
library of files and have pre-cached information on their devices, subject to a
per-node storage capacity constraint. A similar problem has already been
considered in an ``infrastructure'' setting, where all users receive a common
multicast (coded) message from a single omniscient server (e.g., a base station
having all the files in the library) through a shared bottleneck link. In this
work, we consider a D2D ``infrastructure-less'' version of the problem. We
propose a caching strategy based on deterministic assignment of subpackets of
the library files, and a coded delivery strategy where the users send linearly
coded messages to each other in order to collectively satisfy their demands. We
also consider a random caching strategy, which is more suitable to a fully
decentralized implementation. Under certain conditions, both approaches can
achieve the information theoretic outer bound within a constant multiplicative
factor. In our previous work, we showed that a caching D2D wireless network
with one-hop communication, random caching, and uncoded delivery, achieves the
same throughput scaling law of the infrastructure-based coded multicasting
scheme, in the regime of large number of users and files in the library. This
shows that the spatial reuse gain of the D2D network is order-equivalent to the
coded multicasting gain of single base station transmission. It is therefore
natural to ask whether these two gains are cumulative, i.e.,if a D2D network
with both local communication (spatial reuse) and coded multicasting can
provide an improved scaling law. Somewhat counterintuitively, we show that
these gains do not cumulate (in terms of throughput scaling law).Comment: 45 pages, 5 figures, Submitted to IEEE Transactions on Information
Theory, This is the extended version of the conference (ITW) paper
arXiv:1304.585
Continuous client-side query evaluation over dynamic linked data
Existing solutions to query dynamic Linked Data sources extend the SPARQL language, and require continuous server processing for each query. Traditional SPARQL endpoints already accept highly expressive queries, so extending these endpoints for time-sensitive queries increases the server cost even further. To make continuous querying over dynamic Linked Data more affordable, we extend the low-cost Triple Pattern Fragments (TPF) interface with support for time-sensitive queries. In this paper, we introduce the TPF Query Streamer that allows clients to evaluate SPARQL queries with continuously updating results. Our experiments indicate that this extension significantly lowers the server complexity, at the expense of an increase in the execution time per query. We prove that by moving the complexity of continuously evaluating queries over dynamic Linked Data to the clients and thus increasing bandwidth usage, the cost at the server side is significantly reduced. Our results show that this solution makes real-time querying more scalable for a large amount of concurrent clients when compared to the alternatives
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