19,336 research outputs found
On Mobility Management in Multi-Sink Sensor Networks for Geocasting of Queries
In order to efficiently deal with location dependent messages in multi-sink wireless sensor networks (WSNs), it is key that the network informs sinks what geographical area is covered by which sink. The sinks are then able to efficiently route messages which are only valid in particular regions of the deployment. In our previous work (see the 5th and 6th cited documents), we proposed a combined coverage area reporting and geographical routing protocol for location dependent messages, for example, queries that are injected by sinks. In this paper, we study the case where we have static sinks and mobile sensor nodes in the network. To provide up-to-date coverage areas to sinks, we focus on handling node mobility in the network. We discuss what is a better method for updating the routing structure (i.e., routing trees and coverage areas) to handle mobility efficiently: periodic global updates initiated from sinks or local updates triggered by mobile sensors. Simulation results show that local updating perform very well in terms of query delivery ratio. Local updating has a better scalability to increasing network size. It is also more energy efficient than ourpreviously proposed approach, where global updating in networks have medium mobility rate and speed
Brief Announcement: Broadcasting Time in Dynamic Rooted Trees is Linear
We study the broadcast problem on dynamic networks with processes. The
processes communicate in synchronous rounds along an arbitrary rooted tree. The
sequence of trees is given by an adversary whose goal is to maximize the number
of rounds until at least one process reaches all other processes. Previous
research has shown a lower bound and an
upper bound. We show the first linear upper bound for this
problem, namely . Our result
follows from a detailed analysis of the evolution of the adjacency matrix of
the network over time.Comment: 5 pages, 1 figure, published in PODC'22, further work:
arXiv:2211.1015
On the Properties of Gromov Matrices and their Applications in Network Inference
The spanning tree heuristic is a commonly adopted procedure in network
inference and estimation. It allows one to generalize an inference method
developed for trees, which is usually based on a statistically rigorous
approach, to a heuristic procedure for general graphs by (usually randomly)
choosing a spanning tree in the graph to apply the approach developed for
trees. However, there are an intractable number of spanning trees in a dense
graph. In this paper, we represent a weighted tree with a matrix, which we call
a Gromov matrix. We propose a method that constructs a family of Gromov
matrices using convex combinations, which can be used for inference and
estimation instead of a randomly selected spanning tree. This procedure
increases the size of the candidate set and hence enhances the performance of
the classical spanning tree heuristic. On the other hand, our new scheme is
based on simple algebraic constructions using matrices, and hence is still
computationally tractable. We discuss some applications on network inference
and estimation to demonstrate the usefulness of the proposed method
Asymptotically Tight Bounds on the Time Complexity of Broadcast and Its Variants in Dynamic Networks
AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction
Increasingly, commercial content providers (CPs) offer streaming solutions using peer-to-peer (P2P) architectures, which promises significant scalabil- ity by leveraging clientsâ upstream capacity. A major limitation of P2P live streaming is that playout rates are constrained by clientsâ upstream capac- ities â typically much lower than downstream capacities â which limit the quality of the delivered stream. To leverage P2P architectures without sacri- ficing quality, CPs must commit additional resources to complement clientsâ resources. In this work, we propose a cloud-based service AngelCast that enables CPs to complement P2P streaming. By subscribing to AngelCast, a CP is able to deploy extra resources (angel), on-demand from the cloud, to maintain a desirable stream quality. Angels do not download the whole stream, nor are they in possession of it. Rather, angels only relay the minimal fraction of the stream necessary to achieve the desired quality. We provide a lower bound on the minimum angel capacity needed to maintain a desired client bit-rate, and develop a fluid model construction to achieve it. Realizing the limitations of the fluid model construction, we design a practical multi- tree construction that captures the spirit of the optimal construction, and avoids its limitations. We present a prototype implementation of AngelCast, along with experimental results confirming the feasibility of our service.Supported in part by NSF awards #0720604, #0735974, #0820138, #0952145, #1012798 #1012798 #1430145 #1414119. (0720604 - NSF; 0735974 - NSF; 0820138 - NSF; 0952145 - NSF; 1012798 - NSF; 1430145 - NSF; 1414119 - NSF
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