4,557 research outputs found
Self-Correcting Broadcast in Distributed Hash Tables
We present two broadcast algorithms that can be used on top of distributed hash tables (DHTs) to perform group communication and arbitrary queries. Unlike other P2P group communication mechanisms, which either embed extra information in the DHTs or use random overlay networks, our algorithms take advantage of the structured DHT overlay networks without maintaining additional information. The proposed algorithms do not send any redundant messages. Furthermore the two algorithms ensure 100% coverage of the nodes in the system even when routing information is outdated as a result of dynamism in the network. The first algorithm performs some correction of outdated routing table entries with a low cost of correction traffic. The second algorithm exploits the nature of the broadcasts to extensively update erroneous routing information at the cost of higher correction traffic. The algorithms are validated and evaluated in our stochastic distributed-algorithms simulator
zCap: a zero configuration adaptive paging and mobility management mechanism
Today, cellular networks rely on fixed collections of cells (tracking areas) for user equipment localisation. Locating users within these areas involves broadcast search (paging), which consumes radio bandwidth but reduces the user equipment signalling required for mobility management. Tracking areas are today manually configured, hard to adapt to local mobility and influence the load on several key resources in the network. We propose a decentralised and self-adaptive approach to mobility management based on a probabilistic model of local mobility. By estimating the parameters of this model from observations of user mobility collected online, we obtain a dynamic model from which we construct local neighbourhoods of cells where we are most likely to locate user equipment. We propose to replace the static tracking areas of current systems with neighbourhoods local to each cell. The model is also used to derive a multi-phase paging scheme, where the division of neighbourhood cells into consecutive phases balances response times and paging cost. The complete mechanism requires no manual tracking area configuration and performs localisation efficiently in terms of signalling and response times. Detailed simulations show that significant potential gains in localisation effi- ciency are possible while eliminating manual configuration of mobility management parameters. Variants of the proposal can be implemented within current (LTE) standards
From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics
Cascades are ubiquitous in various network environments. How to predict these
cascades is highly nontrivial in several vital applications, such as viral
marketing, epidemic prevention and traffic management. Most previous works
mainly focus on predicting the final cascade sizes. As cascades are typical
dynamic processes, it is always interesting and important to predict the
cascade size at any time, or predict the time when a cascade will reach a
certain size (e.g. an threshold for outbreak). In this paper, we unify all
these tasks into a fundamental problem: cascading process prediction. That is,
given the early stage of a cascade, how to predict its cumulative cascade size
of any later time? For such a challenging problem, how to understand the micro
mechanism that drives and generates the macro phenomenons (i.e. cascading
proceese) is essential. Here we introduce behavioral dynamics as the micro
mechanism to describe the dynamic process of a node's neighbors get infected by
a cascade after this node get infected (i.e. one-hop subcascades). Through
data-driven analysis, we find out the common principles and patterns lying in
behavioral dynamics and propose a novel Networked Weibull Regression model for
behavioral dynamics modeling. After that we propose a novel method for
predicting cascading processes by effectively aggregating behavioral dynamics,
and propose a scalable solution to approximate the cascading process with a
theoretical guarantee. We extensively evaluate the proposed method on a large
scale social network dataset. The results demonstrate that the proposed method
can significantly outperform other state-of-the-art baselines in multiple tasks
including cascade size prediction, outbreak time prediction and cascading
process prediction.Comment: 10 pages, 11 figure
Let the Tree Bloom: Scalable Opportunistic Routing with ORPL
Routing in battery-operated wireless networks is challenging, posing a tradeoff between energy and latency. Previous work has shown that opportunistic routing can achieve low-latency data collection in duty-cycled networks. However, applications are now considered where nodes are not only periodic data sources, but rather addressable end points generating traffic with arbitrary patterns.
We present ORPL, an opportunistic routing protocol that supports any-to-any, on-demand traffic. ORPL builds upon RPL, the standard protocol for low-power IPv6 networks. By combining RPL's tree-like topology with opportunistic routing, ORPL forwards data to any destination based on the mere knowledge of the nodes' sub-tree. We use bitmaps and Bloom filters to represent and propagate this information in a space-efficient way, making ORPL scale to large networks of addressable nodes. Our results in a 135-node testbed show that ORPL outperforms a number of state-of-the-art solutions including RPL and CTP, conciliating a sub-second latency and a sub-percent duty cycle. ORPL also increases robustness and scalability, addressing the whole network reliably through a 64-byte Bloom filter, where RPL needs kilobytes of routing tables for the same task
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