7,056 research outputs found
Overlapping Multi-hop Clustering for Wireless Sensor Networks
Clustering is a standard approach for achieving efficient and scalable
performance in wireless sensor networks. Traditionally, clustering algorithms
aim at generating a number of disjoint clusters that satisfy some criteria. In
this paper, we formulate a novel clustering problem that aims at generating
overlapping multi-hop clusters. Overlapping clusters are useful in many sensor
network applications, including inter-cluster routing, node localization, and
time synchronization protocols. We also propose a randomized, distributed
multi-hop clustering algorithm (KOCA) for solving the overlapping clustering
problem. KOCA aims at generating connected overlapping clusters that cover the
entire sensor network with a specific average overlapping degree. Through
analysis and simulation experiments we show how to select the different values
of the parameters to achieve the clustering process objectives. Moreover, the
results show that KOCA produces approximately equal-sized clusters, which
allows distributing the load evenly over different clusters. In addition, KOCA
is scalable; the clustering formation terminates in a constant time regardless
of the network size
D-SLATS: Distributed Simultaneous Localization and Time Synchronization
Through the last decade, we have witnessed a surge of Internet of Things
(IoT) devices, and with that a greater need to choreograph their actions across
both time and space. Although these two problems, namely time synchronization
and localization, share many aspects in common, they are traditionally treated
separately or combined on centralized approaches that results in an ineffcient
use of resources, or in solutions that are not scalable in terms of the number
of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three
different and independent algorithms to jointly solve time synchronization and
localization problems in a distributed fashion. The First two algorithms are
based mainly on the distributed Extended Kalman Filter (EKF) whereas the third
one uses optimization techniques. No fusion center is required, and the devices
only communicate with their neighbors. The proposed methods are evaluated on
custom Ultra-Wideband communication Testbed and a quadrotor, representing a
network of both static and mobile nodes. Our algorithms achieve up to three
microseconds time synchronization accuracy and 30 cm localization error
Dynamic Parameter Allocation in Parameter Servers
To keep up with increasing dataset sizes and model complexity, distributed
training has become a necessity for large machine learning tasks. Parameter
servers ease the implementation of distributed parameter management---a key
concern in distributed training---, but can induce severe communication
overhead. To reduce communication overhead, distributed machine learning
algorithms use techniques to increase parameter access locality (PAL),
achieving up to linear speed-ups. We found that existing parameter servers
provide only limited support for PAL techniques, however, and therefore prevent
efficient training. In this paper, we explore whether and to what extent PAL
techniques can be supported, and whether such support is beneficial. We propose
to integrate dynamic parameter allocation into parameter servers, describe an
efficient implementation of such a parameter server called Lapse, and
experimentally compare its performance to existing parameter servers across a
number of machine learning tasks. We found that Lapse provides near-linear
scaling and can be orders of magnitude faster than existing parameter servers
Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks
In distributed target tracking for wireless sensor networks, agreement on the
target state can be achieved by the construction and maintenance of a
communication path, in order to exchange information regarding local likelihood
functions. Such an approach lacks robustness to failures and is not easily
applicable to ad-hoc networks. To address this, several methods have been
proposed that allow agreement on the global likelihood through fully
distributed belief consensus (BC) algorithms, operating on local likelihoods in
distributed particle filtering (DPF). However, a unified comparison of the
convergence speed and communication cost has not been performed. In this paper,
we provide such a comparison and propose a novel BC algorithm based on belief
propagation (BP). According to our study, DPF based on metropolis belief
consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus
is the fastest in tree graphs. Moreover, we found that BC-based DPF methods
have lower communication overhead than data flooding when the network is
sufficiently sparse
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