1,659 research outputs found
Deterministic Communication in Radio Networks
In this paper we improve the deterministic complexity of two fundamental
communication primitives in the classical model of ad-hoc radio networks with
unknown topology: broadcasting and wake-up. We consider an unknown radio
network, in which all nodes have no prior knowledge about network topology, and
know only the size of the network , the maximum in-degree of any node
, and the eccentricity of the network .
For such networks, we first give an algorithm for wake-up, based on the
existence of small universal synchronizers. This algorithm runs in
time, the
fastest known in both directed and undirected networks, improving over the
previous best -time result across all ranges of parameters, but
particularly when maximum in-degree is small.
Next, we introduce a new combinatorial framework of block synchronizers and
prove the existence of such objects of low size. Using this framework, we
design a new deterministic algorithm for the fundamental problem of
broadcasting, running in time. This is
the fastest known algorithm for the problem in directed networks, improving
upon the -time algorithm of De Marco (2010) and the
-time algorithm due to Czumaj and Rytter (2003). It is also the
first to come within a log-logarithmic factor of the lower
bound due to Clementi et al.\ (2003).
Our results also have direct implications on the fastest \emph{deterministic
leader election} and \emph{clock synchronization} algorithms in both directed
and undirected radio networks, tasks which are commonly used as building blocks
for more complex procedures
Deterministic Digital Clustering of Wireless Ad Hoc Networks
We consider deterministic distributed communication in wireless ad hoc
networks of identical weak devices under the SINR model without predefined
infrastructure. Most algorithmic results in this model rely on various
additional features or capabilities, e.g., randomization, access to geographic
coordinates, power control, carrier sensing with various precision of
measurements, and/or interference cancellation. We study a pure scenario, when
no such properties are available. As a general tool, we develop a deterministic
distributed clustering algorithm. Our solution relies on a new type of
combinatorial structures (selectors), which might be of independent interest.
Using the clustering, we develop a deterministic distributed local broadcast
algorithm accomplishing this task in rounds, where
is the density of the network. To the best of our knowledge, this is
the first solution in pure scenario which is only polylog away from the
universal lower bound , valid also for scenarios with
randomization and other features. Therefore, none of these features
substantially helps in performing the local broadcast task. Using clustering,
we also build a deterministic global broadcast algorithm that terminates within
rounds, where is the diameter of the
network. This result is complemented by a lower bound , where is the path-loss parameter of the
environment. This lower bound shows that randomization or knowledge of own
location substantially help (by a factor polynomial in ) in the global
broadcast. Therefore, unlike in the case of local broadcast, some additional
model features may help in global broadcast
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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