11,981 research outputs found
Space-Time Hierarchical-Graph Based Cooperative Localization in Wireless Sensor Networks
It has been shown that cooperative localization is capable of improving both
the positioning accuracy and coverage in scenarios where the global positioning
system (GPS) has a poor performance. However, due to its potentially excessive
computational complexity, at the time of writing the application of cooperative
localization remains limited in practice. In this paper, we address the
efficient cooperative positioning problem in wireless sensor networks. A
space-time hierarchical-graph based scheme exhibiting fast convergence is
proposed for localizing the agent nodes. In contrast to conventional methods,
agent nodes are divided into different layers with the aid of the space-time
hierarchical-model and their positions are estimated gradually. In particular,
an information propagation rule is conceived upon considering the quality of
positional information. According to the rule, the information always
propagates from the upper layers to a certain lower layer and the message
passing process is further optimized at each layer. Hence, the potential error
propagation can be mitigated. Additionally, both position estimation and
position broadcasting are carried out by the sensor nodes. Furthermore, a
sensor activation mechanism is conceived, which is capable of significantly
reducing both the energy consumption and the network traffic overhead incurred
by the localization process. The analytical and numerical results provided
demonstrate the superiority of our space-time hierarchical-graph based
cooperative localization scheme over the benchmarking schemes considered.Comment: 14 pages, 15 figures, 4 tables, accepted to appear on IEEE
Transactions on Signal Processing, Sept. 201
Study on Energy Consumption and Coverage of Hierarchical Cooperation of Small Cell Base Stations in Heterogeneous Networks
The demand for communication services in the era of intelligent terminals is
unprecedented and huge. To meet such development, modern wireless
communications must provide higher quality services with higher energy
efficiency in terms of system capacity and quality of service (QoS), which
could be achieved by the high-speed data rate, the wider coverage and the
higher band utilization. In this paper, we propose a way to offload users from
a macro base station(MBS) with a hierarchical distribution of small cell base
stations(SBS). The connection probability is the key indicator of the
implementation of the unload operation. Furthermore, we measure the service
performance of the system by finding the conditional probability-coverage
probability with the certain SNR threshold as the condition, that is, the
probability of obtaining the minimum communication quality when the different
base stations are connected to the user. Then, user-centered total energy
consumption of the system is respectively obtained when the macro base
station(MBS) and the small cell base stations(SBS) serve each of the users. The
simulation results show that the hierarchical SBS cooperation in heterogeneous
networks can provide a higher system total coverage probability for the system
with a lower overall system energy consumption than MBS.Comment: 6 pages, 7 figures, accepted by ICACT201
Towards a collaborative model for wireless sensor networks
Collaboration is crucial to Wireless Sensor Networks (WSNs) as a result of
the typical resource limitations of wireless sensor nodes. In this paper, we
present a model of collaborative work for WSNs. This model is called Wireless
Sensor Networks Supported Cooperative Work (WSNSCW) and was created
for these specific networks. We also present the formalization of some entities
of the model and its properties. This is a generic model that is being used as a
basis for the development of a 3D awareness tool for WSNs.info:eu-repo/semantics/publishedVersio
A collaborative model for representing wireless sensor networks' entities and properties
Wireless sensor nodes are, typically, resource limited. Therefore,
the major functions of the Wireless Sensor Network (WSN) cannot
be accomplished without collaboration among sensor nodes. In this
paper, we present the Wireless Sensor Networks Supported
Cooperative Work (WSNSCW) model. The key contribution of this
model, when comparing to other models, is allowing for the
representation of all the components and properties of a WSN. We
also present the main entities and properties of this graph-based
model, as well as its formalization.info:eu-repo/semantics/publishedVersio
An Energy Efficient Cooperative Hierarchical MIMO Clustering Scheme for Wireless Sensor Networks
In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
Distributed signal processing for wireless sensor networks enables that
different devices cooperate to solve different signal processing tasks. A
crucial first step is to answer the question: who observes what? Recently,
several distributed algorithms have been proposed, which frame the
signal/object labelling problem in terms of cluster analysis after extracting
source-specific features, however, the number of clusters is assumed to be
known. We propose a new method called Gravitational Clustering (GC) to
adaptively estimate the time-varying number of clusters based on a set of
feature vectors. The key idea is to exploit the physical principle of
gravitational force between mass units: streaming-in feature vectors are
considered as mass units of fixed position in the feature space, around which
mobile mass units are injected at each time instant. The cluster enumeration
exploits the fact that the highest attraction on the mobile mass units is
exerted by regions with a high density of feature vectors, i.e., gravitational
clusters. By sharing estimates among neighboring nodes via a
diffusion-adaptation scheme, cooperative and distributed cluster enumeration is
achieved. Numerical experiments concerning robustness against outliers,
convergence and computational complexity are conducted. The application in a
distributed cooperative multi-view camera network illustrates the applicability
to real-world problems.Comment: 12 pages, 9 figure
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