519 research outputs found
Energy Scaling Laws for Distributed Inference in Random Fusion Networks
The energy scaling laws of multihop data fusion networks for distributed
inference are considered. The fusion network consists of randomly located
sensors distributed i.i.d. according to a general spatial distribution in an
expanding region. Among the class of data fusion schemes that enable optimal
inference at the fusion center for Markov random field (MRF) hypotheses, the
scheme with minimum average energy consumption is bounded below by average
energy of fusion along the minimum spanning tree, and above by a suboptimal
scheme, referred to as Data Fusion for Markov Random Fields (DFMRF). Scaling
laws are derived for the optimal and suboptimal fusion policies. It is shown
that the average asymptotic energy of the DFMRF scheme is finite for a class of
MRF models.Comment: IEEE JSAC on Stochastic Geometry and Random Graphs for Wireless
Network
Time-slotted voting mechanism for fusion data assurance in wireless sensor networks under stealthy attacks
In wireless sensor networks, data fusion is often performed in order to reduce the overall message transmission from the sensors toward the base station. We investigate the problem of data fusion assurance in multi-level data fusion or transmission in this paper. Different to a recent approach of direct voting where the base station polls other nodes directly regarding to the received fusion result, we propose a scheme that uses the time-slotted voting technique. In this scheme, each fusion node broadcasts its fusion data or "vote" during its randomly assigned time slot. Only the fusion result with enough number of votes will be accepted. Thus, our scheme eliminates the polling process and eases the energy consumption burden on the base station or the fusion data receiver, which could well be the intermediate nodes. Our analysis and simulation results support our claim of superiority of the proposed scheme
Clustered wireless sensor networks
The study of topology in randomly deployed wireless sensor networks (WSNs) is important in addressing the fundamental issue of stochastic coverage resulting from randomness in the deployment procedure and power management algorithms. This dissertation defines and studies clustered WSNs, WSNs whose topology due to the deployment procedure and the application requirements results in the phenomenon of clustering or clumping of nodes. The first part of this dissertation analyzes a range of topologies of clustered WSNs and their impact on the primary sensing objectives of coverage and connectivity. By exploiting the inherent advantages of clustered topologies of nodes, this dissertation presents techniques for optimizing the primary performance metrics of power consumption and network capacity. It analyzes clustering in the presence of obstacles, and studies varying levels of redundancy to determine the probability of coverage in the network. The proposed models for clustered WSNs embrace the domain of a wide range of topologies that are prevalent in actual real-world deployment scenarios, and call for clustering-specific protocols to enhance network performance. It has been shown that power management algorithms tailored to various clustering scenarios optimize the level of active coverage and maximize the network lifetime. The second part of this dissertation addresses the problem of edge effects and heavy traffic on queuing in clustered WSNs. In particular, an admission control model called directed ignoring model has been developed that aims to minimize the impact of edge effects in queuing by improving queuing metrics such as packet loss and wait time
Distributed Detection over Gaussian Multiple Access Channels with Constant Modulus Signaling
A distributed detection scheme where the sensors transmit with constant
modulus signals over a Gaussian multiple access channel is considered. The
deflection coefficient of the proposed scheme is shown to depend on the
characteristic function of the sensing noise and the error exponent for the
system is derived using large deviation theory. Optimization of the deflection
coefficient and error exponent are considered with respect to a transmission
phase parameter for a variety of sensing noise distributions including
impulsive ones. The proposed scheme is also favorably compared with existing
amplify-and-forward and detect-and-forward schemes. The effect of fading is
shown to be detrimental to the detection performance through a reduction in the
deflection coefficient depending on the fading statistics. Simulations
corroborate that the deflection coefficient and error exponent can be
effectively used to optimize the error probability for a wide variety of
sensing noise distributions.Comment: 30 pages, 12 figure
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