85,567 research outputs found
Fundamentals of Large Sensor Networks: Connectivity, Capacity, Clocks and Computation
Sensor networks potentially feature large numbers of nodes that can sense
their environment over time, communicate with each other over a wireless
network, and process information. They differ from data networks in that the
network as a whole may be designed for a specific application. We study the
theoretical foundations of such large scale sensor networks, addressing four
fundamental issues- connectivity, capacity, clocks and function computation.
To begin with, a sensor network must be connected so that information can
indeed be exchanged between nodes. The connectivity graph of an ad-hoc network
is modeled as a random graph and the critical range for asymptotic connectivity
is determined, as well as the critical number of neighbors that a node needs to
connect to. Next, given connectivity, we address the issue of how much data can
be transported over the sensor network. We present fundamental bounds on
capacity under several models, as well as architectural implications for how
wireless communication should be organized.
Temporal information is important both for the applications of sensor
networks as well as their operation.We present fundamental bounds on the
synchronizability of clocks in networks, and also present and analyze
algorithms for clock synchronization. Finally we turn to the issue of gathering
relevant information, that sensor networks are designed to do. One needs to
study optimal strategies for in-network aggregation of data, in order to
reliably compute a composite function of sensor measurements, as well as the
complexity of doing so. We address the issue of how such computation can be
performed efficiently in a sensor network and the algorithms for doing so, for
some classes of functions.Comment: 10 pages, 3 figures, Submitted to the Proceedings of the IEE
A Key Establishment Scheme for Mobile Wireless Sensor Networks Using Post-Deployment Knowledge
Establishment of pairwise keys between sensor nodes in a sensor network is a
difficult problem due to resource limitations of sensor nodes as well as
vulnerability to physical captures of sensor nodes by the enemy. Public-key
cryptosystems are not much suited for most resource-constrained sensor
networks. Recently, elliptic curve cryptographic techniques show that public
key cryptosystem is also feasible for resource-constrained sensor networks.
However, most researchers accept that the symmetric key cryptosystems are
viable options for resource-constrained sensor networks. In this paper, we
first develop a basic principle to address the key pre-distribution problem in
mobile sensor networks. Then, using this developed basic principle, we propose
a scheme which takes the advantage of the post-deployment knowledge. Our scheme
is a modified version of the key prioritization technique proposed by Liu and
Ning. Our improved scheme provides reasonable network connectivity and
security. Moreover, the proposed scheme works for any deployment topology.Comment: Published in International Journal of Computer Networks &
Communications (IJCNC) Vol.3, No.4, July 201
Extremal Properties of Three Dimensional Sensor Networks with Applications
In this paper, we analyze various critical transmitting/sensing ranges for
connectivity and coverage in three-dimensional sensor networks. As in other
large-scale complex systems, many global parameters of sensor networks undergo
phase transitions: For a given property of the network, there is a critical
threshold, corresponding to the minimum amount of the communication effort or
power expenditure by individual nodes, above (resp. below) which the property
exists with high (resp. a low) probability. For sensor networks, properties of
interest include simple and multiple degrees of connectivity/coverage. First,
we investigate the network topology according to the region of deployment, the
number of deployed sensors and their transmitting/sensing ranges. More
specifically, we consider the following problems: Assume that nodes, each
capable of sensing events within a radius of , are randomly and uniformly
distributed in a 3-dimensional region of volume , how large
must the sensing range be to ensure a given degree of coverage of the region to
monitor? For a given transmission range, what is the minimum (resp. maximum)
degree of the network? What is then the typical hop-diameter of the underlying
network? Next, we show how these results affect algorithmic aspects of the
network by designing specific distributed protocols for sensor networks
The impact of physical conditions on network connectivity in wireless sensor network
In Wireless Sensor Networks, end-to-end routing paths need to be established when nodes want to communicate with the desired destination. For nodes assumed to be static, many routing protocols such as Directed Diffusion have been proposed to meet this requirement efficiently. The performance of such routing protocols is relative to the given network connectivity. This paper addresses mobile sensor nodes taking into account the diversity of scattered node density and investigates how physical conditions impact on network connectivity which in turn influences routing performance. Three analysis metrics: path availability, path duration, and interavailable path time are proposed to quantify the impact of different physical conditions on network connectivity. Simulation results show that the network connectivity varies significantly as a function of different physical conditions
Using transfer entropy to measure the patterns of information flow though cortex : application to MEG recordings from a visual Simon task
Poster presentation: Functional connectivity of the brain describes the network of correlated activities of different brain areas. However, correlation does not imply causality and most synchronization measures do not distinguish causal and non-causal interactions among remote brain areas, i.e. determine the effective connectivity [1]. Identification of causal interactions in brain networks is fundamental to understanding the processing of information. Attempts at unveiling signs of functional or effective connectivity from non-invasive Magneto-/Electroencephalographic (M/EEG) recordings at the sensor level are hampered by volume conduction leading to correlated sensor signals without the presence of effective connectivity. Here, we make use of the transfer entropy (TE) concept to establish effective connectivity. The formalism of TE has been proposed as a rigorous quantification of the information flow among systems in interaction and is a natural generalization of mutual information [2]. In contrast to Granger causality, TE is a non-linear measure and not influenced by volume conduction. ..
Connectivity in Secure Wireless Sensor Networks under Transmission Constraints
In wireless sensor networks (WSNs), the Eschenauer-Gligor (EG) key
pre-distribution scheme is a widely recognized way to secure communications.
Although connectivity properties of secure WSNs with the EG scheme have been
extensively investigated, few results address physical transmission
constraints. These constraints reflect real-world implementations of WSNs in
which two sensors have to be within a certain distance from each other to
communicate. In this paper, we present zero-one laws for connectivity in WSNs
employing the EG scheme under transmission constraints. These laws help specify
the critical transmission ranges for connectivity. Our analytical findings are
confirmed via numerical experiments. In addition to secure WSNs, our
theoretical results are also applied to frequency hopping in wireless networks.Comment: Full version of a paper published in Annual Allerton Conference on
Communication, Control, and Computing (Allerton) 201
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