36,302 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
Strategic Network Formation with Attack and Immunization
Strategic network formation arises where agents receive benefit from
connections to other agents, but also incur costs for forming links. We
consider a new network formation game that incorporates an adversarial attack,
as well as immunization against attack. An agent's benefit is the expected size
of her connected component post-attack, and agents may also choose to immunize
themselves from attack at some additional cost. Our framework is a stylized
model of settings where reachability rather than centrality is the primary
concern and vertices vulnerable to attacks may reduce risk via costly measures.
In the reachability benefit model without attack or immunization, the set of
equilibria is the empty graph and any tree. The introduction of attack and
immunization changes the game dramatically; new equilibrium topologies emerge,
some more sparse and some more dense than trees. We show that, under a mild
assumption on the adversary, every equilibrium network with agents contains
at most edges for . So despite permitting topologies denser
than trees, the amount of overbuilding is limited. We also show that attack and
immunization don't significantly erode social welfare: every non-trivial
equilibrium with respect to several adversaries has welfare at least as that of
any equilibrium in the attack-free model.
We complement our theory with simulations demonstrating fast convergence of a
new bounded rationality dynamic which generalizes linkstable best response but
is considerably more powerful in our game. The simulations further elucidate
the wide variety of asymmetric equilibria and demonstrate topological
consequences of the dynamics e.g. heavy-tailed degree distributions. Finally,
we report on a behavioral experiment on our game with over 100 participants,
where despite the complexity of the game, the resulting network was
surprisingly close to equilibrium.Comment: The short version of this paper appears in the proceedings of WINE-1
Random graphs from a block-stable class
A class of graphs is called block-stable when a graph is in the class if and
only if each of its blocks is. We show that, as for trees, for most -vertex
graphs in such a class, each vertex is in at most blocks, and each path passes through at most blocks.
These results extend to `weakly block-stable' classes of graphs
Core percolation in random graphs: a critical phenomena analysis
We study both numerically and analytically what happens to a random graph of
average connectivity "alpha" when its leaves and their neighbors are removed
iteratively up to the point when no leaf remains. The remnant is made of
isolated vertices plus an induced subgraph we call the "core". In the
thermodynamic limit of an infinite random graph, we compute analytically the
dynamics of leaf removal, the number of isolated vertices and the number of
vertices and edges in the core. We show that a second order phase transition
occurs at "alpha = e = 2.718...": below the transition, the core is small but
above the transition, it occupies a finite fraction of the initial graph. The
finite size scaling properties are then studied numerically in detail in the
critical region, and we propose a consistent set of critical exponents, which
does not coincide with the set of standard percolation exponents for this
model. We clarify several aspects in combinatorial optimization and spectral
properties of the adjacency matrix of random graphs.
Key words: random graphs, leaf removal, core percolation, critical exponents,
combinatorial optimization, finite size scaling, Monte-Carlo.Comment: 15 pages, 9 figures (color eps) [v2: published text with a new Title
and addition of an appendix, a ref. and a fig.
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