38,664 research outputs found
Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks
Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Spectral Efficiency Scaling Laws in Dense Random Wireless Networks with Multiple Receive Antennas
This paper considers large random wireless networks where
transmit-and-receive node pairs communicate within a certain range while
sharing a common spectrum. By modeling the spatial locations of nodes based on
stochastic geometry, analytical expressions for the ergodic spectral efficiency
of a typical node pair are derived as a function of the channel state
information available at a receiver (CSIR) in terms of relevant system
parameters: the density of communication links, the number of receive antennas,
the path loss exponent, and the operating signal-to-noise ratio. One key
finding is that when the receiver only exploits CSIR for the direct link, the
sum of spectral efficiencies linearly improves as the density increases, when
the number of receive antennas increases as a certain super-linear function of
the density. When each receiver exploits CSIR for a set of dominant interfering
links in addition to the direct link, the sum of spectral efficiencies linearly
increases with both the density and the path loss exponent if the number of
antennas is a linear function of the density. This observation demonstrates
that having CSIR for dominant interfering links provides a multiplicative gain
in the scaling law. It is also shown that this linear scaling holds for direct
CSIR when incorporating the effect of the receive antenna correlation, provided
that the rank of the spatial correlation matrix scales super-linearly with the
density. Simulation results back scaling laws derived from stochastic geometry.Comment: Submitte
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