We suggest a novel approach to handle the ongoing explosive increase in the
demand for video content in wireless/mobile devices. We envision femtocell-like
base stations, which we call helpers, with weak backhaul links but large
storage capacity. These helpers form a wireless distributed caching network
that assists the macro base station by handling requests of popular files that
have been cached. Due to the short distances between helpers and requesting
devices, the transmission of cached files can be done very efficiently.

In distributed storage systems that employ erasure coding, the issue of
minimizing the total {\it repair bandwidth} required to exactly regenerate a
storage node after a failure arises. This repair bandwidth depends on the
structure of the storage code and the repair strategies used to restore the
lost data.

In distributed storage systems that use coding, the issue of minimizing the
communication required to rebuild a storage node after a failure arises. We
consider the problem of repairing an erased node in a distributed storage
system that uses an EVENODD code. EVENODD codes are maximum distance separable
(MDS) array codes that are used to protect against erasures, and only require
XOR operations for encoding and decoding. We show that when there are two
redundancy nodes, to rebuild one erased systematic node, only 3/4 of the
information needs to be transmitted.

We investigate the problem of allocating energy from renewable sources to
flexible consumers in electricity markets. We assume there is a renewable
energy supplier that provides energy according to a time-varying (and possibly
unpredictable) supply process. The plant must serve consumers within a
specified delay window, and incurs a cost of drawing energy from other
(possibly non-renewable) sources if its own supply is not sufficient to meet
the deadlines.

Gossip algorithms are attractive for in-network processing in sensor networks
because they do not require any specialized routing, there is no bottleneck or
single point of failure, and they are robust to unreliable wireless network
conditions. Recently, there has been a surge of activity in the computer
science, control, signal processing, and information theory communities,
developing faster and more robust gossip algorithms and deriving theoretical
performance guarantees. This article presents an overview of recent work in the
area.

Regenerating codes allow distributed storage systems to recover from the loss
of a storage node while transmitting the minimum possible amount of data across
the network. We present a systematic computer search for optimal systematic
regenerating codes. To search the space of potential codes, we reduce the
potential search space in several ways. We impose an additional symmetry
condition on codes that we consider.

In this paper we study a Markov Chain Monte Carlo (MCMC) Gibbs sampler for
solving the integer least-squares problem. In digital communication the problem
is equivalent to performing Maximum Likelihood (ML) detection in Multiple-Input
Multiple-Output (MIMO) systems. While the use of MCMC methods for such problems
has already been proposed, our method is novel in that we optimize the
"temperature" parameter so that in steady state, i.e. after the Markov chain
has mixed, there is only polynomially (rather than exponentially) small
probability of encountering the optimal solution.

This is a tale of two linear programming decoders, namely channel coding
linear programming decoding (CC-LPD) and compressed sensing linear programming
decoding (CS-LPD). So far, they have evolved quite independently. The aim of
the present paper is to show that there is a tight connection between, on the
one hand, CS-LPD based on a zero-one measurement matrix over the reals and, on
the other hand, CC-LPD of the binary linear code that is obtained by viewing
this measurement matrix as a binary parity-check matrix.