Abstract: Through recent technology advances in the eld of wireless energy transmission, Wireless Rechargeable Sensor Networks
(WRSN) have emerged. In this new paradigm for
WSNs a mobile entity called Mobile Charger (MC) traverses
the network and replenishes the dissipated energy of sensors.
In this work we rst provide a formal denition of the charging
dispatch decision problem and prove its computational
hardness. We then investigate how to optimize the tradeo
s of several critical aspects of the charging process such
as a) the trajectory of the charger, b) the dierent charging
policies and c) the impact of the ratio of the energy
the MC may deliver to the sensors over the total available
energy in the network. In the light of these optimizations,
we then study the impact of the charging process to the
network lifetime for three characteristic underlying routing
protocols; a greedy protocol, a clustering protocol and an
energy balancing protocol. Finally, we propose a Mobile
Charging Protocol that locally adapts the circular trajectory
of the MC to the energy dissipation rate of each sub-region
of the network. We compare this protocol against several
MC trajectories for all three routing families by a detailed
experimentalevaluation. The derived ndings demonstrate
signicant performance gains, both with respect to the no
charger case as well as the dierent charging alternatives; in
particular, the performance improvements include the network
lifetime, as well as connectivity, coverage and energy
balance properties.
Abstract: This paper addresses the efficient processing of
top-k queries in wide-area distributed data
repositories where the index lists for the attribute
values (or text terms) of a query are distributed
across a number of data peers and the
computational costs include network latency,
bandwidth consumption, and local peer work.
We present KLEE, a novel algorithmic
framework for distributed top-k queries,
designed for high performance and flexibility.
KLEE makes a strong case for approximate top-k
algorithms over widely distributed data sources.
It shows how great gains in efficiency can be
enjoyed at low result-quality penalties. Further,
KLEE affords the query-initiating peer the
flexibility to trade-off result quality and expected
performance and to trade-off the number of
communication phases engaged during query
execution versus network bandwidth
performance. We have implemented KLEE and
related algorithms and conducted a
comprehensive performance evaluation. Our
evaluation employed real-world and synthetic
large, web-data collections, and query
benchmarks. Our experimental results show that
KLEE can achieve major performance gains in
terms of network bandwidth, query response
times, and much lighter peer loads, all with small
errors in result precision and other result-quality
measures
Abstract: Experimentally driven research for wireless sensor networks is invaluable to provide benchmarking and comparison of new ideas. An increasingly common tool in support of this is a testbed composed of real hardware devices which increases the realism of evaluation. However, due to hardware costs the size and heterogeneity of these testbeds is usually limited. In addition, a testbed typically has a relatively static configuration in terms of its network topology and its software support infrastructure, which limits the utility of that testbed to specific case-studies. We propose a novel approach that can be used to (i) interconnect a large number of small testbeds to provide a federated testbed of very large size, (ii) support the interconnection of heterogeneous hardware into a single testbed, and (iii) virtualise the physical testbed topology and thus minimise the need to relocate devices. We present the most important design issues of our approach and evaluate its performance. Our results indicate that testbed virtualisation can be achieved with high efficiency and without hindering the realism of experiments.