Abstract: With the proliferation of wireless sensor net-
works and mobile technologies in general, it is possible to
provide improved medical services and also to reduce costs
as well as to manage the shortage of specialized personnel.
Monitoring a person’s health condition using sensors pro-
vides a lot of benefits but also exposes personal sensitive
information to a number of privacy threats. By recording
user-related data, it is often feasible for a malicious or
negligent data provider to expose these data to an unau-
thorized user. One solution is to protect the patient’s pri-
vacy by making difficult a linkage between specific
measurements with a patient’s identity. In this paper we
present a privacy-preserving architecture which builds
upon the concept of
k
-anonymity; we present a clustering-
based anonymity scheme for effective network manage-
ment and data aggregation, which also protects user’s
privacy by making an entity indistinguishable from other
k
similar entities. The presented algorithm is resource
aware, as it minimizes energy consumption with respect to
other more costly, cryptography-based approaches. The
system is evaluated from an energy-consuming and net-
work performance perspective, under different simulation
scenarios.
Abstract: This paper presents experimental measurements of bulk data
transfer in a wireless multi-hop sensor network environment. We
investigate the effect of the number of the hops and the
conditions of the surrounding environment on the performance of
the network in terms of achieved transfer rates. Our findings
validate the theoretically established results on the relation
between the throughput and the network diameter, i.e.~is inversely
proportional to the network diameter and in particular the number
of hops needed for data to reach its destination. Furthermore, we
indicate how throughput is (significantly) affected by the type of
the physical environment, i.e.~it drops as the harshness of the
ambient conditions increases.
Abstract: Wireless sensor network research usually focuses on the reliable and efficient collection of data. In
this paper we focus on the next step in the lifetime of traces: we aim at investigating and evaluating, by
qualitative and quantitative means, data repositories of already collected measurements. Concerning the
collected datasets, several important topics arise like the need of exchanging traces between researchers
using a common representation of the traces and the need for common classication of the traces based on
a commonly-agreed set of statistical characteristics for in retrospect utilization. In order to qualitatively
address these issues, we propose the use of a novel set of metrics focusing on the in-network data aggregation
problem class. These metrics enable reliable evaluation of algorithms using the same benchmark traces (both
in average cases and \stressful" setups) removing the need for running algorithms in a real testbed, at least
in the initial development stage. We present the results of our research as a rst approach for addressing this
problem, and in order to conrm our method, we characterized several traces with the proposed metrics.
We validate the metrics by predicting the performance of three data-aggregation schemes using the available
traces and checking the results by actually running the algorithms
Abstract: The existence of good probabilistic models for the job
arrival process and job characteristics is important for
the improved understanding of grid systems and the
prediction of their performance. In this study, we
present a thorough analysis of the job inter-arrival
times, the waiting times at the queues, the execution
times, and the data sizes exchanged at the
kallisto.hellasgrid.gr cluster, which is part of the
EGEE Grid infrastructure. By computing the Hurst
parameter of the inter-arrival times we find that the
job arrival process exhibits self-similarity/long-range
dependence. We also propose simple and intuitive
models for the job arrival process and the job
execution times. The models proposed were validated
and were found to be in very good agreement with our
empirical measurements.