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: Clustering is a crucial network design approach to enable large-scale wireless sensor networks (WSNs) deployments. A large variety of clustering approaches has been presented focusing on different performance metrics. Such protocols usually aim at minimizing communication overhead, evenly distributing roles among the participating nodes, as well as controlling the network topology. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal wireless communication channels and perfect energy consumption estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper, we design a new clustering protocol that adapts to the changes in the environment and the needs and goals of the user applications. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the \textsf{WISEBED} framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the clustering problem, regarding adapting to environmental conditions and the quality of topology control. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research \& development communities.
Abstract: This article presents a novel crawling and
clustering method for extracting and pro-
cessing cultural data from the web in a fully
automated fashion. Our architecture relies
upon a focused web crawler to download we
b documents relevant to culture. The
focused crawler is a web crawler that
searches and processes only those web pages
that are relevant to a particular topic. After downloading the pages, we extract from
each document a number of words for each th
ematic cultural area, filtering the docu-
ments with non-cultural content; we then create multidimensional document vectors
comprising the most frequent cultural term o
ccurrences. We calculate the dissimilarity
between the cultural-related document vect
ors and for each cultural theme, we use
cluster analysis to partition the documents in
to a number of clusters. Our approach is
validated via a proof-of-concept applica
tion which analyzes hundreds of web pages
spanning different cultural thematic areas.
Abstract: This paper presents ongoing work on using data mining clustering to support the evaluation of software
systems' maintainability. As input for our analysis we employ software measurement data extracted from
Java source code. We propose a two-steps clustering process which facilitates the assessment of a system's
maintainability at rst, and subsequently an in-cluster analysis in order to study the evolution of each
cluster as the system's versions pass by. The process is evaluated on Apache Geronimo, a J2EE 1.4 open
source Application Server. The evaluation involves analyzing several versions of this software system in
order to assess its evolution and maintainability over time. The paper concludes with directions for future
work.
Abstract: Clustering is an important research topic for wireless sensor
networks (WSNs). A large variety of approaches has been
presented focusing on di erent performance metrics. Even
though all of them have many practical applications, an ex-
tremely limited number of software implementations is avail-
able to the research community. Furthermore, these very few
techniques are implemented for speci c WSN systems or are
integrated in complex applications. Thus it is very difficult
to comparatively study their performance and almost impos-
sible to reuse them in future applications under a di erent
scope. In this work we study a large body of well estab-
lished algorithms. We identify their main building blocks
and propose a component-based architecture for developing
clustering algorithms that (a) promotes exchangeability of
algorithms thus enabling the fast prototyping of new ap-
proaches, (b) allows cross-layer implementations to realize
complex applications, (c) o ers a common platform to com-
paratively study the performance of di erent approaches,
(d) is hardware and OS independent. We implement 5 well
known algorithms and discuss how to implement 11 more.
We conduct an extended simulation study to demonstrate
the faithfulness of our implementations when compared to
the original implementations. Our simulations are at very
large scale thus also demonstrating the scalability of the
original algorithms beyond their original presentations. We
also conduct experiments to assess their practicality in real
WSNs. We demonstrate how the implemented clustering
algorithms can be combined with routing and group key es-
tablishment algorithms to construct WSN applications. Our
study clearly demonstrates the applicability of our approach
and the bene ts it o ers to both research & development
communities.
Abstract: We investigate the Vehicle Routing Problem with Time Windows (VRPTW) under an eco-friendly framework that demands the delivery of balanced and compact customer clusters. We present a new approach consisting of three major phases: (i) a first clustering of customers with compatible time windows; (ii) a second clustering of customers with close geographic proximity based on various methods (natural cuts, KaHIP, quad trees); (iii) a refinement phase that either splits a cluster into smaller ones, or merges clusters to form a bigger compact cluster. Our approach turns out to be beneficial when used in an on-line environment, where changes to the initial tour are requested (add a new customer to the tour or drop some customers). The new method serves as a warm starting point for re-evaluating and further optimizing the solution of VRPTW. Experiments with real data sets demonstrate that our approach compares favorably with standard approaches that start from a basic (cold) solution.
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 de nition 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 di erent 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
experimental evaluation. The derived ndings demonstrate
signi cant performance gains, both with respect to the no
charger case as well as the di erent charging alternatives; in
particular, the performance improvements include the network
lifetime, as well as connectivity, coverage and energy
balance properties.
Abstract: Wireless Sensor Networks are comprised of a vast number of ultra-small, autonomous computing and communication devices, with restricted energy, that co-operate to accomplish a large sensing task. In this work: a) We propose extended versions of two data propagation protocols for such networks: the Sleep-Awake Probabilistic Forwarding Protocol (SW-PFR) and the Hierarchical Threshold sensitive Energy Efficient Network protocol (H-TEEN). These non-trivial extensions improve the performance of the original protocols, by introducing sleep-awake periods in the PFR protocol to save energy, and introducing a hierarchy of clustering in the TEEN protocol to better cope with large networks, b) We implemented the two protocols and performed an extensive simulation comparison of various important measures of their performance with a focus on energy consumption, c) We investigate in detail the relative advantages and disadvantages of each protocol, d) We discuss a possible hybrid combination of the two protocols towards optimizing certain goals.
Abstract: We investigate the impact of multiple, mobile sinks on
efficient data collection in wireless sensor networks. To
improve performance, our protocol design focuses on minimizing
overlaps of sink trajectories and balancing the service load
among the sinks. To cope with high network dynamics, placement
irregularities and limited network knowledge we propose three different
protocols: a) a centralized one, that explicitly equalizes spatial coverage;
this protocol assumes strong modeling assumptions, and also serves as a kind
of performance lower bound in uniform networks of low dynamics b)
a distributed protocol based on mutual avoidance of sinks c) a clustering
protocol that distributively groups service areas towards balancing the load per sink.
Our simulation findings demonstrate significant gains in latency, while keeping the success
rate and the energy dissipation at very satisfactory levels even under
high network dynamics and deployment heterogeneity.
Abstract: We study geometric versions of the min-size k-clustering
problem, a clustering problem which generalizes clustering to minimize
the sum of cluster radii and has important applications. We prove that
the problem can be solved in polynomial time when the points to be clus-
tered are located on a line. For Euclidean spaces of higher dimensions,
we show that the problem is NP-hard and present polynomial time ap-
proximation schemes. The latter result yields an improved approximation
algorithm for the related problem of k-clustering to minimize the sum of
cluster diameters.
Abstract: In this paper we study deliberate attacks on the infrastructure of large scale-free networks. These attacks are based on the importance of individual vertices in the network in order to be successful, and the concept of centrality (originating from social science) has already been utilized in their study with success. Some measures of centrality however, as betweenness, have disadvantages that do not facilitate the research in this area. We show that with the aid of scale-free network characteristics such as the clustering coefficient we can get results that balance the current centrality measures, but also gain insight into the workings of these networks.
Abstract: Wireless sensor networks are composed of a vast number of ultra-small, fully autonomous computing, communication, and sensing devices, with very restricted energy and computing capabilities, that cooperate to accomplish a large sensing task. Such networks can be very useful in practice. The authors propose extended versions of two data propagation protocols: the Sleep-Awake Probabilistic Forwarding (SW-PFR) protocol and the Hierarchical Threshold-Sensitive Energy-Efficient Network (H-TEEN) protocol. These nontrivial extensions aim at improving the performance of the original protocols by introducing sleep-awake periods in the PFR case to save energy and introducing a hierarchy of clustering in the TEEN case to better cope with large network areas. The authors implemented the two protocols and performed an extensive comparison via simulation of various important measures of their performance with a focus on energy consumption. Data propagation under this approach exhibits high fault tolerance and increases network lifetime.
Abstract: Wireless sensor networks are composed of a vast number of ultra-small, fully autonomous computing, communication, and sensing devices, with very restricted energy and computing capabilities, that cooperate to accomplish a large sensing task. Such networks can be very useful in practice. The authors propose extended versions of two data propagation protocols: the Sleep-Awake Probabilistic Forwarding (SW-PFR) protocol and the Hierarchical Threshold-Sensitive Energy-Efficient Network (H-TEEN) protocol. These nontrivial extensions aim at improving the performance of the original protocols by introducing sleep-awake periods in the PFR case to save energy and introducing a hierarchy of clustering in the TEEN case to better cope with large network areas. The authors implemented the two protocols and performed an extensive comparison via simulation of various important measures of their performance with a focus on energy consumption. Data propagation under this approach exhibits high fault tolerance and increases network lifetime.
Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.
Abstract: Efficient task scheduling is fundamental for the success of the Grids,
since it directly affects the Quality of Service (QoS) offered to the users. Efficient
scheduling policies should be evaluated based not only on performance
metrics that are of interest to the infrastructure side, such as the Grid resources
utilization efficiency, but also on user satisfaction metrics, such as the percentage
of tasks served by the Grid without violating their QoS requirements. In this
paper, we propose a scheduling algorithm for tasks with strict timing requirements,
given in the form of a desired start and finish time. Our algorithm aims
at minimizing the violations of the time constraints, while at the same time
minimizing the number of processors used. The proposed scheduling method
exploits concepts derived from spectral clustering, and groups together for assignment
to a computing resource the tasks so to a) minimize the time overlapping
of the tasks assigned to a given processor and b) maximize the degree of
time overlapping among tasks assigned to different processors. Experimental
results show that our proposed strategy outperforms greedy scheduling algorithms
for different values of the task load submitted.
Abstract: We consider random systems of linear equations over GF(2) in which every equation binds k variables. We obtain a precise description of the clustering of solutions in such systems. In particular, we prove that with probability that tends to 1 as the number of variables, n, grows: for every pair of solutions \sigma, \tau, either there exists a sequence of solutions \sigma,...,\tau, in which successive elements differ by O(log n) variables, or every sequence of solutions \sigma,...,\tau, contains a step requiring the simultaneous change of \Omega(n) variables. Furthermore, we determine precisely which pairs of solutions are in each category. Our results are tight and highly quantitative in nature. Moreover, our proof highlights the role of unique extendability as the driving force behind the success of Low Density Parity Check codes and our techniques also apply to the problem of so-called pseudo-codewords in such codes.
