Abstract: In the maximum cover problem, we are given a collection of
sets over a ground set of elements and a positive integer w, and we are
asked to compute a collection of at most w sets whose union contains the
maximum number of elements from the ground set. This is a fundamental
combinatorial optimization problem with applications to resource allo-
cation. We study the simplest APX-hard variant of the problem where
all sets are of size at most 3 and we present a 6=5-approximation algo-
rithm, improving the previously best known approximation guarantee.
Our algorithm is based on the idea of ¯rst computing a large packing of
disjoint sets of size 3 and then augmenting it by performing simple local
improvements.

Abstract: In the maximum cover problem, we are given a collection of
sets over a ground set of elements and a positive integer w, and we are
asked to compute a collection of at most w sets whose union contains the
maximum number of elements from the ground set. This is a fundamental
combinatorial optimization problem with applications to resource allo-
cation. We study the simplest APX-hard variant of the problem where
all sets are of size at most 3 and we present a 6=5-approximation algo-
rithm, improving the previously best known approximation guarantee.
Our algorithm is based on the idea of ¯rst computing a large packing of
disjoint sets of size 3 and then augmenting it by performing simple local
improvements.

Abstract: We here present the Forward Planning Situated Protocol (FPSP), for scalable, energy efficient and fault tolerant data propagation in situated wireless sensor networks. To deal with the increased complexity of such deeply networked sensor systems, instead of emphasizing on a particular aspect of the services provided, i.e. either for low-energy periodic, or low-latency event-driven, or high-success query-based sensing, FPSP uses two novel mechanisms that allow the network operator to adjust the performance of the protocol in terms of energy, latency and success rate on a per-task basis. We emphasize on distributedness, direct or indirect interactions among relatively simple agents, flexibility and robustness.
The protocol operates by employing a series of plan & forward phases through which devices self-organize into forwarding groups that propagate data over discovered paths. FPSP performs a limited number of long range, high power data transmissions to collect information regarding the neighboring devices. The acquired information, allows to plan a (parameterizable long by {\"e}) sequence of short range, low power transmissions between nearby particles, based on certain optimization criteria. All particles that decide to respond (based on local criteria) to these long range transmissions enter the forwarding phase during which information is propagated via the acquired plan. Clearly, the duration of the forwarding phases is characterized by the parameter {\"e}, the transmission medium and the processing speed of the devices. In fact the parameter {\"e} provides a mechanism to adjust the protocol performance in terms of the latency--energy trade-off. By reducing {\"e} the latency is reduced at the cost of spending extra energy, while by increasing {\"e}, the energy dissipation is reduced but the latency is increased.
To control the success rate--energy trade-off, particles react locally on environment and context changes by using a set of rules that are based on response thresholds that relate individual-level plasticity with network-level resiliency, motivated by the nature-inspired method for dividing labor, a metaphor of social insect behavior for solving problems [1]. Each particle has an individual response threshold {\`E} that is related to the "local" density (as observed by the particle, [2]); particles engage in propagation of events when the level of the task-associated stimuli exceeds their thresholds. Let s be the intensity of a stimulus associated with a particular sensing task, set by the human authorities. We adopt the response function T_{{\`e}}(s) = s^{n}over s^{n} + {\`e}^{n}, the probability of performing the task as a function of s, where n > 1 determines the steepness of the threshold. Thus, when {\`e} is small (i.e. the network is sparse) then the response probability increases; when s increases (i.e. for critical sensing tasks) the response probability increases as well.
This role-based approach where a selective number of devices do the high cost planning and the rest of the network operates in a low cost state leads to systems that have increased energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).

Abstract: Wireless Sensor Networks are by nature highly dynamic and communication between sensors is completely ad hoc, especially when mobile devices are part of the setup. Numerous protocols and applications proposed for such networks
operate on the assumption that knowledge of the neighborhood is a priori available to all nodes. As a result, WSN deployments need to use or implement from scratch a neighborhood discovery mechanism. In this work we present a new protocol based on adaptive periodic beacon exchanges. We totally avoid continuous beaconing by adjusting the rate of broadcasts using the concept of consistency over the understanding of neighborhood that nearby devices share. We propose, implement and evaluate our adaptive neighborhood discovery protocol over our experimental testbed and using large scale simulations. Our results indicate that the
new protocol operates more eciently than existing reference implementations while it provides valid information to applications that use it. Extensive performance evaluation indicates that it successfully reduces generated network traffic by 90% and increases network lifetime by 20% compared to existing mechanisms that rely on continuous beaconing.

Abstract: We present new combinatorial approximation algorithms for k-setcover. Previous approaches are based on extending the greedy algorithm by efficiently handling small sets. The new algorithms further extend them by utilizing the natural idea of computing large packings of elements into sets of large size. Our results improve the previously best approximation bounds for the k-setcover problem for all values of k ≥ 6. The analysis technique could be of independent interest; the upper bound on the approximation factor is obtained by bounding the objective value of a factor-revealing linear program.

Abstract: We present new combinatorial approximation algorithms for
k-setcover. Previous approaches are based on extending the greedy al-
gorithm by e±ciently handling small sets. The new algorithms further
extend them by utilizing the natural idea of computing large packings
of elements into sets of large size. Our results improve the previously
best approximation bounds for the k-setcover problem for all values
of k ¸ 6. The analysis technique could be of independent interest; the
upper bound on the approximation factor is obtained by bounding the
objective value of a factor-revealing linear program.

Abstract: The study of the path coloring problem is motivated by the allocation of optical bandwidth to communication requests in all-optical networks that utilize Wavelength Division Multiplexing (WDM). WDM technology establishes communication between pairs of network nodes by establishing transmitter-receiver paths and assigning wavelengths to each path so that no two paths going through the same fiber link use the same wavelength. Optical bandwidth is the number of distinct wavelengths. Since state-of-the-art technology allows for a limited number of wavelengths, the engineering problem to be solved is to establish communication minimizing the total number of wavelengths used. This is known as the wavelength routing problem. In the case where the underlying network is a tree, it is equivalent to the path coloring problem.
We survey recent advances on the path coloring problem in both undirected and bidirected trees. We present hardness results and lower bounds for the general problem covering also the special case of sets of symmetric paths (corresponding to the important case of symmetric communication). We give an overview of the main ideas of deterministic greedy algorithms and point out their limitations. For bidirected trees, we present recent results about the use of randomization for path coloring and outline approximation algorithms that find path colorings by exploiting fractional path colorings. Also, we discuss upper and lower bounds on the performance of on-line algorithms.

Abstract: Collecting sensory data using a mobile data sink has
been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves
significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.

Abstract: Collecting sensory data using a mobile data sink has been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.

Abstract: Collecting sensory data using a mobile data sink has
been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding
density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to
produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves
significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.

Abstract: We examine multi-player pervasive games that rely on the
use of ad-hoc mobile sensor networks. The unique feature in
such games is that players interact with each other and their
surrounding environment by using movement and presence
as a means of performing game-related actions, utilizing sen-
sor devices. We brie
y discuss the fundamental issues and
challenges related to these type of games and the scenar-
ios associated with them. We have also developed a frame-
work, called Fun in Numbers (FinN) that handles a number
of these issues, such as such as neighbors discovery, local-
ization, synchronization and delay-tolerant communication.
FinN is developed using Java and is based on a multilayer ar-
chitecture, which provides developers with a set of templates
and services for building and operating new games.

Abstract: The Greek School Network (GSN) is the nationwide network that connects all units of primary and secondary education in Greece. GSN offers a significant set of diverse services to more than 15.000 schools and administrative units, and more than 60.000 teachers, placing GSN second in infrastructure size nationwide. GSN has relied on the emerging power of open source software to build cutting-edge services capable of covering internal administrative and monitoring needs, end user demands, and, foremost, modern pedagogical requirements for tools and services. GSN provides a wide set of advanced services, varying from web mail to virtual classrooms and synchronous/asynchronous tele-education. This paper presents an evaluation of GSN open source services based on the opinions of users who use GSN for educational purposes, and on usage and traffic measurement statistics. The paper reaches the conclusion that open source software provides a sound technological platform that meets the needs for cutting edge educational services deployment, and innovative, competitive software production for educational networks.

Abstract: Wireless Sensor Networks consist of a large number of small, autonomous devices, that are able to interact with their inveronment by sensing and collaborate to fulfill their tasks, as, usually, a single node is incapable of doing so; and they use wireless communication to enable this collaboration. Each device has limited computational and energy resources, thus a basic issue in the applicastions of wireless sensor networks is the low energy consumption and hence, the maximization of the network lifetime.
The collected data is disseminated to a static control point – data sink in the network, using node to node - multi-hop data propagation. However, sensor devices consume significant amounts of energy in addition to increased implementation complexity, since a routing protocol is executed. Also, a point of failure emerges in the area near the control center where nodes relay the data from nodes that are farther away. Recently, a new approach has been developed that shifts the burden from the sensor nodes to the sink. The main idea is that the sink has significant and easily replenishable energy reserves and can move inside the area the sensor network is deployed, in order to acquire the data collected by the sensor nodes at very low energy cost. However, the need to visit all the regions of the network may result in large delivery delays.
In this work we have developed protocols that control the movement of the sink in wireless sensor networks with non-uniform deployment of the sensor nodes, in order to succeed an efficient (with respect to both energy and latency) data collection. More specifically, a graph formation phase is executed by the sink during the initialization: the network area is partitioned in equal square regions, where the sink, pauses for a certain amount of time, during the network traversal, in order to collect data.
We propose two network traversal methods, a deterministic and a random one. When the sink moves in a random manner, the selection of the next area to visit is done in a biased random manner depending on the frequency of visits of its neighbor areas. Thus, less frequently visited areas are favored. Moreover, our method locally determines the stop time needed to serve each region with respect to some global network resources, such as the initial energy reserves of the nodes and the density of the region, stopping for a greater time interval at regions with higher density, and hence more traffic load. In this way, we achieve accelerated coverage of the network as well as fairness in the service time of each region.Besides randomized mobility, we also propose an optimized deterministic trajectory without visit overlaps, including direct (one-hop) sensor-to-sink data transmissions only.
We evaluate our methods via simulation, in diverse network settings and comparatively to related state of the art solutions. Our findings demonstrate significant latency and energy consumption improvements, compared to previous research.

Abstract: Wireless sensor networks are comprised of a vast number of devices, situated in an area of interest that self organize in a structureless network, in order to monitor/record/measure an environmental variable or phenomenon and subsequently to disseminate the data to the control center.
Here we present research focused on the development, simulation and evaluation of energy efficient algorithms, our basic goal is to minimize the energy consumption. Despite technology advances, the problem of energy use optimization remains valid since current and emerging hardware solutions fail to solve it.
We aim to reduce communication cost, by introducing novel techniques that facilitate the development of new algorithms. We investigated techniques of distributed adaptation of the operations of a protocol by using information available locally on every node, thus through local choices we improve overall performance. We propose techniques for collecting and exploiting limited local knowledge of the network conditions. In an energy efficient manner, we collect additional information which is used to achieve improvements such as forming energy efficient, low latency and fault tolerant paths to route data. We investigate techniques for managing mobility in networks where movement is a characteristic of the control center as well as the sensors. We examine methods for traversing and covering the network field based on probabilistic movement that uses local criteria to favor certain areas.
The algorithms we develop based on these techniques operate a) at low level managing devices, b) on the routing layer and c) network wide, achieving macroscopic behavior through local interactions. The algorithms are applied in network cases that differ in density, node distribution, available energy and also in fundamentally different models, such as under faults, with incremental node deployment and mobile nodes. In all these settings our techniques achieve significant gains, thus distinguishing their value as tools of algorithmic design.

Abstract: We here present Fun in Numbers (FinN), a framework for developing pervasive applications and interactive installations for entertainment and educational purposes. Using ad hoc mobile wireless sensor network nodes as the enabling devices, FinN allows for the quick prototyping of applications that utilize input from multiple physical sources (sensors and other means of interfacing), by offering a set of programming templates and services, such as topology discovery, localization and synchronization, that hide the underlying complexity. We present the target application domains of FinN, along with a set of multiplayer games and interactive installations. We describe the overall architecture of our platform
and discuss some key implementation issues of the application domains. Finally, we present the experience gained by deploying the applications developed with our platform.

Abstract: We study the problem of localizing and tracking multiple moving targets in wireless sensor
networks, from a network design perspective i.e. towards estimating the least possible number
of sensors to be deployed, their positions and operation chatacteristics needed to perform the
tracking task. To avoid an expensive massive deployment, we try to take advantage of
possible coverage ovelaps over space and time, by introducing a novel combinatorial model
that captures such overlaps.
Under this model, we abstract the tracking network design problem by a combinatorial
problem of covering a universe of elements by at least three sets (to ensure that each point in
the network area is covered at any time by at least three sensors, and thus being localized). We
then design and analyze an efficient approximate method for sensor placement and operation,
that with high probability and in polynomial expected time achieves a (log n) approximation
ratio to the optimal solution. Our network design solution can be combined with alternative
collaborative processing methods, to suitably fit different tracking scenaria.

Abstract: We study the problem of localizing and tracking multiple moving targets in wireless sensor networks, from a network design perspective i.e. towards estimating the least possible number of sensors to be deployed, their positions and operation characteristics needed to perform the tracking task. To avoid an expensive massive deployment, we try to take advantage of possible coverage overlaps over space and time, by introducing a novel combinatorial model that captures such overlaps.
Under this model, we abstract the tracking network design problem by a combinatorial problem of covering a universe of elements by at least three sets (to ensure that each point in the network area is covered at any time by at least three sensors, and thus being localized). We then design and analyze an efficient approximate method for sensor placement and operation, that with high probability and in polynomial expected time achieves a {\`E}(logn) approximation ratio to the optimal solution. Our network design solution can be combined with alternative collaborative processing methods, to suitably fit different tracking scenarios.

Abstract: In this work, we propose an energy-efficient multicasting algorithm
for wireless networks for the case where the transmission
powers of the nodes are fixed. Our algorithm is
based on the multicost approach and selects an optimal
energy-efficient set of nodes for multicasting, taking into account:
i) the node residual energies, ii) the transmission
powers used by the nodes, and iii) the set of nodes covered.
Our algorithm is optimal, in the sense that it can
optimize any desired function of the total power consumed
by the multicasting task and the minimum of the current
residual energies of the nodes, provided that the optimization
function is monotonic in each of these parameters. Our
optimal algorithm has non-polynomial complexity, thus, we
propose a relaxation producing a near-optimal solution in
polynomial time. The performance results obtained show
that the proposed algorithms outperform established solutions
for energy-aware multicasting, with respect to both
energy consumption and network lifetime. Moreover, it is
shown that the near-optimal multicost algorithm obtains
most of the performance benefits of the optimal multicost
algorithm at a smaller computational overhead.

Abstract: The Internet of Things is shaping up to be the ideal vehicle for introducing pervasive computing in our everyday lives, especially in the form of smart home and building management systems. However, although such technologies are gradually becoming more mainstream, there is still a lot of ground to be covered with respect to public buildings and specifically ones in the educational sector. We discuss here \Green Mindset", an action focusing on energy efficiency and
sustainability in Greek public schools. A large-scale sensor infrastructure has been deployed to 12 public school buildings across diverse settings. We report on the overall design and implementation of the system, as well as on some first results coming from the data produced. Our system provides a flexible and efficient basis for realizing a unified approach to monitoring energy consumption and environmental parameters,
that can be used both for building administration
and educational purposes.

Abstract: We present here, Fun in Numbers, a framework for developing multiplayer pervasive games that rely on the use of ad hoc mobile sensor networks. The unique feature in such games is that players interact with each other and their surrounding environment by using movement and presence as a means of performing game-related actions, utilizing sensor devices. We present the fundamental issues and challenges related to these type of games and the scenarios associated with them is provided. Our framework is developed using Java and is based on a multilayer architecture, which provides developers with a set of templates and services for building and operating new games. Our framework handles a number of challenging fundamental and practical issues, such as synchronization, network congestion, delay-tolerant communication and neighbors discovery. We also present our platform and identify issues that arise in pervasive games which utilize sensor network nodes. The implemented games show how to use non-conventional user interface methods to breathe new life into familiar concepts, like the multiplayer games played out in open space.

Abstract: Consider an information network with harmful procedures called attackers (e.g., viruses); each attacker uses a probability distribution to choose a node of the network to damage. Opponent to the attackers is the system protector scanning and cleaning from attackers some part of the network (e.g., an edge or a path), which it chooses independently using another probability distribution. Each attacker wishes to maximize the probability of escaping its cleaning by the system protector; towards a conflicting objective, the system protector aims at maximizing the expected number of cleaned attackers.
We model this network scenario as a non-cooperative strategic game on graphs. We focus on the special case where the protector chooses a single edge. We are interested in the associated Nash equilibria, where no network entity can unilaterally improve its local objective. We obtain the following results:
{\^a}{\"i}¿½{\"i}¿½ No instance of the game possesses a pure Nash equilibrium.
{\^a}{\"i}¿½{\"i}¿½Every mixed Nash equilibrium enjoys a graph-theoretic structure, which enables a (typically exponential) algorithm to compute it.
{\^a}{\"i}¿½{\"i}¿½ We coin a natural subclass of mixed Nash equilibria, which we call matching Nash equilibria, for this game on graphs. Matching Nash equilibria are defined using structural parameters of graphs, such as independent sets and matchings.
{\^a}{\"i}¿½{\"i}¿½We derive a characterization of graphs possessing matching Nash equilibria. The characterization enables a linear time algorithm to compute a matching Nash equilibrium on any such graph with a given independent set and vertex cover.
{\^a}{\"i}¿½{\"i}¿½ Bipartite graphs are shown to satisfy the characterization. So, using a polynomial-time algorithm to compute a perfect matching in a bipartite graph, we obtain, as our main result, an efficient graph-theoretic algorithm to compute a matching Nash equilibrium on any instance of the game with a bipartite graph.

Abstract: In this paper we propose an energy-efficient broadcast algorithm for wireless networks for the case where the transmission powers of the nodes are fixed. Our algorithm is based on the multicost approach and selects an optimal energy-efficient set of nodes for broadcasting, taking into account: i) the node residual energies, ii) the transmission powers used by the nodes, and iii) the set of nodes that are covered by a specific schedule. Our algorithm is optimal, in the sense that it can optimize any desired function of the total power consumed by the broadcasting task and the minimum of the current residual energies of the nodes, provided that the optimization function is monotonic in each of these parameters. Our algorithm has non-polynomial complexity, thus, we propose a relaxation producing a near-optimal solution in polynomial time. Using simulations we show that the proposed algorithms outperform other established solutions for energy-aware broadcasting with respect to both energy consumption and network lifetime. Moreover, it is shown that the near-optimal multicost algorithm obtains most of the performance benefits of the optimal multicost algorithm at a smaller computational overhead.

Abstract: This Volume contains the 11 papers corresponding to poster and demo presentations
accepted to the 7th ACM/IEEE International Symposium on Modeling,
Analysis and Simulation ofWireless and Mobile Systems (MSWiM 04),
that is held October 4-6, 2004, in Venice, Italy.
MSWiM 2004 (http://www.cs.unibo.it/mswim2004/) is intended to provide
an international forum for original ideas, recent results and achievements on
issues and challenges related to mobile and wireless systems.
A Call for Posters was announced and widely disseminated, soliciting posters
that report on recent original results or on-going research in the area of wireless
and mobile networks. Prospective authors were encouraged to submit interesting
results on all aspects of modeling, analysis and simulation of mobile and
wireless networks and systems. The scope and topics of the Posters Session
were the same as those included in the MSWiM Call for Papers (see above).
Poster presentations were meant to provide authors with early feedback on
their research work and enable them to present their research and exchange
ideas during the Symposium.
All submissions to the call for posters as well as selected papers submitted
to MSWiM 04 were considered and reviewed. The review process resulted in
accepting the set of 11 papers included in this Volume. Accepted posters will
also be on display during the Symposium.
The set of papers in this Proceedings covers a wide range of important topics
in wireless and mobile computing, including channel allocation in wireless
networks, quality of service provisioning in IEEE 802.11 wireless LANs, IP
mobility support, energy conservation, routing in mobile adhoc networks, resource
sharing, wireless access to the WWW, sensor networks etc. The performance
evaluation techniques used include both analysis and simulation.
We hope that the poster papers included in this Volume will facilitate a fruitful
and lively discussion and exchange of interesting and creative ideas during
the Symposium.
We wish to thank the MSWiM Steering Committee Chair Azzedine Boukerche
and the Program Co-Chairs ofMSWiM 04 Carla-Fabiana Chiasserini and
Lorenzo Donatiello for their valuable help in the selection procedure. Also, the
MSWiM 04 Publicity Co-Chairs Luciano Bononi, Helen Karatza and Mirela
Sechi Moretti Annoni Notare for disseminating the Call for Posters.
We wish to warmly thank the Poster Proceedings Chair Ioannis Chatzigiannakis
for carefully doing an excellent job in preparing the Volume you now
hold in your hands.

Abstract: In this paper we present an efficient general simulation strategy for
computations designed for fully operational BSP machines of n ideal processors,
on n-processor dynamic-fault-prone BSP machines. The fault occurrences are failstop
and fully dynamic, i.e., they are allowed to happen on-line at any point of the
computation, subject to the constraint that the total number of faulty processors
may never exceed a known fraction. The computational paradigm can be exploited
for robust computations over virtual parallel settings with a volatile underlying
infrastructure, such as a NETWORK OF WORKSTATIONS (where workstations may be
taken out of the virtual parallel machine by their owner).
Our simulation strategy is Las Vegas (i.e., it may never fail, due to backtracking
operations to robustly stored instances of the computation, in case of locally
unrecoverable situations). It adopts an adaptive balancing scheme of the workload
among the currently live processors of the BSP machine.
Our strategy is efficient in the sense that, compared with an optimal off-line
adversarial computation under the same sequence of fault occurrences, it achieves an O
¡
.log n ¢ log log n/2¢
multiplicative factor times the optimal work (namely, this
measure is in the sense of the “competitive ratio” of on-line analysis). In addition,
our scheme is modular, integrated, and considers many implementation points.
We comment that, to our knowledge, no previous work on robust parallel computations
has considered fully dynamic faults in the BSP model, or in general distributed
memory systems. Furthermore, this is the first time an efficient Las Vegas
simulation in this area is achieved.

Abstract: Searchius is a collaborative search engine that produces
search results based solely on user provided web-related
data. We discuss the architecture of this system and how
it compares to current state-of-the-art search engines. We
show that the global users¢ preference over pages can be
efficiently used as a metric of page quality, and that the
inherent organization of the collected data can be used to
discover related URLs. We also conduct an extensive experimental
study, based on the web related data of 36483
users, to analyze the qualitative and quantitative characteristics
of user collected URL collections, to investigate how
well the users URL collections cover the web and discover
the characteristics that affect the quality of the search results
under the proposed setting.

Abstract: We consider the frugal coverage problem, an interesting vari-
ation of setcover de¯ned as follows. Instances of the problem consist of
a universe of elements and a collection of sets over these elements; the
objective is to compute a subcollection of sets so that the number of
elements it covers plus the number of sets not chosen is maximized. The
problem was introduced and studied by Huang and Svitkina [7] due to
its connections to the donation center location problem. We prove that
the greedy algorithm has approximation ratio at least 0:782, improving
a previous bound of 0:731 in [7]. We also present a further improvement
that is obtained by adding a simple corrective phase at the end of the
execution of the greedy algorithm. The approximation ratio achieved in
this way is at least 0:806. Our analysis is based on the use of linear
programs which capture the behavior of the algorithms in worst-case
examples. The obtained bounds are proved to be tight.

Abstract: In this paper, we discuss the integration of Wireless
Sensor Networks (WSN) and smart objects with the Web. We present a set of research challenges which we believe are the most important ones rising from this integration and propose a prototype system, Uberdust, which addresses such challenges. Uberdust is a brokerage web service for connecting smart objects to the Internet of Things, providing storage, sharing and discovery of real-time and historical data from smart objects, devices & building installations around the world via the Web. Our system provides high-level language-independent APIs so IoT application developers may choose their favorite programming or scripting languages.

Abstract: In this work, we introduce the notion of time to some well-known combinatorial optimization problems. In particular, we study problems defined on temporal graphs. A temporal graph D=(V,A) may be viewed as a time-sequence G_1,G_2,...,G_l of static graphs over the same (static) set of nodes V. Each G_t = D(t) = (V,A(t)) is called the instance of D at time t and l is called the lifetime of D. Our main focus is on analogues of traveling salesman problems in temporal graphs. A sequence of time-labeled edges (e.g. a tour) is called temporal if its labels are strictly increasing. We begin by considering the problem of exploring the nodes of a temporal graph as soon as possible. In contrast to the positive results known for the static case, we prove that, it cannot be approximated within cn, for some constant c > 0, in general temporal graphs and within (2 − \varepsilon), for every constant \varepsilon > 0, in the special case in which D(t) is connected for all 1 <= t <= l, both unless P = NP. We then study the temporal analogue of TSP(1,2), abbreviated TTSP(1,2), where, for all 1 <= t <= l, D(t) is a complete weighted graph with edge-costs from {1,2} and the cost of an edge may vary from instance to instance. The goal is to find a minimum cost temporal TSP tour. We give several polynomial-time approximation algorithms for TTSP(1,2). Our best approximation is (1.7 + \varepsilon) for the generic TTSP(1,2) and (13/8 + \varepsilon) for its interesting special case in which the lifetime of the temporal graph is restricted to n. In the way, we also introduce temporal versions of Maximum Matching, Path Packing, Max-TSP, and Minimum Cycle Cover, for which we obtain polynomial-time approximation algorithms and hardness results.

Abstract: Recent activity in the field of Internet-of-Things experimentation has focused on the federation of discrete testbeds, thus placing less effort in the integration of other related technologies, such as smartphones; also, while it is gradually moving to more application-oriented paths, such as urban settings, it has not dealt in large with applications having social networking features. We argue here that current IoT infrastructure, testbeds and related software technologies should be used in such a context, capturing real-world human mobility and social networking interactions, for use in evaluating and fine-tuning realistic mobility models and designing human-centric applications. We discuss a system for producing traces for a new generation of human-centric applications, utilizing technologies such as Bluetooth and focusing on human interactions. We describe the architecture for this system and the respective implementation details presenting two distinct deployments; one in an office environment and another in an exhibition/conference event (FET'11, The European Future Technologies Conference and Exhibition) with 103 active participants combined, thus covering two popular scenarios for human centric applications. Our system provides online, almost real-time, feedback and statistics and its implementation allows for rapid and robust deployment, utilizing mainstream technologies and components.