Abstract: This work addresses networked embedded systems enabling the seam-
less interconnection of smart building automations to the Internet and
their abstractions as web services. In our approach, such abstractions are
used to primarily create a exible, holistic and scalable system and allow
external end-users to compose and run their own smart/green building
automation application services on top of this system.
Towards this direction, in this paper we present a smart building test-
bed consisting of several sensor motes and spanning across seven rooms.
Our test-bed's design and implementation simultaneously addresses sev-
eral corresponding system layers; from hardware interfaces, embedded
IPv6 networking and energy balancing routing algorithms to a RESTful
architecture and over the web development of sophisticated, smart, green
scenarios. In fact, we showcase how IPv6 embedded networking combined
with RESTful architectures make the creation of building automation ap-
plications as easy as creating any other Internet Web Service.
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: A considerable part of recent research in smart cities and IoT has focused on achieving energy savings in buildings and supporting aspects related to sustainability. In this context, the educational community is one of the most important ones to consider, since school buildings constitute a large part of non-residential buildings, while also educating students on sustainability matters is an investment for the future. In this work, we discuss a methodology for achieving energy savings in schools based on the utilization of data produced by an IoT infrastructure installed inside school buildings and related educational scenarios. We present the steps comprising this methodology in detail, along with a set of tangible results achieved within the GAIA project. We also showcase how an IoT infrastructure can support activities in an educational setting and produce concrete outcomes, with typical levels of 20% energy savings.
Abstract: Motivated by emerging applications, we consider sensor networks where the sensors themselves (not just the sinks) are mobile. Furthermore, we focus on mobility scenarios characterized by heterogeneous, highly changing mobility roles in the network. To capture these high dynamics of diverse sensory motion we propose a novel network parameter,
the mobility level, which, although simple and local, quite accurately takes into account both the spatial and speed characteristics of motion. We then propose adaptive data dissemination protocols that use the mobility level estimation to optimize performance, by basically exploiting high mobility (redundant message ferrying) as a cost-effective replacement of flooding, e.g. the sensors tend to dynamically propagate less data in the presence
of high mobility, while nodes of high mobility are favored for moving data around. These dissemination schemes are enhanced by a distance-sensitive probabilistic message flooding inhibition mechanism that further reduces communication cost, especially for fast nodes of high mobility level, and as distance to data destination decreases. Our simulation findings
demonstrate significant performance gains of our protocols compared to non-adaptive protocols, i.e. adaptation increases the success rate and reduces latency (even by 15%) while at the same time significantly reducing energy dissipation (in most cases by even 40%). Also, our adaptive schemes achieve significantly higher message delivery ratio and
satisfactory energy-latency trade-offs when compared to flooding when sensor nodes have
limited message queues.
Abstract: We introduce a new modelling assumption for wireless sensor networks, that of node redeployment (addition of sensor devices during protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under these modelling assumptions, we design, implement and evaluate a new power conservation scheme for efficient data propagation. Our scheme is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards improved operation choices. The scheme is simple, distributed and does not require exchange of control messages between nodes.
Implementing our protocol in software we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of the network simulator ns-2. We focus on highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-offs between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known Directed Diffusion propagation protocol) and good trade-offs achieved. Furthermore, the redeployment of additional sensors during network evolution and/or the heterogeneous deployment of sensors, drastically improve (when compared to ``equal total power" simultaneous deployment of identical sensors at the start) the protocol performance (i.e. the success rate increases up to four times} while reducing energy dissipation and, interestingly, keeping latency low).
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: Motivated by emerging applications, we consider sensor networks where the sensors themselves
(not just the sinks) are mobile. Furthermore, we focus on mobility
scenarios characterized by heterogeneous, highly changing mobility
roles in the network.
To capture these high dynamics of diverse sensory motion
we propose a novel network parameter, the mobility level, which, although
simple and local, quite accurately takes into account both the
spatial and speed characteristics of motion. We then propose
adaptive data dissemination protocols that use the
mobility level estimation to optimize performance, by basically
exploiting high mobility (redundant message ferrying) as a cost-effective
replacement of flooding, e.g., the sensors tend to dynamically propagate
less data in the presence of high mobility, while nodes of high mobility
are favored for moving data around.
These dissemination schemes are enhanced by a distance-sensitive
probabilistic message flooding inhibition mechanism that
further reduces communication cost, especially for fast nodes
of high mobility level, and as distance to data destination
decreases. Our simulation findings demonstrate significant
performance gains of our protocols compared to non-adaptive
protocols, i.e., adaptation increases the success rate and reduces
latency (even by 15\%) while at the same time significantly
reducing energy dissipation (in most cases by even 40\%).
Also, our adaptive schemes achieve significantly
higher message delivery ratio and satisfactory energy-latency
trade-offs when compared to flooding when sensor nodes have limited message queues.
Abstract: Motivated by emerging applications, we consider sensor networks where the sensors themselves
(not just the sinks) are mobile. We focus on mobility
scenarios characterized by heterogeneous, highly changing mobility
roles in the network.
To capture these high dynamics
we propose a novel network parameter, the mobility level, which, although
simple and local, quite accurately takes into account both the
spatial and speed characteristics of motion. We then propose
adaptive data dissemination protocols that use the
mobility level estimation to improve performance. By basically
exploiting high mobility (redundant message ferrying) as a cost-effective
replacement of flooding, e.g., the sensors tend to dynamically propagate
less data in the presence of high mobility, while nodes of high mobility
are favored for moving data around.
These dissemination schemes are enhanced by a distance-sensitive
probabilistic message flooding inhibition mechanism that
further reduces communication cost, especially for fast nodes
of high mobility level, and as distance to data destination
decreases. Our simulation findings demonstrate significant
performance gains of our protocols compared to non-adaptive
protocols, i.e., adaptation increases the success rate and reduces
latency (even by 15\%) while at the same time significantly
reducing energy dissipation (in most cases by even 40\%).
Also, our adaptive schemes achieve significantly
higher message delivery ratio and satisfactory energy-latency
trade-offs when compared to flooding when sensor nodes have limited message queues.
Abstract: We introduce a new modelling assumption in wireless sensor networks, that of node redeployment (addition of sensor devices during the protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under this model, we design, implement and evaluate a power conservation scheme for efficient data propagation. Our protocol is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards best operation choices. Our protocol operates does not require exchange of control messages between nodes to coordinate.Implementing our protocol we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of Ns2. We focus in highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-off between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known Directed Diffusion propagation paradigm) and good trade-offs. Furthermore, redeployment of sensors during network evolution and/or heterogeneous deployment of sensors drastically improve (when compared to equal total "power" simultaneous deployment of identical sensors at the start) the protocol performance (the success rate increases up to four times while reducing energy dissipation and, interestingly, keeping latency low).
Abstract: Raising awareness among young people and changing their behaviour and habits concerning energy usage is key to achieving sustained energy saving. Additionally, young people are very sensitive to environmental protection so raising awareness among children is much easier than with any other group of citizens. This work examines ways to create an innovative Information & Communication Technologies (ICT) ecosystem (including web-based, mobile, social and sensing elements) tailored specifically for school environments, taking into account both the users (faculty, staff, students, parents) and school buildings, thus motivating and supporting young citizens¢ behavioural change to achieve greater energy efficiency. A mixture of open-source IoT hardware and proprietary platforms on the infrastructure level, are currently being utilized for monitoring a fleet of 18 educational buildings across 3 countries, comprising over 700 IoT monitoring points. Hereon presented is the system¢s high-level architecture, as well as several aspects of its implementation, related to the application domain of educational building monitoring and energy efficiency. The system is developed based on open-source technologies and services in order to make it capable of providing open IT-infrastructure and support from different commercial hardware/sensor vendors as well as open-source solutions. The system presented can be used to develop and offer new app-based solutions that can be used either for educational purposes or for managing the energy efficiency of the building. The system is replicable and adaptable to settings that may be different than the scenarios envisioned here (e.g., targeting different climate zones), different IT infrastructures and can be easily extended to accommodate integration with other systems. The overall performance of the system is evaluated in real-world environment in terms of scalability, responsiveness and simplicity.
Abstract: Divisible load scenarios occur in modern media server applications since most multimedia applications typically require access to continuous and discrete data. A high performance Continuous Media (CM) server greatly depends on the ability of its disk IO subsystem to serve both types of workloads efficiently. Disk scheduling algorithms for mixed media workloads, although they play a central role in this task, have been overlooked by related research efforts. These algorithms must satisfy several stringent performance goals, such as achieving low response time and ensuring fairness, for the discrete-data workload, while at the same time guaranteeing the uninterrupted delivery of continuous data, for the continuous-data workload. The focus of this paper is on disk scheduling algorithms for mixed media workloads in a multimedia information server. We propose novel algorithms, present a taxonomy of relevant algorithms, and study their performance through experimentation. Our results show that our algorithms offer drastic improvements in discrete request average response times, are fair, serve continuous requests without interruptions, and that the disk technology trends are such that the expected performance benefits can be even greater in the future.
Abstract: Evaluating target tracking protocols for wireless sensor networks that can localize multiple mobile devices, can be a very challenging task. Such protocols usually aim at minimizing communication overhead, data processing for the participating nodes, as well as delivering adequate tracking information of the mobile targets in a timely manner. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal network localization and perfect distance 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 localization protocol, where mobile assets can be tracked passively via software agents. 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 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 target tracking problem, regarding power consumption and the quality of tracking information. Finally we also conduct some very focused simulations to assess the scalability of our protocol in very large networks and multiple mobile assets.
Abstract: In this paper we describe a new simulation platform for heterogeneous distributed systems comprised of small programmable objects (e.g., wireless sensor networks) and traditional networked processors. Simulating such systems is complicated because of the need to coordinate compilers and simulators, often with very different interfaces, options, and fidelities.
Our platform (which we call ADAPT) is a flexible and extensible environment that provides a highly scalable simulator with unique characteristics. While the platform provides advanced functionality such as real-time simulation monitoring, custom topologies and scenarios, mixing real and simulated nodes, etc., the effort required by the user and the impact to her code is minimal. We here present its architecture, the most important design decisions, and discuss its distinct features and functionalities. We integrate our simulator to the Sun SPOT platform to enable simulation of sensing applications that employ both low-end and high-end devices programmed with different languages that are internetworked with heterogeneous technologies. We believe that ADAPT will make the development of applications that use small programmable objects more widely accessible and will enable researchers to conduct a joint research approach that combines both theory and practice.
Abstract: Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.
Abstract: This paper deals with early obstacles recognition in wireless sensor networks under various traffic
patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a
constant factor of 2ð+1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.
Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteria optimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.
Abstract: We consider two approaches that model timetable information in public transportation systems
as shortest-path problems in weighted graphs. In the time-expanded approach, every event at
a station, e.g., the departure of a train, is modeled as a node in the graph, while in the timedependent
approach the graph contains only one node per station. Both approaches have been
recently considered for (a simplified version of) the earliest arrival problem, but little is known
about their relative performance. Thus far, there are only theoretical arguments in favor of the
time-dependent approach. In this paper, we provide the first extensive experimental comparison of
the two approaches. Using several real-world data sets, we evaluate the performance of the basic
models and of several new extensions towards realistic modeling. Furthermore, new insights on
solving bicriteria optimization problems in both models are presented. The time-expanded approach
turns out to be more robust for modeling more complex scenarios, whereas the time-dependent
approach shows a clearly better performance.
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: Urban ecosystems are becoming one of the most potentially attractive scenarios for innovating new services and technologies. In parallel, city managers, urban utilities and other stakeholders are fostering the intensive use of advanced technologies aiming at improving present city performance and its sustainability. The deployment of such technology entails the generation of massive amounts of information which in many cases might become useful for other services and applications. Hence, aiming at taking advantage of such massive amounts of information and deployed technology as well as breaking the potential digital barrier that can be raised, some easy-to-use tools have to be made available to the urban stakeholders. These tools integrated in a platform, operated directly or indirectly by the city, provides a singular opportunity for exploiting the concept of connected city whilst fostering innovation in all city dimensions and making the co-creation concept a reality and eventually impacting on government policies.
Abstract: Few IoT systems monitoring energy consumption in buildings have focused on the educational community. IoT in the educational domain can jump-start a process of sustainability awareness and behavioral change towards energy savings, as well as provide tangible financial savings. We present a real-world multi-site IoT deployment, comprising 19 school buildings, aiming at enabling IoT-based energy awareness and sustainability lectures, promoting energy-saving behaviors supported by IoT data. We discuss scenarios where IoT-enabled applications are integrated into school life, providing an engaging and hands-on approach, based on real data, generating value in terms of educational and energy savings outcomes. We also present a set of first results, based on the analysis of school-building data, which highlight potential ways to identify irregularities and inefficiencies.
Abstract: In this work we study energy efficient routing strategies
for wireless ad-hoc networks. In this kind of networks,
energy is a scarce resource and its conservation
and efficient use is a major issue. Our strategy follows
the multi-cost routing approach, according to which a
cost vector of various parameters is assigned to each
link. The parameters of interest are the number of hops
on a path, and the residual energy and the transmission
power of the nodes on the path. These parameters
are combined in various optimization functions,
corresponding to different routing algorithms, for selecting
the optimal path. We evaluate the routing algorithms
proposed in a number of scenarios, with respect
to energy consumption, throughput and other performance
parameters of interest. From the experiments
conducted we conclude that routing algorithms that take
into account energy related parameters, increase the
lifetime of the network, while achieving better performance
than other approaches, such as minimum hop
routing.
Abstract: In this work we study the implementation of multicost rout-
ing in a distributed way in wireless mobile ad hoc networks.
In contrast to traditional single-cost routing, where each
path is characterized by a scalar, in multicost routing a
vector of cost parameters is assigned to each network link,
from which the cost vectors of candidate paths are calcu-
lated. These parameters are combined in various optimiza-
tion functions, corresponding to different routing algorithms,
for selecting the optimal path. Up until now the performance
of multicost and multi-constrained routing in wireless ad hoc
networks has been evaluated either at a theoretical level or
by assuming that nodes are static and have full knowledge
of the network topology and nodes� state. In the present
paper we assess the performance of multicost routing based
on energy-related parameters in mobile ad hoc networks by
embedding its logic in the Dynamic Source Routing (DSR)
algorithm, which is a well-known fully distributed routing
algorithm. We use simulations to compare the performance
of the multicost-DSR algorithm to that of the original DSR
algorithm and examine their behavior under various node
mobility scenarios. The results confirm that the multicost-
DSR algorithm improves the performance of the network in
comparison to the original DSR algorithm in terms of energy efficiency. The multicost-DSR algorithm enhances the
performance of the network not only by reducing energy
consumption overall in the network, but also by spreading
energy consumption more uniformly across the network, pro
longing the network lifetime and reducing the packet drop
probability. Furthermore the delay suffered by the packets
reaching their destination for the case of the multicost-DSR
algorithm is shown to be lower than in the case of the orig
inal DSR algorithm.
Abstract: A number of Future Internet testbeds are being deployed around the world for research experimentation and development. SmartSantander
is an infrastructure of massive scale deployed inside a city centre. We argue that utilising the concept of participatory sensing can augment the functionality and potential use-cases of such a system and be beneficiary in a number of scenarios. We discuss
the concept of extending SmartSantander with participatory sensing through the use of volunteers¢ smartphones. We report on our design and implementation, which allows for developers to write
their code for Android devices and then deploy and execute on the devices automatically through our system. We have tested our implementation in a number of scenarios in two cities with the help
of volunteers with promising results; the data collected enhance the ones by fixed infrastructure both quantitatively and qualitatively across the cities, while also engaging citizens more directly.
Abstract: Internet of Things technologies are considered the next big
step in Smart Building installations. Although such technologies have
been widely studied in simulation and experimental scenarios it is not so
obvious how problems of real world installations should be dealt with. In
this work we deploy IoT devices for sensing and control in a multi-office
space and employ technologies such as CoAP, RESTful interfaces and
Semantic Descriptions to integrate them with the Web. We report our
research goals, the challenges we faced, the decisions we made and the
experience gained from the design, deployment and operation of all the
hardware and software components that compose our system.
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: In this work, we explore context-aware application scenarios that become possible utilizing semantically-rich information derived from real-world mobility and presence traces. Traces produced by people carrying personal mobile devices, capturing social and contextual interactions, serve as enables for Future Internet applications. We discuss the fundamental concepts, technical issues and related research challenges. We propose a reference architecture for setting up a system that collects such traces in a Smart City environment. We present the algorithms used to process the traces and infer interactions and interests for the observed populations. We conduct two 3-day trial deployments: one in an office environment and the other in the context of a Smart Conference application. We discuss our findings regarding the system's capability to track interactions and the overall efficacy of the application.
Abstract: We provide an improved FPTAS for multiobjective shortest paths—a fundamental (NP-hard) problem in multiobjective optimization—along with a new generic method for obtaining FPTAS to any multiobjective optimization problem with non-linear objectives. We show how these results can be used to obtain better approximate solutions to three related problems, multiobjective constrained [optimal] path and non-additive shortest path, that have important applications in QoS routing and in traffic optimization. We also show how to obtain a FPTAS to a natural generalization of the weighted multicommodity flow problem with elastic demands and values that models several realistic scenarios in transportation and communication networks.
Abstract: This article studies the transmission control
protocol (TCP) synchronization effect in optical burst
switched networks.Synchronization of TCP flows appears
when optical bursts with segments from different flows inside
are dropped in the network causing flow congestion windows decreasing simultaneously. In this article,this imminent
effect is studied with different assembly schemes and network scenarios.Different metrics are applied to quantitatively assess synchronization with classical assembly
schemes.A new burst assembly scheme is proposed that
statically or dynamically allocates flows to multiple assembly queues to control flow aggregation within the assembly
cycle.The effectiveness of the scheme has been evaluated,
showing a good improvement in optical link utilization
Abstract: This paper describes recent research activities and results in the area of photonic switching
carried out within the Virtual Department on Switching (VDS) of the European e-Photon/
ONe Network of Excellence. Contributions from outstanding European research groups in
this field are collected to offer a platform for future research in optical switching. The paper
contains the main topics related to network scenarios, switch architectures and experiments,
with an effort to investigate synergies and challenging opportunities for collaboration
and integration of research expertise in the field.
Abstract: We consider the line planning problem in public transporta-
tion, under a robustness perspective. We present a mechanism for robust
line planning in the case of multiple line pools, when the line operators
have a different utility function per pool. We conduct an experimen-
tal study of our mechanism on both synthetic and real-world data that
shows fast convergence to the optimum. We also explore a wide range of
scenarios, varying from an arbitrary initial state (to be solved) to small
disruptions in a previously optimal solution (to be recovered). Our ex-
periments with the latter scenario show that our mechanism can be used
as an online recovery scheme causing the system to re-converge to its
optimum extremely fast.
Abstract: Geographic routing is becoming the protocol of choice for
many sensor network applications. The current state of the art is unsatisfactory:
some algorithms are very efficient, however they require a
preliminary planarization of the communication graph. Planarization induces
overhead and is thus not realistic for some scenarios such as the
case of highly dynamic network topologies. On the other hand, georouting
algorithms which do not rely on planarization have fairly low success
rates and fail to route messages around all but the simplest obstacles.
To overcome these limitations, we propose the GRIC geographic routing
algorithm. It has absolutely no topology maintenance overhead, almost
100% delivery rates (when no obstacles are added), bypasses large convex
obstacles, finds short paths to the destination, resists link failure
and is fairly simple to implement. The case of hard concave obstacles
is also studied; such obstacles are hard instances for which performance
diminishes.
Abstract: In emerging pervasive scenarios, data is collected by sensing devices in streams that occur at several distributed points of observation. The size of the data typically far exceeds the storage and computational capabilities of the tiny devices that have to collect and process them. A general and challenging task is to allow (some of) the nodes of a pervasive network to collectively perform monitoring of a neighbourhood of interest by issuing continuous aggregate queries on the streams observed in its vicinity. This class of algorithms is fully decentralized and diffusive in nature: collecting all the data at a few central nodes of the network is unfeasible in networks of low capability devices or in the presence of massive data sets. Two main problems arise in this scenario: (i) the intrinsic complexity of maintaining statistics over a data stream whose size greatly exceeds the capabilities of the device that performs the computation; (ii) composing the partial outcomes computed at different points of observation into an accurate, global statistic over a neighbourhood of interest, which entails coping with several problems, last but not least the receipt of duplicate information along multiple paths of diffusion.
Streaming techniques have emerged as powerful tools to achieve the general goals described above, in the first place because they assume a computational model in which computational and storage resources are assumed to be far exceeded by the amount of data on which computation occurs. In this contribution, we review the main streaming techniques and provide a classification of the computational problems and the applications they effectively address, with an emphasis on decentralized scenarios, which are of particular interest in pervasive networks
Abstract: In this work, we discuss 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 discuss the fundamental
issues and challenges related to these type of games and the
scenarios associated with them. We also present and evaluate
an example of such a game, called the “Hot Potato”, developed
using the Sun SPOT hardware platform. We provide a set of
experimental results, so as to both evaluate our implementation
and also to identify issues that arise in pervasive games which
utilize sensor network nodes, which show that there is great
potential in this type of games.
Abstract: We study the load balancing problem in the context of a set of clients each wishing to run a job on a server selected among a subset of permissible servers for the particular client. We consider two different scenarios. In selfish load balancing, each client is selfish in the sense that it selects to run its job to the server among its permissible servers having the smallest latency given the assignments of the jobs of other clients to servers. In online load balancing, clients appear online and, when a client appears, it has to make an irrevocable decision and assign its job to one of its permissible servers. Here, we assume that the clients aim to optimize some global criterion but in an online fashion. A natural local optimization criterion that can be used by each client when making its decision is to assign its job to that server that gives the minimum increase of the global objective. This gives rise to greedy online solutions. The aim of this paper is to determine how much the quality of load balancing is affected by selfishness and greediness.
We characterize almost completely the impact of selfishness and greediness in load balancing by presenting new and improved, tight or almost tight bounds on the price of anarchy and price of stability of selfish load balancing as well as on the competitiveness of the greedy algorithm for online load balancing when the objective is to minimize the total latency of all clients on servers with linear latency functions.
Abstract: We study the load balancing problem in the context of a set of clients each
wishing to run a job on a server selected among a subset of permissible servers for
the particular client. We consider two different scenarios. In selfish load balancing,
each client is selfish in the sense that it chooses, among its permissible servers, to
run its job on the server having the smallest latency given the assignments of the
jobs of other clients to servers. In online load balancing, clients appear online and,
when a client appears, it has to make an irrevocable decision and assign its job to
one of its permissible servers. Here, we assume that the clients aim to optimize some
global criterion but in an online fashion. A natural local optimization criterion that
can be used by each client when making its decision is to assign its job to that server that gives the minimum increase of the global objective. This gives rise to greedy
online solutions. The aim of this paper is to determine how much the quality of load
balancing is affected by selfishness and greediness.
We characterize almost completely the impact of selfishness and greediness in
load balancing by presenting new and improved, tight or almost tight bounds on the
price of anarchy of selfish load balancing as well as on the competitiveness of the
greedy algorithm for online load balancing when the objective is to minimize the
total latency of all clients on servers with linear latency functions. In addition, we
prove a tight upper bound on the price of stability of linear congestion games.
Abstract: In this work we present a new simulation toolkit that we call TRAILS (Toolkit for Realism and Adaptivity In Large-scale Simulations), which extends the \NS simulator by adding several important functionalities and optimizing certain
critical simulator operations. The added features focus on providing the user with the necessary tools to better study wireless networks of high dynamics; in particular, to implement advanced mobility patterns, obstacle presence and disaster scenarios, and failures injection. These scenarios and patterns can dynamically change throughout the execution of the simulation based on network related parameters. Moreover, we define a set of utilities that can facilitate the use of \NS providing advanced statistics and easy-to-use logging mechanisms. This functionality is implemented in a simple and flexible architecture, that follows design patterns, object oriented and generic programming principles, maintaining a proper balance between reusability, extendability and ease of use. We evaluate the performance of TRAILS and show that it offers significant speed-ups (at least 4 times faster) regarding the execution time of \NS in certain important, common wireless settings. Our results also show that this is achieved with minimum overhead in terms of memory usage.
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.
Abstract: In many fields of application, shortest path finding problems
in very large graphs arise. Scenarios where large numbers of on-line
queries for shortest paths have to be processed in real-time appear for example
in traffic information systems. In such systems, the techniques considered
to speed up the shortest path computation are usually based on
precomputed information. One approach proposed often in this context
is a space reduction, where precomputed shortest paths are replaced by
single edges with weight equal to the length of the corresponding shortest
path. In this paper, we give a first systematic experimental study of
such a space reduction approach. We introduce the concept of multi-level
graph decomposition. For one specific application scenario from the field
of timetable information in public transport, we perform a detailed analysis
and experimental evaluation of shortest path computations based
on multi-level graph decomposition.
