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) = snover sn + {\`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 energyefficiency 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: Smart Dust is a special case of wireless sensor networks, comprised of a vast number of ultra-small fully autonomous computing, communication and sensing devices, with very restricted energy and computing capabilities, that co-operate to accomplish a large sensing task. Smart Dust can be very useful in practice, i.e. in the local detection of remote crucial events and the propagation of data reporting their realization to a control center.
In this paper, we propose a new energy efficient and fault tolerant protocol for data propagation in smart dust networks, the Variable Transmission Range Protocol (VTRP). The basic idea of data propagation in VTRP is the varying range of data transmissions, i.e. we allow the transmission range to increase in various ways. Thus, data propagation in our protocol exhibits high fault-tolerance (by bypassing obstacles or faulty sensors) and increases network lifetime (since critical sensors, i.e. close to the control center are not overused). As far as we know, it is the first time varying transmission range is used.
We implement the protocol and perform an extensive experimental evaluation and comparison to a representative protocol (LTP) of several important performance measures with a focus on energy consumption. Our findings indeed demonstrate that our protocol achieves significant improvements in energyefficiency and network lifetime.
Abstract: In this work we propose a new energy efficient and fault tolerant protocol for data propagation in wireless sensor networks, the Variable Transmission Range Protocol VTRP. The basic idea of data propagation in VTRP is the varying range of data transmissions, ie. we allow the transmission range to increase in various ways. Thus data propagation in our protocol exhibits high fault-tolerance (by bypassing obstacles or faulty sensors) and increases network lifetime (since critical sensors, ie. close to the control center are not overused). As far as we know, it is the first time varying transmission range is used.
We implement the protocol and perform an extensive experimental evaluation and comparison to a representative protocol (LTP) of several important performance measures with a focus on energy consumption. Our findings indeed demonstrate that our protocol achieves significant improvements in energyefficiency and network lifetime.
Abstract: Raising awareness among young people on the
relevance of behaviour change for achieving energy savings is widely considered as a key approach towards long-term and costeffective energyefficiency policies. The GAIA Project aims to deliver a comprehensive solution for both increasing awareness on energyefficiency and achieving energy savings in school buildings. In this framework, we present a novel rule engine that, leveraging a resource-based graph model encoding relevant application domain knowledge, accesses IoT data for producing energy savings recommendations. The engine supports configurability, extensibility and ease-of-use requirements, to be easily applied and customized to different buildings. The paper introduces the main design and implementation details and presents a set of preliminary performance results.
Abstract: Smart Dust is a set of a vast number of ultra-small fully
autonomous computing and communication devices, with very
restricted energy and computing capabilities, that co-operate to
quickly and efficiently accomplish a large sensing task. Smart
Dust can be very useful in practice i.e. in the local detection of
a remote crucial event and the propagation of data reporting its
realization. In this work we continue (see [POMC02]) our
effort towards the research on smart dust from a basic algorithmic
point of view. Under a simple but realistic model for smart dust
we present an interesting problem, which is how to propagate
efficiently information on an event detected locally. Then we
present a new smart dust protocol, which we call the
``Sleep-Awake" protocol, for information propagation that explicitly uses the energy saving features (i.e. the alteration of sleeping and awake time periods) of the smart dust particles. By using both probabilistic some first analysis and extensive
experiments, we provide some first concrete results for the
success probability and the time and energyefficiency of the
protocol, in terms of parameters of the smart dust network. We
note that the study of the interplay of these parameters allows us
to program the smart dust network characteristics accordingly.
Abstract: We propose a MAC protocol for mobile ad hoc networks that
uses power control for the RTS/CTS and DATA frame
transmissions in order to improve energy and capacity
utilization efficiency. Unlike IEEE 802.11, in our scheme the
RTS frames are not sent using the maximum transmission
power to silence neighbouring nodes, and the CTS frames do
not silence all receiving nodes to the same degree. In contrast,
the transmission power of the RTS frames follows a slow
start principle, while the CTS frames, which are sent at
maximum transmission power, prevent the neighbouring
nodes from transmitting their DATA frames with power more
than a computed threshold, while allowing them to transmit at
power levels less than that threshold. This is done by
including in the RTS and the CTS frames additional
information, such as the power of the transmissions, and the
interference tolerance of the nodes. Moreover the DATA
frames are sent at the minimum required transmission power
increased by a small margin to ensure connectivity with the
intended receiver, so as to cause minimal interference to
neighbouring nodes and allow for future interference to be
added to the receiver of the DATA frames. The power to be
used by the transmitter is computed by the recipient of the
RTS frame and is included in the CTS frame. It is expected
that a network with such a power management scheme would
achieve a better throughput performance and more power
savings than a network without such a scheme.
Abstract: We propose a MAC protocol for mobile ad hoc networks that
uses power control for the RTS/CTS and DATA frame
transmissions in order to improve energy and capacity
utilization efficiency. Unlike IEEE 802.11, in our scheme the
RTS frames are not sent using the maximum transmission
power to silence neighbouring nodes, and the CTS frames do
not silence all receiving nodes to the same degree. In contrast,
the transmission power of the RTS frames follows a slow
start principle, while the CTS frames, which are sent at
maximum transmission power, prevent the neighbouring
nodes from transmitting their DATA frames with power more
than a computed threshold, while allowing them to transmit at
power levels less than that threshold. This is done by
including in the RTS and the CTS frames additional
information, such as the power of the transmissions, and the
interference tolerance of the nodes. Moreover the DATA
frames are sent at the minimum required transmission power
increased by a small margin to ensure connectivity with the
intended receiver, so as to cause minimal interference to
neighbouring nodes and allow for future interference to be
added to the receiver of the DATA frames. The power to be
used by the transmitter is computed by the recipient of the
RTS frame and is included in the CTS frame. It is expected
that a network with such a power management scheme would
achieve a better throughput performance and more power
savings than a network without such a scheme.
Abstract: Designing wireless sensor networks is inherently complex; many aspects such as energyefficiency, limited resources, decentralized collaboration, fault tolerance have to be tackled. To be effective and to produce applicable results, fundamental research has to be tested, at least as a proof-of-concept, in large scale environments, so as to assess the feasibility of the new concepts, verify their large scale effects (not only at technological level, but also as for their foreseeable implications on users, society and economy) and derive further requirements, orientations and inputs for the research. In this paper we focus on the problems of interconnecting existing testbed environments via the Internet and providing a virtual unifying laboratory that will support academia, research centers and industry in their research on networks and services. In such a facility important issues of trust, security, confidentiality and integrity of data may arise especially for commercial (or not) organizations. In this paper we investigate such issues and present the design of a secure and robust architectural model for interconnecting testbeds of wireless sensor networks.
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, energyefficiency 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: Energy consumption reserves a large portion of the budget for school buildings. At the same time, the students that use such facilities are the adults of the years to come and thus, should they embrace energy-aware behaviors, then sustainable results with respect to energyefficiency are anticipated. GAIA is a research project targeting this user domain, proposing a set of applications that a) aims at raising awareness, prompting action and fostering engagement in energyefficiency enhancement, and b) is adaptable to the needs of each facility/community. This application set relies on an IoT sensing infrastructure, as well as on the use of humans as sensors to create situational awareness.
Abstract: Wireless sensor networks are comprised of a vast number of
ultra-small autonomous computing, communication and sensing devices,
with restricted energy and computing capabilities, that co-operate
to accomplish a large sensing task. Such networks can be very useful
in practice, e.g.~in the local monitoring of ambient conditions and
reporting them to a control center. In this paper we propose a
distributed group key establishment protocol that uses mobile agents
(software) and is particularly suitable for energy constrained,
dynamically evolving ad-hoc networks. Our approach totally avoids
the construction and the maintenance of a distributed structure that
reflects the topology of the network. Moreover, it trades-off
complex message exchanges by performing some amount of additional
local computations in order to be applicable at dense and dynamic
sensor networks. The extra computations are simple for the devices
to implement and are evenly distributed across the participants of
the network leading to good energy balance. We evaluate the
performance of our protocol in a simulated environment and compare
our results with existing group key establishment protocols. The
security of the protocol is based on the Diffie-Hellman problem and
we used in our experiments its elliptic curve analog. Our findings
basically indicate the feasibility of implementing our protocol in
real sensor network devices and highlight the advantages and
disadvantages of each approach given the available technology and
the corresponding efficiency (energy, time) criteria.
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, energyefficiency 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: The use of Augmented Reality (AR) technologies is currently being investigated in numerous and diverse application domains. In this work, we discuss the ways in which we are integrating AR into educational in-class activities for the GAIA project, aiming to enhance existing tools that target behavioral changes towards energyefficiency in schools. We combine real-time IoT data from a sensing infrastructure inside a fleet of school buildings with AR software running on tablets and smartphones, as companions to a set of educational lab activities aimed at promoting energy awareness in a STEM context. We also utilize this software as a means to ease access to IoT data and simplify device maintenance. We report on the design and current status of our implementation, describing functionality in the context of our target applications, while also relaying our experiences from the use of such technologies in this application domain.
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 energyefficiency. 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 energyefficiency. 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 energyefficiency 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: 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 energyefficiency 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 energyefficiency 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 energyefficiency and delivery success.
Abstract: We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property is crucial for maximizing the time the network is functional, by avoiding early energy depletion of a large portion of sensors. We propose a distributed, adaptive data propagation algorithm that exploits limited, local network density information for achieving energy-balance while at the same time
minimizing energy dissipation.
We investigate both uniform and heterogeneous sensor placement distributions. By a detailed experimental evaluation and comparison with well-known energy-balanced protocols, we show that our density-based protocol improves energyefficiency signicantly while also having better energy balance properties.
Furthermore, we compare the performance of our protocol with a centralized, o-line optimum solution derived by a linear program which maximizes the network lifetime and show that it achieves near-optimal performance for uniform sensor deployments.
Abstract: This chapter aims at presenting certain important aspects of the design of lightweight, event-driven algorithmic solutions for data dissemination in wireless sensor networks that provide support for reliable, efficient and concurrency-intensive operation. We wish to emphasize that efficient solutions at several levels are needed, e.g.~higher level energy efficient routing protools and lower level power management schemes. Furthermore, it is important to combine such different level methods into integrated protocols and approaches. Such solutions must be simple, distributed and local. Two useful algorithmic design principles are randomization (to trade-off efficiency and fault-tolerance) and adaptation (to adjust to high network dynamics towards improved operation). In particular, we provide a) a brief description of the technical specifications of state-of-the-art sensor devices b) a discussion of possible models used to abstract such networks, emphasizing heterogeneity, c) some representative power management schemes, and d) a presentation of some characteristic protocols for data propagation. Crucial efficiency properties of these schemes and protocols (and their combinations, in some cases) are investigated by both rigorous analysis and performance evaluations through large scale simulations.
Abstract: Wireless sensor networks are comprised of a vast number of ultra-small autonomous computing, communication and sensing devices, with restricted energy and computing capabilities, that co-operate to accomplish a large sensing task. Such networks can be very useful in practice, e.g.~in the local monitoring of ambient conditions and reporting them to a control center. In this paper we propose a new lightweight, distributed group key establishment protocol suitable for such energy constrained networks. Our approach basically trade-offs complex message exchanges by performing some amount of additional local computations. The extra computations are simple for the devices to implement and are evenly distributed across the participants of the network leading to good energy balance. We evaluate the performance our protocol in comparison to existing group key establishment protocols both in simulated and real environments. The intractability of all protocols is based on the Diffie-Hellman problem and we used its elliptic curve analog in our experiments. Our findings basically indicate the feasibility of implementing our protocol in real sensor network devices and highlight the advantages and disadvantages of each approach given the available technology and the corresponding efficiency (energy, time) criteria.
Abstract: Wireless sensor networks are comprised of a vast number of ultra-small autonomous computing, communication and sensing devices, with restricted energy and computing capabilities, that co-operate to accomplish a large sensing task. Such networks can be very useful in practice, e.g.~in the local monitoring of ambient conditions and reporting them to a control center. In this paper we propose a new lightweight, distributed group key establishment protocol suitable for such energy constrained networks. Our approach basically trade-offs complex message exchanges by performing some amount of additional local computations. The extra computations are simple for the devices to implement and are evenly distributed across the participants of the network leading to good energy balance. We evaluate the performance our protocol in comparison to existing group key establishment protocols both in simulated and real environments. The intractability of all protocols is based on the Diffie-Hellman problem and we used its elliptic curve analog in our experiments. Our findings basically indicate the feasibility of implementing our protocol in real sensor network devices and highlight the advantages and disadvantages of each approach given the available technology and the corresponding efficiency (energy, time) criteria.
Abstract: We propose a new data dissemination protocol for wireless sensor networks, that basically pulls some additional knowledge about the network in order to subsequently improve data forwarding towards the sink. This extra information is still local, limited and obtained in a distributed manner. This extra knowledge is acquired by only a small fraction of sensors thus the extra energy cost only marginally affects the overall protocol efficiency. The new protocol has low latency and manages to propagate data successfully even in the case of low densities. Furthermore, we study in detail the effect of failures and show that our protocol is very robust. In particular, we implement and evaluate the protocol using large scale simulation, showing that it significantly outperforms well known relevant solutions in the state of the art.
Abstract: Smart Dust is a set of a vast number of ultra-small fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that co-operate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice, i.e., in the local detection of a remote crucial event and the propagation of data reporting its realization. In this work we make an effort towards the research on smart dust from an algorithmic point of view. We first provide a simple but realistic model for smart dust and present an interesting problem, which is how to propagate efficiently information on an event detected locally. Then we present various smart dust protocols for local detection and propagation that are simple enough to be implemented on real smart dust systems, and perform, under some simplifying assumptions, a rigorous average case analysis of their efficiency and energy consumption (and their interplay). This analysis leads to concrete results showing that our protocols are very efficient and robust. We also validate the analytical results by extensive experiments.
Abstract: In this work we study the problem of scheduling tasks with dependencies in multiprocessor architectures where processors have different speeds.
We present the preemptive algorithm "Save-Energy" that given a schedule of tasks it post processes it to improve the energyefficiency without any deterioration of the makespan. In terms of time efficiency, we show that preemptive scheduling in an asymmetric system can achieve the same or better optimal makespan than in a symmetric system. Motivited by real multiprocessor systems, we investigate architectures that exhibit limited asymmetry: there are two essentially different speeds. Interestingly, this special case has not been studied in the field of parallel computing and scheduling theory; only the general case was studied where processors have K essentially different speeds. We present the non-preemptive algorithm "Remnants'' that achieves almost optimal makespan. We provide a refined analysis of a recent scheduling method. Based on this analysis, we specialize the scheduling policy and provide an algorithm of (3 + o(1)) expected approximation factor. Note that this improves the previous best factor (6 for two speeds). We believe that our work will convince researchers to revisit this well studied scheduling problem for these simple, yet realistic, asymmetric multiprocessor architectures.
Abstract: The energy balance property (i.e., all nodes having the same energy throughout the network evolution) contributes significantly (along with energyefficiency) to the maximization of the network lifespan and network connectivity. The problem of achieving energy balanced propagation is well studied in static networks, as it has attracted a lot of research attention.
Recent technological advances have enabled sensor devices to be attached to mobile entities of our every day life (e.g. smart-phones, cars, PDAs etc), thus introducing the formation of highly mobile sensor networks.
Inspired by the aforementioned applications, this work is (to the best of our knowledge) the first studying the energy balance property in wireless networks where the nodes are highly and dynamically mobile. In particular, in this paper we propose a new diverse mobility model which is easily parameterized and we also present a new protocol which tries to adaptively exploit the inherent node mobility in order to achieve energy balance in the network in an efficient way.
Abstract: Raising awareness among young people, and especially students, on the relevance of behavior change for achieving energy savings is increasingly being considered as a key enabler towards long-term and cost-effective energyefficiency policies. However, the way to successfully apply educational interventions focused on such targets inside schools is still an open question. In this paper, we present our approach for enabling IoT-based energy savings and sustainability awareness lectures and promoting data-driven energy-saving behaviors focused on a high school audience. We present our experiences toward the successful application of sets of educational tools and software over a real-world Internet of Things (IoT) deployment. We discuss the use of gamification and competition as a very effective end-user engagement mechanism for school audiences. We also present the design of an IoT-based hands-on lab activity, integrated within a high school computer science curriculum utilizing IoT devices and data produced inside the school building, along with the Node-RED platform. We describe the tools used, the organization of the educational activities and related goals. We report on the experience carried out in both directions in a high school in Italy and conclude by discussing the results in terms of achieved energy savings within an observation period.
Abstract: Several networking technologies targeting the IoT application space currently compete within the smart city domain, both in outdoor and indoor deployments. However, up till now, there is no clear winner, and results from real-world deployments have only recently started to surface. In this paper, we present a comparative study of 2 popular IoT networking technologies, LoRa and IEEE 802.15.4, within the context of a research-oriented IoT deployment inside school buildings in Europe, targeting energyefficiency in education. We evaluate the actual performance of these two technologies in real-world settings, presenting a comparative study on the effect of parameters like the built environment, network quality, or data rate. Our results indicate that both technologies have their advantages, and while in certain cases both are perfectly adequate, in our use case LoRa exhibits a more robust behavior. Moreover, LoRa˘s characteristics make it a very good choice for indoor IoT deployments such as in educational buildings, and especially in cases where there are low bandwidth requirements.
Abstract: We propose a new data dissemination protocol for wireless sensor networks, that basically pulls some additional knowledge about the network in order to subsequently improve data forwarding towards the sink. This extra information is still local, limited and obtained in a distributed manner. This extra knowledge is acquired by only a small fraction of sensors thus the extra energy cost only marginally affects the overall protocol efficiency. The new protocol has low latency and manages to propagate data successfully even in the case of low densities. Furthermore, we study in detail the effect of failures and show that our protocol is very robust. In particular, we implement and evaluate the protocol using large scale simulation, showing that it significantly outperforms well known relevant solutions in the state of the art.
Abstract: Educational buildings constitute 17% of the non-residential building stock in the EU [1], while recent work shows that a focus on energy use in schools can potentially yield an array of rewards, in concert with educational excellence and a healthy learning environment [2].
Having these in mind, GAIA1, a Horizon2020 EC-funded project, is developing an IoT platform
that combines sensing, web-based and gamification elements, in order to address the
educational community. Its primary aim is to increase awareness about energy consumption
and sustainability, based on real sensor data produced by the school buildings where students and teachers live and work, while also lead towards behavior change in terms of energyefficiency.
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 energyefficiency 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: Recent rapid developments in micro-electro-mechanical systems
(MEMS), wireless communications and digital electronics have already
led to the development of tiny, low-power, low-cost sensor devices.
Such devices integrate sensing, limited data processing and restricted
communication capabilities.
Each sensor device individually might have small utility, however the
effective distributed co-ordination of large numbers of such devices can
lead to the efficient accomplishment of large sensing tasks. Large numbers
of sensors can be deployed in areas of interest (such as inaccessible
terrains or disaster places) and use self-organization and collaborative
methods to form an ad-hoc network.
We note however that the efficient and robust realization of such large,
highly-dynamic, complex, non-conventional networking environments is
a challenging technological and algorithmic task, because of the unique
characteristics and severe limitations of these devices.
This talk will present and discuss several important aspects of the
design, deployment and operation of sensor networks. In particular, we
provide a brief description of the technical specifications of state-of-theart
sensor, a discussion of possible models used to abstract such networks,
a discussion of some key algorithmic design techniques (like randomization,
adaptation and hybrid schemes), a presentation of representative
protocols for sensor networks, for important problems including data
propagation, collision avoidance and energy balance and an evaluation
of crucial performance properties (correctness, efficiency, fault-tolerance)
of these protocols, both with analytic and simulation means.
Abstract: In this paper we propose an energy-aware broadcast algorithm for wireless networks. Our algorithm is based on the multicost approach and selects the set of nodes that by transmitting implement broadcasting in an optimally energy-efficient way. The energy-related parameters taken into account are the node transmission power and the node residual energy. The algorithm{\^a}€™s complexity however is non-polynomial, and therefore, we propose a relaxation producing a near-optimal solution in polynomial time. We also consider a distributed information exchange scheme that can be coupled with the proposed algorithms and examine the overhead introduced by this integration. Using simulations we show that the proposed algorithms outperform other solutions in the literature in terms of energyefficiency. Moreover, it is shown that the near-optimal algorithm obtains most of the performance benefits of the optimal algorithm at a smaller computational overhead.
Abstract: One oft-cited strategy towards sustainability is improving energyefficiency inside public buildings. In this context, the educational buildings sector presents a very interesting and important case for the monitoring and management of buildings, since it addresses both energy and educational issues. In this work, we present and discuss the hardware IoT infrastructure substrate that provides real-time monitoring in multiple school buildings. We believe that such a system needs to follow an open design approach: rely on hardware-agnostic components that communicate over well-defined open interfaces. We present in detail the design of our hardware components, while also providing insights to the overall system design and a first set of results on their operation. The presented hardware components are utilized as the core hardware devices for GAIA, an EU research project aimed at the educational community. As our system has been deployed and tested in several public school buildings in Greece, we also report on its validation.
Abstract: Recent rapid technological developments have led to the
development of tiny, low-power, low-cost sensors. Such devices
integrate sensing, limited data processing and communication
capabilities.The effective distributed collaboration
of large numbers of such devices can lead to the efficient
accomplishment of large sensing tasks.
This talk focuses on several aspects of energyefficiency.
Two protocols for data propagation are studied: the first
creates probabilistically optimized redundant data transmissions
to combine energyefficiency with fault tolerance,
while the second guarantees (in a probabilistic way) the
same per sensor energy dissipation, towards balancing the
energy load and prolong the lifetime of the network.
A third protocol (in fact a power saving scheme) is also
presented, that directly and adaptively affects power dissipation
at each sensor. This “lower level” scheme can be
combined with data propagation protocols to further improve
energyefficiency.
Abstract: In this work we focus on the energyefficiency challenge in wireless sensor networks, from both an on-line perspective (related to routing), as well as a network design perspective (related to tracking). We investigate a few representative, important aspects of energyefficiency: a) the robust and fast data propagation b) the problem of balancing the energy
dissipation among all sensors in the network and c) the problem of efficiently tracking moving
entities in sensor networks. Our work here is a methodological survey of selected results that
have alre dy appeared in the related literature.
In particular, we investigate important issues of energy optimization, like minimizing the total
energy dissipation, minimizing the number of transmissions as well as balancing the energy
load to prolong the system˘s lifetime. We review characteristic protocols and techniques in the recent literature, including probabilistic forwarding and local optimization methods. We study the problem of localizing and tracking multiple moving targets from a network design perspective i.e. towards estimating the least possible number of sensors, their positions and operation characteristics needed to efficiently 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 covering combinatorial problem and then design and analyze an efficient approximate method for sensor placement
and operation.
Abstract: In this work we study the problem of scheduling tasks with dependencies in multiprocessor architectures where processors have different speeds. We examine the energy-efficiency and time efficiency of scheduling in an asymmetric system.
Abstract: Smart Dust is a set of a ast number of ultra-small fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that cooperate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice i.e. in the local detection of a remote crucial event and the propagation of data reporting its realization. In this work we make an effort towards the research on smart dust from a basic algorithmic point of view. We first provide a simple but realistic model for smart dust and present an interesting problem, which is how to propagate efficiently information on an event detected locally. Then we present smart dust protocols for local detection and propagation that are simple enough to be implemented on real smart dust systems, and perform, under some simplifying assumptions, a rigorous average case analysis of their efficiency and energy consumption (and their interplay). This analysis leads to concrete results showing that our protocols are very efficient.
Abstract: Smart Dust is a set of a vast number of ultra-small fully
autonomous computing and communication devices, with very restricted
energy and computing capabilities, that co-operate to quickly and efficiently
accomplish a large sensing task.
Smart Dust can be very useful in practice
i.e. in the local detection of a remote crucial event and
the propagation of data reporting its realization.
In this work we make an effort towards the research on smart dust
from a basic algorithmic point of view.
We first provide a simple but realistic model for smart dust
and present an interesting problem, which is how to propagate efficiently
information on an event detected locally.
Then we present smart dust protocols for local detection
and propagation that are simple enough to be implemented
on real smart dust systems, and perform, under some simplifying assumptions,
a rigorous average case analysis of their efficiency and energy consumption
(and their interplay).
This analysis leads to concrete results showing that our protocols
are very efficient.
Abstract: Today˘s students are the citizens of tomorrow, and they should have the skills and tools to understand and respond to climate change. Green Awareness in Action (GAIA) has built an IoT infrastructure within 25 schools in Europe, in order to enable lectures that target sustainability and energyefficiency, based on data produced inside school buildings. The school community has reacted very positively to this approach and has reduced energy consumption as a consequence.
Abstract: A lot of activity is being devoted to studying issues related to energy consumption and efficiency in our buildings, and especially on public buildings. In this context, the educational public buildings should bean important part of the equation. At the same time, there is an evident need for open datasets, which should be publicly available for researchers to use. We have implemented a real-world multi-site Inter-net of Things (IoT) deployment, comprising 25 school buildings across Europe, primarily designed as a foundation for enabling IoT-based energy awareness and sustainability lectures and promoting data-driven energy-saving behaviors. In this work, we present some of the basic aspects to producing datasets from this deployment and discuss its potential uses. We also provide a brief discussion on data derived from a preliminary analysis of thermal comfort-related data produced from this infrastructure.