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 energy efficiency and high fault-tolerance since these long range planning phases allow to bypass obstacles (where no sensors are available) or faulty sensors (that have been disabled due to power failure or other natural events).
Abstract: 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: 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 energy efficiency 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 energy efficiency and network lifetime.
Abstract: In this work we present the architecture and implementation of WebDust, a software platform for managing multiple, heterogeneous (both in terms of software and hardware), geographically disparate sensor networks. We describe in detail the main concepts behind its design, and basic aspects of its implementation, including the services provided to end-users and developers. WebDust uses a peer-to-peer substrate, based on JXTA, in order to unify multiple sensor networks installed in various geographic areas. We aim at providing a software framework that will permit developers to deal with the new and critical aspects that networks of sensors and tiny devices bring into global computing, and to provide a coherent set of high level services, design rules and technical recommendations, in order to be able to develop the envisioned applications of global sensor networks. Furthermore, we give an overview of a deployed distributed testbed, consisting of a total 56 nodes and describing in more detail two specific testbed sites and the integration of the related software and hardware technologies used for its operation with our platform. Finally, we describe the design and implementation of an interface option provided to end-users, based on the popular Google Earth application.
Abstract: We study the problem of data propagation in sensor networks,
comprised of a large number of very small and low-cost nodes,
capable of sensing, communicating and computing. The distributed
co-operation of such nodes may lead to the accomplishment of large
sensing tasks, having useful applications in practice. We present
a new protocol for data propagation towards a control center
(``sink") that avoids flooding by probabilistically favoring
certain (``close to optimal") data transmissions.
This protocol is very simple to implement in sensor devices
and operates under total absence
of co-ordination between sensors. We consider a network model of randomly deployed sensors of sufficient density.
As shown by a geometry analysis,
the protocol is correct, since it always propagates data
to the sink, under ideal network conditions (no failures). Using
stochastic processes, we show that the protocol is very energy efficient. Also, when part of the network is inoperative, the
protocol manages to propagate data very close to the sink, thus in
this sense it is robust. We finally present and discuss
large-scale experimental findings validating the analytical
results.
Abstract: We study the problem of data propagation in sensor networks,
comprised of a large number of very small and low-cost nodes,
capable of sensing, communicating and computing. The distributed
co-operation of such nodes may lead to the accomplishment of large
sensing tasks, having useful applications in practice. We present a new protocol for data propagation towards a control center ("sink") that avoids flooding by probabilistically favoring certain ("close to optimal") data transmissions. Motivated by certain applications and also as a starting point for a rigorous analysis, we study here lattice-shaped sensor networks. We however show that this lattice shape emerges even in randomly deployed sensor networks of sufficient sensor density. Our work is inspired and builds upon the directed diffusion paradigm.
This protocol is very simple to implement in sensor devices, uses only local information and operates under total absence of co-ordination between sensors. We consider a network model of randomly deployed sensors of sufficient density. As shown by a geometry analysis, the protocol is correct, since it always propagates data to the sink, under ideal network conditions (no failures). Using stochastic processes, we show that the protocol is very energy efficient. Also, when part of the network is inoperative, the protocol manages to propagate data very close to the sink, thus in this sense it is robust. We finally present and discuss large-scale experimental findings validating the analytical results.
Abstract: As water supplies become scarce and polluted, there is an urgent
need to irrigate more efficiently in order to optimize water
use. In this paper, we present a WSN based, smart homeirrigation
system that consists of heterogeneous motes, special
sensors and actuators. The system is fully adaptive not
only to environmental conditions but also to the specific water
needs that different plants may have. This way, it manages
to perform efficient home irrigation, while it provides
an IPv6-capable managing system.
Abstract: In this paper we present a platform for developing mobile, locative and collaborative distributed games comprised of small programmable object technologies (e.g., wireless sensor networks) and traditional networked processors.
The platform is implemented using a combination of JAVA
Standard and Mobile editions, targeting also mobile phones
that have some kind of sensors installed. We briefly present
the architecture of our platform and demonstrate its capabilities by reporting two pervasive multiplayer games. The key
characteristic of these games is that players interact with each
other and their surrounding environment by moving, running
and gesturing as a means to perform game related actions, using small programmable object technologies.
Abstract: We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually “learns” the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.
Abstract: We investigate the problem of efficient data collection in wireless sensor networks where both the sensors and the sink move. We especially study the important, realistic case where the spatial distribution of sensors is non-uniform and their mobility is diverse and dynamic. The basic idea of our protocol is for the sink to benefit of the local information that sensors spread in the network as they move, in order
to extract current local conditions and accordingly adjust its trajectory. Thus, sensory motion anyway present in the network serves as a low cost replacement of network information propagation. In particular, we investigate two variations of our method: a)the greedy motion of the sink towards the region of highest density each time and b)taking into account the aggregate density in wider network regions. An extensive comparative evaluation to relevant data collection methods (both randomized and optimized deterministic), demonstrates that our approach achieves significant performance gains, especially in non-uniform placements (but also in uniform ones). In fact, the greedy version of our approach is more suitable in networks where the concentration regions appear in a spatially balanced manner, while the aggregate scheme is more appropriate in networks where the concentration areas are geographically correlated.
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: Wireless Sensor Networks are by nature highly dynamic and communication between sensors is completely ad hoc, especially when mobile devices are part of the setup. Numerous protocols and applications proposed for such networks
operate on the assumption that knowledge of the neighborhood is a priori available to all nodes. As a result, WSN deployments need to use or implement from scratch a neighborhood discovery mechanism. In this work we present a new protocol based on adaptive periodic beacon exchanges. We totally avoid continuous beaconing by adjusting the rate of broadcasts using the concept of consistency over the understanding of neighborhood that nearby devices share. We propose, implement and evaluate our adaptive neighborhood discovery protocol over our experimental testbed and using large scale simulations. Our results indicate that the
new protocol operates more eciently than existing reference implementations while it provides valid information to applications that use it. Extensive performance evaluation indicates that it successfully reduces generated network traffic by 90% and increases network lifetime by 20% compared to existing mechanisms that rely on continuous beaconing.
Abstract: We study the problem of secure routing in wireless sensor networks where the sensors and the sink can move during the execution of remote monitoring applications and communication is not necessarily directed towards the sink. We present a new routing protocol that builds upon a collection of mechanisms so that the integrity and confidentiality of the information reported to the controlling authorities is secured. The mechanisms are simple to implement, rely only on local information and require O(1) storage per sensor. The protocol adapts to mobility and security challenges that may arise throughout the execution of the application. We take special care for wireless sensor networks that monitor dynamically changing environments and applications that require its operation for extended periods of time. APSR can detect when the current network conditions are about to change and becomes ready for adaption to the new conditions. We demonstrate how to deal with inside and outside attacks even when the network is adapting to internal and/or external events.
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 investigate the problem of ecient wireless energy recharging in Wireless Rechargeable Sensor Networks (WRSNs). In
such networks a special mobile entity (called the Mobile Charger) traverses the network and wirelessly replenishes the energy
of sensor nodes. In contrast to most current approaches, we envision methods that are distributed, adaptive and use limited
network information. We propose three new, alternative protocols for ecient recharging, addressing key issues which we
identify, most notably (i) to what extent each sensor should be recharged (ii) what is the best split of the total energy between
the charger and the sensors and (iii) what are good trajectories the MC should follow. One of our protocols (
LRP
) performs
some distributed, limited sampling of the network status, while another one (
RTP
) reactively adapts to energy shortage alerts
judiciously spread in the network. As detailed simulations demonstrate, both protocols signi cantly outperform known state
of the art methods, while their performance gets quite close to the performance of the global knowledge method (
GKP
) we
also provide, especially in heterogeneous network deployments.
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 energy efficiency 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 energy efficiency 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: We investigate the problem of efficient data collection in wireless sensor networks where both the sensors and the sink move. We especially study the important, realistic case where the spatial distribution of sensors is non-uniform and their mobility is diverse and dynamic. The basic idea of our protocol is for the sink to benefit of the local information that sensors spread in the network as they move, in order to extract current local conditions and accordingly adjust its trajectory. Thus, sensory motion anyway present in the network serves as a low cost replacement of network information propagation. In particular, we investigate two variations of our method: a) the greedy motion of the sink towards the region of highest density each time and b) taking into account the aggregate density in wider network regions. An extensive comparative evaluation to relevant data collection methods (both randomized and optimized deterministic), demonstrates that our approach achieves significant performance gains, especially in non-uniform placements (but also in uniform ones). In fact, the greedy version of our approach is more suitable in networks where the concentration regions appear in a spatially balanced manner, while the aggregate scheme is more appropriate in networks where the concentration areas are geographically correlated. We also investigate the case of multiple sinks by suggesting appropriate distributed coordination methods.
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: Wireless Sensor Networks are complex systems consisting of a number of relatively simple autonomous sensing devices spread on a geographical area. The peculiarity of these devices lies on the constraints they face in relation to their energy reserves and their computational, storage and communication capabilities. The utility of these sensors is to measure certain environmental conditions and to detect critical events in relation to these measurements. Those events thereupon have to be reported to a specific central station namely the “sink”. This data propagation generally has the form of a hop-by-hop transmission. In this framework we work on distributed data propagation protocols which are taking into account the energy reserves of the sensors. In particular following the work of Chatzigiannakis et al. on the Probabilistic Forwarding Protocol (PFR) we present the distributed probabilistic protocol EFPFR, which favors transmission from the less depleted sensors in addition to favor transmissions close to the “optimal line”. This protocol is simple and relies only on local information for propagation decisions. Its main goal is to limit the total amount of energy dissipated per event and therefore to extend the network’s operation duration.
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 energy efficiency signi cantly 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: Wireless sensor networks are comprised of a vast number of devices, situated in an area of interest that self organize in a structureless network, in order to monitor/record/measure an environmental variable or phenomenon and subsequently to disseminate the data to the control center.
Here we present research focused on the development, simulation and evaluation of energy efficient algorithms, our basic goal is to minimize the energy consumption. Despite technology advances, the problem of energy use optimization remains valid since current and emerging hardware solutions fail to solve it.
We aim to reduce communication cost, by introducing novel techniques that facilitate the development of new algorithms. We investigated techniques of distributed adaptation of the operations of a protocol by using information available locally on every node, thus through local choices we improve overall performance. We propose techniques for collecting and exploiting limited local knowledge of the network conditions. In an energy efficient manner, we collect additional information which is used to achieve improvements such as forming energy efficient, low latency and fault tolerant paths to route data. We investigate techniques for managing mobility in networks where movement is a characteristic of the control center as well as the sensors. We examine methods for traversing and covering the network field based on probabilistic movement that uses local criteria to favor certain areas.
The algorithms we develop based on these techniques operate a) at low level managing devices, b) on the routing layer and c) network wide, achieving macroscopic behavior through local interactions. The algorithms are applied in network cases that differ in density, node distribution, available energy and also in fundamentally different models, such as under faults, with incremental node deployment and mobile nodes. In all these settings our techniques achieve significant gains, thus distinguishing their value as tools of algorithmic design.
Abstract: We here present Fun in Numbers (FinN), a framework for developing pervasive applications and interactive installations for entertainment and educational purposes. Using ad hoc mobile wireless sensor network nodes as the enabling devices, FinN allows for the quick prototyping of applications that utilize input from multiple physical sources (sensors and other means of interfacing), by o ffering a set of programming templates and services, such as topology discovery, localization and synchronization, that hide the underlying complexity. We present the target application domains of FinN, along with a set of multiplayer games and interactive installations. We describe the overall architecture of our platform
and discuss some key implementation issues of the application domains. Finally, we present the experience gained by deploying the applications developed with our platform.
Abstract: We 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: In this thesis we investigate the problems of data routing and data collection in wireless sensor networks characterised by intense and higly diverse mobility. We propose a set of protocols that takes exploits the motion of the sensors in order to inform the sink about the network topology. We experimentally evaluate these protocolls in a wide range of topologies, including both homogeneous and heterogeneous ones.
We also investigate random walks as simple motion strategies for mobile sinks that perform data collection from static WSN's. We propose three new random walks that improve latency compared to already known ones, as well as a new metric called Proximity Variation. This metric captures the different way each random walks traverses the network area.
Abstract: Data collection is usually performed in wireless sensor networks by the sensors
relaying data towards a static control center (sink). Motivated by important
applications (mostly related to ambient intelligence and remote monitoring)
and as a first step towards introducing mobility, we propose the basic
idea of having a sink moving in the network area and collecting
data from sensors. We propose four characteristic mobility patterns
for the sink along with different data collection strategies. Through a
detailed simulation study, we evaluate several important performance properties of
each approach. Our findings demonstrate that by taking advantage
of the sink's mobility and shifting work from sensors to the powerful sink,
we can significantly reduce the energy spent in relaying traffic and thus greatly
extend the lifetime of the network.
Abstract: Through recent technology advances in the eld of wireless energy transmission, Wireless Rechargeable Sensor Networks
(WRSN) have emerged. In this new paradigm for
WSNs a mobile entity called Mobile Charger (MC) traverses
the network and replenishes the dissipated energy of sensors.
In this work we rst provide a formal de nition of the charging
dispatch decision problem and prove its computational
hardness. We then investigate how to optimize the tradeo
s of several critical aspects of the charging process such
as a) the trajectory of the charger, b) the di erent charging
policies and c) the impact of the ratio of the energy
the MC may deliver to the sensors over the total available
energy in the network. In the light of these optimizations,
we then study the impact of the charging process to the
network lifetime for three characteristic underlying routing
protocols; a greedy protocol, a clustering protocol and an
energy balancing protocol. Finally, we propose a Mobile
Charging Protocol that locally adapts the circular trajectory
of the MC to the energy dissipation rate of each sub-region
of the network. We compare this protocol against several
MC trajectories for all three routing families by a detailed
experimental evaluation. The derived ndings demonstrate
signi cant performance gains, both with respect to the no
charger case as well as the di erent charging alternatives; in
particular, the performance improvements include the network
lifetime, as well as connectivity, coverage and energy
balance properties.
Abstract: We study the problem of localizing and tracking multiple moving targets in wireless sensor
networks, from a network design perspective i.e. towards estimating the least possible number
of sensors to be deployed, their positions and operation chatacteristics needed to perform the
tracking task. To avoid an expensive massive deployment, we try to take advantage of
possible coverage ovelaps over space and time, by introducing a novel combinatorial model
that captures such overlaps.
Under this model, we abstract the tracking network design problem by a combinatorial
problem of covering a universe of elements by at least three sets (to ensure that each point in
the network area is covered at any time by at least three sensors, and thus being localized). We
then design and analyze an efficient approximate method for sensor placement and operation,
that with high probability and in polynomial expected time achieves a (log n) approximation
ratio to the optimal solution. Our network design solution can be combined with alternative
collaborative processing methods, to suitably fit different tracking scenaria.
Abstract: We study the problem of localizing and tracking multiple moving targets in wireless sensor networks, from a network design perspective i.e. towards estimating the least possible number of sensors to be deployed, their positions and operation characteristics needed to perform the tracking task. To avoid an expensive massive deployment, we try to take advantage of possible coverage overlaps over space and time, by introducing a novel combinatorial model that captures such overlaps.
Under this model, we abstract the tracking network design problem by a combinatorial problem of covering a universe of elements by at least three sets (to ensure that each point in the network area is covered at any time by at least three sensors, and thus being localized). We then design and analyze an efficient approximate method for sensor placement and operation, that with high probability and in polynomial expected time achieves a {\`E}(logn) approximation ratio to the optimal solution. Our network design solution can be combined with alternative collaborative processing methods, to suitably fit different tracking scenarios.
Abstract: We study the important problem of tracking moving
targets in wireless sensor networks. We try to overcome the
limitations of standard state of the art tracking methods based on
continuous location tracking, i.e. the high energy dissipation and
communication overhead imposed by the active participation of
sensors in the tracking process and the low scalability, especially
in sparse networks. Instead, our approach uses sensors in a
passive way: they only record and judiciously spread information
about observed target presence in their vicinity; this information
is then used by the (powerful) tracking agent to locate the target
by just following the traces left at sensors. Our protocol is greedy,
local, distributed, energy efficient and very successful, in the
sense that (as shown by extensive simulations) the tracking agent
manages to quickly locate and follow the target; also, we achieve
good trade-offs between the energy dissipation and latency.
Abstract: We study the problem of energy-balanced data propagation
in wireless sensor networks. The energy balance property
guarantees that the average per sensor energy dissipation
is the same for all sensors in the network, during
the entire execution of the data propagation protocol. This
property is important since it prolongs the network¢s lifetime
by avoiding early energy depletion of sensors.
We propose a new algorithm that in each step decides
whether to propagate data one-hop towards the final destination
(the sink), or to send data directly to the sink. This
randomized choice balances the (cheap) one-hop transimssions
with the direct transimissions to the sink, which are
more expensive but “bypass” the sensors lying close to the
sink. Note that, in most protocols, these close to the sink
sensors tend to be overused and die out early.
By a detailed analysis we precisely estimate the probabilities
for each propagation choice in order to guarantee
energy balance. The needed estimation can easily be performed
by current sensors using simple to obtain information.
Under some assumptions, we also derive a closed form
for these probabilities.
The fact (shown by our analysis) that direct (expensive)
transmissions to the sink are needed only rarely, shows that
our protocol, besides energy-balanced, is also energy efficient.
Abstract: We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors in the network, during the entire execution of the data propagation protocol. This property is important since it prolongs the network¢:s lifetime by avoiding early energy depletion of sensors.
We propose a new algorithm that in each step decides whether to propagate data one-hop towards the final destination (the sink), or to send data directly to the sink. This randomized choice balances the (cheap) one-hop transimssions with the direct transimissions to the sink, which are more expensive but “bypass” the sensors lying close to the sink. Note that, in most protocols, these close to the sink sensors tend to be overused and die out early.
By a detailed analysis we precisely estimate the probabilities for each propagation choice in order to guarantee energy balance. The needed estimation can easily be performed by current sensors using simple to obtain information. Under some assumptions, we also derive a closed form for these probabilities.
The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy-balanced, is also energy efficient.
Abstract: We study the problem of fast and energy-efficient
data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory
mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually ”learns”
the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.
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: Fun in Numbers (FinN) is a platform for developing and playing mobile, locative and collaborative distributed games
using wireless sensors. Using FinN, a very large and diverse set of games can be enhanced, by maximizing the on-game
experience and collecting statistics for off-line, web-based view. At the same time the essence of such games remains the
same: fun in large numbers, in every place and at any time. FinN is implemented using a combination of JAVA Standard
and Mobile editions, while on the hardware part we use wireless sensor devices, called Sun SPOTs. In the future, mobile
phones that have some kind of sensors embedded, or other custom devices can be used for the same purpose. We report a
number of examples of games created with FinN and briefly present the architecture of our platform.
Abstract: The possibilities offered by utilizing sensors and pervasive computing technologies for creating large-scale multiplayer games are discussed in this chapter. Such game installations constitute a new social form of play taking place in public spaces. A main characteristic is the need to scale to a large number of users and engage players located simultaneously in dispersed areas, thus connected both on a local and Internet level. Fun in Numbers is a platform for developing and playing mobile, locative and collaborative distributed games and interactive installations, based on the participation of large numbers of people and their movement in the physical space. Players interact with each other using a wide range of hardware devices that are either generic (smartphones) or specific (sensor devices A set of related fundamental issues drawn upon the experience from several public events, where the FinN platform supported as many as 50 local users at the same time, is hereby presented.
Abstract: Typical sensor nodes are resource constrained devices containing user level applications, operating system components, and device drivers in a single address space, with no form of memory protection. A malicious user could easily capture a node and tamper the applications running, in order to perform different types of attacks. In this paper, we propose a remote live forensics protection architecture that prevents the execution of tampered software while alarming the owners of the sensors network. Using sandboxing to restrict application memory accesses within the address space and forensic techniques to validate the authenticity of the running applications we prevent malicious code from being executed while specifying the intrusion.
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 work, we showcase a set of implemented multiplayer games and interactive installations based on Fun in Numbers (FINN). FinN allows the quick prototyping of applications that utilize input from multiple physical sources (sensors and other means of interfacing), by offering a set of programming templates and services, such as proximity, localization and synchronization, that hide the underlying complexity.
Abstract: We propose local mechanisms for efficiently marking the broader network region around obstacles, for data propagation to early enough avoid them towards near-optimal routing paths. In particular, our methods perform an online identification of sensors lying near obstacle boundaries,which then appropriately emit beacon messages in the network towards establishing efficient obstacle avoidance paths. We provide a variety of beacon dissemination schemes that satisfy different trade-offs between protocol overhead and performance. Compared to greedy, face routing and trustbased methods in the state of the art, our methods achieve significantly shorter propagation paths, while introducing much lower overhead and converging faster to near-optimality.
Abstract: We propose local mechanisms for efficiently marking the
broader network region around obstacles, for data propagation
to early enough avoid them towards near-optimal
routing paths. In particular, our methods perform an online
identification of sensors lying near obstacle boundaries,
which then appropriately emit beacon messages in the network
towards establishing efficient obstacle avoidance paths.
We provide a variety of beacon dissemination schemes that
satisfy different trade-offs between protocol overhead and
performance. Compared to greedy, face routing and trustbased
methods in the state of the art, our methods achieve
significantly shorter propagation paths, while introducing
much lower overhead and converging faster to near-optimality.
Abstract: We investigate the problem of how to achieve energy balanced data propagation in distributed wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors in the network, throughout the execution of the data propagation protocol. This property is crucial for prolonging the network lifetime, by avoiding early energy depletion of sensors.
We survey representative solutions from the state of the art. We first present a basic algorithm that in each step probabilistically decides whether to propagate data one-hop towards the final destination (the sink), or to send it directly to the sink. This randomized choice trades-off the (cheap, but slow) one-hop transmissions with the direct transmissions to the sink, which are more expensive but bypass the bottleneck region around the sink and propagate data fast. By a detailed analysis using properties of stochastic processes and recurrence relations we precisely estimate (even in closed form) the probability for each propagation option necessary for energy balance.
The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy balanced, is also energy efficient. We then enhance this basic result by surveying some recent findings including a generalized algorithm and demonstrating the optimality of this two-way probabilistic data propagation, as well as providing formal proofs of the energy optimality of the energy balance property.
Abstract: In this paper, we describe the implementation of
applying and testing the ”Lightweight Target Tracking using
Passive Traces algorithm” [1] on a FIRE wireless sensors testbed
located in the Theoretical Computer Science/Sensors Lab in
Geneva, Switzerland. We provide information about the hardware
installation and configuration, the changes we did to the
algorithm to adapt it to a real testbed as well as the tools we
implemented to operate the network and receive feedback from
the algorithm’s operation. Finally, we discuss the performance
evaluation findings of our implementation.
Abstract: — This work discusses PatrasSense, a system utilizing a range of technologies for monitoring environmental conditions, based on the concept of participatory monitoring. We discuss here the main issues in this category of applications, and its relation to the Real World Internet vision. We are interested in the use of sensors for collecting data related to the quality of life in urban areas, making it publicly available. We describe a possible architecture for such a system and a plan for deployment in the city of Patras, Greece. We have thus far implemented a set of features and conducted an initial set of small-scale experiments using sensor devices mounted on vehicles. We report on the respective results
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 energy efficiency.
Two protocols for data propagation are studied: the first
creates probabilistically optimized redundant data transmissions
to combine energy efficiency 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
energy efficiency.
Abstract: Wireless sensor and actor networks are comprised of a large number of small, fully autonomous computing, communication, sensing and actuation devices, with very restricted energy and computing capabilities. Such devices co-operate to accomplish a large sensing and acting task. Sensors gather information for an event in the physical world and notify the actors that perform appropriate actions by making a decision on receipt of the sensed information. Such networks can be very useful in practice i.e.~in the local detection of remote crucial events and the propagation of relevant data to decision
centers that perform appropriate actions upon the environment, thus realizing sensing and acting from a distance.
In this work we present a communication protocol that enables scalable, energy efficient and fault tolerant coordination while allowing to prioritize sensing tasks in situated wireless sensor and actor networks. The sensors react locally on environment and context changes and interact with each other in order to adjust the performance of the network in terms of energy, latency and success rate on a per-task basis. To deal with the increased complexity of such large-scale systems, our protocol pulls some additional knowledge about the network in order to subsequently improve data forwarding towards the actors.
We implement and evaluate the protocol using large scale simulation, showing its suitability in networks where sensor to actor and actor to actor coordination are important for accomplishing tasks of different priorities.
Abstract: In this work we focus on the energy efficiency 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 energy efficiency: 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 present three new distributed, probabilistic data propagation protocols for Wireless Sensor Networks which aim at maximizing the network's operational life and improve its performance. The keystone of these protocols' design is fairness which declares that fair portions of network's work load should be assigned to each node, depending on their role in the system. All the three protocols, EFPFR, MPFR and TWIST, emerged from the study of the rigorously analyzed protocol PFR. Its design elements were identified and improvements were suggested and incorporated into the introduced protocols. The experiments conducted show that our proposals manage to improve PFR's performance in terms of success rate, total amount of energy saved, number of alive sensors and standard deviation of the energy left. Indicatively we note that while PFR's success rate is 69.5%, TWIST is achieving 97.5% and its standard deviation of energy is almost half of that of PFR.
Abstract: Raising awareness among young people and changing their behavior and habits concerning energy usage and the environment is key to achieving a sustainable planet. The goal to address the global climate problem requires informing the population on their roles in mitigation actions and adaptation of sustainable behaviors. Addressing climate change and achieve ambitious energy and climate targets requires a change in citizen behavior and consumption practices. IoT sensing and related scenario and practices, which address school children via discovery, gamification, and educational activities, are examined in this paper. Use of seawater sensors in STEM education, that has not previously been addressed, is included in these educational scenaria.
Abstract: Embedded computing devices dominate our everyday activities, from cell phones to wireless sensors that collect and process data for various applications. Although desktop and high-end server security seems to be under control by the use of current security technology, securing the low-end embedded computing systems is a difficult long-term problem. This is mainly due to the fact that the embedded systems are constrained by their operational environment and the limited resources they are equipped with. Recent research activities focus on the deployment of lightweight cryptographic algorithms and security protocols that are well suited to the limited resources of low-end embedded systems. Elliptic Curve Cryptography (ECC) offers an interesting alternative to the classical public key cryptography for embedded systems (e.g., RSA and ElGamal), since it uses smaller key sizes for achieving the same security level, thus making ECC an attractive and efficient alternative for deployment in embedded systems. In this chapter, the processing requirements and architectures for secure network access, communication functions, storage, and high availability of embedded devices are discussed. In addition, ECC-based state-of-the-art lightweight cryptographic primitives for the deployment of security protocols in embedded systems that fulfill the requirements are presented.
Abstract: In wireless sensor networks data propagation is usually
performed by sensors transmitting data towards a static control center (sink). Inspired by important applications (mostly related to ambient intelligence) and as a first step towards introducing mobility, we propose the idea of having a sink moving in the network area and collecting data from sensors. We propose four characteristic mobility patterns for the sink along with different data collection strategies. Through a detailed simulation study, we evaluate several important performance properties of each protocol. Our findings demonstrate that by taking advantage of the sink's mobility, we can significantly reduce the energy spent in relaying traffic and thus greatly extend the lifetime of the network.
Abstract: One of the most eminent problems in sensor networks is the
routing of data to a central destination in a robust and e±cient manner.
In this work we propose a new scalable protocol for propagating infor-
mation about a sensed event towards a receiving center. Using only local
information and total absence of coordination between sensors our pro-
tocol achieves to propagate the sensed data to a receiving center by ac-
tivating only those nodes that lie very close to the optimal path between
the source of the event and the destination, resulting in low activation of
the network's sensors. Thus the protocol is very energy e±cient. Further-
more, our protocol is robust as it manages to propagate the information
even when sensors fail with certain probability.
Abstract: We consider the important problem of energy balanced data propagation in wireless sensor networks and we extend and generalize
previous works by allowing adaptive energy assignment. We consider the data gathering problem where data are generated by the sensors and
must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering
the data to a neighbouring sensor. Direct and neighbouring transmissions require different levels of energy consumption. Basically, the protocols balance the energy consumption among the sensors by computing the adequate ratios of direct and neighbouring transmissions. An abstract model of energy dissipation as a random walk is proposed, along with rigorous performance analysis techniques. Two efficient distributed algorithms are presented and analysed, by both rigorous means and simulation.
The first one is easy to implement and fast to execute. The protocol assumes that sensors know a-priori the rate of data they generate.
The sink collects and processes all these information in order to compute the relevant value of the protocol parameter. This value is transmitted
to the sensors which individually compute their optimal ratios of direct and neighbouring transmissions. The second protocol avoids the necessary a-priori knowledge of the data rate generated by sensors by inferring the relevant information from the observation of the data paths.
Furthermore, this algorithm is based on stochastic estimation methods and is adaptive to environmental changes.
Abstract: Random scaled sector graphs were introduced as a generalization of random geometric graphs to model networks of sensors using optical communication. In the random scaled sector graph model vertices are placed uniformly at random into the [0, 1]2 unit square. Each vertex i is assigned uniformly at random sector Si, of central angle {\'a}i, in a circle of radius ri (with vertex i as the origin). An arc is present from vertex i to any vertex j, if j falls in Si. In this work, we study the value of the chromatic number ƒ{\^O}(Gn), directed clique number {\`u}(Gn), and undirected clique number {\`u}2 (Gn) for random scaled sector graphs with n vertices, where each vertex spans a sector of {\'a} degrees with radius rn = \~{a}ln n/n. We prove that for values {\'a} < ƒ{\^I}, as n ¨ ‡ w.h.p., ƒ{\^O}(Gn) and {\`u}2 (Gn) are {\`E}(ln n/ln ln n), while {\`u}(Gn) is O(1), showing a clear difference with the random geometric graph model. For {\'a} > ƒ{\^I} w.h.p., ƒ{\^O}(Gn) and {\`u}2 (Gn) are {\`E} (ln n), being the same for random scaled sector and random geometric graphs, while {\`u}(Gn) is {\`E}(ln n/ln ln n).
Abstract: Unattended surveillance of public transportation infrastructures that may generate alarms in the case of a destructive event, and provide information on the current status of the infrastructure as well as on the short interval before the event, can empower the Search and Rescue teams with useful information that may save lives increasing their overall effectiveness. This paper presents a novel system that aims at providing a scalable, unattended surveillance system with networked embedded systems, cameras and sensors for public transportations sector.
