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 sinkmobility), 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 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 consider sensor networks where the sensor nodes are attached on entities that move in a highly dynamic, heterogeneous manner. To capture this mobility diversity we introduce a new network parameter, the direction-aware mobility
level, which measures how fast and close each mobile node is expected to get to the data destination (the sink). We then provide local, distributed data dissemination protocols
that adaptively exploit the node mobility to improve performance. In particular, "high" mobility is used as a low cost replacement for data dissemination (due to the ferrying of data), while in the case of "low" mobility either a) data propagation redundancy is increased (when highly mobile neighbors exist) or b) long-distance data transmissions are used (when the entire neighborhood is of low mobility) to accelerate data dissemination towards the sink. An extensive performance comparison to relevant methods from
the state of the art demonstrates signicant improvements i.e. latency is reduced by even 4 times while keeping energy dissipation and delivery success 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. We also investigate the case of multiple sinks by suggesting appropriate distributed coordination methods.
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 sinkmobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves
significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.
Abstract: Collecting sensory data using a mobile data sink has been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sinkmobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.
Abstract: Collecting sensory data using a mobile data sink has
been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sinkmobility scheme, that adjusts to local network conditions, such as the surrounding
density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to
produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves
significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.
Abstract: Wireless Sensor Networks consist of a large number of small, autonomous devices, that are able to interact with their inveronment by sensing and collaborate to fulfill their tasks, as, usually, a single node is incapable of doing so; and they use wireless communication to enable this collaboration. Each device has limited computational and energy resources, thus a basic issue in the applicastions of wireless sensor networks is the low energy consumption and hence, the maximization of the network lifetime.
The collected data is disseminated to a static control point – data sink in the network, using node to node - multi-hop data propagation. However, sensor devices consume significant amounts of energy in addition to increased implementation complexity, since a routing protocol is executed. Also, a point of failure emerges in the area near the control center where nodes relay the data from nodes that are farther away. Recently, a new approach has been developed that shifts the burden from the sensor nodes to the sink. The main idea is that the sink has significant and easily replenishable energy reserves and can move inside the area the sensor network is deployed, in order to acquire the data collected by the sensor nodes at very low energy cost. However, the need to visit all the regions of the network may result in large delivery delays.
In this work we have developed protocols that control the movement of the sink in wireless sensor networks with non-uniform deployment of the sensor nodes, in order to succeed an efficient (with respect to both energy and latency) data collection. More specifically, a graph formation phase is executed by the sink during the initialization: the network area is partitioned in equal square regions, where the sink, pauses for a certain amount of time, during the network traversal, in order to collect data.
We propose two network traversal methods, a deterministic and a random one. When the sink moves in a random manner, the selection of the next area to visit is done in a biased random manner depending on the frequency of visits of its neighbor areas. Thus, less frequently visited areas are favored. Moreover, our method locally determines the stop time needed to serve each region with respect to some global network resources, such as the initial energy reserves of the nodes and the density of the region, stopping for a greater time interval at regions with higher density, and hence more traffic load. In this way, we achieve accelerated coverage of the network as well as fairness in the service time of each region.Besides randomized mobility, we also propose an optimized deterministic trajectory without visit overlaps, including direct (one-hop) sensor-to-sink data transmissions only.
We evaluate our methods via simulation, in diverse network settings and comparatively to related state of the art solutions. Our findings demonstrate significant latency and energy consumption improvements, compared to previous research.
Abstract: 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: 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 sinkmobility), 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: Data propagation in wireless sensor
networks is usually performed as a multihop process.
Thus,
To deliver a single
message, the resources of many sensor nodes are used and
a lot of energy is spent.
Recently, a novel approach is catching momentum because of important applications;
that of having a mobile sink move inside the network area and collect
the data with low energy cost.
Here we extend this line of research by proposing and evaluating three new protocols.
Our protocols are novel in
a) investigating the impact of having {many} mobile sinks
b) in weak models with restricted mobility, proposing and evaluating
a mix of static and mobile sinks and c) proposing a distributed
protocol that tends to {equally spread the sinks} in the network to
further improve performance.
Our protocols are simple, based on randomization and assume locally
obtainable information. We perform an extensive evaluation via simulation; our
findings demonstrate that our solutions scale very well with respect to the number of sinks
and significantly reduce energy consumption and delivery delay.
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.