90 research outputs found
Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network along a Line
We are motivated by the need, in some applications, for impromptu or
as-you-go deployment of wireless sensor networks. A person walks along a line,
starting from a sink node (e.g., a base-station), and proceeds towards a source
node (e.g., a sensor) which is at an a priori unknown location. At equally
spaced locations, he makes link quality measurements to the previous relay, and
deploys relays at some of these locations, with the aim to connect the source
to the sink by a multihop wireless path. In this paper, we consider two
approaches for impromptu deployment: (i) the deployment agent can only move
forward (which we call a pure as-you-go approach), and (ii) the deployment
agent can make measurements over several consecutive steps before selecting a
placement location among them (which we call an explore-forward approach). We
consider a light traffic regime, and formulate the problem as a Markov decision
process, where the trade-off is among the power used by the nodes, the outage
probabilities in the links, and the number of relays placed per unit distance.
We obtain the structures of the optimal policies for the pure as-you-go
approach as well as for the explore-forward approach. We also consider natural
heuristic algorithms, for comparison. Numerical examples show that the
explore-forward approach significantly outperforms the pure as-you-go approach.
Next, we propose two learning algorithms for the explore-forward approach,
based on Stochastic Approximation, which asymptotically converge to the set of
optimal policies, without using any knowledge of the radio propagation model.
We demonstrate numerically that the learning algorithms can converge (as
deployment progresses) to the set of optimal policies reasonably fast and,
hence, can be practical, model-free algorithms for deployment over large
regions.Comment: 29 pages. arXiv admin note: text overlap with arXiv:1308.068
Impromptu Deployment of Wireless Relay Networks: Experiences Along a Forest Trail
We are motivated by the problem of impromptu or as- you-go deployment of
wireless sensor networks. As an application example, a person, starting from a
sink node, walks along a forest trail, makes link quality measurements (with
the previously placed nodes) at equally spaced locations, and deploys relays at
some of these locations, so as to connect a sensor placed at some a priori
unknown point on the trail with the sink node. In this paper, we report our
experimental experiences with some as-you-go deployment algorithms. Two
algorithms are based on Markov decision process (MDP) formulations; these
require a radio propagation model. We also study purely measurement based
strategies: one heuristic that is motivated by our MDP formulations, one
asymptotically optimal learning algorithm, and one inspired by a popular
heuristic. We extract a statistical model of the propagation along a forest
trail from raw measurement data, implement the algorithms experimentally in the
forest, and compare them. The results provide useful insights regarding the
choice of the deployment algorithm and its parameters, and also demonstrate the
necessity of a proper theoretical formulation.Comment: 7 pages, accepted in IEEE MASS 201
Deploy-As-You-Go Wireless Relay Placement: An Optimal Sequential Decision Approach using the Multi-Relay Channel Model
We use information theoretic achievable rate formulas for the multi-relay
channel to study the problem of as-you-go deployment of relay nodes. The
achievable rate formulas are for full-duplex radios at the relays and for
decode-and-forward relaying. Deployment is done along the straight line joining
a source node and a sink node at an unknown distance from the source. The
problem is for a deployment agent to walk from the source to the sink,
deploying relays as he walks, given that the distance to the sink is
exponentially distributed with known mean. As a precursor, we apply the
multi-relay channel achievable rate formula to obtain the optimal power
allocation to relays placed along a line, at fixed locations. This permits us
to obtain the optimal placement of a given number of nodes when the distance
between the source and sink is given. Numerical work suggests that, at low
attenuation, the relays are mostly clustered near the source in order to be
able to cooperate, whereas at high attenuation they are uniformly placed and
work as repeaters. We also prove that the effect of path-loss can be entirely
mitigated if a large enough number of relays are placed uniformly between the
source and the sink. The structure of the optimal power allocation for a given
placement of the nodes, then motivates us to formulate the problem of as-you-go
placement of relays along a line of exponentially distributed length, and with
the exponential path-loss model, so as to minimize a cost function that is
additive over hops. The hop cost trades off a capacity limiting term, motivated
from the optimal power allocation solution, against the cost of adding a relay
node. We formulate the problem as a total cost Markov decision process,
establish results for the value function, and provide insights into the
placement policy and the performance of the deployed network via numerical
exploration.Comment: 21 pages. arXiv admin note: substantial text overlap with
arXiv:1204.432
A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches
Wireless communication networks have been witnessing an unprecedented demand
due to the increasing number of connected devices and emerging bandwidth-hungry
applications. Albeit many competent technologies for capacity enhancement
purposes, such as millimeter wave communications and network densification,
there is still room and need for further capacity enhancement in wireless
communication networks, especially for the cases of unusual people gatherings,
such as sport competitions, musical concerts, etc. Unmanned aerial vehicles
(UAVs) have been identified as one of the promising options to enhance the
capacity due to their easy implementation, pop up fashion operation, and
cost-effective nature. The main idea is to deploy base stations on UAVs and
operate them as flying base stations, thereby bringing additional capacity to
where it is needed. However, because the UAVs mostly have limited energy
storage, their energy consumption must be optimized to increase flight time. In
this survey, we investigate different energy optimization techniques with a
top-level classification in terms of the optimization algorithm employed;
conventional and machine learning (ML). Such classification helps understand
the state of the art and the current trend in terms of methodology. In this
regard, various optimization techniques are identified from the related
literature, and they are presented under the above mentioned classes of
employed optimization methods. In addition, for the purpose of completeness, we
include a brief tutorial on the optimization methods and power supply and
charging mechanisms of UAVs. Moreover, novel concepts, such as reflective
intelligent surfaces and landing spot optimization, are also covered to capture
the latest trend in the literature.Comment: 41 pages, 5 Figures, 6 Tables. Submitted to Open Journal of
Communications Society (OJ-COMS
Cross-layer latency-aware and -predictable data communication
Cyber-physical systems are making their way into more aspects of everyday life. These systems are increasingly distributed and hence require networked communication to coordinatively fulfil control tasks. Providing this in a robust and resilient manner demands for latency-awareness and -predictability at all layers of the communication and computation stack. This thesis addresses how these two latency-related properties can be implemented at the transport layer to serve control applications in ways that traditional approaches such as TCP or RTP cannot. Thereto, the Predictably Reliable Real-time Transport (PRRT) protocol is presented, including its unique features (e.g. partially reliable, ordered, in-time delivery, and latency-avoiding congestion control) and unconventional APIs. This protocol has been intensively evaluated using the X-Lap toolkit that has been specifically developed to support protocol designers in improving latency, timing, and energy characteristics of protocols in a cross-layer, intra-host fashion. PRRT effectively circumvents latency-inducing bufferbloat using X-Pace, an implementation of the cross-layer pacing approach presented in this thesis. This is shown using experimental evaluations on real Internet paths. Apart from PRRT, this thesis presents means to make TCP-based transport aware of individual link latencies and increases the predictability of the end-to-end delays using Transparent Transmission Segmentation.Cyber-physikalische Systeme werden immer relevanter für viele Aspekte des Alltages. Sie sind zunehmend verteilt und benötigen daher Netzwerktechnik zur koordinierten Erfüllung von Regelungsaufgaben. Um dies auf eine robuste und zuverlässige Art zu tun, ist Latenz-Bewusstsein und -Prädizierbarkeit auf allen Ebenen der Informations- und Kommunikationstechnik nötig. Diese Dissertation beschäftigt sich mit der Implementierung dieser zwei Latenz-Eigenschaften auf der Transport-Schicht, sodass Regelungsanwendungen deutlich besser unterstützt werden als es traditionelle Ansätze, wie TCP oder RTP, können. Hierzu wird das PRRT-Protokoll vorgestellt, inklusive seiner besonderen Eigenschaften (z.B. partiell zuverlässige, geordnete, rechtzeitige Auslieferung sowie Latenz-vermeidende Staukontrolle) und unkonventioneller API. Das Protokoll wird mit Hilfe von X-Lap evaluiert, welches speziell dafür entwickelt wurde Protokoll-Designer dabei zu unterstützen die Latenz-, Timing- und Energie-Eigenschaften von Protokollen zu verbessern. PRRT vermeidet Latenz-verursachenden Bufferbloat mit Hilfe von X-Pace, einer Cross-Layer Pacing Implementierung, die in dieser Arbeit präsentiert und mit Experimenten auf realen Internet-Pfaden evaluiert wird. Neben PRRT behandelt diese Arbeit transparente Übertragungssegmentierung, welche dazu dient dem TCP-basierten Transport individuelle Link-Latenzen bewusst zu machen und so die Vorhersagbarkeit der Ende-zu-Ende Latenz zu erhöhen
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