10,640 research outputs found
RESOURCE AND ENVIRONMENT AWARE SENSOR COMMUNICATIONS: FRAMEWORK, OPTIMIZATION, AND APPLICATIONS
Recent advances in low power integrated circuit devices,
micro-electro-mechanical system (MEMS) technologies, and
communications technologies have made possible the deployment of
low-cost, low power sensors that can be integrated to form wireless
sensor networks (WSN). These wireless sensor networks have vast
important applications, i.e.: from battlefield surveillance system
to modern highway and industry monitoring system; from the emergency
rescue system to early forest fire detection and the very
sophisticated earthquake early detection system. Having the broad
range of applications, the sensor network is becoming an integral
part of human lives. However, the success of sensor networks
deployment depends on the reliability of the network itself. There
are many challenging problems to make the deployed network more
reliable. These problems include but not limited to extending
network lifetime, increasing each sensor node throughput, efficient
collection of information, enforcing nodes to collaboratively
accomplish certain network tasks, etc. One important aspect in
designing the algorithm is that the algorithm should be completely
distributed and scalable. This aspect has posed a tremendous
challenge in designing optimal algorithm in sensor networks.
This thesis addresses various challenging issues encountered in
wireless sensor networks. The most important characteristic in
sensor networks is to prolong the network lifetime. However, due to
the stringent energy requirement, the network requires highly energy
efficient resource allocation. This highly energy-efficient resource
allocation requires the application of an energy awareness system.
In fact, we envision a broader resource and environment aware
optimization in the sensor networks. This framework reconfigures the
parameters from different communication layers according to its
environment and resource. We first investigate the application of
online reinforcement learning in solving the modulation and transmit
power selection. We analyze the effectiveness of the learning
algorithm by comparing the effective good throughput that is
successfully delivered per unit energy as a metric. This metric
shows how efficient the energy usage in sensor communication is. In
many practical sensor scenarios, maximizing the energy efficient in
a single sensor node may not be sufficient. Therefore, we continue
to work on the routing problem to maximize the number of delivered
packet before the network becomes useless. The useless network is
characterized by the disintegrated remaining network. We design a
class of energy efficient routing algorithms that explicitly takes
the connectivity condition of the remaining network in to account.
We also present the distributed asynchronous routing implementation
based on reinforcement learning algorithm. This work can be viewed
as distributed connectivity-aware energy efficient routing. We then
explore the advantages obtained by doing cooperative routing for
network lifetime maximization. We propose a power allocation in the
cooperative routing called the maximum lifetime power allocation.
The proposed allocation takes into account the residual energy in
the nodes when doing the cooperation. In fact, our criterion lets
the nodes with more energy to help more compared to the nodes with
less energy. We continue to look at the problem of cooperation
enforcement in ad-hoc network. We show that by combining the
repeated game and self learning algorithm, a better cooperation
point can be obtained. Finally, we demonstrate an example of
channel-aware application for multimedia communication. In all case
studies, we employ optimization scheme that is equipped with the
resource and environment awareness. We hope that the proposed
resource and environment aware optimization framework will serve as
the first step towards the realization of intelligent sensor
communications
Efficient Wireless Security Through Jamming, Coding and Routing
There is a rich recent literature on how to assist secure communication
between a single transmitter and receiver at the physical layer of wireless
networks through techniques such as cooperative jamming. In this paper, we
consider how these single-hop physical layer security techniques can be
extended to multi-hop wireless networks and show how to augment physical layer
security techniques with higher layer network mechanisms such as coding and
routing. Specifically, we consider the secure minimum energy routing problem,
in which the objective is to compute a minimum energy path between two network
nodes subject to constraints on the end-to-end communication secrecy and
goodput over the path. This problem is formulated as a constrained optimization
of transmission power and link selection, which is proved to be NP-hard.
Nevertheless, we show that efficient algorithms exist to compute both exact and
approximate solutions for the problem. In particular, we develop an exact
solution of pseudo-polynomial complexity, as well as an epsilon-optimal
approximation of polynomial complexity. Simulation results are also provided to
show the utility of our algorithms and quantify their energy savings compared
to a combination of (standard) security-agnostic minimum energy routing and
physical layer security. In the simulated scenarios, we observe that, by
jointly optimizing link selection at the network layer and cooperative jamming
at the physical layer, our algorithms reduce the network energy consumption by
half
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Multiflow Transmission in Delay Constrained Cooperative Wireless Networks
This paper considers the problem of energy-efficient transmission in
multi-flow multihop cooperative wireless networks. Although the performance
gains of cooperative approaches are well known, the combinatorial nature of
these schemes makes it difficult to design efficient polynomial-time algorithms
for joint routing, scheduling and power control. This becomes more so when
there is more than one flow in the network. It has been conjectured by many
authors, in the literature, that the multiflow problem in cooperative networks
is an NP-hard problem. In this paper, we formulate the problem, as a
combinatorial optimization problem, for a general setting of -flows, and
formally prove that the problem is not only NP-hard but it is
inapproxmiable. To our knowledge*, these results provide
the first such inapproxmiablity proof in the context of multiflow cooperative
wireless networks. We further prove that for a special case of k = 1 the
solution is a simple path, and devise a polynomial time algorithm for jointly
optimizing routing, scheduling and power control. We then use this algorithm to
establish analytical upper and lower bounds for the optimal performance for the
general case of flows. Furthermore, we propose a polynomial time heuristic
for calculating the solution for the general case and evaluate the performance
of this heuristic under different channel conditions and against the analytical
upper and lower bounds.Comment: 9 pages, 5 figure
Algorithmic Aspects of Energy-Delay Tradeoff in Multihop Cooperative Wireless Networks
We consider the problem of energy-efficient transmission in delay constrained
cooperative multihop wireless networks. The combinatorial nature of cooperative
multihop schemes makes it difficult to design efficient polynomial-time
algorithms for deciding which nodes should take part in cooperation, and when
and with what power they should transmit. In this work, we tackle this problem
in memoryless networks with or without delay constraints, i.e., quality of
service guarantee. We analyze a wide class of setups, including unicast,
multicast, and broadcast, and two main cooperative approaches, namely: energy
accumulation (EA) and mutual information accumulation (MIA). We provide a
generalized algorithmic formulation of the problem that encompasses all those
cases. We investigate the similarities and differences of EA and MIA in our
generalized formulation. We prove that the broadcast and multicast problems
are, in general, not only NP hard but also o(log(n)) inapproximable. We break
these problems into three parts: ordering, scheduling and power control, and
propose a novel algorithm that, given an ordering, can optimally solve the
joint power allocation and scheduling problems simultaneously in polynomial
time. We further show empirically that this algorithm used in conjunction with
an ordering derived heuristically using the Dijkstra's shortest path algorithm
yields near-optimal performance in typical settings. For the unicast case, we
prove that although the problem remains NP hard with MIA, it can be solved
optimally and in polynomial time when EA is used. We further use our algorithm
to study numerically the trade-off between delay and power-efficiency in
cooperative broadcast and compare the performance of EA vs MIA as well as the
performance of our cooperative algorithm with a smart noncooperative algorithm
in a broadcast setting.Comment: 12 pages, 9 figure
- …