11,440 research outputs found
Delay Optimal Event Detection on Ad Hoc Wireless Sensor Networks
We consider a small extent sensor network for event detection, in which nodes
take samples periodically and then contend over a {\em random access network}
to transmit their measurement packets to the fusion center. We consider two
procedures at the fusion center to process the measurements. The Bayesian
setting is assumed; i.e., the fusion center has a prior distribution on the
change time. In the first procedure, the decision algorithm at the fusion
center is \emph{network-oblivious} and makes a decision only when a complete
vector of measurements taken at a sampling instant is available. In the second
procedure, the decision algorithm at the fusion center is \emph{network-aware}
and processes measurements as they arrive, but in a time causal order. In this
case, the decision statistic depends on the network delays as well, whereas in
the network-oblivious case, the decision statistic does not depend on the
network delays. This yields a Bayesian change detection problem with a tradeoff
between the random network delay and the decision delay; a higher sampling rate
reduces the decision delay but increases the random access delay. Under
periodic sampling, in the network--oblivious case, the structure of the optimal
stopping rule is the same as that without the network, and the optimal change
detection delay decouples into the network delay and the optimal decision delay
without the network. In the network--aware case, the optimal stopping problem
is analysed as a partially observable Markov decision process, in which the
states of the queues and delays in the network need to be maintained. A
sufficient statistic for decision is found to be the network-state and the
posterior probability of change having occurred given the measurements received
and the state of the network. The optimal regimes are studied using simulation.Comment: To appear in ACM Transactions on Sensor Networks. A part of this work
was presented in IEEE SECON 2006, and Allerton 201
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Cross-layer design of multi-hop wireless networks
MULTI -hop wireless networks are usually defined as a collection of nodes
equipped with radio transmitters, which not only have the capability to
communicate each other in a multi-hop fashion, but also to route each others’ data
packets. The distributed nature of such networks makes them suitable for a variety of
applications where there are no assumed reliable central entities, or controllers, and
may significantly improve the scalability issues of conventional single-hop wireless
networks.
This Ph.D. dissertation mainly investigates two aspects of the research issues
related to the efficient multi-hop wireless networks design, namely: (a) network
protocols and (b) network management, both in cross-layer design paradigms to
ensure the notion of service quality, such as quality of service (QoS) in wireless mesh
networks (WMNs) for backhaul applications and quality of information (QoI) in
wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of
this Ph.D. dissertation, different network settings are used as illustrative examples,
however the proposed algorithms, methodologies, protocols, and models are not
restricted in the considered networks, but rather have wide applicability.
First, this dissertation proposes a cross-layer design framework integrating
a distributed proportional-fair scheduler and a QoS routing algorithm, while using
WMNs as an illustrative example. The proposed approach has significant performance
gain compared with other network protocols. Second, this dissertation proposes
a generic admission control methodology for any packet network, wired and
wireless, by modeling the network as a black box, and using a generic mathematical
0. Abstract 3
function and Taylor expansion to capture the admission impact. Third, this dissertation
further enhances the previous designs by proposing a negotiation process,
to bridge the applications’ service quality demands and the resource management,
while using WSNs as an illustrative example. This approach allows the negotiation
among different service classes and WSN resource allocations to reach the optimal
operational status. Finally, the guarantees of the service quality are extended to
the environment of multiple, disconnected, mobile subnetworks, where the question
of how to maintain communications using dynamically controlled, unmanned data
ferries is investigated
Medium Access Control for Wireless Sensor Networks based on Impulse Radio Ultra Wideband
This paper describes a detailed performance evaluation of distributed Medium
Access Control (MAC) protocols for Wireless Sensor Networks based on Impulse
Radio Ultra Wideband (IR-UWB) Physical layer (PHY). Two main classes of Medium
Access Control protocol have been considered: Slotted and UnSlotted with
reliability. The reliability is based on Automatic Repeat ReQuest (ARQ). The
performance evaluation is performed using a complete Wireless Sensor Networks
(WSN) simulator built on the Global Mobile Information System Simulator
(GloMoSim). The optimal operating parameters are first discussed for IR-UWB in
terms of slot size, retransmission delay and the number of retransmission, then
a comparison between IR-UWB and other transmission techniques in terms of
reliability latency and power efficiency
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
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