4,023 research outputs found
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
A survey on energy efficient techniques in wireless sensor networks
International audienceThe myriad of potential applications supported by wireless sensor networks (WSNs) has generated much interest from the research community. Various applications range from small size low industrial monitoring to large scale energy constrained environmental monitoring. In all cases, an operational network is required to fulfill the application missions. In addition, energy consumption of nodes is a great challenge in order to maximize network lifetime. Unlike other networks, it can be hazardous, very expensive or even impossible to charge or replace exhausted batteries due to the hostile nature of environment. Researchers are invited to design energy efficient protocols while achieving the desired network operations. This paper focuses on different techniques to reduce the consumption of the limited energy budget of sensor nodes. After having identified the reasons of energy waste in WSNs, we classify energy efficient techniques into five classes, namely data reduction, control reduction, energy efficient routing, duty cycling and topology control. We then detail each of them, presenting subdivisions and giving many examples. We conclude by a recapitulative table
QoS BASED ENERGY EFFICIENT ROUTING IN WIRELESS SENSOR NETWORK
A Wireless Sensor Networks (WSN) is composed of a large number of low-powered
sensor nodes that are randomly deployed to collect environmental data. In a WSN,
because of energy scarceness, energy efficient gathering of sensed information is one
of the most critical issues. Thus, most of the WSN routing protocols found in the
literature have considered energy awareness as a key design issue. Factors like
throughput, latency and delay are not considered as critical issues in these protocols.
However, emerging WSN applications that involve multimedia and imagining sensors
require end-to-end delay within acceptable limits. Hence, in addition to energy
efficiency, the parameters (delay, packet loss ratio, throughput and coverage) have
now become issues of primary concern. Such performance metrics are usually
referred to as the Quality of Service (QoS) in communication systems. Therefore, to
have efficient use of a sensor nodeβs energy, and the ability to transmit the imaging
and multimedia data in a timely manner, requires both a QoS based and energy
efficient routing protocol. In this research work, a QoS based energy efficient routing
protocol for WSN is proposed. To achieve QoS based energy efficient routing, three
protocols are proposed, namely the QoS based Energy Efficient Clustering (QoSEC)
for a WSN, the QoS based Energy Efficient Sleep/Wake Scheduling (QoSES) for a
WSN, and the QoS based Energy Efficient Mobile Sink (QoSEM) based Routing for a
Clustered WSN.
Firstly, in the QoSEC, to achieve energy efficiency and to prolong
network/coverage lifetime, some nodes with additional energy resources, termed as
super-nodes, in addition to normal capability nodes, are deployed. Multi-hierarchy
clustering is done by having super-nodes (acting as a local sink) at the top tier, cluster
head (normal node) at the middle tier, and cluster member (normal node) at the lowest
tier in the hierarchy. Clustering within normal sensor nodes is done by optimizing the
network/coverage lifetime through a cluster-head-selection algorithm and a
sleep/wake scheduling algorithm. QoSEC resolves the hot spot problem and prolongs
network/coverage lifetime.
Secondly, the QoSES addressed the delay-minimization problem in sleep/wake
scheduling for event-driven sensor networks for delay-sensitive applications. For this
purpose, QoSES assigns different sleep/wake intervals (longer wake interval) to
potential overloaded nodes, according to their varied traffic load requirement defined
a) by node position in the network, b) by node topological importance, and c) by
handling burst traffic in the proximity of the event occurrence node. Using these
heuristics, QoSES minimizes the congestion at nodes having heavy traffic loads and
ultimately reduces end-to-end delay while maximizing the throughput.
Lastly, the QoSEM addresses hot spot problem, delay minimization, and QoS
assurance. To address hot-spot problem, mobile sink is used, that move in the network
to gather data by virtue of which nodes near to the mobile sink changes with each
movement, consequently hot spot problem is minimized. To achieve delay
minimization, static sink is used in addition to the mobile sink. Delay sensitive data is
forwarded to the static sink, while the delay tolerant data is sent through the mobile
sink. For QoS assurance, incoming traffic is divided into different traffic classes and
each traffic class is assigned different priority based on their QoS requirement
(bandwidth, delay) determine by its message type and content. Furthermore, to
minimize delay in mobile sink data gathering, the mobile sink is moved throughout
the network based on the priority messages at the nodes. Using these heuristics,
QoSEM incur less end-to-end delay, is energy efficient, as well as being able to
ensure QoS.
Simulations are carried out to evaluate the performance of the proposed protocols
of QoSEC, QoSES and QoSEM, by comparing their performance with the established
contemporary protocols. Simulation results have demonstrated that when compared
with contemporary protocols, each of the proposed protocol significantly prolong the
network and coverage lifetime, as well as improve the other QoS routing parameters,
such as delay, packet loss ratio, and throughput
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
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