529 research outputs found
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
Antioxidants: nanotechnology and biotechnology fusion for medicine in overall
Antioxidant is a chemical
substance that is naturally found in our
food. It can prevent or reduce the
oxidative stress of the physiological
system. Due to the regular usage of
oxygen, the body continuously
produces free radicals. Excessive
number of free radicals could cause
cellular damage in the human body that
could lead to various diseases like
cancer, muscular degeneration and
diabetes. The presence of antioxidants
helps to counterattack the effect of
these free radicals. The antioxidant can
be found in abundance in plants and
most of the time there are problems
with the delivery. The solution is by
using nanotechnology that has
multitude potential for advanced
medical science. Nano devices and
nanoparticles have significant impact
as they can interact with the subcellular
level of the body with a high degree of
specificity. Thus, the treatment can be
in maximum efficacy with little side
effect
Evaluation of Tunable Data Compression in Energy-Aware Wireless Sensor Networks
Energy is an important consideration in wireless sensor networks. In the current compression evaluations, traditional indices are still used, while energy efficiency is probably neglected. Moreover, various evaluation biases significantly affect the final results. All these factors lead to a subjective evaluation. In this paper, a new criterion is proposed and a series of tunable compression algorithms are reevaluated. The results show that the new criterion makes the evaluation more objective. Additionally it indicates the situations when compression is unnecessary. A new adaptive compression arbitration system is proposed based on the evaluation results, which improves the performance of compression algorithms
Energy efficient data collection and dissemination protocols in self-organised wireless sensor networks
Wireless sensor networks (WSNs) are used for event detection and data collection in
a plethora of environmental monitoring applications. However a critical factor limits
the extension of WSNs into new application areas: energy constraints. This thesis
develops self-organising energy efficient data collection and dissemination protocols in
order to support WSNs in event detection and data collection and thus extend the use
of sensor-based networks to many new application areas.
Firstly, a Dual Prediction and Probabilistic Scheduler (DPPS) is developed. DPPS
uses a Dual Prediction Scheme combining compression and load balancing techniques
in order to manage sensor usage more efficiently. DPPS was tested and evaluated
through computer simulations and empirical experiments. Results showed that DPPS
reduces energy consumption in WSNs by up to 35% while simultaneously maintaining
data quality and satisfying a user specified accuracy constraint.
Secondly, an Adaptive Detection-driven Ad hoc Medium Access Control (ADAMAC)
protocol is developed. ADAMAC limits the Data Forwarding Interruption problem
which causes increased end-to-end delay and energy consumption in multi-hop sensor
networks. ADAMAC uses early warning alarms to dynamically adapt the sensing
intervals and communication periods of a sensor according to the likelihood of any
new events occurring. Results demonstrated that compared to previous protocols such
as SMAC, ADAMAC dramatically reduces end-to-end delay while still limiting energy
consumption during data collection and dissemination. The protocols developed in this thesis, DPPS and ADAMAC, effectively alleviate
the energy constraints associated with WSNs and will support the extension of sensorbased
networks to many more application areas than had hitherto been readily possible
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Uav-assisted data collection in wireless sensor networks: A comprehensive survey
Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energystorage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments
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