767 research outputs found
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
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
Machine Learning in Wireless Sensor Networks for Smart Cities:A Survey
Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications
Development of an Adaptive Environmental Management System for Lejweleputswa District: A Participatory Approach through Fuzzy Cognitive Maps
Published ThesisEnvironmental pollution caused by mines within the district of Lejweleputswa in Free
State is a major contributor to health issues and the inability to grow crops within the
mining communities. Mining industries continue to develop environmental
management systems/plans to mitigate the impact their operations has on the society.
Even with these plans, there are still issues of environmental pollution affecting the
society. Though there are Information Communication and Technology (ICT) based
pollution monitoring solutions, their use is dismal due to lack of appreciation or
understanding of how they disseminate information. Furthermore, non-adopting
community members are being regarded as inherently conservative or irrational, but
these community members argue that the recommendations and technologies brought
to them are not always appropriate to their circumstances. There was concern that
local people’s knowledge of their environment, farming systems, and their social as
well as economic situation had been ignored and underestimated when ICTs solutions
are being implemented (Warburton & Martin, 1999). Another challenge is that there is
no station to monitor pollution for small communities such as Nyakallong in the district.
This result in mining communities depending on their own local knowledge to observe and monitor mining related environmental pollution. However, this local knowledge
has never been tested scientifically or analysed to recognize its usability or
effectiveness. Mining companies tend to ignore this knowledge from the communities
as it is treated like common information with no much scientific value. As a step
towards verifying or validating this local knowledge, fuzzy cognitive maps were used
to model, analyse and represent this linguistic local knowledge.
Although this local knowledge assists in mitigating environmental pollution,
incorporating it with scientific knowledge will improve its relevance, trustworthiness
and acceptability by majority of community members and policy-makers. Information
and Communication Technologies (ICTs) can accelerate this integration; this is the
focus of this research. The increased usages of Information Technology being witnessed today makes it the
most important factor for the world to depend on for solutions to many of today’s and
tomorrow’s problems. These solutions make use of various forms for dissemination
purposes, one of the most versatile dissemination device is a mobile phone since majority of the world’s population do own a mobile phone. In this way information is
easily accessible by almost everyone that needs it.
A novel environmental management solution was designed to work within the mining
communities of Lejweleputswa. The research started off by designing a unique
integration framework that creates the much-needed link between local knowledge
and scientific knowledge. The framework was then converted into an adaptable
environmental pollution management system prototype made up of three components;
(1) gathering environmental pollution knowledge; (2) environmental monitoring and;
(3) environmental dissemination and communication. To achieve sustainability,
relevance and acceptability, local knowledge was integrated in each of the three
components while mobile phones were used as both input and output devices for the
system. In order to facilitate collection and conservation of local knowledge on
environmental monitoring, an elaborate android-based mobile application was
developed. Wireless sensor-based gas sensor boards were acquired, and deployed
as a compliment to conventional monitoring stations, they were used to gather
scientific knowledge. To allow for public access to the system’s data, a web portal and an SMS-based component were also implemented. In order to collect local knowledge
from community, a case study of Nyakallong community in Lejweleputswa was carried
out. On completion of the system prototype, it was evaluated by participants from the
community; 90% of respondents gave a score of ‘excellent ‘
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