5,549 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
BAN-GZKP: Optimal Zero Knowledge Proof based Scheme for Wireless Body Area Networks
BANZKP is the best to date Zero Knowledge Proof (ZKP) based secure
lightweight and energy efficient authentication scheme designed for Wireless
Area Network (WBAN). It is vulnerable to several security attacks such as the
replay attack, Distributed Denial-of-Service (DDoS) attacks at sink and
redundancy information crack. However, BANZKP needs an end-to-end
authentication which is not compliant with the human body postural mobility. We
propose a new scheme BAN-GZKP. Our scheme improves both the security and
postural mobility resilience of BANZKP. Moreover, BAN-GZKP uses only a
three-phase authentication which is optimal in the class of ZKP protocols. To
fix the security vulnerabilities of BANZKP, BAN-GZKP uses a novel random key
allocation and a Hop-by-Hop authentication definition. We further prove the
reliability of our scheme to various attacks including those to which BANZKP is
vulnerable. Furthermore, via extensive simulations we prove that our scheme,
BAN-GZKP, outperforms BANZKP in terms of reliability to human body postural
mobility for various network parameters (end-to-end delay, number of packets
exchanged in the network, number of transmissions). We compared both schemes
using representative convergecast strategies with various transmission rates
and human postural mobility. Finally, it is important to mention that BAN-GZKP
has no additional cost compared to BANZKP in terms memory, computational
complexity or energy consumption
Reliable Energy-Efficient Routing Algorithm for Vehicle-Assisted Wireless Ad-Hoc Networks
We investigate the design of the optimal routing path in a moving vehicles
involved the internet of things (IoT). In our model, jammers exist that may
interfere with the information exchange between wireless nodes, leading to
worsened quality of service (QoS) in communications. In addition, the transmit
power of each battery-equipped node is constrained to save energy. We propose a
three-step optimal routing path algorithm for reliable and energy-efficient
communications. Moreover, results show that with the assistance of moving
vehicles, the total energy consumed can be reduced to a large extend. We also
study the impact on the optimal routing path design and energy consumption
which is caused by path loss, maximum transmit power constrain, QoS
requirement, etc.Comment: 6 pages, 5 figures, rejected by IEEE Globecom 2017,resubmit to IEEE
WCNC 201
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