The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …

Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …

In wireless networks, context awareness and intelligence are capabilities that enable each
host to observe, learn, and respond to its complex and dynamic operating environment in an …

Wireless sensor network (WSN) consists of a large number of sensors and sink nodes which
are used to monitor events or environmental parameters, such as movement, temperature …

Rapid adoption of mobile devices, coupled with the increase in prominence of mobile
applications and services, resulted in unprecedented infrastructure requirements for mobile …

Designers face many system optimization problems when building distributed systems.
Traditionally, designers have relied on optimization techniques that require either prior …

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

In the last decade, several routing algorithms have been proposed in ad hoc wireless
networks. However, most of them require either a high bandwidth, to maintain a full routing …

Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost,
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …

This paper presents a distributed Reinforcement Learning (RL) framework for synthesizing
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …