241 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
An intelligent-agent approach for managing congestion in W-CDMA networks
PhDResource Management is a crucial aspect in the next generation cellular networks
since the use of W-CDMA technology gives an inherent flexibility in managing the
system capacity. The concept of a “Service Level Agreement” (SLA) also plays a
very important role as it is the means to guarantee the quality of service provided to
the customers in response to the level of service to which they have subscribed.
Hence there is a need to introduce effective SLA-based policies as part of the radio
resource management.
This work proposes the application of intelligent agents in SLA-based control in
resource management, especially when congestion occurs. The work demonstrates the
ability of intelligent agents in improving and maintaining the quality of service to
meet the required SLA as the congestion occurs.
A particularly novel aspect of this work is the use of learning (here Case Based
Reasoning) to predict the control strategies to be imposed. As the system environment
changes, the most suitable policy will be implemented. When congestion occurs, the
system either proposes the solution by recalling from experience (if the event is
similar to what has been previously solved) or recalculates the solution from its
knowledge (if the event is new). With this approach, the system performance will be
monitored at all times and a suitable policy can be immediately applied as the system
environment changes, resulting in maintaining the system quality of service
Neighbors-Aware Proportional Fair scheduling for future wireless networks with mixed MAC protocols
Abstract In this paper, we consider a beyond-5G scenario, where two types of users, denoted as scheduled and uncoordinated nodes, coexist on the same set of radio resources for sending data to a base station. Scheduled nodes rely solely on a centralized scheduler within the base station for the assignment of resources, while uncoordinated nodes use an unslotted Carrier Sense Multiple Access (CSMA) protocol for channel access. We propose and evaluate through simulations: (a) a novel centralized resource scheduling algorithm, called Neighbors-Aware Proportional Fair (N-PF) and (b) a novel packet length adaptation algorithm, called Channel-Aware (CA) Packet Length Adaptation algorithm for the scheduled nodes. The N-PF algorithm considers the uplink channel state conditions and the number of uncoordinated nodes neighboring each scheduled node in the aggregate scheduling metric, in order to maximize packet transmission success probability. The CA algorithm provides an additional degree of freedom for improving the performance, thanks to the fact that scheduled nodes with lower number of hidden terminals, i.e., having higher packet capture probability, are assigned longer packet transmission opportunities. We consider two benchmark schemes: Proportional Fair (PF) algorithm, as a resource scheduling algorithm, and a discrete uniform distribution (DUD) scheme for packet lengths distribution. Simulation results show that the proposed schemes can result in significant gain in terms of network goodput, without compromising fairness, with respect to two benchmark solutions taken from the literature
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