Machine learning-based zero-touch network and service management:a survey

The exponential growth of mobile applications and services during the last years has challenged the existing network infrastructures.Consequently,the arrival of multiple management solutions to cope with this explosion along the end-to-end network chain has increased the complexity in the coordinated orchestration of different segments composing the whole infrastructure.The Zero-touch Network and Service Management(ZSM)concept has recently emerged to automatically orchestrate and manage network resources while assuring the Quality of Experience(QoE)demanded by users.Machine Learning(ML)is one of the key enabling technologies that many ZSM frameworks are adopting to bring intelligent decision making to the network management system.This paper presents a comprehensive survey of the state-of-the-art application of ML-based techniques to improve ZSM performance.To this end,the main related standardization activities and the aligned international projects and research efforts are deeply examined.From this dissection,the skyrocketing growth of the ZSM paradigm can be observed.Concretely,different standardization bodies have already designed reference architectures to set the foundations of novel automatic network management functions and resource orchestration.Aligned with these advances,diverse ML techniques are being currently exploited to build further ZSM developments in different aspects,including multi-tenancy management,traffic monitoring,and architecture coordination,among others.However,different challenges,such as the complexity,scalability,and security of ML mechanisms,are also identified,and future research guidelines are provided to accomplish a firm development of the ZSM ecosystem.

Beacon-Less Geographic Routing in Real Wireless Sensor Networks

Geographic Routing （GR） algorithms require nodes to periodically transmit HELLO messages to allow neighbors to know their positions （beaconing mechanism）. Beacon-less routing algorithms have recently been proposed to reduce the control overheads due to these messages. However, existing beacon-less algorithms have not considered realistic physical layers. Therefore, those algorithms cannot work properly in realistic scenarios. In this paper we present a new beacon-less routing protocol called BOSS. Its design is based on the conclusions of our open-field experiments using Tmote-sky sensors. BOSS is adapted to error-prone networks and incorporates a new mechanism to reduce collisions and duplicate messages produced during the selection of the next forwarder node. We compare BOSS with Beacon-Less Routing （BLR） and Contention-Based Forwarding （CBF） algorithms through extensive simulations. The results show that our scheme is able to ache.eve almost perfect packet delivery ratio （like BLR） while having a low bandwidth consumption （even lower than CBF）. Additionally, we carried out an empirical evaluation in a real testbed that shows the correctness of our simulation results.