Periodic Reporting for period 1 - REDESIGN (distRibutED, sElf-adaptable, and Scalable wIreless foG Networks)
Reporting period: 2019-01-01 to 2020-12-31
The Internet of Things (loT) epitomizes the cutting edge technology to realize theoretical and practical facets of computer networking, and has initiated a prominent transition in the how communication and interactions of our world. The strong need to remedy the growing concerns regarding dealing with the huge data influx in real-time and functioning the available bandwidth bounds led to the birth of fog computing, which, intensively but not exclusively, operate along the network edge. Fog computing is a novel paradigm realizing distributed computing, network services and storage from beyond Cloud Computing Data Centers up until the devices along the network edge. This notion extends the inherent operations and services of Cloud computing, thus enabling new applications and services. As Fog-based processing occurs along the network edge, the end results reflect highly improved location awareness, low latency and QoS in streaming and real-time applications.
In addition, to better satisfy future communication demands, fog-based wireless networks are proposed to achieve high spectral efficiency, energy efficiency and low latency by fully utilizing the signal processing, resource management and storage capabilities of edge devices. Owing to fog computing, cloud computing and heterogenous networking, fog-based networks have great potentials in meeting diverse demands. A fog-based network allows the efficiency of the solution to be improved while reducing latency and energy consumption of traditional IoT-based solutions by reducing the amount of data that need to be exchanged with the cloud. This is a crucial aspect in the fog-based networks paradigm since the local processing of data at IoT objects and fog nodes allows avoiding their transmission to the cloud, hence reducing the (relevant) energy consumption for the requested transmission.
To fully support this novel fog-based networks paradigm, existing classical solutions must be rethought and tackled from different points of view. In particular, network components forming this novel paradigm must be augmented with self-configuration, management, healing, and energy awareness functionalities to locally process data and make autonomous decisions. These requirements have led fog-based network designers to consider self- adaptive solutions with fog nodes endowed with intelligent mechanisms able to process data and autonomously adapt their behaviors in response to changes affecting either the system itself or the environment in which they are deployed.
To this end, the purpose of REDESIGN is to aid the design of new communication techniques for wireless fog-based networks and to examine and verify their usefulness to improve the network performances in terms of self-adaptability, energy efficiency, and service latency. During the action, the applications of game theory and machine learning to fog-based networks are investigated. The resource allocation problem in fog-based networks, reconfigurable intelligent surface-based communications, and dense mm-Wave communications are studied, and novel efficient algorithms are devised.
In addition, to better satisfy future communication demands, fog-based wireless networks are proposed to achieve high spectral efficiency, energy efficiency and low latency by fully utilizing the signal processing, resource management and storage capabilities of edge devices. Owing to fog computing, cloud computing and heterogenous networking, fog-based networks have great potentials in meeting diverse demands. A fog-based network allows the efficiency of the solution to be improved while reducing latency and energy consumption of traditional IoT-based solutions by reducing the amount of data that need to be exchanged with the cloud. This is a crucial aspect in the fog-based networks paradigm since the local processing of data at IoT objects and fog nodes allows avoiding their transmission to the cloud, hence reducing the (relevant) energy consumption for the requested transmission.
To fully support this novel fog-based networks paradigm, existing classical solutions must be rethought and tackled from different points of view. In particular, network components forming this novel paradigm must be augmented with self-configuration, management, healing, and energy awareness functionalities to locally process data and make autonomous decisions. These requirements have led fog-based network designers to consider self- adaptive solutions with fog nodes endowed with intelligent mechanisms able to process data and autonomously adapt their behaviors in response to changes affecting either the system itself or the environment in which they are deployed.
To this end, the purpose of REDESIGN is to aid the design of new communication techniques for wireless fog-based networks and to examine and verify their usefulness to improve the network performances in terms of self-adaptability, energy efficiency, and service latency. During the action, the applications of game theory and machine learning to fog-based networks are investigated. The resource allocation problem in fog-based networks, reconfigurable intelligent surface-based communications, and dense mm-Wave communications are studied, and novel efficient algorithms are devised.
During REDESIGN, the following research work was carried out.
a. A practical power consumption model for wireless devices
By considering practical power models, we have obtained that the spectral and energy efficiencies change significantly compared with considering non-practical models.
b. Joint optimization of service latency and energy consumption in fog-based networks
We analyzed resource allocation problems for the joint minimization of both service latency and energy consumption metrics.
c. Resource allocation in ultra-dense mm-Wave communications
The problem of joint transmitter-receiver beamwidths and power allocation problem in ultra-dense mm-Wave network deployments was investigated.
d. Deep Q-learning for computing Nash equilibrium
We developed a new data efficient Deep-Q-learning methodology for learning Nash equilibria of a non-cooperative game.
e. Resource allocation in reconfigurable intelligent surfaces-based communications
We characterized the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.
a. A practical power consumption model for wireless devices
By considering practical power models, we have obtained that the spectral and energy efficiencies change significantly compared with considering non-practical models.
b. Joint optimization of service latency and energy consumption in fog-based networks
We analyzed resource allocation problems for the joint minimization of both service latency and energy consumption metrics.
c. Resource allocation in ultra-dense mm-Wave communications
The problem of joint transmitter-receiver beamwidths and power allocation problem in ultra-dense mm-Wave network deployments was investigated.
d. Deep Q-learning for computing Nash equilibrium
We developed a new data efficient Deep-Q-learning methodology for learning Nash equilibria of a non-cooperative game.
e. Resource allocation in reconfigurable intelligent surfaces-based communications
We characterized the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.
Fog computing is an emerging domain building a glue between today’s control and automation systems and the cloud, supporting new services for modern communication systems and smart cities. The ER advanced our knowledge on emerging services based on fog computing, around artificial intelligence technologies, tactile internet, and 6G connectivity. The project resulted in a holistic solution enabling the dynamic instantiation in fog computing facilities of highly demanding personal cloud services by third-parties for end-users with guaranteed QoS/QoE, anywhere, anytime, while ensuring: high network performance and low complexity interfaces.
A journal publication resulted from the research work on enhanced 6G connectivity based on reconfigurable intelligent surfaces:
- Alessio Zappone, Marco Di Renzo, Farshad Shams, Xuewen Qian, Mérouane Debbah: Overhead-Aware Design of Reconfigurable Intelligent Surfaces in Smart Radio Environments. IEEE Trans. Wirel. Commun. 20(1): 126-141 (2021)
A journal publication resulted from the research work on enhanced 6G connectivity based on reconfigurable intelligent surfaces:
- Alessio Zappone, Marco Di Renzo, Farshad Shams, Xuewen Qian, Mérouane Debbah: Overhead-Aware Design of Reconfigurable Intelligent Surfaces in Smart Radio Environments. IEEE Trans. Wirel. Commun. 20(1): 126-141 (2021)