With a growing number of connected devices relying on the Industrial, Scientific, and Medical radio bands for communication, spectrum scarcity is one of the most important challenges currently and in the future. The existing collision avoidance techniques either apply a random back-off when spectrum collision is detected or assume that the knowledge about other nodes’ spectrum occupation is known. While these solutions have shown to perform reasonably well in intra-Radio Access Technology environments, they can fail if they are deployed in dense multi-technology environments as they are unable to address the inter-Radio Access Technology interference. In this paper, we present Spectrum Prediction Collision Avoidance (SPCA): an algorithm that can predict the behavior of other surrounding networks, by using supervised deep learning; and adapt its behavior to increase the overall throughput of both its own Multiple Frequencies Time Division Multiple Access network as well as that of the other surrounding networks. We use Convolutional Neural Network (CNN) that predicts the spectrum usage of the other neighboring networks. Through extensive simulations, we show that the SPCA is able to reduce the number of collisions from 50% to 11%, which is 4.5 times lower than the regular Multiple Frequencies Time Division Multiple Access (MF-TDMA) approach. In comparison with an Exponentially Weighted Moving Average (EWMA) scheduler, SPCA reduces the number of collisions from 29% to 11%, which is a factor 2.5 lower.

As the number of devices connected to the internet and the amount of data they generate increases, the wireless spectrum is becoming an essential and scarce resource. Most connected devices use wireless technologies that use the industrial, scientific, and medical (ISM) radio bands. As a result, different technologies are interfering with each other. Today's existing collision avoidance techniques either apply a random back-off when a signal collision is detected or assume that knowledge about other nodes' spectrum occupation is known. These approaches are competent approaches to optimise inter-network spectrum usage, but fail to optimise overall channel capacity and throughput of all neighbouring wireless networks. In this paper, we present a Deep Neural Network (DNN) approach that can predict spectrum occupation of unknown neighbouring networks in the near future by using online supervised learning in a multi-agent setting. This prediction can be employed by existing network schedulers to avoid collisions with surrounding networks or other electromagnetic sources. The DNN is trained in an online way, as the problem is a partially observable stochastic game with continuous action space. Our findings show a reduction in the number of collisions between the own network and neighbouring networks of 30%, and an increase in overall throughput of 10% in a medium-sized network with an unknown set of neighbouring networks.

In the unlicensed spectrum, many wireless technologies (e.g Wi-Fi, Bluetooth) use the same spectrum for wireless transmission. This often results in cross-technology interference effects, which are hard to address. Without new methods to manage this shared spectrum, wireless communication is increasingly challenging as too many nodes attempt at accessing the same spectrum. Collaboration between different wireless networks that use the same spectrum will be required to handle this massive amount of devices. In this paper, we present two algorithms based on Neural Networks (NNs) to demonstrate that a function approximation can accurately predict free slots in a Multiple Frequencies Time Division Multiple Access (MF-TDMA) network. By observing the spectrum, we are able to do online learning and let the corresponding NN predict the behavior of the spectrum a second in advance using our approach. We are able to reduce the number of collisions by half if the nodes from other networks are sending data following a Poisson distribution. When the nodes of the other network follow a more periodic traffic pattern, a collision reduction of factor 15 could be achieved.

Hybrid clouds, consisting of multiple individual smaller clouds with heterogeneous capabilities, are becoming more and more popular through concepts such as inter-clouds, fog-and edge-computing. They provide fast computations without introducing large network latency. However, such cloud environments often contain unreliable nodes and links that are failure prone. Therefore, the deployment of applications requiring availability guarantees is a current research challenge. If such a placement algorithm is CPU-, memory-, network-and availability-aware, the applications can use the resources as optimal as possible with a small failure probability. The optimal deployment of applications in the network infrastructure is a NP-hard problem and, as a consequence, exact algorithms to solve it are not scalable. In this paper we propose GRECO, a distributed genetic algorithm to place service-oriented applications on a hybrid cloud, by defining a representation of an application placement in a biased-random-key chromosome and using a fault-tolerance distributed pool model. When compared with an existing Integer Linear Programming approach, simulations show that GRECO is scalable (speedup of a factor of 1000) and obtain near optimal performance results.

With the emergence of new technologies and standards for wireless communications and an increase in application and user requirements, the number and density of deployed wireless ad hoc networks is increasing. For deterministic ad hoc networks, Time-Division Multiple Access (TDMA) is a popular medium access scheme, with many distributed TDMA scheduling algorithms being proposed. However, with increasing traffic demands and the number of wireless devices, proposed protocols are facing scalability issues. Besides, these protocols are achieving suboptimal spatial spectrum reuse as a result of the unsolved exposed node problem. Due to a shortage of available spectrum, a shift from fixed spectrum allocation to more dynamic spectrum sharing is anticipated. For dynamic spectrum sharing, improved distributed scheduling protocols are needed to increase spectral efficiency and support the coexistence of multiple co-located networks. Hence, in this paper, we propose a dynamic distributed multi-channel TDMA (DDMC-TDMA) slot management protocol based on control messages exchanged between one-hop network neighbors and execution of slot allocation and removal procedures between sender and receiver nodes. DDMC-TDMA is a topology-agnostic slot management protocol suitable for large-scale and high-density ad hoc networks. The performance of DDMC-TDMA has been evaluated for various topologies and scenarios in the ns-3 simulator. Simulation results indicate that DDMC-TDMA offers near-optimal spectrum utilization by solving both hidden and exposed node problems. Moreover, it proves to be a highly scalable protocol, showing no performance degradation for large-scale and high-density networks and achieving coexistence with unknown wireless networks operating in the same wireless domain.

This paper presents the architecture and the basic principles behind the design and implementation of the SCATTER system, a wireless end-to-end communication system that participated in the DARPA Second Spectrum Collaboration Challenge (SC2). The focus is mainly on presenting the architecture and the supported interactions between the different components of the system in order to deliver a true dynamic collaborative spectrum allocation and usage, while coexisting with numerous unknown heterogeneous wireless technologies.