Experimental research on wireless networks is arduous and usually involves high costs. Hence, means for rapid prototyping along high-fidelity evaluation are highly desirable. This chapter presents Mininet-WiFi as a tool to emulate WMNs allowing high-fidelity experiments that replicate real networking stacks, protocols, and more. In order to bring the most complete experience possible, this chapter also provides practical guidelines on how to emulate WMNs. First, we introduce wireless interface modes tailored to wireless mesh supported by the Linux-based systems in addition to the most common wireless mesh routing protocols. Second, we showcase the emulation of IEEE 802.11p-based networks in vehicle and drone communication scenarios.

Recent advances in BSN promise to revolutionize the healthcare sector. This chapter presented a detailed review of fundamental concepts of BSN, the understanding of which is critical for the design and development of BSN solutions for medical and nonmedical applications. Basics of BSN architecture and communication modules have been discussed along with the corresponding system, propagation, and noise models. The operation and mathematical formulation of performance parameters of intra-BSN, inter-BSN, and beyond-BSN modules have been presented. The requirements, for example, recent protocols and challenges associated with each layer of the BSN communication stack, have been discussed. Emerging security threats for BSN have been highlighted along with the famous security solutions. Finally, the opportunities and open research directions for BSN have been detailed. In conclusion, there is a need to manage challenges such as energy efficiency, size, cost, reliability, and biocompatibility of BSN nodes. Furthermore, state-of-the-art customizable protocols are also needed to support emerging BSN applications.

In this chapter, we give the required background in order to properly introduce the software-defined radio (SDR) and discuss its usage in wireless mesh networks (WMN) applications such as wireless Internet of Things (IoT) and vehicular communications (V2X). These applications require a mesh interconnection of radio devices to ensure efficient data collection and forwarding. A number of standards have been specified for these networks' applications such as IEEE 802.15.4e and IEEE 802.11 p. These standards are rigid in their definition of physical layer (PHY) specifications. Indeed, their transceivers implement predefined modulators/demodulators, and they are not aware of new constraints of radio environments and applications, such as scarcity of the spectrum and the limited capacity of channels. We introduce SDR as a reconfigurable radio where transmitter and receiver functions are implemented in software rather than in hardware. For the IEEE 802.15.4e-based IoT mesh networks, we explore the feasibility to define in software the possible PHYs to deal with the scarcity of the spectrum. For the V2X, we expose the signal superposition in order to increase the channel capacity. We also highlight major challenges and recent developments in SDR.

Unmanned aerial vehicle (UAV) or drone teams are deployed for a plethora of applications. Regardless of whether the drones work cooperatively in a distributed manner or assigned to individual tasks by a centralized controller, it is envisioned that some form of connectivity is required for the success of the mission. Connectivity of drones to ground control, ground users, and/or other drones can be maintained via cellular networks relying on an existing infrastructure or by incorporating connectivity needs into the mission plan, which depend on the application at hand. As an alternative, in this chapter, we consider a UAV relay network deployed to support mission-oriented UAV networks, where the mission and communication tasks are decoupled from each other. The relay UAVs are not capable of any mission tasks. Their purpose is to form a mesh network that connects the mission UAVs to a centralized controller and indirectly to each other. We employ a modular relay positioning and trajectory planning algorithm that guarantees connectivity needs of the UAV mission team with minimum number of relays and feasible trajectories, where the cost, network structure, and setup can be changed without relying on an existing infrastructure. We illustrate the usability of the algorithm for different applications, by studying scenarios that require all-time, periodic, or event-driven connectivity, and we analyze the performance in terms of connectivity and resource usage when the relay network is deployed.

The speed at which data is flooded from big multi-sensor networks and the noise generated with such a large volume of data pose various new challenges in the field of big data and, especially, those relating to data quality.

The aim of this research is to combine two different artificial intelligence techniques, represented by multi-agent technologies and fuzzy logic, and to provide a generic approach for noise elimination and relevant data extraction in big data environments.

The specificity of the multi-agent system in this approach is that agents are powered by machine learning algorithms so it can learn to adjust the parameters of the fuzzy logic inference system to produce the best quality of data by eliminating only the noisy data and avoiding the loss of information. Therefore, agents will learn to strike a balance between extracting relevant information and losing information, which means that they will learn how to produce optimal performances in terms of data quality and energy consumption. This will extend the lifetime of the sensor network also.

The need of combining QC and ML to fulfill the requirements of future WMNs envisages the definition of proper network architectures with the introduction of new logical entities. After describing the role of different ML algorithms for WMNs and the main properties of QC, this work introduced the application of quantum ML for WMNs. Specifically, it proposed a centralized and a distributed architecture where quantum ML capabilities are placed in the cloud or at the edge, respectively. Design principles and information exchange procedures are deeply discussed for both these architectures, also highlighting their advantages, disadvantages, and possible future research directions.

The aim of the chapters contained in this book has been to introduce, overview, and discuss the concept of wireless mesh network (WMN) and its adoption in different heterogeneous contexts and scenarios. We have highlighted how this network topology has (and will have in the future) several implications in the architecture's modeling and requires to take decisions and actions at different layers.