Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Power Beacon’s deployment optimization for wirelessly powering massive Internet of Things networks

Abstract. The fifth-generation (5G) and beyond wireless cellular networks promise the native support to, among other use cases, the so-called Internet of Things (IoT). Different from human-based cellular services, IoT networks implement a novel vision where ordinary machines possess the ability to autonomously sense, actuate, compute, and communicate throughout the Internet. However, as the number of connected devices grows larger, an urgent demand for energy-efficient communication technologies arises. A key challenge related to IoT devices is that their very small form factor allows them to carry just a tiny battery that might not be even possible to replace due to installation conditions, or too costly in terms of maintenance because of the massiveness of the network. This issue limits the lifetime of the network and compromises its reliability. Wireless energy transfer (WET) has emerged as a potential candidate to replenish sensors’ batteries or to sustain the operation of battery-free devices, as it provides a controllable source of energy over-the-air. Therefore, WET eliminates the need for regular maintenance, allows sensors’ form factor reduction, and reduces the battery disposal that contributes to the environment pollution. In this thesis, we review some WET-enabled scenarios and state-of-the-art techniques for implementing WET in IoT networks. In particular, we focus our attention on the deployment optimization of the so-called power beacons (PBs), which are the energy transmitters for charging a massive IoT deployment subject to a network-wide probabilistic energy outage constraint. We assume that IoT sensors’ positions are unknown at the PBs, and hence we maximize the average incident power on the worst network location. We propose a linear-time complexity algorithm for optimizing the PBs’ positions that outperforms benchmark methods in terms of minimum average incident power and computation time. Then, we also present some insights on the maximum coverage area under certain propagation conditions

Information discovery in multi-dimensional autonomous wireless sensor networks

 The thesis proposed four novel algorithms of information discovery for Multidimensional Autonomous Wireless Sensor Networks (WSNs) that can significantly increase network lifetime and minimize query processing latency, resulting in quality of service improvements that are of immense benefit to Multidimensional Autonomous WSNs are deployed in complex environments (e.g., mission-critical applications)

Impact of directional antennas on routing and neighbor discovery in wireless ad-hoc networks

Wireless ad-hoc networks are data networks that are deployed without a fixed infrastructure nor central controllers such as access points or base stations. In these networks, data packets are forwarded directly to the destination node if they are within the transmission range of the sender or sent through a multi-hop path of intermediary nodes that act as relays. This paradigm where a fixed infrastructure is not needed, is tolerant to topology changes and allows a fast deployment have been considered as a promissory technology that is suitable for a large number of network implementations, such as mobile hand-held devices, wireless sensors, disaster recovery networks, etc. Recently, smart directional antennas have been identified as a robust technology that can boost the performance of wireless ad-hoc networks in terms of coverage, connectivity, and capacity. Contrary to omnidirectional antennas, which can radiate energy in all directions, directional antennas can focus the energy in a specific direction, extending the coverage range for the same power level. Longer ranges provide shorter paths to destination nodes and also improve connectivity. Moreover, directional antennas can reduce the number of collisions in a contention-based access scheme as they can steer the main lobe in the desired direction and set nulls in all the others, thereby they minimize the co-channel interference and reduce the noise level. Connections are more reliable due to the increased link stability and spatial diversity. Shorter paths, as well as alternative paths, are also available as a consequence of the use of directional antennas. All these features combined results in a higher network capacity. Most of the previous research has focused on adapting the existing medium access control and routing protocols to utilize directional communications. This research work is novel because it improves the neighbor discovery process as it allows to discover nodes in the second neighborhood of a given node using a gossip based procedure and by sharing the relative position information obtained during this stage with the routing protocol with the aim of reducing the number of hops between source and destination. We have also developed a model to evaluate the energy consumed by the nodes when smart directional antennas are used in the ad-hoc network. This study has demonstrated that by adapting the beamwidth of the antennas nodes are able to reach furthest nodes and consequently, reduce the number of hops between source and destination. This fact not only reduces the end-to-end delay and improves the network throughput but also reduces the average energy consumed by the whole network

Collaborative Edge Computing in Mobile Internet of Things

The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportunities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision systems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool instead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abundance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative computing, collaborative security and collaborative privacy intelligence. The first framework, Opportunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spatial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third frame- work, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user’s privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Magneto-inductive wireless underground sensor networks: novel longevity model, communication concepts and workarounds to key theoretical issues using analogical thinking

This research has attempted to devise novel workarounds to key theoretical issues in magneto-inductive wireless underground sensor networks (WUSNs), founded on analogical thinking (Gassmann & Zeschky 2008). The problem statement for this research can be summarized as follows. There has been a substantial output of research publications in the past 5 years, devoted to theoretically analysing and resolving the issues pertaining to deployment of MI based WUSNs. However, no alternate solution approaches to such theoretical analyses have been considered. The goal of this research was to explore such alternate solution approaches. This research has used the principle of analogical thinking in devising such alternate solution approaches. This research has made several key contributions to the existing body of work. First, this research is the first of its kind to demonstrate by means of review of state-of-the-art research on MI based WUSNs, the largely theoretical genus of the research to the exclusion of alternate solution approaches to circumvent key theoretical issues. Second, this research is the first of its kind to introduce the notion of analogical thinking as a solution approach in finding viable workarounds to theoretical impediments in MI based WUSNs, and validate such solution approach by means of simulations. Third, this research is the first of its kind to explore novel communication concepts in the realm of MI based WUSNs, based on analogical thinking. Fourth, this research is the first of its kind to explore a novel longevity model in the realm of MI based WUSNs, based on analogical thinking. Fifth, this research is also the first to extend the notion of analogical thinking to futuristic directions in MI based WUSNs research, by means of providing possible indicators drawn from various other areas of contemporary research. In essence, the author believes that the findings of this research mark a paradigm shift in the research on MI based WUSNs

Energy aware and privacy preserving protocols for ad hoc networks with applications to disaster management

Disasters can have a serious impact on the functioning of communities and societies. Disaster management aims at providing efficient utilization of resources during pre-disaster (e.g. preparedness and prevention) and post-disaster (e.g. recovery and relief) scenarios to reduce the impact of disasters. Wireless sensors have been extensively used for early detection and prevention of disasters. However, the sensor\u27s operating environment may not always be congenial to these applications. Attackers can observe the traffic flow in the network to determine the location of the sensors and exploit it. For example, in intrusion detection systems, the information can be used to identify coverage gaps and avoid detection. Data source location privacy preservation protocols were designed in this work to address this problem. Using wireless sensors for disaster preparedness, recovery and relief operations can have high deployment costs. Making use of wireless devices (e.g. smartphones and tablets) widely available among people in the affected region is a more practical approach. Disaster preparedness involves dissemination of information among the people to make them aware of the risks they will face in the event of a disaster and how to actively prepare for them. The content is downloaded by the people on their smartphones and tablets for ubiquitous access. As these devices are primarily constrained by their available energy, this work introduces an energy-aware peer-to-peer file sharing protocol for efficient distribution of the content and maximizing the lifetime of the devices. Finally, the ability of the wireless devices to build an ad hoc network for capturing and collecting data for disaster relief and recovery operations was investigated. Specifically, novel energy-adaptive mechanisms were designed for autonomous creation of the ad hoc network, distribution of data capturing task among the devices, and collection of data with minimum delay --Abstract, page iii