Navigation is a field that has been fascinating humankind for thousands of years and these pillars have been evolving with new technological advancements.There are three ‘pillars’ that define the performance or usefulness of a navigation technologies –cost, accuracy, and continuity. The past two decades have witnessed major progress in navigation technologies. The current market in positioning and navigation is clearly dominated by GNSS. Besides being globally available, it meets two important pillars: accuracy and cost by providing the whole range of navigation accuracies at very low cost. It is also highly portable, has low power consumption, and is wellsuited for integration with other sensors, communication links, and databases.At this point in the development of navigation technology, the need for alternative positioning systems only arises because GNSS does meet the continuity pillar as it does notwork in all environments. Furthermore, there has been a constant market push to develop navigation systems that are accurate, continuous and easy to afford. Needless to say, that cost,and space constraints are currently driving manufacturers of cars, portable devices (e.g. smartphones), and autonomous systems (e.g. self-driving,drones and agriculture machine systems) systems to investigate and develop next generation of low cost and small size navigation systems to meet the fast-growing autonomous vehicles and location services market demands. This presentation will provide a state of the art and future trends of sensors used for navigation of autonomous vehicles: possibilities, limitations and various design approaches. Emphasis will be on sensors and technologies that can navigate autonomous vehicles everywhereand at any time independent of weather and light conditions. Some of the current developed and possible future system’s accuracy performance will be demonstrated through different implementations/applicationsusing Propound Positioning Inc technologies.

Aerial Experimentation and Research Platform for Advanced Wireless (AERPAW) is one of the four PAWR projects funded by the National Science Foundation in the United States. AERPAW supports large-scale outdoor advanced wireless experiments with autonomous unmanned aerial and ground vehicles (UAVs/UGVs) and it is remotely accessible by the nationwide research community. Users of AERPAW have the opportunity to design and rapidly prototype next-generation wireless technologies, systems, and applications in both emulated and real-world environments. This talk will review the overall scope, and existing outdoor infrastructure including eight dedicated fixed nodes (towers, rooftop nodes, and a light pole) and their capabilities, as well as future plans for the AERPAW project. We will describe various different experiment examples involving software-defined radios, a commercial Ericsson 4G/5G network, RF sensors from Keysight, and a smart agriculture system with various LoRa gateways and sensors, all of them interacting with autonomous UAVs and UGVs in a large-scale outdoor environment. We will overview our digital twin that allows AERPAW users to exclusively design their outdoor experiments in a containerized virtual environment. We will also provide various research examples carried out on the platform since AERPAW declared general availability in November 2021.

The inspiration for a stacked intelligent metasurface (SIM) originates from the similarities between an RIS that has multiple meta-atoms and a hidden layer in a deep neural network (DNN) that also consists of multiple neurons with adjustable complex weights. However, a DNN has multiple layers of neurons that gives it a remarkable signal processing ability. In a similar way, by staking multiple layers of meta-atoms, we can perform complex signal processing operations on the signals in the electromagnetic (EM) wave domain without requiring digital beamforming and high-precision digital to analog conversions. In this talk, we will begin by developing a path-loss model for an SIM assisted wireless communication system. Based on the proposed path-loss model, we will formulate an optimization problem for producing a given target radiation pattern on a 2D plane located at a certain distance from the center of the SIM. The corresponding algorithms for the continuous and discrete settings will be discussed. We will show that, thanks to the use of multiple layers, complex target radiation patterns are easily generated. The fine-grained control over the spatial distribution of radiated power achieved with the help of an SIM has several interesting multi-user applications in 6G communication systems.

Millimeter wave (mmWave) communications have garnered significant attention from both academia and industry, thanks to the abundant spectrum resources available in the mmWave frequency bands, capable of supporting gigabit-per-second data rates for bandwidth-intensive wireless applications. However, mmWave communications present challenges, particularly in the form of coverage gaps due to sparse and rank-deficient channels. In an exciting development, reconfigurable intelligent surfaces (RIS), also known as intelligent reflecting surfaces, have emerged as a disruptive technology with the potential to enhance mmWave communications. Composed of numerous low-cost, passive, and small reflecting elements, RIS offers a promising solution. However, it’s important to note that the performance gains from RIS depend on well-designed reflection coefficients and access to necessary channel state information. In this presentation, I will introduce effective RIS designs and channel estimation schemes tailored to different mmWave communication systems, leveraging various RIS configurations. We’ll delve into how we can leverage the inherent channel rank deficiency of mmWavechannels to develop low-complexity yet highly effective phase shift designs and channel estimation methods. Through these insights, we aim to unlock the true potential of mmWave communications with RIS technology.

As we move forward into the 6G era, the efficient management of spectrum resources is paramount for unlocking the full potential of emerging technologies. In this talk, we will delve into the dynamic landscape of Spectrum Management and explore cutting-edge trends and methodologies that are reshaping the way we allocate, optimize, and utilize this invaluable resource.

Our journey will take us through the realm of Spectrum Advanced Analytics, where AI and data science techniques play a pivotal role in revolutionizing the traditional approaches to spectrum management. We will discuss how these technologies are enabling us to benchmark global trends in spectrum management with unparalleled precision, providing insights that were previously unimaginable.

Modern society is witnessing the emergence of complex networked systems driven by exchanges of information among their elements, such as robotic swarms, autonomous systems, social networks, and Internet-of-Things (IoT) architectures. In these applications, data is collected from heterogeneous sources and is generally dispersed across geographic locations. In this context, it is imperative to design learning algorithms that are better suited to the reality of networked units. New methodologies are necessary to account for “coupling” among “intelligent” agents in a manner that respects privacy, enables multi-tasking, promotes fairness, and is robust to malicious interference. Motivated by these considerations, we provide an overview of algorithms for learning and decision-making that exploit important characteristics of social interactions over graphs. We refer to the framework as Social Machine Learning: it handles heterogeneity in data more gracefully, learns with performance guarantees, is more resilient to adversarial attacks, and promotes explainable and fair learning. The framework exploits three properties that are normally missing from existing learning approaches: diversity, decentralization, and group dynamics.

Over four decades, wireless networks continue to play a fundamental role in data transmission and communicating information between users. Wireless networks have gone through five large-scale revolutions, resulting in 1G, 2G, 3G, 4G, and most recently 5G networks. In 1G and 2G, voice and text were possible. In 3G and 4G, picture and video become commonplace. In today’s 5G, live ultra-high-definition three-dimensional data, virtual reality and augmented reality services can be employed.

In this talk, I will start by providing our vision for next generation networks. We emphasize that human-centric mobile communications will continue be the most important application in such future network. In this context, I will start by highlighting the digital divide that separates world countries into “haves” and “have-nots” as illustrated by our imbalance index project. Then, I will focus on the necessary wireless networking solutions that overcome the digital divide and bring better coverage, robustness, and smartness. I will support this mission by introducing our recent advances in IoT context and location-aware mechanism associated with our recent wireless traffic prediction method. Moving forward, I will review our recent advances in non-terrestrial networks, which includes both UAVs and satellite. In this fold, I will introduce several complex optimization and machine learning methods that predicts UAV trajectory, data rate, and energy consumption. I will show satellite systems are essential for today’s traffic intensive applications while maintaining an accepted end-to-end latency for delay sensitive applications. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks.

Climate change has disastrous consequences for the natural water cycles in South Asia and the Indus basin, leading to frequent floods and droughts, elevated food & energy insecurity and the depletion of critical water resources such as glaciers, seasonal snow, and groundwater. For cities and rural communities in the intensely irrigated river basinsof this region,the availability of timely information about uncertain hydrometeoroligical variableshas become a necessityto achieve high crop water productivity and cope with conditions leading to heatwaves and urban flooding. For the scientific community, hidden state variables such as the spatio-temporal evolution of soil moisture, the intra-seasonal variations of snow-depth,and local precipitation provide critical insights.In this talk, we summarize our on-going work onbuilding highly-localizedearth observation networks using a fusion of Internet of Things (IoT), Unmanned Aerial Vehicles (UAV), satellite remote sensing products and data-driven modeling. While several global efforts have aimed at providing a wide coverage of soil moisture, streamflows, groundwater levels, snow extent and evapotranspiration using satellite remote sensing, there is a dire need for more localized and more frequent Earth observation-basedproducts. We share our efforts to enable field-scale irrigation schedulingto thousandsof farmers, provide early warning to communities for flash floods and hill torrentsin remote mountainous communities, and calibrateaccurate hydrological models towardspredictingsnowmelt-runoffs inmany small catchmentsof northern Pakistan. Oursmall-scale data-collection efforts, agent-based socio-hydrological models and basin-scale integrated assessment models areenabling government agenciesin Pakistanto pilotlarge-scale policy interventions for irrigation demand management, explore nature-based solutionsin agriculture, prepare for rare events in transboundary rivers, and generate scenarios for investment options in the Water-Energy-Food-Climate nexus.

Schistosomiasis (bilharzia), one of the most relevanthuman parasitosis, is confirmed by microscopic detection of eggs in stool, urine, or organ biopsies after the infection of a patient.The urinary and intestinal schistosomiasis is a significant public health problem in Senegal with a prevalencerate varying between 0.3% and 1%. The lack of safe drinking water, sanitary infrastructureas well as daily life activities in rural areaswhich aredonearound water points,increase human water contact.Quite often, without symptoms and morbidity, rural people who have positive serology do not go to the hospital for diagnostic and then continue to infect water point.The proposed hybrid sensor-based epidemiology considersseveral metrics based on physical and chemical environment factorsof water pointscollected by sensor networks andan epidemiological model that forecasts the evolution of the density of parasites and snails causing schistosomiasis. Furthermore, we rely on the theory of belief functionsto combine the forecasts made by bothmodels by inferringone day ahead waterpointinfestation state.Therefore, the data fusion approach uses information coming from various sources. It enables to detect contaminated water points and thus takes proactive decisions such as prohibiting the infected area or preventing miracidium from infecting the mollusc or the parasitic larvae entering through the skin of humans. Indeed, the main goal is to stop the Schistosomiasis life cycle.

Fisheries sector holds a prominent role in Senegalese economy according to foreign exchange earnings (exports) and vital needs of the population. Fishery remains artisanal and involves more than 19000 dugout canoes with different sizes. Nowadays, according to climate changes and fishes that are far away from coastal a lot of crashes occurs with roughly 100 deaths per year.Unfortunately, there is no rescue communication system for these dugout canoes and senegalese cellular networks do not cover distance upper than 7km from coasts. It is in this context that we propose SAFeComNet (Safer Artisanal Fishing intEgrating a COMmunity NETwork), which takes into account dugout canoes up to more than 20Km from the coast, whether they are in areas connected to cellular networks or in unconnected areas.I’ll present the SAFeComNetarchitecture during my talk at the Fourth “KAUST 6G Summit”.