Emrecan Demirors

This demo presents SeizNet, an innovative system for predicting epileptic seizures benefiting from a multi-modal sensor network and utilizing Deep Learning (DL) techniques. Epilepsy affects approximately 65 million people worldwide, many of whom experience drug-resistant seizures. SeizNet aims at providing highly accurate alerts, allowing individuals to take preventive measures without being disturbed by false alarms. SeizNet uses a combination of data collected through either invasive (intracranial electroencephalogram (iEEG)) or non-invasive (electroencephalogram (EEG) and electrocardiogram (ECG)) sensors, and processed by advanced DL algorithms that are optimized for real-time inference at the edge, ensuring privacy and minimizing data transmission. SeizNet achieves > 97% accuracy in seizure prediction while keeping the size and energy restrictions of an implantable device.

In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resistant epilepsy. To alleviate the impact of seizure, predictive systems have been developed that can notify such patients of an impending seizure, allowing them to take precautionary measures. SeizNet leverages DL techniques and combines data from multiple recordings, specifically intracranial electroencephalogram (iEEG) and electrocardiogram (ECG) sensors, that can significantly improve the specificity of seizure prediction while preserving very high levels of sensitivity. SeizNet DL algorithms are designed for efficient real-time execution at the edge, minimizing data privacy concerns, data transmission overhead, and power inefficiencies associated with cloud-based solutions. Our results indicate that SeizNet outperforms traditional single-modality and non-personalized prediction systems in all metrics, achieving up to 99% accuracy in predicting seizure, offering a promising new avenue in refractory epilepsy treatment.

Arena is an open-access wireless testing platform based on a grid of antennas mounted on the ceiling of a large office-space environment. Each antenna is connected to programmable software-defined radios (SDR) enabling sub-6 GHz 5G-and-beyond spectrum research. With 12 computational servers, 24 SDRs synchronized at the symbol level, and a total of 64 antennas, Arena provides the computational power and the scale to foster new technology development in some of the most crowded spectrum bands. Arena is based on a three-tier design, where the servers and the SDRs are housed in a double rack in a dedicated room, while the antennas are hung off the ceiling of a 2240 square feet office space and cabled to the radios through 100 ft-long cables. This ensures a reconfigurable, scalable, and repeatable real-time experimental evaluation in a real wireless indoor environment. In this paper, we introduce the architecture, capabilities, and system design choices of Arena, and provides details of the software and hardware implementation of various testbed components. Furthermore, we describe key capabilities by providing examples of published work that employed Arena for applications as diverse as synchronized MIMO transmission schemes, multi-hop ad hoc networking, multi-cell 5G networks, AI-powered Radio-Frequency fingerprinting, secure wireless communications, and spectrum sensing for cognitive radio.

Current cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors. Their configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from performing fine grained performance optimization, especially on a per-user basis. To address these key issues, this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention (“zero-touch”). CellOS leverages softwarization and automatic optimization principles to bridge Software-Defined Networking (SDN) and cross-layer optimization. Unlike state-of-the-art SDN-inspired solutions for cellular networking, CellOS: (i) Hides low-level network details through a general virtual network abstraction; (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices. CellOS has been implemented and evaluated on an indoor testbed with two different LTE-compliant implementations: OpenAirInterface and srsLTE. We further demonstrated CellOS capabilities on the long-range outdoor POWDER-RENEW PAWR 5G platform. Results from scenarios with multiple base stations and users show that CellOS is platform-independent and self-adapts to diverse network deployments. Our investigation shows that CellOS outperforms existing solutions on key metrics, including throughput (up to 86% improvement), energy efficiency (up to 84%) and fairness (up to 29%).

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

Arena is an open-access wireless testing platform based on a grid of antennas mounted on the ceiling of a 2240 square feet office-space environment. Each antenna is connected to programmable software-defined radios enabling sub-6 GHz 5G-and-beyond spectrum research. With 12 computational servers, 24 software defined radios synchronized at the symbol level, and a total of 64 antennas, Arena provides the computational power and the scale to foster new technology development in some of the most crowded spectrum bands, ensuring a reconfigurable, scalable, and repeatable real-time experimental evaluation in a real wireless indoor environment. We demonstrate some of the many possible capabilities of Arena in three cases: MIMO Capabilities, Ad Hoc Network, and Cognitive Radio Network.

Arena is an open-access wireless testing platform based on a grid of antennas mounted on the ceiling of a large office-space environment. Each antenna is connected to programmable software-defined radios enabling sub-6 GHz 5G-and-beyond spectrum research. With 12 computational servers, 24 software defined radios synchronized at the symbol level, and a total of 64 antennas, Arena provides the computational power and the scale to foster new technology development in some of the most crowded spectrum bands. Arena is based on a clean three-tier design, where the servers and the software defined radios are housed in a double rack in a dedicated room, while the antennas are hung off the ceiling of a 2240 square feet office space and cabled to the radios through 100 ft long cables. This ensures a reconfigurable, scalable, and repeatable real-time experimental evaluation in a real wireless indoor environment. This article introduces for the first time architecture, capabilities, and system design choices of Arena, and provide details of the software and hardware implementation of the different testbed components. Finally, we showcase some of the capabilities of Arena in providing a testing ground for key wireless technologies, including synchronized MIMO transmission schemes, multi-hop ad hoc networking, multi-cell LTE networks, and spectrum sensing for cognitive radio.

In this paper we present HIRO-NET, Heterogeneous Intelligent Robotic Network. HIRO-NET is an emergency infrastructure-less network tailored to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy (BLE)and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster stricken area. HIRO-NET main goal is to enable users in the disaster to exchange text messages in order to share critical information and request help from first responders. The mesh network discovery problem is analyzed and a network protocol specifically designed to facilitate the exploration process is presented. We show how HIRO-NET robots successfully discover, bridge and interconnect local mesh networks. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g. Twitter, Gmail)to the whole network. Results suggest that a temporary emergency network to cover a metropolitan area can be created in tens of minutes.