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1. Rethinking Wireless Network Management Through Sensor-driven Contextual Analysis

   https://doi.org/10.1145/3376897.3377863  

   Irshad, Shazal; Rozner, Eric; Bhartia, Apurv; Chen, Bo (March 2020, HotMobile '20: Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications)

   null (Ed.)

   Wireless network management is important to ensure the performance, utilization, allocation, and robustness of the network is optimized. Until now, wireless network management has typically been dictated by in-band information, such as wireless measurements, client locations, or even device state. This position paper explores fundamental new ways to manage the network by utilizing out-of-band data provided by a rich deployment of sensors. Out-of-band data can capture information about the users, objects, or environments associated with a network device, meaning that richer contextual policies can be implemented in the network. We propose an architecture called SenseNet, which builds upon three recent trends: (1) the massive deployment of sensors today, (2) the existence of deep-learning algorithms to glean meaningful insights from the sensory data, and (3) the provisioning of edge computing resources to provide real-time actuation of new sensor-based policies. A brief evaluation shows the feasibility and motivates SenseNet and afterwards challenges towards practical deployment are discussed. 

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2. WideScan: Exploiting Out-of-Band Distortion for Device Classification Using Deep Learning

   https://doi.org/10.1109/GLOBECOM42002.2020.9348138  

   Elmaghbub, Abdurrahman; Hamdaoui, Bechir; Natarajan, Arun (December 2020, 2020 IEEE Global Communications Conference)

   null (Ed.)

   Wireless device classification techniques play a vital role in supporting spectrum awareness applications, such as spectrum access policy enforcement and unauthorized network access monitoring. Recent works proposed to exploit distortions in the transmitted signals caused by hardware impairments of the devices to provide device identification and classification using deep learning. As technology advances, the manufacturing impairment variations among devices become extremely insignificant, and hence the need for more sophisticated device classification techniques becomes inescapable. This paper proposes a scalable, RF data-driven deep learning-based device classification technique that efficiently classifies transmitting radios from a large pool of bit-similar, high-end, high-performance devices with same hardware, protocol, and/or software configurations. Unlike existing techniques, the novelty of the proposed approach lies in exploiting both the in-band and out-of-band distortion information, caused by inherent hardware impairments, to enable scalable and accurate device classification. Using convolutional neural network (CNN) model for classification, our results show that the proposed technique substantially outperforms conventional approaches in terms of both classification accuracy and learning times. In our experiments, the testing accuracy obtained under the proposed technique is about 96% whereas that obtained under the conventional approach is only about 50% when the devices exhibit very similar hardware impairments. The proposed technique can be implemented with minimum receiver design tuning, as radio technologies, such as cognitive radios, can easily allow for both in-band and out-of band sampling. 

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3. Energy-Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

   https://doi.org/10.1109/ICC40277.2020.9149186  

   Guo, Jun; Karimi-Bidhendi, Saeed; Jafarkhani, Hamid (June 2020, IEEE International Conference on Communications (ICC-20))

   We study a wireless ad-hoc sensor network (WASN) where N sensors gather data from the surrounding environment and transmit their sensed information to M fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs that minimizes a Lagrangian combination of sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd-like algorithm to optimize node deployment. Simulation results show that our proposed algorithm outperforms the existing deployment algorithms, on average. 

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4. An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort Sensing

   https://doi.org/10.3390/s23010364  

   Rosenberger, Joseph; Guo, Zhong; Coffman, Austin; Agdas, Duzgun; Barooah, Prabir (January 2023, Sensors)

   We present an open-source wireless network and data management system for collecting and storing indoor environmental measurements and perceived comfort via participatory sensing in commercial buildings. The system, called a personal comfort and indoor environment measurement (PCIEM) platform, consists of several devices placed in office occupants’ work areas, a wireless network, and a remote database to store the data. Each device, called a PCFN (personal comfort feedback node), contains a touchscreen through which the occupant can provide feedback on their perceived comfort on-demand, and several sensors to collect environmental data. The platform is designed to be part of an indoor climate control system that can enable personalized comfort control in real-time. We describe the design, prototyping, and initial deployment of a small number of PCFNs in a commercial building. We also provide lessons learned from these steps. Application of the data collected from the PCFNs for modeling and real-time control will be reported in future work. We use hardware components that are commercial and off-the-shelf, and our software design is based on open-source tools that are freely and publicly available to enable repeatability. 

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5. Performance of Real-Time Geospatial Spectrum Sharing (RGSS) between 5G Communication Networks and Earth Exploration Satellite Services

   https://doi.org/10.1109/DySPAN53946.2021.9677268  

   Eichen, Elliot (December 2021, IEEE)

   A proof of concept system that enables real-time geospatial spectrum sharing between 5G/6G networks and Earth Exploration Satellite Services (EESS) has been developed. A simple algorithm that pauses network transmissions when there is potential interference from 5G/6G transmitters provides 99.6% network availability in the 24 GHz NR2 band while protecting all currently working EESS radiometers operating in the 23.8 GHz band. A more sophisticated algorithm that modifies transmission power levels and (if necessary) network traffic (similar to the methodologies used by Citizens Broadband Radio Service) can reduce interference so that there is no adverse impact on network availability. In addition to preventing interference, RGSS provides other significant benefits to both the wireless and the weather/climate communities, including improving network performance and coverage, the ability to support changes in network architectures, network elements, endpoints, and new or more sensitive radiometers, and a simple mechanism to test and police compliance with out-of-band emission requirements. RGSS is also compatible with existing spectrum management systems. 

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