ACM SIGCOMM 2022, Amsterdam, The Netherlands

ACM SIGCOMM 2022 Workshop on Networked Sensing Systems for a Sustainable Society (NET4us 2022)

Workshop Program

  • Monday, August 22, 2022 CEST (Room: Veilingzaal)

  • 1:30 pm - 1:40 pm CEST      Workshop welcome

  • 1:40 pm - 3:00 pm CEST      Session I

  • 1:40 pm - 2:30 pm CEST

    Keynote: Blockchain for 6G

    Chonggang Wang, PhD, IEEE Fellow (InterDigital, Inc.)

    • Abstract: It is envisioned that the 6G wireless system will be a more intelligent, open, transparent, converged, distributed, and shared infrastructure. At the same time, 6G networks need to be trustworthy and provide user-centric security and privacy protection. 6G trends (e.g., native AI, decentralized and converged communication and computing) demand a new paradigm that can deliver incentivization, decentralized trust, security, and higher performance. As a decentralized communication, networking, and computing technology, blockchain fits these 6G trends and can empower 6G networks. This talk focuses on opportunities and challenges of leveraging blockchain for 6G. It will first briefly discuss 6G trends and the latest progress in blockchain technology. Then, selected use cases and architectural designs of blockchain for 6G will be presented (e.g., blockchain for wireless resource management, blockchain for wireless AI). Finally, future directions and visions on blockchain for 6G will be shared.


  • 2:40 pm - 2:55 pm CEST

    A Preliminary Analysis of Data Collection and Retrieval Scheme for Green Information-Centric Wireless Sensor Networks

    Shintaro Mori (Fukuoka University)

    • Abstract: This paper addresses a wireless sensor network technology that supports the deployment of sustainable IoT applications essential to future zero-carbon smart cities. We propose a novel data collection and retrieval scheme to adopt an information-centric network into wireless sensor networks for energy efficiency. The results of laboratory-based experiments using a testbed and prototype network demonstrate the feasibility and applicability of the proposed scheme in terms of network throughput, latency, jitter, and energy consumption.


  • 3:00 pm - 3:30 pm CEST Break

  • 3:30 pm - 5:00 pm CEST      Session II

  • 3:30 pm - 3:45 pm CEST

    RealTimeAir: A Real-Time Federated Crowd Sensing Hyper-Local Air Quality Data Service

    Simon Hart, Joseph Doyle (Queen Mary University of London)

    • Abstract: Poor air quality has been responsible for millions of premature deaths. Acknowledging the critical role air quality plays in the future of their populations, governments across the world have been installing networks of fixed location air quality measurement instruments. But these monitoring stations are expensive and therefore spatially sparse, typically publishing summaries of hourly averages of pollutant measurements once per day. Data so sparse spatially and temporally offers little to inform the street user or policy maker as to what is happening at a more granular level, thus reducing the ability to avoid pollutants. This paper investigates the feasibility of using consumer grade mobile sensors as a means to contribute to a real time federated hyper-local crowd sensing air quality data service, RealTimeAir (RTA), underpinned by government reference sensors. We compare two mobile sensors and examine the correlation of the measurements between them. We investigate the correlation between these sensors and the more expensive fixed monitoring stations. We consider the variation of measurements over time and space to investigate the need for greater granularity of these measurements. Finally, we present a low pollutant exposure route finder as a use case for the proposed system.


  • 3:45 pm - 4:00 pm CEST

    On the Prediction of Air Quality within Vehicles using Outdoor Air Pollution: Sensors and Machine Learning Algorithms

    Thomas Baldi, Giovanni Delnevo, Roberto Girau, Silvia Mirri (University of Bologna)

    • Abstract: Environmental conditions within vehicles represent a significant element of the driver's well-being and comfort. In particular, exposure to air pollution has been proven to affect human cognitive performances, hence it could represent a risk to driving safety. Monitoring internal and external environmental data could provide interesting hints, helpful in predicting trends and situations potentially dangerous and/or unease, that should be reported, enhancing the driver's awareness. This paper presents a study we have conducted with the aim of predicting indoor vehicle environmental conditions, thanks to a campaign of data collection. In particular, we have adopted a multi-sensor kit, installed within and outside a vehicle, then we have exploited driving sessions in a urban environment. Different machine learning algorithms have been adopted to test their accuracy in predicting internal conditions, on the basis of external ones, discussing the obtained results.


  • 4:00 pm - 4:15 pm CEST

    Saving Energy on Smartphones through Edge Computing: an Experimental Evaluation

    Chiara Caiazza (University of Pisa); Valerio Luconi (IIT-CNR); Alessio Vecchio (University of Pisa)

    • Abstract: Edge computing is a network architecture in which computing and storage capabilities are moved at the fringes of the Internet, close to the end-users. The main goal of edge computing is to enable responsive services, thanks to much shorter paths compared to the ones encountered when communicating with remotely positioned cloud servers. In this paper, we report experimental results concerning an overlooked benefit of edge computing: energy is saved on client devices. We carried out an experimental evaluation using both software-based and hardware-based energy estimation methods. Results show that, for HTTP-based communication, the lifetime of a device can be extended significantly when using the edge instead of a remote cloud.


  • 4:15 pm - 4:30 pm CEST

    A Renewable Energy-Aware Distributed Task Scheduler for Multi-sensor IoT Networks

    Elizabeth Liri (University of California, Riverside); K. K. Ramakrishnan (University of California, Riverside); Koushik Kar (Rensselaer Polytechnic Institute)

    • Abstract: IoT devices are becoming increasingly complex, support multiple sensors and often rely on batteries and renewable energy. Scheduling algorithms can help to manage their energy usage. When multiple devices cooperatively monitor an environment, scheduling sensing tasks across a distributed set of IoT devices can be challenging because they have limited information about other devices, limited energy and communication bandwidth. In addition, sharing information between devices can be costly in terms of energy. Our Tier-based Task scheduling protocol (T2), is an energy efficient distributed scheduler for a network of multi-sensor IoT devices. T2, adapting on an epoch-by-epoch basis distributes task executions throughout an epoch to minimize temporal sensing overlap without exceeding task deadlines. Our experiments show that T2 schedules an IoT device's sensing task start time before its deadline expires. When compared against a simple periodic scheduler, T2 schedules closer to the optimal centralized EDF scheduler.


  • 4:30 pm - 4:45 pm CEST

    Kaala: Scalable, End-to-End, IoT System Simulator

    Udhaya Kumar Dayalan, Rostand A. K. Fezeu, Timothy J. Salo, Zhi-Li Zhang (University of Minnesota)

    • Abstract: We introduce Kaala, a scalable, hybrid, end-to-end IoT system simulator that can integrate with diverse, real-world IoT cloud services. Many IoT simulators run in isolation and do not interface with real-world IoT cloud systems or servers. This isolation makes it difficult for experiments to fully replicate the diversity that exists in end-to-end, real-world systems. Kaala is intended to bridge the gap between IoT simulation experiments and the real world. The simulator can interact with cloud IoT services, such as those offered by Amazon, Microsoft and Google. Kaala leverages vendor-provided software development kits (SDKs) to implement the vendor-specific protocols that are necessary permit simulated IoT devices and gateways to seamlessly communicate with real-world cloud IoT systems. Kaala has the ability to simulate a large number of diverse IoT devices, as well as to simulate events that may simultaneously affect several sensors. Evaluation results show that Kaala is able to, with minimal overhead, seamlessly connect simulated IoT devices to real-world cloud IoT systems.


  • 4:45 pm - 5:00 pm CEST

    An Instance-based Deep Transfer Learning Approach for Resource-Constrained Environments

    Gibson Kimutai (University of Rwanda); Anna Förster (University of Bremen)

    • Abstract: Although Deep Learning (DL) is revolutionising practices across fields, it requires a large amount of data and computing resources, requires considerable training time, and is thus expensive. This study proposes a transfer learning approach by adopting a simplified version of a standard Convolution Neural Network (CNN), which is successful in another domain. We explored three transfer learning approaches: freezing all layers except the first and the last layer of the CNN model, which we had modified, freezing the first layer, updating the weights of the rest of the layers, and fine-tuning the entire network. Furthermore, we trained a DL model from scratch to act as a baseline. We performed the experiments on the Edge Impulse platform. We evaluated the models based on plant-village, tea diseases and land use datasets. Fine-tuning and training the whole network produced the best precision, accuracy, recall, f-measure and sensitivity across the datasets. All three transfer learning schemes significantly reduced the training by more than half. Further, we deployed the fine-tuned model in detecting diseases in tea two months after the idea's conception, and it showed a good correlation with the experts' decisions. The evaluation results showed that it is viable to perform transfer learning among domains to accelerate solutions deployments. Additionally, Edge Impulse is ideal in resource-constrained environments, especially in developing countries lacking computing resources and expertise to train DL models from scratch. This insight can propel the development and rollout of various applications addressing the Sustainable Development Goals targeted at zero hunger and no poverty, among other goals.


Call for Papers

Sustainable environmental sensing systems, as the converging point of several technologies, can have tremendous potential to deliver social value. Multiple industries are progressively investing in environmental sustainability initiatives and technologies to improve quality of life while safeguarding natural resources. For example, the Internet of Things could be a game-changer for sustainability, as reported by the World Economic Forum, which indicated that 84% of IoT deployments are currently addressing, or have the potential to handle, some of the 17 SDGs.

Different challenges need to be addressed, from designing "better" sensing objects, i.e., energy-efficient, low power, and able to communicate via wide-area networks, to turning to the general public for deploying such systems and crowdsourcing data using social sensing or citizen science. With the "Networked sensing systems for a sustainable society" (NET4us) workshop, we aim to present and showcase the latest advances in sustainable networked sensing systems to monitor different urban and rural conditions to improve both people's quality of life and reduce climate change. Therefore, we seek original, unpublished papers addressing key issues and challenges in this growing area.

Topics of Interest

  • Analysis, simulation, testbed, and measurement campaigns
  • Converged communication, computing, and storage technologies
  • Crowd-sensing, human-centric sensing, Citizen science
  • Data collection, organization and dissemination methods
  • Digital twins networking and system
  • Emerging applications (e.g., IoT for Agriculture, Smart City, Smart Energy, Smart Health, Smart Transport, Public Safety, Humanitarian Technologies, Society 5.0)
  • Enabling communication technologies for IoT (e.g., wireless, optical, acoustic, molecular, quantum)
  • Energy efficiency, energy harvesting, power management, and green operation
  • Industrial Network and Industry 4.0
  • IoT for Open Science
  • Lightweight security design
  • Novel Internet of Everything (IoE) paradigms (e.g., Internet of Bio-Nano Things, Internet of Drones, Internet of People)
  • Rural area sensing and connectivity solutions
  • Smart objects, cooperative devices, environments, middleware, platforms, and tools
  • Socio-economic, security and privacy, standardization, policy, and regulatory aspects
  • TinyML systems and application
  • Ultra-low-power IoT technologies and embedded systems architectures
  • Virtualization: Multiple sensors aggregated, or sensors shared by multiple users

Submission Instructions

Submissions must be original, unpublished work, and not under consideration at another conference or journal. The maximum length of the submitted paper must be six (6) pages long, excluding references and appendices, in two-column 10pt ACM format. LaTeX sources can be found at this link. Papers must include author names and affiliations for single-blind peer reviewing by the PC. Authors of accepted submissions are expected to present and discuss their work at the workshop.

Please submit your paper via

If you have any questions or problems with your submission, please get in touch with Pietro Manzoni (

Important Dates

  • May 11, 2022 May 25, 2022

    Paper submission deadline

  • June 17, 2022

    Workshop paper notification

  • July 1, 2022

    Camera-ready deadline


  • General Chairs
  • Pietro Manzoni

    Universitat Politècnica de València, Spain

  • Catia Prandi

    University of Bologna, Italy - ITI/LARSyS

  • Program Committee Chairs
  • Marco Zennaro

    ICTP, Trieste, Italy

  • Nathalie Mitton

    Inria Lille-Nord Europe / FUN, France

  • Ruidong Li

    Kanazawa University, Japan

  • Program Committee
  • Alessandro Redondi

    Polytechnic of Milan, Italy

  • Anna Maria Vegni

    University of Roma Tre, Italy

  • Antonella Molinaro

    University Mediterranea of Reggio Calabria, Italy

  • Aris Leivadeas

    École de technologie supérieure ÉTS, Canada

  • Carlos Calafate

    Universitat Politecnica de Valencia, Spain

  • Claudia Campolo

    University Mediterranea of Reggio Calabria, Italy

  • Claudio E. Palazzi

    Università degli Studi di Padova, Italy

  • Dimitrio Zorbas

    Nazarbayev University, Kazakhstan

  • Enrique Hernandez-Orallo

    Universitat Politecnica de Valencia, Spain

  • Giovanni Delnevo

    University of Bologna, Italy

  • Javier Del Ser Lorente

    Tecnalia, Spain

  • José Maria Cecilia

    Universitat Politecnica de Valencia, Spain

  • Juan Carlos Cano

    Universitat Politecnica de Valencia, Spain

  • Lucas Pereira

    Técnico Lisboa, Portugal

  • Marco Picone

    Università degli studi di Modena e Reggio Emilia, Italy

  • Michela Meo

    Polytechnic University of Turin, Italy

  • Miroslav Voznak

    Technical University of Ostrava, Czech Republic

  • Roberto Girau

    Università di Bologna, Italy

  • Rosdiadee Nordin

    Universiti Kebangsaan Malaysia, Malaysia

  • Shintaro Mori

    Fukuoka University, Japan

  • Shoji Kasahara

    Nara Institute of Science and Technology, Japan

  • Silvia Mirri

    University of Bologna, Italy

  • Spyridon Mastorakis

    University of Nebraska at Omaha, USA

  • Valeria Loscri

    Inria Lille - Nord Europe, France

  • Vasilis Friderikos

    King's College London, United Kingdom