Workshops

Final list of accepted SenSys 2024 workshops

Note: All workshops will take place on November 4 (Monday), 2024

Workshop 1: Sixth ACM International Workshop on Blockchain-enabled Networked Sensor Systems (BlockSys 2024)

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

Sensing technologies are being widely used in environments such as smart homes, smart buildings, vehicular networks, etc. Information collected from networked sensor systems is valuable if shared and tracked correctly. However, today’s sensing-cloud paradigm does not genetically support trust management and privacy preservation; it also does not encourage information sharing in multi-stakeholder settings through incentives and payment mechanisms. The emerging blockchain and other distributed ledger technologies offer the possibility to 1) ensure data protection, 2) monetise information exchange, 3) reduce sharing and maintenance costs, and 4) manage trust in multi-stakeholder settings. We solicit high-quality position papers and research papers that address opportunities and challenges at the intersection of networked sensing/IoT, smart cities, and blockchain. We aim to set up a stage for industry and academia to share wins and lessons combining both disciplines. We welcome contributions to all relevant research on applications, systems, networks, and security.

Organizers

Bhaskar Krishnamachari (University of Southern California, USA)
Salil Kanhere (University of New South Wales, Australia)
Gowri Sankar Ramachandran (Queensland University of Technology, Australia)
Duc (David) A. Tran (University of Massachusetts at Boston, USA)
Zhiyuan Jiang (Shanghai University, China)

Website: https://acmblocksys.github.io/blocksys2024/

Submission: BlockSys 2024 Submission System




Workshop 2: Resource-efficient Mobile and Embedded LLM System in AIoT

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

The workshop aims to address the emerging challenges and opportunities in resource-efficient mobile and embedded Deep Learning (DL) systems within the context of the Artificial Intelligence of Things (AIoT). AIoT systems represent a convergence of AI and IoT devices, enabling intelligent perception and decision-making capabilities. However, traditional IoT frameworks often operate within rigid structures, limiting adaptability and hindering effective coordination between sensing and computation, especially in dynamic environments. This workshop focuses on enhancing the adaptability of AIoT systems through continual optimization of sensing and computing processes. By maximizing resource utilization and fostering self-evolving AIoT systems, we aim to improve real-time responsiveness and enable seamless integration of AI and IoT functionalities. The workshop seeks to explore innovative solutions that overcome the limitations of traditional IoT frameworks, paving the way for more responsive, adaptive, and intelligent AIoT systems.

Organizers

Bin Guo (Northwestern Polytechnical University, China)
Lei Xie (Nanjing University, China)
Fan Dang (Tsinghua University, China)
Sicong Liu (Northwestern Polytechnical University, China)
Chuyu Wang (Nanjing University, China)

Website: https://rmels2024.github.io/RMELS2024/

Submission: RMEL 2024 Submission System




Workshop 3: 12th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys 2024)

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

Sensing systems are one of the technological cornerstones for future applications in smart energy, future transportation, environmental monitoring and smart cities. Today, one of the major challenges of using such systems in real deployments is related to energy consumption and guaranteeing adequate lifetime. The field of energy harvesting is making significant strides and is beginning to gain more traction in the field of sensing systems and the Internet of Things. Innovative solutions in on-board hardware for energy scavenging, energy adaptive algorithms, and power management policies of the nodes in the network are the ultimate frontiers, and they enable unlimited and either uninterrupted or deliberately intermittent operation (zero-energy networks). The workshop brings together members of the research community and industrial practitioners to systematically explore the challenges, issues and opportunities in the research, design, and engineering of energy-harvesting, energy-neutral, and intermittent techniques for distributed sensing systems. The organisers solicit novel and previously unpublished technical articles describing advances in sensing systems to achieve an energy-neutral condition through energy harvesting and aggressive power saving mechanisms. Practical deployments and implementation experiences are warmly invited to demonstrate the effectiveness of zero-energy networks.

Organizers

Jingtong Hu (University of Pittsburgh, USA)
Pi-Cheng Hsiu (Academia Sinica, Taiwan)

Website: https://www.enssys.org

Submission: ENSsys 2024 Submission System




Workshop 4: First International Workshop on IoT Datasets for Multi-modal Large Model

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

The topic of Internet of Things (IoT) datasets for multi-modal large models is centered on the integration and utilization of diverse data sources generated by IoT devices for training and enhancing the capabilities of large-scale machine learning models. IoT datasets typically involve a wide range of data types, including but not limited to sensor data, video streams, audio signals, and text logs. These datasets capture various aspects of the physical world, from environmental conditions to human activities, and provide rich contextual information for model training. The key aspect of multi-modal large models is their ability to process and analyze multiple types of data simultaneously. By leveraging IoT datasets, these models can gain a deeper understanding of the world by combining different types of information. For example, a model trained on both sensor data and video streams can detect patterns and correlations between environmental conditions and human behavior, enabling more accurate predictions and decision-making. There are still many challenges and opportunities associated with handling large-scale, distributed, and heterogeneous IoT data. This includes issues such as data collection, preprocessing, storage, and privacy concerns, as well as the development of efficient algorithms and techniques for data integration and analysis. To overcome these challenges, we would like to invite international researchers to share and present their latest research progresses on datasets, methodology and application about IoT datasets for multi-modal large models.

Organizers

Weixi Gu (China Academy of Industrial Internet, China)
Shuai Wang (Hong Kong University of Science and Technology, China)
Weiwei Jiang (Beijing University of Posts and Telecommunications, China)

Website: https://jwwthu.github.io/SenSys2024.html

Submission: Submission System




Workshop 5: First International Workshop on Radio Frequency (RF) Computing (RFCom 2024)

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

RF computing is a new computing paradigm, which directly utilizes the RF signal as both the information carrier and the computation object within the RF space to achieve signal processing and transformation. For example, the RF computing device can manipulate the RF signal from the properties of amplitude, frequency, phase, propagation direction, and polarization characteristics, to accomplish the information injection, extraction, and modification. Compared with the purely digital computing, the RF computing is well-adapted to the RF signals' analog and continuous physical properties. By omitting or simplifying the frequent analog-to-digital conversion during the information process, the RF computing can significantly reduce power consumption, eliminate computational latency, and enhance computation efficiency. Moreover, the RF computing also expands the design space of IoT systems, making the programmable IoT systems possible. This workshop will bring together researchers and system developers from academia and industry, to share ideas and experiences related to RF computing for IoT.

Organizers

Xiuzhen Guo (Zhejiang University, China)
Xiaolong Zheng (Beijing University of Posts and Telecommunications, China)
Chenshu Wu (The University of Hong Kong)
Yuan He (Tsinghua University, China)



Workshop 6: Third International Workshop on Social and Metaverse Computing, Sensing and Networking (SocialMeta 2024)

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

While online social network services and applications have found unprecedented presence today (e.g., messaging, gaming, advertising, and recommendation), multiple novel and emerging technologies have already started to shape the next-generation online social networks, including the “metaverse”. On one hand, Augmented/Virtual Reality (AR/VR), Internet of Things (IoT), and Multi-access Edge Computing (MEC) are bringing new mobile computing and communication paradigms for provisioning and accessing online social networks, social media, massive gaming, etc. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML), characterized by recent breakthroughs of large language model, are finding successful applications to online social networks, including detecting/preventing spam, fraud, and misinformation. Also it enhances service experience via intelligent user interactions (e.g., recognition) with texts, images, and voices. Yet, how to exploit and synergize all such state-of-the-art technologies to revolutionize online social networks is still in its infancy and demands comprehensive investigation and research. The goal of the series of the International Workshop on Social and Metaverse Computing, Sensing and Networking (SocialMeta) is to bring together scientists, researchers, engineers, and practitioners to identify new problems and discuss the latest research ideas and results regarding online social networks, sensing and the metaverse.

Organizers

Qingyuan Gong (Fudan University, China)
Xinlei He (Hong Kong University of Science and Technology (Guangzhou), China)
Yupeng Li (Hong Kong Baptist University, China)
Lei Jiao (University of Oregon, USA)

Website: https://socialmeta2024.github.io

Submission: SocialMeta 2024 Submission System




Workshop 7: Second International Workshop on Human-Centered Sensing, Networking, and Multi-Device Systems (HumanSys 2024)

Workshop Paper Due     4 September 2024, 23:59 AoE
Workshop Paper Notification     2 October 2024, 23:59 AoE
Workshop Paper Camera Ready     16 October 2024, 23:59 AoE

Workshop goal

With the growing urbanization and aging population worldwide, there is an increasing emphasis on human health, behaviors, and experience in daily living spaces, calling for the development of human-centered sensing, networking, and intelligent systems. Many important tasks, such as sensing multi-modal interaction between humans and environments, developing novel human-centered AI models, and integrating user needs into the design of intelligent systems, etc., have not yet been well understood and fully taken into consideration in current system developments. This workshop thus focuses on fundamental problems in this area, involving human activities and interactions with future intelligent systems. We will bring together researchers, developers, and practitioners in related fields from academia, industry, and service providers, to share ideas and experiences related to human-centered technologies and applications.

Organizers

Yiwen Dong (Stanford University, USA)
Xiuzhen Guo (Zhejiang University, China)
Alessandro Montanari (Nokia Bell Labs, UK)
Danny Hughes (KU Leuven, Belgium)
Zhi Wang (Zhejiang University, China)