The SenSys’22 workshop and tutorial day will be Sunday, November 6th. We look forward to welcoming everyone to Boston!

SenSys’22 workshops have harmonized deadlines. For all workshops, key dates are:

Workshop Paper Due     September 5th, 2022  September 19th, 2002
Workshop Paper Notification     October 3rd, 2022  (Workshop-Specific)
Workshop Paper Camera Ready     October 17th, 2022 (Firm: Date from ACM Publishing)



The 10th International Workshop on Energy Harvesting and Energy-Neutral Systems (ENSSys’22)

Complementing the topics of SenSys 2022, this workshop will bring researchers together to explore the challenges, issues and opportunities in the research, design, and engineering of energy-harvesting, energy-neutral, and intermittent sensing systems. These are enabling technologies for future applications in smart energy, transportation, environmental monitoring and smart cities. Innovative solutions in hardware for energy scavenging, adaptive algorithms, and power management policies are needed to enable either uninterrupted or intermittent Operation.

ENSsys is now entering its 10th iteration, and is currently the longest running workshop at SenSys. As a workshop with a well-established core community, the emphasis of ENSsys this year is community growth and outreach. ENSsys will feature demos that provide “hands-on” (including remote “hands-on,” as appropriate) experience with energy harvesting systems as well as paper sessions with full papers and position papers.

ENSSys’22 website:

First Workshop on Urban Sensor Networks (USN’22)

Sustainable and resilient urbanization will be one of humanity’s grand challenges as the global urban population grows from about half of all people today to about two-thirds of all people by 2050. The rapid growth of cities presents numerous opportunities to use internet of things and wireless technologies to provide fine-grained insights into air quality, public health, mobility, and more to address the challenges that dense urban populations face today and in the future. Creating truly effective and transformative technology solutions requires a multidisciplinary approach incorporating both computer scientists and electrical engineers developing state of the art sensing, communication, and data science techniques as well as domain experts from urban planning, civil engineering, atmospheric science, and public health. Additionally, deploying sensing solutions at scale remains challenging and requires connecting with city and community leaders and working in partnership to develop effective solutions to power, connect, calibrate, and maintain a network for several years.

The Urban Sensor Networks (USN) workshop aims to bring together this diverse community of researchers to share strategies to make the smart city dream become a reality. The workshop will provide a platform for attendees to share their work and to learn how it fits as part of the broader USN field. Our goal is to connect researchers working in high impact urban sensing problem domains with the cutting-edge technologies of SenSys that can make these solutions a reality. Attendees will have the opportunities to present short papers on their work, hear invited talks from speakers who have implemented large scale USNs, and participate in a discussion forum to highlight common challenges and approaches with the goal of establishing new research collaborations.

USN'22 website:

Fifth International SenSys+BuildSys Workshop on Data: Acquisition to Analysis (DATA’22)

As the enthusiasm for and success of the Internet of Things (IoT), Cyber-Physical Systems (CPS), and Smart Buildings grows, so too does the volume and variety of data collected by these systems. How do we ensure that this data is of high quality, and how do we maximize the utility of collected data such that many projects can benefit from the time, cost, and effort of deployments?

The Data: Acquisition To Analysis (DATA) workshop aims to look broadly at interesting data from interesting sensing systems. The workshop considers problems, solutions, and results from all across the real-world data pipeline. We solicit submissions on unexpected challenges and solutions in the collection of datasets, on new and novel datasets of interest to the community, and on experiences and results—explicitly including negative results—in using prior datasets to develop new insights.

The workshop aims to bring together a community of application researchers and algorithm researchers in the sensing systems and building domains to promote breakthroughs from integration of the generators and users of datasets. The workshop will foster cross-domain understanding by enabling both the understanding of application needs and data collection limitations.

DATA'22 website:

The Fourth ACM International Workshop on Blockchain-enabled Networked Sensor Systems (BlockSys’22)

Sensing technologies are being widely used in environments such as smart home, smart building, smart cities, 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 a possibility to 1) ensure data protection, 2) monetize information exchange, 3) reduce sharing and maintenance costs, and 4) to 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, edge computing, and blockchain. We aim to set up a stage for industry and academia to share wins and lessons combining both disciplines. We welcome research contributions on applications, systems, networks, security, and privacy.

BlockSys'22 website:

The Fourth Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT)

Artificial intelligence (AI) and machine learning (ML) are key enabling technologies for many Internet of Things (IoT) applications. However, the collection and processing of data for AI and ML is very challenging in the IoT domain. For example, there are usually a large number of low-powered sensors deployed in large geographical areas with possibly intermittent network connectivity. The sensors and their collected data may be owned by different users or organizations, which can bring further obstacles to data collection due to privacy concerns and noisy labels provided by different users. The successful application of AI/ML approaches in such scenarios with noisy and decentralized data is difficult. In addition, the amount of collected data that can be used for training AI/ML models is usually proportional to the number of users in the system, but the system may not be able to attract many users without a well-trained AI/ML model, and it is challenging to solve this dilemma.


First Workshop on Internet-of-Things and Sensing for Agriculture and Food Systems (AgSys)

Meeting the food and nutrition demands of the world population of over 10 billion by 2050 is a grand challenge that humanity is faced with. The UN in its 2019 State of Food and Agriculture report identified that despite these needs, over 40% of all food grown today is wasted. This can happen anywhere from on the farm to post-harvest stages in the food supply chain. Factors influencing this unsustainable wastage range anywhere from inadequate monitoring to effects of climate change. Data driven agriculture has already shown promise in creating more sustainable agriculture practices. Proper monitoring of the environment in which food grows, is stored or transported can also greatly help reduce this waste and make the Agri Food industry on the whole more sustainable. With this industry spanning across the globe to source food and meet demands, there are various areas of technological innovation that have direct impact; Such as

  • The need for low-cost and low power sensing solutions that enable large scale monitoring. Enabled by IoT and low-power sensing tech, literature in this area has impacted farming and agriculture practices, and are slowly having impact on the post-harvest supply chain as well.

  • Remote sensing with drones and satellites has helped bring timely automation to several processes in large area farming. This has resulted in reduced need for chemical interventions, better carbon sequestration practices etc.

  • Innovation in communication that spans multiple continents. With food traveling across the earth to reach its destination, connectivity across this global path is a major challenge that is being currently addressed. Innovation with mobile IoT communication and Satellite sensor networks are paving way to create this global network.

With these challenges in mind, we’d like to provide a platform for researchers to present their innovations in IoT, sensing as well as Communication networks that can help create a sustainable future for the food industry. With the involvement of our academic and Industry partners, we hope to refine the problem space and connect the technology with practical needs that can help us meet the future food demands.


The Fourth Workshop on Continual and Multimodal Learning for Internet of Things (CML-IOT)

The growth of the Internet of Things (IoT) has brought an ever-growing number of connected sensors, continuously streaming large quantities of multimodal data. These data come from a wide range of different sensing modalities and have distinct statistical characteristics over time, which are hardly captured by traditional learning methods. Continual and multimodal learning allows the integration, adaptation, and generalization of knowledge learned from experiential and heterogeneous data to new situations. Therefore, continual and multimodal learning is an important step to enable efficient information inference for IoT systems. Major challenges for continual and multimodal learning for real-world data include:

  • Fusing and transferring knowledge between multimodal data under constrained resources.
  • Continual learning, despite missing, imbalanced or noisy data under constrained resources.
  • Preserving privacy and retaining security when learning knowledge from multimodal data collected by multiple stakeholders.
  • Developing large-scale distributed learning systems to efficiently learn from continual and multimodal data.

The Workshop on Continual and Multimodal Learning for Internet of Things (CML-IOT) aims to explore the intersection of continual machine learning and multimodal modeling with applications on the Internet of Things. We welcome works addressing these issues in diverse domains as well as algorithmic and systematic approaches to leverage continual learning on multimodal data. We aim to provide researchers a platform to share their novel ideas and work on continual and multimodal data for IoT and real-world computing systems and foster an interdisciplinary community to tackle these challenges together.


Tutorial: Energy storage for low-power wireless embedded systems

Description: Many wireless embedded systems have severe constraints on their operation because they rely on a battery or supercapacitor for their power needs. Low-power and energy-efficient operation has therefore been a central topic of research for the community. However, system design and performance evaluation have generally focused on power consumption and energy harvesting, rather than the energy storage device itself. A better understanding of energy storage devices can enable the development of systems and software that achieve longer lifetime, as well as to more accurate performance evaluation and prediction.

This half-day tutorial will give attendees a solid introduction to modern energy storage devices. The first part of the tutorial is an overview of batteries and supercapacitors and their charge and discharge behavior, focusing on low-power embedded wireless devices. We will also discuss the implications of recent developments in energy storage technology, such as small LTO batteries and hybrid supercapacitors. The second part of the tutorial is an overview of techniques for modeling and predicting battery and device lifetime – this remains an important open problem. Finally, our UU CoRe battery testbed is a unique resource for large-scale, application-oriented measurement of energy storage devices. In the third part of the tutorial, we will present a testbed demonstration and give attendees the opportunity to collect some traces.

Organizers: The tutorial is organized by Laura Marie Feeney, Per Gunningberg, and Christian Rohner ({lmfeeney, perg, chrohner}, all from Uppsala University. The organizers are networking and systems researchers who have been active for over ten years in measurement and modeling of energy storage devices for low-power wireless networks. As “computer scientists who have had to learn battery stuff”, they are well placed to translate their experience to the SenSys community.

Practical information: This is a half-day tutorial. Approximately 2/3 will be lecture and discussion; the remaining 1/3 will be a testbed demonstration and practical measurement activities.

Tutorial: Location Estimation from the Ground Up

The tutorial will present the fundamental ideas involved in estimating a location from indirect measurements (time of arrival, angle of arrival, distance, etc.) and will briefly explain how these ideas are used in both well known localization system, like GNSS, and in the location estimation system that I built, ATLAS.

In particular, the tutorial will cover forming constraints that relate location to observed quantities (e.g. time of arrival), maximum likelihood and least-squares minimization, estimating uncertainty (the covariance matrix of estimated locations), a-priori information theoretic bounds (e.g., the Cramer-Rao bound), joint vs. sequential estimation (e.g., estimating arrival times and then estimation locations from arrival times), state-space modeling (Kalman filtering and smoothing, including fusion of information from inertial sensors), and mixed-integer modeling (e.g. RTK and coarse-time navigation). The tutorial will also cover important algorithmic techniques, including elimination of linear dependencies in separable problems, linear transformation of derivatives, and mixed-integer solvers.

Obviously, in half a day there is only time to give an overview of these techniques, but this overview will be sufficient for understanding the big picture, for understanding the intuition behind these techniques and results (including ones that are typically difficult to understand, like Kalman filtering and mixed-integer modeling), and will provide a solid basis for subsequent self study.

Tutorial Website: