- The 2nd Workshop on Blockchain-enabled Networked Sensor Systems (BlockSys'19)
- The 7th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys)
- The 1st Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)
- The 2nd International Workshop on Data: Acquisition To Analysis (DATA)
- The 1st International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT)
SenSys and BuildSys are co-located this year! Please see if your work fits with any of the BuildSys 2019 co-located workshops.
Networked sensor systems as a key component of internet of things (IoT) have been widely used in various environments such as smart home, smart building, vehicular network, wearable computing, robotics systems, industrial constrol systems, etc. They capture human and physical-world dynamics and feed the data into large-scale analytical backends. Today's cloud-centric paradigm, however, 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. As a result, complementary technologies that can offer to ensure data protection, incentivize information exchange, and reduce sharing and maintenance costs are highly desired.
We have seen the increasing interest in tackling such problems by using emerging blockchain and other distributed ledger technologies. BlockSys'19 aims to set up a stage for industry and academia to share wins and lessons: we solicit high quality research and position papers that address opportunities and challenges at the intersection of networked sensing/IoT and blockchain. We also welcome on-going work, demos and tutorials.
- Gowri Sankar Ramachandran (University of Southern California, USA)
- Nairan Zhang (Facebook, USA)
Complementing the topics of ACM SenSys 2019, ENSsys 2019 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 an enabling technology 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 and intermittent operation.
High quality technical articles are solicited, describing advances in sensing systems powered by energy harvesting, as well as those which describe practical deployments and implementation experiences.
Workshop paper submission link: https://enssys19.hotcrp.com
The 1st Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML) focuses on the work that combines sensor signals from the physical world with machine learning, particularly in ways that are distributed to the device or use edge and fog computing. The development and deployment of ML at the very edge remain a technological challenge constrained by computing, memory, energy, network bandwidth and data privacy and security limitations. This is especially true for battery operated devices and always-on use cases and applications. This workshop will provide a forum for sensing, networking and machine learning researchers to present and share their latest research on building machine learning enabled sensor systems. Sensys-ML focuses on providing extensive feedback on Work In Progress papers involving machine learning (TinyML/UltraML) on sensor systems.
Workshop paper submission link: https://sensys-ml19.hotcrp.com
- Poonam Yadav (University of Cambridge, UK)
- Valerie Liptak (Amazon, USA)
As the enthusiasm for and sucess 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 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.
- Shijia Pan (Carnegie Mellon University, USA)
- Pat Pannuto (University of California at Berkeley, USA)
- Flora Salim (RMIT University, Australia)
- Mikkel Baun Kjærgaard (University of Southern Denmark, Denmark)
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.
This workshop focuses on how to address the above and other unique challenges of applying AI/ML in IoT systems. We invite researchers and practitioners to submit papers describing original work, experiences, or vision related to the entire lifecycle of an IoT system powered by AI and ML.
Workshop paper submission link: https://aichallengeiot19.hotcrp.com
- Shiqiang Wang (IBM T.J. Watson Research Center, USA)
- Mani Srivastava (University of California at Los Angeles, USA)
Wireless network testbeds are important for realistic, at-scale experimental evaluation of new radio technologies, protocols and network architectures. With a somewhat belated reality check on 5G, larger tests and demonstration sites have become even more important in the validation of next generation wireless platforms. In order to address at least some of the challenges of advancing fundamental wireless research, the US National Science Foundation (NSF), in collaboration with the 28-member industry consortium, has formed a public-private partnership to support the creation of up to four city-scale experimental platforms - the NSF’s Platforms for Advanced Wireless Research (PAWR) initiative (https://advancedwireless.org).
This tutorial will introduce the PAWR COSMOS ("Cloud enhanced Open Software defined MObile wireless testbed for city-Scale deployment") platform (https://www.cosmos-lab.org). COSMOS is a joint project involving Rutgers, Columbia, and NYU along with several partner organizations including New York City, City College of New York, University of Arizona, Silicon Harlem, and IBM. The COSMOS advanced wireless testbed is being deployed in New York City with technical focus on ultra-high-bandwidth and low-latency wireless communications with tightly coupled edge computing, and emphasis on the millimeter-wave (mmWave) radio communications and dynamic optical switching.
Tutorial registration link: https://forms.gle/9grPhxR3U8HtMGUq5
- Ivan Seskar (Rutgers University, USA)
- Dipankar Raychaudhuri (Rutgers University, USA)
- Thanasis Korakis (New York University, USA)
- Gil Zussman (Columbia University, USA)
- Tingjun Chen (Columbia University, USA)
- Craig Gutterman (Columbia University, USA)
- Jakub Kolodziejski (Rutgers University, USA)
- Michael Sherman (Rutgers University, USA)
- Panagiotis Skrimponis (New York University, USA)