Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space.
CCS CONCEPTS• Computer systems organization → Real-time system architecture; • Information systems → Information integration.
Edge computing and Federated Learning (FL) can work in tandem to address issues related to privacy and collaborative distributed learning in untrusted IoT environments. However, deployment of FL in resource-constrained IoT devices faces challenges including asynchronous participation of such devices in training, and the need to prevent malicious devices from participating. To address these challenges we present CoLearn, which build on the open-source Manufacturer Usage Description (MUD) implementation osMUD and the FL framework PySyft. We deploy CoLearn on resourceconstrained devices in a lab environment to demonstrate (i) an asynchronous participation mechanism for IoT devices in machine learning model training using a publish/subscribe architecture, (ii) a mechanism for reducing the attack surface in FL architecture by allowing only IoT MUD-compliant devices to participate in the training phases, and (iii) a trade-of between communication bandwidth usage, training time and device temperature (thermal fatigue). CCS CONCEPTS • Networks → Network algorithms; Network experimentation; Network privacy and anonymity; • Computer systems organization → Embedded and cyber-physical systems; • Computing methodologies → Neural networks; Machine learning.
Today, the personal data of many people is stored in the databases of a large number of organizations. Some of them use this data for advertising purposes, disturbing customers with constant calls and SMS messages. The problem is that one cannot tell for sure by looking at an unfamiliar number whether it is an unwanted call from an organization. One can use specialized software tools to solve this problem. This article is devoted to comparing their functionality.
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