Detecting anomalies within smart buildings using do-it-yourself internet of things

Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.EPSRC PETRAS (EP/S035362/1) and GCHQ National Resilience Fellowshi

Cyber physical anomaly detection for smart homes: A survey

Twenty-first-century human beings spend more than 90\% of their time in indoor environments. The emergence of cyber systems in the physical world has a plethora of benefits towards optimising resources and improving living standards. However, because of significant vulnerabilities in cyber systems, connected physical spaces are exposed to privacy risks in addition to existing and novel security challenges. To mitigate these risks and challenges, researchers opt for anomaly detection techniques. Particularly in smart home environments, the anomaly detection techniques are either focused on network traffic (cyber phenomena) or environmental (physical phenomena) sensors' data. This paper reviewed anomaly detection techniques presented for smart home environments using cyber data and physical data in the past. We categorise anomalies as known and unknown in smart homes. We also compare publicly available datasets for anomaly detection in smart home environments. In the end, we discuss essential key considerations and provide a decision-making framework towards supporting the implementation of anomaly detection systems for smart homes

Reliability comparison of transmit/receive diversity and error control coding in low-power medium access control protocols

Low-power medium access control (MAC) protocols used for communication of energy constraint wireless embedded devices do not cope well with situations where transmission channels are highly erroneous. Existing MAC protocols discard corrupted messages which lead to costly retransmissions. To improve transmission performance, it is possible to include an error correction scheme and transmit/receive diversity. It is possible to add redundant information to transmitted packets in order to recover data from corrupted packets. It is also possible to make use of transmit/receive diversity via multiple antennas to improve error resiliency of transmissions. Both schemes may be used in conjunction to further improve the performance. In this study, the authors show how an error correction scheme and transmit/receive diversity can be integrated in low-power MAC protocols. Furthermore, the authors investigate the achievable performance gains of both methods. This is important as both methods have associated costs (processing requirements; additional antennas and power) and for a given communication situation it must be decided which methods should be employed. The€™ results show that, in many practical situations, error control coding outperforms transmission diversity; however, if very high reliability is required, it is useful to employ both schemes together