LoRaWAN is a Low-Power Wide Area Network (LPWAN) technology designed for Internet of Things (IoT) deployments; this paper presents experiences from deploying a city-scale LoRaWAN network across Southampton, UK. This network was deployed to support an installation of air quality monitors and to explore the capabilities of LoRaWAN. This deployment uses a mixture of commercial off-the-shelf gateways and custom gateways. These gateway locations were chosen based on network access, site permission and accessibility, and are not necessarily the best locations theoretically. Over 135,000 messages have been transmitted by the twenty devices analysed. Over the course of the complete deployment, 72.4 % of the messages were successfully received by the data server. Of the messages that were received, 99% were received within 10 s of transmission. We conclude that LoRaWAN is an applicable communication technology for city-scale air quality monitoring and other smart city applications.

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min, and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

This paper examines dynamic identity, as it pertains to the Internet of Things (IoT), and explores the practical implementation of a mitigation technique for some of the key weaknesses of a conventional dynamic identity model. This paper explores human-centric and machine-based observer approaches for confirming device identity, permitting automated identity confirmation for deployed systems. It also assesses the advantages of dynamic identity in the context of identity revocation permitting secure change of ownership for IoT devices. The paper explores use-cases for human and machine-based observation for authentication of device identity when devices join a Command and Control(C2) network, and considers the relative merits for these two approaches for different types of system.

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