0098-3063 (c) 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCE.2019.2962605, IEEE Transactions on Consumer Electronics 1 ‘Smart’ Is Not Free: Energy Consumption of Consumer Home Automation Systems Chrispin Gray, Member, IEEE, Robert Ayre, Kerry Hinton, and Leith Campbell, Member, IEEE, Abstract—The proliferation of consumer Home Automation Systems (HAS), which is increasingly based on the Internet of Things (IoT) architecture, comes with added costs for the additional electrical energy required to power the automation interfaces and the standby energy consumption required to maintain connectivity and/or “smartness”. In this paper, we use a bottom-up approach to develop novel system-level energy consumption models for consumer HAS devices and quantify the energy consumption for a typical HAS. We then assess the potential impact on global Information and Communications Technology (ICT) energy use. We show that, on average, HAS may consume over one-third of the annual energy used in a mid- sized home, with non-trivial impact on the global ICT energy footprint. Index Terms—Energy consumption; power models; smart home; home automation; Internet of Things (IoT). I. I NTRODUCTION A Home Automation System (HAS), which consists of sensors and actuators and the network that connects them, basically enables a “Smart Home”, which will also in- clude consumer “smart” devices like TV sets and refrigerators. Here, we focus just on the HAS itself. A recent report estimates that home automation would make up nearly 50% of all Internet of Things (IoT) devices by 2020 [1]. While the deployment of billions of devices through IoT services could offer many benefits, it comes with the need for additional electrical energy. Research work in the area of consumer HAS energy consumption has, for the most part, been focused on the monitoring, design and development of Smart Home Energy Management Systems (SHEMS) using a number of wire- less network technology options (e.g. Bluetooth Low Energy (BLE), ZigBee, Wi-Fi), while few have focused on developing and optimising energy consumption models for HAS. Han and Lim [2] proposed a context-aware SHEMS, using a ZigBee sensor network, with a new on-demand routing protocol. Manuscripts received August 2, 2019. This work was supported by Alcatel- Lucent Bell Labs, The State Government of Victoria, Australia, and The University of Melbourne. Chrispin Gray is with Department of Electrical and Electronic Engineer- ing, University of Melbourne, Parkville, Victoria 3010, Australia (e-mail: chrispin.gray@unimelb.edu.au) Robert Ayre is with the Department of Electrical and Electronic Engi- neering, University of Melbourne, Parkville, Victoria 3010, Australia (e-mail: rayre@unimelb.edu.au) Kerry Hinton is with the Department of Electrical and Electronic Engi- neering, University of Melbourne, Parkville, Victoria 3010, Australia (e-mail: k.hinton@unimelb.edu.au) Leith Campbell is with the Department of Electrical and Electronic Engi- neering, University of Melbourne, Parkville, Victoria 3010, Australia (e-mail: leith.campbell@unimelb.edu.au) The system aims to automate consumer device control while providing timely energy usage data to consumers. Han et al. [3] proposed a ZigBee and PLC-based SHEMS. Their system simultaneously monitors energy consumption and renewable energy generation to automatically schedule device usage, using a home server, with the goal of cost minimisation. Chen and Lin [4] employed smart plugs to monitor appliance (non- smart) load characteristics and applied analytics to determine power-down opportunities to minimise energy usage. A study by Friedli et. al. [5] for the International Energy Agency (IEA) estimated the global energy consumption of selected IoT use-case applications using a combination of measurement and publicly available manufacturer datasheets. Mehdi and Roshchin [6] studied different home electric appliances based on their load characteristics. By applying task-scheduling techniques, Mehdi and Roshchin presented models that are optimised to minimise energy consumption. Langhammer and Kays [7] presented energy models for HAS networks and proposed a performance evaluation metric for a host of indoor, HAS-enabling wireless network technologies, including BLE, KNX-RF and IEEE 802.15.4 variants. In both [6] and [7], however, critical enabling devices like the gateways are not considered. The authors of this paper have further studied and compared the power consumption design characteristics of five commonly employed wireless technology options for HAS, including BLE, ZigBee and Wi-Fi as detailed in [8]. In this paper, we estimate the energy consumption of consumer HAS from the household to global level, using a bottom-up approach. We use a combination of direct measure- ment and modelling to estimate energy consumption of each component of a typical modern consumer HAS in the context of the IoT. We then estimate the global energy impact of these systems if current IoT device installation projections [1], [5] come to pass. Modern consumer HAS services generally include novel, network-enabled IoT devices, a gateway device and an attached cloud service. Hence, traditional network- enabled devices like Smart TVs and home entertainment systems are not included. For smart consumer appliances, only the added IoT module (e.g. sensor and communication modules) is considered. The paper is organised as follows. The network architecture of a consumer HAS is described in Section II and the device measurements described in Section III. Simplified energy mod- els are detailed in Section IV, with network traffic measure- ments from an example HAS application given in Section V. The energy consumption estimates for an average household fitted with a fully-equipped HAS are given in Section VI. Published deployment scenarios are used to estimate the