Smart homes reduce human intervention in controlling the Heating Ventilation and Air Conditioning (HVAC) systems for maintaining a comfortable indoor environment. The embedded intelligence in the sensor nodes is limited due to the limited processing power and memory in the sensor node. Cloud computing has become increasingly popular due to its capability of providing computer utilities as internet services. In this work, a model for intelligent controller by integrating Internet of Things (IoT) with cloud computing and web services is proposed. The wireless sensor nodes for monitoring the indoor environment and HVAC inlet air, and wireless base station for controlling the actuators of HVAC have been developed. The sensor nodes and base station communicate through RF transceivers at 915 MHz. Random neural network (RNN) models are used for estimating the number of occupants, and for estimating the Predicted mean vote (PMV) based setpoints for controlling the heating, ventilation and cooling of the building. Three test cases are studied (Case 1-data storage and implementation of RNN models on the cloud, Case 2-RNN models implementation on base station, Case 3-distributed implementation of RNN models on sensor nodes and base stations) for determining the best architecture in terms of power consumption. The results have shown that by embedding the intelligence in the base station and sensor nodes (i.e. Case 3), the power consumption of the intelligent controller was 4.4% less than Case 1 and 19.23 % less than Case 2.

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes.

Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.

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The critical requirements for devices connected to the Internet of Things (IoT) are long battery life, long coverage range, and low deployment cost. In this work, we developed a machine learning based smart controller for the HVAC of commercial building using LoRa and compared it with short range RF communication in an indoor setting. The comparison was made in terms of battery life, coverage range and memory size. The effect of changing the transmission power of LoRa on battery consumption of the sensor node was also evaluated. Results show that coverage range of LoRa was 60.4% more than short range communication inside a building. The smart controller was capable of identifying when the room was unoccupied and turning off the HVAC which reduced the energy consumption up to 19.8%. Introduction According to Cisco [1], 50 billion devices will be connected to the internet by 2020. Different types of devices can be connected to the internet from small devices (RFIDs, Sensors) to large devices like TVs, Cameras etc., and mobile devices like vehicles. The Internet of Things (IoT) interconnects these devices and exchanges data between these devices. Therefore, Machine to Machine (M2M) communication is required for exchange of data between devices in IoT. Communication between devices in IoT has already been done by multi-hop short range communication (ZigBee, Bluetooth and RF communication) [2]-[4]. Short range communication (Zigbee, Bluetooth, RF communication) operates in unlicensed ISM bands centred at 2.4 GHz, 868/915 MHz. 433 MHz and 169 MHz. The coverage of these short-range communication (unidirectional or bi-directional) is usually in few meters but they can achieve high data rate. In applications where the distance between sensor nodes and base station is large, short range communication standards are not feasible. More recently industry has been developing in low power wide area networks (LPWAN). LPWAN is introduced as a promising alternative between multi-hop short range communication which operates in unlicensed frequency band and long range cellular communication which operates in licensed frequency band. Basic requirements for LPWAN are long coverage, less power consumption, low deployment cost, low device cost, support large number of devices and easily expandable [5]. Long range (LoRa) alliance [6], Sigfox [7], and Weightless [8] are examples of LPWAN. In our previous work [9], we implemented the smart controller for heating ventilation and cooling (HVAC) of commercial building. Random neural networks (RNN) were used for machine learning of