The incremented popularity of Internet of Things (IoT), thanks to improvements both in hardware and software of sensors over the last years, enables the possibility to monitor and gather any kind of data. Additionally, the arrangement of heterogeneous sensors, capable of perceiving information about their surroundings, into a rich Wireless Sensor Network (WSN), allows the appearance of complex systems in which resources are managed more efficiently. Smart cities, buildings, parkings, emergency services are appearing, where control over energy consumption and better sustainability are coupled with an improvement of the comfort of occupants. In this paper, we address the problem of energy optimization in smart buildings, considering both the planning and operational aspects. Specifically, the first aim is to propose an optimal deployment of the WSN inside a building. For this, we present a model able to identify the optimal locations for different types of sensors and gateways, by optimizing energy consumption while fulfilling connectivity, resource, protection, and clustering coverage constraints. Once the IoT system is deployed, we address the problem of how the building actually functions, according to the behaviour of the occupants. In particular, we propose a Building Management System (BMS) capable of efficiently and automatically manage the building elements using human behavioural models, thus lowering the overall building energy consumption whilst maintaining acceptable levels of comfort.

Internet of Things or IoT is meant to be the future of the current Internet. It is commonly defined as a network of physical and virtual objects, devices or things that are capable of collecting surrounding data and exchanging it between them or through the Internet. To enable these data collection, devices are embedded with sensors, software and electronics and the exchange capability is achieved by connecting them to local area networks or to the Internet.The origins of the Internet of Things are diffuse. Even though the word was first coined in 1999 by Kevin Ashton, co-founder and executive director of the Auto-ID Center at MIT, for companies such as CISCO, the IoT was born in 2009, when more devices than people were connected to the Internet. At that time, the number of connected devices were 10 billion, but the expectations are generous. It is thought that by 2020, more than 50 billion devices will be connected to the Internet.As it can be extracted from the numbers, during the last few years, the Internet of Things has seen an unexpected increase in popularity, mainly thanks to the following technology improvements:• Smaller, more durable and powerful sensors. New manufactured sensors are seeing their size substantially reduced, allowing their placement in small spaces and also in delicate and dangerous scenarios.• Increased efficiency. One of the key aspects of the Internet of Things paradigm is the wireless interconnection between devices. Thus, these devices must be equipped with autonomous power supplies that limit their lifespan. To cope with this problem, manufacturers are aiming for efficient processors and software engineers are specifically designing software and communication technologies for IoT in which lower energy consumption 1

Wireless Sensor Networks (WSN) have lately been gaining momentum thanks to the hardware improvements and standardization software efforts. Moreover, the appearance of Internet of Things (IoT) and its reliance on sensors are helping to widely extend the usage of WSNs. However, such networks present drawbacks, mainly because of limited sensor batteries and their vulnerability against physical attacks due to the lack of protection and security. Additionally, not all the sensors inside the network have the same responsibility in terms of traffic handling. In this paper, we firstly analyze the fact that some nodes are more critical than others, considering the most critical node the one that, once incapacitated, causes the most deterioration on the network performance. Such performance is analyzed using two metrics, namely network latency and lifetime. We present a result comparison between a Mixed Integer Programming (MIP) model and a Greedy Randomized Adaptive Search Procedure (GRASP) meta-heuristic for small networks. For bigger networks, GRASP meta-heuristic results are presented to understand how the network degrades as the number of both critical and network nodes increase, by distributing them into two different areas: fixed and incremental to maintain node density.

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