No abstract

A research field that makes use of information and communication technologies to provide solutions to contemporaneous environmental challenges such as greenhouse gas emissions and global warming is the 'smart building' field. The use of wireless sensor and actuator networks (WSANs) emerges as an alternative for the use of information and communication technologies in the smart buildings. However, most of smart building applications make use of centralised architectures with sensing nodes transmitting messages to a base station wherein, effectively, the control and decision processes happen. In this context, we present CONDE, a decentralised CONtrol and DEcision-making system for smart building applications using WSANs. CONDE main contributions are as follows: (i) the decentralisation of the control and decision-making processes among WSAN nodes, saving energy of both the WSAN and the building; (ii) the integration of applications through sharing the sensed data and chaining decisions between applications within the WSAN, also saving energy of both the WSAN and the building; and (iii) the provision of a consensual multilevel decision that takes into account the cooperation among nodes to have a broader view of the monitored building. Performed experiments have shown CONDE gains in terms of the following: (i) response time; (ii) system efficiency; and (iii) energy savings from the network and the building.

No abstract

Running a cost-effective human blood transfusion supply chain challenges decision makers in blood services world-wide. In this paper, we develop a Markov decision process with the objective of minimising the overall costs of internal and external collections, storing, producing and disposing of blood bags, whilst explicitly considering the probability that a donated blog bag will perish before demanded. The model finds an optimal policy to collect additional bags based on the number of bags in stock rather than using information about the age of the oldest item. Using data from the literature, we validate our model and carry out a case study based on data from a large blood supplier in South America. The study helped achieve an overall increase of 4.5% in blood donations in one year.