With Smart Home technology taking off and smart home assistants (like Amazon Alexa, Google Home and Apple Siri) becoming more prevalent in homes the start of a new home technology revolution is underway. Homeowners can monitor and control the house heating and cooling systems, security systems, door locks, garage doors and more all from where ever they have access to internet on their smartphone. Artificial intelligence (AI) will add to that ability by allowing decisions about the home to be made without the need of direct input from the homeowner. For instance a trusted dog walker walks up to the front door during their scheduled time to take Fido out for a walk. The dog walker's face is seen via camera which an artificial intelligence assistant recognizes and knows they are there during the correct time and allows the door to be unlocked so Fido can enjoy some outdoor time while the homeowner is away.

With Smart Home technology taking off and smart home assistants (like Amazon Alexa, Google Home and Apple Siri) becoming more prevalent in homes the start of a new home technology revolution is underway. Homeowners can monitor and control the house heating and cooling systems, security systems, door locks, garage doors and more all from where ever they have access to internet on their smartphone. Artificial intelligence (AI) will add to that ability by allowing decisions about the home to be made without the need of direct input from the homeowner. For instance a trusted dog walker walks up to the front door during their scheduled time to take Fido out for a walk. The dog walker's face is seen via camera which an artificial intelligence assistant recognizes and knows they are there during the correct time and allows the door to be unlocked so Fido can enjoy some outdoor time while the homeowner is away.

We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query based sensor set selection with predictive MSE performance guarantee. For the first problem, we developed a low complexity algorithm based on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we address the second problem, and develop an efficient algorithm for query based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy method which solves the combinatorial optimization problem in an efficient manner.