The emerging Internet of Things (IoT) has lead to a dramatic increase in type, quantity, and the number of functions that can be offered in a smart environment. Future smart environments will be even richer in terms of the number of devices and functionality provided by them. This poses two major challenges a) an end user has to search through a vast number of IoT devices to identify the suitable devices that satisfy their preferences, and b) it is extremely difficult for users to manually define fine-grained security policies to support workflows involving multiple functions. This paper introduces an intelligent new approach to overcome these challenges by a) enabling users to describe their required functionalities in the form of activity workflow, b) automatically selecting a group of devices to satisfy users functional requirements and maximise their preferences over device usage, c) systematically generating local network access control policies to ensure enforce the principle of least privilege. We study different heuristic search algorithms to find the preferred devices for a given workflow. Experiments results show that the Genetic Algorithm is the best, among the algorithms that we test, as it offers a balance between efficiency and effectiveness.
@cite_24: A smart home system can provide better services to assist users if it knows what user activities will occur beforehand. Early research in activity prediction has indicated that the result of prediction is unique, but the accuracy remains unsatisfactory if only one result is considered. To solve this problem, this paper proposes a method of leveraging multiple models. In this work, we use a Bayesian network to build a model to predict which activity will happen, and then the predicted results go through a property filtering to get the final result. Due to the possibility that residents in a smart home may have different activity patterns, we have built a Bayesian network model to learn conditional probability of resident activities from the dataset of CASAS project. At last, we compare the results and show that our method has improved coverage and accuracy in activity prediction. This proposed method belongs to an ongoing project involving learning and control in a smart home system.
@cite_47: Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
Researchers have studied the possibility of automatically deriving and predicting users activity @cite_47 @cite_24 . @cite_47 proposed Apriori algorithm to detect activity patterns in smart home environment to automate users activities. Similarly @cite_24 used Bayesian network to learn and predict users activities which can be used to generate personalised activities. These works focused predicting and automating users activities which can be represented as workflow, however, it didn't tackle how to build it and which devices will be used. Therefore, our goal is to a) select all devices that fulfil a workflow requirements and b) Choosing devices from the selected list that maximise user preference. For example, a user may prefer to use a brand @math devices for temperature sensing while prefer brand @math for thermostat function. In association with selected devices, policies will be generated automatically to ensure least privilege access, and segregate devices used by different users to protect their privacy.