Dynamic Constraint Reasoning in Smart Environments

Degeler, Viktoriya; Lazovik, Alexander

doi:10.1109/ictai.2013.34

Cited by 10 publications

(3 citation statements)

References 8 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [4], Degeler and Lazovik use dynamic constraint reasoning for smart environment management. The desired behavior of the home is speciied using logical rules.…”

Section: Constraint Reasoning In Ambient Intelligencementioning

confidence: 99%

Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments

Rust

Picard

Ramparany

2022

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

In this paper, we investigate multi-agent techniques to install autonomy and adaptation in IoT-based smart environment settings, like smart home scenarios. We particularly make use of the smart environment configuration problem (SECP) framework, and map it to a distributed optimization problem (DCOP). This consists in enabling smart objects to coordinate and self-configure as to meet both user-defined requirements and energy efficiency, by operating a distributed constraint reasoning process over a computation graph. As to cope with the dynamics of the environment and infrastructure (e.g. by adding or removing devices), we also specify the k -resilient distribution of graph-structured computations supporting agent decisions, over dynamic and physical multi-agent systems. We implement a self-organizing distributed repair method, based on a distributed constraint optimization algorithm to adapt the distribution as to ensure the system still performs collective decisions and remains resilient to upcoming changes. We provide a full stack of mechanisms to install resilience in operating stateless DCOP solution methods, which results in a robust approach using a fast DCOP algorithm to repair any stateless DCOP solution methods at runtime. We experimentally evaluate the performances of these techniques when operating stateless DCOP algorithms to solve SECP instances.

show abstract

“…In [4], Degeler and Lazovik use dynamic constraint reasoning for smart environment management. The desired behavior of the home is speciied using logical rules.…”

Section: Constraint Reasoning In Ambient Intelligencementioning

confidence: 99%

Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments

Rust

Picard

Ramparany

2022

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

show abstract

“…The amount of time is fixed, though it need not be continuous. The formal definitions for each policy, except for the battery policy which we introduce for the purpose of the present work, can be found in [23].…”

Section: F Scheduling Policiesmentioning

confidence: 99%

Energy Smart Buildings: Parallel Uniform Cost-Search with Energy Storage and Generation

Setz¹,

Haghshenas²,

Aiello³

2022

Preprint

View full text Add to dashboard Cite

The amalgamation of Internet of Things and the smart grid enables the energy optimal scheduling of appliances based on user needs and dynamic energy prices. Additionally, progress in local storage technology calls for exploiting additional sources of flexibility. In this paper, we propose a scheduling approach for building operation management, considering factors such as energy storage, local energy generation, and dynamic energy prices. In addition, we propose a new optimization strategy to discover the optimal scheduling of devices. Our approach utilizes parallel uniform cost-search to explore the complex search space and to find the optimal schedule within a user-acceptable amount of time. The evaluation utilizes realworld data for the devices, and the price signals, while the architecture is designed following a micro-service approach, enabling modularity and loose-coupling. The evaluation shows that including local energy storage as part of the optimization problem further reduces overall costs by up to 22.64% when compared to schedules without energy storage. Parallel uniform cost-search decreases the time to find the optimal schedule by a factor of 4.7 with respect to the traditional uniform cost-search algorithm.

show abstract

“…act is denoted as the set of relevant activities. An interesting alternative is to use dependency graph as proposed in this work [29] or to divide all the activities into effects ontology to shortlist relevant activities [30].…”

Section: Advances In Artificial Intelligencementioning

confidence: 99%

Weighted Constraint Satisfaction for Smart Home Automation and Optimization

Tay

Botzheim

Kubota

2016

Advances in Artificial Intelligence

View full text Add to dashboard Cite

Automation of the smart home binds together services of hardware and software to provide support for its human inhabitants. The rise of web technologies offers applicable concepts and technologies for service composition that can be exploited for automated planning of the smart home, which can be further enhanced by implementation based on service oriented architecture (SOA). SOA supports loose coupling and late binding of devices, enabling a more declarative approach in defining services and simplifying home configurations. One such declarative approach is to represent and solve automated planning through constraint satisfaction problem (CSP), which has the advantage of handling larger domains of home states. But CSP uses hard constraints and thus cannot perform optimization and handle contradictory goals and partial goal fulfillment, which are practical issues smart environments will face if humans are involved. This paper extends this approach to Weighted Constraint Satisfaction Problem (WCSP). Branch and bound depth first search is used, where its lower bound is estimated by bacterial memetic algorithm (BMA) on a relaxed version of the original optimization problem. Experiments up to 16-step planning of home services demonstrate the applicability and practicality of the approach, with the inclusion of local search for trivial service combinations in BMA that produces performance enhancements. Besides, this work aims to set the groundwork for further research in the field.

show abstract

Dynamic Constraint Reasoning in Smart Environments

Cited by 10 publications

References 8 publications

Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments

Resilient Distributed Constraint Reasoning to Autonomously Configure and Adapt IoT Environments

Energy Smart Buildings: Parallel Uniform Cost-Search with Energy Storage and Generation

Weighted Constraint Satisfaction for Smart Home Automation and Optimization

Contact Info

Product

Resources

About