SMASH: a Semantic-enabled Multi-agent Approach for Self-adaptation of Human-centered IoT

Abstract

Nowadays, IoT devices have an enlarging scope of activities spanning from sensing, computing to acting and even more, learning, reasoning and planning. As the number of IoT applications increases, these objects are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings and have access to online contextual information. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. Hence, IoT applications must be equipped with self-adaptation techniques to manage their run-time uncertainties locally or in cooperation with each other. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in humancentered environments. In this paper, we have considered the Smart Home as the case study of smart environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home'In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.

A Survey of Adaptive Multi-Agent Networks and Their Applications in Smart Cities

“…The multi-agentoriented programming (MAOP) platforms such as JaCaMo have a structured approach based on three concepts of agent, environment, and organization dimensions [176]. They also successfully integrated with the IoTs to offer self-adaptive applications in humancentric environments [177]. Systems of systems is another efficient methodology to develop meta-models to manage MASs with different subsystems [178].…”

Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things

“…Self-adaptation of HCIoT aims to manage run-time uncertainties in a dynamic environment and to adjust the functionality of IoT objects in order to achieve desired goals during execution. SMASH [1] is a multi-agent approach for selfadaptation of HCIoT that autonomously adapts IoT objects to uncertainties of their environment. This process is based on the integration of value-reasoning and goal-reasoning mechanisms to planning and acting [8] of a multi-agent system [9].…”