Developing Agent-Based Smart Objects for IoT Edge Computing: Mobile Crowdsensing Use Case

Abstract

Software agents have been exploited to handle the inherent dynamicity in the Internet of Things (IoT) systems, as agents are capable of autonomous, reactive and proactive operation in response to changes in their local environment. Agents, operating at the network edge, enable leveraging cloud resources into the proximity of the user devices. However, poor interoperability with the existing IoT systems and the lack of a systematic methodology for IoT system development with the agent paradigm have hindered the utilization of software agent technologies in IoT. In this paper, we describe the development process and the system architecture of a mobile crowdsensing service, provided by an agent-based smart object that comprises agents in both edge and user devices. Mobile crowdsensing is an example of such an application that relies on large-scale participatory sensor networks, where participants have active roles in producing information about their environment with their smartphones. This scheme introduces challenges in handling dynamic opportunistic resource availability, due to mobility and unpredicted actions of the participants. We present how ACOSO-Meth (Agent-oriented Cooperative Smart Object-Methodology) guidelines the development process systematically from the analysis to the actual agentbased implementation of a crowdsensing service. The implementation is done with the ROAgent framework that utilizes resource-oriented architecture and REST principles to integrate agent-based smart objects seamlessly with the programmable Web.

Edge-Based Microservices Architecture for Internet of Things: Mobility Analysis Case Study

“…While the first three are operational approaches, aiming to support the service validation, verification and simulation, the last one provides high-level descriptive representations of both functional and non-functional properties of services. Further, software agent-based computing has been reported for the modeling and design of MEC and, in general, edge services in [9,10].…”

“…In edge computing, mobility prediction aims for optimizing both the communication and edge application performance in the opportunistic IoT environment in large scale [13]. For example, edge-based location-and context-aware crowdsensing solutions have been developed through microservices [16], mobility enhanced Web-integrated smart objects [10] and with autonomous software agents [17].…”

Distributed Artificial Intelligence with Multi-Agent Systems for MEC

“…These agents are therefore natively and seamlessly integrated into the Web, through a RESTful API for interactions with agents, services and other Web components. This framework was extended towards edge computing in [25], [26]. However, these agents are largely reactive, without the deliberative capabilities leading to autonomous intelligent operation, e.g.…”

“…We demonstrate the MAS-based edge service in MEC system, based on our previous work in [25]. An agent-based edge service provides user mobility traces, based on how their devices are connected to the edge system, and detects flocks of users moving to the same direction.…”

“…As described in detail in [25], the agent-based service is implemented with the ROAgent framework. Therefore, the agents already interact through RESTful interfaces, which provides seamless integration into the MEC system and gives access to its services.…”