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A Software Product Line Process to Develop Agents for the IoT.

Ayala I, Amor M, Fuentes L, Troya JM - Sensors (Basel) (2015)

Bottom Line: Our goal is to enhance the development of IoT applications using agents and software product lines (SPL).Specifically, we propose to use Self-StarMASMAS, multi-agent system) agents and to define an SPL process using the Common Variability Language.In this contribution, we propose an SPL process for Self-StarMAS, paying particular attention to agents embedded in sensor motes.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Lenguajes y Ciencias de la Computación, Andalucía Tech, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain. ayala@lcc.uma.es.

ABSTRACT
One of the most important challenges of this decade is the Internet of Things (IoT), which aims to enable things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service. IoT systems are usually composed of heterogeneous and interconnected lightweight devices that support applications that are subject to change in their external environment and in the functioning of these devices. The management of the variability of these changes, autonomously, is a challenge in the development of these systems. Agents are a good option for developing self-managed IoT systems due to their distributed nature, context-awareness and self-adaptation. Our goal is to enhance the development of IoT applications using agents and software product lines (SPL). Specifically, we propose to use Self-StarMASMAS, multi-agent system) agents and to define an SPL process using the Common Variability Language. In this contribution, we propose an SPL process for Self-StarMAS, paying particular attention to agents embedded in sensor motes.

No MeSH data available.


Partial view of the goal model of ShopperAgent.
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f7-sensors-15-15640: Partial view of the goal model of ShopperAgent.

Mentions: Goal modeling and analysis processes focus on the application specific goals and use the logic-based techniques available in SPL tools, like SPLOT [40], to check consistency and detect conflicts between agent goals. Using SPL tools, we can detect inconsistencies between goals and contexts. Additionally, using resolution models (see Section 3), we can detect conflicts between goals and plans. Therefore, in this case, the SPL is used just as an analysis tool, not for developing the system. This procedure allows the software architect, who is familiar with SPL and CVL, to analyze agent goals without requiring any extra knowledge or external tools. Therefore, this process is only applied to each agent that has a goal-oriented reasoning engine. The output of the goal modeling and analysis processes (the refined goal model in Figure 2) is an SPL that contains a set of goals, plans and context of the agent that is consistent and whose conflicts are detected. Figure 7 depicts a partial view of the goal model of ShopperAgent, which follows the semantics of CVL. Due to limitations of space, in Figure 7, only the goals of the ShopperAgent are depicted. In the last phase of our development process, the refined goal model is weaved with the IoT multi-agent system architecture configuration to obtain the final application architecture (see Figure 2). To do so, a model-driven development process implemented using the ATLASTransformation Language (ATL) [41] is used [42].


A Software Product Line Process to Develop Agents for the IoT.

Ayala I, Amor M, Fuentes L, Troya JM - Sensors (Basel) (2015)

Partial view of the goal model of ShopperAgent.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541848&req=5

f7-sensors-15-15640: Partial view of the goal model of ShopperAgent.
Mentions: Goal modeling and analysis processes focus on the application specific goals and use the logic-based techniques available in SPL tools, like SPLOT [40], to check consistency and detect conflicts between agent goals. Using SPL tools, we can detect inconsistencies between goals and contexts. Additionally, using resolution models (see Section 3), we can detect conflicts between goals and plans. Therefore, in this case, the SPL is used just as an analysis tool, not for developing the system. This procedure allows the software architect, who is familiar with SPL and CVL, to analyze agent goals without requiring any extra knowledge or external tools. Therefore, this process is only applied to each agent that has a goal-oriented reasoning engine. The output of the goal modeling and analysis processes (the refined goal model in Figure 2) is an SPL that contains a set of goals, plans and context of the agent that is consistent and whose conflicts are detected. Figure 7 depicts a partial view of the goal model of ShopperAgent, which follows the semantics of CVL. Due to limitations of space, in Figure 7, only the goals of the ShopperAgent are depicted. In the last phase of our development process, the refined goal model is weaved with the IoT multi-agent system architecture configuration to obtain the final application architecture (see Figure 2). To do so, a model-driven development process implemented using the ATLASTransformation Language (ATL) [41] is used [42].

Bottom Line: Our goal is to enhance the development of IoT applications using agents and software product lines (SPL).Specifically, we propose to use Self-StarMASMAS, multi-agent system) agents and to define an SPL process using the Common Variability Language.In this contribution, we propose an SPL process for Self-StarMAS, paying particular attention to agents embedded in sensor motes.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Lenguajes y Ciencias de la Computación, Andalucía Tech, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain. ayala@lcc.uma.es.

ABSTRACT
One of the most important challenges of this decade is the Internet of Things (IoT), which aims to enable things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service. IoT systems are usually composed of heterogeneous and interconnected lightweight devices that support applications that are subject to change in their external environment and in the functioning of these devices. The management of the variability of these changes, autonomously, is a challenge in the development of these systems. Agents are a good option for developing self-managed IoT systems due to their distributed nature, context-awareness and self-adaptation. Our goal is to enhance the development of IoT applications using agents and software product lines (SPL). Specifically, we propose to use Self-StarMASMAS, multi-agent system) agents and to define an SPL process using the Common Variability Language. In this contribution, we propose an SPL process for Self-StarMAS, paying particular attention to agents embedded in sensor motes.

No MeSH data available.