MATe: Multiagent Architecture for Taming e-Devices

In recent years, an explosive growth has been observed in the use of wireless devices, mainly due to the decrease in cost, size, and energy consumption. Researches in the Internet of Things have focused on how to continuously monitor these devices in different scenarios, such as vehicle, weather, and biodiversity tracking, considering both scalability and efficiency while searching and updating their information. For this, current alternatives use a combination of a widely recognized method, called data aggregation, and a widely adopted distributed structure, called Distributed Hash Table, which minimize the number of transmissions and save energy. However, scalability is still a key challenge when the group comprises a large number of devices. In this paper, we propose a scalable architecture that distributes the data aggregation responsibility to the devices of group frontier, and creates agents to manage groups and the interaction among them. Experimental results showed the viability of adopting this architecture if compared to the most widely used approaches.

[1]  David R. Karger,et al.  Chord: A scalable peer-to-peer lookup service for internet applications , 2001, SIGCOMM '01.

[2]  Márk Jelasity,et al.  PeerSim: A scalable P2P simulator , 2009, 2009 IEEE Ninth International Conference on Peer-to-Peer Computing.

[3]  Antony I. T. Rowstron,et al.  Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems , 2001, Middleware.

[4]  Chuang Lin,et al.  Attribute-Aware Data Aggregation Using Potential-Based Dynamic Routing in Wireless Sensor Networks , 2013, IEEE Transactions on Parallel and Distributed Systems.

[5]  Eleonora Borgia,et al.  The Internet of Things vision: Key features, applications and open issues , 2014, Comput. Commun..

[6]  Craig W. Reynolds Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[7]  Azzedine Boukerche,et al.  SLA: Speed and Location Aware LTE Scheduler for Vehicular Safety Applications , 2015, MobiWac.

[8]  Nicholas R. Jennings,et al.  Coordinating Measurements for Air Pollution Monitoring in Participatory Sensing Settings , 2015, AAMAS.

[9]  Chau Yuen,et al.  SenseFlow: An Experimental Study of People Tracking , 2015, RealWSN@SenSys.

[10]  Anarosa A. F. Brandão,et al.  Towards conscientious peers: Combining agents and peers for efficient and scalable video segment retrieval for VoD services , 2015, Eng. Appl. Artif. Intell..

[11]  Vladimir Rocha,et al.  A Scalable Multiagent Architecture for Monitoring Biodiversity Scenarios , 2017 .