ANT: Agent Stigmergy-Based IoT-Network for Enhanced Tourist Mobility

In this work, we propose ANT, a network of agents which exchange information in a stigmergy-based fashion. In ANT, each system’s actor distributes pheromones as a way of indicating places’ attractiveness, as well as for building a proper routing path to those sites. The goal of ANT is to improve the chances of discovering points of interest, as well as to reduce the time required for doing so. We have applied ANT to a tourist mobility scenario, where both people and things (events, restaurants, performances, etc.) participate. ANT has achieved notable success in this example case. We find that probability of discovering temporary events and dates improves by more than 35%, while the mean time employed to determine static point decreases by more than a third. We also introduce a mobile-based architecture which performs ANT tasks efficiently and easily for the user.

[1]  Juan C. Burguillo,et al.  moreTourism: Mobile recommendations for tourism , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[2]  Shuguang Liu A hybrid population heuristic for the heterogeneous vehicle routing problems , 2013 .

[3]  Weimin Zheng,et al.  Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction , 2017 .

[4]  Kevin Curran,et al.  Context-aware intelligent recommendation system for tourism , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[5]  Laura Sebastia,et al.  On the design of individual and group recommender systems for tourism , 2011, Expert Syst. Appl..

[6]  Charalampos Konstantopoulos,et al.  A survey on algorithmic approaches for solving tourist trip design problems , 2014, Journal of Heuristics.

[7]  U. Netlogo Wilensky,et al.  Center for Connected Learning and Computer-Based Modeling , 1999 .

[8]  Colin Torney,et al.  Context-dependent interaction leads to emergent search behavior in social aggregates , 2009, Proceedings of the National Academy of Sciences.

[9]  Damianos Gavalas,et al.  A web-based pervasive recommendation system for mobile tourist guides , 2011, Personal and Ubiquitous Computing.

[10]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[11]  J. Q. Hu,et al.  On the tour planning problem , 2012, Ann. Oper. Res..

[12]  Bernd Ludwig,et al.  Context relevance assessment and exploitation in mobile recommender systems , 2012, Personal and Ubiquitous Computing.

[13]  Wolfgang Wörndl,et al.  RouteMe: A Mobile Recommender System for Personalized, Multi-Modal Route Planning , 2017, UMAP.

[14]  Tobias Höllerer,et al.  I’m feeling LoCo: A Location Based Context Aware Recommendation System , 2012 .

[15]  Luis Alfonso Ureña López,et al.  GeOasis: A knowledge-based geo-referenced tourist assistant , 2012, Expert Syst. Appl..

[16]  Wolfgang Wörndl,et al.  Recommending a sequence of interesting places for tourist trips , 2017, J. Inf. Technol. Tour..

[17]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[18]  Mark D. Dunlop,et al.  A mobile guide for serendipitous exploration of cities , 2011, Mobile HCI.

[19]  Yohei Kurata,et al.  CT-Planner 5 : a Computer-Aided Tour Planning Service Which Profits Both Tourists and Destinations , 2015 .

[20]  Stephen A. Brewster,et al.  Audio Bubbles: Employing Non-speech Audio to Support Tourist Wayfinding , 2009, HAID.

[21]  C. A. Condat,et al.  Modeling cancer immunotherapy: Assessing the effects of lymphocytes on cancer cell growth and motility , 2013 .

[22]  Francesco Ricci,et al.  ITR: A Case-Based Travel Advisory System , 2002, ECCBR.

[23]  Francisco Orozco,et al.  Ant Colony Optimization Model for Tsunamis Evacuation Routes , 2014, Comput. Aided Civ. Infrastructure Eng..

[24]  Alexandre Yahi,et al.  Aurigo: an Interactive Tour Planner for Personalized Itineraries , 2015, IUI.

[25]  Joanna Karbowska-Chilinska,et al.  Genetic Algorithm Solving the Orienteering Problem with Time Windows , 2013, ICSS.

[26]  Shih-Wei Lin,et al.  A simulated annealing heuristic for the multiconstraint team orienteering problem with multiple time windows , 2015, Appl. Soft Comput..

[27]  Farhad Samadzadegan,et al.  Time-dependent personal tour planning and scheduling in metropolises , 2011, Expert Syst. Appl..

[28]  Aristides Gionis,et al.  Customized tour recommendations in urban areas , 2014, WSDM.

[29]  D. Coomes,et al.  Latitudinal gradients as natural laboratories to infer species' responses to temperature , 2013 .

[30]  Panos E. Kourouthanassis,et al.  Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior , 2015, Pervasive Mob. Comput..

[31]  Dirk Van Oudheusden,et al.  The orienteering problem: A survey , 2011, Eur. J. Oper. Res..

[32]  Matthias Fuchs,et al.  Building a Mobile Tourist Guide based on Tourists’ On-Site Information Needs , 2009 .

[33]  Nicolas Jouandeau,et al.  S-MASA: A stigmergy based algorithm for multi-target search , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[34]  Daniel Borrajo,et al.  Planning for tourism routes using social networks , 2017, Expert Syst. Appl..

[35]  Christian Gahm,et al.  Vehicle routing with private fleet, multiple common carriers offering volume discounts, and rental options , 2017 .

[36]  Md Abdul Awal,et al.  A hybrid approach to plan itinerary for tourists , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[37]  San-Yih Hwang,et al.  iTravel: A recommender system in mobile peer-to-peer environment , 2013, J. Syst. Softw..

[38]  Murat Karakaya,et al.  Efficient route planning for an unmanned air vehicle deployed on a moving carrier , 2016, Soft Comput..

[39]  J. Kirschvink Sensory biology: Radio waves zap the biomagnetic compass , 2014, Nature.

[40]  Charalampos Konstantopoulos,et al.  Personalized routes for mobile tourism , 2011, 2011 IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[41]  John Geraghty,et al.  Genetic Algorithm Performance with Different Selection Strategies in Solving TSP , 2011 .

[42]  P.-P. Grasse La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[43]  Marco Dorigo,et al.  Ant algorithms and stigmergy , 2000, Future Gener. Comput. Syst..