Smart Cities via Data Aggregation

Cities have an ever increasing wealth of sensing capabilities, recently including also internet of things (IoT) systems. However, to fully exploit such sensing capabilities with the aim of offering effective city-sensing-driven applications still presents certain obstacles. Indeed, at present, the main limitation in this respect consists of the vast majority of data sources being served on a “best effort” basis. To overcome this limitation, we propose a “resilient and adaptive IoT and social sensing platform”. Resilience guarantees the accurate, timely and dependable delivery of the complete/related data required by smart-city applications, while adaptability is introduced to ensure optimal handling of the changing requirements during application provision. The associated middleware consists of two main sets of functionalities: (a) formulation of sensing requests: selection and discovery of the appropriate data sources; and (b) establishment and control of the necessary resources (e.g., smart objects, networks, computing/storage points) on the delivery path from sensing devices to the requesting applications. The middleware has the intrinsic feature of producing sensing information at a certain level of detail (geographical scope/timeliness/accuracy/completeness/dependability) as requested by the applications in a given domain. The middleware is assessed and validated at a proof-of-concept level through innovative, dependable and real-time applications expected to be highly reproducible across different cities.

[1]  C. Lynch Big data: How do your data grow? , 2008, Nature.

[2]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[3]  T. Luckenbach,et al.  TinyREST – a Protocol for Integrating Sensor Networks into the Internet , 2005 .

[4]  Liang-Jie Zhang,et al.  CCOA: Cloud Computing Open Architecture , 2009, 2009 IEEE International Conference on Web Services.

[5]  Reza Curtmola,et al.  Fostering participaction in smart cities: a geo-social crowdsensing platform , 2013, IEEE Communications Magazine.

[6]  Klaus Moessner,et al.  Enabling smart cities through a cognitive management framework for the internet of things , 2013, IEEE Communications Magazine.

[7]  J Thirumaran,et al.  Internet of Things (IoT) and Machine-to-Machine (M2M) communications , 2015 .

[8]  Kishor S. Trivedi,et al.  Combining Cloud and sensors in a smart city environment , 2012, EURASIP J. Wirel. Commun. Netw..

[9]  Benoit Christophe,et al.  Searching the 'Web of Things' , 2011, 2011 IEEE Fifth International Conference on Semantic Computing.

[10]  Vlad Trifa,et al.  Towards physical mashups in the Web of Things , 2009, 2009 Sixth International Conference on Networked Sensing Systems (INSS).

[11]  Fredrik Rusek,et al.  Iterative receivers with channel estimation for multi-user MIMO-OFDM: complexity and performance , 2012, EURASIP Journal on Wireless Communications and Networking.

[12]  Rebecca Goolsby,et al.  Social media as crisis platform: The future of community maps/crisis maps , 2010, TIST.

[13]  Markus Jakobsson,et al.  Controlling data in the cloud: outsourcing computation without outsourcing control , 2009, CCSW '09.

[14]  Henrique Santos,et al.  Open Government and Citizen Participation in Law Enforcement via Crowd Mapping , 2012, IEEE Intelligent Systems.

[15]  Allen B. MacKenzie,et al.  Cognitive networks: adaptation and learning to achieve end-to-end performance objectives , 2006, IEEE Communications Magazine.

[16]  Carlo Ghezzi,et al.  Self-adaptive software needs quantitative verification at runtime , 2012, CACM.

[17]  Bradley R. Schmerl,et al.  On Patterns for Decentralized Control in Self-Adaptive Systems , 2010, Software Engineering for Self-Adaptive Systems.

[18]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[19]  Jeff Magee,et al.  Self-Managed Systems: an Architectural Challenge , 2007, Future of Software Engineering (FOSE '07).