Table 2.

Summary of surveys.

References	Description	Main Proposals
[6,18,19]	General purpose IoT surveys	General visions of IoT. Key features and the driver technologies of IoT. Phases and interaction with the physical-cyber world.
[10,20,22,23,24,25,26,27]	Surveys oriented to data	Technologies in IoT-based products. Techniques of IoT from the data perspective. Data stream and data stream processing. RDF and SPARQL as method for conceptual description and query language respectively in IoT. Extraction of RDF triples from unstructured data streams.
[29,30,31]	Surveys about the integration of Cloud computing and IoT	Cloud computing and IoT are different technologies, but are complementary. Cloud becomes an intermediate layer between smart objects and applications. Integration components: cloud platforms, cloud infrastructures and IoT middleware.
[22,32,33,34,35,36,37,38,39,40,41,42,66]	Surveys oriented to data mining	Relationships between data mining, KDD and big data for IoT. Processing of big data and sensor information. Data mining algorithms. Data stream clustering.
[6,19,20,21,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67]	Potential IoT applications	General IoT applications: smart cities and homes, environment monitoring, health, energy, business. Classification according several domains: transportation and logistics, healthcare, smart environment (home, office, plant), personal and social. Security and surveillance. Huge spectrum of applications of IoT. IoT applications in industries. RFID technology, wireless sensor networks, barcodes, smart phones, social networks, and cloud computing. Food supply chain. Different devices (capabilities) in IoT. Architectures based on WSN and RFID.
[6,19,21,43,44]	Open research issues for IoT	Standardization, mobility support, naming, transport protocol, traffic characterization, authentication, data integrity, privacy and digital forgetting. Computing, communication and identification technologies, distributed systems technology, distributed intelligence, security, data confidentiality, privacy and trust. Data quality and uncertainty, co-space data, transaction handling, Frequently updated timestamped structured data, distributed and mobile data, semantic enrichment and semantic event processing, mining, knowledge discovery, security, privacy and social concerns. Challenges for industrial use: technology, standardization, security and privacy. IoT and social networks and IoT and context-aware computing.