Compact extensible authentication protocol for the internet of things : enabling scalable and efficient security commissioning

Internet of Things security is one of the most challenging parts of the domain. Combining strong cryptography and lifelong security with highly constrained devices under conditions of limited energy consumption and no maintenance time is extremely difficult task. This paper presents an approach that combines authentication and bootstrapping protocol (TEPANOM) with Extensible Authentication Protocol (EAP) framework optimized for the IEEE 802.15.4 networks. The solution achieves significant reduction of network resource usage. Additionally, by application of EAP header compacting approach, further network usage savings have been reached. The EAP-TEPANOM solution has achieved substantial reduction of 42% in the number of transferred packets and 35% reduction of the transferred data. By application of EAP header compaction, it has been possible to achieve up to 80% smaller EAP header. That comprises further reduction of transferred data for 3.84% for the EAP-TEPANOM method and 10% for the EAP-TLS-ECDSA based methods. The results have placed the EAP-TEPANOM method as one of the most lightweight EAP methods from ones that have been tested throughout this research, making it feasible for large scale deployments scenarios of IoT

Efficiently observing Internet of Things resources

The Constrained Application Protocol (CoAP) is a lightweight protocol that enables the implementation of RESTful embedded web services. Observe is one of the CoAP extensions, which allow servers to send every resource state change to interested clients. In this paper we present an interesting extension to the observe option, called conditional observation, where clients specify notification criteria along their observation request. We evaluate the feasibility of implementing this on a constrained device and evaluate the correct operation for a simple scenario. It is shown that the use of conditional observations can result in a reduced number of packets and power consumption compared to normal observe in combination with client-side filtering

Internet of Things and Big Data Analytics for Smart and Connected Communities

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy

Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain)

Montserrat Mountain is located near Barcelona in Catalonia, in the northeast of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rockfalls. The increasing number of visitors in the monastery area, reaching 2.4 million per year, has highlighted the risk derived from rockfalls for this building area and also for the terrestrial accesses, both roads and the rack railway. A risk mitigation plan has been launched, and its first phase during 2014-2016 has been focused largely on testing several monitoring techniques for their later implementation. The results of the pilot tests, performed as a development from previous sparse experiences and data, are presented together with the first insights obtained. These tests combine four monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non-uniform atmospheric phase screen due to the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying the frequency of small or even previously unnoticed rockfalls, and monitoring rock block centimetre scale displacements; the monitoring of rock joints implemented through a wireless sensor network with an ad hoc design of ZigBee loggers developed by ICGC; and, finally, monitoring singular rock needles with Total Station.Peer ReviewedPostprint (author's final draft

A NEMO-HWSN solution to support 6LoWPAN network mobility in hospital wireless sensor network

IPv6 Low-power Personal Area Networks (6LoWPANs) have recently found renewed interest because of the emergence of Internet of Things (IoT). Mobility support in 6LoWPANs for large-scale IP-based sensor technology in future IoT is still in its infancy. The hospital wireless network is one important 6LoWPAN application of the IoT, it keeps continuous monitoring of vital signs of moveing patients. Proper mobility management is needed to maintain connectivity between patient nodes and the hospital network. In this paper, first we survey IPv6 mobility protocols and propose a solution for a hospital architecture based on 6LoWPAN technology. Moreover, we discuss an important metric like signaling overload to optimize the power consumption and how it can be optimized through the mobility management. This metric is more effective on the mobile router as a coordinator in network mobility since a mobile router normally constitutes a bottleneck in such a system. Finally, we present our initial results on a reduction of the mobility signaling cost and the tunneling traffic on the mobile PAN

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on theWeb such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.Project (the SmartSDK project is co-funded by the EU’s Horizon2020 programme under agreement number 723174 - c 2016 EC and the CONACYT’s agreement number 737373) Doctorado IndustrialAdministración y Dirección de EmpresasIngeniería, Industria y ConstrucciónTurism

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.This research was carried out within the research Project Alcoi Tourist Lab framework, co-funded by the Alcoi City Council & the Valencian Innovation Agency. The research was also partially funded by project UAPOSTCOVID19-10 from the University of Alicante. Finally, this research was partly supported by the EU CEF project GreenMov, CARM HORECOV-21 project (https://horecovid.com/ (accessed on 12 January 2022)). is financed through the Call for Public Aid destined to finance the Strategic projects contemplated in the Research and Innovation Strategy for Smart Specialization - RIS3MUR Strategy by the Autonomous Community of the Region of Murcia, through the Ministry of Economic Development, Tourism and Employment within the framework of the FEDER Region of Murcia Operational Program 2014–2020 within the framework Thematic Objective 1. Strengthen research, technological development and innovation by 80% and with CARM’s own funds in 20%, and finally the EU project H2020 NIoVE (833742)

Using several monitoring techniques to measure the rock mass deformation in the Montserrat Massif

Montserrat Mountain is located near Barcelona in Catalonia, at the north-east corner of Spain, and its massif is formed by conglomerate interleaved by siltstone/sandstone with steep slopes very prone to rock falls. The increasing visitor's number in the monastery area, reaching 2.4 million per year, has pointed out the risk derived from rock falls for this building area and also for the terrestrial accesses, both roads and rack railway. A risk mitigation plan is currently been applied for 2014-2016 that contains monitoring testing and implementation as a key point. The preliminary results of the pilot tests carried out during 2014 are presented, also profiting from previous sparse experiences and data, and combining 4 monitoring techniques under different conditions of continuity in space and time domains, which are: displacement monitoring with Ground-based Synthetic Aperture Radar and characterization at slope scale, with an extremely non uniform atmospheric phase screen because of the stepped topography and atmosphere stratification; Terrestrial Laser Scanner surveys quantifying frequency for unnoticed activity of small rock falls, and monitoring rock block displacements over 1cm; monitoring of rock joints with a wireless net of sensors; and tentative surveying for singular rocky needles with Total Station