Article 1

Cyber Risk from IoT devices and Networks

Petar Radanliev

Cyber risk and its associated economic impact are growing with the integration of artificial intelligence (AI) in human-computer interactions. Some examples include connected devices into more aspects of modern life, including banking, finance, and insurance. Cyber-attacks are increasing in frequency, and the severity of future attacks could be much greater than what has been observed to date. The critical question is how sufficient is our current cybersecurity to safeguard from such cyber risk.

 


Article 2

IoT and COVID-19

Ahmed Banafa

COVID-19 has impacted countries, communities, and individuals in countless ways, from business and school closures to job losses not to undermined loss of lives. As governments scramble to address these problems, different solutions based on technologies like IoT have sprung up to help in dealing with this worldwide health crisis. As a result, COVID-19 may well have been the ultimate catalyst of the Internet of Things (IoT) [3].

 


Article 3The Great Potential of Rural Areas in Spain and Portugal for the Implementation of New Technologies

Javier Parra, Javier Prieto, and Juan M. Corchado

The population density of the least populated regions in Spain is below 0.98 inhabitants per square kilometer, which is lower than in Lapland. These data cause great concern among European institutions. To solve this problem, efforts are being made to boost the technological potential of those areas through Precision Agriculture Solutions. IoT is often resorted to as a means of combatting this problem.

 


Article 4In-band Full-duplex for Enhanced IoT Connectivity

Leo Laughlin, Himal A. Suraweera, and Ioannis Krikidis

The Internet of Things (IoT) promises to affect great change across multiple domains, with wireless connectivity being the key feature that unlocks the power of monitoring and computing in a plethora of applications. Whilst wireless is central to IoT, the predicted growth in traffic must be accommodated within the already crowded wireless spectrum, and IoT presents a wide range of differing wireless connectivity requirements.

 

 

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Article 5

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Article 5

IEEE Internet of Things Magazine

Internet of Things Magazine logo The Internet of Things Magazine (IoTM) publishes high-quality articles on IoT technology and end-to-end IoT solutions. IoTM articles are written by and for practitioners and researchers interested in practice and applications, and selected to represent the depth and breadth of the state of the art. The technical focus of IoTM is the multi-disciplinary, systems nature of IoT solutions.

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Article 5

IEEE Xplore®

Stay Connected to IEEE Xplore When Working Remotely
If your organization has an institutional subscription to IEEE Xplore® and you need to work remotely due to school and workplace closures, you can still access IEEE Xplore and continue your work and research while offsite. Try these tips for remote access or contact IEEE for help. IEEE is here to support you, making certain that your IEEE subscription continues to be accessible to all users so they can continue to work regardless of location. 

 

This Month's Contributors

Petar Radanliev is a Post-Doctoral Research Associate at the University of Oxford.
Read More >>

Ahmed Banafa has extensive experience in research, operations and management, with a focus on IoT, Blockchain, Cybersecurity and AI.
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Javier Parra is a Ph.D. Assistant Professor in the Department of Business and Economics Administration at the University of Salamanca.
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Javier Prieto (S’11–M’12–SM’19) is a distinguished researcher at the University of Salamanca.
Read More >>

Juan M. Corchado (M’10) is a Full Professor with Chair at the University of Salamanca and the Director of the BISITE (Bioinformatics, Intelligent Systems, and Educational Technology) research group.
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Leo Laughlin (GS’13–M’15) is Co-founder and CEO of Forefront RF Ltd, a company developing self-interference cancellation technologies for wireless applications.
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Himal A. Suraweera (S’04, M’07, SM’15) received the B.Sc. Eng. Degree (Hons.) from the University of Peradeniya, Sri Lanka, in 2001, and the Ph.D. degree from Monash University, Australia, in 2007.
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Ioannis Krikidis (S'03-M'07-SM'12-F'19) received the diploma in Computer Engineering from the Computer Engineering and Informatics Department (CEID) of the University of Patras, Greece, in 2000, and the M.Sc and Ph.D degrees from Ecole Nationale Superieure des Telecommunications (ENST), Paris, France, in 2001 and 2005, respectively, all in electrical engineering.
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The IEEE Internet of Things (IoT) eNewsletter is a bi-monthly online publication that features practical and timely technical information and forward-looking commentary on IoT developments and deployments around the world. Designed to bring clarity to global IoT-related activities and developments and foster greater understanding and collaboration between diverse stakeholders, the IEEE IoT eNewsletter provides a broad view by bringing together diverse experts, thought leaders, and decision-makers to exchange information and discuss IoT-related issues.

Cyber Risk from IoT Devices and Networks

Petar Radanliev
July 23, 2020

 

Cyber risk and its associated economic impact are growing with the integration of artificial intelligence (AI) in human-computer interactions. Some examples include connected devices into more aspects of modern life, including banking, finance, and insurance. Cyber-attacks are increasing in frequency, and the severity of future attacks could be much greater than what has been observed to date. The critical question is how sufficient is our current cybersecurity to safeguard from such cyber risk.

The growth of artificial intelligence in human-computer interactions can also expose risks and vulnerabilities at the edge (IoT) of the network. A new assessment is required for detecting and reducing the new types of cybersecurity threats and simplifying compliance with internal, industry, and government regulations. One solution for protecting the edge is to integrate AI in the data collection and analytics of risk through fog computing for predictive outputs. While new cybersecurity is constantly been developed (e.g. ISO 3000), probabilistic data for risk analytics is not collected at the edge. Calculating cyber risks at the edge creates a new role of AI in cyber risk analytics with confidence intervals and time-bound ranges. This would protect data integrity while securing predictive analytic outputs and integrating solutions in these new types of fog computing cybersecurity.

What Makes It Difficult to Quantify Risk from the IoT

The challenge in quantifying cyber risk from the IoT emerges with the complexities, pervasiveness, and automation of IoT technology[1]. Existing risk quantification approaches are not designed to calculate such high-connectivity systems. This categorizes many IoT cyber risks as invisible in the risk assessment process. Adding to this, IoT devices often do not have a mechanism for reporting attempted hacks. Systems such as connected printers or conference room systems are increasingly at risk, because of the new connected devices (e.g. smart lights or smart locks). Such risk can be reduced by connecting and authenticating the devices through the cloud, but that would trigger additional costs for the companies. Without quantification of the potential impact, companies could be reluctant to invest in additional costs (e.g. cloud connection and authentication). This creates a direct link between quantification of impact and willingness for cybersecurity investment. There are existing methods for cyber risk assessment[2], and there are methods for cyber risk analytics[3], but there are no cyber risk impact assessment models that use networks as sensors for real-time intelligence for predictive analytic outputs. Integrating real-time dynamic probabilistic data could enable predictive intelligence. A new AI-enhanced method for cyber risk analytics, integrated into the data collection and analytics of cyber risk assessment, could enable dynamic risk assessment, while the probabilistic data of risk frequency and magnitude would enable understanding risk exposure.

Why is IoT Risk Quantification Necessary

With the current lack of standards and regulations to govern the compliance process, the risk from IoT devices is becoming a liability. Speaking in legal terms, companies are required to take reasonable precautions to protect personal data and information. With the increasing volume of IoT devices, it is difficult to be compliant with this legal requirement. Hence, the definition of what can be considered reasonable becomes blurred. Government legislations are in the process of being created, but it is unlikely that such legislation will come soon and even more unlikely that the legislation would be unified and global. It is more likely that legislation will emerge on a case-by-case basis, starting with autonomous vehicles, judging from the media coverage on the ease of hacking. It is also possible that such new legislation would create more damage than good. For example, if new legislations criminalize all hacking, including ethical and white hat, it would be even more challenging for companies to identify vulnerabilities. Currently, fog computing is used primarily as an enforcer to limit damage from rogue devices. The AI alternatives we discuss for automated risk surveillance would improve information knowledge management e.g. predictive analytics, supported with real-time dynamic intelligence. Such information knowledge management enables measuring the cost and probabilities of cyber-attacks from human-computer interactions. The main obstacle in assessing the impact of cyber risk is the lack of probabilistic data. This is mostly caused by the lack of appropriate data collection strategies. As a result, the growth of cyber risk finance and insurance markets are lacking empirical data and are unable to price cyber risk with the same precision as in traditional insurance lines. Even more concerning, with the lack of probabilistic data, the estimates of the current costs of unpredictable ‘black swan’ cyber-attacks are entirely speculative. The postulate of automated risk surveillance supported with real-time intelligence would lead to improved information knowledge management and enable predictive risk intelligence.

How Can IoT Risk Be Quantified

Traditional risk assessment approaches could assist in conducting an initial IoT risk assessment. One example is comparing the benefits with risks on individual IoT device-by-device. However, the IoT enables many entry points, each entry point creating a security issue. Hence, new automated DevSecOps approaches that anticipate the uniqueness of connected technologies are required for calculating the IoT risk. Connecting the economic impact of cyber risk to human-computer interactions in different information knowledge management systems with artificial intelligence can provide predictive feedback sensors. Dynamic real-time data mechanisms would also assist and enable a better understanding of the problem before cyber-attacks. The reliability of cyber risk impact assessments could increase significantly if decisionmakers have a dynamic and self-adapting AI-enhanced methodology to assess, predict, analyze, and address the economic risks of cyber-attacks. However, the volume of data generated creates diverse challenges in a variety of verticals (ex. machine learning, ethics, business models). Simultaneously, to build design of cybersecurity architecture for complex coupled systems, while understanding the economic impact, demands bold new solutions for optimization and decision making. Much of that is application-oriented and by default interdisciplinary, requiring hybrid researchers, with experiences in different academic areas. To design cybersecurity architecture for integration of economic impact assessment in the cyber risk assessment must meet public acceptability, security standards, and legal scrutiny. With consideration on the above, the integration of areas such as economic impact modeling, policy and governance will contribute to knowledge by integrating economic impact and cyber risk assessment models that have not been previously integrated, and thus promote the field of developing a dynamic and self-adapting AI-enhanced data analytics methodology to assess, predict, analyze and address the economic risks of cyber-attacks.

Quantifying the Impact of IoT Risk

How can the economic impact of IoT cyber risk be quantified?

  • New approaches for cyber risk quantification: a new Cyber Value at Risk framework was presented on the World Economic Forum, based on the Value at Risk statistical technique. The framework can be applied to estimate cyber risk losses over a given period and to answer the question of how much would the risk be reduced if we invest a given amount[4]. The components of the framework consist of analyzing the dependencies between vulnerabilities, assets, and profile of attackers. The rationale is that the number of attacks depends on the value of the assets and the trends in the attacking community. However, in our recent publications on this topic[5], we discovered that the lack of probabilistic data leads to qualitative cyber risk assessment approaches, where the outcome represents a speculative assumption [6]. Emerging quantitative models are effectively designed with ranges and confidence intervals based on expert opinions and not probabilistic data[7]. Furthermore, the majority of the cybersecurity frameworks today apply diverse qualitative methods, [e.g. OCTAVE [1]; TARA [2]; CMMI [3]; CMM [4]], that advocate reaching the required cybersecurity maturity level. The issue is that the current cyber state needs to be transformed into a given target cyber state [5] through the implementation guidance [6] and reaching a target state without being able to quantitatively assess the outcome, represents a speculative assumption. There are several emerging quantitative cyber risk models, [e.g. FAIR [7], and CyVaR [8]], that are complementing the work of NIST and the International Organisation for Standardisation (ISO) [9], e.g. ISO 27032 and ISO 27001. Quantitative risk impact estimation is needed for estimating cybersecurity, cyber risk, and cyber insurance [10]. The argument is, however, that without a dynamic real-time risk assessment methodology, that apples AI for cyber risk data analytics, the estimations can be outdated and imprecise. What is currently needed is a predictive cyber risk analytics model that is based on confidence intervals and time-bound ranges. This would enable designing dynamic real-time risk analytics from existing cyber risk approaches e.g. NIST, FAIR, OCTAVE, TARA, ISO, CMMI, CyVaR, and the IoTMM.
  • Weaknesses of qualitative approaches: Qualitative approaches are predominating the risk assessment process at present. The issue is that qualitative approaches are resource-intensive and often unreliable. Since qualitative approaches are often based on experts’ opinions, they are prone to different interpretations and are influenced by political and cultural forces.
  • The quantitative approach enables organizations to re-focus cybersecurity efforts: The Cyber Value at Risk approach estimates the maximum loss that can occur in a worst-case scenario. Such scenarios include larger losses than estimated with other methods. The benefit of applying the Cyber Value at Risk is that understanding the maximum loss which is different than the expected loss, enables a better understanding of the uncertainty. This quantification enables calculating the Return of Investment (ROI). Hence, offers a better understanding of opportunity and risk. However, the lack of probabilistic data has led to a design that aims to present statistical results without statistical data. A new quantitative approach needs to be developed for an enhanced forward-facing predictive model, supported with mathematical and statistical methods, including dependency modeling, probability, linear regression, decision trees, clustering, and Bayesian inference. Such an approach would undoubtedly require collecting probabilistic data at the edge with an AI-enhanced approach. As critical domains extract value from centralized and edge analytics, this will likely further increase the attack surface for adversaries to poison or trick machine learning models to undermine their integrity or availability. Furthermore, this complexity is compounded by the sectors and applications that AI cognitive engines for risk analytics can be applied to, e.g. many changing requirements, while data and conditions are not fully understood. Therefore, some form of validation is required before AI cognitive engines for risk analytics can be applied in practice.
  • Elements that will enable cyber risk quantification: The most valuable element for quantification is the availability of risk metrics. Currently, there is a lack of risk metrics. To address this, governments need to work with the private sector to identify and develop appropriate standards for the collection, distribution, and availability of cyber risk metrics. This could be achieved with a national information-sharing platform, strengthening the supervision of critical infrastructure and sectors that are elevating the cyber risks. The cyber insurance companies have also not matured and evolved as fast as cyber risks have. Cyber insurance companies could expand operations into performing quantitative cyber risk assessment before offering cyber insurance products. But manipulating personal data in real-time can be controversial. Hence, the threat event frequency should be developed along with an assessment of how imposter devices might compromise edge computing systems. This assessment should adapt AI cognitive engines for data collection and analytics with dynamic real-time feedback for predictive intelligence on threat event frequency and the magnitude loss.

Final Remarks

To promote research in cyber risk assessment, research should be published in open source, e.g. the source code of the Cyber Value at Risk model[8]. The findings up-to-date indicate that SMEs are frequently not adequately protected from cyber risk and the main cause is the high costs. Large enterprises on the other hand are inadequately protected from SMEs operating as the third party in the supply chains. This, combined with the increasing sophistication of cyber-attacks, amplifies the maximum loss scenario. Simultaneously, the returns from cybersecurity investments are declining.  While new cybersecurity is constantly been developed (e.g. ISO 3000), probabilistic data for risk analytics is not collected at the edge. Hence, the role of AI in future cyber risk analytics should be related to the use of confidence intervals and time-bound ranges. The objective of such an approach would be to protect data integrity while securing predictive analytic outputs and integrating such solutions in these new types of fog computing cybersecurity. In fog computing, the IoT-augmented physical reality is open to adversarial behaviors that are yet uncharted and poorly understood, especially the socio-technical dimensions.

By integrating AI in risk analytics, a new approach can be devised for cognitive data analytics, creating a stronger resilience of systems through cognition in their physical, digital, and social dimensions. Such an approach would revolve around understanding how and when compromises happen, to enable systems to adapt and continue to operate safely and securely when they have been compromised. Cognition through AI and cognitive real-time intelligence would enable systems to recover and become more robust. Since some companies (AppDynamics[9]) are already using AI (Cognition Engine[10]) to defend, adapt and recover systems in response to adverse events, others could build upon that knowledge to design a similar model for securing the edge. The crucial factor is assuring that systems can continuously adapt and employ AI techniques to understand and mitigate the vulnerabilities of adverse events.

[1] https://www.cs.ox.ac.uk/files/9680/2017-itpro-ncd_author-final.pdf

[2] https://www.fairinstitute.org/

[3] P. Radanliev et al., “Future developments in cyber risk assessment for the internet of things,” Comput. Ind., vol. 102, pp. 14–22, Nov. 2018.

[4] https://www2.deloitte.com/lu/en/pages/risk/articles/benefits-limits-cyber-value-at-risk.html

[5] P. Radanliev et al., “Future developments in cyber risk assessment for the internet of things,” Comput. Ind., vol. 102, pp. 14–22, Nov. 2018.

[6] P. Radanliev, D. De Roure, S. Cannady, R. . Montalvo, R. Nicolescu, and M. Huth, “Economic impact of IoT cyber risk - analysing past and present to predict the future developments in IoT risk analysis and IoT cyber insurance,” in Living in the Internet of Things: Cybersecurity of the IoT - 2018, 2018, no. CP740, p. 3 (9 pp.).

[7] P. Radanliev et al., “Integration of Cyber Security Frameworks, Models and Approaches for Building Design Principles for the Internet-of-things in Industry 4.0,” in Living in the Internet of Things: Cybersecurity of the IoT, 2018, p. 41 (6 pp.).

[8] https://www.fairinstitute.org/blog/what-is-a-cyber-value-at-risk-model

[9] https://www.appdynamics.com/

[10] https://www.appdynamics.com/cognition-engine/

References

  1. R. A. Caralli, J. F. Stevens, L. R. Young, and W. R. Wilson, “Introducing OCTAVE Allegro: Improving the Information Security Risk Assessment Process,” Hansom AFB, MA, 2007.
  2. J. Wynn et al., “Threat Assessment & Remediation Analysis (TARA) Methodology Description Version 1.0,” Bedford, MA, 2011.
  3. CMMI, “What Is Capability Maturity Model Integration (CMMI)®? | CMMI Institute,” CMMI Institute, 2017. [Online]. Available: http://cmmiinstitute.com/capability-maturity-model-integration. [Accessed: 26-Dec-2017].
  4. U.S. Department of Energy, “Cybersecurity Capability Maturity Model (C2M2) | Department of Energy,” Washington, DC, 2014.
  5. C. NIST, Cybersecurity Framework | NIST. 2016.
  6. M. Barrett, J. Marron, V. Yan Pillitteri, J. Boyens, G. Witte, and L. Feldman, “Draft NISTIR 8170, The Cybersecurity Framework: Implementation Guidance for Federal Agencies,” Maryland, 2017.
  7. FAIR, “Quantitative Information Risk Management | The FAIR Institute,” Factor Analysis of Information Risk , 2017. [Online]. Available: http://www.fairinstitute.org/. [Accessed: 26-Dec-2017].
  8. FAIR, “What is a Cyber Value-at-Risk Model?,” 2017. [Online]. Available: http://www.fairinstitute.org/blog/what-is-a-cyber-value-at-risk-model. [Accessed: 26-Dec-2017].
  9. ISO, “ISO - International Organization for Standardization,” 2017. [Online]. Available: https://www.iso.org/home.html. [Accessed: 26-Dec-2017].
  10. H. Öğüt, S. Raghunathan, and N. Menon, “Cyber Security Risk Management: Public Policy Implications of Correlated Risk, Imperfect Ability to Prove Loss, and Observability of Self-Protection,” Risk Anal., vol. 31, no. 3, pp. 497–512, Mar. 2011.

 


 

Petar RadanlievPetar Radanliev is a Post-Doctoral Research Associate at the University of Oxford. He obtained his Ph.D. at the University of Wales in 2014 and continued with postdoctoral research at Imperial College London, Massachusetts Institute of Technology, and the University of Oxford. His current research focusses on cybersecurity, cyber risk standards for the IoT, risk quantification, and risk analytics.

 

 

IoT and COVID-19

Ahmed Banafa
July 23, 2020

 

COVID-19 has impacted countries, communities, and individuals in countless ways, from business and school closures to job losses not to undermined loss of lives. As governments scramble to address these problems, different solutions based on technologies like IoT have sprung up to help in dealing with this worldwide health crisis. As a result, COVID-19 may well have been the ultimate catalyst of the Internet of Things (IoT) [3].

Internet of Things (IoT) platforms revenue will reach $66 billion in 2020, a 20% increase over last year’s figure. The increase in revenue will be generated, for example, by businesses seeking greater resilience in areas including supply chain and asset management, against external factors such as the disruption caused by the global COVID-19 pandemic. That will enable the IoT market to overcome the anticipated widespread economic disruption over 2020 and beyond. Meanwhile, connected solutions are proving their worth in today’s crisis, making them a critical part of many organizations near-term technology roadmap [1].

Figure 1: IoT and COVID-19.

Figure 1: IoT and COVID-19.

 

In the following, we summarize some of the areas where IoT will flourish because of COVID-19 impact.

IoT & WFH

IoT essentially consists of 4 components: Sensors, Networks, Cloud, and Applications (SNCA), and COVID-19 pushed their adoption and implementation to the max with the sudden pivot of many companies to Work From Home (WFH) option. Even before the pandemic, the IoT technologies that were of most interests to companies were sensors (84%), data processing (77%), and cloud platforms (76%). Remote working has been the standard for many companies for the last few months and will continue to be so wherever possible. It offers more flexibility, less time wasted on home-work trips, it allows companies to save on physical spaces, and to have teams working in different locations, IoT connected devices will make it a more appealing and easy option for many organizations [2].

IoT & Blockchain

With Blockchain we can share any transaction/information, real-time, between relevant parties present as nodes in the chain in a secure and immutable fashion. In this case, had there been a blockchain network where WHO, Health Ministry of each country, and maybe even relevant nodal hospitals of each country, were connected, sharing real-time information, about any new communicable disease, then the world might have woken up much earlier. We might have seen travel restrictions given sooner, quarantining policies set sooner and social distancing implemented faster. And maybe fewer countries would have got impacted.

What every country is doing now fighting this pandemic, would have been restricted to fewer countries and on a much smaller scale. The usage of a Blockchain to share the information early on might have saved the world a lot of pain and deaths. This is an area where IoT and Blockchain converged, with all the info. coming from the sensors/nodes and traveling over available networks to be processed in the cloud and presented via applications in the hands of health workers and authorities, blockchain will secure the data all the way [3].

IoT & E-Commerce

With the disruption of supply chain networks because of COVID-19, inventory control was one of the biggest challenges retailers and wholesalers had to face during lockdown – and this difficulty may continue until the end of the year. But indisputably, companies that were already using NFC labels for example to control inventory in and out of warehouses have had this task made easier. On the other hand, the track & trace systems used by some carrier companies have proved essential to keep the e-commerce in full operation and to manage the delays in deliveries in real-time. In other words, IoT has become a way to offer a faster and more transparent service to the final consumer. If the containment changes our consumption habits, this could be one of the new demands.

IoT & Telemedicine

COVID-19 pandemic will kickstart IoT adoption in many sectors but especially in the healthcare sector, keeping in mind the strain on healthcare systems caused by the crisis has brought into focus the potential efficiency benefits that can be gained from remote monitoring in healthcare. The sector has been historically slow to integrate IoT technologies into its ecosystem, however, current researches anticipate that the continuing pandemic will drive the adoption of remote monitoring to minimize public interactions [1].

The healthcare institutes in the world are facing difficulties in providing medical care and reducing the risk of exposure. Emphasis on contactless medical care drives the healthcare centers to look up to the IoT solution providers for an effective approach to tackle diseases.

The Internet of Medical Things (IoMT) along with cloud technologies and AI offers an opportunity to help healthcare professionals to monitor their patients, access the data and provide treatment from a remote location, this is possible by using devices like smart thermometers, smart wearables, track and trace apps, robots, and smart medical devices [2].

References

  1. https://futureiot.tech/analysts-say-covid-19-pandemic-will-spur-iot-adoption/
  2. https://blog.infraspeak.com/iot-covid-19/ 
  3. https://www.bbvaopenmind.com/en/technology/digital-world/blockchain-technology-and-covid-19/

 

Ahmed BanafaAhmed Banafa has extensive experience in research, operations and management, with a focus on IoT, Blockchain, Cybersecurity and AI. His researches cited in studies by international organizations like NATO, WTO, and APEC. He is a reviewer and a technical contributor for the publication of several technical books. He served as an instructor at well-known universities and colleges, including Stanford University, University of California, Berkeley; California State University-East Bay; San Jose State University; and University of Massachusetts. He is the recipient of several awards, including Distinguished Tenured Staff Award, Instructor of the year for 4 years in a row, and Certificate of Honor from the City and County of San Francisco. He was named as No.1 tech voice to follow, technology fortune teller and influencer by LinkedIn in 2018 by LinkedIn, his research featured in many reputable sites and magazines including Forbes, IEEE and MIT Technology Review, and Interviewed by ABC, CBS, NBC, CNN, BBC, NPR and Fox TV and Radio stations. He is a member of the MIT Technology Review Global Panel.  He studied Electrical Engineering at Lehigh University, Cybersecurity at Harvard University and Digital Transformation at Massachusetts Institute of Technology (MIT). He is the author of the books: “Secure and Smart Internet of Things (IoT) using Blockchain and Artificial Intelligence (AI)”, and “Blockchain Technology and Applications” . Winner of Author & Artist Award 2019 of San Jose State University for "Secure and Smart IoT" Book."

 

 

The Great Potential of Rural Areas in Spain and Portugal for the Implementation of New Technologies

Javier Parra, Javier Prieto, and Juan M. Corchado
July 23, 2020

 

The population density of the least populated regions in Spain is below 0.98 inhabitants per square kilometer, which is lower than in Lapland. These data cause great concern among European institutions. To solve this problem, efforts are being made to boost the technological potential of those areas through Precision Agriculture Solutions. IoT is often resorted to as a means of combatting this problem.

The regions of western Spain and central Portugal must take further steps towards updating and digitizing their production systems.

It is highly important for Europe to offer equal technological opportunities to all regions, and not only to the most developed ones. No one should be left behind in the technological revolution.

One of the keys to beginning the implementation of technological processes in chiefly rural areas is the generation of infrastructures prepared to support R&D. Once these infrastructures are generated, a technological and business network must be created to sustain and expand research, development, and innovation, and to ensure that it reaches all places. Specifically, our focus is on the regions of western Spain and central Portugal.

 The technological potential of those areas arises from the possibility of industrializing and automating the processes involved in crop and livestock farming, and tourism. The implementation of technology in areas that suffer from depopulation will be a new opportunity for their population to flourish.

Livestock and agriculture are the sectors that will have the greatest opportunity to benefit from technology. Access to different data sources makes it possible to take timely decisions and to create automated and intelligent processes in agronomic activities. This will lead to the revaluation of the sector, making it more profitable and attractive for the population, thus promoting an increase in population.

For agriculture to become a more sustainable sector; conserving our resources and the biodiversity of the environment, rural resources must be managed as efficiently as possible, and this makes the Internet of Things an essential tool in the rural environment.

Farmers who want to know the ideal moment for sowing or harvesting, or want to stay informed on the exact state of their crops (humidity, temperature, the toxicity of the soil, fertilization, etc.) can find their best ally in sensorization. If they also wish to carry out actions promptly, they can automate processes on their farms and control through their smartphone, saving time and money, and achieving great improvements in the quality of the products.

Likewise, livestock farmers can obtain great advantages from technology. Herds may be monitored employing sensors and connection tools that allow them to control the animals (feeding, health, etc.) and locate them.

Another key sector for the settlement of the population is tourism, which will also benefit from technology in that it will facilitate access, monitor the flow of visitors, a closer interaction between the visitor and the environment.

One of the uses that are becoming more common in the rural environment is that of devices connected through wireless communications. Such devices become intelligent tools that connect, forming a large information network that allows its users to act immediately in areas as diverse as agriculture, livestock, forest control, conservation, tourism or local and regional administration, etc.

The implementation of new technologies opens up great opportunities for the creation of employment and thus, the generation of wealth. However, the sensorization of processes entails possible security problems, so users and companies will have to invest in cybersecurity systems, VPN networks, intrusion detection systems, etc.

In connection with those efforts, the IoT Digital Innovation Hub has promoted several projects aimed at the implementation of ICT in the above-mentioned regions.

These projects are within the interregional program PocTep that promotes cross-border cooperation projects with the financial and structural support of the European Union.

The DISRUPTIVE project’s objective is to enrich state-of-the-art of ICTs through collaboration among different entities in the PocTep region. This project strives to improve research and innovation infrastructures in the area. By achieving excellence in research and innovation, boosting business competitiveness through technological growth, and encouraging financial and social growth, those regions will have new possibilities. Moreover, those objectives are of great interest to European institutions. Within the PocTep region, this project focuses on innovation in Castilla y León and northern Portugal.

This is a challenge in which the Bisite Research Group of the University of Salamanca, CARTIF, the National Institute of Cybersecurity, the University of Valladolid, the Higher Institute of Engineering of Porto, ProducTech, the Polytechnic Institute of Bragança and ICE are participating.

In addition to the already mentioned objective of promoting research, technological development, and innovation, the project also seeks to bring scientific excellence to the area, as well as productive transformation.

This project will produce notable changes in the development and competitiveness of the region.

The pursuit of the IOTEC and DIGITEC projects is similar to that of the Disruptive project, however, they focus on generating a network of technical and technological participants that, like the latter, will promote research, technological development, and innovation in Castilla y León and central Portugal.

In this case, the participants of the project are the BISITE research group, AETICAL, ADEZOS, CARTIF, ICE, IPN, Tice.pt, and Inòvcluster.

Similarly, this project intends to use the created infrastructures to generate a technological network throughout the territory.

In addition to the development of projects that generate wealth in the areas where IoT Digital Innovation Hub is implemented, it will participate in the organization of the IEEE GLOBECOM 2021 conference, which will take place in Spain for the very first time in 2021. It will be held at the IFEMA and is a reference event for the communication and innovation of society worldwide. The General Chairs of this edition will be Professor Juan Manuel Corchado from the University of Salamanca and also Professor Ana García from the Universidad Carlos III de Madrid. This conference brings together more than 2000 researchers from around the world and this time will be held in Madrid between 7 and 11 December next year.

Other professors who will be part of the organizing committee, include experts such as Sennur Ulukus (University of Maryland) who will act as the Technical Program Committee Chair, Joel Rodrigues (National Institute of Telecommunications of Brazil) the Executive Chair, or Javier Prieto (University of Salamanca) the Operations Chair.


 

Javier ParraJavier Parra is a Ph.D. Assistant Professor in the Department of Business and Economics Administration at the University of Salamanca. Since 2009, Javier has been complementing his work as a professor with the management of different technology departments in private companies. Javier is a member of the BISITE Research Group where he develops his research interests related to economics, technological finance, econometrics, and digital intelligence.

 

Javier PrietoJavier Prieto (S’11–M’12–SM’19) is a distinguished researcher at the University of Salamanca. He is an associate editor of the IEEE Communications Letters and Editor-in-chief of the Smart Cities Journal in the IoT section. He is a member of the Technical Committee on Cognitive Networks of IEEE ComSoc and the Technical Committee on RFID Technologies of the IEEE MTT-S. He also serves as the coordinator of the IEEE Blockchain Initiative in Spain.

 

Juan M CorchadoJuan M. Corchado (M’10) is a Full Professor with Chair at the University of Salamanca and the Director of the BISITE (Bioinformatics, Intelligent Systems, and Educational Technology) research group. He is also a Visiting Professor at the Osaka Institute of Technology, Visiting Professor at the Universiti Malaysia Kelantan, and a Member of the Advisory Group on Online Terrorist Propaganda of the European Counter Terrorism Centre (EUROPOL). He currently serves as the director of the IOT Digital Innovation Hub and as the President of the AIR Institute. He has also been the President of the IEEE Systems, Man, and Cybernetics Spanish Chapter.