Effect of sucrose replacement and resistant starch addition on textural properties of gluten-free doughs and biscuits

There is a need to develop low-sugar healthy products. The aim of this research was to evaluate the effect of maltitol and inulin as sucrose replacement alongside resistant starch (RS) and green banana flour (GBF) on the texture and physical properties of gluten-free doughs and biscuits formulated with buckwheat, sorghum and lentil flours. These properties are important to predict the dough workability, how easy the biscuits could be mass-produced and determine consumers’ acceptability. Results showed that partial and complete substitution of sucrose could be achieved and appropriate concentration of resistant starch or green banana flour contributed to better dough and biscuit texture. RS content showed the biggest influence on dough stickiness and biscuit hardness and could be used to correct the negative effect of sucrose replacement and to maximise both the dough processability and biscuit acceptability

Industrial Internet-of-Things security enhanced with deep learning approaches for smart cities

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security

Emerging drone trends for blockchain-based 5G networks: open issues and future perspectives

Unmanned aerial vehicles, commonly known as drones, are receiving growing research interest due to their ability to carry a multitude of sensors and to connect to mobile networks. They are also able to move freely across the air, which enables the creation of numerous applications that were until now considered impracticable. However, such applications may require high computational resources, reliable connection, and high data transmission rates to accomplish different tasks. Therefore, in this work, first, we discuss 5G communication networks and mobile edge computing (MEC) as promising technologies that can provide several benefits to drone-enabled environments and solve some of the presented issues. We also comment on 5G and MEC approaches, presenting the state of the art and seeking to solve each of the latter issues presented. Afterward, we introduce new security concerns of drone communication networks, given their recent popularity. These concerns are related to the possibility of malicious users taking advantage of this brand new technology, which has made many governments ban drones due to public safety. Next, blockchain technology is brought in as a novel solution to the security issues due to its decentralized nature, making it inherently safe. This article also surveys contributions that make use of each of the technologies mentioned to improve the emerging drone industry. Subsequently, we discuss open issues and future perspectives

Performance Assessment of Fragmentation Mechanisms for Vehicular Delay-Tolerant Networks

[EN] Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular communications where vehicles cooperate with each other, acting as the communication infrastructure, to provide low-cost asynchronous opportunistic communications. These communication technologies assume variable delays and bandwidth constraints characterized by a non-transmission control protocol/internet protocol architecture but interacting with it at the edge of the network. VDTNs are based on the principle of asynchronous communications, bundle-oriented communication from the DTN architecture, employing a store-carry-and-forward routing paradigm. In this sense, VDTNs should use the tight network resources optimizing each opportunistic contact among nodes. Given the limited contact times among nodes, fragmentation appears as a possible solution to improve the overall network performance, increasing the bundle delivery probability. This article proposes the use of several fragmentation approaches (proactive, source, reactive, and toilet paper) for VDTNs. They are discussed and evaluated through a laboratory testbed. Reactive and toilet paper approaches present the best results. It was also shown that only the source fragmentation approach presents worst results when compared with non-fragmentation approaches.This study was partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, by the Euro-NF Network of Excellence of the Seventh Framework Programme of EU, in the framework of the Specific Joint Research Project VDTN, and by the INESC-ID multiannual funding through the PIDDAC program funds and National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 and PTDC/EEA-TEL/099074/2008 (MPSat) Projects.Dias, JAFF.; Rodrigues, JJPC.; Isento, JN.; Pereira, PRBA.; Lloret, J. (2011). Performance Assessment of Fragmentation Mechanisms for Vehicular Delay-Tolerant Networks. EURASIP Journal on Wireless Communications and Networking. 2011(195):1-14. https://doi.org/10.1186/1687-1499-2011-195S1142011195Tatchikou R, Biswas S, Dion F: Cooperative vehicle collision avoidance using inter-vehicle packet forwarding. In Presented at the IEEE Global Telecommunications Conference (IEEE GLOBECOM 2005). St. Louis, MO, USA; 2005.Park JS, Lee U, Oh SY, Gerla M, Lun DS: Emergency related video streaming in VANET using network coding. In The Third ACM International Workshop on Vehicular Ad Hoc Networks. (VANET 2006), Los Angeles, CA, USA; 2006:102-103.Buchenscheit A, Schaub F, Kargl F, Weber M: A VANET-based emergency vehicle warning system. Presented at the First IEEE Vehicular Networking Conference (IEEE VNC 2009), Tokyo, Japan 2009.Nekovee M: Sensor networks on the road: the promises and challenges of vehicular ad hoc networks and vehicular grids. In Proceedings of the Workshop on Ubiquitous Computing and e-Research. Edinburgh, UK; 2005.Blum J, Eskandarian A, Hoffmman L: Challenges of intervehicle ad hoc networks. IEEE Trans. Intell. Transport. Syst 2004, 5(4):347-351. 10.1109/TITS.2004.838218Yousefi S, Mousavi MS, Fathy M: Vehicular ad hoc networks (VANETs): challenges and perspectives. 6th International Conference on ITS Telecommunications (ITST 2006) 2006, 761-766.Füßler H, Torrent-Moreno M, Transier M, Festag A, Hartenstein H: Thoughts on a protocol architecture for vehicular ad-hoc networks. In Presented at the 2nd International Workshop on Intelligent Transportation (WIT 2005). Hamburg, Germany; 2005.Cerf V, Burleigh S, Hooke A, Torgerson L, Durst R, Scott K, Fall K, Weiss H: Delay-tolerant networking architecture. RFC 4838 2007. [Online] [ http://www.rfc-editor.org/rfc/rfc4838.txt ]Soares VNGJ, Farahmand F, Rodrigues JJPC: A layered architecture for vehicular delay-tolerant networks. In The Fourteenth IEEE Symposium on Computers and Communications (ISCC 2009). Sousse, Tunisia; 2009:122-127.Rodrigues JJPC, Soares VNGJ, Farahmand F: Stationary relay nodes deployment on vehicular opportunistic networks. In Mobile Opportunistic Networks: Architectures, Protocols and Applications. Edited by: Denko M. CRC Press, Taylor & Francis Group (hardcover); 2011:227-243.Postel J: Internet Protocol. RFC 791 1981. [Online] [ http://www.ietf.org/rfc/rfc791.txt ]Kent CA, Moguk JC: Fragmentation considered harmful. SIGCOMM Comput Commun Rev 1995, 25(1):75-87. 10.1145/205447.205456Kim B-S, Fang Y, Wong TF, Kwon Y: Throughput enhancement through dynamic fragmentation in wireless LANs. IEEE Trans Veh Technol 2005, 54(4):1415-1425. 10.1109/TVT.2005.851361Ginzboorg P, Niemi V, Ott J: Message Fragmentation in Disruptive Networks. Nokia Research Center, Technical Report; 2009.Legner M: Map-Based Geographic Forwarding in Vehicular Networks. Department of Informatic, University of Stuttgart; 2002.Li Q, Rus D: Sending messages to mobile users in disconnected ad-hoc wireless networks. 6th Annual International Conference on Mobile Computing and Networking, New York, USA 2000, 44-55.Vahdat A, Becker B: Epidemic Routing for Partially-Connected Ad-Hoc Networks. Duke University, Technical Report; 2000.Briesemeister L, Hommel G: Overcoming fragmentation in mobile ad-hoc networks. J Commun Netw 2000, 2(3):182-187.Liu H, Sheng H, Lv Z, Li L, Ma C: A cross layer design of fragmentation and priority scheduling in vehicular ad hoc networks. 7th World Congress on Intelligent Control and Automation (WCICA 2008) 2008, 6157-6160.Joshi HP: Distributed robust geocast: a multicast protocol for inter-vehicle communication. Master Thesis, North Carolina State University; 2006.Bachir A, Benslimane A: A multicast protocol in ad hoc networks: Inter-vehicles geocast. Proceedings of the 57th IEEE Vehicular Technology Conference, Korea 2003, 2456-2460.Mikko P, Ari K, Ott J: Message fragmentation in opportunistic DTNs. In 9th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM 2008). Newport Beach, CA, USA; 2008.Farrell S, Symington S, Weiss H: Delay-tolerant networking security overview. Internet Draft 2009. [Online] [ http://tools.ietf.org/html/draft-irtf-dtnrg-sec-overview-06 ]Magaia N, Pereira PR, Casaca A, Rodrigues J, Dias JA, Isento JN, Cervelló-Pastor C, Gallego J: Bundles fragmentation in vehicular delay-tolerant networks. 7th Euro-nf conference on next generation internet, Kaiserslautern, Germany 2011.Soares V, Rodrigues J, Farahmand F, Denko M: Exploiting node localization for performance improvement of vehicular delay-tolerant networks. In IEEE International Conference on Communications (ICC 2010). Cape Town, South Africa; 2010.Rubinstein MG, Abdesselm FB, Cavalcanti SR, Campista MEM, Alves RSA, Costa LHMK, Amorim MD, Duarte OCMB: Measuring the capacity of in-car to in-car vehicular networks. IEEE Commun Mag 2009, 47(11):128-136.Spyropoulos T, Psounis K, Raghavendra C S: Spray and wait: an efficient routing scheme for intermittently connected mobile networks. In ACM SIGCOMM 2005--Workshop on Delay Tolerant Networking and Related Networks (WDTN-05). Philadelphia, PA, USA; 2005:252-259.Lindgren A, Doria A, Davies E, Grasic S: Probabilistic routing protocol for intermittently connected networks (2010). Internet Draft 2010. [Online] [ http://tools.ietf.org/html/draft-irtf-dtnrg-prophet-06 ]Teshima S, Ohta T, Kohno E, Kakuda Y: A data transfer scheme using autonomous clustering in VANETs environment. In 10th International Symposium on Autonomous Decentralized Systems (ISADS 2011). Tokyo, Japan; 2011:477-482.Psounis K: Efficient Routing for Safety Applications in Vehicular Networks. METRANS Project DTRS98-G0019, Electrical Engineering. University of Southern California, Los Angeles, USA; 2009.Li X, Shu W, Li M, Huang H, Min-You Wu: DTN routing in vehicular sensor networks. In IEEE Global Telecommunications Conference (IEEE GLOBECOM 2008). New Orleans, USA; 2008:1-5

Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives

With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving toward the edge of the network. For numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the edge computing paradigm. Together with machine learning, edge computing has become a powerful local decision-making tool, fostering the advent of edge learning. However, the latter has become delay-sensitive and resource-thirsty in terms of hardware and networking. New methods have been developed to solve or minimize these issues, as proposed in this study. We first investigated representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we proposed an ELI-based video data prioritization framework that only considers data with events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, we critically examined various communication aspects related to edge learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss the challenges and present issues that remain

Bundles fragmentation in vehicular delay-tolerant networks

Vehicular Delay-Tolerant Networks use the delaytolerant architecture and protocols to overcome the disruptions in network connectivity. These concepts help in cases where the network is sparse or with large variations in density or there is no end-to-end connectivity, by providing a communications solution for non real-time applications. This paper presents data fragmentation techniques to optimize the efficiency of data delivery for the case of the short node contacts that characterize vehicle networks. The techniques were tested in a laboratory environment with portable digital assistants and Lego Mindstorm NXT robotic cars. If no fragmentation is used, only small messages are successfully transferred. Proactive fragmentation fragments messages to a predefined size in the source node. Reactive fragmentation adjusts the fragment sizes to the real duration of the contact when it is broken. Reactive fragmentation showed a good efficiency in adapting the fragmentation in real time to the contact duration. Proactive fragmentation can perform slightly better if the fragment sizes are carefully chosen as it requires less processing. As this choice is difficult, reactive fragmentation is more practical to use.Postprint (published version

Bundles fragmentation in vehicular delay-tolerant networks

Vehicular Delay-Tolerant Networks use the delaytolerant architecture and protocols to overcome the disruptions in network connectivity. These concepts help in cases where the network is sparse or with large variations in density or there is no end-to-end connectivity, by providing a communications solution for non real-time applications. This paper presents data fragmentation techniques to optimize the efficiency of data delivery for the case of the short node contacts that characterize vehicle networks. The techniques were tested in a laboratory environment with portable digital assistants and Lego Mindstorm NXT robotic cars. If no fragmentation is used, only small messages are successfully transferred. Proactive fragmentation fragments messages to a predefined size in the source node. Reactive fragmentation adjusts the fragment sizes to the real duration of the contact when it is broken. Reactive fragmentation showed a good efficiency in adapting the fragmentation in real time to the contact duration. Proactive fragmentation can perform slightly better if the fragment sizes are carefully chosen as it requires less processing. As this choice is difficult, reactive fragmentation is more practical to use