Deep learning-based edge caching for multi-cluster heterogeneous networks

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability

Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms

[EN] By offering low-latency and context-aware services, fog computing will have a peculiar role in the deployment of Internet of Things (IoT) applications for smart environments. Unlike the conventional remote cloud, for which consolidated architectures and deployment options exist, many design and implementation aspects remain open when considering the latest fog computing paradigm. In this paper, we focus on the problems of dynamically discovering the processing and storage resources distributed among fog nodes and, accordingly, orchestrating them for the provisioning of IoT services for smart environments. In particular, we show how these functionalities can be effectively supported by the revolutionary Named Data Networking (NDN) paradigm. Originally conceived to support named content delivery, NDN can be extended to request and provide named computation services, with NDN nodes acting as both content routers and in-network service executors. To substantiate our analysis, we present an NDN fog computing framework with focus on a smart campus scenario, where the execution of IoT services is dynamically orchestrated and performed by NDN nodes in a distributed fashion. A simulation campaign in ndnSIM, the reference network simulator of the NDN research community, is also presented to assess the performance of our proposal against state-of-the-art solutions. Results confirm the superiority of the proposal in terms of service provisioning time, paid at the expenses of a slightly higher amount of traffic exchanged among fog nodes.This research was partially funded by the Italian Government under grant PON ARS01_00836 for the COGITO (A COGnItive dynamic sysTem to allOw buildings to learn and adapt) PON Project.Amadeo, M.; Ruggeri, G.; Campolo, C.; Molinaro, A.; Loscri, V.; Tavares De Araujo Cesariny Calafate, CM. (2019). Fog Computing in IoT Smart Environments via Named Data Networking: A Study on Service Orchestration Mechanisms. Future Internet. 11(11):1-21. https://doi.org/10.3390/fi11110222S1211111Lee, I., & Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. doi:10.1016/j.bushor.2015.03.008Cicirelli, F., Guerrieri, A., Spezzano, G., Vinci, A., Briante, O., Iera, A., & Ruggeri, G. (2018). Edge Computing and Social Internet of Things for Large-Scale Smart Environments Development. IEEE Internet of Things Journal, 5(4), 2557-2571. doi:10.1109/jiot.2017.2775739Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854-864. doi:10.1109/jiot.2016.2584538Openfog Consortiumhttp://www.openfogconsortium.org/Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, kc, Crowley, P., … Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44(3), 66-73. doi:10.1145/2656877.2656887Amadeo, M., Ruggeri, G., Campolo, C., & Molinaro, A. (2019). IoT Services Allocation at the Edge via Named Data Networking: From Optimal Bounds to Practical Design. IEEE Transactions on Network and Service Management, 16(2), 661-674. doi:10.1109/tnsm.2019.2900274ndnSIM 2.0: A New Version of the NDN Simulator for NS-3https://www.researchgate.net/profile/Spyridon_Mastorakis/publication/281652451_ndnSIM_20_A_new_version_of_the_NDN_simulator_for_NS-3/links/5b196020a6fdcca67b63660d/ndnSIM-20-A-new-version-of-the-NDN-simulator-for-NS-3.pdfAhlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012). A survey of information-centric networking. IEEE Communications Magazine, 50(7), 26-36. doi:10.1109/mcom.2012.6231276NFD Developer’s Guidehttps://named-data.net/wp-content/uploads/2016/03/ndn-0021-diff-5..6-nfd-developer-guide.pdfPiro, G., Amadeo, M., Boggia, G., Campolo, C., Grieco, L. A., Molinaro, A., & Ruggeri, G. (2019). Gazing into the Crystal Ball: When the Future Internet Meets the Mobile Clouds. IEEE Transactions on Cloud Computing, 7(1), 210-223. doi:10.1109/tcc.2016.2573307Zhang, G., Li, Y., & Lin, T. (2013). Caching in information centric networking: A survey. Computer Networks, 57(16), 3128-3141. doi:10.1016/j.comnet.2013.07.007Yi, C., Afanasyev, A., Moiseenko, I., Wang, L., Zhang, B., & Zhang, L. (2013). A case for stateful forwarding plane. Computer Communications, 36(7), 779-791. doi:10.1016/j.comcom.2013.01.005Amadeo, M., Briante, O., Campolo, C., Molinaro, A., & Ruggeri, G. (2016). Information-centric networking for M2M communications: Design and deployment. Computer Communications, 89-90, 105-116. doi:10.1016/j.comcom.2016.03.009Tourani, R., Misra, S., Mick, T., & Panwar, G. (2018). Security, Privacy, and Access Control in Information-Centric Networking: A Survey. IEEE Communications Surveys & Tutorials, 20(1), 566-600. doi:10.1109/comst.2017.2749508Ndn-ace: Access Control for Constrained Environments over Named Data Networkinghttp://new.named-data.net/wp-content/uploads/2015/12/ndn-0036-1-ndn-ace.pdfZhang, Z., Yu, Y., Zhang, H., Newberry, E., Mastorakis, S., Li, Y., … Zhang, L. (2018). An Overview of Security Support in Named Data Networking. IEEE Communications Magazine, 56(11), 62-68. doi:10.1109/mcom.2018.1701147Cisco White Paperhttps://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdfAazam, M., Zeadally, S., & Harras, K. A. (2018). Deploying Fog Computing in Industrial Internet of Things and Industry 4.0. IEEE Transactions on Industrial Informatics, 14(10), 4674-4682. doi:10.1109/tii.2018.2855198Hou, X., Li, Y., Chen, M., Wu, D., Jin, D., & Chen, S. (2016). Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures. IEEE Transactions on Vehicular Technology, 65(6), 3860-3873. doi:10.1109/tvt.2016.2532863Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., … Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: A complete survey. Journal of Systems Architecture, 98, 289-330. doi:10.1016/j.sysarc.2019.02.009Baktir, A. C., Ozgovde, A., & Ersoy, C. (2017). How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions. IEEE Communications Surveys & Tutorials, 19(4), 2359-2391. doi:10.1109/comst.2017.2717482Duan, Q., Yan, Y., & Vasilakos, A. V. (2012). A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing. IEEE Transactions on Network and Service Management, 9(4), 373-392. doi:10.1109/tnsm.2012.113012.120310Amadeo, M., Campolo, C., & Molinaro, A. (2016). NDNe: Enhancing Named Data Networking to Support Cloudification at the Edge. IEEE Communications Letters, 20(11), 2264-2267. doi:10.1109/lcomm.2016.2597850Krol, M., Marxer, C., Grewe, D., Psaras, I., & Tschudin, C. (2018). Open Security Issues for Edge Named Function Environments. IEEE Communications Magazine, 56(11), 69-75. doi:10.1109/mcom.2018.170111711801-2:2017 Information Technology—Generic Cabling for Customer Premiseshttps://www.iso.org/standard/66183.htm

Trust Management in Social Internet of Things (SIoT): A Survey

A survey on trust management in the Social Internet of Things (SIoT) is provided, beginning with a discussion of SIoT architectures and relationships. Using a variety of publication databases, we describe efforts that focus on various trust management aspects of SIoT. Trust management models comprise three themes: trust computation, aggregation, and updates. Our study presents a detailed discussion of all three steps. Trust computation and trust aggregation depend upon Trust Attributes (TAs) for the calculation of local and global trust values. Our paper discusses many strategies for aggregating trust, but “Weighted Sum” is the most frequently used in the relevant studies. Our paper addresses trust computation and aggregation scenarios. Our work classifies research by TAs (Social Trust, Quality of Service). We’ve categorized the research (reputation-based, recommendation-based, knowledge-based) depending on the types of feedback/opinions used to calculate trust values (global feedback/opinion, feedback from a friend, trustor’s own opinion considering the trustee’s information). Our work classifies studies (policy-based, prediction-based, weighted sum-based/weighted linear combination-based) by trust computation/aggregation approach. Two trust-update schemes are discussed: time-driven and event-driven schemes, while most trust management models utilize an event-driven scheme. Both trust computation and aggregation need propagating trust values in a centralized, decentralized, or semi-centralized way. Our study covers classifying research by trust updates and propagation techniques. Trust models should provide resiliency to SIoT attacks. This analysis classifies SIoT attacks as collaborative or individual. We also discuss scenarios depicted in the relevant studies to incorporate resistance against trust-related attacks in SIoT. Studies suggest context-based or context-free trust management strategies. Our study categorizes studies based on context-based or context-free approaches. To gain the benefits of an immutable, privacy-preserving approach, a future trust management system should utilize Blockchain technology to support non-repudiation and tracking of trust relationships

Federated Learning in Mobile Edge Networks: A Comprehensive Survey

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional cloudbased Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in F

Reduction of wind induced microphone noise using singular spectrum analysis technique

Wind induced noise in microphone signals is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. This thesis is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling.Thethesis presents a new approach to wind noise problem. Instead of filtering, a separation technique is developed. Signals are separated into wanted sounds of specific interest and wind noise based on the statistical feature of wind noise. The new technique is based on the Singular Spectrum Analysis methodwhich has recently seen many successful paradigms in the separation of biomedical signals, e.g., separating heart soundfrom lung noise. It has also been successfully implemented to de-noise signals in various applications.The thesis set out with particular emphasison investigating the factor that determines and improves the separability towards obtaining satisfactory results in terms of separating wind noise components out from noisy acoustic signals. A systematicapproach has been established and developed within the framework of singular spectral separation of acoustic signals contaminated by wind noise. This approach, which utilisesa conceptual framework, has, in its final form, three key objectives; grouping, reconstruction and separability. This approach is offered through introducing new mathematical models particularly for window length optimisation along with new descriptive figures.The research question has therefore been addressed considering developing algorithms according to updated requirements from method justification to verification and validation of the developed system. This thesis follows suitable testing criteria by conducting several experiments and a case-study design, with in-depth analysis of the results using visual tools of the method and related techniques.For system verification, an empirical study using testing signals thatintroduces a large number of experiments has been conducted. Empirical study with real-world sounds has been introduced next in system validation phase after rigorously selecting and preparing the dataset whichis drawn from two main sources: freefield1010 dataset, internet-based Freesound recordings. Results show that microphone wind noise is separable in the singular spectrum domain after validating and critically evaluating the developed system objectively. The findings indicate the effectiveness of the developed grouping and reconstruction techniques with significant improvement in the separability evidenced by w-correlation matrix.The developed method might be generalised to other outdoor sound acquisition applications