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Prof. Paolo Bellavista
Department of Computer Science and Engineering (DISI), University of Bologna, 40136 Bologna, Italy

Basic Info


Research Keywords & Expertise

0 EDGE COMPUTING
0 fog computing
0 Pervasive and Mobile Computing
0 Wireless sensor and actuator networks
0 Vehicular sensor networks

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fog computing
EDGE COMPUTING
Wireless sensor and actuator networks
Vehicular sensor networks
Pervasive and Mobile Computing
Cyber physical systems for Industry 4.0
Online stream processing of sensing dataflows
IoT and big data processing

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Short Biography

Paolo Bellavista received his MSc and PhD degrees in computer science engineering from the University of Bologna, Italy, where he is now a full professor of distributed and mobile systems. His research activities span from pervasive wireless computing to location/context-aware services, from edge cloud computing to middleware for Industry 4.0 applications. He is currently the scientific coordinator of a large H2020 big data innovation action called IoTwins (delivers distributed digital twins for the manufacturing industry). He serves on the editorial boards of IEEE Communications Surveys and Tutorials, ACM Computing Surveys, IEEE Transactions on Network and Service Management, Elsevier Pervasive Mobile Computing, and the Elsevier Journal of Network and Computing Applications, among the others.

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Project

Project Goal: https://cordis.europa.eu/project/rcn/223969/en

Starting Date:01 September 2019

Current Stage: First year of project execution

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Project

Project Goal: https://simdome.eu/

Starting Date:01 February 2019

Current Stage: First year of project execution

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Journal article
Published: 21 July 2021 in Sensors
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Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization; thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead.

ACS Style

Paolo Bellavista; Christian Esposito; Luca Foschini; Carlo Giannelli; Nicola Mazzocca; Rebecca Montanari. Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing. Sensors 2021, 21, 4955 .

AMA Style

Paolo Bellavista, Christian Esposito, Luca Foschini, Carlo Giannelli, Nicola Mazzocca, Rebecca Montanari. Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing. Sensors. 2021; 21 (15):4955.

Chicago/Turabian Style

Paolo Bellavista; Christian Esposito; Luca Foschini; Carlo Giannelli; Nicola Mazzocca; Rebecca Montanari. 2021. "Interoperable Blockchains for Highly-Integrated Supply Chains in Collaborative Manufacturing." Sensors 21, no. 15: 4955.

Journal article
Published: 17 June 2021 in Sensors
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Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

ACS Style

Isam Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams. Sensors 2021, 21, 4160 .

AMA Style

Isam Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari. QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams. Sensors. 2021; 21 (12):4160.

Chicago/Turabian Style

Isam Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. 2021. "QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams." Sensors 21, no. 12: 4160.

Journal article
Published: 01 May 2021 in IEEE Transactions on Services Computing
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ACS Style

Paolo Bellavista; Antonio Corradi; Andy Edmonds; Luca Foschini; Alessandro Zanni; Thomas Michael Bohnert. Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking. IEEE Transactions on Services Computing 2021, 14, 710 -723.

AMA Style

Paolo Bellavista, Antonio Corradi, Andy Edmonds, Luca Foschini, Alessandro Zanni, Thomas Michael Bohnert. Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking. IEEE Transactions on Services Computing. 2021; 14 (3):710-723.

Chicago/Turabian Style

Paolo Bellavista; Antonio Corradi; Andy Edmonds; Luca Foschini; Alessandro Zanni; Thomas Michael Bohnert. 2021. "Elastic Provisioning of Stateful Telco Services in Mobile Cloud Networking." IEEE Transactions on Services Computing 14, no. 3: 710-723.

Editorial
Published: 15 February 2021 in Pervasive and Mobile Computing
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ACS Style

Paolo Bellavista; Carlo Giannelli; Mirco Musolesi; Marco Picone. Editorial for this SI on “Location Based Services and Applications in the era of Internet of Things”. Pervasive and Mobile Computing 2021, 71, 101350 .

AMA Style

Paolo Bellavista, Carlo Giannelli, Mirco Musolesi, Marco Picone. Editorial for this SI on “Location Based Services and Applications in the era of Internet of Things”. Pervasive and Mobile Computing. 2021; 71 ():101350.

Chicago/Turabian Style

Paolo Bellavista; Carlo Giannelli; Mirco Musolesi; Marco Picone. 2021. "Editorial for this SI on “Location Based Services and Applications in the era of Internet of Things”." Pervasive and Mobile Computing 71, no. : 101350.

Conference paper
Published: 01 December 2020 in GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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The Industrial Internet of Things (IIoT) provides automation solutions for industrial processes through the interconnection of different sensors, actuators and robotic devices to the Internet, enabling for the automation of manufacturing processes through Factory Automation. However, IIoT processes are often critical, and require very high Quality of Service (QoS) to work properly, as well as network scalability and flexibility. Fog computing, a paradigm that brings computation and storage devices closer to the edge of the network to enhance QoS, as well as Software-Defined Networking (SDN), which enables for network scalability and flexibility, can be integrated into IIoT architectures in the form of fog nodes that integrate both, computation resources and SDN capabilities, to meet these needs. However, the QoS of the IIoT system depends on the placement of these fog nodes, creating a need to obtain placements that optimize QoS in order to meet the requirements by minimizing the latency between the fog nodes and the IIoT devices that consume their services. In this paper, this fog node placement problem is formalized and solved by means of Mixed Integer Programming. We also show relevant experimental results of our formulation and analyze its performance.

ACS Style

Juan Luis Herrera; Paolo Bellavista; Luca Foschini; Jaime Galen-Jimanez; Juan M. Murillo; Javier Berrocal. Meeting Stringent QoS Requirements in IIoT-based Scenarios. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 2020, 1 -6.

AMA Style

Juan Luis Herrera, Paolo Bellavista, Luca Foschini, Jaime Galen-Jimanez, Juan M. Murillo, Javier Berrocal. Meeting Stringent QoS Requirements in IIoT-based Scenarios. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020; ():1-6.

Chicago/Turabian Style

Juan Luis Herrera; Paolo Bellavista; Luca Foschini; Jaime Galen-Jimanez; Juan M. Murillo; Javier Berrocal. 2020. "Meeting Stringent QoS Requirements in IIoT-based Scenarios." GLOBECOM 2020 - 2020 IEEE Global Communications Conference , no. : 1-6.

Conference paper
Published: 01 December 2020 in GLOBECOM 2020 - 2020 IEEE Global Communications Conference
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The easily reachable IoT edge devices have caused the accumulation of vast amounts of geo-referenced data traces that can help in performing deep insightful analytics. Geospatial data in real geometries are normally clumped into batches and has strong autocorrelation properties which can be exploited in discovering interesting insights. Current plain Cloud computing frameworks are not attuned to the shape of data. Most importantly, data splitting is an important precursor in data parallelization mechanisms. Current systems mostly focus on general data workloads, thus are giving attention mostly to load balancing while splitting the data to Cloud computing resources. However, many benefits can be reaped by being attuned to the spatial characteristics while distributing the data, thus striking a plausible balance between load balancing and spatial data locality preservation normally leads to achieving better time-based QoS goals, which then leads to an optimized provisioning of Cloud computing resources. In this paper, we have designed a spatial batch processing engine that comprises a custom spatial data locality aware partitioning method for disseminating spatial data loads in Cloud computing clusters. We have also extended a state-of-art benchmark density-based clustering method that is known as DBSCAN-MR and implemented a standard compliant prototype on top of a best-in-breed de facto Cloud-based main memory processing framework, Apache Spark. Our results show that our partitioning method with the associated spatial query optimizers can achieve gains that significantly outperform baselines

ACS Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. Locality-Preserving Spatial Partitioning for Geo Big Data Analytics in Main Memory Frameworks. GLOBECOM 2020 - 2020 IEEE Global Communications Conference 2020, 1 -6.

AMA Style

Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari. Locality-Preserving Spatial Partitioning for Geo Big Data Analytics in Main Memory Frameworks. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. 2020; ():1-6.

Chicago/Turabian Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. 2020. "Locality-Preserving Spatial Partitioning for Geo Big Data Analytics in Main Memory Frameworks." GLOBECOM 2020 - 2020 IEEE Global Communications Conference , no. : 1-6.

Journal article
Published: 27 October 2020 in IEEE Transactions on Network and Service Management
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Current cloud-enabled NoSQL database frameworks support flexible and scalable storage of huge amounts of data arriving through various and often heterogeneous channels. However, they do not natively provide optimised processing of spatial data, thus making it more difficult to perform accurate data analytics needed in many smart city application scenarios. To improve the performance of spatial data computation in the NoSQL MongoDB storage framework, this paper proposes a novel data partitioning method based on dimensionality reduction. The underlying key idea is to reduce a spatial data representation from multi to single dimensionality, by still maintaining its geometrical meaning and by employing a specific geo-encoding scheme, i.e., a geohash string. In particular, the geohash string is used as a sharding key in order to store geometrically-nearby objects into the same chunks (and consequently into the same shard). In addition, as a distinctive feature, we have extended the MongoDB framework with a custom spatial QoS-aware optimizer that exploits our novel partitioning scheme to support two, typically expensive, types of spatial queries with QoS guarantees. Those queries are containment (and consequently top-N) and proximity. The paper also contributes to the existing literature with extensive experimental results about the performance of both our partitioning method and query optimizer; the reported results show that our solutions outperform baselines by orders of magnitude.

ACS Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. Efficient QoS-Aware Spatial Join Processing for Scalable NoSQL Storage Frameworks. IEEE Transactions on Network and Service Management 2020, 18, 2437 -2449.

AMA Style

Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari. Efficient QoS-Aware Spatial Join Processing for Scalable NoSQL Storage Frameworks. IEEE Transactions on Network and Service Management. 2020; 18 (2):2437-2449.

Chicago/Turabian Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. 2020. "Efficient QoS-Aware Spatial Join Processing for Scalable NoSQL Storage Frameworks." IEEE Transactions on Network and Service Management 18, no. 2: 2437-2449.

Conference paper
Published: 01 September 2020 in 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
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The diversity of sensing options that IoT offers imposed requirements to evolve stream processing engines so to cope with highly heterogeneous and fast-pace data streams challenging their computing capacities. Location intelligence applications aim at exploiting those geo-referenced data in generating visualizations and dashboards that provide deep insights for assisting decision making in smart cities and urban planning. As data arriving are mostly geo-referenced and the rate is fluctuating in pace and skewness, computations upon streams should depend on approximation by applying methods such as sampling. Representativeness in sampling designs remains the pivotal concern in the literature. In spatial data streams contexts, it loosely means selecting proportional counts of spatial tuples from each group of tuples that belong to the same real geometry (i.e., geographically residing in the same proximity) within each streaming time window. This is challenging in streaming settings because spatial data is parametrized, loosing hence it is real geometries, which requires costly geometric operations to project them back to maps. To close this void, we have designed SpatialSPE in a previous work and incorporated an efficient fine-grained spatial online sampling method (SAOS) transparently within its layers. In this paper, we extend SAOS (the novel method is termed ex-SAOS) by new features that allow efficient online spatial sampling on a coarser level, which is a requirement in smart city scenarios. Our results show that ex-SAOS is efficient and effectively extends SAOS for more general smart city and urban computing scenarios.

ACS Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. Spatially Representative Online Big Data Sampling for Smart Cities. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) 2020, 1 -6.

AMA Style

Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari. Spatially Representative Online Big Data Sampling for Smart Cities. 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). 2020; ():1-6.

Chicago/Turabian Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. 2020. "Spatially Representative Online Big Data Sampling for Smart Cities." 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) , no. : 1-6.

Conference paper
Published: 01 September 2020 in 2020 IEEE International Conference on Smart Computing (SMARTCOMP)
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Industry 4.0 outlines the trend of the massively adoption of Internet of Things (IoT) nodes in supply chains, manufacturing, and factories in general. The industry digitalization is the key enabler to ease the productive process, drastically reduce its costs, and boost up the associated business. In this context, Arrowhead Tools (AHT) is a H2020 EU project provided by ECSEL that targets automation and digitalization solutions for the industry in Europe. AT is based on a framework, named Arrowhead Framework (AHF), developed and provided by the previous Arrowhead (AH) project. AHF is open source and addresses IoT-based automation and integration by abstracting IoT objects to services. AHF enables IoT interoperability and provides real time data handling, security features, automation system engineering, and automation systems scalability. In this paper, after a rapid overview of the AT project and the AHF architecture, we originally introduce the concept of Tool and Tool Chain for Industry 4.0 in AH. We also present a vertical AHT use case along with its implementation, as well as all the steps to turn a service/application into an AH-compliant Tool.

ACS Style

Riccardo Venanzi; Federico Montori; Paolo Bellavista; Luca Foschini. Industry 4.0 Solutions for Interoperability: a Use Case about Tools and Tool Chains in the Arrowhead Tools Project. 2020 IEEE International Conference on Smart Computing (SMARTCOMP) 2020, 429 -433.

AMA Style

Riccardo Venanzi, Federico Montori, Paolo Bellavista, Luca Foschini. Industry 4.0 Solutions for Interoperability: a Use Case about Tools and Tool Chains in the Arrowhead Tools Project. 2020 IEEE International Conference on Smart Computing (SMARTCOMP). 2020; ():429-433.

Chicago/Turabian Style

Riccardo Venanzi; Federico Montori; Paolo Bellavista; Luca Foschini. 2020. "Industry 4.0 Solutions for Interoperability: a Use Case about Tools and Tool Chains in the Arrowhead Tools Project." 2020 IEEE International Conference on Smart Computing (SMARTCOMP) , no. : 429-433.

Article
Published: 19 August 2020 in Wireless Networks
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The principles of physics and system sciences are increasingly used in the field of network engineering to design network protocols. This work proposes an energy and congestion aware routing (ECAR) algorithm inheriting the concepts of the potential field. It uses depth and time-variant network parameters to forward the data packets through low congestion and an energy-balanced path. We define a novel forward aware energy density as a decision metric along with residual energy and queue-length for forwarding data packets. It results in network-wide balanced residual energy and enhanced network lifetime. The proposed ECAR algorithm is evaluated for the transmission rounds before the first dead node (FDN) is detected. It is found that in typical traffic conditions, there was an average increment of 45% transmission rounds till the FDN appeared. Moreover, the simulated and theoretical findings are compared using statistical measures that justify its energy and congestion awareness.

ACS Style

Ankush Jain; K. K. Pattanaik; Ajay Kumar; Paolo Bellavista. Energy and congestion aware routing based on hybrid gradient fields for wireless sensor networks. Wireless Networks 2020, 27, 175 -193.

AMA Style

Ankush Jain, K. K. Pattanaik, Ajay Kumar, Paolo Bellavista. Energy and congestion aware routing based on hybrid gradient fields for wireless sensor networks. Wireless Networks. 2020; 27 (1):175-193.

Chicago/Turabian Style

Ankush Jain; K. K. Pattanaik; Ajay Kumar; Paolo Bellavista. 2020. "Energy and congestion aware routing based on hybrid gradient fields for wireless sensor networks." Wireless Networks 27, no. 1: 175-193.

Journal article
Published: 17 August 2020 in IEEE Access
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Distributed intrusion detection systems (IDS) are primarily deployed across the network to monitor, detect, and report anomalies, as well as to respond in real-time. Predominantly, an IDS is equipped with a set of rules that it needs to infer to be able to perform efficient detection. However, generating false alarms is a major challenge in any IDS implementation. Additionally, the sheer number of IoT devices that generate alarms in a moderately large sensor network may be overwhelming. In order to reduce alarms, this paper contributes to the field by proposing an original framework that limits the number of generated messages without compromising detection accuracy. The primary idea is to exploit mid-level nodes called collectors where similar alerts are collected and analyzed independently. Priority is assigned to each alert and similar alert are fused to respective collectors for more informed decision making. In addition, Kademlia based Distributed Hash Table (DHT) is used for efficient alert transportation and distributed fusion of similar alerts. In order to minimize false alarm rate, event correlation is used to find similarity between events fused by different detection sensors. The framework is implemented in a fog-based environment to assess and evaluate the efficiency of the proposed system in edge network. The architecture is evaluated with the recognized DARPA 1999 dataset; the reported results show that the proposed technique reduces message generation by 62% while achieving false positive accuracy over 80%.

ACS Style

Mansoor Nasir; Khan Muhammad; Paolo Bellavista; Mi Young Lee; Muhammad Sajjad. Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables. IEEE Access 2020, 8, 175194 -175204.

AMA Style

Mansoor Nasir, Khan Muhammad, Paolo Bellavista, Mi Young Lee, Muhammad Sajjad. Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables. IEEE Access. 2020; 8 (99):175194-175204.

Chicago/Turabian Style

Mansoor Nasir; Khan Muhammad; Paolo Bellavista; Mi Young Lee; Muhammad Sajjad. 2020. "Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables." IEEE Access 8, no. 99: 175194-175204.

Article
Published: 27 July 2020 in Journal of Network and Systems Management
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The high abundance of IoT devices have caused an unprecedented accumulation of avalanches of geo-referenced IoT spatial data that if could be analyzed correctly would unleash important information. This can feed decision support systems for better decision making and strategic planning regarding important aspects of our lives that depend heavily on location-based services. Several spatial data management systems for IoT data in Cloud has recently gained momentum. However, the literature is still missing a comprehensive survey that conceptualize a convenient framework that classify those frameworks under appropriate categories. In this survey paper, we focus on the management of big geospatial data that are generated by IoT data sources. We also define a conceptual framework and match the works of the recent literature with it. We then identify future research frontiers in the field depending on the surveyed works.

ACS Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. Big Spatial Data Management for the Internet of Things: A Survey. Journal of Network and Systems Management 2020, 28, 990 -1035.

AMA Style

Isam Mashhour Al Jawarneh, Paolo Bellavista, Antonio Corradi, Luca Foschini, Rebecca Montanari. Big Spatial Data Management for the Internet of Things: A Survey. Journal of Network and Systems Management. 2020; 28 (4):990-1035.

Chicago/Turabian Style

Isam Mashhour Al Jawarneh; Paolo Bellavista; Antonio Corradi; Luca Foschini; Rebecca Montanari. 2020. "Big Spatial Data Management for the Internet of Things: A Survey." Journal of Network and Systems Management 28, no. 4: 990-1035.

Article
Published: 10 July 2020 in Journal of Network and Systems Management
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Software Defined Networking (SDN), with its clear distinction of control and data planes, as well with its simple paradigm of logically centralized controller with global visibility of the whole targeted network status, is gaining momentum in different scenarios. However, its effective exploitation in Wireless Mesh Networks (WMNs) is still an open and emerging research topic, mainly due to the high dynamicity of some related deployment environments (e.g., spontaneous WMNs) and to the need of efficient solutions capable of locality-enhanced optimizations. Here we originally present motivations, challenges, design guidelines, and a prototype middleware implementation for SDN-based management of selected (most appropriate) traffic flows. Our solution advances the state of the art in the field by (i) allowing high flexibility via deployment and de/activation of flow management policies at provisioning time, (ii) supporting dynamicity via proper efficient handling node of join/leave events, and (iii) increasing scalability via a partially decentralized approach based on dynamically determined WMN “islands”, usually managed by separated SDN controllers that can seldom interact to federate their management decisions. In addition to design/implementation insights and to the availability of the prototype code, this paper provides the community with a significant novel contribution in terms of experimental performance results, which quantitatively demonstrate the feasibility and the effectiveness of the proposed approach.

ACS Style

Paolo Bellavista; Alessandro Dolci; Carlo Giannelli; Dmitrij David Padalino Montenero. SDN-Based Traffic Management Middleware for Spontaneous WMNs. Journal of Network and Systems Management 2020, 28, 1575 -1609.

AMA Style

Paolo Bellavista, Alessandro Dolci, Carlo Giannelli, Dmitrij David Padalino Montenero. SDN-Based Traffic Management Middleware for Spontaneous WMNs. Journal of Network and Systems Management. 2020; 28 (4):1575-1609.

Chicago/Turabian Style

Paolo Bellavista; Alessandro Dolci; Carlo Giannelli; Dmitrij David Padalino Montenero. 2020. "SDN-Based Traffic Management Middleware for Spontaneous WMNs." Journal of Network and Systems Management 28, no. 4: 1575-1609.

Conference paper
Published: 01 June 2020 in ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
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Edge Computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.

ACS Style

Paolo Bellavista; Roberto Della Penna; Luca Foschini; Domenico Scotece. Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example. ICC 2020 - 2020 IEEE International Conference on Communications (ICC) 2020, 1 -7.

AMA Style

Paolo Bellavista, Roberto Della Penna, Luca Foschini, Domenico Scotece. Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 2020; ():1-7.

Chicago/Turabian Style

Paolo Bellavista; Roberto Della Penna; Luca Foschini; Domenico Scotece. 2020. "Machine Learning for Predictive Diagnostics at the Edge: an IIoT Practical Example." ICC 2020 - 2020 IEEE International Conference on Communications (ICC) , no. : 1-7.

Conference paper
Published: 01 June 2020 in ICC 2020 - 2020 IEEE International Conference on Communications (ICC)
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Compared to Cloud computing, Fog computing is proving to support challenging scenarios imposing stricter delay requirements, e.g., tactile Internet and Industrial Internet of Things (IIoT), and demanding increased flexibility, e.g., dynamic Smart Cities and users’ follow-me provisioning cases. However, such scenarios are characterized by increased heterogeneity of nodes in terms of hardware/software characteristics, of time-varying services/applications possibly offered by multiple service providers, and frequent joining/leaving of nodes. The paper originally proposes Multi-Layer Advanced Networking Environment (Multi-LANE), a Multi-Layer Routing (MLR) solution based on Software Defined Networking (SDN). Multi-LANE dynamically selects and exploits routing strategies and mechanisms suitable for applications with heterogeneous capabilities and requirements. Based on application requirements and its centralized point of view, our SDN controller determines the most suitable path and configures the proper MLR forwarding mechanism, ranging from traditional IP and sequence-based overlay to more articulated ones based on payload content type and value inspection.

ACS Style

Paolo Bellavista; Carlo Giannelli; Dmitrij David Padalino Montenero. Multi Layer Routing in SDN-enabled Fog Environments. ICC 2020 - 2020 IEEE International Conference on Communications (ICC) 2020, 1 -7.

AMA Style

Paolo Bellavista, Carlo Giannelli, Dmitrij David Padalino Montenero. Multi Layer Routing in SDN-enabled Fog Environments. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). 2020; ():1-7.

Chicago/Turabian Style

Paolo Bellavista; Carlo Giannelli; Dmitrij David Padalino Montenero. 2020. "Multi Layer Routing in SDN-enabled Fog Environments." ICC 2020 - 2020 IEEE International Conference on Communications (ICC) , no. : 1-7.

Journal article
Published: 20 May 2020 in IEEE Transactions on Network and Service Management
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If compared with Cloud computing, Fog computing is proving to support challenging scenarios imposing strict delay requirements, e.g., tactile Internet and Industrial Internet of Things (IIoT), and increased flexibility, e.g., dynamic Smart City and users’ follow-me provisioning case. In fact, by exploiting computing, storage, and connectivity resources in the proximity of sensors and actuators (for IIoT) and of mobile nodes carried by citizens (for Smart Cities), significant portions of services and functionalities can be migrated outside datacenters. However, such scenarios are characterized by increased heterogeneity of nodes in terms of hardware/software, of time-varying applications possibly offered by multiple service providers at the same time, and frequent joining/leaving of nodes as a typical behavior. To overcome these issues, the paper originally proposes Multi-Layer Advanced Networking Environment (Multi-LANE), a Multi Layer Routing (MLR) solution based on Software Defined Networking (SDN) that specifically targets the emerging and promising Fog-based deployment environments. Multi-LANE dynamically selects and exploits (even at the same time) different routing strategies and mechanisms suitable for applications with heterogeneous features and requirements. Based on its centralized point of view, our Multi-LANE SDN controller determines the most suitable path and configures the proper MLR forwarding mechanism, ranging from traditional IP and sequence-based overlays to more articulated ones based on the inspection of payload content types and values. In addition to design/implementation insights and to the availability of the Multi-LANE prototype, this paper also provides the community with a significant contribution in terms of novel models for forwarding mechanisms specialized for Fog computing scenarios.

ACS Style

Paolo Bellavista; Carlo Giannelli; Dmitrij David Padalino Montenero. A Reference Model and Prototype Implementation for SDN-Based Multi Layer Routing in Fog Environments. IEEE Transactions on Network and Service Management 2020, 17, 1460 -1473.

AMA Style

Paolo Bellavista, Carlo Giannelli, Dmitrij David Padalino Montenero. A Reference Model and Prototype Implementation for SDN-Based Multi Layer Routing in Fog Environments. IEEE Transactions on Network and Service Management. 2020; 17 (3):1460-1473.

Chicago/Turabian Style

Paolo Bellavista; Carlo Giannelli; Dmitrij David Padalino Montenero. 2020. "A Reference Model and Prototype Implementation for SDN-Based Multi Layer Routing in Fog Environments." IEEE Transactions on Network and Service Management 17, no. 3: 1460-1473.

Conference paper
Published: 01 May 2020 in 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)
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Nowadays, the exploitation of distributed ledger technology (DLT) is increasing among different domains and use cases. Not only within the context of cryptocurrencies, DLT could help the cooperation among untrusted parties in a wide variety of application scenarios. In particular, crowdsensing platforms can benefit from DLT because they need to federate systems belonging to different organizations to share end-user profiles, finally free to move within different domains, maintaining their identity. In this paper, we propose an edge-based distributed ledger architecture for supporting decentralised incentives in a specific mobile crowdsensing paltform called ParticipAct. To motivate the choice we describe two different deployments of ParticipAct, one based on a classical client-server architecture and the other one based on an edge-based model, and we highlight their pro and cons. In particular, our more notable findings rely on an approach based on edge computing and highlight how the three-tier solution improves the scalability, the performance, the security and the fault tolerance of the infrastructure responsible for the management of the federation among untrusted crowdsensing platforms.

ACS Style

Paolo Bellavista; Marco Cilloni; Giuseppe Di Modica; Rebecca Montanari; Pasquale Carlo Maiorano Picone; Michele Solimando. An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) 2020, 781 -787.

AMA Style

Paolo Bellavista, Marco Cilloni, Giuseppe Di Modica, Rebecca Montanari, Pasquale Carlo Maiorano Picone, Michele Solimando. An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing. 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID). 2020; ():781-787.

Chicago/Turabian Style

Paolo Bellavista; Marco Cilloni; Giuseppe Di Modica; Rebecca Montanari; Pasquale Carlo Maiorano Picone; Michele Solimando. 2020. "An Edge-based Distributed Ledger Architecture for Supporting Decentralized Incentives in Mobile Crowdsensing." 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) , no. : 781-787.

Conference paper
Published: 27 April 2020 in Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking
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Edge computing and Federated Learning (FL) can work in tandem to address issues related to privacy and collaborative distributed learning in untrusted IoT environments. However, deployment of FL in resource-constrained IoT devices faces challenges including asynchronous participation of such devices in training, and the need to prevent malicious devices from participating. To address these challenges we present CoLearn, which build on the open-source Manufacturer Usage Description (MUD) implementation osMUD and the FL framework PySyft. We deploy CoLearn on resource-constrained devices in a lab environment to demonstrate (i) an asynchronous participation mechanism for IoT devices in machine learning model training using a publish/subscribe architecture, (ii) a mechanism for reducing the attack surface in FL architecture by allowing only IoT MUD-compliant devices to participate in the training phases, and (iii) a trade-off between communication bandwidth usage, training time and device temperature (thermal fatigue).

ACS Style

Angelo Feraudo; Poonam Yadav; Vadim Safronov; Diana Andreea Popescu; Richard Mortier; Shiqiang Wang; Paolo Bellavista; Jon Crowcroft. CoLearn. Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking 2020, 1 .

AMA Style

Angelo Feraudo, Poonam Yadav, Vadim Safronov, Diana Andreea Popescu, Richard Mortier, Shiqiang Wang, Paolo Bellavista, Jon Crowcroft. CoLearn. Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking. 2020; ():1.

Chicago/Turabian Style

Angelo Feraudo; Poonam Yadav; Vadim Safronov; Diana Andreea Popescu; Richard Mortier; Shiqiang Wang; Paolo Bellavista; Jon Crowcroft. 2020. "CoLearn." Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking , no. : 1.