United States

The Internet of Vehicles (IoV) has been recognized as a powerful application of the Internet of Things (IoT) in the Intelligent Transportation System (ITS), providing intelligence for interconnection between devices, interaction with the environment, and thus, greater efficiency in sensor data exploitation. Therefore, leveraging the huge capability of sensors embedded in vehicles to offer a Sensing As A Service (Se-aaS) represents a great solution to exploit under-used sensor resources and continue providing sensors despite their positions and mobility patterns.
Nevertheless, the high network mobility and the fast topology changes in IoV impact the vehicle availability and complicate the service provision. To this aim, we propose a vehicle sensor virtualization in a Cloud IoV architecture that encompasses functional blocks of mobile sensor suppliers, Sensor Cloud Service Provider (SCSP), and service consumers. Moreover, we propose a reinforcement learning-based solution for vehicle sensor selection to predict and dynamically select the physical sensors composing the vehicle virtual sensor. The conducted simulations show the effectiveness of the proposed solution.

IEEE NCA 2022

Reinforcement Learning-based Virtual Sensor Provision in Internet of Vehicles (IoV)

virtual sensor provision

vehicle sensor virtualization

reinforcement learning (rl)

internet of vehicles (iov)

q-learning

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

### Welcome to IEEE NCA 2022!

NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing. NCA, which is sponsored by the IEEE Computer Society, reaches out to both researchers and practitioners, and to both academia and industry. The conference features keynotes, technical presentations, and workshops.

All papers presented at the main conference and the workshops will be submitted to IEEE Xplore for publication and indexing.

[More about NCA](https://www.nca-ieee.org/2022/index.html)

**Organization**

* Steering Committee Chair: Dimiter R. Avresky, *IRIANC, Boston/Munich, USA/Germany*
* General Chair: Michele Colajanni, *University of Bologna, Italy*
* Program Chair: Miguel Pardal, *INESC-ID, Lisboa, Portugal*
* Program Chair: Luca Ferretti, *University of Modena and Reggio Emilia, Italy*
* Financial Chair: Mauro Andreolini, *University of Modena and Reggio Emilia, Italy*

**The topics of interest include, but are not limited to:**

* Theory of Network Computing
* Autonomic Network Computing
* Content Delivery Network (CDN)
* High Speed Networks/Protocols and Middleware
* Routing Mechanisms
* Overlay Networks/Peer-to-Peer Systems
* System Area Networks (SAN)/Clusters
* Performance Modeling/Quality of Services (QoS) Issues
* Web Caching and Switching
* Dependable Wide, Local, and System Area Networks
* Network Security
* Intrusion-Tolerant Networked Systems
* Scalable and Dependable Servers and Data Centers
* Middleware for Dependable Network Computing
* Self* (Configuring, Healing, Optimizing, Protecting, Organizing, Aware)
* Platforms for Network Computing
* Network Protocols: Verification and Validation
* Autonomic Network Computing for Achieving Self* Properties and Applications Availability
* Seamless and Virtual Capabilities for Implementing Self* Properties
* Software Defined Networking (SDN): Principles and Implementation
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* Network Functions Virtualization (NFV): Design and Optimization
* Network Cloud Computing
* Machine Learning Techniques for Cloud Resources Management
* Big Data in Clouds for IoT
* Future Internet and Communicating Objects
* Mobile Ad-Hoc Networks (MANET)
* Sensor Networks
* Fog/Edge Computing
* Energy-Efficient Computation and Communication
* Applications to Real and/or Complex Problems, Practical Experiences and Prototypes
* Programming Environments for Distributed Systems
* Blockchain
* Byzantine Fault Tolerance
* Web and Mobile Security
* Security of Networked Applications (including Automotive, Industrial and Cyberphysical Systems)


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NCA 2022, the International Symposium on Network Computing and Applications, is a successful series of conferences that serves as a large international forum for presenting and sharing recent research results and technological developments in the fields of Network and Cloud Computing.

"State machine replication (SMR) allows nodes to jointly maintain a consistent ledger, even when a part of nodes are Byzantine. To defend against and/or limit the impact of attacks launched by Byzantine nodes, there have been blocks that combine reputation mechanisms to SMR, where each node
has a reputation value based on its historical behaviours, and the
node’s voting power will be proportional to its reputation. Despite
the promising features of reputation-based SMR, existing studies
do not provide formal treatment on the reputation mechanism on
SMR protocols, including the types of behaviours affecting the
reputation, the security properties of the reputation mechanism,
and the extra security properties of SMR using reputation
mechanisms.
In this paper, we provide the first formal study on the
reputation-based SMR. We define the security properties of the
reputation mechanism w.r.t. these misbehaviours. Based on the
formalisation of the reputation mechanism, we formally define the
reputation-based SMR, and identify a new property reputationconsistency that is necessary for ensuring reputation-based SMR’s
safety. We then design a simple reputation mechanism that
achieves all security properties in our formal model. To demonstrate the practicality, we combine our reputation mechanism to
the Sync-HotStuff SMR protocol, yielding a simple and efficient
reputation-based SMR at the cost of only an extra ∆ in latency,
where ∆ is the maximum delay in synchronous networks."

Reputation-based state machine replication

"In this paper, we examine the inherent properties
of the Byzantine Reliable Broadcast (BRB) primitive as pertain
to the ability to provide causal ordering. We prove the following
results. First, we analyze Bracha’s BRB algorithm and show
that under the failure-free model, safety is guaranteed across
broadcasts. Second, we also prove that Bracha’s BRB algorithm
guarantees safety across broadcasts under the crash failure model
tolerating any number of crash failures. Third, we prove that
Bracha’s BRB algorithm cannot provide weak or strong safety
under the Byzantine failure model. Fourth, we prove that neither
the Imbs-Raynal BRB protocol nor any (2,*)-round BRB protocol
can provide causal order even if all processes are correct, and
they must incur additional latency to causally order messages at a
higher layer. The inherent causal ordering properties of Bracha’s
BRB can be of use under favourable circumstances in practical
applications, given the widespread adoption of the protocol."

Causal Ordering Properties of Byzantine Reliable Broadcast Primitives

Gossip-based dissemination protocols are important building blocks of large-scale distributed systems as they may impact both the systems' efficiency and fault tolerance. There exist many flavors of gossip dissemination protocols. IDA-Gossip is one of the gossip dissemination protocols proposed in the context of blockchains to efficiently disseminate large messages. It relies on multi-chunk gossip dissemination, erasure coding, and Merkle hash trees. However, despite its claimed efficiency, there is no in-depth analysis of this protocol to understand its behavior under different conditions (e.g., with injected faults). In this work, we evaluate the behavior of IDA-Gossip by relying on extensive experiments and simulations. Specifically, we evaluate IDA-Gossip both in terms of performance and resilience to faults by varying its configuration parameters and the number of faulty nodes, respectively. This study results in several takeaways. First, IDA-Gossip provides excellent dissemination latency compared to classic gossip. Second, it provides excellent coverage even with 40 percent of faulty nodes in the system. Finally, the use of erasure coding provides an important advantage to IDA-Gossip compared to classic multi-chunk gossip dissemination protocols.

In-depth analysis of the IDA-Gossip protocol

"Byzantine fault tolerant (BFT) consensus allows the construction of robust, distributed systems via the state-machine replication (SMR) approach. Still, after more than 40 years of research, limitations on performance and scalability for practical systems remain. A large corpus of existing work improves on consensus complexity, performance and introduces a multitude of optimization techniques. The state-of-the-art is complex. On the other hand, many protocols designed for practical deployments are built on strong, common assumptions about underlying communication and authentication primitives. To fulfill these assumptions, often, commodity tools and libraries are employed without further analysis and caution for negative interplay.
Instead of contributing to the existing complexity, we choose a different approach. In this work, we outline the feasibility and potential impact of the optimization of common building blocks of BFT-SMR systems. We systemize existing work in terms of common model assumptions and identify optimization potential.  Finally, we choose the building block of networking transport as a representative example and analyze its optimization space, both in context of general BFT-SMR systems and a case study of the HotStuff protocol. We describe behavior, challenges, and desired configuration of network transports for use in byzantine agreement, and identify lossy links as the main catalyst for significant performance differences between protocols and configurations."

BFT-Blocks: The Case for Analyzing Networking in Byzantine Fault Tolerant Consensus

Most of today's online services (e.g. social networks, search engines, marketplace places) are centralized, which most users recognize as unsatisfactory for various reasons (e.g. centralized governance, censorship, loss of control over personal data). Blockchain technologies promise a new Web revolution (Web 3.0) through the decentralization of online services. However, one of the fundamental limitations for this revolution to happen at a planetary scale is the poor performance of today's permissionless blockchains. In this paper, we propose ALDER, a generic construction that multiplexes off-the-shelf permissionless blockchain protocols to address the performance bottleneck due to store-validate-forward block dissemination techniques in blockchain protocols.
We apply ALDER to two representative blockchains, namely Algorand (Proof-of-Stake) and Bitcoin (Proof-of-Work), to illustrate the benefits it brings to blockchain performance. Our evaluations show that ALDER can drastically improve the throughput of blockchains when bottlenecks exist. 

ALDER: Unlocking blockchain performance by multiplexing consensus protocols

Causal ordering of broadcasts is a widely used
requirement in collaborative distributed software systems. We
consider Byzantine-tolerant causal ordering of broadcasts in a
replicated data store implemented over an incomplete graph
network topology, wherein messages of the broadcast are sent
via flooding. We propose two protocols to achieve this. The
incomplete graph topology also occurs naturally in wireless
networks and in overlay peer-to-peer networks. We identify four
properties – safety, liveness, no impersonation, and no avatars
– that a Byzantine-tolerant causal broadcast algorithm for a
replicated data store over an incomplete graph must satisfy. We
also reformulate the traditional properties – validity, integrity,
self-delivery, and reliability (or termination) – specified for a
complete graph in the literature for a replicated data store system
over an incomplete graph topology. We then analyze whether
Byzantine processes can mount attacks on these properties in our
two protocols. We show results for the classical communication
model and the local broadcast model.

Byzantine Fault-Tolerant Causal Broadcast on Incomplete Graphs

Nowadays, tourists turn to digital platforms to discover new places to explore. CROSS City is a smart tourism mobile application that enhances the user experience of tourists visiting points of interest in a route by rewarding them in the end, if they actually visited all locations. From a technical standpoint, the user location is certified resorting to strategies that take advantage of both the diversity of the existing Wi-Fi network infrastructure throughout the city, as well as the presence of other users at the same site using Bluetooth. This work developed a new, peer-to-peer location certification strategy and added gamification elements to encourage users to keep the wireless radios turned on and use the app more. This work was evaluated both in laboratory experiments and with users in a real-world scenario which demonstrated that the new Bluetooth peer-based strategy is both feasible and resistant to collusion attacks.

Bluetooth Peer-to-Peer Location Certification with a Gamified Mobile Application

In this work we present a set of issues affecting the research topic of cybersecurity on in-vehicle networks and we publicly address them by comparing three selected works against the same attack scenarios available in public datasets.

Comparison of Machine Learning-based anomaly detectors for Controller Area Network

Under the vision of industry 4.0, industrial networks are expected to accommodate a large amount of aggregated traffic of both operation and information technologies to enable the integration of innovative services and new applications. In this respect, guaranteeing the uninterrupted operation of the installed systems is an indisputable condition for network management. Network measurement and performance monitoring of the underlying communication states can provide invaluable insight for safeguarding the system performance by estimating required and available resources for flexible integration without risking network interruption or degrading network performance. In this work, we propose a data-driven in-band telemetry method to monitor the aggregated traffic of the network at the switch level. The method learns and models the communication states by local network-level measurement of communication intensity. The approximated model parameters provide information for network management for prognostic purposes and congestion avoidance resource planning when integrating new applications. Applying the method also addresses the consequence of telemetry data overhead on QoS since the transmission of telemetry packets can be done based on the current state of the network. The monitoring at the switch level is a step towards the Network-AI for future industrial networks. 

Data driven method for In band Network Telemetry Monitoring of Aggregated Traffic

Industrial Internet-of-Things (IoT) massively deploys intelligent computing in industrial production and manufacturing environments seeking automation, reliability, and control. Machine Learning models provide intelligent decisions to drive manufacturing systems to the next level of productivity, efficiency, and safety. One of the critical challenges that must be faced is the deployment of Machine Learning models at the network edge to detect data anomalies caused by Industrial IoT hardware failures, since industrial IoT devices are prone to errors and failures. These anomalies can harm the industrial IoT system by producing false alarms, consuming network resources, and affecting productivity. Because of that, it is critical to rely on low latency and high precision detection systems to verify the data received from industrial IoT devices. In light of this, we assessed key performance indicators of five machine learning models running at edge computing, to provide in-depth discussions. The performance results were obtained from an oil refinery scenario using a real industrial IoT dataset. The performance was measured in terms of (a) Accuracy, (b) Precision, (c) Recall, (d) F1 score, (e) Training time, and (f) Response time.

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