A database for publications published by researchers and students at SimulaMet.
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- Journal articles (78)
- Books (4)
- Proceedings, refereed (96)
- Book chapters (2)
- Talks, keynote (8)
- PhD theses (1)
- Proceedings, non-refereed (16)
- Posters (3)
- Technical reports (10)
- Talks, invited (118)
- Talks, contributed (9)
- Public outreach (29)
- Miscellaneous (6)
Journal articles
Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks
IEEE Internet of Things Journal 7, no. 1 (2020): 247-257.Status: Published
Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks
In this paper, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize content placement and content delivery in vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. Simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the non-cooperative and random edge caching schemes.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Internet of Things Journal |
| Volume | 7 |
| Issue | 1 |
| Pagination | 247-257 |
| Date Published | 10/2019 |
| Publisher | IEEE |
On the Usability of Transport Protocols other than TCP: A Home Gateway and Internet Path Traversal Study
Computer Networks 173 (2020).Status: Published
On the Usability of Transport Protocols other than TCP: A Home Gateway and Internet Path Traversal Study
Network APIs are moving towards protocol agility, where applications express their needs but not a static protocol binding, and it is up to the layer below the API to choose a suitable protocol. The IETF Transport Services (TAPS) Working Group is standardizing a protocol-independent transport API and offering guidance to implementers. Apple’s recent “Network.framework” is specifically designed to allow such late and dynamic binding of protocols. When the network stack autonomously chooses and configures a protocol, it must first test which protocols are locally available and which work end-to-end (“protocol racing”). For this, it is important to know the set of available options, and which protocols should be tried first: Does it make sense to offer unchecked payload delivery, as with UDP-Lite? Is a UDP-based protocol like QUIC always a better choice, or should native SCTP be tried? This paper develops answers to such questions via (i) a NAT study in a local testbed, (ii) bidirectional Internet tests, (iii) a large scale Internet measurement campaign. The examined protocols are: SCTP, DCCP, UDP-Lite, UDP with a zero checksum and three different UDP encapsulations.
| Afilliation | Communication Systems |
| Project(s) | NorNet, The Center for Resilient Networks and Applications, NEAT: A New, Evolutive API and Transport-Layer Architecture for the Internet |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | Computer Networks |
| Volume | 173 |
| Date Published | 05/2020 |
| Publisher | Elsevier |
| ISSN | 1389-1286 |
| Keywords | DCCP, Internet, NAT, Protocol Testing, SCTP, UDP-Lite |
| DOI | 10.1016/j.comnet.2020.107211 |
Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments
IEEE Journal on Selected Areas in Communications (JSAC) Early access (2020).Status: Published
Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics, Simula Metropolitan Center for Digital Engineering |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Journal on Selected Areas in Communications (JSAC) |
| Volume | Early access |
| Publisher | IEEE |
| DOI | 10.1109/JSAC.2020.3000365 |
Online Identification of Groups of Flows Sharing a Network Bottleneck
IEEE/ACM Transactions on Networking (2020).Status: Accepted
Online Identification of Groups of Flows Sharing a Network Bottleneck
Abstract—Most Internet hosts today support multiple access technologies and network interfaces. Multipath transport protocols, like MPTCP, are being deployed (e.g., in smartphones), allowing transparent simultaneous use of multiple links. Besides providing increased resilience to link failures, multipath trans- ports may better exploit available (aggregate) capacity across all interfaces. The safest way to ensure fairness is to assume that any subflows of a multipath end-to-end connection may share bottleneck links, but knowledge of non-shared bottlenecks could allow multipath senders to exploit more capacity without being unfair to other flows. The problem of reliably detecting the existence of (non)-shared bottlenecks is not trivial and is compounded by the fact that bottlenecks may change due to traffic dynamics. In this paper we focus on practical methods to reliably group flows that share, possibly dynamic, bottlenecks online and in a passive manner (i.e., without injecting measurement traffic). We introduce a novel dynamic clustering algorithm that we apply to update our previous shared bottleneck flow grouping (SBFG) method standardized by the IETF, based on delay statistics. We also adapt an offline SBFG method based on wavelet filters to enable it for online operation. These SBFG methods are evaluated by a simple testbed, rigorous simulation and real-world Internet experiments in a testbed comprised of multihomed hosts. Our results suggest that there is no clear winner, and selection of the “best” SBFG method will have to consider tradeoffs regarding accuracy, lag, and application requirements.
| Afilliation | Communication Systems |
| Project(s) | RITE: Reducing Internet Transport Latency, Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE/ACM Transactions on Networking |
| Publisher | IEEE/ACM |
| Keywords | dynamic clustering algorithms, Internet congestion control, multipath congestion control., shared bottleneck detection |
| Notes | Further details when they are available. |
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
IEEE Communications Magazine (2020).Status: Accepted
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open- source along with a lightweight data visualization platform.
| Afilliation | Communication Systems |
| Project(s) | Department of Mobile Systems and Analytics |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Communications Magazine |
| Publisher | IEEE |
| Notes | Preprint available here: https://arxiv.org/abs/2005.02341 |
Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks
IEEE Internet of Things Journal 7, no. 1 (2020): 247-257.Status: Published
Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks
In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Internet of Things Journal |
| Volume | 7 |
| Issue | 1 |
| Pagination | 247-257 |
| Date Published | 01/2020 |
| Publisher | IEEE |
| URL | https://ieeexplore.ieee.org/abstract/document/8879573 |
| DOI | 10.1109/JIOT.2019.2945640 |
Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics
IEEE Transactions on Industrial Informatics 16, no. 3 (2020): 2134-2143.Status: Published
Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics
Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 16 |
| Issue | 3 |
| Pagination | 2134 - 2143 |
| Date Published | 03/2020 |
| Publisher | IEEE |
| ISSN | Print ISSN: 1551-3203 Electronic ISSN: 1941-0050 |
| URL | https://ieeexplore.ieee.org/document/8843942/authors#authors |
| DOI | 10.1109/TII.2019.2942179 |
Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems
IEEE Network 34, no. 3 (2020): 50-56.Status: Published
Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems
Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Network |
| Volume | 34 |
| Issue | 3 |
| Pagination | 50-56 |
| Date Published | 05/2020 |
| Publisher | IEEE |
| ISSN | Print ISSN: 0890-8044 Electronic ISSN: 1558-156X |
| URL | https://ieeexplore.ieee.org/document/9105934 |
| DOI | 10.1109/MNET.011.1900317 |
Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics
IEEE Transactions on Industrial Informatics 16, no. 8 (2020): 5467-5477.Status: Published
Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics
Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles, as well as unpredictable vehicle routes, make efficient content distribution a challenge. To cope with this challenge, in this article we develop a social-aware nobile edge computing and caching mechanism by exploiting the relation between vehicles and roadside units. By leveraging a deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Industrial Informatics |
| Volume | 16 |
| Issue | 8 |
| Pagination | 5467 - 5477 |
| Publisher | IEEE |
oint Transaction Relaying and Block Verification Optimization for Blockchain Empowered D2D Communication
IEEE Transactions on Vehicular Technology 69, no. 1 (2020): 828-841.Status: Published
oint Transaction Relaying and Block Verification Optimization for Blockchain Empowered D2D Communication
Device to device (D2D) communication is a promising paradigm to support data sharing in the fifth generation (5G) small cell networks. However, D2D communication is more vulnerable to security and privacy threats due to direct wireless connection between the proximity devices. In this paper, we exploit blockchain technology to propose a new distributed and secure data sharing framework named D2D blockchain, where we deploy a set of Access Points (APs) to verify end users' transactions. Besides, in order to enable the end users to receive the required data on time in the D2D blockchain, we propose two new schemes for the transaction confirmation procedure including a relay-assisted transaction relaying scheme and a Delegated Proof-of-Stake (DPoS) based lightweight block verification scheme. Furthermore, to incentivize the relay devices to help in relaying transactions in congested areas, and to encourage the verifiers to contribute their resources in block verification, we design a two-stage contract theory based joint optimization scheme. To measure the quality of the relayed transaction and the verified block, we formulate two models: value of transaction relaying (VoTR) and value of block verification (VoBV), and then design the optimal contract for each relay device and each verifier. Security analysis and numerical results illustrate that the proposed D2D blockchain and the designed joint optimization scheme are secure and efficient for data sharing among end users.
| Afilliation | Communication Systems |
| Project(s) | Simula Metropolitan Center for Digital Engineering, The Center for Resilient Networks and Applications |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | IEEE Transactions on Vehicular Technology |
| Volume | 69 |
| Issue | 1 |
| Pagination | 828 - 841 |
| Publisher | IEEE |