Paal Engelstad

• Khan, Mehak; Borges Moreno e Mello, Gustavo; Habib, Laurence; Engelstad, Paal & Yazidi, Anis (2024). HITS-based Propagation Paradigm for Graph Neural Networks. ACM Transactions on Knowledge Discovery from Data. ISSN 1556-4681. 18(4), p. 1–23. doi: 10.1145/3638779.
• Abolghasemi, Roza; Viedma, Enrique Herrera; Engelstad, Paal E.; Djenouri, Youcef & Yazidi, Anis (2024). A graph neural approach for group recommendation system based on pairwise preferences. Information Fusion. ISSN 1566-2535. 107. doi: 10.1016/j.inffus.2024.102343. Full text in Research Archive
• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2024). Relative evaluation of probabilistic methods for spatio-temporal wind forecasting. Journal of Cleaner Production. ISSN 0959-6526. 434. doi: 10.1016/j.jclepro.2023.139944. Full text in Research Archive
• Rauniyar, Ashish; Østerbø, Olav Norvald; Håkegård, Jan Erik & Engelstad, Paal E. (2023). Ergodic Performance Analysis of Reconfigurable Intelligent Surface Enabled Bidirectional NOMA. In Gregory, Mark A. & Li, Shuo (Ed.), Proceedings of the 2023 33rd International Telecommunication Networks and Applications Conference (ITNAC). IEEE conference proceedings. ISSN 979-8-3503-1713-8. p. 128–133. doi: 10.1109/ITNAC59571.2023.10368523. Full text in Research Archive Show summary

  This paper proposes and investigates a reconfigurable intelligent surface (RIS) enabled bidirectional non-orthogonal multiple access (NOMA) network termed as NOMA-RIS. Here, RIS allows multiple NOMA users in one group to communicate with or share information with multiple NOMA users in another group. Specifically, the two NOMA user groups send the data intended for exchange to the RIS. RIS reflects the NOMA signals allowing bidirectional communication between two NOMA user groups. In particular, under the Rician fading environment, we pay close attention to how well RIS-enabled bidirectional NOMA networks operate. Analytical expressions for tight upper bounds for the ergodic capacity are mathematically derived and verified with the simulation results. Comprehensive performance comparisons are presented, showing that our proposed bidirectional NOMA-RIS system can achieve enhanced capacity gains than RIS-enabled traditional orthogonal multiple access schemes. These comparisons also offer practical insights into the impact of various system parameters on the overall network performance.

• Meng, Li; Goodwin, Morten; Yazidi, Anis & Engelstad, Paal E. (2023). Unsupervised State Representation Learning in Partially Observable Atari Games. In Tsapatsoulis, Nicolas (Eds.), Computer Analysis of Images and Patterns. CAIP 2023. Springer. ISSN 978-3-031-44239-1. p. 212–222. doi: 10.1007/978-3-031-44240-7_21. Full text in Research Archive Show summary

  State representation learning aims to capture latent factors of an environment. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused on using masks as an augmentation technique to represent the latent generative factors better. Partially observable environments in reinforcement learning have not yet been carefully studied using unsupervised state representation learning methods. In this article, we create an unsupervised state representation learning scheme for partially observable states. We conducted our experiment on a previous Atari 2600 framework designed to evaluate representation learning models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has shown state-of-the-art performance on this benchmark but remains inferior to its supervised counterpart. Our approach improves ST-DIM when the environment is not fully observable and achieves higher F1 scores and accuracy scores than the supervised learning counterpart. The mean accuracy score averaged over categories of our approach is 66%, compared to 38% of supervised learning. The mean F1 score is 64% to 33%. The code can be found on https://github.com/mengli11235/MST_DIM.

• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal E. (2023). A Unified Graph Formulation for Spatio-Temporal Wind Forecasting. Energies. ISSN 1996-1073. 16(20). doi: 10.3390/en16207179. Full text in Research Archive
• Khan, Mehak; Borges Moreno e Mello, Gustavo; Engelstad, Paal E.; Habib, Laurence & Yazidi, Anis (2023). HITS-GNN: A Simplified Propagation Scheme for Graph Neural Networks. In Tsumoto, Shusaku; Ohsawa, Yukio; Chen, Lei; Van den Poel, Dirk; Hu, Xiaohua; Motomura, Yoichi; Takagi, Takuya; Wu, Lingfei; Xie, Ying; Abe, Akihiro & Raghavan, Vijay (Ed.), 2022 IEEE International Conference on Big Data . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-8045-1. doi: 10.1109/BigData55660.2022.10020815.
• Rauniyar, Ashish; Østerbø, Olav Norvald; Håkegård, Jan Erik & Engelstad, Paal E. (2023). Exploiting Cooperative Downlink NOMA in D2D Communications. Sensors. ISSN 1424-8220. 23(8). doi: 10.3390/s23083958. Full text in Research Archive Show summary

  We propose and investigate a bidirectional device-to-device (D2D) transmission scheme that exploits cooperative downlink non-orthogonal multiple access (NOMA) (termed as BCD-NOMA). In BCD-NOMA, two source nodes communicate with their corresponding destination nodes via a relaying node while exchanging bidirectional D2D messages simultaneously. BCD-NOMA is designed for improved outage probability (OP) performance, high ergodic capacity (EC) and high energy efficiency by allowing two sources to share the same relaying node for data transmission to their corresponding destination nodes while also facilitating bidirectional D2D communications exploiting downlink NOMA. Simulation and analytical expressions of the OP, EC and ergodic sum capacity (ESC) under both perfect and imperfect successive interference cancellation (SIC) are used to demonstrate the effectiveness of BCD-NOMA compared to conventional schemes.

• Holen, Martin; Ruud, Else-Line Malene; Warakagoda, Narada Dilp; Granmo, Ole-Christoffer; Engelstad, Paal E. & Knausgård, Kristian Muri (2022). Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles. In Iliadis, Lazaros; Jayne, Chrisina; Tefas, Anastasios & Pimenidis, Elias (Ed.), Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science. Springer Nature. ISSN 978-3-031-08223-8. p. 461–474. doi: 10.1007/978-3-031-08223-8_38. Full text in Research Archive Show summary

  Providing full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function.

• Meng, Li; Goodwin, Morten; Yazidi, Anis & Engelstad, Paal (2022). Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise. IEEE Transactions on Games (TG). ISSN 2475-1502. 15(4), p. 580–589. doi: 10.1109/TG.2022.3185330. Full text in Research Archive
• Meng, Li; Yazidi, Anis; Goodwin, Morten & Engelstad, Paal (2022). Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples. Proceedings of the Northern Lights Deep Learning Workshop. doi: 10.7557/18.6237. Full text in Research Archive Show summary

  In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims to incorporate semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning. Our results show that Expert Q-learning is indeed useful and more resistant to the overestimation bias. The baseline Q-learning algorithm exhibits unstable and suboptimal behavior in non-deterministic settings, whereas Expert Q-learning demonstrates more robust performance with higher scores, illustrating that our algorithm is indeed suitable to integrate state values from expert examples into Q-learning.

• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures. Applied Energy. ISSN 0306-2619. 333. doi: 10.1016/j.apenergy.2022.120565. Full text in Research Archive
• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Probabilistic Wind Park Power Prediction using Bayesian Deep Learning and Generative Adversarial Networks. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2362. doi: 10.1088/1742-6596/2362/1/012005. Full text in Research Archive
• Abolghasemi, Roza; Khadka, Rabindra; Lind, Pedro; Engelstad, Paal E.; Viedma, Enrique Herrera & Yazidi, Anis (2022). Predicting missing pairwise preferences from similarity features in group decision making. Knowledge-Based Systems. ISSN 0950-7051. 256. doi: 10.1016/j.knosys.2022.109860. Full text in Research Archive Show summary

  In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost importance, since many experts provide incomplete preferences. In this paper, we propose a new method called the entropy-based method for estimating the missing values in the FPR. We compared the accuracy of our algorithm for predicting the missing values with the best candidate algorithm from state of the art achievements. In the proposed entropy-based method, we took advantage of pairwise preferences to achieve good results by storing extra information compared to single rating scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and thus the solution for the matrix factorization can be expressed in the form of latent expert features and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in the same latent feature space. By virtue of this embedding, another novelty of our approach is to use the similarity of experts, as well as the similarity between alternatives, to infer the missing values even when only minimal data are available for some alternatives from some experts. Note that current approaches may fail to provide any output in such cases. Apart from estimating missing values, another salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives. It is worth mentioning that ranking alternatives have many possible applications in GDM, especially in group recommendation systems (GRS).

• Rauniyar, Ashish; Østerbø, Olav Norvald; Håkegård, Jan Erik & Engelstad, Paal E. (2022). Secrecy Performance Analysis of Cooperative Nonorthogonal Multiple Access in IoT Networks. IEEE Sensors Journal. ISSN 1530-437X. 22(19), p. 19030–19045. doi: 10.1109/JSEN.2022.3199540. Full text in Research Archive Show summary

  Different system models utilizing Non-orthogonal multiple access (NOMA) have been successfully studied to meet the growing capacity demands of the Internet of Things (IoT) devices for the next-generation networks. However, analyzing the anti-eavesdropping for NOMA systems under different scenarios and settings still needs further exploration before it can be practically deployed. Therefore, in this paper, we study the secrecy performance of a cooperative NOMA system in IoT networks where two source nodes communicate with their respective destination nodes via a common relay in the presence of an eavesdropper. Specifically, two source node sends their data in parallel over the same frequency band to the common relay node using uplink NOMA. Then, the relay node forwards the decoded symbols to the respective destination nodes using downlink NOMA in the presence of an eavesdropper. To enhance the security performance of the considered system, we study and propose an artificial noise (AN)-aided scheme in which the two destination nodes emit a jamming signal to confuse the eavesdropper while receiving the signal from the common relay node. We also study the effect of NOMA power allocation, perfect successive interference cancellation (pSIC), and imperfect SIC (ipSIC) on the considered system. Analytical expressions for the Ergodic capacity, Ergodic secrecy sum rate (ESSR), and secrecy outage probability (SOP) are mathematically derived and verified with the simulation results. Our results demonstrate that a significantly higher ESSR and lower SOP of the system can be attained compared to a conventional NOMA system without a destination-assisted jamming signal scheme.

• Bentsen, Lars Ødegaard; Warakagoda, Narada Dilp; Stenbro, Roy & Engelstad, Paal (2022). Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2265. doi: 10.1088/1742-6596/2265/2/022035. Full text in Research Archive
• Aryan, Ramtin; Yazidi, Anis; Brattensborg, Frode Henriksen; Kure, Øivind & Engelstad, Paal E. (2022). SDN Spotlight: A real-time OpenFlow troubleshooting framework. Future Generation Computer Systems. ISSN 0167-739X. 133, p. 364–377. doi: 10.1016/j.future.2022.03.014. Full text in Research Archive Show summary

  Troubleshooting in SDN-based networks is still a cumbersome task that can overwhelm human attention. Various anomalies, such as installation failure, disordered rules, and loops, remain unnoticed even when the most recent detection methods are used. In this paper, we address the issue of verifying SDN policies by actively probing the data plane. SDN Spotlight is presented as an anomaly detection framework that tries to detect installation failures, rule conflicts, and loops. In contrast to recent work, such as Monocle and Pronto, SDN Spotlight verifies a chain of rules using a single probing packet. This approach also reduces the number of monitoring rules, which has a direct effect on saving TCAM memory usage and minimizing the packet matching time. SDN Spotlight addresses two problems: verifying rule installation and forwarding behavior verification. Within the SDN Spotlight framework, we introduce two different approaches for forwarding anomaly detection: Hedge-SDN Spotlight and Open-SDN Spotlight. Furthermore, we devise an efficient and fast probe generation algorithm that generates one single probing packet per chain of rules. As opposed to other related work, Hedge-SDN Spotlight does not yield false positives and false negatives when detecting loops and forwarding failures. The results of the experiment demonstrate that SDN Spotlight is much faster than the SDNProbe and SDN traceroute method, in some cases by a factor of up to seven times as fast.

• Abolghasemi, Roza; Engelstad, Paal E.; Herrera-Viedma, Enrique & Yazidi, Anis (2022). A personality-aware group recommendation system based on pairwise preferences. Information Sciences. ISSN 0020-0255. 595, p. 1–17. doi: 10.1016/j.ins.2022.02.033. Full text in Research Archive Show summary

  Human personality plays a crucial role in decision-making and it has paramount importance when individuals negotiate with each other to reach a common group decision. Such situations are conceivable, for instance, when a group of individuals want to watch a movie together. It is well known that people influence each other’s decisions, the more assertive a person is, the more influence they will have on the final decision. In order to obtain a more realistic group recommendation system (GRS), we need to accommodate the assertiveness of the different group members’ personalities. Although pairwise preferences are long-established in group decision-making (GDM), they have received very little attention in the recommendation systems community. Driven by the advantages of pairwise preferences on ratings in the recommendation systems domain, we have further pursued this approach in this paper, however we have done so for GRS. We have devised a three-stage approach to GRS in which we 1) resort to three binary matrix factorization methods, 2) develop an influence graph that includes assertiveness and cooperativeness as personality traits, and 3) apply an opinion dynamics model in order to reach consensus. We have shown that the final opinion is related to the stationary distribution of a Markov chain associated with the influence graph. Our experimental results demonstrate that our approach results in high precision and fairness.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Exploiting SWIPT for IoT NOMA-based Diamond Relay Networks. In Mühlhäuser, Max; Polyzos, George C.; Michahelles, Florian; Guinea, Alejandro Sánchez & Wang, Lin (Ed.), MobiQuitous '20: Computing, Networking and Services, Virtual Event / Darmstadt, Germany, December 7-9, 2020. ACM 2020. ACM Digital Library. ISSN 978-1-4503-8840-5. p. 137–146. doi: 10.1145/3448891.3448931. Show summary

  Combining non-orthogonal multiple access (NOMA) with cooperative relaying networks has been considered as a viable approach for increasing the spectral efficiency and capacity enhancement of the system. With the exponential growth of the Internet of Things (IoT), integrating simultaneous wireless information and power transfer (SWIPT) with NOMA and diamond relaying networks can increase capacity and energy efficiency at the same time. Moreover, a diamond network topology has been established as a standard cooperative relaying model in the 3rd Generation Partnership Project (3GPP). Therefore, in this paper, we investigate SWIPT for IoT NOMA-based diamond relay networks wherein a source node transmits two symbols to a destination node through two energy harvesting (EH) based decode-and-forward relay nodes using the principle of downlink NOMA in the first time slot. The two EH based relay nodes harvest the energy from the source’s signal and then transmits the decoded symbol to the destination node using the uplink NOMA protocol in the next time slot. Analytical expressions for the achievable rate of the symbols and the achievable rate of the considered system are derived and verified with the simulation results. Moreover, an asymptotic closed-form solution for the achievable rate is also provided for mathematical tractability and simplified analysis. Our results demonstrate the effect of energy harvesting parameters and the effectiveness of the considered system over the similar system model using conventional orthogonal multiple access.

• Hagos, Desta Haileselassie; Yazidi, Anis; Kure, Øivind & Engelstad, Paal (2020). A Machine Learning-based Tool for Passive OS Fingerprinting with TCP Variant as a Novel Feature. IEEE Internet of Things Journal. ISSN 2327-4662. doi: 10.1109/JIOT.2020.3024293. Full text in Research Archive
• Rauniyar, Ashish; Yazidi, Anis; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). A Reinforcement Learning based Game Theoretic Approach for Distributed Power Control in Downlink NOMA . In Avresky, Dimiter R. (Eds.), 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-8327-5. doi: 10.1109/NCA51143.2020.9306737. Show summary

  Optimal power allocation problem in wireless networks is known to be usually a complex optimization problem. In this paper, we present a simple and energy-efficient distributed power control in downlink Non-Orthogonal Multiple Access (NOMA) using a Reinforcement Learning (RL) based game theoretical approach. A scenario consisting of multiple Base Stations (BSs) serving their respective Near User(s) (NU) and Far User(s) (FU) is considered. The aim of the game is to optimize the achievable rate fairness of the BSs in a distributed manner by appropriately choosing the power levels of the BSs using trials and errors. By resorting to a subtle utility choice based on the concept of marginal price costing where a BS needs to pay a virtual tax offsetting the result of the interference its presence causes for the other BS, we design a potential game that meets the latter objective. As RL scheme, we adopt Learning Automata (LA) due to its simplicity and computational efficiency and derive analytical results showing the optimality and convergence of the game to a Nash Equilibrium (NE). Numerical results not only demonstrate the convergence of the proposed algorithm to a desirable equilibrium maximizing the fairness, but they also demonstrate the correctness of the proposal followed by thorough comparison with random and heuristic approaches.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). An Adaptive User Pairing Strategy for Uplink Non-Orthogonal Multiple Access. In Darwazeh, Izzat & Aghvami, Hamid (Ed.), 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-4491-7. doi: 10.1109/PIMRC48278.2020.9217383. Full text in Research Archive Show summary

  Non-orthogonal multiple access (NOMA) is consid-ered an important candidate for the next-generation cellular networks to address the issue of exponentially growing data traffic from technologies like the Internet of Things (IoT). Efficient user pairing between multiple users is needed to enhance the capacity of uplink NOMA systems, and this paper investigates a user pairing strategy that enhances the capacities of a cell for uplink NOMA. In general, NOMA separates nodes into two equally-sized groups where half of nodes with the best channel conditions are allocated to one group, while the other half of nodes are allocated to the other group. Then, NOMA pairs each node in one group with another node in the other group. First, we investigate an efficient pairing scheme that maximizes the sum capacity for the uplink NOMA system, with an even number of nodes, i.e. when there is an equal number of cellular users in each group. An additional problem arises when there is an odd number of nodes, and the number of users in one group is thus one larger than in the other, leaving one node potentially unpaired. Instead of using regular OMA for the unpaired node, we propose an efficient adaptive user pairing strategy where the unpaired node is allowed to pair with one of the other pairs, forming a cluster of three nodes. The objective of our proposed scheme is to find the best pair of nodes to serve the unpaired node in order to increase the overall sum capacity. Moreover, the impact of perfect and imperfect successive interference cancellation (SIC) is studied on the user pairing strategy for uplink NOMA systems. Numerical results verify the effectiveness of our proposed adaptive user pairing strategy over the user pairing scheme utilizing conventional NOMA and OMA schemes.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Capacity Performance of D2D IoT Relay NOMA-SWIPT Systems with Direct Links. In Herencsar, Norbert & Benedetto, Francesco (Ed.), 2020 43rd International Conference on Telecommunications and Signal Processing (TSP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-6377-2. p. 117–123. doi: 10.1109/TSP49548.2020.9163552. Show summary

  We investigate the Ergodic capacity (EC) performance of device-to-device (D2D) Internet of Things (IoT) relay non-orthogonal multiple access (NOMA)- simultaneous wireless information and power transfer (SWIPT) systems where the relayed communication is supported with direct link. A two-user case is considered in which a base station transmits symbols to two NOMA users, and the energy harvesting (EH) based relay node via a direct link. The EH based relay node harvests the energy from the BS's signal and again transmits a superimposed composite NOMA signal intended for the user with poor channel condition and for its D2D user to offload its data traffic. A D2D user offloading is considered to further enhance the spectral efficiency of the system. We derive the analytical expressions for the EC of each of the user and the ergodic sum capacity (ESC) of the system and validate them with simulation results. In such settings, our results demonstrate that the EC of a node and the ESC of the system can be improved through the maximal ratio combining (MRC) scheme compared to a system with single signal decoding scheme. Our results also indicate that the overall ESC of the NOMA-SWIPT system can be improved by having a direct link for a user in the system.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Capacity Performance of NOMA-SWIPT Aided IoT Relay Systems with Direct Link. In Huang, Longbo; Koutsopoulos, Iordanis & Subramanian, Vijay (Ed.), 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-3-903176-29-4. p. 222–229. Show summary

  For the delay-tolerant transmission mode, ergodic capacity (EC) is an appropriate measure for the performance analysis of the system. In this paper, we investigate the EC and ergodic sum capacity (ESC) performance of cooperative non-orthogonal multiple access (NOMA) simultaneous wireless information and power transfer (SWIPT) aided Internet of Things (IoT) relay systems with direct links over the Rayleigh fading channels. To the best of our knowledge, there is no published literature that investigates the EC and ESC of the NOMA-SWIPT aided IoT relay systems with the direct link, in which one source or base station (BS) transmits symbol to two destination nodes through the direct link and with the help of EH based relay node. Specifically, we study the time switching (TS), and power splitting (PS) relaying architecture for increasing the spectral and energy-efficiency of the considered system. Analytical expressions for the EC and the ESC are mathematically derived and validated by the simulation results. Our results not only provide a thorough comparison of the TS and PS relaying EH architecture for the considered system model, but it also demonstrates that the ESC performance could be significantly improved through the optimal choice of the power splitting ε factor for PS relaying with NOMA compared to TS relaying with NOMA.

• Aryan, Ramtin; Yazidi, Anis; Bouhoula, Adel & Engelstad, Paal E. (2020). Net Auto-Solver: A formal approach for automatic resolution of OpenFlow anomalies . Conference on Local Computer Networks. ISSN 0742-1303. doi: 10.1109/LCN48667.2020.9314851. Full text in Research Archive
• Hagos, Desta Haileselassie; Løland, Martin V.; Yazidi, Anis; Kure, Øivind & Engelstad, Paal E. (2020). Advanced passive operating system fingerprinting using machine learning and deep learning. Computer Communications and Networks. ISSN 1095-2055. p. 1–11. doi: 10.1109/ICCCN49398.2020.9209694. Full text in Research Archive
• Upreti, Ramesh; Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Engelstad, Paal E. & Yazidi, Anis (2020). Adaptive Pursuit Learning for Energy‐efficient Target Coverage in Wireless Sensor Networks. Concurrency and Computation. ISSN 1532-0626. doi: 10.1002/cpe.5975. Full text in Research Archive Show summary

  With the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option. Therefore, intelligent scheduling of the sensor nodes for optimizing its energy-efficient operation and thereby extending the lifetime of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learn-ing automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with anLA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). On the Performance of Bidirectional NOMA-SWIPT Enabled IoT Relay Networks. IEEE Sensors Journal. ISSN 1530-437X. 21(2), p. 2299–2315. doi: 10.1109/JSEN.2020.3018905. Full text in Research Archive Show summary

  In this paper, a bidirectional relaying (BR) non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) termed as BR NOMA-SWIPT is proposed and investigated for the Internet of Things (IoT) relay networks. Here, multiple NOMA users in one group can communicate or exchange information with multiple NOMA users in another group through a common energy harvesting (EH) based relay. The EH based relay exploits the radio frequency (RF) energy supplied by the two NOMA user groups to recharge itself, and then it exchanges the information between them. Specifically, the two groups of NOMA users transmit the information intended for the exchange to the relay node using the uplink NOMA protocol. The relay node first harvests the RF energy through the signals of the two group of NOMA users, and then it carries out the exchange of information between two NOMA user groups by using the downlink NOMA protocol. To the best of our knowledge, there is no existing study or research on BR NOMA with SWIPT. Therefore, in this paper, we examine in detail, the performance of BR NOMA-SWIPT enabled IoT relay networks. Specifically, we study a popular EH time switching (TS) relaying architecture with BR and NOMA. We also study the effect of both perfect successive interference cancellation (pSIC) and imperfect SIC (ipSIC) on the proposed BR NOMA-SWIPT system. Analytical expressions for the outage probability and ergodic capacity are mathematically derived. The analytical results of our proposed system model are validated by the simulation results, and representative performance comparisons are presented thoroughly, which not only provides practical insights into the effect of different system parameters on the overall network performance, but it also demonstrates that our proposed BR NOMA-SWIPT can attain significant throughput and capacity gains as compared to conventional BR multiple access schemes.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Ergodic Sum Capacity Analysis of NOMA‐SWIPT Enabled IoT Relay Systems. Internet Technology Letters (ITL). 4(2). doi: 10.1002/itl2.218. Show summary

  Instead of using the participating relay node only for relaying the data of the source node, in this letter, we have also considered transmitting the data of the relay node along with the source node data based on time switch-ing (TS), power splitting (PS) relaying and NOMA. Therefore, in this context, we investigate the ergodic sum capacity (ESC) analysis of simultaneous wireless information and power transfer (SWIPT) based non-orthogonal multiple access (NOMA) for the Internet of Things (IoT) relay systems. Furthermore, we also study the effect of both perfect successive interference cancellation (pSIC) and imperfect SIC (ipSIC) on the proposed system. Analytical expressions for the ESC under both pSIC and ipSIC scenarios are mathematically derived and corroborated through Monte-Carlo simulations. Our result demonstrates that the PS relaying with NOMA is more energy-efficient than TS relaying with NOMAfor the proposed system.

• Aryan, Ramtin; Yazidi, Anis; Kure, Øivind & Engelstad, Paal E. (2020). A parallel approach for detecting OpenFlow rule anomalies based on a general formalism. Concurrency and Computation. ISSN 1532-0626. doi: 10.1002/cpe.5907. Full text in Research Archive Show summary

  As the policies of a software-defined networking (SDN) network can be updateddynamically and often at a high pace, conflicts between policies can easily occur. Dueto the large number of switches and heterogeneous policies within a typical SDN net-work, detecting those conflicts is a laborious and challenging task. This article presentsthree main contributions. First, we devise an offline method for detecting unmatchedOpenFlow rules, that is, rules that are never fired. In our taxonomy such anomaliescan stem from either invalid or irrelevant unmatched rules. Second, we introduce anew set of definitions for the intraanomalies between rules in the same table, whichmight occur when using the multiaction feature of an OpenFlow rule. Third, our detec-tion method has been enhanced to support parallel execution, which makes it a viablesolution for troubleshooting large-scale networks. We provide some comprehensiveexperimental results based on both synthetic and real-life setup the synthetic set upis designed in such a way that the rule matching takes place in the last rules of theswitch and thus putting more stress on the rule detection process. The parallel methodis shown to outperform the single-threaded checking method by order of magnitudeup to 21.

• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2020). Capacity Enhancement of NOMA‐SWIPT IoT Relay System with Direct Links over Rayleigh Fading Channels. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. doi: 10.1002/ett.3913. Show summary

  It is known that when the direct links between the base station (BS) and the users exist and are nonnegligible, consolidating direct links could significantly enhance the performance of the cooperative relaying systems. Therefore, taking the impact of direct link into account, in this article, we investigate the capacity enhancement of the nonorthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) for the Internet of Things (IoT) relay systems over the Rayleigh fading channels. Specifically, for the considered NOMA‐SWIPT system with direct links, we study a time‐switching energy harvesting (EH) architecture in which a BS transmits two symbols to the two users through the direct links and via an EH‐based relay node. On the receiving user node, we employ maximum ratio combining (MRC) to show the capacity enhancement of the considered system. Moreover, analytical expressions for the Ergodic capacity and Ergodic Sum Capacity are mathematically derived for the MRC and single signal decoding scheme, and the analytical expressions are corroborated with the Monte‐Carlo simulation results. This not only reveals the effect of different EH parameters on the system performance but it also demonstrates the capacity enhancement of the considered NOMA‐SWIPT system compared to a similar system without direct links and to conventional OMA schemes.

• Aryan, Ramtin; Brattensborg, Frode Henriksen; Yazidi, Anis & Engelstad, Paal (2019). Checking the OpenFlow Rule Installation and Operational Verification. In Andersson, Karl; Tan, Hwee-Pink & Oteafy, Sharief (Ed.), 44th Annual IEEE Conference on Local Computer Networks (LCN 2019). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-1028-8. p. 250–253. doi: https:/doi.org/10.1109/LCN44214.2019.8990808.
• Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis & Griwodz, Carsten (2019). A Deep Learning Approach to Dynamic Passive RTT. In Wang, Yu & Wang, Mea (Ed.), 2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC). IEEE Press. ISSN 9781728110257. p. 1–10. doi: 10.1109/IPCCC47392.2019.8958727. Full text in Research Archive
• Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Yazidi, Anis & Engelstad, Paal E. (2019). Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning. In Jemili, Imem & Mosbah, Mohamed (Ed.), Distributed Computing for Emerging Smart Networks. Springer Nature. ISSN 978-3-030-40131-3. p. 133–147. doi: 10.1007/978-3-030-40131-3_9. Full text in Research Archive Show summary

  Over the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSNin the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to coverall targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

• Hagos, Desta Haileselassie; Engelstad, Paal E. & Yazidi, Anis (2019). Classification of Delay-based TCP Algorithms From Passive Traffic Measurements. In Gkoulalas-Divanis, Aris; Marchetti, Mirco & Avresky, Dimiter R. (Ed.), 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2522-0. doi: 10.1109/NCA.2019.8935063.
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2019). Performance Analysis of RF Energy Harvesting and Information Transmission based on NOMA with Interfering Signal for IoT Relay Systems. IEEE Sensors Journal. ISSN 1530-437X. 19(17), p. 7668–7682. doi: 10.1109/JSEN.2019.2914796. Full text in Research Archive
• Aryan, Ramtin; Yazidi, Anis & Engelstad, Paal E. (2018). An Incremental Approach for Swift OpenFlow Anomaly Detection . Conference on Local Computer Networks. ISSN 0742-1303. doi: 10.1109/LCN.2018.8638226. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF Energy Harvesting and Information Transmission in IoT Relay Systems based on Time Switching and NOMA. In Gregory, Mark A (Eds.), 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). IEEE conference proceedings. ISSN 978-1-5386-7177-1. p. 168–174. doi: 10.1109/ATNAC.2018.8615403. Show summary

  A huge expansion of billions of Internet of Things (IoT) sensor and devices is expected over the next few years which will consume more power. Therefore, energy efficiency is a major concern for the development of fifth generation (5G) wireless systems. In wireless communication systems, energy harvesting (EH) is an emerging paradigm that allows the sensor nodes to recharge themselves through radio frequency (RF) signals directed to them from the source node and then relaying or transmitting the information. Although a myriad of works have been carried out in the literature for EH, the absolute vast majority of those works only consider RF EH at relay node and transmission of source node data successfully to its destination node. Those approaches do not consider the data transmission of the relay node that may be an IoT node which needs to transmit its data along with the source node data to their respective destinations. Thus, such approaches are clearly ineffective for energy efficient IoT relay systems. In this paper, we rather focus on RF EH and information transmission based on time switching (TS) relaying and non-orthogonal multiple access (NOMA) for IoT relay systems. A source node information data is relayed through power constrained IoT relay node that first harvests the energy from source node RF signal using TS protocol and then transmits source node information along with its information using NOMA protocol. We have mathematically derived analytical expressions for outage probability, throughput and sum-throughput for our proposed system. We have also formulated an algorithm to find out optimal TS factor that maximizes the sum-throughput for our proposed system. Our proposed system analytical results are validated by the simulation results.

• Kure, Endre Hegland; Engelstad, Paal E.; Maharjan, Sabita; Gjessing, Stein & Zhang, Yan (2018). Distributed Uplink Offloading for IoT in 5G Heterogeneous Networks under Private Information Constraints. IEEE Internet of Things Journal. ISSN 2327-4662. 6(4), p. 6151–6164. doi: 10.1109/JIOT.2018.2886703.
• Kure, Endre Hegland; Engelstad, Paal E.; Maharjan, Sabita; Gjessing, Stein; Zhang, Xing & Zhang, Yan (2018). Energy Usage Forecasting for LTE: A Network-Wide Traffic Measurements Study. In NN, N (Eds.), 2018 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM). IEEE Press. ISSN 978-1-5386-4727-1. doi: 10.1109/GLOCOMW.2018.8644423. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF Energy Harvesting and Information Transmission based on Power Splitting and NOMA for IoT Relay Systems, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). IEEE conference proceedings. ISSN 978-1-5386-7659-2. doi: 10.1109/NCA.2018.8548068.
• Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis & Kure, Øivind (2018). Recurrent neural network-based prediction of TCP transmission states from passive measurements, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA). IEEE conference proceedings. ISSN 978-1-5386-7659-2. p. 1–10. doi: 10.1109/NCA.2018.8548064. Full text in Research Archive
• Rauniyar, Ashish; Engelstad, Paal E. & Østerbø, Olav Norvald (2018). RF energy harvesting and information transmission based on NOMA for wireless powered IoT relay systems. Sensors. ISSN 1424-8220. 18:3254(10), p. 1–22. doi: 10.3390/s18103254. Full text in Research Archive
• Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis & Kure, Øivind (2018). Towards a Robust and Scalable TCP Flavors Prediction Model from Passive Traffic. In F. Abdelzaher, Tarek (Eds.), Proceedings of 27th International Conference on Computer Communication Networks (ICCCN), 2018. Hangzhou, China. IEEE Sarnoff Symposium. ISSN 978-1-5386-5156-8. p. 1–11. doi: 10.1109/ICCCN.2018.8487396.
• Rauniyar, Ashish; Engelstad, Paal E. & Moen, Hans Jonas Fossum (2018). A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things. IEEE Vehicular Technology Conference (VTC) Proceedings. ISSN 1550-2252. 2018-June, p. 1–7. doi: 10.1109/VTCSpring.2018.8417665.
• Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis & Kure, Oivind (2018). A machine learning approach to TCP state monitoring from passive measurements. IFIP Wireless Days. ISSN 2156-9711. 2018-April, p. 164–171. doi: 10.1109/WD.2018.8361713.
• Hagos, Desta Haileselassie; Engelstad, Paal E.; Yazidi, Anis & Kure, Øivind (2018). General TCP State Inference Model From Passive Measurements Using Machine Learning Techniques. IEEE Access. ISSN 2169-3536. 6, p. 28372–28387. doi: 10.1109/ACCESS.2018.2833107. Full text in Research Archive
• Aryan, Ramtin; Yazidi, Anis; Engelstad, Paal E. & Kure, Øivind (2017). A General Formalism for Defining and Detecting OpenFlow Rule Anomalies. In Jens, Tölle (Eds.), 42nd IEEE Conference on Local Computer Networks. IEEE conference proceedings. ISSN 978-1-5090-6523-3. p. 426–434. doi: 10.1109/LCN.2017.94. Full text in Research Archive
• Do, van Thanh; Engelstad, Paal E.; Feng, Boning & Do, Van Thuan (2017). A near real time SMS Grey Traffic detection. In Mezhuyev, Vitaliy & Benedicenti, Luigi (Ed.), Proceedings of 2017 6th International Conference on Software and Computer Applications (ICSCA 2017) February 26-28, 2017 Bangkok, Thailand. Association for Computing Machinery (ACM). ISSN 978-1-4503-4857-7. p. 244–249. doi: 10.1145/3056662.3056687. Show summary

  Lately, mobile operators experience threats from SMS grey routes which are used by fraudsters to evade SMS fees and to deny them millions in revenues. But more serious are the threats to the user’s security and privacy and consequently the operator’s reputation. Therefore, it is crucial for operators to have adequate solutions to protect both their network and their customers against this kind of fraud. Unfortunately, so far there is no sufficiently efficient countermeasure against grey routes. This paper proposes a near real time SMS grey traffic detection which makes use of Counting Bloom Filters combined with blacklist and whitelist to detect SMS grey traffic on the fly and to block them. The proposed detection has been implemented and proved to be quite efficient. The paper provides also comprehensive explanation of SMS grey routes and the challenges in their detection. The implementation and verification are also described thoroughly.

• Do, Van Thuan; Engelstad, Paal E.; Feng, Boning & Do, van Thanh (2017). Detection of DNS tunneling in mobile networks using machine learning. Lecture Notes in Electrical Engineering. ISSN 1876-1100. 424, p. 221–230. doi: 10.1007/978-981-10-4154-9_26.
• (2017). Data Leakage Prevention for Secure Cross-Domain Information Exchange. IEEE Communications Magazine. ISSN 0163-6804. 55(10), p. 37–43. doi: 10.1109/MCOM.2017.1700235. Full text in Research Archive
• Hagos, Desta Haileselassie; Yazidi, Anis; Kure, Øivind & Engelstad, Paal E. (2017). Enhancing security attacks analysis using regularized machine learning techniques. Advanced Information Networking and Applications. ISSN 1550-445X. p. 909–918. doi: 10.1109/AINA.2017.19. Full text in Research Archive
• Hammer, Hugo Lewi; Yazidi, Anis; Bai, Aleksander & Engelstad, Paal E. (2017). Improving Classification of Tweets Using Linguistic Information from a Large External Corpus. In Maglaras, Leandros (Eds.), Industrial Networks and Intelligent Systems. Springer. ISSN 978-3-319-52568-6. p. 122–134. doi: 10.1007/978-3-319-52569-3_11. Full text in Research Archive
• Pham, Hai Ngoc; Zhang, Yan; Skeie, Tor; Engelstad, Paal E. & Eliassen, Frank (2016). Joint energy-efficient cooperative spectrum sensing and power allocation in Cognitive Machine-to-Machine Communications, Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC 2016). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-0304-4. doi: 10.1109/IWCMC.2016.7577208.
• Yazidi, Anis; Hammer, Hugo Lewi; Bai, Aleksander & Engelstad, Paal E. (2016). On the Feasibility of Machine Learning as a Tool for Automatic Security Classification: A Position Paper. In NN, NN (Eds.), 2016 International Conference on Computing, Networking and Communications, ICNC 2016. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-8579-4. doi: 10.1109/ICCNC.2016.7440588.
• (2016). Data loss prevention based on text classification in controlled environments. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10063 LNCS, p. 131–150. doi: 10.1007/978-3-319-49806-5_7.
• Rauniyar, Ashish; Engelstad, Paal E.; Feng, Boning & Do, van Thanh (2016). Crowdsourcing-based Disaster Management using Fog Computing in Internet of Things Paradigm. In Bilof, Randall (Eds.), 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-4607-2. p. 490–494. doi: 10.1109/CIC.2016.074.
• Do, Van Thuan; Engelstad, Paal E.; Feng, Boning & Do, van Thanh (2016). Strengthening mobile network security using machine learning. In Younas, Muhammad; Awan, Irfan; Kryvinska, Natalia; Strauss, Christine & Do, van Thanh (Ed.), Mobile Web and Intelligent Information Systems 13th International Conference, MobiWIS 2016 Vienna, Austria, August 22-24, 2016 Proceedings. Springer. ISSN 978-3-319-44214-3. p. 173–183. doi: 10.1007/978-3-319-44215-0_14. Show summary

  Lately, several episodes of tapping and tracking of mobile phones in Europe including Norway have been revealed, showing the vulnerabilities of both the mobile network and mobile phones. A better protection of the user’s confidentiality and privacy is urgently required. This paper will present an in-novative mobile network security system using machine learning. The paper will start with a vulnerability and threat analysis of the evolving mobile network, which is a fusion of mobile wireless technologies and Internet technologies, complemented with the Internet of Things. The main part of the paper will concentrate on clarifying how machine learning can help improving mobile network security. The focus will be on elucidating what makes machine learning superior to other techniques. A special case study on the detection of IMSI Catcher, the fake base station that is used in mobile phone tracking and tapping, will be explained.

• Hammer, Hugo Lewi; Yazidi, Anis; Bai, Aleksander & Engelstad, Paal E. (2016). Improving classification of tweets using word-word co-occurrence information from a large external corpus. In Ossowski, Sascha (Eds.), Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC '16). Association for Computing Machinery (ACM). ISSN 978-1-4503-3739-7. p. 1174–1177. doi: 10.1145/2851613.2851986.
• Engelstad, Paal E.; Hammer, Hugo Lewi; Kongsgård, Kyrre Wahl; Yazidi, Anis; Nordbotten, Nils Agne & Bai, Aleksander (2016). Automatic security classification with Lasso. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 9503, p. 399–410. doi: 10.1007/978-3-319-31875-2_33.
• (2015). On the Trade-Off Between Multi-Level Security Classification Accuracy and Training Time. In Al-Dabass, David; Saad, Ismail; Bolong, Nurmin & Hijazi, Mohd Hanafi Ahmad (Ed.), Third International Conference on Artificial Intelligence, Modelling and Simulation. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-8675-3. p. 349–355. doi: 10.1109/AIMS.2015.62.
• (2015). On the usability of clustering for topic-oriented multi-level security models. In Al-Dabass, David; Romero, Gregorio; Orsoni, Alessandra & Pantelous, Athanasios (Ed.), 9th IEEE European modelling symposium on Mathematical Modelling and Computer Simulation (EMS), Proceedings. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-0206-1. p. 14–20. doi: 10.1109/EMS.2015.13.
• (2015). Security Challenges with Cross-Domain Information Exchange: Integrity and Guessing Attacks. MILCOM IEEE Military Communications Conference. ISSN 2155-7578. p. 1596–1605. doi: 10.1109/MILCOM.2015.7357673. Full text in Research Archive
• Bratterud, Alfred; Walla, Alf-André; Haugerud, Hårek; Engelstad, Paal E. & Begnum, Kyrre (2015). IncludeOS: A minimal, resource efficient unikernel for cloud services. IEEE International Conference on Cloud Computing Technology and Science. ISSN 2330-2186. p. 250–257. doi: 10.1109/CloudCom.2015.89. Full text in Research Archive
• Yazidi, Anis; Hammer, Hugo Lewi; Bai, Aleksander & Engelstad, Paal E. (2015). On Enhancing the Label Propagation Algorithm for Sentiment Analysis Using Active Learning with an Artificial Oracle. In Leszek, Rutkowski; Marcin, Korytkowski; Rafal, Scherer; Ryszard, Tadeusiewicz; Lotfi, A. Zadeh & Jacek, M. Zurada (Ed.), Artificial Intelligence and Soft Computing: 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part II. Springer. ISSN 978-3-319-19369-4. p. 799–810. doi: 10.1007/978-3-319-19369-4_71.
• Yazidi, Anis; Bai, Aleksander; Hammer, Hugo Lewi & Engelstad, Paal E. (2015). A Simple and Efficient Algorithm for Lexicon Generation Inspired by Structural Balance Theory. In Ali, Moonis; Kwon, Sig Young; Chang-Hwan, Lee; Kim, Juntae & Kim, Yongdai (Ed.), 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer. ISSN 978-3-319-19066-2. p. 336–347. doi: 10.1007/978-3-319-19066-2_33.
• Engelstad, Paal E.; Hammer, Hugo Lewi; Yazidi, Anis & Bai, Aleksander (2015). Advanced Classification Lists (Dirty Word Lists) for Automatic Security Classification. In Bin, Xie; Hee, Yong Youn & Min, Chen (Ed.), Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9199-3. p. 44–53. doi: 10.1109/CyberC.2015.103.
• Engelstad, Paal E.; Hammer, Hugo Lewi; Yazidi, Anis & Bai, Aleksander (2015). Analysis of Time-Dependencies in Automatic Security Classification. In Bin, Xie; Hee, Yong Youn & Min, Chen (Ed.), Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9199-3. p. 54–61. doi: 10.1109/CyberC.2015.104.
• Hammer, Hugo Lewi; Kongsgård, Kyrre Wahl; Bai, Aleksander; Yazidi, Anis; Nordbotten, Nils Agne & Engelstad, Paal E. (2015). Automatic Security Classification by Machine Learning for Cross-Domain Information Exchange. MILCOM IEEE Military Communications Conference. ISSN 2155-7578. p. 1590–1595. doi: 10.1109/MILCOM.2015.7357672. Full text in Research Archive
• Hammer, Hugo Lewi; Yazidi, Anis; Bai, Aleksander & Engelstad, Paal E. (2015). Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus. In Abdelmalek, Amine; Ladjel, Bellatreche; Zakaria, Elberrichi; Erich J., Neuhold & Robert, Wrembel (Ed.), 5th IFIP TC 5 International Conference, CIIA 2015. Springer. ISSN 978-3-319-19577-3. p. 205–216. doi: 10.1007/978-3-319-19578-0_17. Full text in Research Archive
• Bai, Aleksander; Hammer, Hugo Lewi; Yazidi, Anis & Engelstad, Paal E. (2014). Constructing sentiment lexicons in Norwegian from a large text corpus. In Liu, Xingang; Hsu, Ching-Hsien; Kang, Kai & Chen, Weifeng (Ed.), The 17th IEEE International conference on Computational science and Engineering (CSE 2014) . IEEE conference proceedings. ISSN 978-1-4799-7980-6. p. 231–237. doi: 10.1109/CSE.2014.73.
• Hammer, Hugo Lewi; Bai, Aleksander; Yazidi, Anis & Engelstad, Paal E. (2014). Building sentiment Lexicons applying graph theory on information from three Norwegian thesauruses. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. doi: 10.1109/cse.2014.73. Full text in Research Archive
• Grønsund, Pål R.; Engelstad, Paal E.; Pawelczak, Przemyslaw; Grøndalen, Ole; Lehne, Per Hjalmar & Cabric, Danijela (2013). Spectrum sensing aided long-term spectrum management in cognitive radio networks. Conference on Local Computer Networks. ISSN 0742-1303. 38. doi: 10.1109/lcn.2013.6761241.
• Borgenholt, Gaute; Begnum, Kyrre & Engelstad, Paal (2013). Audition: a DevOps-oriented service optimization and testing framework for cloud environments. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive
• Shams, S M Saif; Engelstad, Paal Einar & Kvalbein, Amund (2012). On Randomness in ISP-friendly P2P Applications. Advanced Information Networking and Applications. ISSN 1550-445X. p. 431–437. doi: 10.1109/AINA.2012.101.
• Evensen, Kristian; Kaspar, Dominik; Griwodz, Carsten; Halvorsen, Pål; Hansen, Audun Fosselie & Engelstad, Paal (2012). Using bandwidth aggregation to improve the performance of quality-adaptive streaming. Signal processing. Image communication. ISSN 0923-5965. 27(4), p. 312–328. doi: 10.1016/j.image.2011.10.007.
• Grønsund, P.; MacKenzie, R.; Lehne, Per Hjalmar; Briggs, K.; Grøndalen, Ole & Engelstad, Paal E. (2012). Towards spectrum micro-trading, Future Network and Mobile Summit (FUNEMS 2012), Berlin, Germany, July 2012. IEEE. ISSN 978-1-905824-29-8.
• Shams, S M Saif; Engelstad, Paal E. & Kvalbein, Amund (2012). PreeN: Improving steady-state performance of ISP-friendly P2P applications, Proceedings of the 13th International Conference, ICDCN 2012,Lecture Notes in Computer Science, Theoretical Computer Science and General Issues, Vol. 7129. Springer. ISSN 978-3-642-25959-3. doi: 10.1007/978-3-642-25959-3_34.
• (2012). O-CTP: Hybrid Opportunistic Collection Tree Protocol for Wireless Sensor Networks, Proceedings of the 37th Conference on Local Computer Networks Workshops (LCN Workshops 2012 IEEE). IEEE. ISSN 978-1-4673-2130-3. doi: 10.1109/lcnw.2012.6424086.
• Evensen, Kristian Riktor; Kaspar, Dominik; Griwodz, Carsten; Halvorsen, Pål; Hansen, Audun Fosselie & Engelstad, Paal E. (2011). Improving the Performance of Quality-Adaptive Video Streaming over Multiple Heterogeneous Access Networks. In N., N. (Eds.), MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-0518-1. p. 57–68. doi: 10.1145/1943552.1943560. Show summary

  Devices capable of connecting to multiple, overlapping networks simultaneously are becoming increasingly common. For example, most laptops are equipped with LAN- and WLAN-interfaces, and smart phones can typically connect to both WLANs and 3G mobile networks. At the same time, streaming high-quality video is becoming increasingly popular. However, due to bandwidth limitations or the unreliable and unpredictable nature of some types of networks, streaming video can be subject to frequent periods of rebuffering and characterised by a low picture quality. In this paper, we present a client-side request scheduler that distributes requests for the video over multiple heterogeneous interfaces simultaneously. Each video is divided into independent segments with constant duration, enabling segments to be requested over separate links, utilizing all the available bandwidth. To increase performance even further, the segments are divided into smaller subsegments, and the sizes are dynamically calculated on the fly, based on the throughput of the different links. This is an improvement over our earlier subsegment approach, which divided segments into fixed size subsegments. Both subsegment approaches were evaluated with on-demand streaming and quasi-live streaming. The new subsegment approach reduces the number of playback interruptions and improves video quality significantly for all cases where the earlier approach struggled. Otherwise, they show similar performance.

View all works in Cristin

• Aryan, Ramtin; Brattensborg, Frode; Yazidi, Anis & Engelstad, Paal E. (2019). Checking the OpenFlow Rule Installation and Operational Verification.
• HaileselassieHagos, Desta; Engelstad, Paal E. & Yazidi, Anis (2019). Classification of Delay-based TCP Algorithms From Passive Traffic Measurements.
• Aryan, Ramtin; Yazidi, Anis & Engelstad, Paal E. (2018). An Incremental Approach for Swift OpenFlow Anomaly Detection .
• Aryan, Ramtin; Yazidi, Anis; Engelstad, Paal E. & Kure, Øivind (2017). A General Formalism for Defining and Detecting OpenFlow Rule Anomalies.
• (2017). An Internal/Insider Threat Score for Data Loss Prevention and Detection.
• Van Thuan, Do; Engelstad, Paal E.; Feng, Boning & Do, van Thanh (2017). Detection of DNS tunneling in mobile networks using machine learning.
• Do, van Thanh; Engelstad, Paal E.; Feng, Boning & Do, Van Thuan (2017). A near real time SMS Grey Traffic detection.
• (2017). Data Loss Prevention for Cross-Domain Instant Messaging. Show summary

  This paper proposes a cascading classifier for inspecting and validating the payload of chat messages in (military) instant messaging. The first step in the cascading classifier pipeline is an anomaly detection-based method whose purpose is to ensure that the message channel is not used to exfiltrate non-message data such as images, documents, binary files or encrypted content. Messages that pass the filtering phase then proceed to have their content analyzed for the presence of known sensitive information. This data loss prevention step is enhanced by incorporating an author profile signal that assesses the validity of the claimed authorship by capturing the stylometric signature embedded in each user's past message stream. The hypothesis being that the inference and subsequent inclusion of latent author traits such as gender, age and ethnicity will aid the data loss prevention solution by reducing the number of incorrect classifications. Experiments were conducted using message traffic that was generated during a field-training exercise conducted by members of the armed forces, as well as an internal repository of classified documents and a myriad of non-message based data sources. The results demonstrated that our proposed traffic-filtering classifier is successful in distinguishing between legitimate and illegitimate traffic. Further, the experiments showed that constructing authorship verification models, using sparse messages as a training set, is feasible and that including this signal in the data loss prevention solution leads to a significant increase in the predictive performance for the cross-domain messaging setting.

• Do, Van Thuan; Engelstad, Paal E.; Boning, Feng & Do, van Thanh (2016). Strengthening mobile network security using machine learning.
• Engelstad, Paal E.; Hammer, Hugo Lewi; Yazidi, Anis & Bai, Aleksander (2015). Advanced Classification Lists (Dirty Word Lists) for Automatic Security Classification.
• Engelstad, Paal E.; Hammer, Hugo Lewi; Yazidi, Anis & Bai, Aleksander (2015). Analysis of Time-Dependencies in Automatic Security Classification.
• Hammer, Hugo Lewi; Yazidi, Anis; Bai, Aleksander & Engelstad, Paal E. (2015). Building domain specific sentiment lexicons combining information from many sentiment lexicons and a domain specific corpus.
• Yazidi, Anis; Bai, Aleksander; Hammer, Hugo Lewi & Engelstad, Paal E. (2015). A Simple and Efficient Algorithm for Lexicon Generation Inspired by Structural Balance Theory.
• Yazidi, Anis; Hammer, Hugo Lewi; Bai, Aleksander & Engelstad, Paal E. (2015). On Enhancing the Label Propagation Algorithm for Sentiment Analysis Using Active Learning with an Artificial Oracle.
• Bai, Aleksander; Hammer, Hugo Lewi; Yazidi, Anis & Engelstad, Paal E. (2014). Constructing sentiment lexicons in Norwegian from a large text corpus.
• (2014). Resilient internetwork routing with QoS support over heterogeneous mobile military networks.
• Begnum, Kyrre & Engelstad, Paal E. (2013). HIOA får sin egen sky. [Internet]. https://www.hioa.no/Aktuelle-saker.
• (2012). O-CTP: Hybrid Opportunistic Collection Tree Protocol for Wireless Sensor Networks.
• Shams, S M Saif; Engelstad, Paal E. & Kvalbein, Amund (2012). PreeN: Improving steady-state performance of ISP-friendly P2P applications.
• Grønsund, P.; MacKenzie, R.; Lehne, Per Hjalmar; Briggs, K.; Grøndalen, Ole & Engelstad, Paal E. (2012). Towards spectrum micro-trading.

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