Amirhosein Taherkordi

• Han, Song; Ma, Hui; Taherkordi, Amirhosein; Lan, Dapeng & Chen, Yange (2024). Privacy-preserving data integration scheme in industrial robot system based on fog computing and edge computing. IET Communications. ISSN 1751-8628. doi: 10.1049/cmu2.12749.
• Fazel-Najafabadi, Azam; Abbasi, Mahdi; Attar, Hani H.; Amer, Ayman; Taherkordi, Amir & Shokrollahi, Azad [Show all 8 contributors for this article] (2024). High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments. Tsinghua Science and Technology. ISSN 1007-0214. 29(4), p. 1118–1137. doi: 10.26599/TST.2023.9010088.
• Liu, Su; Guan, Peiyuan; Yu, Jiong & Taherkordi, Amirhosein (2023). FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks. Computer Networks. ISSN 1389-1286. 237. doi: 10.1016/j.comnet.2023.110100.
• Liu, Liqing; Yuan, Xiaoming; Chen, Decheng; Zhang, Ning; Sun, Haifeng & Taherkordi, Amirhosein (2023). Multi-user dynamic computation offloading and resource allocation in 5g mec heterogeneous networks with static and dynamic subchannels. IEEE Transactions on Vehicular Technology. ISSN 0018-9545. 72(11), p. 14924–14938. doi: 10.1109/TVT.2023.3285069.
• Liu, Liqing; Yuan, Xiaoming; Zhang, Ning; Chen, Decheng; Yu, Keping & Taherkordi, Amirhosein (2023). Joint computation offloading and data caching in multi-Access edge computing enabled internet of vehicles. IEEE Transactions on Vehicular Technology. ISSN 0018-9545. 72(11), p. 14939–14954. doi: 10.1109/TVT.2023.3285073.
• Li, Chunhai; Chen, Qiyong; Chen, Mingfeng; Su, Zhaoyu; Ding, Yong & Lan, Dapeng [Show all 7 contributors for this article] (2023). Blockchain enabled task offloading based on edge cooperation in the digital twin vehicular edge network. The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA). ISSN 2192-113X. 12(1). doi: 10.1186/s13677-023-00496-6. Full text in Research Archive
• Yuan, Xiaoming; Chen, Jiahui; Yang, Jiayu; Zhang, Ning; Yang, Tingting & Han, Tao [Show all 7 contributors for this article] (2023). FedSTN: Graph Representation Driven Federated Learning for Edge Computing Enabled Urban Traffic Flow Prediction. IEEE transactions on intelligent transportation systems (Print). ISSN 1524-9050. 24(8), p. 8738–8748. doi: 10.1109/TITS.2022.3157056.
• Liu, Lei; Zhao, Ming; Yu, Miao; Jan, Mian Ahmad; Lan, Dapeng & Taherkordi, Amirhosein (2023). Mobility-Aware Multi-Hop Task Offloading for Autonomous Driving in Vehicular Edge Computing and Networks. IEEE transactions on intelligent transportation systems (Print). ISSN 1524-9050. 24(2), p. 2169–2182. doi: 10.1109/TITS.2022.3142566.
• Wei, Zhaohui; Pei, Qingqi; Zhang, Ning; Liu, Xuefeng; Wu, Celimuge & Taherkordi, Amirhosein (2023). Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee. IEEE Internet of Things Journal. ISSN 2327-4662. 10(4), p. 3179–3191. doi: 10.1109/JIOT.2021.3127886.
• Yoshizawa, Takahito; Singelée, Dave; Muehlberg, Jan Tobias; Delbruel, Stephane; Taherkordi, Amirhosein & Hughes, Danny [Show all 7 contributors for this article] (2023). A Survey of Security and Privacy Issues in V2X Communication Systems. ACM Computing Surveys. ISSN 0360-0300. 55(9). doi: 10.1145/3558052.
• Wang, Kan; Liu, Xuan; Zhou, Hongfang; Lan, Dapeng; Gao, Zhen & Taherkordi, Amirhosein [Show all 8 contributors for this article] (2023). Reinforcement learning-based cost-efficient service function chaining with CoMP zero-forcing beamforming in edge networks. Future Generation Computer Systems. ISSN 0167-739X. 141, p. 355–368. doi: 10.1016/j.future.2022.11.022.
• Shahryari, Mohammad-Salar; Farzinvash, Leili; Feizi-Derakhshi, Mohammad-Reza & Taherkordi, Amirhosein (2023). High-throughput and energy-efficient data gathering in heterogeneous multi-channel wireless sensor networks using genetic algorithm. Ad hoc networks. ISSN 1570-8705. 139. doi: 10.1016/j.adhoc.2022.103041.
• Sepahvand, Majid; Abdali-Mohammadi, Fardin & Taherkordi, Amirhosein (2023). An adaptive teacher–student learning algorithm with decomposed knowledge distillation for on-edge intelligence. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 117. doi: 10.1016/j.engappai.2022.105560.
• Shi, Yongpeng; Wu, Jinsong; Liu, Lei; Lan, Dapeng & Taherkordi, Amirhosein (2022). Energy-Aware Relay Optimization and Power Allocation in Multiple Unmanned Aerial Vehicles Aided Satellite-Aerial-Terrestrial Networks. IEEE Systems Journal. ISSN 1932-8184. 16(4), p. 5293–5304. doi: 10.1109/JSYST.2022.3147491.
• Sepahvand, Majid; Abdali-Mohammadi, Fardin & Taherkordi, Amirhosein (2022). Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications. Expert Systems With Applications. ISSN 0957-4174. 202. doi: 10.1016/j.eswa.2022.117474.
• Yang, Tingting; Ning, Jiahong; Lan, Dapeng; Zhang, Jiawei; Yang, Yang & Wang, Xudong [Show all 7 contributors for this article] (2022). Kubeedge Wireless for Integrated Communication and Computing Services Everywhere. IEEE wireless communications. ISSN 1536-1284. 29(2), p. 140–145. doi: 10.1109/MWC.004.2100038.
• Lan, Dapeng; Taherkordi, Amirhosein; Eliassen, Frank; Liu, Lei; Delbruel, Stephane & Dustdar, Schahram [Show all 7 contributors for this article] (2022). Task Partitioning and Orchestration on Heterogeneous Edge Platforms: The Case of Vision Applications. IEEE Internet of Things Journal. ISSN 2327-4662. 9(10), p. 7418–7432. doi: 10.1109/JIOT.2022.3153970.
• Shahraki, Amin; Abbasi, Mahmoud; Taherkordi, Amirhosein & Jurcut, Anca Delia (2022). Active Learning for Network Traffic Classification: A Technical Study. IEEE Transactions on Cognitive Communications and Networking (TCCN). ISSN 2372-2045. doi: 10.1109/TCCN.2021.3119062.
• Mao, Sun; Wu, Jinsong; Liu, Lei; Lan, Dapeng & Taherkordi, Amirhosein (2022). Energy-Efficient Cooperative Communication and Computation for Wireless Powered Mobile-Edge Computing. IEEE Systems Journal. ISSN 1932-8184. 16(1), p. 287–298. doi: 10.1109/JSYST.2020.3020474.
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2021). Ataraxis: A Deep Learning Approach for Hardwareless In-Vehicle Presence Detection, IEEE International Conference on Cognitive Machine Intelligence (CogMI). Institute of Electrical and Electronics Engineers (IEEE). ISSN 978-1-6654-1621-4. p. 17–26. doi: 10.1109/CogMI52975.2021.00012. Full text in Research Archive Show summary

  Accurately detecting the mobile contexts, in which public transport vehicles and their passengers operate, is key for future intelligent context-aware services in transportation systems. A prominent example is in-vehicle presence detection that, for instance, can be used to provide services such as automated ticketing, dynamic vehicle distribution, and live route optimization. To use such services in practice, the in-vehicle presence detection needs to be close to infallible. However, most existing solutions in this field suffer from low spatiotemporal accuracy. To address this challenge, in this paper we introduce ATARAXIS, an approach to hardwareless in-vehicle presence detection. In particular, we develop a deep convolutional neural network that can be trained to detect if a user is inside a public transportation vehicle such as a tram, subway, or bus from the raw sensor events generated by the sensors in a single ordinary smartphone. We show that this information can be used to infer the in-vehicle presence of users over time when combined with other sources such as the GPS trace of the user and that of the public transport vehicles. ATARAXIS has the capability to distinguish between the four user modes driving a car, riding a bike, walking, and using public transport with an accuracy of 98.69%. That is higher than existing techniques for transport mode detection. Since we are interested in applying ATARAXIS in practice, we also made experiments on the battery consumption and CPU overhead to be expected. The results show that the use of ATARAXIS in smartphones incurs a negligible computational overhead and power consumption, even though we base our approach on sensor data collection and a deep learning model.

• Lohrasbinasab, Iraj; Shahraki, Amin; Taherkordi, Amirhosein & Delia Jurcut, Anca (2021). From statistical- to machine learning-based network traffic prediction. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. doi: 10.1002/ett.4394. Full text in Research Archive
• Oplenskedal, Magnus Karsten; Herrmann, Peter & Taherkordi, Amir (2021). DEEPMATCH2: A comprehensive deep learning-based approach for in-vehicle presence detection. Information Systems. ISSN 0306-4379. doi: 10.1016/j.is.2021.101927. Full text in Research Archive Show summary

  The accurate detection of the mobile context information of public transportation vehicles and their passengers is a key feature to realize intelligent transportation systems. A topical example is in-vehicle presence detection that can, e.g., be used to ticket passengers automatically. Unfortunately, most existing solutions in this field suffer from low spatiotemporal accuracy which impedes their use in practice. In previous work, we addressed this challenge through a deep learning-based framework, called DeepMatch, that allows us to detect in-vehicle presence with a high degree of accuracy. DeepMatch utilizes the smartphone of a passenger to analyse and match the event streams of its own sensors with the event streams of counterpart sensors provided by a reference unit that is installed inside the vehicle. This is achieved through a new learning model architecture using Stacked Convolutional Autoencoders to compress sensor input streams by feature extraction and dimensionality reduction as well as a deep convolutional neural network to match the streams of the user phone and the reference device. The sensor stream compression is offloaded to the smartphone, while the matching is performed in a server. In this paper, we introduce DeepMatch2. It is an amended version of DeepMatch that reduces the amount of data to be transferred from the user and reference devices to the server by the factor of four. Further, DeepMatch2 improves the already good accuracy of DeepMatch from 97.81% to 98.51%. Moreover, we propose a travel inference algorithm, based on DeepMatch2, to detect the duration of whole passenger trips in public transport vehicles with a high degree of precision. This is needed to create intelligent and highly reliable auto-ticketing systems. Thanks to the high accuracy of 98.51% by DeepMatch2, the inferences can be carried out with a negligible error rate.

• Zhang, Min; Eliassen, Frank; Taherkordi, Amirhosein; Jacobsen, Hans-Arno; Chung, Hwei-Ming & Zhang, Yan (2021). Demand Response Games for Peer-to-Peer Energy Trading with the Hyperledger Blockchain. IEEE Transactions on Systems, Man & Cybernetics. Systems. ISSN 2168-2216. doi: 10.1109/TSMC.2021.3111135.
• Shahraki, Amin; Taherkordi, Amirhosein & Haugen, Øystein (2021). TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks. Computer Networks. ISSN 1389-1286. 194. doi: 10.1016/j.comnet.2021.108125. Full text in Research Archive
• Abbasi, Mahmoud; Shahraki, Amin & Taherkordi, Amirhosein (2021). Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey. Computer Communications. ISSN 0140-3664. 170, p. 19–41. doi: 10.1016/j.comcom.2021.01.021. Full text in Research Archive
• Azangoo, Mohammad; Taherkordi, Amirhosein & Blech, Jan Olaf (2020). Digital Twins for Manufacturing Using UML and Behavioral Specifications. IEEE Conference on Emerging Technologies and Factory Automation. ISSN 1946-0740. p. 1035–1038. doi: 10.1109/ETFA46521.2020.9212165.
• Puka, Ergys; Herrmann, Peter & Taherkordi, Amirhosein (2020). Hybrid Context-aware Message Flooding for Dead Spot Mitigation in V2I Communication. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTC2020-Fall49728.2020.9348721. Full text in Research Archive Show summary

  An often overlooked practical problem in Intelligent Transportation Systems (ITS) is the presence of areas without cellular network connectivity, so-called dead spots, which aggravates the communication between vehicles and the external infrastructure. In our previous work, we suggested to mitigate this problem by using a hybrid data dissemination protocol that combines cellular network communication with ad-hoc networks between vehicles. If the vehicles in such ad-hoc networks are in a dead spot but have a good estimation about the time they will leave it again, messages can be forwarded to the vehicle that is supposed to regain cellular network coverage first. Since this vehicle may transmit the stored messages immediately after having left the dead spot, the delivery time is improved. In this paper, we first analyze the behavior of the aforementioned data dissemination protocol in larger dead spots in which a message may be carried by several vehicles before being delivered via the cellular network. The analysis reveals that messages are not always delivered in the fastest possible time. To address this concern, a new protocol variant named context-aware message flooding protocol is introduced. This protocol, indeed, guarantees the fastest possible forwarding of messages to their recipients. This is achieved at the cost of delivering duplicates that, however, are only produced when the delivery of a message is sped up.

• Yuan, Xiaoming; Tian, Hansen; Wang, Haiyang; Su, Haoru; Liu, Jiemin & Taherkordi, Amirhosein (2020). Edge-Enabled WBANs for Efficient QoS Provisioning Healthcare Monitoring: A Two-Stage Potential Game-Based Computation Offloading Strategy. IEEE Access. ISSN 2169-3536. 8, p. 92718–92730. doi: 10.1109/ACCESS.2020.2992639.
• Yuan, Xiaoming; Zhao, Zheyu; Wang, Haiyang; Zhang, Lin; Taherkordi, Amirhosein & Yu, Hua (2020). MDMH: An improved multi-attribute decision-making and highest response ratio next-based computation offloading approach for wireless body area networks. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. p. 1–20. doi: 10.1002/ett.4070.
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2020). DeepMatch: deep matching for in-vehicle presence detection in transportation, DEBS '20: Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-8028-7. p. 97–108. doi: 10.1145/3401025.3401741. Show summary

  A key feature of modern public transportation systems is the accurate detection of the mobile context of transport vehicles and their passengers. A prominent example is automatic in-vehicle presence detection which allows, e.g., intelligent auto-ticketing of passengers. Most existing solutions, in this field, are based on either using active RFID or Bluetooth Low Energy (BLE) technology, or mobile sensor data analysis. Such techniques suffer from low spatiotemporal accuracy in in-vehicle presence detection. In this paper, we address this issue by proposing a deep learning model and the design of an associated generic distributed framework. Our approach, called DeepMatch, utilizes the smartphone of a passenger to analyze and match the event streams of its own sensors and the event streams of the counterpart sensors in an in-vehicle reference unit. This is achieved through a new learning model architecture using Stacked Convolutional Autoencoders for feature extraction and dimensionality reduction, as well as a dense neural network for stream matching. In this distributed framework, feature extraction and dimensionality reduction is offloaded to the smartphone, while matching is performed in a server, e.g., in the Cloud. In this way, the number of sensor events to be transmitted for matching on the server side will be minimized. We evaluated DeepMatch based on a large dataset taken in real vehicles. The evaluation results show that the statistical accuracy of our approach is 0.978 for in-vehicle presence detection which, as we will argue, is sufficient to be used in, e.g., auto-ticketing systems.

• Lan, Dapeng; Taherkordi, Amir; Eliassen, Frank; Chen, Zhuang & Liu, Lei (2020). Deep Reinforcement Learning for Intelligent Migration of Fog Services in Smart Cities. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12453, p. 230–244. doi: 10.1007/978-3-030-60239-0_16.
• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms. IEEE Transactions on Network and Service Management. ISSN 1932-4537. doi: 10.1109/TNSM.2020.3035315. Full text in Research Archive
• Shahraki, Amin; Taherkordi, Amirhosein; Haugen, Øystein & Eliassen, Frank (2020). Clustering objectives in wireless sensor networks: A survey and research direction analysis. Computer Networks. ISSN 1389-1286. 180. doi: 10.1016/j.comnet.2020.107376. Full text in Research Archive
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2019). Automated Product Localization through Mobile Data Analysis. In Sarwat, Mohamed; Zheng, Baihua & Zheng, Kevin Kai (Ed.), 20th IEEE International Conference on Mobile Data Management (MDM). IEEE conference proceedings. ISSN 978-1-7281-3364-5. p. 18–26. doi: 10.1109/MDM.2019.00-78. Show summary

  Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice.

• Zhang, Min; Eliassen, Frank; Taherkordi, Amirhosein; Jacobsen, Hans-Arno; Chung, Hwei-Ming & Zhang, Yan (2019). Energy Trading with Demand Response in a Community-based P2P Energy Market, 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-8099-5. doi: 10.1109/SmartGridComm.2019.8909798.
• Shooshtarian, Leila; Lan, Dapeng & Taherkordi, Amirhosein (2019). A Clustering-Based Approach to Efficient Resource Allocation in Fog Computing. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. 1080 CCIS, p. 207–224. doi: 10.1007/978-3-030-30143-9_17.
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2019). Automated Product Localization through Mobile Data Analysis. Proceedings (IEEE International Conference on Mobile Data Management). ISSN 1551-6245. p. 18–26. doi: 10.1109/MDM.2019.00-78. Full text in Research Archive Show summary

  Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice.

• Lan, Dapeng; Pang, Zhibo; Fischione, Carlo; Liu, Yu; Taherkordi, Amirhosein & Eliassen, Frank (2019). Latency Analysis of Wireless Networks for Proximity Services in Smart Home and Building Automation: The Case of Thread. IEEE Access. ISSN 2169-3536. 7, p. 4856–4867. doi: 10.1109/ACCESS.2018.2888939. Full text in Research Archive
• Dreibholz, Thomas; Mazumdar, Somnath; Zahid, Feroz; Taherkordi, Amirhosein & Gran, Ernst Gunnar (2019). Mobile Edge as Part of the Multi-Cloud Ecosystem: A Performance Study. In Leporati, Francesco; Danese, Giovanni & Merelli, Ivan (Ed.), Proceedings of the 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-1644-0. p. 59–66. doi: 10.1109/EMPDP.2019.8671599. Full text in Research Archive
• Taherkordi, Amirhosein & Herrmann, Peter (2018). Pervasive Smart Contracts for Blockchains in IoT Systems. In Parizi, Reza M. (Eds.), Proceedings of the 2018 International Conference on Blockchain Technology and Application. Association for Computing Machinery (ACM). ISSN 978-1-4503-6646-5. p. 1–6. doi: 10.1145/3301403.3301405. Full text in Research Archive Show summary

  The blockchain technology has gained tremendous attention thanks to its decentralized structure, immutability, and enhanced security and privacy guarantees. Blockchain has the potential to address security and privacy challenges of Internet of Things (IoT). By hosting and executing \textit{smart contracts}, blockchain allows secure and flexible message communication between IoT devices and traceability in IoT applications. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, pose challenges in designing smart contracts for IoT systems. In this paper, we study those challenges and propose a microservice-based approach to the design of IoT smart contracts. The proposed service model is aimed to encapsulate functionalities such as contract-level communication between IoT devices, access to data-sources within contracts, and supporting interoperability of heterogeneous IoT smart contracts. The flexibility, scalability and modularity of the microservice architecture model make it an efficient approach for developing IoT smart contracts.

• Taherkordi, Amirhosein; Zahid, Feroz; Verginadis, Yiannis & Horn, Geir (2018). Future Cloud Systems Design: Challenges and Research Directions. IEEE Access. ISSN 2169-3536. 6, p. 74120–74150. doi: 10.1109/ACCESS.2018.2883149.
• Taherkordi, Amirhosein; Eliassen, Frank; McDonald, Michael & Horn, Geir (2018). Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud. ACM Transactions on Internet Technology. ISSN 1533-5399. 19(1), p. 7:1–7:24. doi: 10.1145/3151006.
• Zahid, Feroz; Taherkordi, Amirhosein; Gran, Ernst Gunnar; Skeie, Tor & Johnsen, Bjørn Dag (2018). A Self-Adaptive Network for HPC Clouds: Architecture, Framework, and Implementation. IEEE Transactions on Parallel and Distributed Systems. ISSN 1045-9219. 29(12), p. 2658–2671. doi: 10.1109/TPDS.2018.2842224. Full text in Research Archive
• Oplenskedal, Magnus Karsten; Herrmann, Peter; Blech, Jan Olaf & Taherkordi, Amirhosein (2018). Simulation-driven Development of Self-adaptive Transportation Systems, Proceedings of the 2018 IEEE Conference on Network Softwarization. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-4633-5. p. 372–377. doi: 10.1109/NETSOFT.2018.8460073. Show summary

  Modern Intelligent Transportation Systems (ITS) operate highly automatically. Therefore, they have to be able to handle a large variety of situations each demanding a particular system behavior. That aggravates the development of control software that has to guarantee safe and expedient operation in all possible situations. To support a suitable reconfiguration of the controllers to changing environments, the use of self-adaptation seems to be a highly promising approach. In this paper, we propose to combine model-based engineering of control software with simulation. That allows us to create and test controller software in parallel with the physical systems, it shall operate. Moreover, this approach makes it possible to safely confront a transport system with situations that, otherwise, could only be reproduced taking a significant risk. In particular, we introduce a framework for the creation of control software using simulators together with a development structure. The suggested design process is illustrated with a mobile robot example.

• Taherkordi, Amirhosein; Eliassen, Frank & Horn, Geir Henrik (2017). From IoT big data to IoT Big Services. In Shin, Sung Y. (Eds.), Proceedings of the Symposium on Applied Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-4486-9. p. 485–491. doi: 10.1145/3019612.3019700.
• Taherkordi, Amirhosein & Eliassen, Frank (2017). Data-Centric IoT Services Provisioning in Fog-Cloud Computing Systems. In Bilof, Randall (Eds.), Second International Conference on Internet-of-Things Design and Implementation. Association for Computing Machinery (ACM). ISSN 978-1-4503-4966-6. p. 317–318. doi: 10.1145/3054977.3057295.
• Abbas, Nasir; Zhang, Yan; Taherkordi, Amirhosein & Skeie, Tor (2017). Mobile Edge Computing: A Survey. IEEE Internet of Things Journal. ISSN 2327-4662. 5(1), p. 450–465. doi: 10.1109/JIOT.2017.2750180.
• Svae, Alexander; Taherkordi, Amirhosein; Herrmann, Peter & Blech, Jan Olaf (2017). Self-adaptive control in cyber-physical systems: The autonomous train experiment. In Shin, Sung Y.; Shin, Dongwan & Lancastre, Maria (Ed.), Proceedings of the Symposium on Applied Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-4486-9. p. 1436–1443. doi: 10.1145/3019612.3019651. Show summary

  Autonomous systems become more and more important in today’s transport sector. They often operate in dynamic environments in which unpredictable events may occur at any time. These events may affect the safe operation of vehicles, calling for highly efficient control software technologies to reason about and react on their appearance. A crucial efficiency parameter is timeliness as vehicles often operate under high speed. The contribution of this paper is the presentation and analysis of design aspects of dynamic control software in the context of an autonomous train experiment. This is achieved through a self-adaptation software framework intended for autonomous trains and built on a demonstrator using Lego Mindstorms. The main mission of the framework is to collect context information, reason about it, and adapt the train behavior accordingly. The adaptation framework is implemented using the development tool Reactive Blocks and tested on the demonstrator. The evaluation results provide useful insights into the performance of the framework, particularly about the time needed to reason about the context and to carry out reconfigurations.

• Taherkordi, Amirhosein; Herrmann, Peter; Blech, Jan Olaf & Fernandez Fernandez, Alvaro (2017). Service virtualization for self-adaptation in mobile cyber-physical systems. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10380 LNCS, p. 56–68. doi: 10.1007/978-3-319-68136-8_5.
• Taherkordi, Amirhosein & Eliassen, Frank (2016). Scalable modeling of cloud-based IoT services for smart cities. In Kumar, Mohan & Seneviratne, Aruna (Ed.), 2016 IEEE International Conference on Pervasive Computing and Communication Workshops. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5090-1941-0. doi: 10.1109/PERCOMW.2016.7457098.
• Dar, Kashif Sana; Taherkordi, Amirhosein & Eliassen, Frank (2016). Enhancing Dependability of Cloud-Based IoT Services through Virtualization. In Abdelzaher, Tarek; Cao, Jiannong & Marron, Pedro Jose (Ed.), 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-9948-7. p. 106–116. doi: 10.1109/IoTDI.2015.38.
• Horn, Geir Henrik; Eliassen, Frank; Taherkordi, Amirhosein; Venticinque, Salvatore; Di Martino, Beniamino & Bücher, Monika [Show all 7 contributors for this article] (2016). An architecture for using commodity devices and smart phones in health systems. Proceedings of the IEEE Symposium on Computers and Communications. ISSN 1530-1346. 2016-August, p. 255–260. doi: 10.1109/ISCC.2016.7543749.
• Taherkordi, Amirhosein & Eliassen, Frank (2015). Models@run.time for creating in-cloud dynamic cyber-physical ecosystems. IEEE International Conference on Cloud Computing Technology and Science. ISSN 2330-2186. 2015-February(February), p. 976–982. doi: 10.1109/CloudCom.2014.158.
• Taherkordi, Amirhosein; Johansen, Christian; Eliassen, Frank & Römer, Kay (2015). Tokenit: Designing State-Driven Embedded Systems through Tokenized Transitions. In OConner, Lisa (Eds.), Distributed Computing in Sensor Systems (DCOSS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-8856-3. p. 52–61. doi: 10.1109/DCOSS.2015.36.
• Taherkordi, Amirhosein & Eliassen, Frank (2014). Models@run.time for Creating in-Cloud Dynamic Cyber-Physical Ecosystems. In Cai, Wentong (Eds.), CloudCom 2014, IEEE 6th International Conference on Cloud Computing Technology and Science, Singapore, 15-18. December, 2014 . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-4093-6. p. 976–982. doi: 10.1109/CloudCom.2014.158.
• Taherkordi, Amirhosein & Eliassen, Frank (2014). Towards Independent in-Cloud Evolution of Cyber-Physical Systems. In Azumi, Takuya & Lee, Victor (Ed.), The 2nd IEEE International Conference on Cyber-Physical Systems, Networks, and Applications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-5387-5. p. 19–24. doi: 10.1109/CPSNA.2014.12.
• Dar, Kashif Sana; Taherkordi, Amirhosein; Baraki, Harun; Eliassen, Frank & Geihs, Kurt (2014). A Resource Oriented Integration Architecture for the Internet of Things: A Business Processes Perspective. Pervasive and Mobile Computing. ISSN 1574-1192. 20, p. 145–159. doi: 10.1016/j.pmcj.2014.11.005. Show summary

  The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.

• Taherkordi, Amirhosein; Eliassen, Frank & Johnsen, Einar Broch (2013). Behavioural Design of Sensor Network Applications using Activity-Driven States. In Christine, Julien (Eds.), 4th International Workshop on Software Engineering for Sensor Network Applications (SESENA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4673-6269-6. p. 13–18. doi: 10.1109/sesena.2013.6612259.
• Romero, Daniel; Hermosillo, Gabriel; Taherkordi, Amirhosein; Nzekwa, Russel; Rouvoy, Romain & Eliassen, Frank (2013). The DigiHome Service-Oriented Platform. Software, Practice & Experience. ISSN 0038-0644. 43(10), p. 1205–1218. doi: 10.1002/spe.1125.
• Taherkordi, Amirhosein; Loiret, Frederic; Rouvoy, Romain & Eliassen, Frank (2013). Optimizing Sensor Network Reprogramming via In Situ Reconfigurable Components. ACM transactions on sensor networks. ISSN 1550-4867. 9(2). doi: 10.1145/2422966.2422971.
• Le-Trung, Quan; Engelstad, Paal Einar; Skeie, Tor; Eliassen, Frank & Taherkordi, Amirhosein (2011). Mobility Management for All-IP Mobile Networks. In Makaya, Christian & Pierre, Samuel (Ed.), Emerging Wireless Networks - Concepts, Techniques, Applications. Taylor & Francis. ISSN 9781439821350.
• Dar, Kashif Sana; Taherkordi, Amirhosein; Vitenberg, Roman; Rouvoy, Romain & Eliassen, Frank (2011). Adaptable Service Composition for Very-Large-Scale Internet of Things Systems. In Ben Mokhtar, Sonia (Eds.), Middleware 2011: Proceedings of the Workshop on Posters and Demos Track. ACM Publications. ISSN 978-1-4503-1073-4.
• Taherkordi, Amirhosein; Eliassen, Frank; Romero, Daniel & Rouvoy, Romain (2011). RESTful Service Development for Resource-constrained Environments. In Wilde, Erik (Eds.), REST: From Research to Practice. Springer. ISSN 978-1-4419-8302-2. p. 221–236. doi: 10.1007/978-1-4419-8303-9_9.
• Dar, Kashif Sana; Taherkordi, Amirhosein; Rouvoy, Romain & Eliassen, Frank (2011). Adaptable Service Composition for Very-Large-Scale Internet of Things Systems. In Eyers, David (Eds.), Proceedings of the 8th Middleware Doctoral Symposium of the 12th ACM/IFIP/USENIX International Middleware Conference. ACM Publications. ISSN 978-1-4503-1072-7. doi: 10.1145/2093190.2093192.
• Taherkordi, Amirhosein; Loiret, Frederic; Rouvoy, Romain & Eliassen, Frank (2011). A Generic Component-Based Approach for Programming, Composing and Tuning Sensor Software. Computer journal. ISSN 0010-4620. 54(8), p. 1248–1266. doi: 10.1093/comjnl/bxq102.
• Taherkordi, Amirhosein & Eliassen, Frank (2010). An event-driven approach to address reflectivity in sensornets. In Venkatasubramanian, Nalini (Eds.), Proceedings of the 9th International Workshop on Adaptive and Reflective Middleware. ACM Publications. ISSN 978-1-4503-0455-9. p. 52–53.
• Taherkordi, Amirhosein; Rouvoy, Romain & Eliassen, Frank (2010). A component-based approach for service distribution in sensor networks. In Hughes, Danny (Eds.), Proceedings of the 5th International Workshop on Middleware Tools, Services and Run-Time Support for Sensor Networks. ACM Publications. ISSN 978-1-4503-0454-2. p. 22–28.
• Romero, Daniel; Hermosillo, Gabriel; Taherkordi, Amirhosein; Nzekwa, Russel; Rouvoy, Romain & Eliassen, Frank (2010). RESTful Integration of Heterogeneous Devices in Pervasive Environments. In Eliassen, Frank & Kapitza, Rüdiger (Ed.), Distributed Applications and Interoperable Systems. Springer. ISSN 3-642-13644-3. p. 1–14.
• Taherkordi, Amirhosein; Loiret, Frederic; Abdolrazaghi, Azadeh; Rouvoy, Romain; Le-Trung, Quan & Eliassen, Frank (2010). Programming Sensor Networks Using REMORA Component Model, Distributed Computing in Sensor Systems. Springer. ISSN 3-642-13650-8. p. 45–62.
• Le Trung, Quan; Engelstad, Paal Einar; Taherkordi, Amirhosein; Skeie, Tor & Pham, Hai Ngoc (2009). Information Storage, Reduction and Dissemination in Sensor Networks: A Survey. In Gibbs, Simon (Eds.), 6th Annual IEEE Consumer Communications & Networking Conference IEEE CCNC 2009, Workshop Information Retrieval in Sensor Networks. IEEE Sarnoff Symposium. ISSN 978-1-4244-2309-5. p. 505–510.
• Taherkordi, Amirhosein; Le-Trung, Quan; Rouvoy, Romain & Eliassen, Frank (2009). WiSeKit: A Distributed Middleware to Support Application-Level Adaptation in Sensor Networks. In Goos, Gerhard (Eds.), Distributed Applications and Interoperable Systems. Springer. ISSN 978-3-642-02163-3. p. 44–58.
• Taherkordi, Amirhosein; Rouvoy, Romain; Le-Trung, Quan & Eliassen, Frank (2009). Supporting lightweight adaptations in context-aware wireless sensor networks. In Bouroche, Mélanie (Eds.), Proceedings of the 1st International Workshop on Context-Aware Middleware and Services. ACM Publications. ISSN 978-1-60558-525-3. p. 43–48.
• Le Trung, Quan; Taherkordi, Amirhosein; Eliassen, Frank; Pham, Hai Ngoc; Skeie, Tor & Engelstad, Paal Einar (2009). DCM-Arch: An Architecture for Data, Control, and Management in Wireless Sensor Networks. In Awan, Irfan; Younas, Muhammad; Hara, Takahiro & Durresi, Arjan (Ed.), The IEEE 23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Bradford, United Kingdom, May 26-29, 2009. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7695-3638-5. p. 898–905.
• Le-Trung, Quan; Engelstad, Paal Einar; Pham, Vinh; Skeie, Tor; Taherkordi, Amirhosein & Eliassen, Frank (2009). Providing internet connectivity and mobility management for MANETs. International Journal of Web Information Systems. ISSN 1744-0084. 5(2), p. 239–263. doi: 10.1108/17440080910968472.
• Le Trung, Quan; Engelstad, Paal Einar; Skeie, Tor & Taherkordi, Amirhosein (2008). Load-Balance of Intra/Inter-MANET Traffic over Multiple Internet Gateways. In Kotsis, Gabriele (Eds.), The 6th International Conference on Advances in Mobile Computing & Multimedia (MoMM2008). Association for Computing Machinery (ACM). ISSN 978-1-60558-269-6. p. 50–57.
• Taherkordi, Amirhosein; Rouvoy, Romain; Le Trung, Quan & Eliassen, Frank (2008). A self-adaptive context processing framework for wireless sensor networks. In Michiels, Sam (Eds.), Proceedings of the 3rd International Workshop on Middleware for Sensor Networks. ACM Publications. ISSN 978-1-60558-366-2. p. 7–13.
• Taherkordi, Amirhosein; Eliassen, Frank; Rouvoy, Romain & Le Trung, Quan (2008). ReWiSe: A New Component Model for Lightweight Software Reconfiguration in Wireless Sensor Networks. In Meersman, Robert (Eds.), On the Move to Meaningful Internet Systems: OTM2008Workshops. Springer. ISSN 978-3-540-88874-1. p. 415–425.
• Taherkordi, Amirhosein; Mohammadi, Reza & Eliassen, Frank (2008). A Communication-Efficient Distributed Clustering Algorithm for Sensor Networks. In Conner, Lisa (Eds.), 22nd International Conference on Advanced Information Networking and Applications Workshops/Symposia. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-0-7695-3096-3.
• Alkaee Taleghan, Majid; Taherkordi, Amirhosein; Sharifi, Mohsen & Kim, Tai-Hoon (2007). A Survey of System Software for Wireless Sensor Networks. In Island, Jeju (Eds.), Future Generation Communication and Networking. IEEE (Institute of Electrical and Electronics Engineers). ISSN 0-7695-3048-6. p. 402–407.

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• Ramezanifarkhani, Toktam & Taherkordi, Amirhosein (2021). IKTPLUSS Grant Proposal- How to write a successful proposal?
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2021). Ataraxis: A Deep Learning Approach for Hardwareless In-Vehicle Presence Detection. Show summary

  Accurately detecting the mobile contexts, in which public transport vehicles and their passengers operate, is key for future intelligent context-aware services in transportation systems. A prominent example is in-vehicle presence detection that, for instance, can be used to provide services such as automated ticketing, dynamic vehicle distribution, and live route optimization. To use such services in practice, the in-vehicle presence detection needs to be close to infallible. However, most existing solutions in this field suffer from low spatiotemporal accuracy. To address this challenge, in this paper we introduce ATARAXIS, an approach to hardwareless in-vehicle presence detection. In particular, we develop a deep convolutional neural network that can be trained to detect if a user is inside a public transportation vehicle such as a tram, subway, or bus from the raw sensor events generated by the sensors in a single ordinary smartphone. We show that this information can be used to infer the in-vehicle presence of users over time when combined with other sources such as the GPS trace of the user and that of the public transport vehicles. ATARAXIS has the capability to distinguish between the four user modes driving a car, riding a bike, walking, and using public transport with an accuracy of 98.69%. That is higher than existing techniques for transport mode detection. Since we are interested in applying ATARAXIS in practice, we also made experiments on the battery consumption and CPU overhead to be expected. The results show that the use of ATARAXIS in smartphones incurs a negligible computational overhead and power consumption, even though we base our approach on sensor data collection and a deep learning model.

• Puka, Ergys; Herrmann, Peter & Taherkordi, Amirhosein (2020). Hybrid Context-aware Message Flooding for Dead Spot Mitigation in V2I Communication. Show summary

  An often overlooked practical problem in Intelligent Transportation Systems (ITS) is the presence of areas without cellular network connectivity, so-called dead spots, which aggravates the communication between vehicles and the external infrastructure. In our previous work, we suggested to mitigate this problem by using a hybrid data dissemination protocol that combines cellular network communication with ad-hoc networks between vehicles. If the vehicles in such ad-hoc networks are in a dead spot but have a good estimation about the time they will leave it again, messages can be forwarded to the vehicle that is supposed to regain cellular network coverage first. Since this vehicle may transmit the stored messages immediately after having left the dead spot, the delivery time is improved. In this paper, we first analyze the behavior of the aforementioned data dissemination protocol in larger dead spots in which a message may be carried by several vehicles before being delivered via the cellular network. The analysis reveals that messages are not always delivered in the fastest possible time. To address this concern, a new protocol variant named context-aware message flooding protocol is introduced. This protocol, indeed, guarantees the fastest possible forwarding of messages to their recipients. This is achieved at the cost of delivering duplicates that, however, are only produced when the delivery of a message is sped up.

• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2020). Deep Matching for In-Vehicle Presence Detection in Transportation. Show summary

  A key feature of modern public transportation systems is the accurate detection of the mobile context of transport vehicles and their passengers. A prominent example is automatic in-vehicle presence detection which allows, e.g., intelligent auto-ticketing of passengers. Most existing solutions, in this field, are based on either using active RFID or Bluetooth Low Energy (BLE) technology, or mobile sensor data analysis. Such techniques suffer from low spatiotemporal accuracy in in-vehicle presence detection. In this paper, we address this issue by proposing a deep learning model and the design of an associated generic distributed framework. Our approach, called DeepMatch, utilizes the smartphone of a passenger to analyze and match the event streams of its own sensors and the event streams of the counterpart sensors in an in-vehicle reference unit. This is achieved through a new learning model architecture using Stacked Convolutional Autoencoders for feature extraction and dimensionality reduction, as well as a dense neural network for stream matching. In this distributed framework, feature extraction and dimensionality reduction is offloaded to the smartphone, while matching is performed in a server, e.g., in the Cloud. In this way, the number of sensor events to be transmitted for matching on the server side will be minimized. We evaluated DeepMatch based on a large dataset taken in real vehicles. The evaluation results show that the statistical accuracy of our approach is 0.978 for in-vehicle presence detection which, as we will argue, is sufficient to be used in, e.g., auto-ticketing systems.

• Zhang, Min; Eliassen, Frank; Zhang, Yan & Taherkordi, Amirhosein (2019). Energy Trading with Demand Response in a Community-based P2P Energy Market. [Internet]. https://ieeexplore.ieee.org/document/8909798.
• Oplenskedal, Magnus Karsten; Taherkordi, Amirhosein & Herrmann, Peter (2019). Automated Product Localization through Mobile Data Analysis. Show summary

  Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice.

• Taherkordi, Amirhosein & Herrmann, Peter (2018). Pervasive Smart Contracts for Blockchains in IoT Systems. Show summary

  The blockchain technology has gained tremendous attention thanks to its decentralized structure, immutability, and enhanced security and privacy guarantees. Blockchain has the potential to address security and privacy challenges of Internet of Things (IoT). By hosting and executing \textit{smart contracts}, blockchain allows secure and flexible message communication between IoT devices and traceability in IoT applications. The unique characteristics of IoT systems, such as heterogeneity and pervasiveness, pose challenges in designing smart contracts for IoT systems. In this paper, we study those challenges and propose a microservice-based approach to the design of IoT smart contracts. The proposed service model is aimed to encapsulate functionalities such as contract-level communication between IoT devices, access to data-sources within contracts, and supporting interoperability of heterogeneous IoT smart contracts. The flexibility, scalability and modularity of the microservice architecture model make it an efficient approach for developing IoT smart contracts.

• Oplenskedal, Magnus Karsten; Herrmann, Peter; Blech, Jan Olaf & Taherkordi, Amirhosein (2018). Simulation-driven Development of Self-adaptive Transportation Systems. Show summary

  Modern Intelligent Transportation Systems (ITS) operate highly automatically. Therefore, they have to be able to handle a large variety of situations each demanding a particular system behavior. That aggravates the development of control software that has to guarantee safe and expedient operation in all possible situations. To support a suitable reconfiguration of the controllers to changing environments, the use of self-adaptation seems to be a highly promising approach. In this paper, we propose to combine model-based engineering of control software with simulation. That allows us to create and test controller software in parallel with the physical systems, it shall operate. Moreover, this approach makes it possible to safely confront a transport system with situations that, otherwise, could only be reproduced taking a significant risk. In particular, we introduce a framework for the creation of control software using simulators together with a development structure. The suggested design process is illustrated with a mobile robot example.

• Svae, Alexander; Taherkordi, Amirhosein; Herrmann, Peter & Blech, Jan Olaf (2017). Self-Adaptive Control in Cyber-Physical Systems: The Autonomous Train Experiment. Show summary

  Autonomous systems become more and more important in today’s transport sector. They often operate in dynamic environments in which unpredictable events may occur at any time. These events may affect the safe operation of vehicles, calling for highly efficient control software technologies to reason about and react on their appearance. A crucial efficiency parameter is timeliness as vehicles often operate under high speed. The contribution of this paper is the presentation and analysis of design aspects of dynamic control software in the context of an autonomous train experiment. This is achieved through a self-adaptation software framework intended for autonomous trains and built on a demonstrator using Lego Mindstorms. The main mission of the framework is to collect context information, reason about it, and adapt the train behavior accordingly. The adaptation framework is implemented using the development tool Reactive Blocks and tested on the demonstrator. The evaluation results provide useful insights into the performance of the framework, particularly about the time needed to reason about the context and to carry out reconfigurations.

• Taherkordi, Amirhosein; Herrmann, Peter; Blech, Jan Olaf & Fernandez, Alvaro (2016). Service Virtualization for Self-Adaptation in Mobile Cyber-Physical Systems. Show summary

  Mobile Cyber-Physical Systems (mCPS) consist of cooperating units that often operate in an unpredictably changing environment. Thus, they need to adapt quickly to varying spatial and temporal conditions during operation, e.g., to avoid collisions. The control software of the mobile units has to reflect this complex dynamics, and traditional device-level adaptation models are usually not efficient enough to engineer them smoothly. We address this challenge by proposing a Virtual Adaptation Services Framework (VASF). It provides a virtualized application-level view to adaptation requirements, enabling adaptation coordination between cooperative mCPS devices. In particular, VASF allows us to describe the contextual conditions of mCPS by abstract rules that are analyzed at runtime by the tool-set BeSpaceD. Based on this analysis, the control systems of the involved mCPS units are automatically reconfigured using the OSGi framework. The approach is demonstrated with DiddyBorg robots that are operated by Raspberry Pi boards.

• Taherkordi, Amirhosein; Eliassen, Frank & Skeie, Tor (2011). Programming Wireless Sensor Networks: From Static to Adaptive Models. Unipub forlag.

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