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Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be used for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other’s resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. Our simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of −23% in energy consumption, −15% in latency, +18% in throughput, and +9% in fairness.
Dadmehr Rahbari; Muhammad Mahtab Alam; Yannick Le Moullec; Maksim Jenihhin. Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach. IEEE Access 2021, 9, 113832 -113849.
AMA StyleDadmehr Rahbari, Muhammad Mahtab Alam, Yannick Le Moullec, Maksim Jenihhin. Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach. IEEE Access. 2021; 9 ():113832-113849.
Chicago/Turabian StyleDadmehr Rahbari; Muhammad Mahtab Alam; Yannick Le Moullec; Maksim Jenihhin. 2021. "Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach." IEEE Access 9, no. : 113832-113849.
NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide area networks suitable for implementing massive machine type communications. NB-IoT features include e.g. extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly under-estimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of Radio Resource Control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base-station. Using two different commercial off the shelf NB-IoT boards and two Mobile Network Operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors for our proposed model vary between 0.33% and 15.38%.
Sikandar Khan; Muhamamd Mahtab Alam; Yannick LeMoullec; Alar Kussik; Sven Parand; Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. 2021, 1 .
AMA StyleSikandar Khan, Muhamamd Mahtab Alam, Yannick LeMoullec, Alar Kussik, Sven Parand, Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. . 2021; ():1.
Chicago/Turabian StyleSikandar Khan; Muhamamd Mahtab Alam; Yannick LeMoullec; Alar Kussik; Sven Parand; Christos Verikoukis. 2021. "An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver." , no. : 1.
NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide area networks suitable for implementing massive machine type communications. NB-IoT features include e.g. extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly under-estimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of Radio Resource Control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base-station. Using two different commercial off the shelf NB-IoT boards and two Mobile Network Operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors for our proposed model vary between 0.33% and 15.38%.
Sikandar Khan; Muhamamd Mahtab Alam; Yannick LeMoullec; Alar Kussik; Sven Parand; Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. 2021, 1 .
AMA StyleSikandar Khan, Muhamamd Mahtab Alam, Yannick LeMoullec, Alar Kussik, Sven Parand, Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. . 2021; ():1.
Chicago/Turabian StyleSikandar Khan; Muhamamd Mahtab Alam; Yannick LeMoullec; Alar Kussik; Sven Parand; Christos Verikoukis. 2021. "An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver." , no. : 1.
High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.
Kaiser Pärnamets; Tamas Pardy; Ants Koel; Toomas Rang; Ott Scheler; Yannick Le Moullec; Fariha Afrin. Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry. Micromachines 2021, 12, 345 .
AMA StyleKaiser Pärnamets, Tamas Pardy, Ants Koel, Toomas Rang, Ott Scheler, Yannick Le Moullec, Fariha Afrin. Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry. Micromachines. 2021; 12 (3):345.
Chicago/Turabian StyleKaiser Pärnamets; Tamas Pardy; Ants Koel; Toomas Rang; Ott Scheler; Yannick Le Moullec; Fariha Afrin. 2021. "Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry." Micromachines 12, no. 3: 345.
In scenarios, like critical public safety communication networks, On-Scene Available (OSA) user equipment (UE) may be only partially connected with the network infrastructure, e.g., due to physical damages or on-purpose deactivation by the authorities. In this work, we consider multi-hop Device-to-Device (D2D) communication in a hybrid infrastructure where OSA UEs connect to each other in a seamless manner in order to disseminate critical information to a deployed command center. The challenge that we address is to simultaneously keep the OSA UEs alive as long as possible and send the critical information to a final destination (e.g., a command center) as rapidly as possible, while considering the heterogeneous characteristics of the OSA UEs. We propose a dynamic adaptation approach based on machine learning to improve a joint energy-spectral efficiency (ESE). We apply a Q-learning scheme in a hybrid fashion (partially distributed and centralized) in learner agents (distributed OSA UEs) and scheduler agents (remote radio heads or RRHs), for which the next hop selection and RRH selection algorithms are proposed. Our simulation results show that the proposed dynamic adaptation approach outperforms the baseline system by approximately 67% in terms of joint energy-spectral efficiency, wherein the energy efficiency of the OSA UEs benefit from a gain of approximately 30%. Finally, the results show also that our proposed framework with C-RAN reduces latency by approximately 50% w.r.t. the baseline.
Muhidul Islam Khan; Luca Reggiani; Muhammad Mahtab Alam; Yannick Le Moullec; Navuday Sharma; Elias Yaacoub; Maurizio Magarini. Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication. Sensors 2020, 20, 6692 .
AMA StyleMuhidul Islam Khan, Luca Reggiani, Muhammad Mahtab Alam, Yannick Le Moullec, Navuday Sharma, Elias Yaacoub, Maurizio Magarini. Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication. Sensors. 2020; 20 (22):6692.
Chicago/Turabian StyleMuhidul Islam Khan; Luca Reggiani; Muhammad Mahtab Alam; Yannick Le Moullec; Navuday Sharma; Elias Yaacoub; Maurizio Magarini. 2020. "Q-Learning Based Joint Energy-Spectral Efficiency Optimization in Multi-Hop Device-to-Device Communication." Sensors 20, no. 22: 6692.
Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.
Abdul Saboor; Triin Kask; Alar Kuusik; Muhammad Mahtab Alam; Yannick Le Moullec; Imran Khan Niazi; Ahmed Zoha; Rizwan Ahmad. Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review. IEEE Access 2020, 8, 167830 -167864.
AMA StyleAbdul Saboor, Triin Kask, Alar Kuusik, Muhammad Mahtab Alam, Yannick Le Moullec, Imran Khan Niazi, Ahmed Zoha, Rizwan Ahmad. Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review. IEEE Access. 2020; 8 (99):167830-167864.
Chicago/Turabian StyleAbdul Saboor; Triin Kask; Alar Kuusik; Muhammad Mahtab Alam; Yannick Le Moullec; Imran Khan Niazi; Ahmed Zoha; Rizwan Ahmad. 2020. "Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review." IEEE Access 8, no. 99: 167830-167864.
NarrowBand Internet of Things (NB-IoT) is an emerging cellular IoT technology that offers attractive features for deploying low-power wide area networks suitable for implementing massive machine type communications. NB-IoT features include e.g. extended coverage and deep penetration for massive connectivity, longer battery-life, appropriate throughput and desired latency at lower bandwidth. Regarding the device energy consumption, NB-IoT is mostly under-estimated for its control and signaling overheads, which calls for a better understanding of the energy consumption profiling of an NB-IoT radio transceiver. With this aim, this work presents a thorough investigation of the energy consumption profiling of Radio Resource Control (RRC) communication protocol between an NB-IoT radio transceiver and a cellular base-station. Using two different commercial off the shelf NB-IoT boards and two Mobile Network Operators (MNOs) NB-IoT test networks operational at Tallinn University of Technology, Estonia, we propose an empirical baseline energy consumption model. Based on comprehensive analyses of the profile traces from the widely used BG96 NB-IoT module operating in various states of RRC protocol, our results indicate that the proposed model accurately depicts the baseline energy consumption of an NB-IoT radio transceiver while operating at different coverage class levels. The evaluation errors for our proposed model vary between 0.33% and 15.38%.
Sikandar Khan; Muhamamd Mahtab Alam; Yannick Lemoullec; Alar Kussik; Sven Parand; Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. 2020, 1 .
AMA StyleSikandar Khan, Muhamamd Mahtab Alam, Yannick Lemoullec, Alar Kussik, Sven Parand, Christos Verikoukis. An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver. . 2020; ():1.
Chicago/Turabian StyleSikandar Khan; Muhamamd Mahtab Alam; Yannick Lemoullec; Alar Kussik; Sven Parand; Christos Verikoukis. 2020. "An Empirical Modelling for the Baseline Energy Consumption of an NB-IoT Radio Transceiver." , no. : 1.
Existing public safety networks (PSNs) are not designed to cope with disasters such as terrorist attacks, consequently leading to long delays and intolerable response times. First responders’ life threats when accessing the attacked zone are more severe in comparison to other disasters and the accuracy of basic information such as the number of terrorists, the number of trapped people, their locations and identity, etc., is vital to the reduction of the response time. Recent technologies for PSNs are designed to manage natural disaster scenarios; these are not best suited for situations like terrorist attacks because a proper communication infrastructure is required for operating most of the classical PSNs. This serious concern makes it highly desirable to develop reliable and adaptive pervasive public safety communication technologies to counter such a kind of emergency situation. Device-to-device (D2D) communication can be a vital paradigm to design PSNs that are fit for dealing with terrorist attacks thanks to long-term evolution (LTE)-sidelink, which could allow the devices that people carry with themselves in the attacked zone to communicate directly. To our best knowledge, this is the first survey paper on public safety communication in the context of terrorist attacks. We discuss PSN scenarios, architectures, 3rd generation partnership project (3GPP) standards, and recent or ongoing related projects. We briefly describe a system architecture for disseminating the critical information, and we provide an extensive literature review of the technologies that could have a significant impact in public safety scenarios especially in terrorist attacks, such as beamforming and localization for unmanned aerial vehicles (UAVs), LTE sidelink for both centralized (base-station assisted) and decentralized (without base-station) architectures, multi-hop D2D routing for PSN, and jamming and anti-jamming in mobile networks. Furthermore, we also cover the channel models available in the literature to evaluate the performance of D2D communication in different contexts. Finally, we discuss the open challenges when applying these technologies for PSN.
Ali Masood; Davide Scazzoli; Navuday Sharma; Yannick Le Moullec; Rizwan Ahmad; Luca Reggiani; Maurizio Magarini; Muhammad Mahtab Alam. Surveying pervasive public safety communication technologies in the context of terrorist attacks. Physical Communication 2020, 41, 101109 .
AMA StyleAli Masood, Davide Scazzoli, Navuday Sharma, Yannick Le Moullec, Rizwan Ahmad, Luca Reggiani, Maurizio Magarini, Muhammad Mahtab Alam. Surveying pervasive public safety communication technologies in the context of terrorist attacks. Physical Communication. 2020; 41 ():101109.
Chicago/Turabian StyleAli Masood; Davide Scazzoli; Navuday Sharma; Yannick Le Moullec; Rizwan Ahmad; Luca Reggiani; Maurizio Magarini; Muhammad Mahtab Alam. 2020. "Surveying pervasive public safety communication technologies in the context of terrorist attacks." Physical Communication 41, no. : 101109.
Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.
Collins Burton Mwakwata; Hassan Malik; Muhammad Mahtab Alam; Yannick Le Moullec; Sven Parand; Shahid Mumtaz. Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives. Sensors 2019, 19, 2613 .
AMA StyleCollins Burton Mwakwata, Hassan Malik, Muhammad Mahtab Alam, Yannick Le Moullec, Sven Parand, Shahid Mumtaz. Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives. Sensors. 2019; 19 (11):2613.
Chicago/Turabian StyleCollins Burton Mwakwata; Hassan Malik; Muhammad Mahtab Alam; Yannick Le Moullec; Sven Parand; Shahid Mumtaz. 2019. "Narrowband Internet of Things (NB-IoT): From Physical (PHY) and Media Access Control (MAC) Layers Perspectives." Sensors 19, no. 11: 2613.
The development of DORM (integrateD cOmpact naRrowband platforM) node is presented that combines low cost and low power components in a multi-layer architecture to support versatile NB-IoT applications. In our proposed DORM node, the processing component provides an interface to a variety of sensors and the radio component provides connectivity to the IoT cloud as required by IoT applications. Furthermore, an overview of our NB-IoT test network is presented that is deployed at Tallinn University of Technology (TalTech) campus, Estonia with a detailed description of its various functionality layers. In addition, an in-field investigation of the coverage of our NB-IoT system is made whereby our DORM nodes are deployed across the TalTech campus so as to explore its connectivity performance and possible issues in an indoor scenario. We made empirical measurements on NB-IoT coverage for different elevation levels, a key performance indicator that most of the operators would be interested in. We obtained averaged SNR and RSSI values in the range of 18 dB to 23 dB and -65 dBm to -70 dBm, respectively. Our obtained results show that our NB-IoT system provides an excellent connectivity in indoor environments and can satisfy IoT application requirements. However, small variations in these SNR and RSSI values are observed at different elevation levels possibly due to positioning of the measuring nodes, proximity of the antenna with respect to the base-station, building structure and its construction material and the surrounding environment of the placement of our DORM nodes.
Sikandar Zulqarnain Khan; Hassan Malik; Jeffrey Leonel Redondo Sarmiento; Muhammad Mahtab Alam; Yannick Le Moullec. DORM: Narrowband IoT Development Platform and Indoor Deployment Coverage Analysis. Procedia Computer Science 2019, 151, 1084 -1091.
AMA StyleSikandar Zulqarnain Khan, Hassan Malik, Jeffrey Leonel Redondo Sarmiento, Muhammad Mahtab Alam, Yannick Le Moullec. DORM: Narrowband IoT Development Platform and Indoor Deployment Coverage Analysis. Procedia Computer Science. 2019; 151 ():1084-1091.
Chicago/Turabian StyleSikandar Zulqarnain Khan; Hassan Malik; Jeffrey Leonel Redondo Sarmiento; Muhammad Mahtab Alam; Yannick Le Moullec. 2019. "DORM: Narrowband IoT Development Platform and Indoor Deployment Coverage Analysis." Procedia Computer Science 151, no. : 1084-1091.
Sander Ulp; Yannick Le Moullec; Muhammad Mahtab Alam. Energy-Efficient Distributed Leader Selection Algorithm for Energy-Constrained Wireless Sensor Networks. IEEE Access 2018, 7, 4410 -4421.
AMA StyleSander Ulp, Yannick Le Moullec, Muhammad Mahtab Alam. Energy-Efficient Distributed Leader Selection Algorithm for Energy-Constrained Wireless Sensor Networks. IEEE Access. 2018; 7 ():4410-4421.
Chicago/Turabian StyleSander Ulp; Yannick Le Moullec; Muhammad Mahtab Alam. 2018. "Energy-Efficient Distributed Leader Selection Algorithm for Energy-Constrained Wireless Sensor Networks." IEEE Access 7, no. : 4410-4421.
Abdul Saboor; Rizwan Ahmad; Waqas Ahmed; Adnan K. Kiani; Yannick Le Moullec; Muhammad Mahtab Alam. On Research Challenges in Hybrid Medium-Access Control Protocols for IEEE 802.15.6 WBANs. IEEE Sensors Journal 2018, 19, 8543 -8555.
AMA StyleAbdul Saboor, Rizwan Ahmad, Waqas Ahmed, Adnan K. Kiani, Yannick Le Moullec, Muhammad Mahtab Alam. On Research Challenges in Hybrid Medium-Access Control Protocols for IEEE 802.15.6 WBANs. IEEE Sensors Journal. 2018; 19 (19):8543-8555.
Chicago/Turabian StyleAbdul Saboor; Rizwan Ahmad; Waqas Ahmed; Adnan K. Kiani; Yannick Le Moullec; Muhammad Mahtab Alam. 2018. "On Research Challenges in Hybrid Medium-Access Control Protocols for IEEE 802.15.6 WBANs." IEEE Sensors Journal 19, no. 19: 8543-8555.
In this work, we investigate the possibility of employing sparse reconstruction framework for the separation of cardiac and respiratory signal components from the bioimpedance measurements. The signal decomposition is complicated by the nonstationarity of the signal and overlapping of their spectra. The signal has a harmonic structure, which is sparse in the spectral domain. We approach the problem by considering a dictionary with integrated wideband elements describing spectral components of the considered signal. The parameter estimation task is solved by the means of sparse reconstruction where solving the optimization problem returns a sparse vector of relevant dictionary atoms.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21844
Maksim Butsenko; Olev Martens; Andrei Krivošei; Yannick Le Moullec. Sparse Reconstruction Method for Separating Cardiac and Respiratory Components from Electrical Bioimpedance Measurements. Elektronika ir Elektrotechnika 2018, 24, 57-61 .
AMA StyleMaksim Butsenko, Olev Martens, Andrei Krivošei, Yannick Le Moullec. Sparse Reconstruction Method for Separating Cardiac and Respiratory Components from Electrical Bioimpedance Measurements. Elektronika ir Elektrotechnika. 2018; 24 (5):57-61.
Chicago/Turabian StyleMaksim Butsenko; Olev Martens; Andrei Krivošei; Yannick Le Moullec. 2018. "Sparse Reconstruction Method for Separating Cardiac and Respiratory Components from Electrical Bioimpedance Measurements." Elektronika ir Elektrotechnika 24, no. 5: 57-61.
Faisal Ahmed; Corentin Kervadec; Yannick Le Moullec; Gert Tamberg; Paul Annus. Autonomous Wireless Sensor Networks: Implementation of Transient Computing and Energy Prediction for Improved Node Performance and Link Quality. The Computer Journal 2018, 62, 820 -837.
AMA StyleFaisal Ahmed, Corentin Kervadec, Yannick Le Moullec, Gert Tamberg, Paul Annus. Autonomous Wireless Sensor Networks: Implementation of Transient Computing and Energy Prediction for Improved Node Performance and Link Quality. The Computer Journal. 2018; 62 (6):820-837.
Chicago/Turabian StyleFaisal Ahmed; Corentin Kervadec; Yannick Le Moullec; Gert Tamberg; Paul Annus. 2018. "Autonomous Wireless Sensor Networks: Implementation of Transient Computing and Energy Prediction for Improved Node Performance and Link Quality." The Computer Journal 62, no. 6: 820-837.
In the past decade, the energy needs in WBANs have increased due to more information to be processed, more data to be transmitted and longer operational periods. On the other hand, battery technologies have not improved fast enough to cope with these needs. Thus, miniaturized energy harvesting technologies are increasingly used to complement the batteries in WBANs. However, this brings uncertainties in the system since the harvested energy varies a lot during the node operation. It has been shown that reinforcement learning algorithms can be used to manage the energy in the nodes since they are able to make decisions under uncertainty. But the efficiency of these algorithms depends on their reward function. In this paper we explore different reward functions and seek to identify the most suitable variables to use in such functions to obtain the expected behavior. Experimental results with four different reward functions illustrate how the choice thereof impacts the energy consumption of the nodes.
Yohann Rioual; Yannick Le Moullec; Johann Laurent; Muhidul Islam Khan; Jean-Philippe Diguet. Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management. 2018 16th Biennial Baltic Electronics Conference (BEC) 2018, 1 -4.
AMA StyleYohann Rioual, Yannick Le Moullec, Johann Laurent, Muhidul Islam Khan, Jean-Philippe Diguet. Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management. 2018 16th Biennial Baltic Electronics Conference (BEC). 2018; ():1-4.
Chicago/Turabian StyleYohann Rioual; Yannick Le Moullec; Johann Laurent; Muhidul Islam Khan; Jean-Philippe Diguet. 2018. "Reward Function Evaluation in a Reinforcement Learning Approach for Energy Management." 2018 16th Biennial Baltic Electronics Conference (BEC) , no. : 1-4.
The vision of Internet of Things (IoT) is to enable systems across the globe to share data using advanced communication technologies. With the recent technological advancements, IoT-based solutions are no longer a challenging vision. IoT will offer numerous, and potentially revolutionary, benefits to today’s digital world. Future personalized and connected heathcare is one of the promising area to see the benefits of IoT. This paper surveys emerging healthcare applications, including detailed technical aspects required for the realization of a complete endto- end solution for each application. The survey explores the key application-specific requirements from the perspective of communication technologies. Furthermore, a detailed exploration from the existing to the emerging technologies and standards that would enable such applications is presented, highlighting the critical consideration of short-range and long-range communications. Finally, the survey highlights important open research challenges and issues specifically related to IoT-based future healthcare systems.
Muhammad Mahtab Alam; Hassan Malik; Muhidul Islam Khan; Tamas Pardy; Alar Kuusik; Yannick Le Moullec. A Survey on the Roles of Communication Technologies in IoT-Based Personalized Healthcare Applications. IEEE Access 2018, 6, 36611 -36631.
AMA StyleMuhammad Mahtab Alam, Hassan Malik, Muhidul Islam Khan, Tamas Pardy, Alar Kuusik, Yannick Le Moullec. A Survey on the Roles of Communication Technologies in IoT-Based Personalized Healthcare Applications. IEEE Access. 2018; 6 (99):36611-36631.
Chicago/Turabian StyleMuhammad Mahtab Alam; Hassan Malik; Muhidul Islam Khan; Tamas Pardy; Alar Kuusik; Yannick Le Moullec. 2018. "A Survey on the Roles of Communication Technologies in IoT-Based Personalized Healthcare Applications." IEEE Access 6, no. 99: 36611-36631.
In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90–94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.
Faisal Ahmed; Gert Tamberg; Yannick Le Moullec; Paul Annus. Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes. Sensors 2018, 18, 1105 .
AMA StyleFaisal Ahmed, Gert Tamberg, Yannick Le Moullec, Paul Annus. Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes. Sensors. 2018; 18 (4):1105.
Chicago/Turabian StyleFaisal Ahmed; Gert Tamberg; Yannick Le Moullec; Paul Annus. 2018. "Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes." Sensors 18, no. 4: 1105.
Narrowband Internet of Things (NB-IoT) is the prominent technology that fits the requirements of future IoT networks. However, due to the limited spectrum (i.e., 180 kHz) availability for NB-IoT systems, one of the key issues is how to efficiently use these resources to support massive IoT devices? Furthermore, in NB-IoT, to reduce the computation complexity and to provide coverage extension, the concept of time offset and repetition has been introduced. Considering these new features, the existing resource management schemes are no longer applicable. Moreover, the allocation of frequency band for NB-IoT within LTE band, or as a standalone, might not be synchronous in all the cells, resulting in intercell interference (ICI) from the neighboring cells' LTE users or NB-IoT users (synchronous case). In this paper, first a theoretical framework for the upper bound on the achievable data rate is formulated in the presence of control channel and repetition factor. From the conducted analysis, it is shown that the maximum achievable data rates are 89.2 Kbps and 92 Kbps for downlink and uplink, respectively. Second, we propose an interference aware resource allocation for NB-IoT by formulating the rate maximization problem considering the overhead of control channels, time offset, and repetition factor. Due to the complexity of finding the globally optimum solution of the formulated problem, a sub-optimal solution with an iterative algorithm based on cooperative approaches is proposed. The proposed algorithm is then evaluated to investigate the impact of repetition factor, time offset and ICI on the NB-IoT data rate, and energy consumption. Furthermore, a detailed comparison between the non-cooperative, cooperative, and optimal scheme (i.e., no repetition) is also presented. It is shown through the simulation results that the cooperative scheme provides up to 8% rate improvement and 17% energy reduction as compared with the non-cooperative scheme.
Hassan Malik; Haris Pervaiz; Muhammad Mahtab Alam; Yannick Le Moullec; Alar Kuusik; Muhammad Ali Imran. Radio Resource Management Scheme in NB-IoT Systems. IEEE Access 2018, 6, 15051 -15064.
AMA StyleHassan Malik, Haris Pervaiz, Muhammad Mahtab Alam, Yannick Le Moullec, Alar Kuusik, Muhammad Ali Imran. Radio Resource Management Scheme in NB-IoT Systems. IEEE Access. 2018; 6 ():15051-15064.
Chicago/Turabian StyleHassan Malik; Haris Pervaiz; Muhammad Mahtab Alam; Yannick Le Moullec; Alar Kuusik; Muhammad Ali Imran. 2018. "Radio Resource Management Scheme in NB-IoT Systems." IEEE Access 6, no. : 15051-15064.
Smart urban drainage systems (SUDS) with real-time control are considered to be one of the prominent approaches to tackle the impact of climate change which results in heavy rainfall and floods in urban areas. However, the lack of reliable and efficient communication with sensors buried underground is considered as one of the main deficiencies preventing ubiquitous application of SUDS. With the recent developments of new noise insensitive technologies for ultra-narrow band (UNB) and chirp spread spectrum (CSS) communication, introducing new low-power long-range communication technologies (e.g. NB-IoT, LoRa), implementation of such drainage systems is becoming feasible. This paper presents a comparative study of NB-IoT and LoRa and evaluate the potential of these technologies to be used for SUDS. The study is conducted in different underground scenarios such as dry and damp conditions to highlight the potential benefits offered by LoRa and NB-IoT in terms of coverage, information rate, and energy consumption. It has been shown through the simulation results that NB-IoT offers capillary coverage with a high level of scalability as compared to LoRa. Furthermore, NB-IoT also provide high information rate and low energy consumption and is best suited for underground scenarios.
Hassan Malik; Nils Kandler; Muhammad Mahtab Alam; Ivar Annus; Yannick Le Moullec; Alar Kuusik. Evaluation of low power wide area network technologies for smart urban drainage systems. 2018 IEEE International Conference on Environmental Engineering (EE) 2018, 1 -5.
AMA StyleHassan Malik, Nils Kandler, Muhammad Mahtab Alam, Ivar Annus, Yannick Le Moullec, Alar Kuusik. Evaluation of low power wide area network technologies for smart urban drainage systems. 2018 IEEE International Conference on Environmental Engineering (EE). 2018; ():1-5.
Chicago/Turabian StyleHassan Malik; Nils Kandler; Muhammad Mahtab Alam; Ivar Annus; Yannick Le Moullec; Alar Kuusik. 2018. "Evaluation of low power wide area network technologies for smart urban drainage systems." 2018 IEEE International Conference on Environmental Engineering (EE) , no. : 1-5.
Mobile devices are an intrinsic part of the Internet of Things (IoT) paradigm. Device-to-device (D2D) communication is emerging as one of the viable solutions for the radio resource optimization in an IoT infrastructure. However, it also comes with the challenges associated with power allocation as it causes severe interference by reusing the spectrum with the cellular users in an underlay model. Therefore, efficient techniques are required to reduce the interference with proper power allocation. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive power allocation in D2D communication which helps to provide better system throughput as well as D2D throughput with less interference. We perform cooperation by sharing the value function between devices and incorporating a neighboring factor. We design our states for reinforcement learning with appropriate application-defined variables which provide a longer observation space. We compare our work with the existing distributed reinforcement learning method and random allocation of resources. Simulation results show that the proposed algorithm outperforms the distributed reinforcement learning and the random allocation both in terms of overall system throughput as well as D2D throughput by adaptive power allocation.
Muhidul Islam Khan; Muhammad Mahtab Alam; Yannick Le Moullec; Elias Yaacoub. Cooperative reinforcement learning for adaptive power allocation in device-to-device communication. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) 2018, 476 -481.
AMA StyleMuhidul Islam Khan, Muhammad Mahtab Alam, Yannick Le Moullec, Elias Yaacoub. Cooperative reinforcement learning for adaptive power allocation in device-to-device communication. 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). 2018; ():476-481.
Chicago/Turabian StyleMuhidul Islam Khan; Muhammad Mahtab Alam; Yannick Le Moullec; Elias Yaacoub. 2018. "Cooperative reinforcement learning for adaptive power allocation in device-to-device communication." 2018 IEEE 4th World Forum on Internet of Things (WF-IoT) , no. : 476-481.