Estimating accurate radio maps is important for various tasks in wireless communications, such as... more Estimating accurate radio maps is important for various tasks in wireless communications, such as channel modeling, resource allocation, and network planning, to name a few. Due to the changes in the propagation characteristics of the wireless environments, a radio map model learned under a particular wireless environment cannot be directly used in a new wireless environment. Moreover, learning a new model for every environment requires, in general, a large amount of data and is computationally demanding. In this work, we design an effective novel data-driven transfer learning method that transfers and fine-tunes a deep neural network (DNN)-based radio map model learned from an original wireless environment to other wireless environments with a certain level of similarity, allowing the radio map to be estimated with less amount of training data. As opposed to other widely used similarity measures that do not take into account the wireless propagation characteristics, we design a dat...
An online topology estimation algorithm for nonlinear structural equation models (SEM) is propose... more An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corrob...
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
In many applications involving wireless sensor networks (WSNs), the observed data can be modeled ... more In many applications involving wireless sensor networks (WSNs), the observed data can be modeled as signals defined over graphs. As a consequence, an increasing interest has been witnessed to develop new methods to analyze graph signals, leading to the emergence of the field of Graph Signal Processing. One of the most important processing tools in this field is graph filters, which can be easily implemented distributedly over networks by means of cooperation among the nodes. Most of previous works related to graph filters assume the same connection probability in both link directions when transmitting an information between two neighboring nodes. This assumption is not realistic in practice due to the typical random link asymmetry in WSNs caused by interferences and background noise. This paper proposes solutions to cope with the problem of graph filtering of noisy time-varying input graph signals over random time-varying asymmetric WSNs. We design an extension to node-variant graph filters that can provide a trade-off between the expected error and variance, by optimizing the filter coefficients adaptively, resulting in an accurate graph filtering. Numerical experiments carried out over different random WSNs illustrate the efficiency of the proposed solutions.
2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
In this paper, we present the design and prototype implementation of self-powered Internet of Thi... more In this paper, we present the design and prototype implementation of self-powered Internet of Things (IoT) device based on energy harvesting from a small solar panel of size 63mm x 63mm and 0.36W for remote applications. These IoT devices can be deployed in remote places within the range of a gateway. A complete proof of concept IoT device based on ambient energy harvesting is designed, prototyped and tested with super capacitors and Lithium cells in star topology. Based on the measurements, the IoT device can potentially last for one year with 55 seconds transmission interval with the fully charged 120mAh coin cell battery. On the other hand, a fully charged single 5F supercapacitor lasts for 37 hours with the transmission interval of 55 seconds. The range of the node was measured to be 1.8 km with coded transmissions. The node is equipped with ultra-low power sensors for temperature, humidity and light levels, and several more sensors can easily be integrated.
Proceedings of the Northern Lights Deep Learning Workshop, 2022
Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other... more Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the machine learning life-cycle with industrial time-series data, i.e. data acquisition, pre-processing, feature engineering, architecture selection, training, and model verification.
There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computati... more There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.
Most recent works in device-to-device (D2D) underlay communications focus on the optimization of ... more Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-stateinformation (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentrali...
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017
In this paper, we developed a software defined radio (SDR) system for implementing energy detecti... more In this paper, we developed a software defined radio (SDR) system for implementing energy detection spectrum sensing. The SDR module can be used for a wide range of applications. The use of the SDR module is motivated by its high interoperability, availability for relatively cheaper prices and being software independent. Energy detection for cognitive radios is chosen for its simplicity and popularity. However, it is chosen as a representative for a very wide range of measurements and algorithms that can be implemented in the SDR. We have used probabilities of detection and false alarm with the receiver operating characteristics (ROC) curves as performance metrics for the implemented energy detector. We have benchmarked our findings with an energy detector implemented on a Universal Software Radio Peripheral from National Instruments (NI-USRP). Measurements results for the SDR module are in close agreement with the results from NI-USRP using the same setup and also with the theoreti...
ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020
Robust beamforming is an efficient technique to guarantee the desired receiver performance in the... more Robust beamforming is an efficient technique to guarantee the desired receiver performance in the presence of erroneous channel state information (CSI). However, the application of robust beamforming in underlay device-to-device (D2D) communication still requires further investigation. In this paper, we investigate resource allocation problem for underlay D2D communications by considering multiple antennas at the base station (BS) and at the transmitters of D2D pairs. The proposed design problem aims at maximizing the aggregate rate of all D2D pairs and cellular users (CUs) in downlink spectrum. In addition, our objective is augmented to achieve a fair allocation of resources across the D2D pairs. Further, assuming elliptically bounded CSI errors, the formulation ensures maintaining signal to interference plus noise ratio (SINR) above a specified threshold. The derived optimization problem results in a mixed integer non-convex problem and requires exponential complexity to obtain th...
Estimating accurate radio maps is important for various tasks in wireless communications, such as... more Estimating accurate radio maps is important for various tasks in wireless communications, such as channel modeling, resource allocation, and network planning, to name a few. Due to the changes in the propagation characteristics of the wireless environments, a radio map model learned under a particular wireless environment cannot be directly used in a new wireless environment. Moreover, learning a new model for every environment requires, in general, a large amount of data and is computationally demanding. In this work, we design an effective novel data-driven transfer learning method that transfers and fine-tunes a deep neural network (DNN)-based radio map model learned from an original wireless environment to other wireless environments with a certain level of similarity, allowing the radio map to be estimated with less amount of training data. As opposed to other widely used similarity measures that do not take into account the wireless propagation characteristics, we design a dat...
An online topology estimation algorithm for nonlinear structural equation models (SEM) is propose... more An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature approximation. The online learning strategy uses a group-lasso-based optimization framework with a prediction-corrections technique that accounts for the model evolution. The proposed approach has three properties of interest. First, it enjoys node-separable learning, which allows for scalability in large networks. Second, it offers privacy in SEM learning by replacing the actual data with node-specific random features. Third, its performance can be characterized theoretically via a dynamic regret analysis, showing that it is possible to obtain a linear dynamic regret bound under mild assumptions. Numerical results with synthetic and real data corrob...
2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
In many applications involving wireless sensor networks (WSNs), the observed data can be modeled ... more In many applications involving wireless sensor networks (WSNs), the observed data can be modeled as signals defined over graphs. As a consequence, an increasing interest has been witnessed to develop new methods to analyze graph signals, leading to the emergence of the field of Graph Signal Processing. One of the most important processing tools in this field is graph filters, which can be easily implemented distributedly over networks by means of cooperation among the nodes. Most of previous works related to graph filters assume the same connection probability in both link directions when transmitting an information between two neighboring nodes. This assumption is not realistic in practice due to the typical random link asymmetry in WSNs caused by interferences and background noise. This paper proposes solutions to cope with the problem of graph filtering of noisy time-varying input graph signals over random time-varying asymmetric WSNs. We design an extension to node-variant graph filters that can provide a trade-off between the expected error and variance, by optimizing the filter coefficients adaptively, resulting in an accurate graph filtering. Numerical experiments carried out over different random WSNs illustrate the efficiency of the proposed solutions.
2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
In this paper, we present the design and prototype implementation of self-powered Internet of Thi... more In this paper, we present the design and prototype implementation of self-powered Internet of Things (IoT) device based on energy harvesting from a small solar panel of size 63mm x 63mm and 0.36W for remote applications. These IoT devices can be deployed in remote places within the range of a gateway. A complete proof of concept IoT device based on ambient energy harvesting is designed, prototyped and tested with super capacitors and Lithium cells in star topology. Based on the measurements, the IoT device can potentially last for one year with 55 seconds transmission interval with the fully charged 120mAh coin cell battery. On the other hand, a fully charged single 5F supercapacitor lasts for 37 hours with the transmission interval of 55 seconds. The range of the node was measured to be 1.8 km with coded transmissions. The node is equipped with ultra-low power sensors for temperature, humidity and light levels, and several more sensors can easily be integrated.
Proceedings of the Northern Lights Deep Learning Workshop, 2022
Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other... more Aquaponic systems provide a reliable solution to grow vegetables while cultivating fish (or other aquatic organisms) in a controlled environment. The main advantage of these systems compared with traditional soil-based agriculture and aquaculture installations is the ability to produce fish and vegetables with low water consumption. Aquaponics requires a robust control system capable of optimizing fish and plant growth while ensuring a safe operation. To support the control system, this work explores the design process of Deep Learning models based on Recurrent Neural Networks to forecast one hour of pH values in small-scale industrial Aquaponics. This implementation guides us through the machine learning life-cycle with industrial time-series data, i.e. data acquisition, pre-processing, feature engineering, architecture selection, training, and model verification.
There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computati... more There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.
Most recent works in device-to-device (D2D) underlay communications focus on the optimization of ... more Most recent works in device-to-device (D2D) underlay communications focus on the optimization of either power or channel allocation to improve the spectral efficiency, and typically consider uplink and downlink separately. Further, several of them also assume perfect knowledge of channel-stateinformation (CSI). In this paper, we formulate a joint uplink and downlink resource allocation scheme, which assigns both power and channel resources to D2D pairs and cellular users in an underlay network scenario. The objective is to maximize the overall network rate while maintaining fairness among the D2D pairs. In addition, we also consider imperfect CSI, where we guarantee a certain outage probability to maintain the desired quality-of-service (QoS). The resulting problem is a mixed integer non-convex optimization problem and we propose both centralized and decentralized algorithms to solve it, using convex relaxation, fractional programming, and alternating optimization. In the decentrali...
2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017
In this paper, we developed a software defined radio (SDR) system for implementing energy detecti... more In this paper, we developed a software defined radio (SDR) system for implementing energy detection spectrum sensing. The SDR module can be used for a wide range of applications. The use of the SDR module is motivated by its high interoperability, availability for relatively cheaper prices and being software independent. Energy detection for cognitive radios is chosen for its simplicity and popularity. However, it is chosen as a representative for a very wide range of measurements and algorithms that can be implemented in the SDR. We have used probabilities of detection and false alarm with the receiver operating characteristics (ROC) curves as performance metrics for the implemented energy detector. We have benchmarked our findings with an energy detector implemented on a Universal Software Radio Peripheral from National Instruments (NI-USRP). Measurements results for the SDR module are in close agreement with the results from NI-USRP using the same setup and also with the theoreti...
ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020
Robust beamforming is an efficient technique to guarantee the desired receiver performance in the... more Robust beamforming is an efficient technique to guarantee the desired receiver performance in the presence of erroneous channel state information (CSI). However, the application of robust beamforming in underlay device-to-device (D2D) communication still requires further investigation. In this paper, we investigate resource allocation problem for underlay D2D communications by considering multiple antennas at the base station (BS) and at the transmitters of D2D pairs. The proposed design problem aims at maximizing the aggregate rate of all D2D pairs and cellular users (CUs) in downlink spectrum. In addition, our objective is augmented to achieve a fair allocation of resources across the D2D pairs. Further, assuming elliptically bounded CSI errors, the formulation ensures maintaining signal to interference plus noise ratio (SINR) above a specified threshold. The derived optimization problem results in a mixed integer non-convex problem and requires exponential complexity to obtain th...
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