Energy conservation is crucial in wireless ad hoc sensor network design to increase network lifetime. Since communication consumes a major part of the energy used by a sensor node, efficient communication is important. Topology control aims at achieving more efficient communication by dropping links and reducing interference among simultaneous transmissions by adjusting the nodes’ transmission power. Since dropping links make a network more susceptible to node failure, a fundamental problem in wireless sensor networks is to find a communication graph with minimum interference and minimum power assignment aiming at an induced topology that can satisfy fault-tolerant properties. In this paper, we examine and propose linear integer programming formulations and a hybrid meta-heuristic GRASP/VNS (Greedy Randomized Adaptive Search Procedure/Variable Neighborhood Search) to determine the transmission power of each node while maintaining a fault-tolerant network and simultaneously minimize the interference and the total power consumption. Optimal biconnected topologies for moderately sized networks with minimum interference and minimum power are obtained using a commercial solver. We report computational simulations comparing the integer programming formulations and the GRASP/VNS, and evaluate the effectiveness of three meta-heuristics in terms of the tradeoffs between computation time and solution quality. We show that the proposed meta-heuristics are able to find good solutions for sensor networks with up to 400 nodes and that the GRASP/VNS was able to systematically find the best lower bounds and optimal solutions.
DOCUMENT
Reliability is a constraint of low-power wireless connectivity, commonly addressed by the deployment of mesh topology. Accordingly, power consumption becomes a major concern during the design and implementation of such networks. Thus, a mono-objective optimization was implemented in this work to decrease the total amount of power consumed by a low-power wireless mesh network based on Thread protocol. Using a genetic algorithm, the optimization procedure takes into account a pre-defined connectivity matrix, in which the possible distances between all network devices are considered. The experimental proof-of-concept shows that a mean gain of 26.45 dB is achievable in a specific scenario. Through our experimental results, we conclude that the Thread mesh protocol has much leeway to meet the low-power consumption requirement of wireless sensor networks.
LINK
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
Organ-on-a-chip technology holds great promise to revolutionize pharmaceutical drug discovery and development which nowadays is a tremendously expensive and inefficient process. It will enable faster, cheaper, physiologically relevant, and more reliable (standardized) assays for biomedical science and drug testing. In particular, it is anticipated that organ-on-a-chip technology can substantially replace animal drug testing with using the by far better models of true human cells. Despite this great potential and progress in the field, the technology still lacks standardized protocols and robust chip devices, which are absolutely needed for this technology to bring the abovementioned potential to fruition. Of particular interest is heart-on-a-chip for drug and cardiotoxicity screening. There is presently no preclinical test system predicting the most important features of cardiac safety accurately and cost-effectively. The main goal of this project is to fabricate standardized, robust generic heart-on-a-chip demonstrator devices that will be validated and further optimized to generate new physiologically relevant models to study cardiotoxicity in vitro. To achieve this goal various aspects will be considered, including (i) the search for alternative chip materials to replace PDMS, (ii) inner chip surface modification and treatment (chemistry and topology), (iii) achieving 2D/3D cardiomyocyte (long term) cell culture and cellular alignment within the chip device, (iv) the possibility of integrating in-line sensors in the devices and, finally, (v) the overall chip design. The achieved standardized heart-on-a-chip technology will be adopted by pharmaceutical industry. This proposed project offers a unique opportunity for the Netherlands, and Twente in particular, which has relevant expertise, potential, and future perspective in this field as it hosts world-leading companies pioneering various core aspects of the technology that are relevant for organs-on-chips, combined with two world-leading research institutes within the University of Twente.
