Anomaly detection is a key factor in the processing of large amounts of sensor data from Wireless Sensor Networks (WSN). Efficient anomaly detection algorithms can be devised performing online node-local computations and reducing communication overhead, thus improving the use of the limited hardware resources. This work introduces a fixed-point embedded implementation of Online Sequential Extreme Learning Machine (OS-ELM), an online learning algorithm for Single Layer Feed forward Neural Networks (SLFN). To overcome the stability issues introduced by the fixed precision, we apply correction mechanisms previously proposed for Recursive Least Squares (RLS). The proposed implementation is tested extensively with generated and real-world datasets, and compared with RLS, Linear Least Squares Estimation, and a rule-based method as benchmarks. The methods are evaluated on the prediction accuracy and on the detection of anomalies. The experimental results demonstrate that fixed-point OS-ELM can be successfully implemented on resource-limited embedded systems, with guarantees of numerical stability. Furthermore, the detection accuracy of fixed-point OS-ELM shows better generalization properties in comparison with, for instance, fixed-point RLS. © 2013 IEEE.
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.
Existing research on the recognition of Activities of Daily Living (ADL) from simple sensor networks assumes that only a single person is present in the home. In real life there will be situations where the inhabitant receives visits from family members or professional health care givers. In such cases activity recognition is unreliable. In this paper, we investigate the problem of detecting multiple persons in an environment equipped with a sensor network consisting of binary sensors. We conduct a real-life experiment for detection of visits in the oce of the supervisor where the oce is equipped with a video camera to record the ground truth. We collected data during two months and used two models, a Naive Bayes Classier and a Hidden Markov Model for a visitor detection. An evaluation of these two models shows that we achieve an accuracy of 83% with the NBC and an accuracy of 92% with a HMM, respectively.
MULTIFILE
This project aims to develop a measurement tool to assess the inclusivity of experiences for people with varying challenges and capabilities on the auditory spectrum. In doing so, we performed an in-depth exploration of scientific literature and findings from previous projects by Joint Projects. Based on this, we developed an initial conceptual model that focuses on sensory perception, emotion, cognition, and e[ort in relation to hearing and fatigue. Within, this model a visitor attraction is seen as an “experienscape” with four key elements: content, medium, context, and individual. In co-creative interviews with experts by experience with varying challenges on the auditory spectrum, they provided valuable insights that led to a significant expansion of this initial model. This was a relevant step, as in the scientific and professional literature, little is known about the leisure experiences of people with troubled hearing. For example, personal factors such as a person’s attitude toward their own hearing loss and the social dynamics within their group turned out to greatly influence the experience. The revised model was then applied in a case study at Apenheul, focusing on studying differences in experience of their gorilla presentation amongst people with varying challenges on the auditory spectrum.Societal issueThe Netherlands is one of the countries in Europe with the highest density of visitor attractions. Despite this abundance, many visitor attractions are not fully accessible to everyone, particularly to visitors with disabilities who sometimes are not eligible to ride due to safety concerns, yet when eligible generally still encounter numerous barriers. Accessibility of visitor attractions can be approached in various ways. However, because the focus often lies on operational and technical aspects (e.g., reducing stimuli at certain times of the day by turning o[ music, o[ering alternative wheelchair entrances), strategic and community-focused approaches are often overlooked. More importantly, there is also a lack of attention to the experience of visitors with disabilities. This becomes apparent from several studies from Joint Projects, where visitor attractions are being visited together with experts by experience with various disabilities. Nevertheless, experience is often being regarded as the 'core product' of the leisure sector. The right to meet, discover, develop, relax and thus enjoy this core product is hindered for many people with disabilities due to a lack of knowledge, inaccessibility (physical, digital, social, communicative as well as financial) and discrimination in society. Additionally, recreation entrepreneurs still face a significant gap in reaching the potential market of guests with disabilities and their networks. Thus, despite the numerous initiatives in the leisure sector aimed at improving accessibility on technical and operational fronts, often people with disabilities are still not being able to experience the same kind of enjoyment as those without. These observations form the pressing impetus for initiating the current research project, tapping into the numerous opportunities for learning, development and growth on making leisure offer more inclusive.Benefit to societyIn total, the current project approach comes with a number of enrichments in terms of both knowledge and methodology: a mixed-methods approach that allows for comparing data from different sources to obtain a more complete picture of the experience; a methodological co-design process that honours the 'nothing about us without us' principle; and benchmarking for a group (i.e., people with challenges on the auditory spectrum) that despite the size of its population has thus far mostly been overlooked.
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.
