Alarm Management Research Articles

SCADA Master Planning for Water and Wastewater Utilities

Key TakeawaysSupervisory control and data acquisition (SCADA) provides water utilities with data for regulatory compliance, monitoring and alarm management of life safety systems, and protecting public health.Utilities often make the mistake of taking a passive approach to managing a SCADA system, not giving it the special attention and detailed planning it requires to stay current.A SCADA master plan can fit any organization's size, complexity, and budget.

Robust alarm design strategy for medical devices: Application to air-in-line detection and occlusion management

Alarm fatigue in the hospital environment is a recurring problem that can be solved through technical, human, or organizational considerations. The use of technical factors to get a robust alarm management system is illustrated here by two case studies related to air-in-line detection in peritoneal dialysis and insulin pump occlusion management. Air-in-line sensors are usually very sensitive to the presence of small bubbles stuck on tubing and may therefore deliver false alarms. To provide a reliable estimate of the cumulated air volume pumped an IR sensor technology that is insensitive to small bubbles was implemented and tested. Another strategy was used to limit false occlusion alarms in an insulin patch pump. The piezoelectric actuator of this micropump was servo-controlled to the pressure sensor signal to allow insulin to flow into the patient despite the presence of a partial occlusion. The false alarm rate is thus reduced by using a self-adaptive occlusion detection threshold that has been evaluated using numerical simulations.

Ten Years Later, Alarm Fatigue Is Still a Safety Concern.

Ten years after the publication of a landmark article in AACN Advanced Critical Care, alarm fatigue continues to be an issue that researchers, clinicians, and organizations aim to remediate. Alarm fatigue contributes to missed alarms and medical errors that result in patient death, increased clinical workload and burnout, and interference with patient recovery. Led by the American Association of Critical-Care Nurses, national patient safety organizations continue to prioritize efforts to battle alarm fatigue and have proposed alarm management strategies to mitigate the effects of alarm fatigue. Similarly, clinical efforts now use simulation studies, individualized alarm thresholds, and interdisciplinary teams to optimize alarm use. Finally, engineering research efforts have innovated the standard alarm to convey information more effectively for medical users. By focusing on patient and provider safety, clinical workflow, and alarm technology, efforts to reduce alarm fatigue over the past 10 years have been grounded in an evidence-based and personnel-focused approach.

The effects of alarm fatigue on the tendency to make medical errors in nurses working in intensive care units.

Alarm fatigue resulting from exposure to multiple alarms is an important problem that threatens patient safety. The fact that each device in intensive care units works with different alarm systems increases the number and variety of alarms. The aim of this study was to determine the effects of alarm fatigue on the tendency of nurses working in intensive care units to make medical errors. A cross-sectional and correlational design were used in this study. The study was carried out with 382 intensive care nurses who could be reached via an electronic questionnaire. Data were collected using a 'Personal Information Form', the 'Alarm Fatigue Scale (AFS)' and the 'Medical Error Tendency Scale in Nursing (METSN)'. The mean age of the nurses who were included in the study was 31.52 ± 5.66. While 70.2% of the participants were women, 67% had bachelor's degrees, and 65.4% had been working in the intensive care unit for 1-5 years. The mean total METSN score of the participants was 229.29 ± 15.32, and their mean total AFS score was 20.02 ± 6.15. A negative and weak significant correlation was found between the total mean AFS and METSN scores of the participants (r = -0.275; p < .001). As the alarm fatigue levels of the participants increased, their medical error tendencies increased. It was determined that a one-unit increase in the alarm fatigue level of intensive care nurses increased their tendency to make medical errors by 0.263 units (p < .001). It was found that the nurses had a low tendency to make medical errors and moderate levels of alarm fatigue, and an increase in their alarm fatigue levels significantly increased their medical error tendencies. Institutions should establish alarm management procedures in units with multiple alarm systems, such as intensive care units, and examine the effects of alarms on employees.

Nurses’ alarm fatigue, influencing factors, and its relationship with burnout in the critical care units: A cross-sectional study

BackgroundExcessive number of alarms and false and nonactionable alarms may lead to alarm fatigue. Alarm fatigue could easily contribute to burnout. Burnout may reduce nurses' sensitivity to alarms, thus affecting patients' safety due to insufficient response to the alarms. However, no study has examined nurses’ alarm fatigue in Ghana. ObjectivesThe objective of this study was to investigate the level of alarm fatigue and its associated factors, as well as determine its relationship with burnout among nurses working in the critical care units of hospitals in Ghana. MethodsThe cross-sectional study was conducted in critical care units of five hospitals in Ghana from November 2021 to January 2022. A total of 364 nurses were recruited and completed the questionnaire. Alarm fatigue was assessed by the alarm fatigue questionnaire, which was originally developed in Chinese and was translated into English using a standard protocol. Burnout was assessed using the Maslach Burnout Inventory. ResultsThe overall alarm fatigue score was 76.43 ± 27.80 out of 124. Longer years working at the critical care unit (B = −2.50, 95% confidence interval [CI]: −4.62, −0.37) and having policies related to alarm management (B = −10.77, 95% CI: −3.50, −18.04) were associated with a decreased risk of alarm fatigue, while working in neonatal intensive care unit (B = 16.35, 95% CI: 2.48, 30.21) and postanesthesia care unit (B = 15.16; 95% CI: 0.32, 30.01), and having anxiety and stress (B = 8.15, 95% CI: 1.30, 15.00) were associated with an increased risk of alarm fatigue. In addition, alarm fatigue was positively associated with emotional exhaustion (r = 0.52, P < 0.001) and depersonalisation (r = 0.43, P < 0.001) but not personal accomplishment (r = −0.09, P = 0.100). ConclusionCritical care nurses in Ghana experienced higher levels of alarm fatigue, which is affected by multiple factors. There is a significant link between nurses' alarm fatigue and burnout. Our findings provide important guidance for future intervention programs to improve critical care nurses' alarm fatigue by introducing policies on alarm management and improving nurses’ psychological health, with a special focus on nurses with shorter working years and working in neonatal intensive care unit and postanesthesia care unit.

MFM-based alarm root-cause analysis and ranking for nuclear power plants

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

The Alarm Fatigue Challenge in the Neonatal Intensive Care Unit: A "before" and "after" Study.

Alarm fatigue (AF) happens when professionals are exposed to many alarms and they become desensitized to them. It is related to proliferation of devices, not standardized alarm limits, and high prevalence of "nonactionable alarms," i.e., false alarms (triggered by equipment issues) or nuisance alarms (physiological change not requiring clinical action). When AF happens, response time seems to be longer and important alarms could be dismissed. After evaluating the situation in our neonatal intensive care unit (NICU), an alarm management program (AMP) was developed to reduce AF. The objective of this study were to compare the proportion of true alarms, nonactionable alarms, and to measure response time to alarms in the NICU before and after implementing an AMP and also to determine variables associated with nonactionable alarms and response time. This was a cross-sectional study. A total of 100 observations were collected between December 2019 and January 2020. After an AMP was implemented, 100 new observations were collected between June 2021 and August 2021. We estimated the true and nonactionable alarms proportion. Univariate analyses were performed to determine variables associated with nonactionable alarms and response time. Logistic regression was performed to assess independent variables. The proportion of true alarms before and after AMP was 31 versus 57% (p = 0.001), whereas the proportion of nonactionable alarms was 69 versus 43% (p = 0.001). Median response time was significantly reduced (35 versus 12 seconds; p = 0.001). Before AMP, neonates with less intensive care needs had a higher proportion of nonactionable alarms and a longer response time. After AMP, response time was similar for true and nonactionable alarms. For both periods, the need of respiratory support was significantly associated with true alarms (p = 0.001). In the adjusted analysis, response time (p = 0.001) and respiratory support (p = 0.003) remained associated with nonactionable alarms. AF was highly prevalent in our NICU. This study shows that after the implementation of an AMP, response time to alarms and the proportion of nonactionable alarms can be significantly reduced. · AF happens when professionals are exposed to many alarms and they become desensitized to them.. · The presence of AF can compromise patients' safety.. · The implementation of an AMP can reduce AF..

Staff perspectives on the influence of patient characteristics on alarm management in the intensive care unit: a cross-sectional survey study

BackgroundHigh rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff’s perceptions of alarm threshold individualization according to patient characteristics and disease status.MethodsThis is a cross-sectional survey study (February-July 2020). Intensive care nurses and physicians were sampled by convenience. Data was collected using an online questionnaire.ResultsStaff view the individualization of alarm thresholds in the monitoring of vital signs as important. The extent to which alarm thresholds are adapted from the normal range varies depending on the vital sign monitored, the reason for clinical deterioration, and the professional group asked. Vital signs used for hemodynamic monitoring (heart rate and blood pressure) were most subject to alarm individualizations. Staff are ambivalent regarding the integration of novel technological features into alarm management.ConclusionsAll relevant stakeholders, including clinicians, hospital management, and industry, must collaborate to establish a “standard for individualization,” moving away from ad hoc alarm management to an intelligent, data-driven alarm management. Making alarms meaningful and trustworthy again has the potential to mitigate alarm fatigue – a major cause of stress in clinical staff and considerable hazard to patient safety.Trial registrationThe study was registered at ClinicalTrials.gov (NCT03514173) on 02/05/2018.

Alarm rationalization in engineering projects: analyzing cost-saving measures and efficiency gains

Alarm rationalization is a critical process in engineering projects, focusing on optimizing alarm management systems to enhance operational efficiency, reduce costs, and improve safety. This paper explores how alarm rationalization, when implemented effectively, mitigates alarm fatigue, reduces unnecessary alerts, and ensures that operators are only notified of critical issues. By analyzing cost-saving measures and efficiency gains, the study highlights how rationalization techniques can streamline decision-making and improve system reliability across various industries, such as oil and gas, manufacturing, and chemical processing. The rationalization process involves reviewing existing alarm systems, identifying redundant or non-critical alarms, and reclassifying alarms based on priority and operational impact. By refining alarm settings, engineers can decrease operator workload and minimize downtime, contributing to significant cost savings. For example, reducing nuisance alarms not only improves response times but also extends the lifespan of equipment by preventing unnecessary shutdowns. From a global perspective, the adoption of alarm rationalization varies by industry and region, with some sectors benefiting from advanced automation technologies and standardized regulatory guidelines. The implementation of intelligent alarm systems, combined with data analytics and machine learning, further enhances the ability to predict and prevent failures before they escalate into major operational disruptions. This paper also examines case studies of successful alarm rationalization projects, demonstrating tangible efficiency gains such as reduced maintenance costs, improved safety, and optimized production processes. Additionally, the discussion extends to the challenges and best practices in alarm rationalization, such as securing stakeholder buy-in, aligning rationalization efforts with organizational goals, and ensuring compliance with international safety standards. In conclusion, alarm rationalization offers engineering projects an effective pathway to achieve cost efficiency and operational excellence. However, the success of these initiatives depends on robust planning, continuous monitoring, and the integration of advanced technologies.

Realization of Detector Control System of Physics Experiment

In order to meet the experimental requirements, modern physics experiments need to monitor a large number of indicators and give timely control commands. Considering the characteristics of large-scale physical experiments, including long-distance, various equipment and massive data, a distributed detector control system (DCS) was developed. It implements communication with non-standard hardware, data acquisition, alarm management and data archiving functions. A set of EPICS IOCs (input/output controller) is used to communicate with hardware. Because the electronics use IPbus as communication protocol, driver support is developed. The DCS implementation includes user interfaces, an alarm system, a data archiving system, and web services, among other things. At present, this DCS has been used by the experiment of Jiangmen Underground Neutrino Observatory (JUNO) to monitor the system status in electronics test. It provides the necessary reference for the technical route of large-scale monitoring.

RETRACTED ARTICLE: Research on internet financial risk control based on deep learning algorithm

With the rapid development of Internet technology, Internet finance has entered thousands of households, bringing a lot of convenience to people’s lives. As a financial model based on Internet technology, compared with traditional bank loans, online loans have lower operating costs and faster returns, so they are developing rapidly. Providing users with better and faster services while standardizing operations has always been the development goal of various Internet finance companies. At present, many domestic Internet financial enterprises are facing many problems such as difficulty in risk control. Therefore, this paper makes full use of deep learning algorithms to build an Internet financial risk control system. After in-depth analysis and research on the deep learning algorithm, the Internet financial risk control system is divided into several modules. The project mainly includes model management module, user behavior analysis module, alarm management module, monitoring module, product management module, etc., and then by analyzing the test results of each test scenario, it is concluded that the performance test of the system design meets the actual needs of users, and simulates the test. It is carried out in accordance with the constraints and regulations of the test plan, and the performance test meets the standard. The system not only ensures the safety of the company's funds, but also helps the company to form a smooth and effective financial risk control process. This paper designs a class of effective management systems by applying deep learning algorithms to the field of Internet financial risk control.

Predicting microbial water quality in on-site water reuse systems with online sensors

Widespread implementation of on-site water reuse is hindered by the limited availability of monitoring approaches that ensure microbial quality during operation. In this study, we developed a methodology for monitoring microbial water quality in on-site water reuse systems using inexpensive and commercially available online sensors. An extensive dataset containing sensor and microbial water quality data for six of the most critical types of disruptions in membrane bioreactors with chlorination was collected. We then tested the ability of three typological machine learning algorithms – logistic regression, support-vector machine, and random forest – to predict the microbial water quality as “safe” or “unsafe” for reuse. The main criteria for model optimization was to ensure a low false positive rate (FPR) – the percentage of safe predictions when the actual condition is unsafe – which is essential to protect users health. This resulted in enforcing a fixed FPR ≤ 2%. Maximizing the true positive rate (TPR) – the percentage of safe predictions when the actual condition is safe – was given second priority. Our results show that logistic-regression-based models using only two out of the six sensors (free chlorine and oxidation–reduction potential) achieved the highest TPR. Including sensor slopes as engineered features allowed to reach similar TPRs using only one sensor instead of two. Analysis of the occurrence of false predictions showed that these were mostly early alarms, a characteristic that could be regarded as an asset in alarm management. In conclusion, the simplest algorithm in combination with only one or two sensors performed best at predicting the microbial water quality. This result provides useful insights for water quality modeling or for applications where small datasets are a common challenge and a general advantage might be gained by using simpler models that reduce the risk of overfitting, allow better interpretability, and require less computational power.

Current approach to fever of unknown origin in the intensive care unit

This study examines the effects of the interventions in alarm management in intensive care units on reducing alarms on the basis of current evidence.This systematic review and meta-analysis study was conducted between January 1, 2010 and October 31, 2021 by searching 16 databases through an independent search by three researchers. The acronym of PICOS was used and the keywords as well as inclusion/exclusion criteria were determined. The Joanna Briggs Institute meta-Analysis of Statistics Assessment and Review Instruments (JBI-MAStARI) were used to assess methodological quality. Professional meta-analysis Software (ProMeta 3) statistical software package was used for data analysis. The study is preregistered on PROSPERO (CRD42021286770).Intensive care units.The systematic review included 25 studies, whereas the meta-analysis included 15. According to the heterogeneity test in the study, alarm-reducing interventions yielded a heterogeneous distribution. The overall effect size value of all studies, which was found using the random-effects model, was determined to be −0.03 [95 % confidence interval (CI)], a value between −0.16 and 0.10 CI. Based on the results, individual studies showed that the strategies suggesting to ensure alarm safety were effective in reducing the number of alarms; the combined results of the studies in this meta-analysis showed that the interventions made to reduce alarms had too little effect.This study showed that the interventions have a very weak effect size on reducing the number of alarms.

Intensive care unit nurses' perceptions and practices regarding clinical alarms: A descriptive study.

To describe the frequencies of physiologic monitor clinical alarms and to investigate nurses' perceptions and practices regarding clinical alarms in ICUs. A descriptive study. A 24-h continuous nonparticipant observation study was conducted in ICU. Observers observed and recorded the occurrence time, detail information when electrocardiogram monitor alarms triggered. And a cross-sectional study was conducted among ICU nurses by convenience sampling, using the general information questionnaire and the Chinese version of clinical alarms survey questionnaire for medical devices. Data analysis was performed using SPSS 23. A total of 13,829 physiologic monitor clinical alarms were recorded in 14-day observation and 1191 ICU nurses responded to the survey. Most nurses agreed or strongly agreed the sensitivity to alarms and responded quickly (81.28%), smart alarm systems (74.56%), alarm notification systems (72.04%) and set up alarm administrators (59.45%) were useful to improve alarm management, while frequent nuisance alarms disrupted patients care (62.47%) and reduced nurses' trust in alarms (49.03%), environmental noise interfered with nurses' recognition of the alarms (49.12%) and not everyone received education of alarm systems (64.65%). Physiological monitor alarms occur frequently in ICU, and it is necessary to formulate or further optimize alarm management measures. It is recommended to use smart medical devices and alarm notification systems, formulate and implement standardized alarm management policies and norms, and strengthen alarm management education and training, so as to improve the nursing quality and patient safety. The patients in the observation study included all patients admitted to the ICU during the observation period. The nurses in the survey study were conveniently selected through an online survey.

The relationship between alarm fatigue and burnout among critical care nurses: A cross-sectional study.

Alarm fatigue has significant negative impacts on nurses and patient safety. However, the relationship between alarm fatigue and burnout is still unclear. This study aimed to explore the relationship between alarm fatigue and burnout among critical care nurses. A descriptive-analytical cross-sectional study design was used. Data were collected from five hospitals in mainland China between January 2022 and March 2022. A general information questionnaire, the Chinese version of the Intensive Care Unit Nurse Alarm Fatigue Questionnaire, and the Chinese version of the Maslach Burnout Inventory were used. A total of 236 critical care nurses were enrolled in this study. The mean score of alarm fatigue among critical care nurses was 21.11 ± 6.83. The results showed that critical care nurses experienced moderate alarm fatigue levels, and most nurses had moderate to high levels of burnout. The multiple linear regression analyses showed that alarm fatigue was independently associated with emotional exhaustion, depersonalization dimensions, and reduced personal accomplishment dimension. Alarm fatigue was associated with burnout among critical care nurses. Reducing critical care nurses' alarm fatigue may help to alleviate burnout. Managers should provide comprehensive training for nurses and promote the application of artificial intelligence technology in alarm management to reduce alarm fatigue and improve burnout among critical care nurses.

Leak Detection in Natural Gas Pipelines Based on Unsupervised Reconstruction of Healthy Flow Data

Summary Timely detection of leak accidents plays an essential role in the safe operation and risk assessment of natural gas pipelines. However, the scarce leak data and complex operating conditions lead to small samples, data imbalance, and problems with confusing operating conditions. The reliance on leak data limits the recognition performance of the artificial intelligence classification method for leakage operating conditions. A leak detection method based on the unsupervised reconstruction of healthy flow data is established to address these problems. First, an unsupervised neural network is established to reconstruct healthy flow data from real natural gas pipelines. And a model update strategy based on active learning is designed to improve the model’s adaptability for time-varying pipelines. Next, a dynamic alarm threshold strategy that accounts for the knowledge of the experience and statistical characteristics of the data segments is suggested to prevent false alarms caused by ambiguous operating conditions. Finally, unlike most recent work that only considers simulated data or laboratory data, this paper conducts a leak case study on an actual natural gas pipeline in service to improve the robustness of the proposed method in the actual operating environment. The findings of this paper can be used as a reference to analyze pipeline behavior analysis based on pipeline flow trend characteristics and early alarm management.

My smart home: an auto-ethnography of learning to live with smart technologies

Smart home technology is expected to be widespread in the future and to accommodate a green transition to reduce and time-shift energy consumption. However, smart technologies also have social consequences, which are important to understand. At a basic level, we need to know more about learning to live with these technologies and how they influence our everyday practices and routines. Providing in-depth longitudinal insights into these processes, this paper presents an auto-ethnography of living with smart home technology: a 20-month diary kept by one of the authors. The paper uses theories of practice to investigate details of learning processes when interacting with three selected technologies: smart alarm and lighting management, smart control of heating, and a smart electric vehicle (EV). Theories of learning have a well-established tradition within theories of practice, and the concept of “knowing how to go on” and the concept of practical intelligibility are central in this work. This paper investigates the adoption of new smart technologies and how they interact with learning processes in different material and social contexts. Such an approach can lay the groundwork for further empirical research with a broader set of materials. It can also provide knowledge to assist in the design of better technologies and in developing policies and regulations to promote this.

Implementing a Unit-Based Alarm Management Bundle for Critical Care Nurses

Clinical alarms are designed to signal an unsatisfactory patient physiological state and alert staff members to malfunctioning medical equipment. Alarm desensitization and fatigue can occur when clinicians are exposed to an overwhelming number of clinical alarms, particularly nonactionable alarms. Alarm fatigue and alarm management competency related to use of Philips monitoring systems were noted to be problematic among nurses working in the 27-bed surgical intensive care unit of a teaching hospital in northeastern Florida. A quality improvement project was conducted to reduce alarm fatigue and improve nursing competency in managing alarms. The CEASE (Communication, Electrodes, Appropriateness, Setup, and Education) evidence-based alarm management bundle was implemented on the unit, and a representative from the alarm manufacturer conducted in-service training sessions. A clinical alarms survey developed by the Healthcare Technology Foundation was distributed both before and after the intervention to 115 nurses working in the unit. Descriptive and inferential statistics were used to assess for differences between the preintervention and postintervention periods. Nurse participants demonstrated improved alarm management competency, resulting in significant improvements in their perceptions of alarm functionality, settings, response time, and policy adherence. There was a statistically significant decrease in self-reported alarm fatigue, and the CEASE bundle was found to influence nursing practice. The Joint Commission's 2022 goal of improving clinical alarm safety remains a top priority nationwide. Implementation of the CEASE alarm management bundle was effective in reducing surgical intensive care unit nurses' alarm fatigue and improving their alarm management practices.

Investigation of Cooling Systems Faults, Control and Management Models

With the rapid increase in population in the world, people's demands for chilled and frozen foods are also increasing. The healthy and energy efficient cold storage of foods has become an important issue. In this context, studies on the efficiency and performance of energy management models including automatic control techniques on cooling systems have been investigated in recent years. The purpose of this study is to give information about faults, traceable energy management models and control systems in the perspective of cooling systems.&#x0D; Maintenance of cooling systems, malfunctions and their late detection cause time and cost problems. The decrease in the performance of the cooling system or the occurrence of malfunctions cause economic costs as well as energy consumption. Controlling the changes in pressure, temperature and electricity consumption values ​​in cooling systems and comparing them with standard operating conditions is one of the methods used for fault detection. Faults that may occur in cooling systems; compressor, condenser, evaporator, expansion element and fan failures, thermal failures, phase protection relay failures, under/over refrigerant charge, probe failures, night curtain electric motor failure, condenser-evaporator surface pollution. Errors and malfunctions occurring in the cycle cause the parameters under normal operating conditions and accordingly the cooling performance coefficient to change. Traceable energy management models are needed to detect faults and prevent them in a short time. With the design and implementation of these models, long-term damage to the system is prevented by reducing energy, maintenance and repair costs.&#x0D; In the design of traceable energy management models and control systems for cooling systems; set point and management, alarm notification and management, designing algorithms with precise temperature control will provide energy efficiency.

A Modular Architecture of Command-and-Control Software in Multi-Sensor Systems Devoted to Public Security

Preventing terrorist attacks at soft targets has become a priority for our society. The realization of sensor systems for automatic threat detection in crowded spaces, such as airports and metro stations, is challenged by the limited sensing coverage capability of the devices in place due to the variety of dangerous materials, to the scanning rate of the devices, and to the detection area covered. In this context, effectiveness of the physical configuration of the system based on the detectors used, the coordination of the sensor data collection, and the real time data analysis for threat identification and localization to enable timely reactions by the security guards are essential requirements for such integrated sensor-based applications. This paper describes a modular distributed architecture of a command-and-control software, which is independent from the specific detectors and where sensor data fusion is supported by two intelligent video systems. Furthermore, the system installation can be replicated at different locations of a public space. Person tracking and later re-identification in a separate area, and tracking hand-over between different video components, provide the command-and-control with localization information of threats to timely activate alarm management and support the activity of subsequent detectors. The architecture has been implemented for the NATO-funded DEXTER program and has been successfully tested in a big city trial at a metro station in Rome both when integrated with two real detectors of weapons and explosives and as a stand-alone system. The discussion focuses on the software functions of the command-and-control and on the flexibility and re-use of the system in wider settings.