Device for time-based tracking and cost optimization in construction projects

Abstract

A device for time-based tracking and cost optimization in construction projects, the device comprising the following: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

Description

Field of invention:

[0001] The present invention relates to the field of construction project management systems and, in particular, to a time-synchronized, hardware- and software-integrated device that tracks the progress and resource utilization of construction projects in real time. The device enables dynamic cost optimization by correlating data on labor, material, and machine utilization with project planning and budget forecasting systems. It integrates predictive analytics based on machine learning and the synchronization of digital twins for proactive decision support in complex construction environments. Background of the invention:

[0002] Construction projects frequently suffer from delays and budget overruns due to a lack of real-time transparency regarding task progress, resource inefficiency, and suboptimal coordination among stakeholders. Traditional project management tools rely heavily on manual updates, static Gantt charts, and isolated budget management software. These limitations hinder responses to deviations on-site, leading to labor misallocation, machine downtime, over-procurement, and missed milestones. Therefore, there is an urgent need for a dedicated hardware device that functions as a site terminal, automatically collecting and analyzing data in real time and forwarding it to a central control unit for adaptive planning and cost optimization.

[0003] The construction industry has long grappled with the challenge of effectively managing project schedules and budgets. Large construction projects are fraught with numerous variables, including fluctuating labor availability, unpredictable environmental conditions, equipment limitations, and complex material logistics. Project managers must ensure the timely completion of all construction phases—from excavation and foundation work to final completion and acceptance—while maintaining strict cost control. Despite advancements in construction planning methodologies and the proliferation of digital project management platforms, the industry continues to experience significant cost overruns and schedule delays. Traditional approaches to construction management rely heavily on static planning tools, manual updates, periodic reporting, and human coordination.While these methods provide a basic framework for project execution, they are inherently reactive and cannot dynamically adapt to real-time changes on site.

[0004] Existing solutions for construction time and cost tracking primarily include Gantt chart-based planning software, Earned Value Management (EVM) tools, and Enterprise Resource Planning (ERP) systems specifically tailored to the construction industry. These tools are typically used in conjunction with each other, with data manually entered by project managers, foremen, or administrators. Software such as Microsoft Project, Primavera P6, and SAP Construction Project Management offers a range of functions for task planning, resource allocation, and tracking budgeted and actual costs. However, these platforms operate in isolation, and their effectiveness is limited by the quality and timeliness of the data entry. In fast-paced construction environments, manual data updates quickly become outdated, leading to decisions based on lagging indicators rather than current on-site conditions.Furthermore, these systems rarely incorporate detailed real-time data directly from the construction site, such as equipment idle times, real-time workforce presence, or immediate confirmations of material usage.

[0005] To address the challenges of data collection on construction sites, some construction companies are experimenting with IoT-based solutions. They are equipping devices with sensors, materials with RFID tags, and biometric attendance systems for time tracking. However, these systems are often fragmented and operate independently without an integrated framework that links their data to planning or cost optimization techniques. For example, a sensor might record crane utilization, or a GPS module might monitor the movement of a concrete mixer. However, these data streams are not automatically synchronized with the project plan to update the schedule or cost basis. Therefore, such systems do not provide usable information within the context of overall project management.Furthermore, many IoT-based implementations lack scalability, robustness, and adaptability to site-specific conditions, making them unsuitable for widespread use in diverse building environments.

[0006] Another area of ​​development in the construction industry is Building Information Modeling (BIM), which provides a digital representation of a building's physical and functional characteristics. BIM has transformed the collaboration between architects, engineers, and contractors in planning and construction processes. While BIM platforms can integrate scheduling data (commonly referred to as 4D BIM) and cost data (5D BIM), they are primarily used for pre-construction planning and clash detection, rather than real-time project tracking. Integrating live field data into BIM environments remains a significant technical challenge, particularly since most data originates from disparate sources that lack standardization or time synchronization. Furthermore, BIM systems are often desktop-centric or cloud-based and not designed for continuous interaction with robust, on-site hardware, limiting their use in day-to-day construction management.

[0007] Mobile project management apps and cloud-based platforms attempt to bridge the gap between field service and centralized planning by allowing field staff to upload photos, task completion notes, and progress checklists via smartphones or tablets. While this approach improves communication, it still relies on human input, which is prone to delays, inaccuracies, and subjective interpretations. Furthermore, mobile apps often don't integrate well with existing ERP or BIM systems, leading to data silos and redundant workflows. These limitations prevent real-time analytics and restrict the use of predictive analytics for proactive project management.

[0008] Some companies are also experimenting with drones and aerial photography to visually monitor construction progress. While this allows for an understanding of structural development and site coverage at a macro level, this visual data lacks the necessary accuracy to measure task duration, equipment utilization, or labor productivity at a micro level. Furthermore, image processing techniques for extracting meaningful data from drone footage are still under development and require significant computing resources and technical expertise, making them less suitable for everyday use.

[0009] From a cost optimization perspective, current systems typically treat budget forecasting and financial control as downstream processes. Budget deviations are only detected after they occur, and cost control measures are reactive, such as reducing procurement, renegotiating labor contracts, or postponing non-critical activities. This approach fails to identify early warning signs of cost deviations, such as extended machine downtime or repeated rework cycles, which can accumulate into significant budget impacts over time. Without real-time cost correlation mechanisms, project managers are unable to make dynamic adjustments, such as rescheduling activities to avoid peak labor costs or prioritizing critical tasks to prevent delays.

[0010] Besides technical limitations, many existing solutions suffer from a lack of user-friendliness and adaptability to the field. Construction sites are chaotic environments where dust, vibrations, weather, and noise are ever-present. Most digital systems are not designed for rugged use and lack user interfaces tailored to field workers with minimal technical training. Equipment used on construction sites must be robust and intuitive. However, most available options are designed for office-based project managers rather than for field operation by civil engineers or foremen.

[0011] Cybersecurity and data integrity pose increasing challenges. Many construction projects involve confidential designs, procurement contracts, and compliance-relevant schedules. Current systems based on mobile phones or unencrypted IoT transmissions expose construction data to the risk of manipulation, interception, or misuse. The lack of tamper detection mechanisms or secure authentication protocols in most location-based tracking systems compromises the reliability of data used for planning and billing.

[0012] Taken together, these drawbacks highlight a critical gap in the industry: the lack of a unified, secure, real-time on-site device capable of integrating time tracking, resource monitoring, predictive analytics, and cost optimization within a single ecosystem. There is an urgent need for a robust and intelligent hardware solution that can operate autonomously on-site, process multimodal data, synchronize with central planning platforms, and generate actionable insights for real-time decision-making. Such a device must move beyond existing standalone solutions and instead be an integrated component of the construction site infrastructure—serving as a live interface between physical site conditions and digital project management environments. Only with such a holistic approach can the construction industry transition from reactive to proactive management and achieve meaningful improvements in on-time delivery and budget control. Summary of the invention:

[0013] The invention comprises a site-integrated device with multimodal sensors, time-stamping logic, and communication modules connected to a central software platform for time-based tracking and cost optimization. When installed at critical points on a construction site—such as reinforcement zones, formwork areas, crane operating bases, and material receipt checkpoints—the device monitors time, activity status, equipment utilization, and personnel presence. At the heart of the device is a programmable edge computing unit that processes sensor data and synchronizes it with a project-wide time and cost management system. This synchronization is further enhanced by a digital twin representation of the building under construction, enabling the real-time mapping of site data into virtual models for predictive cost control and dynamic planning.

[0014] The main objective of the present invention is to provide an intelligent, on-site device that enables time-based, real-time tracking and dynamic cost optimization in construction projects. The invention closes the long-standing gap between static project planning tools and the realities of the construction site by integrating sensor-based data acquisition, temporal analysis, and predictive computing models into a single hardware-based system. A further objective of the invention is the continuous, autonomous monitoring of labor deployment, equipment usage, and material movement directly from the construction site, without relying solely on manual input or disconnected software tools.The device is designed for seamless synchronization with central project management systems and digital building models (such as BIM), thus creating a closed feedback loop that supports decision-making with current, validated field data. A further objective is to provide construction managers and project stakeholders with real-time alerts, visual dashboards, and predictive analytics to proactively identify risks of cost overruns or schedule delays and adjust resource allocation accordingly. The invention also aims to provide a robust, weatherproof, and user-friendly interface suitable for harsh construction site environments, enabling even non-technical field personnel to interact effectively with the system.Furthermore, the device supports secure data logging, authentication protocols, and encrypted communication to ensure the integrity and confidentiality of project-critical data. A key objective of the invention is to reduce reliance on retrospective reports by introducing a continuous, time-synchronized intelligence layer that improves schedule forecasting accuracy and cost control throughout the entire construction cycle. BRIEF DESCRIPTION OF THE FIGURE

[0015] These and other features, aspects, and advantages of the present invention will be better understood if the following detailed description is read with reference to the accompanying drawing, in which the same symbols consistently represent the same parts. The following applies: Fig. Figure 1 shows a block diagram of a device for time-based tracking and cost optimization in construction projects.

[0016] Experts will also recognize that the elements in the drawing are shown for the sake of simplicity and are not necessarily to scale. For example, the flowcharts illustrate the process by highlighting the main steps to enhance understanding of the aspects of this disclosure. Furthermore, with regard to the design of the device, one or more components of the device may be represented in the drawing by conventional symbols, and the drawing may show only the specific details relevant to understanding the embodiments of this disclosure, so as not to clutter the drawing with details that are readily apparent to those skilled in the art after reading this description. Detailed description of the invention

[0017] For a better understanding of the inventive principles, reference is made below to the embodiment shown in the drawing, which is described in specific terminology. However, this does not limit the scope of the invention. Changes and further modifications of the illustrated system, as well as further applications of the inventive principles, are possible, as would normally occur to a person skilled in the art in the field of invention.

[0018] It is clear to the person skilled in the art that the preceding general description and the following detailed description are exemplary and explanatory of the invention and are not intended as a limitation of it.

[0019] References in this specification to “an aspect”, “another aspect”, or similar expressions mean that a particular feature, structure, or property described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, occurrences of the expressions “in one embodiment”, “in another embodiment”, and similar expressions in this specification may all refer to the same embodiment, but need not.

[0020] The terms "includes," "include," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method that includes a list of steps may not only contain those steps but may also include other steps not expressly listed or inherent in such process or method. Likewise, the statement "includes..." in the case of one or more devices, subsystems, elements, structures, or components does not, without further limitations, preclude the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art in the field of the invention. The system, methods, and examples provided here serve only for illustration and are not to be construed as a limitation.

[0022] Embodiments of the present disclosure are described in detail below with reference to the attached drawing.

[0023] In Fig.Figure 1 shows a block diagram of a device for time-based tracking and cost optimization in construction projects. The System 100 comprises: a robust housing (102) suitable for use on construction sites; a processing unit (104) located within the housing, configured to perform real-time time-stamping, data acquisition, and preprocessing tasks; a multimodal sensor unit (106) operationally connected to the processing unit, comprising at least one motion sensor, an RFID reader (106a), environmental condition sensors, and an image processing module (106b) with optical character recognition; and a real-time clock module (108) operationally connected to the processing unit to ensure temporal synchronization of all sensor data streams.a wireless communication module (110) supporting Wi-Fi, LoRa, and LTE protocols and configured to transmit time-stamped data to a central project server; a storage module (112) operationally coupled to the processing unit to locally buffer time-series data on construction activities during offline operation; a housing-mounted, touchscreen-based human-machine interface (114) configured to allow site personnel to input activity updates and confirm the status of construction tasks; a cost optimization engine (116) running on the central server, configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique incorporating critical path and performance metric principles;furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

[0024] In one embodiment, the processing unit (104) is configured to apply edge-based inference to classify construction activities into predefined task categories. The inference model was trained using historical construction site data and includes labeled sequences of sensor signal patterns that correspond to specific construction processes, such as rebar placement, concreting, or formwork removal.

[0025] In one embodiment, the image processing module (106b) comprises a high-resolution image sensor coupled with an embedded optical character recognition engine. The module is configured to recognize and analyze material delivery lists, barcode labels, or hand-marked labels on packages and extract metadata for material identification and quantity, which is then time-stamped and transmitted to the central server for cost reconciliation.

[0026] In one embodiment, the RFID reader (106b) is configured to scan the workers' identification tags at regular intervals, and the processing unit is also configured to calculate the cumulative working hours in certain zones of the construction site, correlate the work data with the planned task duration, and display patterns of under- or over-utilization to the cost optimization engine.

[0027] In one embodiment, the wireless communication module (110) is configured with an adaptive protocol selection mechanism that continuously evaluates signal strength, bandwidth availability, and latency metrics across the available communication channels and dynamically selects the optimal transmission mode to ensure continuity of data flow under restricted site connectivity conditions.

[0028] In one embodiment, the storage module (112) comprises a tamper-proof, secure storage zone implemented with a hardware security module. The secure zone is configured to store hash-verified logs of sensor data packets and user inputs. The logs are cryptographically signed and regularly backed up to the central server to ensure integrity and auditability.

[0029] In one embodiment, the cost optimization engine (116) comprises a machine learning model trained using a training corpus of time-series data from construction projects, including at least task start / end times, resource allocations, environmental disturbances, and final costs. The model is configured to predict task-specific cost variance probabilities and suggest strategies for rescheduling or resource adjustments to minimize budget overruns.

[0030] In one embodiment, the digital twin interface is synchronized in real time with the device's data output and is furthermore configured to render task-specific progress overlays on a virtual 3D model of the construction site, with the overlays visually highlighting areas with task delays, pending inspections, or material shortages using color-coded indicators based on predefined deviation thresholds.

[0031] In one embodiment, the touch-sensitive human-machine interface (114) comprises a multi-layered user access control system that includes biometric authentication, numeric PIN entry and session logging, and in which on-site foremen are authorized to approve or override automatic task classification decisions with reasoning inputs that are appended to the task timeline for review purposes.

[0032] In one embodiment, the processing unit (104) is also configured to generate real-time alerts based on deviations from the baseline construction schedules. These alerts are issued via audio, visual, and wireless notifications when task progress falls below a threshold completion rate derived from cumulative time-segmented sensor data over a 6-hour sliding window.

[0033] The present invention provides a robust, real-time construction management device that combines hardware-based field data acquisition with advanced engineering computation to enable time-based tracking and cost optimization in construction projects. The core functionality of the device is based on an embedded processing unit that orchestrates data acquisition from a network of integrated sensors, applies local preprocessing and inference operations, and synchronizes this information with a central cost optimization engine on a remote server. The underlying framework of the system utilizes time-synchronized data streams to generate actionable insights into task progress, workload, material flow, and equipment efficiency. This ultimately enables predictive and dynamic adjustment of resource allocation to minimize delays and cost overruns.

[0034] The workflow begins at the edge, where sensor inputs are continuously captured and time-stamped using a high-precision, real-time clock module integrated into the device. Sensors include motion detectors, RFID readers, optical scanners, environmental monitors, and OCR-enabled imaging units. Data from these sources first undergoes a signal conditioning process that includes noise filtering using recursive moving average and confidence assessment based on signal fidelity. For example, RFID-based time tracking is validated against planned attendance windows, while motion detection data is thresholded to differentiate between idle time, transport, and active machine operation.

[0035] The processing unit implements an activity classification module trained on historical construction datasets. This module uses a lightweight, edge-deployable machine learning model, such as a decision tree or a compact convolutional neural network, to classify construction events in near real-time. Each classification result is associated with a timestamp and a confidence level and then passed to a task sequencing module. This module segments the current workday into fixed-duration intervals (e.g., 15 minutes) and aggregates task-like events within these intervals to derive the actual start and end times of specific activities, such as rebar placement, formwork, pouring, the start of curing, or crane loading.

[0036] After aggregation, the time-coded activity data is transmitted via a secure wireless channel—using LoRa, LTE, or WLAN—to the central cost optimization engine in the cloud or on a local project server. At this stage, the technology becomes a multivariable calculation framework that combines elements of the Critical Path Method (CPM), Earned Value Management (EVM), and predictive regression models. The core of this framework is a module for analyzing time and cost variances, which compares the actual task duration and costs with the baseline plan. The baseline data is pre-loaded into the system through integration with the main project schedule and budget.

[0037] The variance analysis module calculates two key performance indicators: the Schedule Performance Index (SPI) and the Cost Performance Index (CPI), calculated for each task area and resource type (labor, materials, equipment). These indices are dynamically updated based on a rolling time window of current data, enabling the system to identify early signs of inefficiency. For example, if the SPI for a specific foundation slab operation falls below 0.85, the system triggers an optimization routine that analyzes idle patterns, team distribution density, and material delivery logs from field data to diagnose the root cause—be it understaffing, delays in formwork release, or crane availability bottlenecks.

[0038] To support predictive control, the cost optimization engine includes a supervised learning model trained on historical project data from similar construction projects. The model uses factors such as ambient temperature, task complexity, overlapping concurrent activities, equipment availability, and employee skill levels to predict the expected duration of the next scheduled tasks. Predicted delays are compared to buffer margins in the critical path. If the buffer margins are negative, the system examines corrective options. These options may include overtime scheduling, parallel resource allocation, or reordering the task sequence—all evaluated using a cost-benefit estimator integrated into the optimization technique.

[0039] The system also includes an engine for inferring task criticality. This engine uses Bayesian probability models to calculate the probability of delay propagating from a task node to subsequent dependent tasks. This probabilistic risk assessment serves to prioritize intervention measures and ensures that optimization efforts are focused on the nodes with the greatest systemic impact. For example, if two tasks are delayed, but one is on the critical path and the other is in a non-critical sequence, the system concentrates resource expansion on the critical path node and allows for leeway at the other node.

[0040] In addition to predictive and corrective control, the system enables real-time visualization through a digital twin synchronization engine. Device output is linked to the building's BIM model via a standardized API, and the system overlays task completion status, material consumption, and resource density onto the 3D model. The digital twin is continuously updated with timestamped field data, allowing project managers to visually validate progress and assess the spatial distribution of delays. Red-highlighted zones indicate underperformance based on SPI and CPI metrics, while yellow zones signify marginal deviations, and green indicates on-time and on-budget performance.

[0041] Another level of technical functionality lies in the network coordination model. If several such devices are deployed on the site, each acts as a temporal node within a distributed network. The central engine aggregates data from the spatially distributed devices and performs a spatiotemporal correlation analysis. This includes identifying dependencies between zones, detecting systemic problems such as recurring crane congestion or delays in material supply, and developing optimized workflows for task sequencing and resource redistribution.

[0042] The entire pipeline is designed for fault tolerance and security. All data packets are time-signed, hashed, and verified by the device's hardware security module. In the event of a network outage, the edge logic stores the sensor data in tamper-proof storage with hashed index logs. Upon reconnection, the buffered data is synchronized with the server and integrated into the optimization timeline without any loss of timing accuracy.

[0043] The combination of edge analytics, time synchronization, central planning intelligence, predictive modeling, and digital twin integration enables the invention to transform construction project management from a reactive, paper-intensive discipline into an intelligence-driven, adaptive, real-time control system. By continuously recalculating schedule and budget variables based on live site data, the technology ensures that decision-makers are informed by actual conditions, not by forecasts or delayed reports. This allows for timely interventions, minimized rework, and optimized resource allocation.

[0044] The device features a robust housing suitable for use in harsh construction site environments. Inside the housing is a microcontroller-based processing unit with an interface to a local real-time clock module, a multimode radio communication module supporting Wi-Fi, LoRa, and LTE, and a memory module for buffered data logging.

[0045] A range of environmental and activity sensors are connected to the housing, including RFID readers for identifying personnel and materials, accelerometers for detecting equipment movement, optical character recognition (OCR) cameras for capturing logistics data (e.g., invoices, material barcodes), and temperature / humidity sensors to correlate weather conditions with construction pace. The device also features an optional GPS module for location-specific tracking of its deployment on larger construction sites.

[0046] A human-machine interface (HMI) touchscreen on the front of the housing allows construction site personnel to interact with the system, enter task updates, and manually report deviations. An integrated speaker and warning system provides real-time alerts or warnings based on deviation thresholds.

[0047] Each device is assigned a node ID, and time synchronization is achieved via a project-wide mesh network established using the communication module. This network feeds data to a central server (cloud or local) that hosts the cost optimization engine. The server executes a multivariable optimization technique based on a modified Critical Path Method (CPM) combined with Earned Value Management (EVM) principles. This technique calculates time-cost trade-offs and continuously adjusts the project budget based on actual and planned data.

[0048] The cost optimization engine receives data from multiple devices distributed across the site. Each device sends time-stamped data packets containing information on activity completion status, RFID-tracked work hours, equipment idle time, and material consumption. The process identifies potential delays, calculates their cost impact, and suggests dynamic resource reallocation—for example, by accelerating procurement plans, modifying shift schedules, or pre-positioning machines—to minimize cost increases.

[0049] To further improve predictability, the system incorporates a machine learning model trained on historical construction datasets. This model forecasts task durations under current environmental and site conditions and provides early warnings if a task is likely to exceed budget or time. Additionally, a digital twin interface synchronizes sensor data with a BIM (Building Information Modeling) model of the structure under construction. This enables a visual overlay of progress metrics, allowing project managers to "see" the construction status in 4D (time being the fourth dimension).

[0050] The device can also operate in standalone mode if communication is interrupted. It stores locally buffered data and synchronizes upon reconnection. Power is supplied by a battery with optional solar panel support for energy autonomy in remote or underdeveloped locations.

[0051] The entire system is secured by end-to-end encryption, with device authentication and tamper detection supported by a hardware security module. Site-wide performance dashboards can be accessed via web and mobile applications linked to the central platform.

[0052] The drawing and the preceding description show examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements of one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a flowchart need not be implemented in the sequence shown; nor does it necessarily have to be performed by all actions. Actions that are not dependent on other actions can also be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations are possible, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and material use. The range of embodiments is at least as broad as specified in the following claims.

[0053] Advantages, further benefits, and problem solutions have been described above with reference to specific embodiments. However, the advantages, benefits, problem solutions, and all components that can lead to an advantage, benefit, or solution occurring or becoming more apparent are not to be construed as critical, necessary, or essential features or components of individual or all claims. REFERENCES 100 A device for time-based tracking and cost optimization in construction projects. 102 Robust housing 104 processing units 106 Multimodal Sensor Suite 106a RFID reader 106b Vision Module 108 A real-time clock module 110 Wireless Communication Module 112 memory module 114 Touchscreen-based human-machine interface 116 Cost Optimization Engine

Claims

[1] A device for time-based tracking and cost optimization in construction projects, the device comprising: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis. [2] Device according to claim 1, wherein the processing unit is configured to apply edge-based inference to classify construction activities into predefined task categories, wherein the inference model was trained using historical construction site data comprising characterized sequences of sensor signal patterns corresponding to specific construction operations, such as the placement of reinforcement, concreting or the removal of formwork. [3] Device according to claim 1, wherein the image processing module comprises a high-resolution image sensor coupled with an embedded optical character recognition engine, wherein the module is configured to recognize and analyze material delivery lists, barcode labels or hand-marked tags on packages and extract metadata for material identification and quantity, which is then time-stamped and transmitted to the central server for cost reconciliation. [4] Device according to claim 1, wherein the RFID reader is configured to scan the workers' identification tags at regular intervals, and the processing unit is further configured to calculate the cumulative working hours in certain zones of the construction site, correlate the work data with the planned task duration, and report patterns of under- or over-utilization to the cost optimization machine. [5] Device according to claim 1, wherein the wireless communication module is configured with an adaptive protocol selection mechanism that continuously evaluates the signal strength, bandwidth availability and latency metrics across the available communication channels and dynamically selects the optimal transmission mode to ensure the continuity of data flow under restricted site connectivity conditions. [6] Device according to claim 1, wherein the storage module comprises a tamper-proof, secure storage zone implemented with a hardware security module, wherein the secure zone is configured to store hash-verified logs of sensor data packets and user inputs, wherein the logs are cryptographically signed and regularly backed up to the central server to ensure integrity and auditability. [7] Device according to claim 1, wherein the cost optimization engine includes a machine learning model trained on a training corpus of time series data from construction projects, comprising at least task start / stop times, resource allocations, environmental disturbances and final costs, wherein the model is configured to predict task-specific cost variance probabilities and suggest replanning or resource adjustment strategies to minimize budget overruns. [8] Device according to claim 1, wherein the digital twin interface is synchronized in real time with the data output of the device and is also configured to render task-specific progress overlays on a virtual 3D model of the construction site, the overlays visually highlighting areas with task delays, pending inspections or material shortages using color-coded indicators based on predefined deviation thresholds. [9] Device according to claim 1, wherein the touch-sensitive human-machine interface comprises a multi-layered user access control system that includes biometric authentication, numeric PIN entry and session logging, and wherein site managers are authorized to approve or override automatic task classification decisions with reasoning inputs that are appended to the task timeline for review purposes. [10] Device according to claim 1, wherein the processing unit is further configured to generate real-time alerts based on deviations from the baseline construction schedules. These alerts are issued via audio, visual, and wireless notifications when task progress falls below a completion rate threshold derived from cumulative, time-segmented sensor data over a 6-hour sliding window.

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