Agent-based, AI-driven hybrid IoT system for real-time sustainability and environmental optimization

Abstract

An agent-based, AI-controlled hybrid IoT system (100) for real-time sustainability and environmental optimization, wherein the system (100) comprises: a multimodal data acquisition layer (1) configured to acquire real-time environmental, energy, resource consumption, equipment status and occupancy data from a variety of distributed sensors distributed across an operating site; a hybrid communication and edge gateway layer (2) that is functionally coupled with the multimodal sensor layer (1), wherein the hybrid communication and edge gateway layer (2) is configured to receive the said data via heterogeneous wired and wireless communication protocols, preprocess the data and transmit it for further analysis; a context data fusion and digital twin layer (3) that is functionally coupled with the hybrid communication and edge gateway layer (2), wherein the context data fusion and digital twin layer (3) is configured to aggregate historical data, real-time captured data, external environment data and system state data to generate a dynamic operational representation of a monitored environment; an agentic AI decision orchestration engine (4) that is functionally coupled with the context data fusion and digital twin layer (3), wherein the agentic AI decision orchestration engine (4) is configured to autonomously generate, prioritize, revise and execute sustainability-oriented decision workflows based on target conditions, policy constraints and predicted environmental impacts; a sustainability analysis and optimization layer (5) that is functionally coupled with the agent-based AI decision orchestration engine (4), wherein the sustainability analysis and optimization layer (5) is configured to calculate optimization measures to reduce energy consumption, emissions, water consumption, waste generation and resource inefficiency while maintaining operational performance; an action and control interface layer (6) that is functionally coupled to the sustainability analysis and optimization layer (5), wherein the action and control interface layer (6) is configured to issue automated or semi-automated control commands to one or more controlled systems, including HVAC systems, lighting systems, pumps, motors, valves, industrial equipment, renewable energy systems and water management infrastructure; and a trust, compliance and audit layer (7) that is functionally coupled with the agent-based AI decision orchestration engine (4) and the action and control interface layer (6), wherein the trust, compliance and audit layer (7) is configured to validate decisions against predefined sustainability rules, maintain traceable decision records and generate compliance-compliant reports, wherein the system (100) continuously monitors environmental and operating conditions and adaptively optimizes resource utilization through autonomous, AI-driven decision execution in real time.

Description

INVENTION AREA

[0001] The present invention relates generally to the field of intelligent environmental monitoring, sustainability management, and industrial and building automation.

[0002] In particular, the present invention relates to an agent-based, AI-controlled hybrid Internet of Things (IoT) system configured for real-time sustainability and environmental optimization by combining distributed sensing, hybrid communication infrastructure, contextual data fusion, digital twin modeling, autonomous AI-based decision orchestration, sustainability analytics, asset control, and compliance-oriented audit intelligence. BACKGROUND OF THE INVENTION

[0003] The subject matter discussed in the background section should not be considered prior art solely because it is mentioned therein. Likewise, a problem mentioned in the background section or related to its subject matter should not be considered to be prior art. The subject matter in the background section merely presents various approaches, which could themselves also be inventions.

[0004] Rapid urbanization, industrial growth, and the increasing operational complexity of buildings, campuses, factories, utilities, and smart infrastructure have significantly increased energy consumption, CO2 emissions, water scarcity, and waste generation. Traditional environmental management monitoring systems are often fragmented, reactive, and manually monitored. Existing building management systems, industrial automation systems, and basic IoT dashboards typically collect sensor data and display alerts, but they often lack the deep contextual information, cross-domain integration, and autonomous decision-making capabilities required for real-time sustainability optimization.

[0005] Most currently known systems operate in isolation, with energy systems, HVAC systems, occupancy management, air quality systems, waste disposal, water management, and renewable energy facilities being monitored independently. Such isolated architectures lead to poor coordination between sensing and control, inefficient resource utilization, delayed responses to environmental changes, and difficulties in achieving sustainability goals. Furthermore, many existing systems rely on fixed-threshold automation that cannot effectively adapt to dynamic occupancy patterns, tariff fluctuations, weather conditions, equipment wear and tear, or external environmental impacts.

[0006] Another limitation of existing systems is the lack of an intelligent decision-making layer capable of breaking down overarching sustainability goals into actionable machine tasks. Conventional automation platforms do not adequately support agent autonomy, contextual reasoning, predictive control, or policy-driven interventions. They are also typically not equipped with digital twin-based scenario simulation to predict the environmental impacts of a given control strategy before its actual implementation.

[0007] Furthermore, compliance with sustainability standards, emissions reporting obligations, energy efficiency benchmarks, and operational audit requirements is becoming increasingly important. Traditional systems lack transparent accountability, traceable control protocols, decision origins, and compliance-oriented reporting mechanisms. This leads to significant limitations in sectors where environmental responsibility, ESG performance, and sustainability certification are crucial.

[0008] Accordingly, there is a need for an improved system that can integrate heterogeneous IoT infrastructures, combine real-time and historical data, create context-related digital representations of the monitored environments, autonomously judge sustainability goals and execute optimized control measures in real time, while maintaining explainability, trust and regulatory readiness.

[0009] As used in the present description and in the following claims, the meaning of "a", "an", and "the" also includes the plural unless the context clearly indicates otherwise. Likewise, the meaning of "in", as used in the present description, includes both "in" and "on", unless the context clearly indicates otherwise.

[0010] The use of value ranges here serves only as a shorthand method to refer to each individual value within the range. Unless otherwise stated, each individual value will be included in the description as if it were listed separately.

[0011] The use of any examples or illustrative phrases (e.g., "as") in relation to certain embodiments described herein serves only to better illustrate the invention and does not constitute a limitation of the scope of the invention as claimed elsewhere. No phrase in the description shall be construed as referring to an unclaimed element that is essential for carrying out the invention.

[0012] The information disclosed above in this "Background" section is provided solely for the purpose of better understanding the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY

[0013] Before describing the systems and methods presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly presented in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application.

[0014] In one aspect, the present invention provides an agent-based, AI-driven hybrid IoT system (100) for real-time sustainability and environmental optimization. The system essentially comprises a multimodal data acquisition layer (1), a hybrid communication and edge gateway layer (2), a layer for contextual data fusion and digital twins (3), an agent-based AI decision orchestration engine (4), a sustainability analysis and optimization layer (5), an activation and control interface layer (6), and a trust, compliance, and audit layer (7).

[0015] The multimodal sensor layer (1) collects real-time data from sensors for environment, energy, resources, occupancy, waste, and equipment status distributed across a monitored site. The hybrid communication and edge gateway layer (2) receives data from heterogeneous devices via various communication protocols, performs local preprocessing and filtering, and forwards the processed data for higher-level analysis. The contextual data fusion and digital twin layer (3) integrates real-time data, historical data, system state data, external environmental data, and operational metadata to create a living digital representation of the monitored infrastructure.

[0016] The agentic AI decision orchestration engine (4) comprises several intelligent software agents that perform event interpretation, goal decomposition, anomaly handling, policy derivation, task scheduling, and cross-agent coordination for autonomous sustainability control. The sustainability analysis and optimization layer (5) computes optimized decisions for energy, emissions, water, waste, air quality, and resource efficiency using forecasting, optimization, rule-based evaluation, and machine learning. The actuation and control interface layer (6) translates the optimized decisions into machine-executable control commands directed to HVAC systems, lighting networks, renewable energy systems, pumps, valves, motors, industrial plants, or other controlled assets.The Trust, Compliance and Audit Layer (7) validates the security, compliance with policies, explainability and traceability of each generated decision and stores auditable records.

[0017] The disclosed system continuously adapts to changing operating conditions and enables real-time environmental optimization, while simultaneously reducing resource waste, minimizing the carbon footprint, improving room comfort, and ensuring compliance with organizational or regulatory sustainability goals. BRIEF DESCRIPTION OF THE DRAWING

[0018] To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments shown in the accompanying drawing. It is understood that this drawing represents only illustrative embodiments of the invention and is therefore not to be considered a limitation of its scope. The invention is described and explained with additional specificity and detail using the accompanying drawing.

[0019] To make the advantages of the present invention easily understandable, a detailed description of the invention is discussed below in conjunction with the accompanying drawing, although it should not be assumed that the scope of the invention is limited to the accompanying drawing, in which: Fig. Figure 1 shows a block diagram of the agent-based, AI-driven hybrid IoT system (100) that illustrates the connection of the multimodal acquisition layer (1), the hybrid communication and edge gateway layer (2), the context data fusion and digital twin layer (3), the agentic AI decision orchestration engine (4), the sustainability analysis and optimization layer (5), the activation and control interface layer (6), and the trust, compliance, and audit layer (7). DETAILED DESCRIPTION

[0020] The present invention relates to an agent-based, AI-controlled hybrid IoT system (100) for real-time sustainability and environmental optimization.

[0021] Fig. shows a detailed block diagram representation of the agent-based, AI-controlled hybrid IoT system (100) for real-time sustainability and environmental optimization.

[0022] The present invention will now be described in detail with reference to exemplary embodiments. The following description serves only for illustration and is not intended to limit the scope of the invention.

[0023] In one embodiment, the present invention provides an agent-based, AI-controlled hybrid IoT system (100) configured to perform real-time sustainability and environmental optimization in one or more monitored environments, such as buildings, factories, campuses, warehouses, utilities, data centers, agricultural facilities, transportation hubs, municipal infrastructure, or smart city zones.

[0024] The system (100) comprises a multimodal sensing layer (1). The multimodal sensing layer (1) includes a variety of sensing devices and measurement nodes installed at various operational locations. These sensor devices may include, but are not limited to, temperature sensors, humidity sensors, carbon dioxide sensors, volatile organic compound sensors, particle sensors, water quality sensors, water flow meters, power meters, current sensors, pressure sensors, noise sensors, light sensors, solar radiation sensors, presence sensors, motion detectors, level sensors, vibration sensors, and equipment health sensors. The multimodal sensing layer (1) is configured to acquire real-time measurements of environmental conditions, indoor and outdoor air quality, power consumption, water consumption, waste accumulation, machine operation, resource consumption, space utilization, and occupancy patterns.In a preferred embodiment, the sensing layer can include both permanently installed sensors and mobile or portable sensors, making the overall architecture hybrid in its sensing topology.

[0025] The system (100) further comprises a hybrid communication and edge gateway layer (2) that is functionally coupled to the multimodal sensor layer (1). The hybrid communication and edge gateway layer (2) is configured to communicate with the sensor devices using a variety of protocols such as Wi-Fi, Ethernet, Zigbee, Bluetooth Low Energy, LoRaWAN, NB-LOT, 5G, Modbus, MQTT, BACnet, CAN-based communication, and other industrial or consumer communication standards. The hybrid communication and edge gateway layer (2) of the system ( ) receives raw sensor data and performs one or more preprocessing operations, including timestamp matching, data normalization, packet inspection, edge analytics, missing value handling, local anomaly marking, compression, filtering, and temporary local buffering.In one embodiment, the edge gateway layer (2) can also support offline fault tolerance, so that local rules and control logic continue to function even if the cloud connection is temporarily interrupted.

[0026] The system (100) further comprises a contextual data fusion and digital twin layer (3) coupled with the hybrid communication and edge gateway layer (2). The contextual data fusion and digital twin layer (3) is configured to aggregate heterogeneous data streams, including real-time sensor readings, historical logs, asset metadata, maintenance records, occupancy schedules, equipment status information, external weather data, utility rate data, carbon intensity feeds, and site-specific environmental information. Based on the fused data, layer (3) generates a dynamic digital twin, or contextual operating model, of the monitored environment.The digital twin can represent the thermal behavior of a building, the energy flow within an industrial process, patterns of renewable energy generation, water consumption in different zones, indoor air quality, waste accumulation trends, and the performance characteristics of equipment. Layer (3) can further simulate one or more future operating conditions and estimate the likely effect of control measures before their actual activation. In this way, the system (100) is able to transition from reactive monitoring to predictive and prescriptive environmental management.

[0027] The system (100) further comprises an agentic AI decision orchestration engine (4) that is operationally connected to the layer (3) for context data fusion and the digital twin. The agentic AI decision orchestration engine (4) is the central intelligent core of the system. In one embodiment, the engine (4) comprises a variety of autonomous and semi-autonomous software agents. These agents may include, for example, a monitoring agent, a target planning agent, an anomaly interpretation agent, a policy compliance agent, a scheduler agent, a control recommendation agent, a negotiation agent, and a verification agent. These agents work together to translate overarching sustainability goals into actionable workflows. For example, if occupancy in a zone increases while air quality decreases and energy prices rise, the engine (4) can determine an optimized sequence of actions, such as…the regulation of ventilation, the adjustment of setpoints, the relocation of non-critical electrical loads, the use of available renewable energy and the creation of a projected savings estimate.

[0028] In a preferred embodiment, the agent-based AI decision orchestration engine (4) is configured to perform goal decomposition, event interpretation, contextual reasoning, conflict resolution, dynamic prioritization, and workflow revision. The engine (4) can generate an initial sustainability workflow and then revise it when new data indicates equipment failures, weather changes, peak load conditions, occupancy fluctuations, or emergency events. Accordingly, the engine (4) behaves not only as a recommendation engine but also as an active agent layer capable of autonomous planning and controlled execution.

[0029] The system (100) further comprises a sustainability analysis and optimization layer (5) connected to the agent-based AI decision orchestration engine (4). The sustainability analysis and optimization layer (5) is configured to calculate optimized control outcomes for reducing energy consumption, CO2 emissions, water consumption, waste generation, and operational inefficiencies. In various embodiments, the analysis layer (5) can employ one or more AI and computational models, such as time-series forecasting, reinforcement learning, predictive maintenance models, statistical optimization based on the principle of " ", graph-based optimization, rule-augmented neural networks, constraint solvers, demand-response models, and emission estimation models. The layer (5) can calculate sustainability measures at the zone, plant, building, campus, or enterprise level.It can also quantify expected reductions in electricity consumption, heat losses, fresh water consumption, waste overflow incidents or CO2 footprint, while ensuring acceptable comfort, productivity, safety and process continuity.

[0030] The system (100) further comprises an action and control interface layer (6) coupled to the sustainability analysis and optimization layer (5). The action and control interface layer (6) is configured to convert optimized results into machine-readable control commands directed to controllable equipment. Such equipment may include HVAC systems, chillers, ventilation units, dampers, lighting systems, pumps, motors, valves, fans, irrigation systems, water treatment plants, smart plugs, industrial controls, battery storage systems, solar inverters, electric charging systems, access-controlled ventilation devices, and waste disposal systems. In one embodiment, the action and control interface layer (6) supports three control modes: an autonomous mode, a human-in-the-loop approval mode, and a recommendation mode.In autonomous mode, validated actions are executed automatically. In human-in-the-loop approval mode, the system proposes a control action and awaits operator confirmation. In recommendation mode, the system provides advisory output without direct action. The selected control mode can depend on confidence levels, policy restrictions, equipment criticality, operator preferences, or safety classification.

[0031] The system (100) further comprises a trust, compliance, and audit layer (7) that is functionally coupled with the agentic AI decision orchestration engine (4) and the actuation and control interface layer (6). The trust, compliance, and audit layer (7) is configured to verify whether a proposed decision complies with operating rules, environmental policies, safety thresholds, regulatory requirements, user-defined priorities, and sustainability commitments. The trust, compliance, and audit layer (7) can also assign an explanatory output to each action, including the decision basis, the input conditions considered, the predicted sustainability impacts, the confidence score, the target affected, and the compliance status. Each generated decision and each executed action can be logged in an auditable manner for later review.Layer (7) can also produce sustainability reports, ESG-supporting documentation, CO2 balance summaries, audit trails and compliance-ready records for internal or external stakeholders.

[0032] During operation, the multimodal acquisition layer (1) continuously collects real-time physical and operational data from a monitored environment. The hybrid communications and edge gateway layer (2) receives and preprocesses the data and forwards it to the contextual data fusion and digital twins layer (3). Layer (3) integrates the data with historical and external context and creates a dynamic state model of the environment. The agentic AI decision orchestration engine (4) receives this contextual representation and identifies one or more sustainability goals, such as reducing peak energy load, improving indoor air quality, minimizing water waste, transitioning operations to renewable energy, or preventing waste accumulation. The sustainability analysis and optimization layer (5) determines the optimal action or sequence of actions.Before execution, the Trust, Compliance, and Audit layer (7) validates the decision. The Actuation and Control Interface layer (6) then transmits the approved command to the relevant controlled systems. The resulting environmental response is again captured by the Acquisition layer (1), creating a continuous, adaptive, closed-loop sustainability control system.

[0033] In an exemplary embodiment, the system (100) can be deployed in a commercial building. The sensing layer (1) measures temperature, humidity, CO2 levels, occupancy, and power consumption across multiple zones. The digital twin layer (3) identifies thermal imbalances and peak energy loads in selected zones. The agentic AI decision orchestration engine (4) determines that cooling should be redistributed, non-critical lighting dimmed, and ventilation adjusted according to real-time occupancy. The analysis layer (5) predicts a reduction in energy consumption and CO2 emissions. The trust layer (7) validates the action with regard to thermal comfort and policy requirements. Subsequently, the actuator layer (6) sends commands to HVAC dampers, thermostats, and lighting controls.

[0034] In another exemplary embodiment, the system (100) can be used in an industrial plant where water consumption, emissions, and plant efficiency play a critical role. The detection layer (1) identifies unusual water consumption and elevated particle concentrations near a process line. The digital twin layer (3) correlates this event with the plant's operating conditions and the ambient weather conditions. The agentic AI decision orchestration engine (4) generates a remediation workflow that includes pump modulation, filtration adjustment, and temporarily rerouting operations to a more energy-efficient configuration. The analysis layer (5) estimates the water savings and emission reductions, while the trust layer (7) verifies safety and regulatory compliance.

[0035] In another embodiment, the system (100) can support the integration of renewable energies by monitoring solar power generation, battery state, local demand, grid tariff, and carbon intensity signals. Based on these inputs, the agentic AI decision orchestration engine (4) can instruct the system to shift certain loads to periods of high renewable energy availability or low tariff intensity, thereby optimizing both costs and sustainability.

[0036] In another embodiment, the system (100) can generate a real-time sustainability dashboard that displays energy savings, CO2 reduction, water efficiency, waste prevention, the share of renewable energy, air quality indicators, and environmental performance at the zone level. This dashboard can be used by facility managers, compliance teams, operators, administrators, or ESG auditors.

[0037] Although specific embodiments have been described here, various modifications, substitutions, additions, and rearrangements can be made without deviating from the spirit and scope of the invention. The described layers can be implemented in centralized, distributed, cloud-based, edge-based, or hybrid deployment architectures. The software agents can be executed as containerized services, embedded controllers, orchestration modules, digital assistants, or policy-constrained autonomous processes.

[0038] The figure and the preceding description provide 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 from 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 shown. Furthermore, the actions of a block diagram need not be implemented in the sequence shown; nor do all actions necessarily have to be performed. In addition, those actions that are not dependent on other actions can be performed in parallel with the other actions. The scope of embodiments is by no means limited by these specific examples.

[0039] Although embodiments of the invention have been described in language relating to structural features and / or methods, it is to be understood that the appended claims of the document are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of embodiments of the invention.

Claims

[1] An agent-based, AI-controlled hybrid IoT system (100) for real-time sustainability and environmental optimization, wherein the system (100) comprises: a multimodal data acquisition layer (1) configured to acquire real-time environmental, energy, resource consumption, equipment status and occupancy data from a variety of distributed sensors distributed across an operating site; a hybrid communication and edge gateway layer (2) that is functionally coupled with the multimodal sensor layer (1), wherein the hybrid communication and edge gateway layer (2) is configured to receive the said data via heterogeneous wired and wireless communication protocols, preprocess the data and transmit it for further analysis; a context data fusion and digital twin layer (3) that is functionally coupled with the hybrid communication and edge gateway layer (2), wherein the context data fusion and digital twin layer (3) is configured to aggregate historical data, real-time captured data, external environment data and system state data to generate a dynamic operational representation of a monitored environment; an agentic AI decision orchestration engine (4) that is functionally coupled with the context data fusion and digital twin layer (3), wherein the agentic AI decision orchestration engine (4) is configured to autonomously generate, prioritize, revise and execute sustainability-oriented decision workflows based on target conditions, policy constraints and predicted environmental impacts; a sustainability analysis and optimization layer (5) that is functionally coupled with the agent-based AI decision orchestration engine (4), wherein the sustainability analysis and optimization layer (5) is configured to calculate optimization measures to reduce energy consumption, emissions, water consumption, waste generation and resource inefficiency while maintaining operational performance; an action and control interface layer (6) that is functionally coupled to the sustainability analysis and optimization layer (5), wherein the action and control interface layer (6) is configured to issue automated or semi-automated control commands to one or more controlled systems, including HVAC systems, lighting systems, pumps, motors, valves, industrial equipment, renewable energy systems and water management infrastructure; and a trust, compliance and audit layer (7) that is functionally coupled with the agent-based AI decision orchestration engine (4) and the action and control interface layer (6), wherein the trust, compliance and audit layer (7) is configured to validate decisions against predefined sustainability rules, maintain traceable decision records and generate compliance-compliant reports, wherein the system (100) continuously monitors environmental and operating conditions and adaptively optimizes resource utilization through autonomous, AI-driven decision execution in real time. [2] System (100) according to claim 1, wherein the multimodal sensing layer (1) comprises one or more sensors selected from temperature sensors, humidity sensors, air quality sensors, fine dust sensors, gas sensors, noise sensors, vibration sensors, water flow sensors, power meters, solar radiation sensors, presence sensors, machine condition sensors and waste level sensors. [3] System (100) according to claim 1, wherein the hybrid communication and edge gateway layer (2) is configured to support a variety of communication protocols, including Wi-Fi, LoRaWAN, Zigbee, Bluetooth Low Energy, NB-IoT, 5G, Ethernet, Modbus, BACnet, MQTT and CAN-based protocols, thereby enabling interoperability between heterogeneous IoT devices. [4] System (100) according to claim 1, wherein the context data fusion and digital twin layer (3) is further configured to generate a predictive environmental model that represents the energy flow, CO2 footprint, thermal conditions, plant utilization, water consumption and waste generation for one or more zones of the monitored environment. [5] System (100) according to claim 1, wherein the agentic AI decision orchestration engine (4) comprises a plurality of software agents configured to perform target decomposition, event interpretation, anomaly handling, task scheduling, policy derivation and cross-agent negotiation to select an optimal environment control strategy. [6] System (100) according to claim 1, wherein the agent-based AI decision orchestration engine (4) is configured to dynamically revise a previously generated control workflow in response to changing environmental conditions, equipment failures, fluctuations in utilization, tariff changes, the availability of renewable energy or emergency events. [7] System (100) according to claim 1, wherein the sustainability analysis and optimization layer (5) uses one or more artificial intelligence models selected from machine learning models, reinforcement learning models, time series forecasting models, graph-based optimization models and rule-augmented neural models to generate optimized resource management decisions. [8] System (100) according to claim 1, wherein the actuation and control interface layer (6) is configured to operate selectively in an autonomous mode, a human-in-the-loop approval mode and a recommendation mode, depending on the criticality level, policy settings or confidence value associated with a generated decision. [9] System (100) according to claim 1, wherein the trust, compliance and audit layer (7) is configured to assign an explainability output to each optimization decision, the explainability output comprising at least a decision basis, the sustainability target affected, the predicted impact, the confidence level and the policy compliance status. [10] System (100) according to claim 1, wherein the system (100) is configured to generate a real-time sustainability performance dashboard that displays at least energy savings, CO2 emission reduction, water efficiency improvement, waste prevention efficiency, renewable energy use and environmental quality indices for a building, industrial plant, campus, smart city area or agricultural infrastructure.

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