I. Low-Energy Adaptive Clustering Hierarchy (LEACH) [5] is a popular algorithm in Wireless Sensor Networks (WSNs) that forms clusters of nearby devices. Each cluster elects a leader (cluster head) responsible for collecting and aggregating data from member nodes before sending it to the base station. This reduces the number of direct transmissions to the base station, significantly lowering energy consumption.

II. Threshold-sensitive Energy-Efficient Network (TEEN) is used for time-sensitive applications where energy is saved by setting thresholds. Sensors only transmit data if values surpass a specified threshold, reducing redundant data transmission.

III. Power-Efficient Gathering in Sensor Information System (PEGASIS) organizes [6] nodes into chains rather than clusters, where each node communicates only with a close neighbor. Forwarding data along the chain to a central base station reduces the overall communication cost.

I. Sensor Medium Access Control (S-MAC) is [7] a duty-cycling MAC protocol where nodes periodically switch between sleep and active states. By reducing active time, S-MAC conserves energy while maintaining data exchange capabilities.

II. Timeout-MAC (T-MAC) [8] improves upon S-MAC by dynamically adjusting duty cycles based on network traffic. Nodes stay active only when there is data to transmit, which can significantly reduce energy waste during low-traffic periods.

I. Compressive Sensing (CS) reduces [9] the number of samples needed for accurate data reconstruction. This approach reduces the amount of transmitted data, lowering energy consumption while still capturing essential information.

II. In-network data aggregation combine data from multiple sources to eliminate redundancy, reducing the total amount of data sent [10]. Algorithms like Data Fusion or Aggregation Trees are common in IoT networks for minimizing energy by preprocessing data at the edge nodes.

I. Energy-Aware Geographic Routing (EAGR) uses [11] location-based information to select routes that minimize energy consumption. By balancing loads and avoiding low-battery nodes, these protocols prolong the network lifetime.

II. Multipath routing protocols such as directed diffusion, energy-aware ad hoc on-demand distance vector use multiple routes for data transmission, which balances energy consumption across the network. This method prevents energy depletion in certain nodes and distributes the energy load [12].

III. Energy harvesting adaptive routing take energy harvesting capabilities into account, routing data through nodes with available harvested energy to maximize network longevity [13].

I. Reinforcement Learning (RL)-based algorithms optimize communication decisions dynamically. For instance, RL can be applied to optimize data offloading decisions and balance computation between edge devices and servers to save energy [14].

II. Predictive wake-up scheduling mainly supervised learning models, predict when devices must transmit data.

Devices can then be scheduled to "wake up" only when needed, minimizing energy used in idle states [15]. III. Deep Q-Networks (DQN) manage energy resources by dynamically adjusting routing, offloading, or communication schedules. For example, a DQN can be trained to find optimal offloading paths for data-intensive tasks, balancing energy efficiency with latency constraints [16].

In a smart agriculture IoT deployment, sensor nodes are placed across large fields to monitor soil moisture, temperature, and humidity to optimize crop yield [17]. These IoT devices are generally battery-powered and located in remote areas where maintenance is infrequent, making energy-efficient communication crucial. To conserve energy, a combination of data aggregation and duty-cycling algorithms was implemented. By using in-network data aggregation techniques, redundant data was minimized before transmission to edge nodes. An adaptive duty-cycling protocol was also used, where devices alternated between active and sleep modes based on environmental conditions and data significance thresholds. The energy-efficient communication protocols extended battery life by up to 30% across the network. Furthermore, using edge computing nodes enabled local data processing, reducing the frequency of data transmissions to a central server and conserving bandwidth and energy. Data aggregation and adaptive duty cycling can significantly reduce the energy load on rural IoT networks, where power sources are limited and maintenance access is challenging.

A health monitoring system using wearable IoT devices was developed to monitor vital signs such as heart rate, oxygen levels, and body temperature in real-time for patients in a clinical setting [18]. These wearables required energy-efficient algorithms to maintain continuous operation without frequent charging. Machine learning-based energy optimization algorithms were implemented to predict low-activity periods and schedule device “wake-up” times accordingly. RL algorithms were also used to balance the trade-off between energy consumption and data accuracy, ensuring that vital signs were only transmitted when critical changes were detected. Predictive wake-up scheduling reduced energy usage by approximately 25%, while RL-based optimization extended device operating time by selectively transmitting important data. This approach allowed wearable devices to run longer, which is particularly valuable for overnight patient monitoring. Machine learning-based optimization, including predictive wake-up scheduling and selective data transmission, can greatly enhance battery life in real-time health monitoring systems by focusing only on transmitting critical data.

A smart city traffic management system installed an IoT network of cameras, sensors, and edge nodes at intersections to monitor traffic flow and pedestrian activity [19]. The goal was to optimize traffic signals based on real-time data, reduce congestion, and improve safety, all while maintaining energy efficiency. Energy-aware geographic routing and task-offloading algorithms were applied. Geographic routing algorithms efficiently relay data from the sensors to edge servers, optimizing energy usage based on device locations. Additionally, task offloading was applied to edge devices with ample power supply, ensuring that data processing tasks were handled locally whenever possible. The traffic management system improved energy efficiency by over 35%, as data processing was conducted at nearby edge servers rather than sent to a central server. This saved energy and decreased latency, allowing for near-instantaneous traffic signal adjustments. For smart city applications, energy-aware routing and task offloading algorithms effectively balance real-time responsiveness with energy efficiency, especially in densely networked environments.

An industrial IoT deployment involves sensors embedded in factory machinery, monitoring parameters like temperature, vibration, and pressure to predict potential equipment failures [20]. The system aimed to ensure continuous operation by predicting maintenance needs while minimizing energy consumption. Multi-Objective Optimization (MOO) algorithms were used to balance energy efficiency, latency, and bandwidth. Additionally, CS was employed to capture and transmit only essential data, reducing transmission loads on the network. The MOO algorithm allowed the system to adjust its data collection and transmission parameters dynamically, reducing energy consumption by approximately 40% compared to a baseline system. CS reduced data volume, leading to further energy savings and decreased unnecessary network congestion. MOO and CS can be very effective for industrial IoT, especially when continuous monitoring is required, but network resources are limited.

In a remote environmental monitoring project, IoT sensors were deployed to track air quality, temperature, and wildlife activity [21]. Limited access to power sources and harsh environmental conditions made energy-efficient communication a top priority. Cluster-based algorithms like LEACH and energy-harvesting adaptive routing were implemented. Data was aggregated at cluster heads before transmission to edge servers by clustering devices. Energy-harvesting techniques (e.g., solar) were also used, where nodes prioritized using solar-powered devices for data transmission when possible. The combination of LEACH clustering and adaptive routing reduced network energy consumption by about 50%. Energy harvesting also extended operational time, allowing the network to function with minimal human intervention.

By Processing data locally on edge devices, Edge AI significantly reduces the volume of data that needs to be sent to cloud servers. For example, a camera in a smart city environment might use edge AI to process video data and send only relevant traffic patterns or incidents to the central server, minimizing energy used in data transmission. Edge AI can filter irrelevant or redundant data, send only the most meaningful data, or aggregate information across multiple devices. This reduces the energy demand from transmitting large data volumes and is particularly valuable in applications with high data output, like industrial IoT and real-time monitoring.

Edge AI models can predict low-activity periods and automatically adjust device duty cycles, allowing devices to enter low-power sleep modes when not actively collecting critical data. For example, AI models may predict that environmental sensors in a smart agriculture setup need only activate at specific times of day, saving energy during low-impact periods. With event-based AI, devices activate only in response to specific triggers, such as a change in sensor readings or the presence of a human in a surveillance area. This approach reduces the need for constant data transmission, saving significant energy, especially in areas with unpredictable activity patterns.

Edge AI enables real-time processing, enabling IoT devices to make instant decisions without consulting a central server. This is especially valuable in energy-critical applications such as healthcare, where wearable devices might monitor heart rate and initiate alerts directly from the edge without the power and time expense of cloud communications. Edge AI can dynamically assess whether a task should be performed locally or offloaded to another edge or cloud server based on current network conditions and energy costs. Machine learning algorithms, such as RL, can continuously learn optimal offloading policies that balance energy usage and performance.

Edge AI can allocate resources (like processing power and bandwidth) based on real-time energy availability and network demands. For instance, in a low-power situation, an edge device may prioritize essential tasks and defer less critical ones to preserve energy, all guided by AI-driven policies. AI-driven scheduling algorithms at the edge can prioritize tasks to reduce energy-intensive operations. For example, in industrial IoT, certain sensors can delay non-urgent readings, conserving energy without compromising essential monitoring quality.

Edge AI algorithms can predict potential device failures by analyzing sensor data patterns. This proactive approach to maintenance reduces the energy cost of continuous monitoring. It enables predictive actions, such as replacing batteries or recalibrating sensors before they fail, thus optimizing energy usage and extending device lifetime. Edge AI can maintain energy efficiency across entire networks by monitoring energy levels and adapting operations based on device conditions. This proactive management is especially beneficial for networks with heterogeneous devices, where devices of different energy capacities must coexist and operate efficiently.

In full offloading, all processing tasks are transferred from the IoT device to an edge or cloud server. This strategy is ideal when the device has limited processing power, needs to conserve energy, or requires processing capabilities that exceed local resources. However, full offloading can increase network traffic and may not be suitable for latency-sensitive applications [24]. In Partial offloading, only selected task parts are offloaded, while some computations remain on the device. This is often achieved through partitioning algorithms, which divide tasks based on energy requirements, available resources, or latency needs. Partial offloading provides a balance by reducing device energy consumption without incurring excessive network delays [25].

opportunistic offloading considers current network conditions, such as bandwidth availability and network congestion, to decide the optimal times for offloading. For instance, offloading may be delayed if the network is busy, saving power and reducing the risk of data transmission failure [26]. In Device-to-Device (D2D) offloading, tasks are offloaded to nearby IoT devices with spare capacity rather than a central server. This approach helps in scenarios where cloud connectivity is limited or energy conservation by avoiding long-distance data transmission is needed [27].

Proximity-based offloading, offloades tasks to the nearest available edge nodes. This approach benefits applications requiring immediate feedback, such as real-time video analytics or autonomous vehicle systems. By leveraging edge nodes close to the data source, latency is minimized, which is especially valuable for IoT applications with stringent delay constraints [28]. Hierarchical offloading in large IoT networks, can involve multiple layers, such as local edge devices, regional servers, and cloud centers. Each layer handles tasks based on their latency and processing requirements. For instance, low-latency tasks are offloaded to edge devices, while high-processing but delay-tolerant tasks go to the cloud. This layered approach optimizes latency and energy use across the network [29].

In Task-based offloading, tasks are classified based on processing needs, data size, and criticality. Resource-intensive tasks may be offloaded to nearby edge nodes or the cloud, while simpler tasks are handled locally. This strategy is often used in smart city environments, where tasks like real-time surveillance and traffic management have unique processing needs [30]. Decisions in priority-based offloading are made based on task priority, where critical tasks are prioritized for offloading to meet deadlines. Low-priority tasks may be delayed or processed locally when resources are limited. This approach works well in scenarios where certain tasks, like emergency alerts in healthcare, must take precedence over routine data collection [30].

Energy-aware offloading algorithms consider the power levels of IoT devices and adjust offloading decisions to conserve battery life. For example, tasks may be offloaded more frequently when a device’s battery is low, preserving energy for essential operations. Certain applications can tolerate minor delays, allowing for energy-efficient offloading strategies prioritizing low energy usage over speed. For instance, in environmental monitoring, data transmission may be delayed without significant impact, allowing devices to conserve power by avoiding frequent transmissions [31].