Fig. 4 illustrates the evolution of (i) temporal difference (TD) loss, and (ii) policy stability over training episodes.

Episode Reward: The cumulative reward per episode is computed as (34)R(e)=∑t=1Trt,where rt is the instantaneous reward and T is the episode length. An increasing and saturating trend indicates policy improvement.

We define convergence when all of the following conditions are satisfied:(37)|R¯(e)−R¯(e−k)|<εR,ℒ(e)<εL,Δπ(e)<επ,where R¯(e) is the moving average reward over a window of k episodes, and εR, εL, and επ are small thresholds. In our experiments, convergence is typically observed within 300–500 episodes.

To quantify the benefit of reinforcement learning, we compare the proposed DQL based controller against the following baselines:

Deterministic Policy: Fixed threshold based protocol selection and static duty cycling.

Table 1 summarizes the comparative performance:

The hybrid method regularly exceeds both deterministic and rule based baselines, yielding around 25%–35% gain in energy efficiency and decreased latency.

We trained a DQL agent on many independent runs with different random seeds in order to confirm dependability. The information shows that:

Reward trajectories converge to similar steady state values across runs.

These observations confirm that the learning process is stable and reproducible, and that the policy generalizes across different initializations.

The convergence pattern indicates that the DQL agent may be able to develop an energy aware communication strategy that takes QoS limitations and energy consumption into account. By offering secure backup choices during early training stages or in unanticipated situations, the hybrid control strategy increases robustness. In general, learning based adaptation works better than static heuristics, particularly under unforeseen or dynamic network situations.

We undertake a sensitivity analysis using the weighting factors λ1 and λ2, which establish the trade off between energy conservation as well as QoS constraints, in order to evaluate the impact of reward function parameters on the system’s operation.

The reward function is defined as:(38)R=−Ecomm−λ1⋅IPDR−λ2⋅Ilatency,where IPDR is the penalty for packet delivery ratio (PDR) breaches, Ilatency is the penalty for deadline violations, and Ecomm is the communication energy cost.

The performance measures listed below are employed:

Energy per Bit:(39)Eb=EtotalNbits,where Etotal is total energy consumed and Nbits is the number of successfully delivered bits.

With a step size of 0.25, we alter λ1 and λ2 throughout the interval [0,1]. The DQL agent is trained till convergence as well as assessed under the same workload and channel circumstances for every configuration.

The observed trade offs are summarized in Table 2.

The results demonstrate a clear trade-off governed by λ1 and λ2:

Increasing λ1 (PDR penalty) improves reliability at the cost of higher energy consumption.

Lower values prioritize energy efficiency over latency and reliability; the extreme circumstances (λ1,λ2→1) improve QoS and lead to more energy per bit.

These results emphasize the necessity of tailoring incentive parameters to the particular needs of each application. Applications that are worried about latency benefit from higher λ2, whereas installations with limited energy resources benefit from smaller λ values. To enable context-aware optimization, the proposed method dynamically modifies these weights.

We conduct an ablation research comparing three control strategies in order to evaluate the effects of this special reinforcement learning component:

Deterministic Only: Fixed threshold based policy for protocol selection, transmission power, and duty cycling.

The following metrics are used for comparison:

Energy per bit (Eb),

Table 3 summarizes the performance across the three approaches.

The results demonstrate that:

The DQL only approach improves energy efficiency and latency compared to deterministic control, confirming that learning based adaptation captures system dynamics more effectively.

While the DQL component allows for fine grained optimization based on learned experience, the deterministic component ensures predictable and consistent execution in unexpected or unknown situations. This combination maintains performance throughout exploration while reducing convergence variance.

We perform a methodical ablation study that progressively activates critical systems including duty cycling (DC), adjustable power control (PC), and protocol switching (PS) in order to ascertain the effects of each component.

Configurations:

Baseline (B0): Fixed power, no duty cycling, single protocol

Each configuration is evaluated using:(42){Etotal, ηsys, L, PDR}.

Results interpretation:

Let ηi denote the efficiency of configuration i. The marginal contribution of each component is quantified as:(43)ΔDC=ηB1−ηB0,ΔPC=ηB2−ηB0,ΔPS=ηB3−ηB0.

We further analyze interaction effects:(44)Δinteraction=ηB6−(ηB2+ηB3−ηB0).

Key findings:

Duty cycling contributes the largest standalone energy reduction (∼12%–18%),

The assessment incorporates crucial real world factors to increase ecological credibility.

The likelihood of packet success is calculated as follows:(45)Psucc=exp⁡(−γthγk),where γk includes interference:(46)γk=PtxGk|hk|2N0B+Ik.

Node positions evolve according to a random waypoint model:(47)dk=‖xi(k)−xj(k)‖,introducing connectivity quality that varies throughout time.

A time varying graph is used to illustrate network connectivity as follows:(48)𝒢(k)=(𝒱,ℰ(k)),where linkages develop and vanish according to channel conditions and distance.

In these circumstances:

Duty cycling adapts to traffic bursts and link intermittency,

Battery lifetime is estimated as:(49)Tlife=Cbat⋅VnomP¯total,where Cbat is capacity (Ah), Vnom is nominal voltage, and P¯total is average power.

For each configuration:(50)T¯life±1.96σTN,guaranteeing statistical assurance.

Improvements in battery longevity are assessed in comparison to the static power baseline:(51)ΔT=Tproposed−Tbaseline.

Across N=10 runs:(52)ΔT=16.4±1.8 hours(95% CI),demonstrating consistent improvement.

To avoid platform bias, we also report:(53)ΔTTbaseline≈25%±3%.

The improvement should be interpreted as:

A relative gain under controlled workload conditions,

Reinforcement learning provides significant advantages over heuristic control, according to the ablation experiment; nevertheless, its efficacy increases when deterministic protection is included. The hybrid approach is suitable for both energy-intensive and dynamic Internet of Things applications because it strikes a balance between resilience and high performance.

To measure the relative influence of every single module, the normalised contribution ratio was developed:Ri=Mbaseline−MiMbaseline,where Ri represents the contribution ratio of component i, Mbaseline denotes the baseline system metric, and Mi corresponds to the measured performance after enabling component i.

We report contribution ratios for:

communication energy reduction,

The experimental analysis demonstrates that:

adaptive protocol selection contributed approximately 11%–14% energy reduction,

The combined hybrid framework achieved approximately 25%–31% total energy improvement compared with the fixed-policy baseline.

To increase experimental rigor, all experiments were repeated in numerous independent runs with identical workload setups.

For each analyzed statistic, the sample average was calculated as follows:μ=1N∑i=1Nxi,and the corresponding standard deviation was calculated using:σ=1N−1∑i=1N(xi−μ)2.

Additionally, 95% confidence intervals were estimated according to:CI=μ±1.96σN.

To confirm statistical significance among the proposed framework as well as baseline approaches, paired t-tests were undertaken across numerous experimental trials. The null hypothesis suggested that there was no substantial difference in performance between the strategies being compared.

The experimental results show statistically significant improvement with:p<0.05,for reducing communication energy, minimizing latency, and increasing throughput across all measured workload situations.

To improve the ablation analysis, component level assessments were performed under a variety of traffic intensities and network circumstances. Our findings show that the limited DQL as well as adaptive protocol selection modules retain consistent performance benefits under:

low-load periodic sensing,

The sensitivity of each optimization component was also evaluated by varying reward-weight coefficients:R=αE+βL+γT,where E, L, and T represent energy, latency, and throughput objectives, respectively.

The research shows that the suggested hybrid framework maintains steady convergence behavior over mild fluctuations in α, β, as well as γ, thereby increasing the resilience and repeatability of the optimization technique.

We clearly state that the measured performance increases are not due to a specific optimization method, but rather to the coordinated interplay of communication adaptability, restricted learning, as well as workload-aware scheduling.

The suggested adaptive framework is applicable to MCU class IoT equipment such as ARM Cortex M platforms. In particular:

the constrained DQL controller is lightweight and compatible with reduced model sizes,

The complexity evaluation may be enhanced to show viability for resource limited embedded systems:Cstep=O(|A|)+O(B⋅d),where |A| denotes the action space size, B represents mini-batch size, and d corresponds to the number of trainable parameters.

To further reduce MCU-side overhead, we highlight:

model pruning,

These strategies allow for lightweight deployment on restricted IoT devices while maintaining adaptive behavior.

To further simulate practical IoT deployments, our tests include different traffic models:

periodic sensing traffic,

The dynamic controller dynamically regulates duty cycles as well as protocol selection depending on the observed traffic conditions:D=TactiveTactive+Tsleep,where D denotes the duty cycle ratio.

This enhancement increases the realism of the analysis as well as prevents making assumptions based simply on static workloads.

We explicitly state that the reported TX2 measurements should be interpreted as:

relative energy efficiency improvements,

rather than direct estimates of MCU-level absolute power consumption.

To minimize experimental bias, all protocols were evaluated under identical conditions:

identical payload size,

Additionally, we emphasize that the proposed framework focuses on transferable optimization behavior rather than platform-specific wattage measurements.

We explicitly identify MCU-level deployment validation as future work. Planned extensions include:

implementation on Cortex-M and ESP32-class devices,

Fig. 5 shows the difference in power use between running an application with the connection on and the idle baseline. At around 2.2–2.4 W, the idle power remains quite constant. This indicates that when there is no active processing or communication, the system requires less energy to function.

When the application and communication stack are operating, the power usage increases a lot. It usually hovers between 5.2 and 5.8 W, although it may reach as high as 6.5 and 7.0 W. These spikes illustrate when communication is more active, as when packets are transmitted, protocols are agreed upon, and retransmissions are made. The number and magnitude of these peaks demonstrate that the major reason runtime energy usage is so high is because it requires more effort to talk to each other.

The application’s power profile demonstrates that it does not consistently consume high power. This indicates that energy consumption correlates more strongly with communication events than with ongoing computational tasks. This suggests that static communication scheduling and protocol overhead warrant attention. In contrast, the idle power profile fluctuates less. This implies that devices exhibiting power fluctuations were actively communicating.

These findings emphasize the importance of improving communication protocols for low-power IoT devices. To reduce peak power demand as well as total energy consumption, we can reduce superfluous transmissions, merge data, and adjust communication settings. Within the proposed adaptive communication context, these advancements result in longer device lifetime as well as energy stability.

The findings reveal that IoT as well as swarm robotics implementations require energy-efficient communication methods, since communication overhead can significantly increase power consumption relative to idle operation.

To ensure that the observed energy reductions correspond to plausible IoT as well as swarm workloads, we analyze the system using three appropriate traffic models, namely periodic, bursty, and event driven communication.

Periodic traffic refers to time triggered sensor applications that generate data packets at predetermined intervals:(62)tk=k⋅Tp,k=1,2,…where the sample time is indicated by Tp. Low rate telemetry systems and environmental monitoring share this idea.

With packets coming in clusters, bursty traffic mimics clustered communications. A Poisson burst arrival process is used to illustrate this:(63)Nb∼Poisson(λb),where λb specifies the mean burst magnitude, followed by inactive periods between bursts. This pattern covers scenarios like image/frame uploads and coordinated swarm updates.

Stochastic events, which are defined as follows, provide event-driven traffic:(64)tk∼Exponential(λe),where the event rate is represented by λe. This model depicts reaction swarm behaviors, alarms, and anomaly detection.

The duty cycle control unit automatically adjusts the active/sleep schedule based on the system state and the measured traffic pattern:(65)D=TactiveTactive+Tsleep.

For periodic traffic, the controller synchronizes the active window with packet generation:

Tactive≈ transmission duration,

This results in extremely effective operation with little idle listening, saving a significant amount of energy.

When there is bursty activity, the control unit compresses the duty cycle during idle times and raises it during bursts:

Increased D during burst intervals to handle high throughput,

This adaptive scaling preserves throughput during spikes while lowering energy loss during quiet periods.

The controller uses a low duty hibernation state with quick waking capabilities for event-driven traffic.

Default low D to minimize idle energy consumption,

This maintains responsiveness while minimizing average power usage.

The efficiency of duty cycle modification is greatly dependent on the traffic model:

Periodic traffic achieves the highest efficiency due to predictable scheduling.

The stated savings in energy (up to ~74%) are regarded as an aggregate impact spanning all traffic conditions, with the greatest advantages found in periodic as well as bursty scenarios. This illustrates that the suggested adaptive architecture is resilient under a variety of workload scenarios and is not restricted to a particular traffic assumption.

Fig. 6 shows how BLE, LoRaWAN, MQTT, and CoAP compare in terms of average power utilization and speed of communication. The findings reveal that each protocol changes energy into data transmission in its own method.

LoRaWAN represents the optimal communication protocol due to its low-power consumption (less than 2 W) achieving approximately 50 bits per joule. This makes it well suited for IoT applications requiring low-power, modest data rates, and long-range operation. BLE exhibits a balanced profile with moderate power consumption (about 5 W) and reasonable efficiency (approximately 20 bits/J). It performs well for short-range communication with energy-constrained devices.

They require higher power (approximately 7–8 W) but exhibit lower efficiency for message transmission and reception. Operating at approximately 10 bits per joule, MQTT is the least efficient protocol. This is primarily attributable to protocol overhead, including connection maintenance and acknowledgment processing. CoAP outperforms MQTT in certain aspects, while BLE and LoRaWAN are faster. This demonstrates that IP-based communication protocols require further optimization.

These studies reveal that having more power doesn’t automatically mean you can communicate better. Protocols for the MAC and physical layers that are light utilize less power than protocols for the application layer that demand greater networking stacks.

The results show how important it is to pick the right protocol when building a system. Use MQTT and CoAP when energy limitations are not as important as interoperability or sophisticated app semantics. Utilize LoRaWAN or BLE on IoT devices that have been adjusted to use less energy whenever energy efficiency is critical. These tradeoffs are used by the suggested adaptive system to control communication speed and power consumption.

Fig. 7 shows how much power a Jetson based IoT node uses to transmit data over MQTT, BLE, and LoRaWAN at different distances, both when there are no obstacles (line of sight) and when there are. All protocols show that power use goes up with distance, which means that transmission power and retransmission overhead go up as the quality of the connection goes down.

In general, BLE and LoRaWAN use less power than MQTT over the entire distance range. BLE is more energy-efficient over short to medium ranges (about 40 m), but LoRaWAN has a more stable power increase and stays efficient over long distances. As the transport layer overhead and the need to keep the connection going all the time, MQTT uses more and more energy, especially when the distance is more than 40 m.

Impeded conditions significantly increase energy consumption across all methods. The effect is most noticeable with MQTT, which reaches its maximum power level at shorter distances than when it is in line of sight mode. When blocked, BLE requires a lot more power, which demonstrates that it is weak to fading and signal loss from many paths. LoRaWAN, on the other hand, is less likely to be blocked and consumes less energy since it has strong modulation and connection flexibility capabilities.

When the distance is more than 50 m, all protocols use almost all of the device’s power capacity. This implies that extending the range without adaptive control isn’t as useful. This saturation illustrates that static transmission arrangements don’t perform properly most of the time.

The data indicate that the cost of communication energy changes a lot depending on the environment. Just picking a protocol isn’t enough to attain the optimum energy efficiency. The findings indicate how significant the recommended adaptive communication architecture is. To conserve energy and make the gadget last longer, it modifies the protocols and transmission settings depending on how far away the device is and how good the channel is.

Fig. 8 illustrates how much power the receiver side of the Jetson device requires for MQTT, BLE, and LoRaWAN, regardless of whether the channel is open or closed. The distance affects how much energy is consumed. On the other hand, reception exhibits a more consistent growth in power usage with distance than transmission does. Still, it’s extremely evident what occurs when a channel goes bad.

BLE utilizes the least amount of power to pick up signals from all distances since its protocol is simple and its radio duty cycle is efficient. LoRaWAN operates in a similar fashion, but it requires more power since it takes longer to get signals and there is more work to perform to keep everything in sync. When the distance is more than 40 m, MQTT needs the greatest power to transmit. This is because it needs to maintain the TCP/IP stack and handle sessions via a broker.

Conditions that are blocked make reception better in all modes. MQTT is very influenced, and the amount of power it uses goes up in a straight line with distance, peaking at 75 m. This sort of behavior is generally associated with more lost packets, retransmissions, and longer waiting times. When blocked, BLE consumes more power, although the difference is extremely tiny across short and medium distances. LoRaWAN is more reliable, allowing it to lose-less whenever things go wrong. This shows that it works effectively in locations wherever signals do not travel easily.

Whenever the distance traveled is sixty meters or greater, the transmitting and receiving strength of MQTT as well as LoRaWAN are equal. This means that the reception windows are extended, and synchronization occurs more frequently. This convergence demonstrates the quantity of energy it requires to receive while SNR is relatively low, which static energy models sometimes disregard.

These data suggest that the amount of additional energy required to receive varies according to technique and context. This has a huge influence on the duration that nodes can live in IoT settings with low-power. The results confirm the proposed adaptive communication architecture, which saves energy by changing the reception windows, protocol selection, and listening intervals in response to changes in channel circumstances.

Fig. 9 demonstrates how BLE, MQTT, and LoRaWAN’s transmission power, communication distance, and data rate are all connected. When the distance becomes longer, all protocols require more power and transfer data more slowly. This implies that wireless signals can’t go as far as they might and connections can’t be as flexible as they could be.

BLE achieves high data rates for short-range communication, but exhibits high power consumption beyond short-ranges, making it unsuitable for long-range or ultra-low-power applications. MQTT functions adequately, though data rate gradually decreases and power consumption fluctuates. This is primarily attributable to its mobility and radio configuration. LoRaWAN maintains low-power consumption over long-ranges, but achieves lower data rates compared to short-range, high-speed alternatives.

The findings reveal that there isn’t one ideal technique to conserve energy in every situation. Protocols with high data speeds need greater power as distance increases. Protocols with low-power and long-range, on the other hand, sacrifice throughput in order to save energy. This emphasizes the relevance of low-power IoT devices’ ability to change how they interact with one another.

The findings provide confidence to the proposed adaptive architecture for energy-efficient IoT and swarm robots. By continually modifying protocols and transmission settings in accordance with distance as well as availability of energy, the system can conserve energy while maintaining high quality communication. Static installations, on the other hand, don’t make these trade offs, which wastes energy.

Fig. 10 demonstrates how the time it takes for BLE, MQTT, and LoRaWAN to communicate, the distance it can travel, and the speed at which data can be transferred are all connected. As the distance increases, the latency of all protocols increases, while the practical data rate decreases. This implies that it will take longer to transport data over long distances, there will be more retransmissions, and link layer designers will have to be more cautious.

BLE has the least latency over short distances since it transfers data rapidly and doesn’t leave much time between connections. The latency does become greater as the distance grows, thus it can only be used for talking to those who are close by. When you add distance, MQTT’s latency goes up a tiny bit since the transport layer and the acknowledgment mechanisms have to work harder. Due to this, its performance profile is even, however it varies according to distance.

Compared to BLE and MQTT, LoRaWAN is slower, especially over long distances. This is mostly due to the limited frequency of usage, long airtime, and sluggish data rates of LPWAN technology. LoRaWAN may connect distant devices, but its high latency renders it inappropriate for applications that require fast replies.

The findings indicate that the trade off between latency, speed, as well as range is significant. High throughput protocols usually accelerate data transmission over short distances. In contrast, operations that operate across long distances are relatively slow. These findings underscore the need of communication systems that can change their protocols and broadcast parameters based on the distance to travel as well as the amount of latency an application may tolerate.

Systems for IoT and swarm robots that consume less energy need to be able to switch their decision. Static setups can’t perform what delay aware protocol switching can achieve. It helps the system fulfill deadlines without consuming additional power or slowing down.

Every study is carried out in a well regulated as well as standardized benchmarking environment to offer an impartial and repeatable assessment of communication methods.

All of the protocols under study (MQTT, BLE, LoRaWAN, as well as CoAP) share the following parameters:

Payload Size: All protocols transmit identical payloads of fixed size Spayload (in bits) per packet.

Energy efficiency is computed using a unified definition across all protocols:(67)η=Ndelivered⋅SpayloadEcomm,where:

Ndelivered is the number of successfully received packets,

Thus, η represents the number of useful payload bits delivered per unit energy (bits/Joule).

The communication energy is measured as:(68)Ecomm=∫0TobsPcomm(t)dt,where Pcomm(t) is the instantaneous power consumption attributable solely to communication activities (transmission, reception, and protocol overhead). Non communication components such as idle CPU and background processes are excluded to ensure protocol level fairness.

To address differences in protocol stack implementations:

Application layer overhead (e.g., MQTT over TCP/IP) is included only when evaluating end-to-end system performance.

This standardized assessment protocol ensures that differences in measured results are only attributable to protocol features as well as adaptive control methods, rather than inconsistencies in workload, payload, or measurement technique. The implementation of a common bits/Joule measure allows for direct and relevant comparisons of energy efficiency spanning different communication systems.

Fig. 11 shows a proposed adaptive methodology that governs protocol selection, compute offloading, as well as execution to preserve energy. The experimental results indisputably support the value of this rigorous decision making technique.

The startup and condition setup procedures define the essential device conditions, resource constraints, and priority levels. This allows many protocols to function in a consistent manner. Before communicating, the framework does a prioritization analysis to verify that only relevant energy transmissions are planned. This is supported by the reported decreases in transmission overhead costs and average energy usage across protocols.

To obtain the observed efficiency gains, the fundamental protocol selection technique must be restricted by a power limitation. When there is enough energy, the system can function autonomously. The computation is moved to a different location or the communication settings are changed if there is insufficient energy. The experimental graphs show substantial energy savings and slower energy scaling due to this selective propensity, particularly if there are obstructs and long distances.

Together, implementation, resource evaluation, and refinement make it easier to handle modifications to the program’s operation. Since the system is still being assessed, it cannot stay in exceptionally energetic states for very long. As a consequence, the results show that battery life is becoming longer and that communication energy consumption is continuously falling. The idea that efficient refining lowers link variability and channel challenges is supported by the fact that efficiency doesn’t significantly deteriorate across long distances.

The results show that rather of operating as a static optimization, the suggested approach functions as a control framework that adapts over time and accounts for energy use. Making modifications after the execution, selecting the appropriate protocol, and determining whether to offload are all quite comparable. As a result, the nodes live longer, communicate more quickly, and use less power overall. This demonstrates that the suggested architecture is appropriate for long term IoT and swarm robotic systems that use little energy and operate under shifting network and ambient circumstances.

Figs. 11 and 12 demonstrate how the suggested adaptive communication architecture would effect the entire system as its entirety, including average power consumption, distribution of energy across component activities, and battery life. The improvement dramatically decreases communication energy usage across all tested operations and is directly related to quantifiable increases in lifetime.

The first subplot indicates a significant decline in average power utilization after optimization, saving roughly 25% for BLE, 33% for LoRaWAN, 30% for MQTT, and 32% for CoAP. These cuts show that protocol aware adaptation through dynamic duty cycling, changing the data rate, and selective transmission scheduling can cut down on unnecessary radio activity while keeping the connection.

The second subplot shows that the battery life has improved. Each methodology has a different basic efficiency, but they all make a single charge last four to six hours longer. The finest one makes a node last 16 to 18 h longer. Optimization helps LoRaWAN in many ways. This illustrates how easy it is to modify the time between broadcasts and set the time frames for reception. Protocols that are really basic, like BLE, have gone a long way. This is crucial because it demonstrates that adaptive control works even when the power is extremely low.

The first subplot in Fig. 13 indicates how much energy each activity consumes on average. Communication uses the greatest energy before optimization. The new system will use around 30% less energy to talk to other nodes, but it will still require roughly the same amount of energy while it’s not being utilized or when it’s performing arithmetic. This illustrates that the optimization only looks at the major energy constraint.

The second subplot in Fig. 13 shows that the battery lasts longer as time goes on. The battery lasts roughly four hours longer once it has been optimized. This result illustrates that the energy savings from each activity may endure for a long period. It also backs up the premise that IoT systems that know how much energy they consume or conserve energy are conceivable.

The results demonstrate that the proposed adaptive framework reduces energy consumption, operates protocol-agnostically, helps batteries last longer, and consistently mitigates communication-related power waste. The approach is viable for energy-constrained IoT and swarm robotic systems as the optimizations apply uniformly across protocols.

Fig. 14 demonstrates how adaptive processing, duty-cycle management, and protocol aware communication all work together to help the system consume less power and survive longer on battery power.

The arrangement of the CPU shows that the number of cores and the operating frequency have a big effect on how much energy is utilized. Using a single core with a reasonable clock frequency of 1.47 GHz lowers power utilization between 6.4 to 4.15 W. However, with flexible scaling in a multi core design, it decreases even more, to 3.24 W. This illustrates that when dealing with IoT devices that must conserve energy, a smart CPU arrangement is more critical than a large processing capability.

The most efficient way to conserve energy is to change the duty cycle. Decreasing this duty cycle from constant to 10%–20% can lower average power consumption by up to 74%, making it acceptable for most IoT applications. This shows that activity-based scheduling might assist accomplish functional objectives while reducing costs for outage and communication.

The research of transmission energy shows that the length of a discussion and the quantity of power used are related. LoRaWAN can carry data over greater distances than BLE, although BLE consumes less power over shorter distances (less than 30–40 m). But sending data outside of this range uses more energy. During this transition period, the adaptive framework chooses the protocol that transfers the least amount of energy per bit, taking into account distance and channel constraints.

The batteries endure a long time, which shows how effectively adaptive control approaches operate. With adaptive control, the duration it can run rises increased from 14 to 18 h, which is a 25% increase. This illustrates that changing the processor, duty cycle, and communication levels all at once is highly advantageous for the system.

The findings reveal that the proposed adaptive framework works effectively in real-time to control duty cycles, communication strategies, and computational effort. This lets IoT and swarm robotic installations operate all the time and consume less power.

The reported 25% improvement in battery lifetime is explicitly bounded with respect to the chosen baseline.

The improvement is computed relative to a fixed power transmission baseline, in which the node operates with static transmit power, fixed duty cycling, and no adaptive optimization. Let Tfixed and Tadaptive denote the battery lifetime under the fixed baseline and the proposed adaptive framework, respectively. The relative improvement is defined as:(69)ΔT(%)=Tadaptive−TfixedTfixed×100.

In our experimental setup, we observe:(70)ΔT≈25%,stating that a non adaptive, rigid architecture has a shorter working lifetime than a dynamic control framework.

It is important to remember that:

This improvement is not a universal gain across all possible baselines, but specifically reflects comparison against a fixed power strategy.

The suggested adaptive communication system improves battery lifespan by up to 25% compared to a static power baseline under reviewed experimental settings.

To give a more plausible perspective on battery life cycle, we explicitly describe the battery model characteristics as well as substitute the implicit linear lifespan assumption with a nonlinear discharge formulation.

The studies presume a chargeable Li-ion battery with the following characteristics:

Nominal Capacity: Cbat=5000 mAh

The total nominal energy is approximated as:(71)Ebat=Cbat⋅Vnom=18.5 Wh.

Instead of assuming linear discharge, we adopt a non linear voltage state of charge (SoC) relationship typical of Li-ion batteries. The discharge behavior can be approximated as:(72)Vbat(SoC)=Vnom−k1(1−SoC)−k2(1−SoC)2,where SoC∈[0,1] and k1,k2 are empirical constants capturing the voltage drop profile.

The instantaneous available energy is then:(73)E(t)=∫t0tVbat(SoC(t))⋅I(t)dt,where I(t) is the current draw.

Battery lifetime is determined by the time at which:(74)Vbat(SoC)≤Vcut.

The optimal usable energy varies with discharge rate as well as load circumstances, in opposition to the linear models. Because of the effects of internal resistance, high current demand induces voltage to decrease more quickly, reducing useable capacity.

This nonlinear model has been used to modify the stated lifetime gains:

Energy savings from adaptive control delay the onset of rapid voltage decline near low SoC.

Battery lifetime improvements are evaluated using a non linear discharge model, where adaptive energy savings extend the usable operating region by delaying the approach to the cutoff voltage, rather than assuming a linear scaling between power reduction and lifetime.

This formulation provides a more realistic explanation of battery management in IoT devices. It underlines that adaptive communication systems reduce mean energy consumption while increasing effective energy use in non linear discharge circumstances.