Table 1 Energy enhancement schemes: main ideas, advantages, and drawbacks.

TSRA³⁸

Stochastic optimization to minimize energy via joint task scheduling, power, and resource allocation

Balances performance and energy dynamically; minimizes average energy

Needs practical validation in real-world scenarios

EASF³⁹

Adaptive sampling using spatio-temporal correlation to reduce energy

Improves network lifetime; reduces energy up to 47%

Potential data loss; relies on reconstruction accuracy

DSOM⁴⁰

MILP model using TPN for predictive maintenance in dynamic scheduling

Handles complex operations; provides optimal decision-making

High computational cost; solver dependency

ESS⁴¹

Real-time CNN-based sleep control for NB-IoT devices in 5G environments

Extends device lifetime; responsive to data changes

Complex implementation; requires continuous connectivity

TACE⁴²

Energy optimization at Smart Homes

Enhances lifetime; better load and energy enhancement

Only for indoor system, susceptible to noise

EERM⁴³

Energy with QoS with Edge-fog-cloud architecture

Enhances QoS with energy enhancement

Scalabilty issues in increase sensor nodes