Table 2.

Comparison of recent energy efficiency measurement techniques.

Sr. no.	Authors and publication years	Parameters	Efficiency measurement technique	Advantages	Disadvantages	Efficiency improved
1	Rehman et al. [7] (2021)	Energy consumption (EC), packet drop ratio (PDR), delivery time (DT), and data leakage (DL)	Comparison of proposed model (energy-efficient IoT e-health model using AI with homomorphic secret sharing) [7] with (attribute-based encryption) ABE [61] and (privacy-enhanced data fusion system) PDFS [62] using simulation	1. The maintainability of the disease diagnosis system is increased. 2. Provides trust for communication in integration with medical cloud.	1. Under high network load, PDR increased 2. No fixed energy consumption for all IoT nodes. 3. Lack of intelligence that is required for avoiding packet collision during the increased speed of edge nodes.	EC = 17% PDR = 20% DT = 24.5% DL = 13%
2	Sodhro et al. [63] (2021)	Energy dissipation (ED), charge dissipation (CD), energy discharge and battery lifetime	Comparison of proposed model (energy-efficient algorithm) [63]with (battery recovery-based lifetime enhancement) BRLE [64] using MATLAB simulation	1. It consumes low energy 2. Increased battery lifetime.	Computational load is high	ED = 6.67% CD = 12.52%
3	Lazarevska et al. [65] (2018)	Energy consumption (EC), network lifetime (NL), packet delivery ratio (PDR), and total control traffic overhead (TCTO)	Comparison of proposed new objective function (NEWOF) and minimum rank with hysteresis objective function (MRHOF) using powertracker tool in CoojaContiki OS.	1. Improvement in the total energy consumption. 2. Improvement in TCTO 3. Less degradation in PDR. 4. Energy efficiency improved	During the implementation of mobility plug-in, this model shows approximately 20% loss.	PDR = 3% EC = 1.45% NL = 8% TCTO = 5.8%
4	Tanzila et al. [66] (2020)	Network throughput (NT), packet loss rate (PLR), end-to-end delay (E2E), energy consumption (EC), and link breakages (LB)	Comparison of proposed algorithm SEF-IoMT [66] against existing solutions EERP [67], CRD [68], and SEAR [69] using NS3	1. Decrease energy consumption thereby providing more efficiency 2. Data delivery toward medical experts is increased. 3. Highly secure with validation and integrity support 4. Lower network delay	1. Improvement required in SEF-IoMT for mobility-based medical scenarios. 2. This framework requires the improvement of energy consumption and network security when dealing with inter-WBAN data transformation	NT = 18% PLR = 44% E2E = 26% EC = 29% LB = 48%
5	Abdulmohsin Hammood et al. [32]	Energy efficiency (EE), power consumption (PC), transmission rate (TR), outage probability (OP)	Comparison of the proposed algorithm inter-WBAN cooperation in an IoMT environment (IWC-IoMT) with noninter-WBAN cooperation (direct transmission in an IoMT environment (DT-IoMT) and two hops in an IoMT environment (TH-IoMT))	1. Greater energy efficiency of IWC-IoMT than DT-IoMT and TH-IoMT. 2. The outage probability of IWC-IoMT is higher than that of DT-IoMT and TH-IoMT during symmetric transmission.	During asymmetric transmission, the outage probability of IWC-IoMT degraded compared to DT-IoMT and TH-IoMT	EE = 10% PC = 2% (PC increased with increase in TR) TR = 3% OP = 5% (with internode distance >2.5 m)