In the past years several solutions and discussions proposed about mentioned challenges. Bradai et al. [25] proposed data scheduling and aggregation under WBAN/WLAN healthcare network. They proposed scheduling algorithms to satisfy QoS needs and to overcome the starvation mode of the packets without the highest priority. This proposed method led to reduce latency and increased network capacity. The disadvantage of this method is transmitting data continuously.

In [1] authors proposed adaptive data collection approach for periodic sensor networks. They used adaptive sampling Based on data variance. The result of this method is reducing energy consumption and the disadvantage of it is unnecessary activation of sensors. Makhoul et al. [3] proposed adaptive data collection approach for periodic sensor networks. It is based on the dependence of measurements variance while taking into account the residual energy that varies over time. They used of adaptive data collection technique and this method resulted in a lower transmission rate. The faults of this method are unnecessary activation of sensors and high power consumption of sensors.

Yoon et al. [2] proposed adaptive sensing and compression rate selection scheme. Authors used of selecting the optimum compression rate for captured data in WSNs. In this way, a node periodically selects a compression algorithm and adjusts its sensing rate accordingly. This method led to reducing power consumption in network. However, the sensors are constantly active.

Lee et al. [26] proposed harvesting and energy aware adaptive sampling algorithm for guaranteeing self-sustainability. Authors introduced new methods to extend the lifetime of network. They proposed adaptive sampling algorithm (ASA) and resuscitation adaptive sampling algorithm (RASA) and compensation adaptive sampling algorithm (CASA) and also adaptive sensor management scheme (ASMS) to apply ASA, CASA and RASA according to the energy state of sensors. The results of mentioned method are network self-stability and decreasing power consumption. However, this method is not applicable in most of networks and environments, because of this method assumes the sensors are rechargeable.

Lu et al. [5] proposed distributed sampling rate allocation for data quality maximization in rechargeable sensor networks. Authors introduced a way to calculate and specify distributed sampling rate .In this technique, authors used an algorithm called EAA to determine the amount of energy that each node can access at each interval and another algorithm called RAA to allocate the optimal data sampling rate for each node. The results of this method are increasing data quality and reducing power consumption. However, this method is not applicable in most of networks because of this method assumes the sensors are rechargeable.

Carol et al. [22] proposed self-adaptive data collection and fusion for health monitoring. They proposed some algorithms for sampling. Authors employs an early warning score system to optimize data transmission and estimates in real-time the sensing frequency and a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. The result of their methods is reducing data transfer rate and the disadvantage of their methods is unnecessary activation of sensors.

Marco et al. [4] authors proposed an adaptive sensing scheme. In this content, a set of low-complexity rules auto-regulates the sensing frequency depending on the observed parameter variation. This method led to reducing data overhead and reducing energy consumption. However, in this method there were not considered emergency conditions on the network.

Keally et al. [27] introduced an adaptive approach to determining optimal sampling rate. In this method, authors used of an appropriate interpolation function to determine the optimal sampling rate. The mentioned function for estimating optimal sampling rate considers two factors: the values measured by the pivot node and the patient's risk data.

Fathy et al. [6] proposed an adaptive method for data reduction in IOT. In this proposed method, authors reduced data sent with using the LMS algorithm and adaptive filters. However, in this method sensors are continuously active and emergency conditions are not included.

Zhou et al. [28] proposed node sleep strategy and energy conservation method based on data compression. They used of representation classification (SRC) algorithm to identify the normal signal and compressed sensing (CS) theory for signal compression sampling, and the compressed signal is sent to the coordinator .This method reduces power consumption by decreasing size of transmitting data and sleeping nodes when signals are normal.

Table 1

Specifications and Disadvantages of State-of-the-art methods

REF | PROPOSED METHOD | RESULTS | DISADVANTAGES |

[25] | scheduling and aggregation under WBAN/WLAN | • reducing latency • increasing network capacity | • transmitting data continuously |

[1] | adaptive sampling | • reducing energy consumption | • unnecessary activation of sensors |

[3] | adaptive data collection | • reducing energy consumption | • unnecessary activation of sensors • high power consumption of sensors |

[2] | adaptive sensing and compression rate selection scheme | • reducing power consumption in network | • sensors are constantly active |

[26] | adaptive sampling algorithm for guaranteeing self-sustainability | • network self-stability • decreasing power consumption | • not applicable because it assumes that the sensors are rechargeable |

[5] | distributed sampling rate allocation for data quality maximization | • increasing data quality • reducing power consumption | • not applicable because it assumes that the sensors are rechargeable |

[22] | self-adaptive data collection and fusion | • reducing data transfer rate | • unnecessary activation of sensors |

[4] | adaptive sensing scheme | • reducing data overhead • reducing energy consumption | • not considered emergency conditions |

Makhoul et al. [29] proposed using DWT lifting scheme for lossless data compression in Wireless Body Sensor Networks. It is based on the Discrete Wavelet Transform using the lifting scheme extended with Lagrange polynomial interpolation. This technique reduces power consumption by decreasing size of data. However, in this technique emergency conditions and sleeping unnecessary nodes did not handle.