Compression sensing is used to overcome the limitations of traditional sampling theory and to apply the concept of compress during the sensing procedure. Researchers have developed wireless body sensor networks by creating the network and using miniature equipment. Small topology, low power consumption, scalable data rates from kilobits per second to megabits per second, low cost, simple hardware deployment, and low processing power for wireless sensor nodes are needed through lightweight, implantable, and shareable WBSN communication tools. Thus, the proposed system used WBSN with IEEE 802.15.4 in simulation Castalia using the language of C++ in addition to the adaptive compression sensing technology. To build a health system that helps people to maintain their health without going to the hospital and get more efficient energy through adaptive compression sensing, energy is obtained more efficiently and thus helps the sensor battery to last longer and will be increased energy-efficient from 228.134 NJ/bit to 433.082 NJ/bit. Finally, wearable devices were used from sensors. The proposed system uses seven sensors: electrocardiogram, pulse oximeter, heart rate, blood pressure, skin temperature, temperature, and humidity. It collects data and transmits it via Wi-Fi to the microcontroller Raspberry Pi3 using the language of python on the Internet of Things. Then, they have been linked to the Blynk platform that showcases the desired results for the individual. With adaptive compressed sensing technology, a compressed data average of 10.7 with an average of 0.372 PRD was achieved. The energy was increased from 228.134 NJ/bit to 433.082 NJ/bit, as well as the throughput from 595758 kbps in one node and become 21.4494 Gbps in multi-node II of the simulation Castalia, as well as efficient power was obtained in the hardware implementation of the proposed system at level 3, which the number of samples to 400 from 3000 samples.

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