Energy-aware cross-layer optimization for EEG-based wireless monitoring applications

Abstract

Body Area Sensor Networks (BASNs) for healthcare applications have gained significant research interests recently due to the growing number of patients with chronic diseases requiring constant monitoring. Because of the limited power source and small form factors, BASNs have distinguished design and operational challenges, particularly focusing on energy optimization. In this paper, an Energy-Delay-Distortion cross-layer design that aims at minimizing the total energy consumption subject to data delay deadline and distortion threshold constraints is proposed. The optimal encoding and transmission energy are computed to minimize the total energy consumption in a delay constrained wireless body area sensor network. This cross-layer framework is proposed, across Application-MAC-Physical layers, under a constraint that all successfully received packets must have their delay smaller than their corresponding delay deadline and with maximum distortion less than the application distortion threshold. Due to the complexity of the optimal-proposed solution, sub-optimal solutions are also proposed. These solutions have close-to-optimal performance with lower complexity. In this context, there is complexity/energy-consumption trade-off, as shown in the simulation results.

Technical Issues and Challenges in Building Human Body Sensor Networks

“…In graphs (Figure 2) below shows energy consumption patterns demonstrated in this research paper. [4] [5] "A Motion-Powered Piezoelectric Pulse Generator for Wireless Sensing via FM Transmission" This paper demonstrates a process of building a motion-powered piezoelectric effect pulse generate for wireless sensing that using FM transmission can become a part of body sensor monitoring system. This device basically helps in energy harvesting attached with body of a subject.…”

“…The patient may be stationed at his/her home, office or is present in hospital itself. Now with the help of body sensor networks [1][2] [4][5] [19] [23], heat stress [1],heart pulse [7][31], heartbeat [31], blood pressure [1] [7][10] [15] [20][22] [31], blood sugar readings, sleep data [6][28] [29] can be transmitted, and analyzed at multilocations for fast response. This wireless body sensor network, however are connected to "Health Cloud [32]" for remote processing by computer algorithms and healthcare service providers.…”

Implementation of Zero Proof Algorithm for Extended Wireless Body Sensor Networks

“…In this protocol one party presents a question ("challenge") and another party must provide a valid answer ("response") to be authenticated, here both the parties should be machines interacting along the transmission of time series data .Hence, the protocols using "zero proof" [19][20] [22]approach for authentication are more suitable for such scenarios. Therefore, for extending and improving the work in this context it is suggested that such algorithm may be used in conjunction with cryptography that makes key management scheme more secure, scalable , and reliable with low storage need to maintain low energy consumption [7][8] tradeoffs.…”

Distributed in-network processing and resource optimization over mobile-health systems

“…However, neither the aforementioned work nor the studies in [22] and [23], have considered a cross-layer approach that takes the application requirements, in-network data processing, and physical layer components jointly into consideration. With regards to our previous work [24][25] [26], we have studied the transmission and processing energy consumption and developed an Energy-Compression-Distortion analysis framework. Using this framework, [24] proposes a cross-layer optimization model that minimizes the total energy consumption, under a TDMA scheduling.…”

“…With regards to our previous work [24][25] [26], we have studied the transmission and processing energy consumption and developed an Energy-Compression-Distortion analysis framework. Using this framework, [24] proposes a cross-layer optimization model that minimizes the total energy consumption, under a TDMA scheduling. The work has been extended in [25] to the case where more than one link can be activated at the same time, using the same TDMA slot.…”