An In-depth Analysis of CUSUM Algorithm for the Detection of Mean and Variability Deviation in Time Series

et al. 2020

IET wirel. sens. syst.

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In body sensor networks (BSNs), medical sensors capture physiological data from the human body and send them to the coordinator who act as a gateway to health care. The main aim of BSNs is to save peoples' lives. Therefore, fast and correct detection of emergencies while maintaining low-energy consumption of sensors is essential requirement of BSNs. In this study, the authors propose a new adaptive data sampling approach, where the sampling ratio is adapted based on the sensed data variation. The idea is to use the modified version of the cumulative sum (CUSUM) algorithm (modified CUSUM) that they previously proposed for wireless sensor networks to monitor the data variability, and adapt the sampling rate accordingly. Modified CUSUM is then applied to the adaptively sampled data to detect anomalies, and the correlation property between physiological parameters will be used to identify emergency cases from false alarms. Several experiments are performed and compared to evaluate the efficiency of their approach, and different parameters are considered.

“…When the value of

C_{i}^{+}

returns to be below the threshold H , the process is declared as in control. The values of K and H depend on the standard deviation

σ_{0}

of the training values

K = k σ_{0}; H = h σ_{0}

It is recommended to select

k = 0.5

and

h = 4 or 5

to attain a good ARL [19, 46].…”

Section: Related Workmentioning

confidence: 99%

C_{i}

.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…As stated in Section 2.4, K =

k σ_{0}

is used to compute

C_{i}

and the decision threshold H is computed as

h σ_{0}

, where

σ_{0}

is the standard deviation of the sensors training values. k and h are chosen to be 0.5 and 4, respectively, to attain a good ARL as recommended in [19, 46].…”

Section: Proposed Approach For Emergency Cases Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toward fast and accurate emergency cases detection in BSNs

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IET wirel. sens. syst.

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A cloud-based foundational infrastructure for water management ecosystem

2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)

Jaoude

et al. 2020

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Water monitoring is one of the critical battles of sustainability for a better future of humanity. 44 countries are considered at high risk of the water crisis, 28 of which are developing countries and have limited capacity to deploy a national scale solution. As a response to the United Nation's sustainability goals and initiatives, this paper proposes an intelligent water monitoring service which acts as a foundational infrastructure for all future water management systems. It also provides municipalities, Non-Governmental Organization and other private initiatives with the tools needed to establish local water monitoring in the scale of villages or rural areas with a very small initial investment.