New trends in sensor coverage modeling and related techniques: A brief synthesis

Boudaren, Mohamed El Yazid; Senouci, Mustapha Réda; Senouci, Mohamed Abdelkrim; Mellouk, Abdelhamid

doi:10.1109/saconet.2014.6867778

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…Boolean (deterministic/disk) sensing model is the simplest and most commonly used sensing model [29]. In this model, if a point (or event) P in the network field is located within the sensing range R of sensor node S, then it is assumed that P is covered/detected by S. The sensing area of S is defined as a disk centered at S with a radius of the sensing range R.…”

Section: C3 Boolean Sensing Modelmentioning

confidence: 99%

Coverage protocols for wireless sensor networks: Review and future directions

2019

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The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverageaware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered.

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Section: C3 Boolean Sensing Modelmentioning

confidence: 99%

Coverage protocols for wireless sensor networks: Review and future directions

2019

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“…To deal with such problem, we have to first define a coverage model for the sensor. Boudaren et al [16] mentioned three kinds of coverage models. Deterministic coverage models classify a point to be either certainly within a specific region (i.e., covered by a sensor) or not.…”

Section: Related Workmentioning

confidence: 99%

Continuous Monitoring of the Ambient Factors via ε-Smooth Support Vector Regression

Jhuo

Chen

Yang

et al. 2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Thanks to the advances of the Internet of Things (IoTs), more and more wireless sensor networks applications have been realized. One of the fundamental but crucial applications is the continuous monitoring of environmental factors including temperature, humidity, illumination, etc. We develop a nonlinear regression model which takes spatial and temporal information into account to construct a globally three-dimensional heat map for a closed space based on very sparse sensor deployment. However, fitting the whole-space heat map with a very limited number of sensor observations gives a very poor estimation when we use a nonlinear model. We call it the coverage hole problem. We utilize the uniform experimental design which is well known in industrial statistics to allocate the synthetic sensors. We estimate those synthetic sensor readings on the basis of linear model locally. We then apply ε-SSVR, a nonlinear support vector regression model to fit the globally three-dimensional heat map by combining real sensor and synthetic sensor readings. The numerical results demonstrate our proposed model can enhance the accuracy significantly.

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“…The design of reliable wireless sensor networks has been studied in many literature including the sensing coverage design [2]- [5]. The sensing coverage means the area that a sensor can provide valid information from its measurement.…”

Section: Introductionmentioning

confidence: 99%

Securing Fresh Data in Wireless Monitoring Networks: Age-of-Information Sensitive Coverage Perspective

Kim,

Lee

2021

Preprint

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With the development of IoT, the sensor usage has been elevated to a new level, and it becomes more crucial to maintain reliable sensor networks. In this paper, we provide how to efficiently and reliably manage the sensor monitoring system for securing fresh data at the data center (DC). A sensor transmits its sensing information regularly to the DC, and the freshness of the information at the DC is characterized by the age of information (AoI) that quantifies the timeliness of information. By considering the effect of the AoI and the spatial distance from the sensor on the information error at the DC, we newly define an error-tolerable sensing (ETS) coverage as the area that the estimated information is with smaller error than the target value. We then derive the average AoI and the AoI violation probability of the sensor monitoring system, and finally present the η-coverage probability, which is the probability that the ETS coverage is greater than η ratio of the maximum sensor coverage. We also provide the optimal transmission power of the sensor, which minimizes the average energy consumption while guaranteeing certain level of the η-coverage probability. Numerical results validate the theoretical analysis and show the tendency of the optimal transmission power according to the maximum number of retransmissions. This paper can pave the way to efficient design of the AoI-sensitive sensor networks for IoT.

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New trends in sensor coverage modeling and related techniques: A brief synthesis

Cited by 13 publications

References 25 publications

Coverage protocols for wireless sensor networks: Review and future directions

Coverage protocols for wireless sensor networks: Review and future directions

Continuous Monitoring of the Ambient Factors via ε-Smooth Support Vector Regression

Securing Fresh Data in Wireless Monitoring Networks: Age-of-Information Sensitive Coverage Perspective

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