A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks

Ye, Dayong; au, Minjie Zhang

doi:10.1109/tcyb.2017.2669996

Cited by 103 publications

(42 citation statements)

References 42 publications

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“…Recent research has begun to study the energy efficiency of IoT architectures. Scheduling sleeping intervals [11][12][13][14][15], determining duty cycles [16] in the sensor layer, and implementing packet routing [17] are among the methods to deliver energy efficiency to the sensor networks. However, the state of the art lacks the attention toward designing adaptive healthcare IoT architectures capable of controlling system-level parameters based on the context of environment and the state of patients.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems

Amiri

Anzanpour

Azimi

et al. 2020

ACM Trans. Comput. Healthcare

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Healthcare applications supported by the Internet of Things enable personalized monitoring of a patient in everyday settings. Such applications often consist of battery-powered sensors coupled to smart gateways at the edge layer. Smart gateways offer several local computing and storage services (e.g., data aggregation, compression, local decision making), and also provide an opportunity for implementing local closed-loop optimization of different parameters of the sensor layer, particularly energy consumption. To implement efficient optimization methods, information regarding the context and state of patients need to be considered to find opportunities to adjust energy to demanded accuracy. Edge-assisted optimization can manage energy consumption of the sensor layer but may also adversely affect the quality of sensed data, which could compromise the reliable detection of health deterioration risk factors. In this article, we propose two approaches: myopic and Markov decision processes (MDPs)-to consider both energy constraints and risk factor requirements for achieving a twofold goal: energy savings while satisfying accuracy requirements of abnormality detection in a patient's vital signs. Vital signs, including heart rate, respiration rate, and oxygen saturation, are extracted from a photoplethysmogram signal and errors of extracted features are compared to a ground truth that is modeled as a Gaussian distribution. We control the sensor's sensing energy to minimize the power consumption while meeting a desired level of satisfactory detection performance. We present experimental results on realistic case studies using a reconfigurable photoplethysmogram sensor in an IoT system, and show that compared to nonadaptive methods, myopic reduces an average of 16.9% in sensing energy consumption with the maximum probability of abnormality misdetection on the order of 0.17 in a 24-hour health monitoring system. In addition, over 4 weeks of monitoring, we demonstrate that our MDP policy can extend the battery life on average of more than 2x while fulfilling the same average probability of misdetection compared to the myopic method. We illustrate results comparing myopic, MDP, and nonadaptive methods to monitor 14 subjects over 1 month. CCS Concepts: • Theory of computation → Stochastic control and optimization;

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Section: Related Workmentioning

confidence: 99%

Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems

Amiri

Anzanpour

Azimi

et al. 2020

ACM Trans. Comput. Healthcare

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“…In [44] authors focused on priority based sleep scheduling for cluster-based commercial WSNs to save network energy and prolong the WSNs lifetime. A sleep or awake scheduling scheme for WSNs is introduced [45]. The aim was to conserve more energy without using duty cycling and improve the network lifetime as long as possible.…”

Section: Related Workmentioning

confidence: 99%

“…The parameters while designing scheduling schemes need to consider the type of sensors, network topology, deployment policy, sensing area, transmission range, 1297 time synchronization, localization, etc. [12]. The optimal goal of designing energy efficient scheduling schemes is to enhance the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Survey: energy efficient protocols using radio scheduling in wireless sensor network

Mathew¹,

Jones²

2020

IJECE

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An efficient energy management scheme is crucial factor for design and implementation of any sensor network. Almost all sensor networks are structured with numerous small sized, low cost sensor devices which are scattered over the large area. To improvise the network performance by high throughput with minimum energy consumption, an energy efficient radio scheduling MAC protocol is effective solution, since MAC layer has the capability to collaborate with distributed wireless networks. The present survey study provides relevant research work towards radio scheduling mechanism in the design of energy efficient wireless sensor networks (WSNs). The various radio scheduling protocols are exist in the literature, which has some limitations. Therefore, it is require developing a new energy efficient radio scheduling protocol to perform multi tasks with minimum energy consumption (e.g. data transmission). The most of research studies paying more attention towards to enhance the overall network lifetime with the aim of using energy efficient scheduling protocol. In that context, this survey study overviews the different categories of MAC based radio scheduling protocols and those protocols are measured by evaluating their data transmission capability, energy efficiency, and network performance. With the extensive analysis of existing works, many research challenges are stated. Also provides future directions for new WSN design at the end of this survey.

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“…Shift operation is combination of leave and join activity. Case 4: Sleep-wake up activity of node If all nodes in WSN are activated all the time, it spends more energy on the network [7]. It comprises idle nodes as well as active nodes.…”

Section: Case 3: Shift Activity Of a Nodementioning

confidence: 99%

Mobility Aware Clustering Routing Algorithm (MACRON) to improve lifetime of Wireless Sensor Network

Bhandai¹,

Sekhar²

2019

IJRTE

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In wireless sensor network energy consumption is the recent research trend. Sleep scheduling and clustering of nodes in mobile environment are two different approaches adopted to elongate the life of network. In this paper Mobility Aware Clustering routing algorithm (MACRON) is proposed by integrating two challenging approaches. In the sleep scheduling approach, the node takes the decision for their states independently in decentralized manner. Using this reward based approach; the node decides its own current state (sleep, wake, idle) and it also predict the state of neighbors without communicating with neighbors. The performance improvement of sleep scheduling algorithm is significant but, to enhance performance it should be integrated with clustering approaches. To support the mobility in wireless sensor network various MAC protocol has been proposed, but they consume huge energy for data transmission. The proposed MACRON algorithm works efficiently in both mobile and stationary network. The performance of proposed algorithm gives decent outcome in varying size of nodes ranging from 10 to 40 in ns-2.

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A Self-Adaptive Sleep/Wake-Up Scheduling Approach for Wireless Sensor Networks

Cited by 103 publications

References 42 publications

Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems

Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems

Survey: energy efficient protocols using radio scheduling in wireless sensor network

Mobility Aware Clustering Routing Algorithm (MACRON) to improve lifetime of Wireless Sensor Network

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