Delay Efficient Scheduling Algorithms for Data Aggregation in Multi-Channel Asynchronous Duty-Cycled WSNs

Jiao, Xianlong; Lou, Wei; Guo, Songtao; Yang, Long; Feng, Xinxi; Wang, Xiaodong; Chen, Guirong

doi:10.1109/tcomm.2019.2924222

Cited by 34 publications

(21 citation statements)

References 38 publications

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“…Moreover, in [44], another scheduling method whose latency required for data aggregation is upper bounded in 12R + ∆ − 2 time slots is proposed. In [17], a scheduling method is proposed to minimize the latency in multi-channel WSNs. In [2], a hierarchical agglomerative aggregation scheduling method is proposed for minimizing the latency of data aggregation in directional WSNs with non-uniform power model.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Latency for Data Aggregation in Wireless Sensor Networks: An Algorithm Approach

Pham

Nguyen

Liu

et al. 2022

ACM Trans. Sen. Netw.

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In wireless sensor networks (WSNs), especially in underwater sensor networks the problem of reporting data to the sink with minimum latency has been widely discussed in many research works. Many studies address on using data aggregation to report the same type of data to the sink without data collision in a short period of time. However, due to the rapid development of sensor technology in recent years, a sensor is allowed to have multiple sensing capabilities, that is, it can generate and collect different types of data. Because different types of data have different meanings and required aggregation functions, only the data that belong to the same type are allowed to be aggregated. In addition, due to the interference of the environment or the noise, the links in the WSNs are often not bidirectional. This motivates us to study the problem of using minimum latency scheduling to aggregate and report data to the sink without data collision in multiple-data-type WSNs having unidirectional links, which is shown to be NP-hard in the paper. In addition, the Relative-Collision-Graph-Based Scheduling Algorithm (RCGBSA) is therefore proposed accordingly. Simulations are conducted to demonstrate the performance of the RCGBSA.

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Section: Related Workmentioning

confidence: 99%

Minimizing Latency for Data Aggregation in Wireless Sensor Networks: An Algorithm Approach

Pham

Nguyen

Liu

et al. 2022

ACM Trans. Sen. Netw.

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show abstract

“…Transmission channels between mobile terminals and edge base stations are likely to be obscured by some buildings, resulting in co-existence of line-of-sight (LoS) and nonline-of-sight (NLoS) communication. In this case, the line-of-sight channel model can no longer accurately describe the transmission channel between the mobile terminal and the edge base station, so we adopt the probabilistic line-of-sight channel model [20,21]. Line-of-sight communication and nonline-of-sight communication have a probability of occurrence, respectively, which is a function of the environment, the density and height of the building, and the elevation angle between the mobile terminal and the edge base station.…”

Section: Channel Modelmentioning

confidence: 99%

“Fed-DRL”: A Timeliness Optimization Method for Dynamic Data Acquisition System Based on Mobile Edge Computing

Zheng

Qiu

et al. 2022

Mathematical Problems in Engineering

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Age of Information (AoI) is a metric to describe the timeliness of a system proposed in recent years. It measures the freshness of the latest received data from the perspective of the target node in the system. This work studies a kind of dynamic data acquisition system for urban security that can update and control the situation of urban environmental security by collecting environmental data. The collected data packets need to be uploaded to the cloud center in time for data update, which has high requirements on the timeliness of the system and freshness of data. However, due to the limited computing capacity of mobile terminals and the pressure of bandwidth for data transmission, problems such as high data execution delay and transmission interruption are caused. Emerging mobile edge computing (MEC), a new model of computing that extends cloud computing capabilities to the edge network, promises to solve these problems. This work focuses on the timeliness of the system, as measured by the average AoI across all mobile terminals. First, a timeliness optimization model is defined, and a multi-agent deep reinforcement learning (DRL) algorithm combined with an attention mechanism is proposed to carry out computing offloading and resource allocation through the continuous interaction between agent and environment; then, in order to improve algorithm performance and data security, the federated learning mode is proposed to train agents; finally, the proposed algorithm is compared with other main baseline algorithms based on deep reinforcement learning. The simulation results show that the proposed algorithm not only outperforms other algorithms in optimizing system timeliness, but also improves the stability of training.

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“…This mechanism, when embedded in a multi channel asynchronous duty cycle scenario, reduces the performance of the WSNs. To address this issue, a delay efficient data aggregation scheduling problem for multi channel asynchronous duty cycled WSNs is proposed in [33]. The proposed approach is based on two concepts, namely, candidate active conflict graphs (CACGs) and feasible active conflict graphs (FACGs).…”

Section: State Of Artmentioning

confidence: 99%

A Level-Wise Periodic Tree Construction Mechanism for Sleep Scheduling in WSN

Tarasia

Swain

Roy

et al. 2020

J. commun. softw. syst. (Online)

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The wireless sensor network (WSN) has been extensively used to monitor and control the natural ecosystem on a large scale like air quality, natural life, etc. Low battery power,low storage, and limited processing ability are the most critical areas of concern in WSN. To reduce energy utilization, the sensor nodes in WSN work in a cyclic process between active and sleep mode. A certain number of nodes are chosen active and they are responsible for sensing as well as data transmission and rest of the nodes are gone to sleep. In order to lengthen the lifetime of network, in this paper we proposed a level wise periodic tree construction algorithm that uses a specific set of nodes to participate in tree construction, instead of all the nodes, to minimize the energy consumption. In this proposed approach, the main idea is to put the nodes, which are currently active and have already spent a significant amount of energy, to sleep mode, while giving chances to the leaf nodes, which has comparatively spent less energy, to become an active node and maintain connectivity. The performance of the proposed protocol is evaluated using the Castalia simulator. The simulation results show that the proposed level-wise periodic tree construction approach increases the durability of the network in conjunction with the non-level approach.

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Delay Efficient Scheduling Algorithms for Data Aggregation in Multi-Channel Asynchronous Duty-Cycled WSNs

Cited by 34 publications

References 38 publications

Minimizing Latency for Data Aggregation in Wireless Sensor Networks: An Algorithm Approach

Minimizing Latency for Data Aggregation in Wireless Sensor Networks: An Algorithm Approach

“Fed-DRL”: A Timeliness Optimization Method for Dynamic Data Acquisition System Based on Mobile Edge Computing

A Level-Wise Periodic Tree Construction Mechanism for Sleep Scheduling in WSN

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