Analysis of Aggregation Delay for Multisource Sensor Data with On-Off Traffic Pattern in Wireless Body Area Networks

Kim, Un-Ha; Kong, Eutteum; Choi, Hyun-Ho; Lee, Jung-Ryun

doi:10.3390/s16101622

Cited by 17 publications

(18 citation statements)

References 14 publications

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“…Since the packets collected by the sensor nodes have a certain correlation in time and space, after receiving the packets the nodes can directly remove redundant parts through data aggregation technology [66]. When the transmission rate is constant, it can directly reduce the delay.…”

Section: Existing Research Related To Delaymentioning

confidence: 99%

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Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Xiang

Liu

Wang

et al. 2019

J Wireless Com Network

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Data collection is the basic functions of the Internet of Things (IoT), in which the sensed data are concentrations from sensor nodes to the sink, with a timely style, so the smart response can be done for emergency. The goal of multi-modal sensor data fusion is to obtain simple and accurate data to enhance system reliability and fault tolerance. Energy efficiency and small delay are the most important indicators which govern the performance of IoT. Convergecast is a low-latency data collection strategy based on effective time division multiple access (TDMA), in which each sensor node generates a packet, and m packets can aggregate to a packet. However, in most practical networks, sensor nodes do not necessarily generate packets during each data collection cycle, but instead generate packets from time to time. In the previous convergecast strategy, each node was fixedly allocated a slot, which increased the delay and wasted energy. A delay and energy-efficient data collection (DEEDC) scheme-based matrix filling theory is proposed to collect data in a randomly generated WSNs with minimum delay and energy consumption. The DEEDC scheme uses a clustering approach. For each cluster, the number of slots required for transmission is calculated by matrix filling theory, not the number of nodes that actually generate data. This ensures that data can be collected in a network with randomly generated data (number of slots ≤ number of nodes), thereby avoiding the allocation of slots for each node and the acquisition of redundant data to lead to the wastage of time and energy. Based on the above, a mixed slot scheduling strategy is proposed to construct energy and delay-efficient, collision-free schedule scheme. After extensive theoretical analysis, by using the DEEDC scheme, the delay is reduced by about 50~80%, and the energy consumed is reduced by about 40~57%.

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Section: Existing Research Related To Delaymentioning

confidence: 99%

“…3. Data aggregation technology [66]. Through this technology, the amount of transmitted data and packet size are effectively reduced, thereby increasing energy utilization and reducing energy consumption.…”

Section: Existing Research Related To Energy Consumptionmentioning

confidence: 99%

Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Xiang

Liu

Wang

et al. 2019

J Wireless Com Network

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“…Wireless sensor networks are widely used in a variety of applications such as the environmental monitoring and urban health monitoring [ 12 , 13 , 14 ], healthcare [ 2 , 15 ], vehicular communication networks [ 16 ], cyber-physical cloud systems [ 17 ], multi-channel cognitive radio networks [ 18 , 19 ], crowdsourcing networks [ 20 , 21 , 22 ], and social networks [ 23 , 24 ]. Because of the growing demand for wireless sensor applications in various aspects, quality of service (QoS) is an important issue in wireless sensor applications [ 25 , 26 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…For the QoS of wireless sensor applications, energy efficiency [ 5 , 11 , 17 , 30 , 31 ] and delay [ 8 , 9 , 27 , 29 ] are two of the most significant indicators. Since wireless sensor nodes are battery-powered, their energy is limited.…”

Section: Introductionmentioning

confidence: 99%

“…The network routing optimization method in the network layer [ 7 , 32 ] and selecting the optimal size of data packets in the application layer [ 2 ] are effective methods, as well. Among these methods, data aggregation is an effective and widely studied method [ 7 , 29 , 36 , 37 , 38 ]. Since there is a correlation between sensing data, the amount of data packets after aggregation is less than the original amount when two or more data packets aggregate, thereby reducing the number of network data packets [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

Liu

Xie

et al. 2018

Sensors

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The quality of service (QoS) regarding delay, lifetime and reliability is the key to the application of wireless sensor networks (WSNs). Data aggregation is a method to effectively reduce the data transmission volume and improve the lifetime of a network. In the previous study, a common strategy required that data wait in the queue. When the length of the queue is greater than or equal to the predetermined aggregation threshold (Nt) or the waiting time is equal to the aggregation timer (Tt), data are forwarded at the expense of an increase in the delay. The primary contributions of the proposed Adaptive Aggregation Routing (AAR) scheme are the following: (a) the senders select the forwarding node dynamically according to the length of the data queue, which effectively reduces the delay. In the AAR scheme, the senders send data to the nodes with a long data queue. The advantages are that first, the nodes with a long data queue need a small amount of data to perform aggregation; therefore, the transmitted data can be fully utilized to make these nodes aggregate. Second, this scheme balances the aggregating and data sending load; thus, the lifetime increases. (b) An improved AAR scheme is proposed to improve the QoS. The aggregation deadline (Tt) and the aggregation threshold (Nt) are dynamically changed in the network. In WSNs, nodes far from the sink have residual energy because these nodes transmit less data than the other nodes. In the improved AAR scheme, the nodes far from the sink have a small value of Tt and Nt to reduce delay, and the nodes near the sink are set to a large value of Tt and Nt to reduce energy consumption. Thus, the end to end delay is reduced, a longer lifetime is achieved, and the residual energy is fully used. Simulation results demonstrate that compared with the previous scheme, the performance of the AAR scheme is improved. This scheme reduces the delay by 14.91%, improves the lifetime by 30.91%, and increases energy efficiency by 76.40%.

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FOG Computing and Low Latency Context-Aware Health Monitoring in Smart Interconnected Environments

Moore

Pham

2018

Advances in Internet, Data &Amp; Web Technologies

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Analysis of Aggregation Delay for Multisource Sensor Data with On-Off Traffic Pattern in Wireless Body Area Networks

Cited by 17 publications

References 14 publications

Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT

Adaptive Aggregation Routing to Reduce Delay for Multi-Layer Wireless Sensor Networks

FOG Computing and Low Latency Context-Aware Health Monitoring in Smart Interconnected Environments

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