A novel spatial correlation model for wireless sensor network applications

Shakya, Rajeev K.; Singh, Yatindra Nath; Verma, Nishchal K.

doi:10.1109/wocn.2012.6335549

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…It does not consider the local interactions of nodes, their remaining energy, their consumption cost etc. [15,19,20]. In this paper, spatial correlation characteristics of sensor nodes is studied in the design the improved SIR model and its comparative analysis.…”

Section: Epidemic Theory and Wireless Sensor Networkmentioning

confidence: 99%

SIR Epidemic Model derived from Spatial Correlation for Worm Propagation in Event-driven Wireless Sensor Network

Chaudhary,

Shakya*,

Kumar

2020

IJITEE

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An event or a physical activity triggers many nodes for sensing the environment in wireless sensor networks for event monitoring applications. These triggered nodes then transmit the sensed data to the control center. To study the virus propagation behavior in event-driven WSNs, sensor coverage which is a quality of service parameter, can be considered in epidemic model design to get more insight. Existing epidemic models have global characteristics when it is considered a realistic behavior of WSNs. In this paper, a comparative analysis is carried out for spatial correlation feature found in sensor nodes based on sensing range. It is extended version of our recent work. We present analysis using Susceptible-Infectious-Recovered (SIR) epidemic model with and without spatial correlation feature. Firstly, we show that the correlated nodes are occurred due to sensing ability for event monitoring applications. These nodes are responsible for transmitting correlated information when an event occurs in the field. A detailed comparative analysis with recent existing SIR epidemic models is presented with results and discussions. A comparison based on basic reproduction number is also discussed. Our Results show the impact of spatial correlation in the behavior of virus spread with time. Comparative study shows the effective use of our model is in designing prevention mechanisms for infection control. It can also be used to study the virus spreading behavior for event-driven scenarios in WSNs .

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Section: Epidemic Theory and Wireless Sensor Networkmentioning

confidence: 99%

SIR Epidemic Model derived from Spatial Correlation for Worm Propagation in Event-driven Wireless Sensor Network

Chaudhary,

Shakya*,

Kumar

2020

IJITEE

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“…That is, the generated sensory data by neighbouring nodes are often correlated. Hence, there has been very active research in data spatial correlation in sensor networks (Shen and Chen, 2014;Bhosale et al, 2015;Liu et al, 2013;Shakya et al, 2012;Crary et al, 2015). Shen and Chen (2014) present an improved method to prolong the lifetime of WSN by exploiting the spatial correlation which is called unequal distributed spatial correlation-based tree clustering for approximate data collection (UDSCTC) algorithm.…”

Section: Spatial Correlationmentioning

confidence: 99%

Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

Harb

Makhoul

Jaber

et al. 2019

IJSNET

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In wireless sensor networks (WSNs), the densely deployment and the dynamic phenomenon provide strong correlation between sensor nodes. This correlation is typically spatiotemporal. This paper proposes an efficient data collection technique, based on spatio-temporal correlation between sensor data, aiming to extend the network lifetime in periodic WSN applications. In the first step, our technique proposes a new model based on an adapted version of Euclidean distance which searches, in addition to the spatial correlation, the temporal correlation between neighbouring nodes. Based on this correlation and in a second step, a subset of sensors are selected for collecting and transmitting data based on a sleep/active algorithm. Our proposed technique is validated via experiments on real sensor data readings. Compared to other existing techniques, the results show the effectiveness of our technique in terms of reducing energy consumption and extending network lifetime while maintaining the coverage of the monitored area.

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“…In order to reduce the redundancy and shrink the total amount of data in sensor networks, data aggregation has been well studied by numerous researchers in recent years [7][8][9]. In [10], data aggregation methods are classified according to the level of sampled data, that is, data compression aggregation [11,12], in-network aggregation [13,14], and representative aggregation [10,15]. Applying any of these data aggregation methods can reduce the redundancy of data in sensor networks to some extent; however, each of them has certain limitations.…”

Section: Related Workmentioning

confidence: 99%

“…There are some other representative aggregation strategies proposed recently with the coverage model and location information to achieve energy saving [10,15]. In [15], Shakya et al addressed the problem of energy-efficient data collection process in the existence of spatial correlation in relatively dense wireless networks. They mainly focus on locationbased correlation characteristics among sensor nodes.…”

Section: Related Workmentioning

confidence: 99%

Location-Based Data Aggregation in 6LoWPAN

Hu¹,

Luo²,

Zhang³

et al. 2015

International Journal of Distributed Sensor Networks

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Location-based information has recently been exploited to assist the aggregated process of data, thereby reducing the spatial redundancy efficiently. The constraints nature in 6LoWPAN becomes one of the major concerns in data aggregation methods. However, traditional CSMA/CA in MAC layer may cause significant transmission and control overhead as well as delay on listening and competing for channels. It is a low efficient way to transfer IPv6 packet due to the big packet header. To overcome these shortages, in this paper, we propose LDAA, a location-based novel data aggregation model that aggregates data from the network layer according to the MAC layer queuing delay. When the queuing delay becomes larger, more packets will be dynamically aggregated into one packet to increase the proportion of application data. Otherwise, the amount of packets involved in aggregation will decrease to improve channels utilization. Simulation results show that our approach could provide better real-time guarantees and reduce data spatial redundancy and energy consumption efficiently.

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A novel spatial correlation model for wireless sensor network applications

Cited by 13 publications

References 7 publications

SIR Epidemic Model derived from Spatial Correlation for Worm Propagation in Event-driven Wireless Sensor Network

SIR Epidemic Model derived from Spatial Correlation for Worm Propagation in Event-driven Wireless Sensor Network

Energy efficient data collection in periodic sensor networks using spatio-temporal node correlation

Location-Based Data Aggregation in 6LoWPAN

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