An efficient energy hole alleviating algorithm for wireless sensor networks

Xue, Yu; Chang, Xiangmao; Zhong, Shuncong; Zhuang, Yi

doi:10.1109/tce.2014.6937317

Cited by 45 publications

(34 citation statements)

References 15 publications

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“…A sensor nodes monitor event and transmit sensory data of event, a node spend its energy to transmit, receive data packets. The mathematical expressions are derived in [12] for sensor node energy consumption for transmission, and receive packets in WSNs. A number of energy efficient routing protocols have been designed for WSNs [13] [14] [15] [16].…”

Section: Related Workmentioning

confidence: 99%

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LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

S.R¹,

S.²,

M.³

2017

IJCA

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Existing reliable-oriented routing protocols computes link reliability based on the packet reception ratio and neglects impact of various parameters such as noise, shadowing, battery-lifespan, uncertainty and geographic locations. In this paper, we propose a Link Reliable Reactive Routing (LR 3 ) protocol for WSNs to accomplish reliable and resilience to out-of-order transmission and path diversity at each hop. The log-normal shadowing model is used to estimate link reliability and a back-off scheme is used to determine delay. A new cost estimated to find forwarding nodes on mentor path that includes link reliability, delay, status of queue at forwarding node and packet advancement at the forwarding node. LR 3 is simulated using NS-2 and results show that it outperforms other reactive routing protocols in terms of packet delivery ratio, latency, link reliability and data transmission cost [1] [2].

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Section: Related Workmentioning

confidence: 99%

“…by back-off delay, as in Equation (12). After computation, a potential node j k that has higher progress is designated as mentor node and node j k rebroadcast RREQ with mentor node ID and RREQ sequence number.…”

Section: P I F D P D mentioning

confidence: 99%

LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

S.R¹,

S.²,

M.³

2017

IJCA

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“…As mentioned above, the sink-based data gathering method is widely used in WSNs [1][2][3][4][5][6]. Similar to [4,29], network in this paper is defined as a circular area with a radius , while the static sink node is deployed at the center , whose coordinate is (0, 0).…”

Section: Network Modelmentioning

confidence: 99%

“…In WSNs, sensor node usually behaves as both data originator and the router [1,2]. The traffic follows a many-to-one pattern, where nodes nearer to the sink carry heavier traffic loads.…”

Section: Introductionmentioning

confidence: 99%

A Type of Energy Hole Avoiding Method Based on Synchronization of Nodes in Adjacent Annuluses for Sensor Network

Sha

Chen

et al. 2016

International Journal of Distributed Sensor Networks

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For the purpose of balancing energy consumption of nodes in Wireless Sensor Networks (WSNs for short), a type of energy hole avoiding method based on synchronization of nodes in adjacent annuluses (SNAA for short) is proposed in this paper. The circular network is divided into virtual annuluses with the same width. Nodes are deployed nonuniformly and their number increases in geometric progression from the outer annuluses to the inner ones which could effectively reduce the work load on nodes near the center. Moreover, each node could find its optimal parent by considering the residual energy of each candidate as well as the distance between the two nodes in adjacent annuluses. And on the basis of synchronization of nodes between adjacent annuluses during their transmitting and receiving phases, a sleep scheduling strategy is also proposed to further reduce the energy consumption of nodes in idle listening mode. Simulation results show that SNAA has a superior performance on energy consumption balance compared to the algorithm proposed (Liu et al., 2013;Wu et al., 2008) and it could also mitigate the energy hole problem in WSNs.

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“…The recent advancements in microsensor technology [1], [2], have realized the concept of the smart home envisaged in the last century [3]. The underlying device interconnection paradigm, namely, Wireless Sensor Network (or Smart Home Sensor Network in this case) connects sensing devices to set up a smart home [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Mobile agent-based cross-layer anomaly detection in smart home sensor networks using fuzzy logic

Usman

Muthukkumarasamy

2015

IEEE Trans. Consumer Electron.

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Despite the rapid advancements in consumer electronics, the data transmitted by sensing devices in a smart home environment are still vulnerable to anomalies due to node faults, transmission errors, or attacks. This affects the reliability of the received sensed data and may lead to the incorrect decision making at both local (i.e., smart home) and global (i.e., smart city) levels. This study introduces a novel mobile agent-based cross-layer anomaly detection scheme, which takes into account stochastic variability in cross-layer data obtained from received data packets, and defines fuzzy logic-based soft boundaries to characterize behavior of sensor nodes. This cross-layer design approach empowers the proposed scheme to detect both node and link anomalies, and also effectively transmits mobile agents by considering the communication link-state before transmission of the mobile agent. The proposed scheme is implemented on a real testbed and a modular application software is developed to manage the anomaly detection system in the smart home. The experimental results show that the proposed scheme detects cross-layer anomalies with high accuracy and considerably reduces the energy consumption caused by the mobile agent transmission in the poor communication link-state situations 1 .

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An efficient energy hole alleviating algorithm for wireless sensor networks

Cited by 45 publications

References 15 publications

LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

LR3: Link Reliable Reactive Routing Protocol for Wireless Sensor Networks

A Type of Energy Hole Avoiding Method Based on Synchronization of Nodes in Adjacent Annuluses for Sensor Network

Mobile agent-based cross-layer anomaly detection in smart home sensor networks using fuzzy logic

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