Deployment-Based Lifetime Optimization Model for Homogeneous Wireless Sensor Network under Retransmission

Li, Ruiying; Liu, Xiaoxi; Xie, Wei; Huang, Ning

doi:10.3390/s141223697

Cited by 16 publications

(17 citation statements)

References 36 publications

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“…A packet need a frame transmission, so the packet size is B × T × L. The cluster operation determines B and T. In order to efficiently utilize the battery of each sensor, L is adaptable according to the time-varying channel conditions. The CMs transmit data packets according to control information in data transmission slot [36,37,38]. …”

Section: System Modelmentioning

confidence: 99%

Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks

Zhang

Jiang

Huang

et al. 2015

Sensors

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Technological advances have led to the emergence of wireless sensor nodes in wireless networks. Sensor nodes are usually battery powered and hence have strict energy constraints. As a result, energy conservation is very important in the wireless sensor network protocol design and the limited power resources are the biggest challenge in wireless network channels. Link adaptation techniques improve the link quality by adjusting medium access control (MAC) parameters such as frame size, data rate, and sleep time, thereby improving energy efficiency. In this paper we present an adaptive packet size strategy for energy efficient wireless sensor networks. The main goal is to reduce power consumption and extend the whole network life. In order to achieve this goal, the paper introduces the concept of a bounded MAB to find the optimal packet size to transfer by formulating different packet sizes for different arms under the channel condition. At the same time, in achieve fast convergence, we consider the bandwidth evaluation according to ACK. The experiment shows that the packet size is adaptive when the channel quality changes and our algorithm can obtain the optimal packet size. We observe that the MAB packet size adaptation scheme achieves the best energy efficiency across the whole simulation duration in comparison with the fixed frame size scheme, the random packet size and the extended Kalman filter (EKF).

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Section: System Modelmentioning

confidence: 99%

Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks

Zhang

Jiang

Huang

et al. 2015

Sensors

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“…In WSNs, nodes are usually randomly broadcasted or manually placed around the perceptual object in order to achieve the target perception data collection and forwarding. Depending on the sensor node structure, there are two kinds of sensor network, namely isomorphic networks and heterogeneous networks [7,8]. Depending on the sensor node structure, there are two kinds of sensor network, namely isomorphic networks and heterogeneous networks [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Complex and heterogeneous networks can be modeled using epidemic spreading [4], the epidemic process of complex and heterogeneous connectivity patterns [5], and non-Markovian social contagion models [6]. Depending on the sensor node structure, there are two kinds of sensor network, namely isomorphic networks and heterogeneous networks [7,8]. As isomorphic networks are composed of low-cost nodes that have the same structure and which are easily deployed, they often encounter problems such as a generally low network energy utilization rate and an unbalanced energy consumption of nodes, leading to premature node death and long-term network failure [9].…”

Section: Introductionmentioning

confidence: 99%

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

Khan

Singh

2018

ETRI Journal

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To solve the problem of unbalanced loads and the short network lifetime of heterogeneous wireless sensor networks, this paper proposes a node‐selection algorithm based on energy balance and dynamic adjustment. The spacing and energy of the nodes are calculated according to the proximity to the network nodes and the characteristics of the link structure. The direction factor and the energy‐adjustment factor are introduced to optimize the node‐selection probability in order to realize the dynamic selection of network nodes. On this basis, the target path is selected by the relevance of the nodes, and nodes with insufficient energy values are excluded in real time by the establishment of the node‐selection mechanism, which guarantees the normal operation of the network and a balanced energy consumption. Simulation results show that this algorithm can effectively extend the network lifetime, and it has better stability, higher accuracy, and an enhanced data‐receiving rate in sufficient time.

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“…Usually, we adopt the deterministic deployment method to meet these demands, and the optimization targets of the deterministic deployment mainly are coverage, connectivity, lifetime, and so on. All the deployment methods are desired to satisfy the demands with high efficiency, and the deployment methods designed by scholars can improve such performance of the WSN as better connectivity and longer lifetime [13][14][15][16]. Besides, there are some studies mainly about the load balance and fault tolerance [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Sensor Deployment Method for Target Coverage

et al. 2018

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In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.

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Deployment-Based Lifetime Optimization Model for Homogeneous Wireless Sensor Network under Retransmission

Cited by 16 publications

References 36 publications

Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks

Study of Multi-Armed Bandits for Energy Conservation in Cognitive Radio Sensor Networks

Energy-balance node-selection algorithm for heterogeneous wireless sensor networks

A Deterministic Sensor Deployment Method for Target Coverage

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