Lifetime maximizing dynamic energy efficient routing protocol for multi hop wireless networks

Bhattacharjee, Sanghita; Bandyopadhyay, S.

doi:10.1016/j.simpat.2012.11.006

Cited by 36 publications

(14 citation statements)

References 21 publications

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“…The results revealed that the approach gains near-optimal performance for constant information gathering rates as well as random ones. Bhattacharjee and Bandyopadhyay 186 introduced an energy efficient load balancing routing scheme that considered node cost in packet transmission from source to sink. The node cost included energy efficiency and overhearing cost to locate energy-efficient next hop node and residual energy node for even distribution of load in order to balance energy consumption.…”

Section: Routing Strategiesmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Section: Routing Strategiesmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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“…9 shows that CCMR exploits clustering and collaboration to create effectively the path {1, 24, 5, 10, 18, 23, 11} from source node 1 to destination node 11 the nodes in the network with 25 network nodes. Nodes 16,13,19,3,25,6,4,14,15,20,12,and 9 are collaborative ones that cooperate with transmission nodes 1, 24, 5, 10, 18, and 23. Fig.…”

Section: Collaborative Multi-hop Pathmentioning

confidence: 99%

“…Yue et al studied the coding-aware routing metric in multi-hop wireless networks [14]. Bhattacharjee et al proposed an energy efficient routing algorithm to balance data traffic among network nodes and improve network lifetime [15].…”

Section: Introductionmentioning

confidence: 99%

A collaborative multi-hop routing algorithm for maximum achievable rate

Jiang

Wang

et al. 2015

Journal of Network and Computer Applications

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“…To overcome the problems such as energy hole and lifetime optimization in static terrestrial WSNs and underwater WSNs, [5][6][7] research into lifetime optimization algorithms. Those algorithms assume that the node locations are fixed.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

Chen

Wang

Ren

et al. 2014

Mathematical Problems in Engineering

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In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

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Lifetime maximizing dynamic energy efficient routing protocol for multi hop wireless networks

Cited by 36 publications

References 21 publications

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

A collaborative multi-hop routing algorithm for maximum achievable rate

Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

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