RaSMaLai: A Randomized Switching algorithm for Maximizing Lifetime in tree-based wireless sensor networks

Imon, Sk Kajal Arefin; Khan, Adnan; Francesco, Mario Di; Das, Sajal K.

doi:10.1109/infcom.2013.6567102

Cited by 26 publications

(31 citation statements)

References 17 publications

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“…In certain sensor network applications, the specific application's performance strongly depends on the amount of data gathered from each sensor node in the network. However, higher data rates result in increased sensing and communication costs across the sensor network, as well as in escalating energy consumption and reduced network lifetime [255]. Thus, there is an inherent trade-offs between the network lifetime and a specific application's performance, while the latter is often correlated to the rate at which the application can reliably send its data across sensor networks.…”

Section: Lifetime-versus-application-performance Trade-offsmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

384

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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Section: Lifetime-versus-application-performance Trade-offsmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

384

202

View full text Add to dashboard Cite

show abstract

“…In the new graph G , the capacity C ii = E i , C ij = +∞, C jj = E j . and the constraints are equivalent, it is concluded that Problem (1) is equivalent to Problem (6).…”

Section: Appendix C Proof Of Lemmamentioning

confidence: 99%

On Maximizing Sensor Network Lifetime by Energy Balancing

Gkatzikis

Fischione

et al. 2018

IEEE Trans. Control Netw. Syst.

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Abstract-Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered Wireless Sensor Networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to a long WSN lifetime. Two prominent techniques to long WSN lifetime are i) optimal sensor activation and ii) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by the energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.

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“…S. K. A. Imon et.al [4][2013] RaSMaLai efficient algorithm is implemented to achieve maximum lifetime of the WSN. called randomized switching for maximizing the life of WSN.…”

Section: Literature Surveymentioning

confidence: 99%

A Protocol for the Effective Utilization of Energy in Wireless Sensor Network

Anand¹,

Radhakrishna²

2018

IJET

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In current technology Adhoc network is most commonly used in communication network. Major parts of the Adhoc network devices are based on the sensor nodes. Sensor devices operated on the battery so energy is vital constraint. Effective Utilization of the energy of the sensor nodes and sensor networks to the full extent is highly impracticable. Optimization of the network root and the load balancing in coverage helps to maximize the lifetime of the sensor network considerably. The research carry out on sensor network, deployment model is proposed which essentially finds the optimized roots with the help of neighbour discovery algorithm which find the neighbour and non neighbour nodes and analysis the load of the network based on Packet Retrieval in RREQ, throughput, average energy consumption, packet delivery ratio and packet drop.

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RaSMaLai: A Randomized Switching algorithm for Maximizing Lifetime in tree-based wireless sensor networks

Cited by 26 publications

References 17 publications

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

On Maximizing Sensor Network Lifetime by Energy Balancing

A Protocol for the Effective Utilization of Energy in Wireless Sensor Network

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