I&lt;sup&gt;2&lt;/sup&gt;C: A holistic approach to prolong the sensor network lifetime

Peng, Yang; Zi, Li; Qiao, Daji; Zhang, Wensheng

doi:10.1109/infcom.2013.6567075

Cited by 15 publications

(16 citation statements)

References 28 publications

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“…In the future, we plan to consider distributed implementation of our algorithm and perform evaluations in real-world sensor networks similar to Peng et al (2013). The other interesting direction for future work is to study the Steiner tree version of our problem.…”

Section: Discussionmentioning

confidence: 99%

“…Such energy consumption is usually a constant, so we can safely merge it into the constant T x . Third, the model can be adapted to other scenarios such as sensor networks with energy harvesting capabilities and sensor networks under low duty cycle operation model, as long as the traffic can be aggregated, by referring to the formulations in Challen et al (2010) and Peng et al (2013), respectively.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We consider the problem of finding an aggregation tree with the maximum lifetime, where lifetime is defined as the time duration from network deployment until a node runs out of energy. Indeed, due to the energy-constrained nature of sensor nodes, lifetime maximization is a fundamental issue in sensor networks (Liu and Cao 2010, Peng et al 2013, Xiang et al 2013, Luo et al 2011, Wu et al 2010, Basagni et al 2009, Tan et al 2011, Zhu et al 2015.…”

Section: Introductionmentioning

confidence: 99%

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Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks

Zhu

Chen

Tang

et al. 2016

INFORMS Journal on Computing

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O ne ultimate goal of wireless sensor networks is to collect the sensed data from a set of sensors and transmit them to some sink node via a data gathering tree. In this work, we are interested in data aggregation, where the sink node wants to know the value for a certain function of all sensed data, such as minimum, maximum, average, and summation. Given a data aggregation tree, sensors receive messages from children periodically, merge them with its own packet, and send the new packet to its parent. The problem of finding an aggregation tree with the maximum lifetime has been proved to be NP-hard and can be generalized to finding a spanning tree with the minimum maximum vertex load, where the load of a vertex is a nondecreasing function of its degree in the tree. Although there is a rich body of research in those problems, they either fail to meet a theoretical bound or need high running time. In this paper, we develop a novel algorithm with provable performance bounds for the generalized problem. We show that the running time of our algorithm is in the order of O mn m n , where m is the number of edges, n is the number of sensors, and is the inverse Ackerman function. Though our work is motivated by applications in sensor networks, the proposed algorithm is general enough to handle a wide range of degree-oriented spanning tree problems, including bounded degree spanning tree problem and minimum degree spanning tree problem. When applied to these problems, it incurs a lower computational cost in comparison to existing methods. Simulation results validate our theoretical analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks

Zhu

Chen

Tang

et al. 2016

INFORMS Journal on Computing

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“…Peng et al [81] invoked intra-and inter-route coordinations for allowing the nodes balance their ED across the network in order to maximize the NL.…”

mentioning

confidence: 99%

“…2014 of hops and the minimum traffic load. Peng et al [81] invoked intra-route coordination for allowing the nodes along the same route to balance their node lifetime durations, which was also combined with inter-route coordination for additionally balancing the lifetime durations of the sensors along different routes in order to collaboratively maximize the NL. This was carried out under a specific delivery delay constraint.…”

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confidence: 99%

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

Yetgin

Cheung

El‐Hajjar

et al. 2017

IEEE Commun. Surv. Tutorials

544

229

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Abstract-Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.

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New approach of GA–PSO‐based clustering and routing in wireless sensor networks

Anand

Pandey

2020

Int J Communication

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SummaryWireless sensor networks (WSNs) not only have become an essential technology in the current scenario but also has been established as a core technology for many applications, like the Internet of Things (IoT). The drawback of limited energy resources in WSN creates the challenge of designing an energy‐efficient network topology and routing path. As these devices are generally not attended for any maintenance, such as battery replacement or charging, these resources need to be intelligently used. The paper represents a methodology for enhancing the lifetime of a network by using a genetic algorithm‐based clusteringand particle swarm optimization‐based routing in WSN. The best cluster head (CH) that collects the data from the remaining nodes is selected using a genetic algorithm, which considers distance and energy parameters. Further, the particle swarm optimization algorithm depends on optimal routing paths that are chosen for all relay nodes to send data to the Base Station (BS). The proposed procedure shows that the relay node supports and facilitates communication among the sink and CH, which enhances energy efficiency. Finally, the quality of service (QoS) of the WSNs is improved, due to which the system would survive longer. The results of simulated experiments show that the proposed approach significantly increases the lifetime of WSN and that energy consumption is reduced when compared with the existing protocols.

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I<sup>2</sup>C: A holistic approach to prolong the sensor network lifetime

Abstract: Abstract-We present a novel holistic approach (called I 2 CIntra-route and Inter-route Coordination) to prolong the sensor network lifetime under the end-to-end delivery delay constraint.

Cited by 15 publications

References 28 publications

Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks

Fast Approximation Algorithm for Maximum Lifetime Aggregation Trees in Wireless Sensor Networks

A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks

New approach of GA–PSO‐based clustering and routing in wireless sensor networks

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