Link selection strategies based on network analysis to determine stable and energy-efficient data gathering trees for mobile sensor networks

Meghanathan, Natarajan

doi:10.1016/j.adhoc.2017.04.007

Cited by 7 publications

(6 citation statements)

References 39 publications

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“…M is the dimension of sensing field area, and T is the number of neighbors NOVER: To calculate the degree of mutual neighborhood among the termination nodes of a link, the NOVER method is utilized. A link that has a small NOVER is to connect two unique networks, and a link that has high NOVER is bound to be among nodes in the same network . The sets of neighbors of nodes u and v defined by N ( u ) and N ( v ) individually.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Pattnaik

Sahu

2020

Int J Communication

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Summary The problems related to energy consumption and improvement of the network lifetime of WSN (wireless sensor network) have been considered. The base station (BS) location is the main concern in WSN. BSs are fixed, yet, they have the ability to move in some situations to collect the information from sensor nodes (SNs). Recently, introducing mobile sinks to WSNs has been proved to be an efficient way to extend the lifespan of the network. This paper proposes the assimilation of the fuzzy clustering approach and the Elephant Herding Optimization (EHO)‐Greedy algorithm for efficient routing in WSN. This work considers the separate sink nodes of a fixed sink and movable sink to decrease the utilization of energy. A fixed node is deployed randomly across the network, and the movable sink node moves to different locations across the network for collecting the data. Initially, the number of nodes is formed into the multiple clusters using the enhanced expectation maximization algorithm. After that, the cluster head (CH) selection done through a fuzzy approach by taking the account of three factors of residual energy, node centrality, and neighborhood overlap. A suitable collection of CH can extremely reduce the utilization of energy and also enhancing the lifespan. Finally, the routing protocol of the hybrid EHO‐Greedy algorithm is used for efficient data transmission. Simulation results display that the proposed technique is better to other existing approaches in regard to energy utilization and the system lifetime.

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Section: Proposed Methodologymentioning

confidence: 99%

“…A link that has a small NOVER is to connect two unique networks, and a link that has high NOVER is bound to be among nodes in the same network. 29 The sets of neighbors of nodes u and v defined by N(u) and N(v) individually.…”

Section: Ch Selection Using Fuzzy Approachmentioning

confidence: 99%

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Pattnaik

Sahu

2020

Int J Communication

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“…These approaches considered various measures such as reliability and energy efficiency. The second category is the tree-based schemes, which provided a backbone tree for data transmission to the base station in IoT systems [7][8][9][10][11].…”

Section: Related Workmentioning

confidence: 99%

“…Transferring important data over the network requires a reliable backbone. The common approach for data gathering in IoT systems is to construct a spanning tree over the available devices [7][8][9][10][11]. The drawback of the existing algorithms is the unreliability of their generated trees.…”

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confidence: 99%

Reliable data gathering in the Internet of Things using artificial bee colony

Najjar-Ghabel¹,

Yousefi²,

Farzinvash³

2018

Turk J Elec Eng & Comp Sci

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The Internet of Things (IoT) technology enables physical devices to communicate with each other for preparing, gathering, and sharing hazard warnings or critical information without human intervention. With respect to emergency applications of IoT technology, an essential issue is to provide an efficient and robust scheme for data gathering. The proposed solution in the existing approaches is to construct a spanning tree over the IoT devices and collect data using the tree. The shortcoming of these algorithms is that they do not take into account the probability of device mobility or failure. In such cases, the spanning tree is split, and it becomes impossible to deliver critical data to the base station on time. In this paper, we propose a reliable spanning tree construction algorithm, which is called reliable spanning tree construction in IoT (RST-IoT). Our algorithm utilizes the artificial bee colony algorithm to generate proper trees. In this method, hop count distances of the devices from the base station, residual energies of the devices, and their mobility probabilities are considered to measure the appropriateness of the trees. Moreover, the proposed algorithm generates a number of trees instead of a single one. These trees are arranged according to their preferences and used for data gathering in succession. Each tree is employed for data gathering upon splitting the preceding one. The simulation results show that RST-IoT improves the reliability of data gathering in emergency applications compared to the previous approaches. IntroductionThe Internet of Things (IoT) is a novel paradigm that considers the interaction of smart things. The main idea of IoT is that smart things or objects, such as sensors, actuators, smart phones, and RFID tags, are spread everywhere ubiquitously. These devices have interactions with each other and are able to collect and exchange data. The IoT paradigm covers a broad range of applications, comprising environmental monitoring [1, 2], transportation services, smart cities [3], emergency situation explorers, and industrial processes controllers [4].Reliable data gathering is an important issue to support emergency applications under IoT technology. The devices send the critical information to the base station for processing and decision making. Furthermore, the base station sends some commands to the devices under its control [5,6]. Transferring important data over the network requires a reliable backbone. The common approach for data gathering in IoT systems is to construct a spanning tree over the available devices [7][8][9][10][11]. The drawback of the existing algorithms is the unreliability of their generated trees. This shortcoming is mostly due to their failure in considering the characteristics of IoT devices. These devices are usually mobile and equipped with low-power batteries. Therefore, it is possible that the spanning tree is split because of the mobility of some devices or their energy exhaustion.

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“…The Received Signal Strength (RSS) plays a very important role to accomplish self configuration characteristics in WSNs, where every node itself finds the neighbor nodes. Furthermore, it determines the distance to the neighbors and link quality with the intentions that it effectively constructs the reliable network with strong link quality by avoiding the collision, interference, packet retransmission and overhearing [17][18][19]. Therefore, it remarkably saves the battery power and enhances the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) in Wireless Sensor Networks

Inam¹,

Zhou²,

Zardari³

et al. 2019

Preprint

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The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

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Link selection strategies based on network analysis to determine stable and energy-efficient data gathering trees for mobile sensor networks

Cited by 7 publications

References 39 publications

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Assimilation of fuzzy clustering approach and EHO‐Greedy algorithm for efficient routing in WSN

Reliable data gathering in the Internet of Things using artificial bee colony

Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) in Wireless Sensor Networks

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