GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks

Hawbani, Ammar; Wang, Xingfu; Karmoshi, Saleem; Kuhlani, Hassan; Ghannami, Aiman; Abudukelimu, Adili; Ghoul, Rafia

doi:10.1155/2017/4509697

Cited by 13 publications

(11 citation statements)

References 21 publications

(87 reference statements)

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“…DRP and PRP cited above (see Figures 10,11,12 ,13). On the other hand, the updating of PDTR does not need much energy, unlike in PRPs, due to the fact that the update of PDTR happens only when any node loses 20% of its energy.…”

Section: Performance Comparison Of the Proposed Pdtr To The State-of-mentioning

confidence: 99%

“…Article [12] also considered this factor in the presented protocol called (STDD). The authors in [13] showed that the Euclidian distance has a direct effect on energy consumption-the more the distance is minimized, the more the consumed energy is reduced. In [5,6], a p-persistence protocol was proposed, such that the forwarding probability is calculated according to the distance between two nodes.…”

mentioning

confidence: 99%

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PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

Ghoul

Djaidja

et al. 2020

Sensors

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This paper proposed a "Probabilistic and Deterministic Tree-based Routing for WSNs (PDTR)". The PDTR builds a tree from the leaves to the head (sink), according to the best elements in the initial probabilistic routing table, measured by the product of hops-count distribution, and transmission distance distribution, to select the best tree-paths. Each sender node forwards the received data to the next hop via the deterministic built tree. After that, when any node loses of its energy, PDTR updates the tree at that node. This update links probabilistically one of that node's children to a new parent, according to the updated probabilistic routing table, measured by the product of the updated: Hops-count distribution, transmission distance distribution, and residual energy distribution at the loss of e energy. By implementing the control parameters in each distribution, PDTR shows the impact of each distribution in the routing path. These control parameters are oriented by the user for different performances. The simulation results prove that selecting the initial best paths to root the packets via unicast, then improving the tree at the node with loss of energy by rooting the packets via anycast, leads to better performance in terms of energy consumption and network lifetime. of 22positions, through other localization technologies [2,3]. Other proposed WSN localization technologies are based on different measurement techniques such as the time-of-arrival (TOA), angle-of-arrival (AOA), and received signal strength indicator (RSSI) [4][5][6][7]. Later,in [8,9], the cooperative localization technology was developed for hybrid networks containing mobile and static sensors. Recently,in [10], the authors proposed a centralized expectation maximization-based passive localization technology for asynchronous receivers (EMplLaR). Being aware of the nodes' location allows the use of many power control techniques to reduce energy consumption.Data forwarding toward the sink via deterministic protocols may cause quick energy depletion for some nodes, due to the sender node's forwarder unique choice, which causes a topology disruption, thus a quick network death. Meanwhile, the probabilistic routing approach came with the improvement, such that each sensor has to select its forwarder from a set of its candidates according to their routing probabilities, based on its neighborhood knowledge, thus providing a longer network lifetime.From the existing probabilistic and deterministic protocols, we concluded that to minimize the energy consumption and prolong the network lifetime, the protocol should depend on three factors. Reference [11] proposed a Hop-based Routing Protocol based on an Energy Efficient Minimum Spanning Tree for WSN. From the simulation results, it was proved that the proposed protocol improved the network in terms of energy consumption and lifetime extension, hence the importance of the hop-count factor. Article [12] also considered this factor in the presented protocol called (STDD). The authors in [13] showed tha...

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Section: Performance Comparison Of the Proposed Pdtr To The State-of-mentioning

confidence: 99%

mentioning

confidence: 99%

PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

Ghoul

Djaidja

et al. 2020

Sensors

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“…Then usingĉ 1 and in eight different directions as seen in Fig. 1b the centerĉ j for j > 1 of the next Cell is determined by an increment or a decrement to the x and y values ofĉ 1 by a 3 2 R (since adjacent Cells are 1 2 R apart, their network center's will be 3 2 R apart). This method is repeated until the network's x, y & diagonal axis are covered by Cells encapsulating a group of sensor nodes surrounded by regular nodes as seen in Compute the network centerC by (11) 3:…”

Section: Initial Constructionmentioning

confidence: 99%

“…At the initialization level, each Cell keeps a link with it's neighboring Cells (i.e. Cells at a distance less than or equal to 3 2 of the Cell radius from it's Cell center). At first entry of the mobile sink, the nearest Cell to it would be designated as the Root-Cell and all its neighboring Cells will become its Children, also called Level-One Cells.…”

Section: Tree Couplingmentioning

confidence: 99%

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Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Busaileh

Hawbani

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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Wireless sensor networks (WSNs) with a static sink suffer from concentrated data traffic in the vicinity of the sink, which increases the burden on the nodes surrounding the sink, and impels them to deplete their batteries faster than other nodes in the network. Mobile sinks solve this corollary by providing a more balanced traffic dispersion, by shifting the traffic concentration with the mobility of the sink. However, it brings about a new expenditure to the network, where prior to delivering data, nodes are obligated to procure the sink's current position. This paper proposes Tuft, a novel hierarchical tree structure that is able to avert the overhead cost from delivering the fresh sink's position while maintaining a uniform dispersion of data traffic concentration. Tuft appropriates the mobility of the sink to its advantage, to increase the uniformity of energy consumption throughout the network. Moreover, we propose Tuft-Cells, a distributed dissemination protocol that models data routing as a Multi-Criteria Decision Making (MCDM) in three steps. To begin with, each criterion constitutes a random variable defined by a mass function. Each of these cirterion serves a proportionately distinguishable alternative, and hence, may conflict. Therefore, the Analytic Hierarchy Process (AHP) quantifies the relationship between criteria. Finally, the final forwarding decision is derived by a weighted aggregation. Tuft is compared with state-of-the-art protocols, and the performance evaluation illustrates that our protocol adheres to the requirements of WSNs, in terms of energy consumption, and success ratio, considering the additional overhead cost brought by the mobility of the sink.

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Energy Efficient Balanced Tree-Based Routing Protocol for Wireless Sensor Network (EEBTR)

Ghoul

Hawbani

et al. 2019

Advances in Intelligent Systems and Computing

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GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks

Cited by 13 publications

References 21 publications

PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks

Tuft: Tree Based Heuristic Data Dissemination for Mobile Sink Wireless Sensor Networks

Energy Efficient Balanced Tree-Based Routing Protocol for Wireless Sensor Network (EEBTR)

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