Effect of network topology on localization algorithm’s performance

Shahzad, Farrukh; Sheltami, Tarek R.; Shakshuki, Elhadi M.

doi:10.1007/s12652-016-0349-4

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Because tracking consumes much energy, [21] only nodes that are closer and their residual power is above the minimum threshold will participate in the tracking of the target. 7…”

Section: Energy Managementmentioning

confidence: 99%

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adam

Anisi

Ali

2020

Future Generation Computer Systems

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The development of pervasive communication devices and the emergence of the Internet of Things (IoT) have acted as an essential part in the feasibility of smart city initiatives. Wireless sensor network (WSN) as a key enabling technology in IoT offers the potential for cities to get smatter. WSNs gained tremendous attention during the recent years because of their rising number of applications that enables remote monitoring and tracking in smart cities. One of the most exciting applications of WSNs in smart cities is detection, monitoring, and tracking which is referred to as object tracking sensor networks (OTSN). The adaptation of OTSN into urban cities brought new exciting challenges for reaching the goal of future smart cities. Such challenges focus primarily on problems related to active monitoring and tracking in smart cities. In this paper, we present the essential characteristics of OTSN, monitoring and tracking application used with the content of smart city. Moreover, we discussed the taxonomy of OTSN along with analysis and comparison. Furthermore, research challenges are investigated concerning energy reservation, object detection, object speed, accuracy in tracking, sensor node collaboration, data aggregation and object recovery position estimation. This review can serve as a benchmark for researchers for future development of smart cities in the context of OTSN. Lastly, we provide future research direction. Highlights One of the most interesting applications of wireless sensor networks in smart cities would be detecting and monitoring or tracking of objects based on object tracking sensor networks (OTSN).  The review delineates the advantages as well as the weakness of existing OTSN and conduct a comparative view.  Furthermore, research challenges in OTSNs and existing methods are investigated with a focal point of energy constraints, object detection, object speed, tracking accuracy, sensor node collaboration, data aggregation and object recovery position estimation in WSN and smart cities.  Open research issues that required further improvement are summarized.

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“…Because tracking consumes much energy, [21] only nodes that are closer and their residual power is above the minimum threshold will participate in the tracking of the target. 7…”

Section: Energy Managementmentioning

confidence: 99%

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Adam

Anisi

Ali

2020

Future Generation Computer Systems

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“…After each node receives and rebroadcasts this message, it constructs local neighbor information. Update the corresponding data structure; the second is the collection phase, and all nodes forward the local neighbor information to the coordinating node [1]. Nesa and Banerjee proposed a distributed topology discovery strategy based on mobile agents.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors

Sun¹

2022

Wireless Communications and Mobile Computing

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Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more.

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“…Without such positional information, the observer cannot match the data with the region and make an appropriate decision. Meanwhile, the exact location information of sensor nodes are of great help in improving the efficiency of network routing [3]. Since the wireless sensor network consists of a large number of sensors and the topology often change especially for the environment with mobile nodes, each sensor nodes need to be equipped with a positioning system, such as GPS.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Miao

Zhu

2018

J Wireless Com Network

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The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node's localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithmprovidesabetterpositionestimationwithlesslocalizationerror.

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Effect of network topology on localization algorithm’s performance

Cited by 10 publications

References 25 publications

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions

Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

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