Analytical Model of Deployment Methods for Application of Sensors in Non-hostile Environment

Kaiwartya, Omprakash; Kumar, Sushil; Abdullah, Abdul Hanan

doi:10.1007/s11277-017-4584-6

Cited by 8 publications

(9 citation statements)

References 29 publications

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“…Tri-hexagon tiling deployment was proposed to combine the advantages of the triangle and hexagon deployment methods. In terms of energy consumption, the THT deployment outperforms the square and hexagon deployments [ 33 ].…”

Section: Background and Related Workmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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A smart city is a geographical area that uses modern technologies to facilitate the lives of its residents. Wireless sensor networks (WSNs) are important components of smart cities. Deploying IoT sensors in WSNs is a challenging aspect of network design. Sensor deployment is performed to achieve objectives like increasing coverage, strengthening connectivity, improving robustness, or increasing the lifetime of a given WSN. Therefore, a sensor deployment method must be carefully designed to achieve such objective functions without exceeding the available budget. This study introduces a novel deployment algorithm, called the Evaluated Delaunay Triangulation-based Deployment for Smart Cities (EDTD-SC), which targets not only sensor distribution, but also sink placement. Our algorithm utilizes Delaunay triangulation and k-means clustering to find optimal locations to improve coverage while maintaining connectivity and robustness with obstacles existence in sensing area. The EDTD-SC has been applied to real-world areas and cities, such as Midtown Manhattan in New York in the United States of America. The results show that the EDTD-SC outperforms random and regular deployments in terms of area coverage and end-to-end-delay by 29.6% and 29.7%, respectively. Further, it exhibits significant performance in terms of resilience to attacks.

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Section: Background and Related Workmentioning

confidence: 99%

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani

Alenazi

2020

Sensors

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“…Many research studies show that the clustering is used for energy constrain and maximizing network lifetime. In order to accomplish high energy efficiency and maximize network lifetime, sensors can be organized into cluster [4]. The cluster head election is an important aspect of WSNs, as designing a protocol for cluster-based communication, researchers face many problems due to complex nature of network topology to attain better energy efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

Toward green computing in wireless sensor networks: prediction-oriented distributed clustering for non-uniform node distribution

Sikandar¹,

Agrawal²,

Tyagi³

et al. 2020

J Wireless Com Network

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Recently, researchers and practitioners in wireless sensor networks (WSNs) are focusing on energy-oriented communication and computing considering next-generation smaller and tiny wireless devices. The tiny sensor-enabled devices will be used for the purpose of sensing, computing, and wireless communication. The hundreds/thousands of WSNs sensors are used to monitor specific activities and report events via wireless communication. The tiny sensor-enabled devices are powered by smaller batteries to work independently in distributed environments resulting in limited maximum lifetime of the network constituted by these devices. Considering the non-uniform distribution of sensor-enabled devices in the next-generation mobility centric WSNs environments, energy consumption is imbalanced among the different sensors in the overall network environments. Toward this end, in this paper, a cluster-oriented routing protocol termed as prediction-oriented distributed clustering (PODC) mechanism is proposed for WSNs focusing on non-uniform sensor distribution in the network. A network model is presented, while categorizing PODC mechanism in two activities including setting cluster of nodes and the activity in the steady state. Further cluster set up activity is described while categorizing in four subcategories. The proposed protocol is compared with individual sensor energy awareness and distributed networking mode of clustering (EADC) and scheduled sensor activity-based individual sensor energy awareness and distributed networking mode of clustering (SA-ADC). The metrics including the overall lifetime of the network and nodes individual energy consumption in realistic next-generation WSNs environments are considered in the experimental evaluation. The results attest the reduced energy consumption centric benefits of the proposed framework PODC as compared to the literature. Therefore, the framework will be more applicable for the smart product development in the next-generation WSNs environments.

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“…There are basically two different ways of deploying the sensor nodes: planned and random. For example, sensors are deployed in a well-calculated style in an approachable area where humans can travel [10]. On the other hand, an ad-hoc method approach is used in a hostile environment, as it is very difficult to deploy the nodes there manually [11].…”

Section: Introductionmentioning

confidence: 99%

Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

Ram

Kumar

et al. 2019

Electronics

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Due to the rapidly growing sensor-enabled connected world around us, with the continuously decreasing size of sensors from smaller to tiny, energy efficiency in wireless sensor networks has drawn ample consideration in both academia as well as in industries’ R&D. The literature of energy efficiency in wireless sensor networks (WSNs) is focused on the three layers of wireless communication, namely the physical, Medium Access Control (MAC) and network layers. Physical layer-centric energy efficiency techniques have limited capabilities due to hardware designs and size considerations. Network layer-centric energy efficiency approaches have been constrained, in view of network dynamics and available network infrastructures. However, energy efficiency at the MAC layer requires a traffic cooperative transmission control. In this context, this paper presents a one-dimensional discrete-time Markov chain analytical model of the Timeout Medium Access Control (T-MAC) protocol. Specifically, an analytical model is derived for T-MAC focusing on an analysis of service delay, throughput, energy consumption and power efficiency under unsaturated traffic conditions. The service delay model calculates the average service delay using the adaptive sleep wakeup schedules. The component models include a queuing theory-based throughput analysis model, a cycle probability-based analytical model for computing the probabilities of a successful transmission, collision, and the idle state of a sensor, as well as an energy consumption model for the sensor’s life cycle. A fair performance assessment of the proposed T-MAC analytical model attests to the energy efficiency of the model when compared to that of state-of-the-art techniques, in terms of better power saving, a higher throughput and a lower energy consumption under various traffic loads.

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Analytical Model of Deployment Methods for Application of Sensors in Non-hostile Environment

Cited by 8 publications

References 29 publications

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Toward green computing in wireless sensor networks: prediction-oriented distributed clustering for non-uniform node distribution

Enabling Green Wireless Sensor Networks: Energy Efficient T-MAC Using Markov Chain Based Optimization

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