A Formal Analytical Modeling and Simulation of Wireless Sensor Home Network

Gaboitaolelwe, Jwaone; Zungeru, Adamu Murtala; Chuma, Joseph; Ditshego, Nonofo M.J.; Semong, Thabo

doi:10.22266/ijies2020.0630.06

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…The paper [21] designs and implements the smart home monitoring system based on embedded Linux system. Thesis [22] studied ZigBee intelligent home system based on IEEE802.15.4 protocol. In this paper [23], Bluetooth protocol version 4.0 is applied to wireless information transmission of smart home system, and a low energy consumption smart home sensor network is designed.…”

Section: Related Workmentioning

confidence: 99%

Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network

Zhou

2022

Journal of Sensors

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Smart home is a building, network, communication, automation, set management, and service, as one of the efficient, comfortable, safe, and environmental protection of the living environment, and can provide personalized living space according to the needs of different users. Due to the advantages of wireless sensor network (WSN), such as strong expansion ability, strong communication ability, and strong self-organization ability, the smart home system based on WSN has become the main development direction in the field of smart home. This paper analyzes the different functions and features of wireless sensor network routing protocol and use scope, aiming at the application layer, using the MQ queue enhance server concurrent processing capacity and system scalability and design based on application layer gateway and terminal node “data matching list,” which are introduced in the design of smart home system based on geographic information location energy-efficient routing protocols. The solution of routing void problem is improved by using information feedback mechanism and alternate nodes. Simulation results show that this method can improve the energy saving of energy-efficient routing protocol. Finally, the intelligent home system experiment platform was built to verify the overall function of the system, and the communication function of the WiFi network and ZigBee network was tested. Based on the analysis of the interference between ZigBee and WiFi network, frequency agility algorithm is used to solve the problem of the same frequency interference, so as to ensure the performance of the system to meet the needs of the smart home system.

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Section: Related Workmentioning

confidence: 99%

Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network

Zhou

2022

Journal of Sensors

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“…For the purpose of demonstrating the enumeration of the different sensor types through the proposed model, we chose a level of heterogeneity equal to 6, always considering the 10,000 sensors to be deployed, and with the initial values of β 1 and γ equal to 0.4 and 0.025 respectively. Referring to Equation (12), the values of β 2 , β 3 , β 4 , β 5 and β 6 were respectively 0.35, 0.30, 0.25, 0.20 and 0.15.…”

Section: Evaluation Of the Different Sensors Of The Ls-wsnsmentioning

confidence: 99%

“…In addition, a poor deployment strategy can lead to unbalanced energy depletion, resulting in empty areas over time while others remain quite dense. Therewith, in the literature, the WSN deployment techniques proposed so far have in most cases been designed to either maximize network connectivity, increase coverage of the area of interest or, of course, extend the network's lifespan [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

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Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.

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“…The development and the deployment of electronic sensors and smart IoT devices on street furniture make it possible to interconnect infrastructures, to provide useful information to public and private services and citizens, or to adapt energy expenditure, among other things [4][5][6]. Moreover, data is at the center of the digital development of services.…”

Section: Introductionmentioning

confidence: 99%

A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

et al. 2020

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In recent years, technological advances and the ever-increasing power of embedded systems have seen the emergence of so-called smart cities. In these cities, application needs are increasingly calling for Large-Scale Wireless Sensor Networks (LS-WSN). However, the design and implementation of such networks pose several important and interesting challenges. These low-cost, low-power devices are characterized by limited computing, memory storage, communication, and battery power capabilities. Moreover, sensors are often required to cooperate in order to route the collected data to a single central node (or sink). The many-to-one communication model that governs dense and widely deployed Wireless Sensor Networks (WSNs) most often leads to problems of network overload and congestion. Indeed, it is easy to show that the closer a node is geographical to the sink, the more data sources it has to relay. This leads to several problems including overloading of nodes close to the sink, high loss rate in the area close to the sink, and poor distribution of power consumption that directly affects the lives of these networks. In this context, we propose a contribution to the problem of LS-WSN energy consumption. We designed a hierarchical 3-tier architecture of LS-WSNs coupled with a modeling of the activities of the different sensors in the network. This architecture that is based on clustering also includes a redeployment function to maintain the topology in case of coverage gaps. The results of the performed simulations show that our architecture maximizes the lifetime than compared solutions.

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A Formal Analytical Modeling and Simulation of Wireless Sensor Home Network

Cited by 3 publications

References 22 publications

Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network

Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network

A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection

A Three-Tier Architecture of Large-Scale Wireless Sensor Networks for Big Data Collection

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