Connectivity analysis of finite wireless multihop networks incorporating boundary effects in shadowing environments

Nagar, Jaiprakash; Chaturvedi, Sanjay K.; Soh, Sieteng

doi:10.1049/iet-com.2020.0043

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…Although this method does not provide accurate distance measurements, the measurements are good enough. The distance between two sensor nodes is calculated by using the path loss model [ 39 , 40 ] given by Equation ( 1 ):

where

represents the total path loss (transmitted power–received power);

represents the path loss with respect to a reference distance,

; d represents the distance between the transmitter and the receiver; and

represents the path loss exponent, indicating the rate at which the received signal strength decreases concerning distance; its value depends upon the propagation environment.

is a Gaussian random variable representing the attenuation caused by fading.…”

Section: System Modelmentioning

confidence: 99%

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Kotiyal

Singh

Sharma

et al. 2021

Sensors

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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

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where

represents the total path loss (transmitted power–received power);

represents the path loss with respect to a reference distance,

; d represents the distance between the transmitter and the receiver; and

represents the path loss exponent, indicating the rate at which the received signal strength decreases concerning distance; its value depends upon the propagation environment.

is a Gaussian random variable representing the attenuation caused by fading.…”

Section: System Modelmentioning

confidence: 99%

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Kotiyal

Singh

Sharma

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, they are being deployed for a large number of military and civilian applications such as border surveillance, enemy tracking andreconnaissance, precision agriculture, landslide monitoring, wildlife monitoring, seismic activity monitoring, industrial automation, ubiquitous and pervasive computing, health industry and so on 4,5,6,7,8,9) . Coverage and connectivity are two essential performance metrics of WSNs because coverage determines how well an RoI is being monitored by the deployed network and the connectivity allows the network nodes to send their sensed data to the sink node 10,11,12,13) . As per the application requirements, the coverage metric of a network can obtained in terms of point coverage, partial area coverage, full area coverage, and k-coverage.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Coverage Performance of a Wireless Sensor Network Considering Boundary Effects in the Presence of Sensor Failure

Mini¹,

Pal²

2021

Evergreen

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Coverage is one of the most critical performance metric of wireless sensor networks (WSNs) because it shows how well a region of interest (RoI) is being monitored by the deployed network. In general, the RoI is either circular or rectangular in shape which have boundary regions. Sensor nodes (SNs) deployed in these regions suffer boundary effects (BEs), i.e., the useful coverage area of an SN deployed near the boundary regions is less as compared to the SNs deployed in the middle of the RoI. It is imperative to consider these BEs while evaluating the performance of WSNs because analytical results derived for large networks are not valid for finite networks. In addition, SNs of a deployed WSN are prone to failure due to a large number of factors such as battery drainage, high temperature, and other environmental conditions. Earlier researchers have ignored the impact of BEs in the presence of sensor failure while evaluating the coverage performance of WSNs. In this work, we derive an analytical model by considering BEs and sensor failure to achieve a closed form expression for the k-coverage performance of a WSN deployed in a rectangular RoI. Further, we analyze the influence of various network parameters such as number of SNs, sensing range, and sensor failure rate on the k-coverage performance of the network. The results obtained using the proposed model show a good match with simulations outcomes with Root Mean Square Error (RMSE) no more than 0.03, thus, validating our model. For 𝑟𝑟 𝑠𝑠 = 80 m and N = 100, 1-coverage probabilities are found to be 0.9874, 0.9313 and 0.8069 for k = 1, 2 and 3 respectively showing that the k-coverage probability deriorates with the increase in the value of k.

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An analytical framework with border effects to estimate the connectivity performance of finite multihop networks in shadowing environments

2021

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Connectivity analysis of finite wireless multihop networks incorporating boundary effects in shadowing environments

Cited by 8 publications

References 27 publications

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks

Estimating the Coverage Performance of a Wireless Sensor Network Considering Boundary Effects in the Presence of Sensor Failure

An analytical framework with border effects to estimate the connectivity performance of finite multihop networks in shadowing environments

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