Investigation of Incorporation of Internet of Things with Wireless Sensor Networks Based on Path Vector Hop Count and Limited Bandwidth Channel IoT Mechanism ^†

Abstract

A wireless sensor network (WSN) consists of sensors with wireless transceivers that link autonomously over many hops. It offers various advantages, including less traffic, more stability, extended wireless communication distances, and broader coverage regions at lower cost. Combining emerging Limited Bandwidth Channel Internet of Things (LBC-IoT) technologies with wireless sensor networks offers interesting applications in the defense, medical, smart conveyance, and marketable sectors. This study initially analyzes WSN and LBC-IoT technologies independently before combining them to look into the networking framework of LBC-IoT and WSN, as well as the associated technologies resulting from the fusion. This article describes the typical network node redeployment strategy for wireless sensors, which can lead to poor node connection and inadequate coverage due to a lack of confined subgroup node exploration. The suggested method for localizing WSN nodes, based on the Hop Count Path Vector (HOP-PV) algorithm, enhances the process of calculating the average hop distance and the number of node hops, resulting in the PVHOP-LBCIOT mechanism. Simulation results indicate that the improved PVHOP-LBCIOT algorithm’s three deployment methods (square, central uniform, and cross) outperform the two approaches of HOP-PV (random deployment) and PVHOP-LBCIOT (border uniform deployment) for an equal number of unknown moving anchor positions (11), a disparate number of unspecified nodes (30-13), and a fixed communication radius (6), with a reduced average error rate of 32.79%, from 38%, and improved accuracy for obtaining unknown node locations. The suggested method for localizing WSN nodes using a single node acting as a mobile anchor point, known as the PVHOP-LBCIOT mechanism, enhances and optimizes the process of computing the average hop distance and the number of node hops. A comparison experiment demonstrates that this hopping algorithm has much greater coverage, node power, linkage, and resilience compared to existing methods.

1. Introduction

A wireless sensor network (WSN) combines integrated systems, desktops, and network interaction techniques into a single integrated circuit capable of collecting, transmitting, and processing data. Mobile operator networks provide good reach of locations. In today’s advanced technology society, connectivity innovation is continually changing to increase the excellence and swiftness of data transfer while also providing basic voice call services. However, due to unequal regional installation of mobile cellular networks, low-population areas are not feasible for high-speed data transport nowadays. As a result, it is not feasible to access data in a WSN directly using mobile cellular networks. Real-time monitoring involves employing small sensors, processing information with embedded microprocessors, and sending it to remote users over wireless networks. The information is then analyzed according to regulations.

1.1. Benefits of WSN Node Positioning Centered on LBC-IoT Access

LBC-IoT (Limited Bandwidth Channel Internet of Things) is a new technology that uses cellular networks to connect highly demanding and low-power devices. Additionally, it enables ultra-long downtime for implementations that need constant access to the WSN. The correct location of nodes is the most important aspect of wireless sensor networks. After a sensor node has been positioned, the WSN may learn the coordinate location of each node and the position connection between nodes, giving each node the necessary conditions for data collection and transmission [1].

The following are the benefits of installing WSN nodes with IoT-LBC access:

• IoT-LBC is a public utility network that is perfect for a variety of IoT-related applications because of its high signal propagation and low power consumption.
• The LBC-IoT main station locating capabilities may be used. The main station signal allows the network nodes of the IoT-LBC network to precisely determine their position. GPS signals are needed to improve base station placement accuracy in IoT-LBC, as 30 m precision is currently not sufficient.
• The IoT-LBC network’s backend processing capacity may be utilized. The first computation process may be carried out via the backend, but the calculation technique is more complicated. It involves finding a large number of unknown nodes and being prepared to send the measurements and the results of the calculations via a 4G/LTE-centered IoT-LBC network.

1.2. WSN Node Location Techniques and Algorithms

WSN node positioning techniques are divided into two types, namely (i) the ranging technique of positioning and (ii) the non-ranging technique of positioning [2].

• Ranging-based positioning strategies involve measuring angular data and straight-line lengths between nodes, followed by calculating undetermined node coordinates using trilateral and triangulation methods of positioning [3].
• The non-ranging technique of positioning does not rely on node measurement of distance and may calculate undetermined node coordinates due to the network connection. The center-of-mass technique, the distance vector calculation procedure HOP-PV, and other approaches are widely used [4].

The performance criterion for positioning systems is based on anchor nodes, which are associated with the arithmetic approach of localization. The computation procedure becomes increasingly difficult as the number of anchor node sites increases. The node uses energy that is directly connected to the zone of coverage [5].

2. Materials and Methods

2.1. PV-HOP Algorithm for Node Location

PV-HOP (Path Vector Hop), a non-ranging technique, is commonly used for WSN node positioning. All nodes are easily accessible to one another and identical to their neighboring nodes; four black circles show stationary anchor points at confirmed locations; every vacant point circle in the two-dimensional area of the X and Y axes indicates an unidentified node; and the positioning calculation procedure is split into two stages [6]:

• The first stage is to calculate the minimum quantity of bars that separate each anchor point from the unknown node point.
• The second stage is to determine the distance that lies between each anchored node and the location node.
• The location marker of the other anchored nodes recorded in the database, along with the total number of hops, may be used to compute each anchored node using a mean hop separation value of 1 [7]. The expression for the mean hop separation among all anchor nodes is given by Equation (1):

$${ S}_i={\displaystyle \frac{{\Sigma }_{j\ne i}\sqrt{(a_i-a_j{)}^2+(b_i-b_j{)}^2}}{{\Sigma }_{j\ne i} h_{ij}}}$$

(1)

where (a_i, b_i) and (a_j, b_j) represent the location markers of the anchored nodes with numbers corresponding to i and j, respectively; h symbolizes the quantity of hop divisions from the anchored node from i to j; and S symbolizes the mean hop separation among all anchor nodes.

It is possible to transmit the mean hop length and calculation result to all unidentified nodes after ensuring that the nodes receive information from anchor nodes on the shortest path. The unknown node is able to calculate how far it is from the corresponding anchored node [8].

2.2. Positioning Technique for Shifting Anchored Point Multi-Position Change

The following positioning techniques are used for shifting an anchored point.

2.2.1. LBC-IoT Mobile Access-Based Wireless Sensor Network

The versatility and robustness of the IoT-LBC mobile communication solution make WSNs suitable for a variety of applications. The IoT-LBC admittance node serves as a mobile reference point with a simple movement rule inside the same aggregate, and HOP-PV calculates the number of transitions to the endpoint node in the background.

2.2.2. Optimization Technique for LBCIOT-PVHOP

The number of transitions that each moving anchored point stays for at various reference locations must be determined using the HOP-PV method, and the leap values may be checked between two points, as shown in Equation (2) [9].

$$g={\displaystyle \frac{{\Sigma }_{j\ne i}\sqrt{(a_i-a_j{)}^2+(b_i-b_j{)}^2}}{{\Sigma }_{j\ne i} {\min (h}_{ik}+h_{jk})}}$$

(2)

The number of transitions from any location on the k node to the anchored points i and j, respectively, is represented by the formula’s variables ℏjk and ℏij. The reference value for the number of transitions between two anchored positions is determined by taking the lowest value of the sum of transitions from every position of the nodes to two anchor locations [10,11,12,13,14,15,16].

3. Results and Discussion

3.1. Simulation Results

With the side length of the square set to 10 m, 40 static nodes are arbitrarily placed inside a square using MATLAB 2015 software. Starting at point 1 (0, 0), one mobile node approaches this cluster and proceeds to the following positions as planned: 2 (3.2, 0), 3 (6.5, 0), 4 (10, 0), 5 (10, 3.5), 6 (10, 6.7), 7 (10, 10), 8 (6.7, 10), 9 (3.3, 10), 10 (0, 10), 11 (0, 6.7), and 12 (0, 3.2). With a beginning node transmission radius R of 6 m, the 12 (0, 3.2) procedure sends the shortest path, a one-node hop, and registers the single or multi-transition content when every node remains in each place, as shown in Figure 1 and Figure 2.

The IoT-LBC mobile cluster’s initial localization is shown by □ in the schematic diagram, while the location of the localized node that has to be identified is indicated by Δ.

3.2. Effect of Quantity of Unexplored Nodes on Results

The number of sites where IoT-LBC anchor nodes may be found influences the complexity of the calculation procedure as well as the time it takes to perform the position calculation.

From the above

Table 1. Changing the number of anchored places and its impact on the mean error rate.

Anchored Point Displacement Count	Error Rate (Average) %
2	41.3
4	38.75
6	36.83
8	34.6
10	33.05
11	32.79

, it is observed that an increase in the anchored point displacement count lowers the average error rate.

3.3. Impact of Allocation Rules of Anchored Position on Outcomes

The placement of IoT-LBC anchor nodes follows specific regulations, and the dispersion of anchor sites affects the results produced. This work compares and analyzes experimental results using four deployment methods: square method, border uniform method, central uniform method, and cross method. The findings are displayed in Figure 3.

The simulation results indicate that our improved PVHOP-LBCIOT algorithm’s three deployment methods (square, central uniform, and cross) outperform the two approaches of HOP-PV (random deployment) and PVHOP-LBCIOT (border uniform deployment) for an equal number of unknown moving anchor positions (12), a constant communication range (6), and an unequal number of unidentified nodes (31-13), with a reduced average error rate of 32.79%. Additionally, the obtained unidentified node location’s accuracy is increased. The findings are shown in Figure 4.

3.4. Evaluation Analysis

The PVHOP-LBCIOT analytical technique utilizes LBC-IoT’s self-positioning and background calculation capabilities, as well as deployment optimization, to calculate unknown node locations. A comparison with the original PV-HOP method shows a significant error reduction. The PVHOP-LBCIOT enhancement algorithm improves both the hop distance and hop number using approaches developed in related research. Simulation experiments are performed using the MATLAB platform, and the standard moving anchored position deployment approach demonstrates a significant reduction in average positioning error rates, indicating the efficiency of the modified methodology.

4. Conclusions

The deployment of network units for sensors that are wireless has advanced significantly as a result of networks’ rapid growth. Traditional methods, however, are not very effective in big quantities and have a restricted coverage area. This article integrates IoT-LBC technology and WSNs, focusing on the IoT-LBC-based WSN topology and associated information integration approaches. The proposed WSN node localization technique, based on the Hop Count Path Vector (HOP-PV) algorithm, enhances the process of calculating the mean hop separation and the number of node hops, resulting in the PVHOP-LBCIOT mechanism. The result of a comparative experiment shows that this hopping algorithm outperforms the old technique in terms of reach, power of the node, connection, and endurance. This work provides fresh perspectives on node redeployment techniques for wireless sensor networks, stimulates further in-depth examination of research findings, and provides a strong framework for specialists and scientists to carry out further research in this field.