IJCNC 04

OPTIMAL COVERAGE PATH PLANNING IN A
WIRELESS SENSOR NETWORK FOR INTELLIGENT
TRANSPORTATION SYSTEM

Saureng Kumar and S C Sharma

Electronics and Computer Discipline, IIT Roorkee, Saharanpur Campus, Saharanpur

ABSTRACT

With the enhancement of the intelligent and communication technology, an intelligent transportation plays a vital role to facilitate an essential service to many people, allowing them to travel quickly and conveniently from place to place. Wireless sensor networks (WSNs) are well-known for their ability to detect physical significant barriers due to their diverse movement, self-organizing capabilities, and the integration of this mobile node on the intelligent transportation system to gather data in WSN contexts is becoming more and more popular as these vehicles proliferate. Although these mobile devices might enhance network performance, however it is difficult to design a suitable transportation path with the limited energy resources with network connectivity. To solve this problem, we have proposed a novel itinerary planning schema data gatherer (IPS-DG) model. Furthermore, we use the path planning module (PPM) which finds the transportation path to travel the shortest distance. We have compared our results under different aspect such as life span, energy consumption, and path length with Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-Hop Weighted Revenue (MWR), Single-Hop Data Gathering Procedure (SHDGP). Our model outperforms in terms of energy usage, shortest path, and longest life span of with LEACH, MWR, SHDGP routing protocols.

KEYWORDS

Wireless Sensor Network, Intelligent Transportation System, Path Planning, Routing protocol.

1. INTRODUCTION

In the past year, interest in wireless sensor networks (WSNs) has grown significantly increases in the development of an intelligent transportation systems (ITS)[1]. WSNs allow for the collection of data from vehicles, roadways, and other infrastructure to improve safety, efficiency, and reliability. It consists of a huge number of tiny sensor nodes having computation, networking, and sensing capabilities that work together to deliver monitoring and sensing services[2]. Due to their communication capabilities, the wireless sensor networks [3]are ubiquitous in our daily lives, monitoring systems such as road traffic management[4], security and surveillance[5], and intrusion detection systems [6] and their inherent advantages of easy deployment in various applications. Despite these advantages, WSNs have difficulties in vehicle alert authentication[7], and the replacement of in-built power units for sensor nodes is done manually, which is a complex process in harsh environments. Thus, researchers have focused on energy management systems[8] by deploying optimal routing processes to enhance network lifespan.[9] Considering the shift from static to moving nodes in WSNs, various techniques have been proposed for path planning. These techniques, including heuristic algorithms[10], evolutionary algorithms[11], and swarm intelligence-based algorithms[12]aim to find efficient and reliable routes while considering energy efficiency, network connectivity, and application requirements.[13] These algorithms are computationally efficient and can handle large-scale networks. However, they may not always find the optimal solution and can be sensitive to the initial conditions. Our proposed IPS-DG model utilizes a single-hop connection to achieve energy-efficient optimal coverage with the moving mobile node (MN), allowing it to gather various information about nodes simultaneously.

The contribution made for this research work are as follows:

1. Proposed a novel IPS-DG model for intelligent transportation system for optimal
coverage path planning
2. Developed an algorithm for the path planning module.
3. Discussed the parameter like energy consumption, path route and life span for sensor
based intelligent transportation system and simulation were obtained through MATLAB
R2020b.
4. Perform the comparative analysis of different routing protocol.

The remaining sections of this paper detail are summarized as Section 1. Present the introduction, Section 2 represent grouping strategy, clustering and proposed assignation-based data gatherer, In the section 3, We proposed a novel IPS-DG model for path planning, energy consumption model, coverage ability & optimization, path planning module algorithm. In the next section, we briefly discuss the outcome of this research. and comparative analysis of energy consumption, lifespan, and path route with our proposed model and conclude the paper.

2. GROUPING STRATEGY BASED NETWORK MODEL

Grouping strategy-based network models are a popular approach in wireless sensor networks (WSNs). These models aim to improve the network’s efficiency by dividing the sensors into groups based on certain criteria, such as their location or function. By doing so, the sensors in each group can share information and communicate more effectively, which lowers the network’s overall communication overhead and energy usage. Additionally, grouping tactics can aid in balancing the energy usage across sensors, extending the life of the network. However, designing an effective grouping strategy is a challenging task and requires careful consideration of various factors, such as the network topology, the application requirements, and the available resources a lot of scope to the researcher to focus this area. At present the grouping tactics has been implementing in the intelligent transportation in order to gather the real time traffic data. Most of the researcher fixed the group of sensor node in the both side of the zebra. This sensor node sense the data and transmit to the base station. This strategy also integrates different routing protocols such as Low-Energy Adaptive Clustering Hierarchy (LEACH)[14], Multi-Hop Weighted Revenue (MWR) [15], and Single-Hop Data-Gathering Procedure (SHDGP) [16]. The performance index is created by defining variables like energy usage and coverage area. The utility value of each node is then used to construct a chained routing scheme [17] In the chained routing scheme the significant benefits in terms of message delivery speed, collision likelihood, and precision was claimed, however an energy efficiency routing protocol (EERP)[18] has been proposed to improves the network lifetime by forwarding data packets via the optimal shortest path. In order to balance between energy and communication expenditure in each routing approach. The author [19] proposed a survey to insight the future direction, through this motivation we have perform the our research work in the direction of optimal coverage path planning in WSNs.

Problem Statement

It is essential to select the right cluster size when utilizing cluster-based approaches, as the performance is dependent on the average cluster size. A larger cluster for communication will put more strain on the workload of the cluster head, while a smaller cluster will result in a worse performance.

It is difficult to balance the need to efficient energy with the risk of packet loss and retransmissions. This could occasionally result in higher energy usage. As a result, it is challenging to strike a balance between transmission power and the quantity of lost or retransmitted packets.

To develop an algorithm that can identify the most efficient path for a mobile sensor node that minimize the total travel time and energy consumption

This research work proposes a new routing protocol IPS-DG model to tackle the problems associated with existing multi-hop routing technologies, which are caused by the complexity of current methods.

2.1. Clustering of Fixed Sink Node

A clustering strategy with a fixed sink system is used to extend network life and lower the energy  consumption. This scheme uses three-layer schema ie; sensor node, access point and a fixed sink.

1. Sensor nodes are grouped into clusters, with each cluster being given a fixed sink as a
cluster head or leader.
2. An access point creates a wirless local area network and allow devices to connect the
network.
3. In order to measure the blind spot, sensor node must be divided into multiple sensor node
4. The fixed sink collect data according to their path route.
5. This will also enable better coverage and redundancy, making the network more robust
and reliable.

Figure 1. Clustering of node with fixed sink

In Figure 1, nodes are clustered around a fixed sink according to a predetermined protocol. After the nodes have been grouped, a supervisor node is chosen as the data gathering point. The network is randomly spread out over an area.

2.2. Clustering of Moving Sink Node

A clustering with the moving sink is another technique employed in wireless sensor networks. In this technique sensor node is grouped into a clustering format have a group leader or group head. This group head gathers and process the data. In this technique the mobile sink moves freely to any other location and can transmit the data to BS. In a typical wireless sensor network (WSN), nodes located further away from the BS tend to use less energy due to slower data speeds and infrequent communication with the BS. However, clustering methods such as HEED (Hybrid Energy-Efficient Distributed Clustering) [20], LEACH(Low-Energy Adaptive Clustering Hierarchy) [21] and TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol [22].have been developed to achieve energy-efficient clustering by selecting cluster leaders based on various metrics such as remaining energy, distance from the BS, or network connectivity. After being chosen, cluster heads can execute data aggregation and forwarding to the BS while non-cluster head nodes can turn down their radios to conserve energy.

Aside from clustering algorithms, other techniques such as data compression, sleep scheduling, and duty cycling can also be used to enhance the energy efficiency of WSNs. By combining these methods with energy-efficient clustering, it is possible to create WSNs that can operate for extended periods without requiring battery replacement or recharge.

2.3. Assignation based on Data Gathering.

An assignment-based data gatherer is a node or device that gathers and aggregates data from other networked sensor nodes. The data gatherer typically has more processing power and memory than the sensor nodes and may be connected to a central server or base station for further analysis and storage of the collected data. The data gatherer plays a crucial role in wireless sensor networks by collecting and organizing data from multiple sensor nodes, enabling efficient data processing and analysis.

Figure 2. Assignation based schema with data gatherer

Figure 2 illustrates the framework of the proposed approach, which involves a sensor node that collects local information through its sensors. These nodes are anticipated to move along a predetermined transmission path [23] or a defined transmission point. Unlike traditional schemes that require the collector to visit every sensor node to collect data and stores it in its memory and moves to a new location, where it can collect data from additional sensor nodes. The mobile collector continues to move around the network in a pre-defined path until it reaches a base station, where it uploads the collected data. This scheme involves the use of a sensor node, which is typically a vehicle equipped with wireless communication capabilities. The vehicle passes through the several group of nodes to transmit the data to the leader node, facilitating the data gathering process.

3. PROPOSED MODEL: ITINERARY PLANNING SCHEMA-DATA GATHERER (IPS-DG)

Itinerary planning schema data gatherer (IPS-DG) is the detailed path travel plan or a route plan along with a data gatherer facility. The proposed model is designed to choose the vehicle’s customized path or route. It helps to create a systematic outline of the entire journey. Most vehicles use the online route tracking system for the journey and need an effective plan for optimal coverage. To overcome these disadvantages, we proposed a new implementation IPS-DG model with the data gatherer facility in the predefined path.

The following assumptions were considered for our proposed IPS-DG model.

1. All sensors should be immobile. Based on the minimum hop routing protocol, these
sensors should cover the communication range within a pre-set distance.
2. Monitor nodes (MNs) should be located within the predefined range
3. A stationary node should be set up within the sensor deployment area, and the data
collector must approach only for uploading the obtained data using a modified device

This schema is used to identify the best route with the intelligent transportation. There are various approaches to itinerary planning models, ranging from rule-based systems to machine learning algorithms. Some models rely on user inputs such as preferred destinations and travel dates, while others use external data sources such as weather forecasts and traffic patterns to optimize the itinerary.

Figure 3. Itinerary planning model with data gatherer

Figure 3. illustrate the itinerary planning model with data gatherer system that allows for the collection and analysis of data across a network. The data gatherer is used to collect and store data from numerous networked sources, including sensors, programs, and databases. Figure 4 shows the flowchart for illustrating the path using a system database and current location method, where the current location of an intelligent transportation system is given to compute the arrival and departure time of each location. For this, Firstly, we determine the optimal path between each pair of sensor nodes through the data gatherer, then we enumerate all possible sequences and eliminate the impossible ones. Secondly, we optimize the possible itinerary based on the minimum waiting time.

Figure 3. Flowchart of proposed model for itinerary planning schema data gatherer

In this study we have considered the following components for optimal coverage for path planning.

1. Compute arrival and departure time: The goal is to reduce waiting time by considering the duration of each activity, the time needed to travel between activities, and the operating hours of the activity locations. The code can be used to plan and optimize the order of activities in a practical scenario, which could be beneficial in various applications, such as scheduling tasks or organizing events to minimize waiting times and improve overall efficiency.
2. Optimize possible sequences: The method involves iterating through all possible sequences such as the duration of each activity, the travel time between activities, and the operating hours of the activity locations and calculating effectiveness in optimizing possible sequences.

The pseudo-code representation of arrival and departure time computation and Optimization of possible sequences are illustrated as follows.