Sound Sensor Based Emergency Vehicle Preemption System using Microcontrolled Nodes

Irish Interdisciplinary Journal of Science & Research (IIJSR)
Vol.6, Iss.2, Pages 76-80, April-June 2022
ISSN: 2582-3981 www.iijsr.com
76
Sound Sensor Based Emergency Vehicle Preemption System using Microcontrolled Nodes
P. Dinesh Kumar1
, T.Jeyashree2
, S. Kaaviya3
, A.Parevaakhini4
& C.Sathika5
1
Assistant Professor, Department of Computer Science and Engineering, Vivekanandha College of Technology for
Women, Namakkal, Tamil Nadu, India. 2-5
Under Graduate Students, Department of Computer Science and Engineering,
Vivekanandha College of Technology for Women, Namakkal, Tamilnadu, India.
DOI: http://doi.org/10.46759/IIJSR.2022.6210
Copyright © 2022 P.Dinesh Kumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Article Received: 12 March 2022 Article Accepted: 21 May 2022 Article Published: 15 June 2022
1. Introduction
The IoT can join numerous manufacturing gadgets geared up with sensing, identity, processing, conversation,
actuation, and networking competencies. Network control and management of manufacturing. This is carried
out via the usage of detecting the frequency of the ambulance heading closer to the visitors signal through the
usage of the sound sensors and tracking the ambulance to skip the respective visitors lane using Zigbee
protocol. We are implementing the AARS system is referred to as (automated ambulance rescue gadget). The
principle intention of this scheme is to offer an easy float for the ambulance to reach the hospitals in time.
Enforcing this mechanism might manipulate automatically the site visitors lights within the course of the
ambulance. The ambulance is managed by the imperative unit which furnishes the scantest path to the
ambulance and also controls the site visitors mild in line with the ambulance area and for this reason reaching
the hospital safely. This module is likewise tremendous on high priority motors including hearth disaster
prevention automobiles, VIP automobiles and Police Jeeps which are heading closer to high density traffic
signals.
System, asset and state of affairs control, or production technique manage permit IoT to be used for industrial
applications and clever production. IoT intelligent structures permit fast production and optimization of
recent merchandise, and fast reaction to product demands. Virtual manage structures to automate process
controls, operator gear and provider records systems to optimize plant protection and protection are inside the
purview of the IoT.
IoT also can be applied to asset management thru predictive renovation, statistical assessment, and
measurements to maximize reliability. Business management systems can be included with smart grids,
enabling energy optimization. Measurements, computerized controls, plant optimization, health and safety
management, and other features are furnished by way of networked sensors.
ABSTRACT
Traffic congestion is diagnosed as principal problems in current urban regions, that have triggered an awful lot uncomfortable for the
ambulance to journey. Moreover, road accidents in the city have been increasing and to bar the loss of life due to accidents is even more
crucial because the range of automobiles grows hastily each 12 months, more and more traffic congestion happens, turning into a huge
trouble for civil engineers in almost all metropolitan cities. Emergency Vehicle Pre-emption systems play a key role in reprioritizing
signalized traffic intersections.
This role is essential for safe and minimal travel put off of Emergency vehicles (EV) passing through avenue intersections. This paintings
especially objectives on presenting answer for the problem faced via ambulances which can be transferring toward the visitors sign for the
duration of excessive density visitors.

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2. Background
In this paper [1], A partial Lagrange multiplier method is used. It blessings from the total unimodularity of the
prevalence matrix in the fashions . This method can be applied to efficiently solve the real-world stochastic
vehicle routing problem. The demerit is, The MILP problem does need to be accurate as long as it is
acceptable. In this paper [2], VTL Priority Intersection Control (VTL-PIC) protocol algorithm is used. It can
detect the presence of an emergency vehicle. A low overhead with the main goal of high average travel time is
the demerit. In this paper [3], the author uses techniques from multi-agent reinforcement learning for large
traffic system. It Proposed Q-learning outperformed the fixed time method under different traffic demands.
The disadvantage of this technique is that protocols tend to maximize the variation of remaining energy of all
the nodes. In this paper [4], the author proposed the geometric fuzzy multiagent system (GFMAS) algorithm
is used to improved efficiency and accurate fuzzy multiagent of the proposed architecture and the demerit is
deriving the sensitivities of the objective and constraint functions with respect to the design variables. The
writer implements [5], an agent-primarily based approach and its applications in exclusive modes of
transportation, which include roadway, railway, and air transportation is used to manipulate and management
systems improves the flexible and compatible. The size of traffic data for each road link will affect the
computation efficiency is the drawback.
3. Proposed System
In a transportation system, traffic lights are signaling the devices positioned at road intersections to control
competing traffic flows. In general, traffic light control includes two major parts: color phase and time
duration. The color phase is a sequence of standard colors (i.e., red, yellow and green). The time duration
describes the displaying periods of the color phases. In the unified traffic management framework, we
propose two online traffic light control strategies. Both of the two strategies automatically set color phases
and calculate the time duration of these phases on competing roads according to the amount of digital
pheromone. The major difference between the two strategies is whether the concerned traffic light agent
considers the downstream traffic. In both strategies, we assume that each traffic light agent by default has the
knowledge of its direct upstream pheromone, which can be easily obtained from corresponding roadside
infrastructure agents. Traffic light agent can obtain the downstream pheromone by communicating with the
neighboring traffic light agents.
Figure 1.1. TLC Not Considering Downstream Traffic (TLC-NCDT)

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Take above Figure 1.1., as an example, and suppose that the pheromone information on road links p1 and p2
is available when computing the duration for traffic lights T L1 and T L2. Also assume that p1, p2 are
one-way roads, thus the two roads have the competing relationship, where only one of the roads can be
switched to green traffic lights at any time point. Based on their pheromone, for example τ (p1,t + 1) > τ(p2,t
+ 1), road p1 should be assigned the green traffic light, and road p2 is red.
Figure 1.2. TLC Considering Downstream Traffic (TLC-CDT)
Take above Figure 1.2., as an example, and suppose that when regulating TL1 and TL2, the pheromone
information on road links p3 and p4 can also be known through communication. To better illustrate the idea,
we assume that vehicles on p1 and p2 respectively go straight to the road links p3 and p4 (e.g., not turning
right or left). Since a concerned traffic light agent always controls whether the upstream vehicles should
proceed to the downstream links, it will affect the traffic on both links. Generally, distributing vehicles to
downstream road links without any restriction may deteriorate the traffic, especially if the downstream road is
already congested. It is therefore ideal to additionally take the downstream visitors under consideration.
4. Flow Diagram
Figure 1.3. Flow Chart

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5. Algorithm
Emergency traffic response Algorithm
Input:
G, road network; R , old routes; t, current time; l, vehicle selection parameter; k, number of paths; Output:
Road network with new agent G;
➢ while #Sensor> 0 do Find road with maximum τ (p,t + 1); Get neighboring links path connected to road p
according to l;
➢ for p do Get the vehicles which need rerouting on link p according to their original intentions to traverse p;
➢ for v ∈ do Compute path according to the global distance;
➢ Compute probability for each of the k paths according to local pheromone amount on the first l
downstream links;
➢ Randomly select one path Set new route;
➢ Update pheromone;
➢ Update path;
➢ Delete the checked road;
➢ Output road network with new pheromone G;
6. Results and Discussions
The proposed method uses two sound sensors to detect the frequency of the ambulance heading towards the
traffic signal. The sound sensor1 is attached to Arduino uno1 and sensor2 is connected to Arduino uno2. The
connection among sesnor1 and sensor2 is done the usage of wireless Xbee protocol. When there's no
ambulance detected at each the sensors, ordinary visitors operation keeps functioning. When the ambulance is
detected at sensor1, the respective lane in which it is travelling is made green for duration of 10s for the
ambulance to reach sensor2 position which is placed at traffic lights and the remaining lanes are made red.
Figure 1.4. Hardware Output

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7. Conclusion and Future Enhancement
In our paper, a novel multiagent pheromone-based traffic management framework is proposed for reducing
traffic congestion, where we unify the vehicle rerouting and traffic light control for traffic congestion
alleviation. In this framework, two different pheromones, i.e., traffic pheromone and intention pheromone are
combined to model and predict the traffic condition. Once congestion is predicted, we first select vehicles
according to the distance to the concerned road and their driving intentions, then we use one probabilistic
strategy based on global distance and local pheromone to reroute those vehicles. At the same time, depending
on whether or not they consider the downstream traffic condition, we develop two pheromone-based
strategies to dynamically control traffic lights to further alleviate the traffic congestion.
Declarations
Source of Funding
This research did not receive any grant from funding agencies in the public or not-for-profit sectors.
Consent for publication
Authors declare that they consented for the publication of this research work.
References
[1] Z. Cao, H. Guo, J. Zhang, D. Niyato, and U. Fastenrath, “Improving the efficiency of stochastic vehicle
routing: A partial Lagrange multiplier method,” IEEE Trans. Veh. Technol., vol. 65, no. 6, pp. 3993–4005,
Jun. 2016.
[2] Z. Cao, H. Guo, J. Zhang, D. Niyato, and U. Fastenrath , “Locating the shortest course in stochastic vehicle
routing: A cardinality minimization technique,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 6, pp. 1688–
1702, Jun. 2016.
[3] Monireh Abdoos, Nasser Mozayani, Ana L. C.Bazzan, “Site traffic mild manage in non-desk bound
environments based totally on multi agent Q-gaining knowledge of,” 14th global IEEE convention on
intelligent Transportation structures (ITSC), no. 12385609, Nov. 2011.
[4] Rainbowbalaji Parasumanna Gokulan, Dipti Srinivasan, “Disbursed Geometric Fuzzy Multiagent urban
traffic signal control,” IEEE Transactions on intelligent Transportation structures, vol. 11, no. 3, pp. 714–727,
Sept. 2010.
[5] Wantanee Viriyasitavat, Ozan K. Tonguz, “Precedence control of Emergency vehicles at Intersections the
usage of Self- prepared visitors manage,” 2012 IEEE Vehicular Technology Convention (VTC Fall), Sept.
2012.

Sound Sensor Based Emergency Vehicle Preemption System using Microcontrolled Nodes

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