Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.1 (1-9)
Vehicular Communications ••• (••••) ••••••
Contents lists available at ScienceDirect
Vehicular Communications
www.elsevier.com/locate/vehcom
Time-bound single-path opportunistic forwarding in disconnected
industrial environments
Saad Kabbaj a , Anis Ur Rahman b,c,∗ , Asad Waqar Malik b,c , Asif Iqbal Baba d ,
Sri Devi Ravana b,∗
a
EMINES – School of Industrial Management at Mohammed VI Polytechnic University, Morocco
Department of Information Systems, Faculty of Computer Science & Information Technology, University of Malaya, Malaysia
c
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
d
Department of Computer Science, Tuskegee University, USA
b
a r t i c l e
i n f o
Article history:
Received 29 February 2020
Received in revised form 13 June 2020
Accepted 9 September 2020
Available online xxxx
Keywords:
Vehicle-to-vehicle network
Industrial internet of things
Emergency message dissemination
Path-based forwarding
a b s t r a c t
Vehicular ad hoc networks have enabled applications for real-time data sharing such as safety and
infotainment services in smart cities. Notably, with the widespread adoption of the internet of things
(IoT), the back-end networks are not designed to carry large amounts of data as it leads up to network
congestion and consumes a significant amount of energy. Moreover, the IoT sensor data requires real-time
transmission for efficient decision making, especially for safety-related warning messages. Often a greedy
approach is usually adopted to select the next-hop vehicle near the destination node; however, without
considering the direction, the performance degrades. In this paper, we propose a vehicle-based realtime data transfer framework using a path-aware dissemination scheme. The messages are forwarded
to vehicles on the road segment leading up to the destination. We compare the proposed scheme to
traditional methods like flooding, random vehicle selection, and distance-based techniques. The results
demonstrate that the proposed path-based technique increases the success rate with a minimum number
of intermediate hops.
© 2020 Elsevier Inc. All rights reserved.
1. Introduction
With the adoption of industry 4.0, the role of the industrial
internet of things (IIoT) has been widened significantly. Potentially the technology has been used with a great promise in smart
industries to collect, update, and share status messages, which
subsequently contributes to industrial data analysis and performance management [1]. Recently, large scale industrial automation
is making strides by the inclusion of autonomous mobile systems.
Unlike traditional on-site robots, these fast-pacing connected systems facilitate data forwarding across large spans of industrial environments with innovative applications like autonomous vehicles,
machine utilization, power management, quality control, and smart
logistics. The goal is to improve the production efficiency and realtime monitoring of the industrial environment [2].
In general, IoT devices are deployed to support data gathering and
real-time information sharing [3]. They facilitate information cov-
* Corresponding authors.
E-mail addresses: saad.kabbaj@emines.um6p.ma (S. Kabbaj),
anis.rahman@um.edu.my (A.U. Rahman), asad.malik@seecs.edu.pk (A.W. Malik),
ababa@tuskegee.edu (A.I. Baba), sdevi@um.edu.my (S.D. Ravana).
https://doi.org/10.1016/j.vehcom.2020.100302
2214-2096/© 2020 Elsevier Inc. All rights reserved.
erage and provide stable connectivity to the control station. That
is, the intermediate devices act as relay nodes conjointly sharing
information and status messages. Furthermore, they work collaboratively to establish single or multiple real-time connections to
the control station depending on the large-scale deployment of IoT
devices [4]. This is made possible by establishing a well-defined
communication infrastructure. However, in large-scale industrial
scenarios, it becomes unfeasible to deploy devices ensuring around
the clock connectivity. Notably, along with intermittent connectivity due to the sparsity and mobility of autonomous mobile systems, there is limited or no communication path redundancy in
such disconnected networks. Consequently, this hinders the timely
dissemination of time-critical messages. Even though there are
solutions to provide redundancy but requires widespread deployment and maintenance of a communication network, necessarily
deeming the solution cost ineffective [5].
As highlighted earlier, data generated by IoT devices is a key asset, and sharing it on time becomes quintessential. With many
data forwarding approaches specifically designed for in such a dynamic environment proposed in the literature to make data forwarding possible. For instance, vehicles along with static devices
are deployed to support the dissemination of critical information
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.2 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Organization – The paper is organized as follows, Section 2 covers
the literature review. The system model is presented in Section 3.
Section 4 presents the proposed framework followed by details of
forwarding approaches used in Section 5. The performance evaluation is presented in Section 6. We conclude the work in Section 7.
on roads [6]. Here, a large number of such devices are required
to support emergency message dissemination, which makes such
fixed installations uneconomical [7]. Moreover, sparse vehicle density affects its network coverage. Similar to connected vehicles on
road networks, modern industrial environments are often engineered comprising smooth surfaces, both indoors and outdoors,
using some form of wheeled locomotion. Thus, there is a possibility of using ad hoc networks and connected smart devices
to carry information. Therefore, in the work, we propose a data
dissemination mechanism using autonomous mobile systems that
are already contributing to the industrial automation process. The
systems are used to establish a data forwarding network from a
source node to the control center. The mechanism is evaluated
with varying vehicular densities to analyze its benefits in the smart
industry and to provide a sustainable communication network.
2. Literature review
In general, the data dissemination protocols are categorized as
infrastructure-, broadcast- and geocast-based protocols. First, the
infrastructure-based protocols use static devices to store and disseminate messages. The protocols exhibit better performance but
require costly infrastructure. Second, the broadcast-based protocols use flooding for data forwarding to all connected devices.
Even though the protocols use different suppression techniques
to reduce network traffic [8]. Last, the geocast data dissemination
protocols send data to devices within a specific geographical area,
referred to as the zone-of-relevance (ZOR). Over the year, there
have been many studies proposing techniques based on one or
multiple aforementioned protocol categories.
Problem Statement – Generally, static network devices are placed
to provide maximum coverage in any traditional industrial environment. However, such deployment is expensive and requires regular maintenance. Moreover, with the ever-changing road network
in a congested industrial site, a large number of such nodes are
required for on-site monitoring of autonomous devices, increasing the total operational cost. Furthermore, autonomous devices
demand continuous monitoring to avoid unpredictable scenarios,
unfortunately, the static network only provides a single communication link from the source to the control unit. Recently, IoT
devices have been used to establish a communication network
after natural disasters or during exploratory missions. Here, the
connected devices act as message relays from the source to the
control center. Similar to this concept, we propose a data dissemination framework that relies on autonomous vehicles. The vehicles
act as data sources and relay nodes directing the network traffic
towards the control center.
Notably, in such industrial environments, vehicle movements
are irregular along unspecified paths. Moreover, it is common to
have only intermittent paths from the source to the destination.
Traditionally, techniques like flooding provide maximum coverage
but with enormous message complexity they lead up to network
congestion affecting the overall system performance. Furthermore,
the environment when connected is classed as an opportunistic
network with intermittent connections between the source and
destination. Often mobility of the nodes is used to their advantage
by carrying forward the data and passing it along when opportunistic contacts are formed. In this paper, we propose an opportunistic path-aware data dissemination scheme between the dynamic source and destination in an industrial environment where
single-path strategy is adopted to establish opportunistic contacts
for single-hop data forwarding. The main contributions of the work
are as follows:
Data dissemination in VANETs – The role of message dissemination has been extensively explored in VANETs using RSUs for efficient data transmission between source and destination. In [9], Lu
et al. have proposed UAV-based data dissemination and scheduling
technique where the UAVs act as flying base stations. They cache
information to improve the quality of service (QoS). The work proposes a spatial dynamic programming algorithm for UAV trajectory
planning. However, the use of UAVs is an expensive in terms of energy and maintenance costs. Moreover, the UAV fail to adopt when
disseminating emergency information, for instance, in the event
of a road accident. Similarly, a position-based data dissemination
technique is proposed in [10]. Here, the next device is selected
based on device mobility direction and its relative speed. More
precisely, they argue that devices moving in opposite directions
are suitable candidates for faster data dissemination.
Grassi et al. [11] use the concept of named data networking
(NDN) for data forwarding in VANETs. Here, the data is fetched
based on its name. The underlying interface is independent of the
application layer, that is the data is decoupled from the host or
destination addresses. Moreover, the propagation of interest and
data packets is extensively explored in [12]. Due to mobility, and
interest, the NDN can lead to broadcast storm problems; therefore,
the authors proposed a dissemination limit to manage the broadcast scenario. This limit is set on receiving the data packet from the
provider; thus, fewer packets are generated compared to the traditional approach in NDN. Similarly, frequent network disconnection
is covered in [13]. Other techniques prolong the energy of the
nodes by selecting the farthest device as a destination node. Thus,
to increase the packet delivery ratio and coverage area [14]. However, in all the aforementioned data forwarding approaches, node
density can play an important role. In [15] the authors present a
traffic-aware routing protocol that considers the density to calculate the expected connectivity. This is later used for data forwarding between the source and the destination.
• Propose a data forwarding scheme for multiple sources to destination in an industrial environment, with intermediate nodes
holding the data packets for a certain time ensuring successful
delivery to next-hop by repeated retransmissions. Since IoT devices are equipped with limited capacity, a time-bound buffer
management policy is adopted to maximize successful delivery.
• Design a single-path data forwarding decision model, a device
deviating from the selected path to the destination either offloads the message to another suitable device on the path to
the destination or else discards the message with no further
transmissions to reduce network congestion.
• Evaluate the proposed approach in terms of packet delivery ratio, end-to-end delay, hop count and network overhead. The
proposed data forwarding scheme is compared to classical
flooding, random selection, and greedy geocast schemes.
Data dissemination in IIoT – Latif et al. [16] propose a message
dissemination protocol to manage the issues related to VANETs
in an industrial environment. The authors adopt a segmentationbased model for IIoT that facilitates the selection of the best vehicle; thus, reducing the broadcast storm problem. Here, the farthest
vehicle is selected for data forwarding, initially, the ideal segment
is explored to find the candidate vehicle followed by normal and
ahead vehicle clusters. The technique is ill-suited for an IIoT environment with its support for limited traffic density and storage
2
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.3 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
capacity. Moreover, any malfunctioning of routing protocols adversely affects system performance. In [17], the authors proposed a
scheme to overcome the connectivity issues using a unified routing
metric in IIoT. The work formulates the issue as integer linear programming (ILP) optimization to determine the optimal route terms
of cost. Since wireless links are often used to monitor devices
and for data gathering, in [18], a generic framework is proposed
to manage such links. The study reviews different factors relevant
to the IIoT domain and performs an analysis of their impact on
performance and resource utilization schemes. Moreover, IIoT environments are usually large and irregular with abundant mobile IoT
devices; therefore, the provision of seamless wireless connectivity
becomes difficult. In [19], a device-to-device caching mechanism
for IIoT bandwidth-hungry content sharing. Furthermore, in-device
caching is adopted to manage intermittent connectivity reducing
response times and enhancing network reliability. Similarly, in [20]
a social awareness-based resource sharing framework for IIoT environment is proposed. The framework is presented as buyers and
sellers scenario using a one-hop-based and relay-based incentive
mechanism. Subsequently, trustworthy mobile devices play a significant role to optimize the underlying resource sharing model in
manned or unmanned environments.
are equipped with IEEE 802.11p wireless technology and computation capability; (c) all vehicles are location-aware with limited and
identical transmission range; (d) there exists a multi-hop routing
protocol based on shortest path; (e) source and target are static
and located at certain points; and (f) the source contains a generic
emergency message intended for the target, sending it via multihop transmission.
3.2. Network model
Consider a vehicular traffic simulation, they can be described as
a graph G = ( V , E ) where V is the set of vehicles and E represents
the set of links between the vehicles. There is a source s and target
t representing the role of static base stations. Assume there exists a
finite directed walk w from s to t, a finite directed trail comprising
distinct edges (e s , · · · , et −1 ) connecting vertices ( v s , · · · , v t ). Similarly, the road network can be formalized as a graph H = ( I , R )
where I are the sets of intersections and R are the road segments
connecting them. A path between two intersections x and y is a
set of road segments (r x , · · · , r y −1 ) with intersections (i x , · · · , i y ).
Furthermore, each vehicle maintains an updated list X of neighboring vehicles, ones within its data-transmission range , defined
as,
Discussion – In summary, the main issue with existing protocols
is that they are not well-suited for highly dynamic networks. Ondemand routing protocols like ad hoc on-demand distance vector
routing AODV [21] and dynamic source routing (DSR) [22], may
suffer from high latency time in route finding, excessive flooding
that leads to network clogging and repetitive link breaks. For instance, in AODV every node maintains a routing table to forward
data packets to the destination. However, to build one, a large
number of messages are exchanged. Similarly, DSR initiates route
discovery and maintenance with RREQ packets that flood the system. With a large number of the intermediate nodes, the number
of RREQ messages that exist in the system increases exponentially.
With frequent topology changes in an industrial environment, the
message overhead of the aforementioned protocols to discover and
maintain routes is significantly high. On the other hand, proactive
protocols like destination-sequenced distance vector (DSDV) protocol [23] suffer from slow reaction times to network restructuring
and failures. As for greedy perimeter stateless routing (GPSR) [24],
it relies on a greedy approach where nodes closer to the destination are selected as the next hop for data forwarding. The original
approach does not take into account the frequently changing speed
and direction. When used in a mobile industrial environment, it is
inaccurate contributing to a high packet drop ratio (PDR). Moreover, once a path is established, even though it is no longer suitable, all following packets use it. Therefore, in a disconnected industrial scenario, there are frequent breaks in connectivity due to
sparsity and mobility of mobile robots, and hence traditional protocols results in lower message transmission success rates.
X = {i ∈ V |1(∃ j ∈ V : di j ⩽ )}
(1)
where di j is the distance between vehicles i and j, whereas 1(·)
is the indicator function. As mentioned earlier each vehicle has an
identical data-transmission range . A vehicle can only transmit
messages to a vehicle or target within its data-transmission range.
3.3. Message structure
We have modified the message structure of ad hoc on-demand
distance vector routing (AODV) protocol, some of the fields are
already present such as message type, hop count, destination, reserved, and originator addresses. In addition to these fields, we
introduce fields: (a) Id, a message identifier; (b) list of records,
one for each previous relay node of the message; (c) list of roads,
the roads that form the shortest path; (d) distance traveled by the
message since its generation, updated after each hop; (e) timestamp on when the message was generated by the source; and
(f) timestamp on when the message is received by the destination.
Every vehicle possesses a data buffer to keep the newly incoming
messages except the ones already in the buffer.
3.4. Radio model
In this paper we are mainly interested in vehicle-to-vehicle
(V2V) communications using the IEEE 802.11p standard. Since the
nodes involved are moving vehicles, the properties of the communication network change frequently. For instance, vehicles traveling
in the opposite directions may end up with short-lived connections; thus, the communication among neighbors is highly dynamic.
Communication modeling – Consider path loss L (d) is the reduction in power density of an electromagnetic wave (the wireless
communication signal between vehicles in our case) as it propagates through space. This weakening is due to the dispersion of the
power during transmission, and obstacles encountered that may
block, scatter, reflect or refract the transmitted signal. This loss is
important to consider when calculating energy needed for signal
transmission, as higher path loss leads to higher energy consumption. The L (d) between two neighboring vehicles at distance d is
computed as [25],
3.1. Basic assumptions
L (d) = 69.6 + 20.9 log(d) + We assume that (a) all vehicles are moving in an urban environment, and each of them has a unique Id; (b) all vehicles
where is the additive white Gaussian noise (AWGN) with mean
and variance [0, σ 2 ].
3. System model
3
(2)
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.4 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Fig. 1. State-transition diagram of the data-forwarding mechanism.
the intended destination. That is, for any vehicle v ∈ V traveling
along road segment φ(r v ), the shortest path is P = (r1 , r2 , · · · , rn )
(where r1 = r v and rn = t) that over all possible n minimizes the
Communication time modeling – The time spent to send a message depends on the distance di j between the transmitter of vehicle i and the receiver of vehicle j. The one hop successful transmission probability P hop is given as [25],
P hop =
1
2
1 + er f
ψ(d)
√
sum
2σ
i =1
2T slot
P hop
Buffer management – The proposed system relies on vehicles with
on-board computing and storage unit. Apart from the conventional
storage used by different applications running on the smart vehicles, there is message buffer with limited capacity. This means
the vehicles are unable to store messages indefinitely in its buffer.
For instance, in the case of flooding, the limited storage buffer allocated can easily spillover. In this work, we implement a buffer
management policy that removes messages from the buffer after
the expiry of T time interval. Here, T is a reasonable deadline
for the message to reach the destination, that is 2d/υ where d is
distance between the source and destination, and υ is the average
speed in the vehicular network. However, due to either the vehicle
moving away for the intended target or due to frequent disconnections in vehicular ad hoc network, the vehicle repeats forwarding
the message after every K time interval. The retransmit decision
model δ invoked every K is defined as,
(4)
where T slot represents the unit time for message transmission.
Communication energy modeling – The energy E spent to forward the messages is given as,
E = E T x + E Rx
f (i i ,i +1 ). Here, f is the weight function corresponding to
the distance between two intersections. Upon receiving a message,
the vehicle uses this path for forwarder selection to avoid congestion and broadcast storm problem, in turn, faster reception of
emergency messages at the target.
(3)
where er f (·) is the deviation function and ψ(d) = P¯v − −
N 0 W mm − L (d). Here, P¯v is the transmission power, is the SNR
threshold, N 0 is the power spectrum density of AWGN, and W mm
is the bandwidth of the millimeter wave transmissions. The propagation latency T between the vehicles is given as [25],
T=
n
−1
(5)
where E T x and E Rx is the energy consumed per bit at the transmitting and receiving devices.
4. Proposed data-forwarding framework
In this section, we present the proposed data dissemination
framework using available vehicles to forward messages from the
source to the destination. Note the destination can be fixed or mobile; however, for simplicity, we consider it fixed. Fig. 1 depicts the
state-transition diagram of the data-forwarding mechanism. Initially, all the messages are received in the input buffer, thereafter
the messages are picked up one by one from this buffer for further
processing. In the diagram, the wait mechanism reflects the buffer
where the arrival and processing rate might differ under extreme
circumstances.
δ=
0
1
if T EXPIRES
otherwise
(6)
where 0 indicates removal from the buffer while 1 indicates retransmission attempt. The attempts continue till the T time interval expires, this is when the message is removed from the buffer.
Next-hop selection – The next-hop selection is important phase to
reduce the total number of hops the message traverses to reach the
target, in turn, reducing the total reception delay. In the proposed
scheme, we utilize the concept of directional forwarding based on
the shortest route to the target. To reduce the transmission delay
due failed retransmission between intermediate node or the node
diverging from the path precomputed to the intended target, we
implement forwarder selection schemes using one or multiple vehicles on the shortest route as candidate next hops. This multiple
next hop selection excludes any vehicles moving in direction opposite to the intended target. Algorithm 1 shows the data forwarding
and expired message removal implementation.
Initialization – In emergency message dissemination scenario, the
location of first responder is generally known. However, the challenge is using the vehicular network to carry-forward the message
from a source s towards the target t. In the proposed work, we assume that the responder locations are fixed and vehicles are used
to forward the messages towards the destination node only. Every
vehicle v ∈ V is location-aware with limited communication range
to navigate during regular commute. Furthermore, the road network formalized as a graph H is a collection of road segments R
with unique ids φ(·) ∈ R. We assume this road network map is
available to every vehicle with shortest path P precomputed to
Shortest-path based protocol – In both the techniques, we use
a shortest-path based protocol for data forwarding. Algorithm 2
4
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.5 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Algorithm 1 Data-forwarding mechanism.
Algorithm 3 On receive.
Input
M: local message list; V : set of vehicles; : data-transmission range
P : shortest path; T : current simulation time
Output status message
Input
L: local message list; Lmax : local buffer capacity
M: incoming message list
Output status message
1: if M IS FULL AND τ EXPIRED then
2:
M ex ← {m ∈ M |1(m.deadline ⩽ T )}
get expired messages
3:
M ← { M − M ex }
discard expired messages
4: end if
5: X ← {i ∈ V |1(∃ j ∈ V : di j ⩽ )}
search for vehicles in range
6: Y ← {i ∈ X |1(∃ p ∈ P : p = φ(i )}
selected vehicle(s)
7: if Y = φ then
8:
Send(M,∀ v ∈ Y )
forward messages to selected vehicle(s)
9:
return 1
10: end if
11: return 0
1: if L IS FULL then
2:
return 0
3: end if
4: N ← L max − | L |
5: X ← M − L
6: L = L ∪ { X 1 · · · X N }
7: return 1
The distance-based data-forwarding protocol is a greedy decision model where the next hop is selected based on its distance
to the target [26]. However, the direction of vehicles is not considered by the decision model. Moreover, for the next hop selection,
the distance among the vehicles is compared to find a suitable candidate, in some cases, compared to the distance from the source to
the target, a vehicle much farther is selected as the next-hop candidate. This affects the data forwarding performance, especially in
time-critical scenarios. To overcome this problem, we introduce the
concept of shortest path for next hop selection where vehicles on
the shortest path road segment are selected. As mentioned earlier,
the locations of the source and destination are known in advance,
the shortest path from the source to the target is precomputed before making the selection decision. The farthest vehicles x for any
vehicle i are defined as,
w i j xi j
i j∈ A
subject to x ⩾ 0
∀i ,
i
xi j −
j
⎧
⎨ 1
x ji = −1
⎩
0
if i = s
if i = t
otherwise
(7)
x(·) is an indicator function for (i, j), 1 when part of the shortest
path, and 0 otherwise. The resulting solution is optimal with all
links labeled either 0 or 1, and the one with 1’s forming a digraph
between the source s and the target t.
∀i ∈ V , ∃x ∈ X i : max(dix )
Input
v: receiving vehicle; R: data-transmission range; P : shortest path
Output status message
5.2. Path-based forwarding
In path-based data forwarding technique, messages are forwarded to all vehicles on the road segment making the shortest path. Upon receiving the messages, every vehicle executes the
same procedure for the next hop selection. For instance, when a
vehicle sends some messages to the set of vehicles X within its
data-transmission range , the vehicle i determines the ones with
road segment φ(·) ∈ P . Here, P is the shortest path from current
source (i) to destination (t). Thus, the candidate vehicles Y for data
forwarding, the ones on the path to the target, are defined as,
1: if v WITHIN AND v ON P then
2:
Send(v,L)
send local message list to receiving vehicle
3:
return 1
4: end if
5: return 0
Data fusion – To handle the multiple receipts of the same message,
the concept of data fusion is adopted where the resulting message
list L after fusion with incoming message list M comprises only
distinct messages. That is, any redundant messages or message exceeding the message list capacity are immediately rejected. The
updated local message list L is given as,
subject to | L | ⩽ L max
(9)
where X is the set of vehicles in data-transmission range.
Algorithm 2 On send.
L = L ∪ {M − L}
available buffer capacity
discard duplicate incoming messages
{ X N +1 · · · X | M | } rejected
5.1. Distance-based forwarding
shows the data forwarding procedure on shortest route to destination. A vehicle v ∈ V scans around to find all its neighboring
vehicles within its data-transmission range. Recall shortest path is
given as,
min
buffer limit reached
Y = {i ∈ X |1(∃ p ∈ P : p = φ(i )}
(10)
where 1(·) is the indicator function.
5.3. Baseline approaches
(8)
We have also tested two baseline approaches to evaluate the
proposed data dissemination schemes. In contrast to forwarding
the message to multiple vehicles on the shortest path, the schemes
only select one vehicle for next hop, presented as follows:
where L max is the maximum capacity of the local message buffer.
Algorithm 3 shows the packet drop implementation in the proposed framework.
5. Forwarder selection approaches
Flooding – is a classical approach to disseminate a message
through the network reaching all nodes. In this approach, each
node rebroadcasts a message it receives to all the vehicles in datatransmission range denoted as the set X . Consequently, multiple
copies of same message are received from different sources. It is
considered a good choice when mobility is high, resulting in better success rates. Furthermore, the coverage area is high in flooding
compared to other techniques. However, the disadvantage being its
inefficient use of network bandwidth [27].
To optimize the use of resources, next-hop selection to forward
the message is still an emerging area in vehicular networks where
different factors like vehicle speed, direction, and distance affect
the data forwarding performance. Furthermore, limited buffer capacity at every vehicle introduces more challenges and interesting
observations. In this work, we compared distance-based message
forwarding to the path-based data forwarding scheme for data dissemination in emergency scenarios.
5
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.6 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Fig. 2. Industrial road network map, showing multiple routes to control unit. The shortest possible route is highlighted in blue. (For interpretation of the colors in the figure(s),
the reader is referred to the web version of this article.)
Random selection on shortest path – is based on another traditional method used for next hop selection. Originally, the source
uniformly selects a node as the next hop among X , the set of vehicles in data-transmission range. In this study, we modify it by
limiting the candidates to ones on the shortest path P from source
i to destination t. The candidate set Y for next hop is defined as,
Y = {i ∈ X |1(∃ p ∈ P : p = φ(i )}
Table 1
Simulation configuration and specification.
(11)
where 1(·) is the indicator function, φ(·) returns the road segment.
Mathematically, the selected next hop is defined as, U { y ∈ Y }.
The method being a variant of path-based next-hop selection, the
technique is useful to reduce the network traffic in VANET environment; however, affects the success rate and coverage area of
the message dissemination.
6. Performance evaluation
To evaluate the aforementioned data dissemination schemes in
VANETs, we consider different parameters to assess the reception
performance such as packet delivery ratio (PDR), end-to-end delay, hop count, and the number of messages in the environment.
The schemes evaluated are: traditional flooding, random selection,
distance-based and path-based data forwarding.
Value
Simulation area
Simulation time
Iterations per run
Road type
Total number of vehicles
Vehicles sources
Vehicle arrival rate
Vehicle destination
Vehicle speed
Vehicle acceleration/deacceleration
Vehicle transmission range
Message data size
Message generation rate
Vehicle buffer size
Retransmit interval K
4650×2625 m
1 hr
5 (five)
Two-way and three-way
Variable
Multiple
100–500 per hour
Random
17–22 m/s
2.6 m/s2
300 m
1 KB
50–500 ms
64 KB
500 ms
CPU
RAM
OS
Simulator
Intel Core T M i5-3470 3.2 GHz
8 GB
Windows 8.1
AnyLogic 8 PLE 8.4.0
any time. Furthermore, the arrival rate is set as 500 vehicles per
minute. The simulation is run for a duration of one hour. The simulation results reported are the average values after five iterations.
Other vehicle specific parameters are shown in Table 1.
6.1. Simulation setup
6.2. Evaluation criteria
To benchmark the aforementioned data dissemination algorithms, we develop the proposed experimental framework using
AnyLogic1 simulation tool. For the vehicular traffic simulation, we
use the road traffic library available in the tool. We use a map
spanning 4650×2625 m for this study as shown in Fig. 2. The
source and destination are indicated as yellow icons whereas the
shortest path between them is highlighted as a dark blue line. The
entire road network supports two-way communication. The typical
message size is considered is 1 KB and vehicle transmission range
is 300 m. The vehicles can hold up to 64 messages in its buffer at
1
Parameter
Packet delivery ratio (PDR) – assesses the performance of a routing protocol. It is the ratio of packets received N R at the destination node to the total number of packets sent N S from the source
node, defined as,
P DR =
NR
NS
× 100%
(12)
Fig. 3 shows the comparison of different data forwarding techniques with respect to PDR. Here, the arrival rate of vehicles per
hour per entry point is used to gauge the performance trend. Initially, flooding performs better compared to the other techniques;
https://www.anylogic.com/.
6
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.7 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Fig. 3. PDR with varying vehicle arrival rate per hour per entry point.
Fig. 4. End-to-end delay compared for all the techniques with varying vehicles arrival rate per hour per entry point.
pacity buffers are efficiently used with only one forwarder selected
per repeat, resulting in less delay.
however, with increasing arrival rate, the success rate of flooding
decreases. This is due to the congestion caused when flooding the
message in all directions. Similar performance pattern is observed
in path-based forwarding, initially, performing well but dropping
with increasing arrival rate. This is mainly due to it being a type
of directional flooding. On the other hand, random and distance
based perform well even at larger arrival rates with less congestion. Another reason affecting the success rate is the limited buffer
size for incoming messages. This becomes apparent at high arrival
rates where random and distance-based methods surpass traditional flooding.
Hop count – is the average number of intermediate nodes used
for a successful reception of message at the target node. With
less number of hops reduces the intermediate delay due to the
store-and-forward procedure. Assuming N is the total number of
received messages, and h is the number of relay nodes traversed
by a message then the average number of hops H is given as,
End-to-end delay – is the average time taken by a message to
reach the destination. This includes any intermediate delays like
buffering time, transmission delay, propagation time, and transfer
time. Assuming N is the total number of messages received, tr and
t g are the message reception and generation times, respectively,
then, the delay D is,
1 N
(t ig − tri )
N
hi
(14)
i =1
Fig. 5 shows that flooding and random data forwarding techniques
take more hops for successful transmission. With increasing vehicle arrival rate, the path-based technique performs better due to
its message forwarding approach on the shortest path. Whereas,
the distance-based technique performs slightly better compared to
flooding. This is due to the fact that the probability of selecting
the same vehicle again as next hop is high due to the distance
criterion used between moving the source and next hop nodes,
with buffer reaching capacity and eventually resulting in message
drops.
N
D=
1 N
H=
(13)
i =1
Fig. 4 shows a significant difference between the data forwarding
techniques in terms of delay. The path-based and baseline flooding
techniques exhibit more delay compared to random and distancebased techniques. This is due to the fact that vehicles have a
limited buffer capacity to hold incoming messages. That is, the
buffer overflows quickly in the case of path-based and flooding,
leading to frequent packet drops and additional delay. We observe
that in the case of flooding messages eventually reach via alternate
paths. In contrast, in the case of path-based with directional flooding, there are more delays compared to traditional flooding. On the
other hand, distance-based and random perform better when using
repeated transmits rather then flooding. Moreover, the limited ca-
Network Congestion – corresponds to the total number of messages relayed in the system, an indicator of total network load.
Assuming that K is the size of the population of vehicles in the
vehicular environment, and l is the total number of messages carried by any vehicle over its lifetime, the total number of messages
P is defined as,
L=
K
i =1
7
li
(15)
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.8 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
Fig. 5. Average number of hops used at varying vehicle arrival rate per hour per entry point.
Fig. 6. Network overhead in terms of total messages generated with varying arrival rate per hour per entry point.
ber of duplicate messages in the network. The main conclusion is
that attributed to the suitable selection of relay nodes, the proposed path-based protocol results in better packet delivery ratio
(up to 81% of messages delivered successfully) with reduced network load compared to baseline flooding protocol. Similar findings
are observed at different vehicular density conditions. Moreover,
three variants of the path-based protocol have been discussed,
demonstrating its adaptability to different network conditions.
Fig. 6 shows the average number of messages carried by vehicles
with respect to arrival rate. We observe that flooding generates
a significant number of messages, resulting in a higher network
load. In contrast, all other techniques ended up generating lesser
messages in the environment, a lower network load.
Summary of results – In the evaluation section, four different techniques are compared in terms of PDR, hop count, delay and network congestion. We observe that every technique has its own set
of pros and cons. In a less dense environment, flooding demonstrates better success rates as it generates a number of redundant messages. But with increasing arrival rates, this redundancy
mechanism eventually congests the network, affecting the overall
message delivery. Furthermore, even though, path-based technique
is based on traditional flooding but being directional significantly
reduces the network overhead. In the case of the two baseline
techniques, distance-based and random selection, the performance
is similar. However, in a distance-based approach, the number of
hops is less due to the distance-based next-hop selection approach.
In summary, the dynamic nature of the vehicular network, makes
path-based more suitable data forwarding approach as it establishes multiple paths to a destination on the shortest route. In case
any of the selected next hops fail, there are alternatives carrying
the same message towards the destination.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
References
[1] M. Zolanvari, M.A. Teixeira, L. Gupta, K.M. Khan, R. Jain, Machine learning-based
network vulnerability analysis of industrial internet of things, IEEE Int. Things
J. 6 (4) (2019) 6822–6834.
[2] H. Abou-zeid, F. Pervez, A. Adinoyi, M. Aljlayl, H. Yanikomeroglu, Cellular v2x
transmission for connected and autonomous vehicles standardization, applications, and enabling technologies, IEEE Consum. Electron. Mag. 8 (6) (2019)
91–98.
[3] C. Guo, D. Li, G. Zhang, M. Zhai, Real-time path planning in urban area via
vanet-assisted traffic information sharing, IEEE Trans. Veh. Technol. 67 (7)
(2018) 5635–5649.
[4] T. Bednarz, C. James, E. Widzyk-Capehart, C. Caris, L. Alem, Distributed collaborative immersive virtual reality framework for the mining industry, in: Machine
Vision and Mechatronics in Practice, Springer, 2015, pp. 39–48.
[5] R. Hemmati, R.-A. Hooshmand, A. Khodabakhshian, State-of-the-art of transmission expansion planning: comprehensive review, Renew. Sustain. Energy
Rev. 23 (2013) 312–319.
[6] V. Vijayakumar, K.S. Joseph, Adaptive load balancing schema for efficient data
dissemination in vehicular ad-hoc network vanet, Alex. Eng. J. 58 (4) (2019)
1157–1166.
[7] S. Dietzel, J. Petit, F. Kargl, B. Scheuermann, In-network aggregation for vehicular ad hoc networks, IEEE Commun. Surv. Tutor. 16 (4) (2014) 1909–1932.
7. Conclusions
In this work, we design a protocol for VANETs that disseminates
messages based on a path-based approach designed in a very simple way and by using a few network parameters. We detail both
the protocol description and simulation of its behavior in a realistic urban scenario. We analyze the performance of the dissemination scheme by evaluating different performance metrics including
packet delivery ratio, end-to-end delay, number of hops, and num8
Downloaded from https://iranpaper.ir
JID:VEHCOM
https://www.tarjomano.com/order
AID:100302 /FLA
[m5G; v1.297] P.9 (1-9)
Vehicular Communications ••• (••••) ••••••
S. Kabbaj, A.U. Rahman, A.W. Malik et al.
[8] N. Wisitpongphan, O.K. Tonguz, J.S. Parikh, P. Mudalige, F. Bai, V. Sadekar,
Broadcast storm mitigation techniques in vehicular ad hoc networks, IEEE
Wirel. Commun. 14 (6) (2007) 84–94.
[9] R. Lu, R. Zhang, X. Cheng, L. Yang, Relay in the sky: a uav-aided cooperative
data dissemination scheduling strategy in vanets, in: ICC 2019-2019 IEEE International Conference on Communications (ICC), IEEE, 2019, pp. 1–6.
[10] M. Ali, A.W. Malik, A.U. Rahman, S. Iqbal, M.M. Hamayun, Position-based emergency message dissemination for internet of vehicles, Int. J. Distrib. Sens. Netw.
15 (7) (2019) 1550147719861585.
[11] G. Grassi, D. Pesavento, G. Pau, R. Vuyyuru, R. Wakikawa, L. Zhang, Vanet via
named data networking, in: 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2014, pp. 410–415.
[12] S.H. Ahmed, S.H. Bouk, M.A. Yaqub, D. Kim, H. Song, J. Lloret, Codie: controlled
data and interest evaluation in vehicular named data networks, IEEE Trans.
Veh. Technol. 65 (6) (2016) 3954–3963.
[13] M. Chahal, S. Harit, A stable and reliable data dissemination scheme based on
intelligent forwarding in vanets, Int. J. Commun. Syst. 32 (3) (2019) e3869.
[14] J.H. Narasappa, D. Rekha, Energy aware methodical data forwarding (eamdf)
mechanism in vanet, J. Intell. Fuzzy Syst. (2019) 1–11 (Preprint).
[15] S. Khan, M. Alam, M. Fränzle, N. Müllner, Y. Chen, A traffic aware segmentbased routing protocol for vanets in urban scenarios, Comput. Electr. Eng. 68
(2018) 447–462.
[16] S. Latif, S. Mahfooz, N. Ahmad, B. Jan, H. Farman, M. Khan, K. Han, Industrial
internet of things based efficient and reliable data dissemination solution for
vehicular ad hoc networks, Wirel. Commun. Mob. Comput. (2018).
[17] Z. Zhao, Y. Shi, B. Diao, B. Wu, Optimal data caching and forwarding in industrial iot with diverse connectivity, IEEE Trans. Ind. Inform. 15 (4) (2018)
2288–2296.
[18] Y. Liu, M. Kashef, K.B. Lee, L. Benmohamed, R. Candell, Wireless network design
for emerging iiot applications: reference framework and use cases, Proc. IEEE
107 (6) (2019) 1166–1192.
[19] A. Orsino, R. Kovalchukov, A. Samuylov, D. Moltchanov, S. Andreev, Y. Koucheryavy, M. Valkama, Caching-aided collaborative d2d operation for predictive
data dissemination in industrial iot, IEEE Wirel. Commun. 25 (3) (2018) 50–57.
[20] W. Sun, J. Liu, Y. Yue, Y. Jiang, Social-aware incentive mechanisms for d2d resource sharing in iiot, IEEE Trans. Ind. Inform. 16 (8) (2020) 5517–5526.
[21] C. Perkins, E. Belding-Royer, S. Das, Rfc3561: ad hoc on-demand distance vector
(aodv) routing, 2003.
[22] D. Johnson, Y.-c. Hu, D. Maltz, et al., The dynamic source routing protocol (dsr)
for mobile ad hoc networks for ipv4, Tech. Rep., RFC 4728, 2007.
[23] G. He, Destination-sequenced distance vector (dsdv) protocol, Networking Laboratory, Helsinki University of Technology, 2002, pp. 1–9.
[24] B. Karp, H.-T. Kung, Gpsr: greedy perimeter stateless routing for wireless networks, in: Proceedings of the 6th Annual International Conference on Mobile
Computing and Networking, 2000, pp. 243–254.
[25] X. Ge, Ultra-reliable low-latency communications in autonomous vehicular networks, IEEE Trans. Veh. Technol. 68 (5) (2019) 5005–5016.
[26] S. Boussoufa-Lahlah, F. Semchedine, L. Bouallouche-Medjkoune, Geographic
routing protocols for vehicular ad hoc networks (vanets): a survey, Veh. Commun. 11 (2018) 20–31.
[27] C.-K. Toh, G. Guichal, S. Bunchua, Abam: on-demand associativity-based multicast routing for ad hoc mobile networks, in: Vehicular Technology Conference
Fall 2000. IEEE VTS Fall VTC2000. 52nd Vehicular Technology Conference (Cat.
No. 00CH37152), vol. 3, IEEE, 2000, pp. 987–993.
9