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Discover Internet of Things
Research
An energy‑aware application module forthefog‑based internet
ofmilitary things
BashirYusufBichi1· SaifulIslam2 · AnasMaazuKademi3· IshfaqAhmad4
Received: 29 April 2022 / Accepted: 7 June 2022
© The Author(s) 2022 OPEN
Abstract
Smart devices in various application areas are becoming increasinglyprevalent for ecient handling ofmultiple critical
activities. One such area of interest is high-security militarized environments. Due to military zones’ harsh and unpredict-
able nature, monitoring devices deployed in such environments must operate without powerinterruption forextended
timeperiods. Therefore, it is essential to choose an appropriate application design for operating these “things” in the
internet of things (IoT)environment such that energy can be conservedthroughout the operating span ofan application.
This paper presents two application modules and analyzes their performance in terms of energy conservation consider-
ing a military-based IoT-Fog architecture. The two modules are: A sequential application module, and amaster-worker
application module. Experimental results show that the master-worker module incurs lower energy consumption and
communication overhead than the sequential application module. Signicantly, the master-worker module exhibits a
lower delay in tuple execution by almost four milliseconds while also accounting for lower simulation time and higher
network utilization. The module achieves signicant savings in energy consumption, making it more eective in handling
smart devices.
Keywords Fog computing· IoT· Master-worker module· Military-based IoT· Sequential module
1 Introduction
The internet of things (IoT) is ubiquitous and is changing the way we live and work. Applications of theIoT are becom-
ing popular in a myriad of elds of life, including education, health, transportation, security, and surveillance [1]. The IoT
is a network of heterogeneous devices capable of capturing and sharing information without human intervention [2].
IoTnetworks are classied as cyber-physical systems of interaction between the abstract cyber system and the physical
environment using the Internet [2]. The aim is to allow these things to autonomously acquire vital information from the
deployment area [3]. For instance, smart devices can be deployed on roadsides and surveillance cameras to help track
oenses or violations of trac rules [4, 5].
Moreover, these things can prove to be a signicant source of information in the military environment due to the
distinct operations, such as smart surveillance and monitoring activities. For instance, Dastjerdi and Buyya [6] describe
* Saif ul Islam, saiu2004@gmail.com; saif.islam@mail.ist.edu.pk; Bashir Yusuf Bichi, byusufbichi@gmail.com; Anas Maazu Kademi,
anas.kademi@yasar.edu.tr; Ishfaq Ahmad, iahmad@cse.uta.edu | 1Department ofComputer Science, COMSATS University Islamabad,
Islamabad, Pakistan. 2Department ofComputer Science, Institute ofSpace Technology, Islamabad44000, Pakistan. 3Department
ofManagement Information Systems, Yasar University, No 37-39 Bornova, Izmir, Turkey. 4Department ofComputer Science
andEngineering, The University ofTexas atArlington, Arlington, USA.
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how sensors in IoT work by sending a stream of data to a given IoT network where the application running in the fog
environment processes data and sends feedback to actuators. Innovative surveillance technologies coupled with IoT
have paved the way for the emergence of the military based Internet of Things [7].
Military environments are increasingly being populated by smart things [3, 7]. These things perform tasks that may
involve collaboration, sensing, communication, and decision-making. Their increasing demand shows how IoTs gain
ground in military activities to enhance and improve information gathering and dissemination. Figure1 shows how
intelligent devices deployed within an army zone interact with the edge for task processing.
Military-based IoT devices(also known as the internet of military things (IoMT)) are usually close to the fog environ-
ment. Fog computing is the extension of the cloud, and its main objective is to bring computing resources closer to
the users [8]. Typically, fog computing resources are available at a one-hop wireless distance. This enables fog devices
to perform context-aware computation and data processing as per user requirements because the fog can provide
location-based customization in terms of content, services, and applications to the IoT devices [8–10]. With the adop-
tion and integration of technologies in the area of cloud, fog, and IoT, the concept of integrated-fog-cloud-IoTs (IFCIoTs)
focuses on issues such as an increase in performance, less energy consumption, and lower network usage compared to
traditional networking components [9, 11].
Being part of an IoT architecture, Military-based IoTs share some common characteristics and challenges, the signi-
cant of which is a growing demand for energy conservation due to the rapid expansion of military-based IoT devices.
Minimizing energy through ecient use of resources has proven successful in the IoT environment. Therefore, one can
expect it to be even more successful when a cost reduction mechanism is employed to manage IoT devices in a military
environment [12]. Managing and conserving energy consumption is highly recommended, as pointed out in [1]. The
authors forecast that the future of military environments is gradually transforming into digital gadgets [1]. These gadgets
constantly consume energy to perform the assigned responsibility. Therefore, energy conservation is highly desired for
sustainable operation. We consider the need for energy conservation due to the energy constraints of IoT sensors that
exclusively rely on battery power. Moreover, these devices may need to communicate over the Internet through wireless
communication and perform surveillance of the environment where they are deployed constantly. Such activities con-
sume a signicant amount of energy, which needs to be minimized for operational, economic, and environmental reasons.
Adopting IoTs into the military introduces a new generation of cyber-physical applications with improved capabilities
specically targeted for combat eectiveness [7]. The sensitivity of a military eld often requires constant surveillance
by employing various sensors within the militarized zones. These sensors are constantly monitoring the area where they
are deployed to detect an abnormal state due to intrusion or security breach. The military eld’s highly dynamic and
sensitive nature makes it unpredictable, prompting the need for constant sensing of the militarized area to detect any
intrusions. However, continuous sensing by the sensors can lead to a considerable increase in energy consumption that
Fig. 1 A military-based IoT-
Fog application architecture
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reduces the lifetime of such devices. Improving the operational lifetime of IoT devices in the militarized environment
is crucial due to energy constraints as they are expected to operate in unattended harsh environments. Therefore, new
solutions are required to reduce the energy consumption of IoT devices within military zones. This paper aims to study
the energy consumption of IoT devices and propose a solution to improve the energy eciency of IoT deployed in fog
conguration. The paper adopts two strategies from [13], namely the sequential and master-worker modules. The details
of the two modules are discussed in Sect.3.
1.1 Motivation andcontribution
Intensied by the integration with the cloud, resource management in Fog computing is intricate because of the diver-
sity and resource limitation of the nodes to respond to the computational request of IoT-enabled systems. Other factors
that add more diculties are the sensing capacity dierences of the devices, distributed application structure, and
their coordination. Developing ecient resource management in an IoT-based fog computing environment requires a
comprehensive strategy.
The management component conceptualized in the architecture consists of Controller and Module Mapping objects
that identify available resources and place them. This is enabled by iFogSim, which uses Sense-Process-Actuate and
distributed dataow model while simulating any application scenario in a Fog computing environment. Figure2 show
Military IoT’s system architecture and interaction with the conceptual relation with the iFogSim components. The physi-
cal components consist of Fog devices, the lower layer devices directly connected with associated sensors and actuators
that realize the Military IoT’s data sensing and control implementation. The sensors generate tuples referred to as tasks.
The logical components include the modules and the edges of the application. While the collection of inter-dependent
modules facilitates distributed data ow, the edges dene a dependency between modules. Two application models,
the master-worker and the Sequential Unidirectional dataow application model, are proposed.
This paper investigates the pattern of energy consumption, network usage, execution time, and tuple latency that
may arise when handling critical IoT devices in a sensitive militarized environment. Our work is focused on these two
basic approaches in the context of application module strategy, the “sequential module” and “master-worker module” for
handling data in IoT. The aim is to build a lower energy consumption scheme when an application is invoked to process
the captured data acquired by IoT devices within the militarized zone.
Complementing the literature, the main contributions of this paper are:
• It evaluates two application modules and analyzes their performance in terms of energy conservation under military-
based IoT-Fog architecture.
• It provides an edge network solution from the logical components of the application and dynamic network consid-
eration while dealing with trade-os.
• Assuming a physical topology conguration with experiments in a realistic edge and core cloud testbed, we develop
further avenues to enable the design of resource management techniques to minimize the latency and maximize
throughput.
Fig. 2 The sequential applica-
tion module
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In the next part of this paper, Sect.2, we analyze the related work. The system model and problem formulation are
presented in Sect.3 and 4, respectively. The proposed strategy is detailed in Sect.5, and the experimental evaluation and
simulation results are discussed in Sect.6. Section7 concludes the paper with directions for future work.
2 Related work
The IoT revolution is transforming the entire spectrum of computing-based services. A new wave of rapid infrastructure
development is sweeping the globe, encompassing virtually everything digital, ranging from data centers, supercom-
puters, clusters, embedded systems, servers, and networks to power grids, sensors, appliances, and mobile devices
instruments [14]. With a broad application in many elds, including healthcare, military surveillance, buildings, and
transportation, the need for high bandwidth and processing capability and energy eciency performing tasks is ever-
present as the modern system is expected to utilize cutting-edge technologies like 6G, IoT, etc.
Hameed etal. [15] devised a cluster-enabled capacity-based load balancing approach, providing lower energy con-
sumption, and improving the performance in vehicular fog distributed computing to process the IoT requests eciently.
They use a dynamic clustering approach that considers vehicles’ position, speed, and direction.
Furthermore, energy emissions have a critical impact on the environment. In [16], a strength Pareto evolutionary
algorithm (SPEA-II)-based higher-level algorithm was proposed to augment power conservation. In this regard, machine
temperature is used as a parameter for job placement.
The design of the data centers also has a critical impact on energy consumption, cost, and performance. These issues
require a compact solution in terms of software and hardware. Khalid and Ahmad [17] developed an evolutionary algo-
rithm based upon a higher-level heuristic designed to nd Pareto optimal decisions for a cloud controller. The technique
considers real-time electric price variations due to load and renewable power availability while assigning requests to
data centers. Meanwhile, An energy-ecient approach called eLCRQ was proposed in [18]. They optimized the energy
consumption of the CPU by exploiting the parallelism. Here and in many other example areas, energy eciency is the
goal that must be addressed.
Also, in live virtual reality (VR) streaming, the considerable bandwidth and the energy required to deliver live VR
frames in the wireless video sensor network (WVSN) become bottlenecks, making it impossible for the application to be
deployed more widely. To solve the bandwidth and energy challenges, Chen etal. [19] proposed a lightweight neural
network-based viewport prediction method for live VR streaming in WVSN.
In more critical areas, the energy-aware application module of IoT from a military perspective is a topic that explores
energy consumption at the things, edge, and data center levels. Although there is no related research work in the eld
under consideration, we present closely related works targeted toward energy conservation in IoT, including military
gadgets IoTs.
In [20], the authors presented a technique that considers the physical components of the IoT systems, including
sensors, standard networking, and other acquisition components, to bridge the gap between theoretical analysis and
practical applicability. The authors apply various application parameters to the proposed scenario, i.e., prediction and
experimental demonstration. The authors conclude that dierent application parameters have varying impacts on the
power consumption of IoT.
Mebrek etal. [21] investigated the nature of energy consumption in the IoT-Fog-Cloud Environment by using an evo-
lutionary algorithm approach. The scenario employed by the authors involves a data center at the top of the network
with fog nodes at the edges that serve the IoT devices. This scenario was parameterized into three components or steps
to derive a solution to minimize energy consumption. The simulation results show that a considerable amount of energy
is reduced when the proposed fog computing architecture is adopted. Another work presented in [22] investigated
various technologies, such as Zigbee or IEEE 802.15.4, low power WSN, ultra-low power Bluetooth low energy (BLE)
wireless standards in a mesh topology, among other technologies. The authors conclude that using BLE reduces energy
consumption signicantly within IoT devices. This is because BLE can extend the sleep time of sensor nodes. The work in
[23] focused on some domains, such as data centers that heavily rely on a continuous ow of energy to operate commu-
nications entities and devices. The energy sources were the primary concern in their discussion. Based on the observation
made on each energy source identied, the authors concluded that a strategic mechanism needs to be put in place to
balance IoT devices’ resource requirements, human experience, and the cost that may be induced to the environment.
Assila etal. [24] proposed a scheme based on a matching game approach to achieve low-energy consumption
in IoT devices and clouds through fog computing facilities by employing a caching technique. The authors use the
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Galer-Shaphey algorithm for matching IoT devices to a suitable fog environment. The simulation result shows a remark-
able reduction in energy consumption.
Table1 further analyses additional closely related research work along with the environment-focused problem,
approach, and the added advantage and the weaknesses of each work. Existing strategies mainly optimize the quality
of service features separately in dierent application environments. Most of the studies aimed at optimizing energy
consumption using various approaches, focusing on the IoT devices’ physical components––they do not consider the
logical components. Very few of these studies use the graph-based computation strategy, although an ecient tool
to represent the association and connections between the devices is found in the network theory. The literature is
augmented with the proposed technique aimed at the application module’s logical components to optimize energy in
military-based IoT devices further.
3 The system model
Military-enabled IoTs require the help of a middleware application that can collect and analyze raw data captured by
IoT devices [7]. The application module contains the necessary instructions for executing the specic IoT application
function. Generally, it includes functions that receive data, process received data, and perform analytical tasks on data
to provide a real-time response to the concerned application [28]. According to [29], the application is modeled as a col-
lection of modules composed of dierent data processing elements. Processing military IoT applications locally or within
a nearby fog is very important for energy conservation, as computation distance is distributed rather than centralized to
a distant server [30]. In this research, we present two application modules to nd better (less) execution time and lower
energy consumption while handling tasks at the things level. The application may consist of distributed fog processes
[28]. These processes are mapped onto computing instances contained within the fog, cloud, and various edge devices.
IoT applications are designed to execute over devices that have the required resources. These resources can be sensors,
actuators, computing, and communication resources. The applications are built based on the distributed data ow (DDF)
model, where dierent functions or modules can be deployed on multiple physical devices. This is because the output
from one module is considered an input for another module [29, 31].
Inspired by [32], we employ the idea of the directed acyclic graphs (DAGs) model for the proposed technique. The
DAG models consist of vertices that contain four application modules and a sensor representing an IoT device. These
sensors send tuples (the fundamental unit of communication among entities) to the application modules along the
edges of the modules. Edges of the DAG show data dependency between modules. The actuator displays the output of
the processed data generated by the IoT sensor.
3.1 The sequential module
The application module processes tasks in a unidirectional pattern in the sequential module. Each module has a specic
job to carry out on the incoming data. It then forwards the output to the next module as an input for processing until it
reaches the actuator as the nal output. The process is illustrated in Fig.2.
In the sample module depicted in Fig.2, sensor emitted tasks are sent to module 1. Module 1 processes the tasks
and produces an output “Result 1”. The output (Result 1) is sent as an input to the next module (i.e., module 2), which
processes it and produces an output “Result 2”. Result 2 is sent to the next module as input. The process continues until
the last module has the outcome which is sent back to the rst module. The rst module then sends the nal output to
the actuator.
3.2 The master‑worker module
The master-worker module works by start sending tasks to a specic module (worker 1). Worker 1 processes the task and
returns the resulting output to the master module. The received output is sent to the next module (worker 2). This process is
illustrated in Fig.3 and is repeated until all the required modules process data. Lastly, the master module is responsible for
sending the processed data to the actuator.
The master module has control over the workers, and, as the name implies, workers only process the tasks sent by the
master module and return the results. The master module then sends the task to the next worker module until the task is
completely processed. The output is then sent to the actuator. As in the sequential application module, the rst module is
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Table 1 Related work
Related work Problem/environment Goal Approach Strength Weakness
[21] Balancing energy consump-
tion in IoT-Fog-Cloud physical
devices
To reduce energy consumption
by IoT application through
evolutionary algorithm
Evolutionary algorithm
approach based on genetic
algorithm
The algorithm reduces energy
consumption and achieves
high utilization
The energy consumption of the
IoT is minimized only when the
number of devices is increased
considerably
[22]Reducing power consumption in
IoT devices for IoT-WSN-based
Environment
To lower energy consumption Implementation using various
low-power wireless technolo-
gies
The BLE extends the lifetime of
a sensor by prolonging the
sleep mode of the sensor
Although the technique reduces
energy consumption, it is
unsuitable for long-range wire-
less communication
[23] Optimizing energy of IoT-based
devices for home appliances To generate optimized clean
and renewable sources of
energy for IoT devices
Investigative based approach to
dierent energy sources Conceptual analysis of minimiz-
ing energy consumption by
balancing demand on the IoT
devices
No practical experiment to prove
the eectiveness of the meth-
odology
[24] Low energy consumption for IoT
devices in the Fog environ-
ment
Lowering energy consumption
at the physical device level Matching game approach by
employing the most used
algorithm called Gale-Shapley
algorithm
The algorithm shows a consid-
erable reduction in energy
consumption
The energy-optimized at the IoT
level, the primary concern, is rel-
atively lower than that achieved
at the edge or fog level
[25] Minimize energy consumption
and latency in the industrial
Sensor network
An energy-ecient task o-
loading mechanism with an
ecient task-device matching
policy
Multilevel feedback queuing
policy and Hall’s theorem Task priority, low complexity,
binary ooading, and device
matching
Minimizing the average queuing
delay of tasks, not execution
delay
[26] Joint optimization of energy
consumption and latency as a
multi-objective problem
Minimizing power consumption
and time delay for IoT devices Metaheuristic methods; NSGAII
genetic algorithm and Bees
algorithm
Making a trade-o between
energy consumption and
latency
Considers several IoT requests but
not type
[27] Energy ecient and secure algo-
rithm for mobile fog cloud Energy eciency a better
throughput, reducing and
detecting malicious data
Hybrid algorithm using block-
chain technology Applying a malicious data
detection (MDD) algorithm Weak interoperability and focuses
on the hardware devices
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always responsible for sending the output to the actuator. A common feature between the two scenarios is the user interface
module. The user interface is deployed to provide the output of the video stream fraction captured by the IoT devices within
the area of interest. For the proposed scenario, we will consider the value for inter-module edges (i.e., tuple input and task)
CPU capacity involved in each module, as shown in Table2.
4 Problem formulation
Energy conservation is essential, especially in a militarized environment. According to [1], the US Department of Defense
(DoD) is employing mechanisms to help in reducing the overall energy consumption of its facilities. These eorts are carried
out to avoid potential energy constraints, which is a critical challenge in adopting IoTs within the military eld. Suri etal. [34]
state that commercial IoTs can easily be recharged from a stable energy source, whereas the case for military-based IoTs is
dierent due to their unique challenges of reachability and accessibility. Therefore, energy conservation is considered a key
challenge for the sustainable adoption of military-based IoTs. This work analyzes the energy consumption, network usage,
and execution time of tasks for the two application scenarios being investigated in this article.
The iFogSim simulator inherits the CloudSim simulator’s energy model [35]. We applied the same model for energy cal-
culation, formulated mathematically in Eq.1.
where
E
is the energy consumed by the nodes during the simulation,
Ec
is the current energy consumption,
TcandTo
are current time and the last utilization update time of the host, and
Po
is the last utilization by the host. Another critical
parameter is the network usage which is given in Eq.2 as used in [34]:
where
NU
is the network usage,
TdandTt
are total delay or latency and total tuple size, respectively. Maxst is the maximum
simulation time.
(1)
E
=Ec+
(
Tc−T0
)
∗P
o
(2)
NU =Td+Tt∕Maxst
Fig. 3 The Master-Worker
application module
Table 2 The simulation
parameters Tuple type CPU requirements (MIs) N/W
length
(bytes)
Tuple input 1000 20,000
Tuple task 1 2000 2000
Interface tuple 500 2000
Tuple task 2 100 100
Tuple output 100 100
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5 Proposed strategy
The application module presented in this paper is based on logical components of iFogSim applications [29]. The applica-
tion modules [13, 29], being inter-dependent entities, promote the concept of distributed application with application
edges representing dependency between modules as discussed in the proposed sequential application module and
master-worker application module. The proposed modules are built upon the context of video surveillance object track-
ing military IoT application presented in [33]. The modules consist of distributed intelligent cameras that can perform
specic functions, such as motion detection, object detection, and object tracking. Other functions include user interface
and pan-tilt-zoom (PTZ) control.
We demonstrate militarized IoT scenarios intending to get the model having minimal energy consumption in the par-
ticular deployment situation. The application model proposed in this paper allows IoT devices, for instance, surveillance
cameras deployed in the militarized eld, to perform a sensor-process-actuator relationship. The processing module
processes raw data captured by the device in sequential or in a master-worker relationship. The application module
consists of 3 processing modules and an interface to display the captured data for the two scenarios employed.
Based on the scenarios mentioned above, we have used the following two algorithms for the modules to enable the
energy-aware placement in IoT Fog-Cloud paradigm. Algorithm1 is the Master-Module mapping, which allows IoT-Fog
placement. It returns the ecient mapping of modules of an application onto a network infrastructure. Take the tasks T
sent to each worker that processes and sends back the output. It may rst sort the tasks and modules according to their
capacity and requirement. The Control Loop of the algorithm runs for all the application tasks that need to be processed.
Algorithm2 is the sequential application module, where sensors send a set of input tasks to the rst modules, which
compute and send the tasks to the immediate next module and the last module. The rst module received the previous
module’s output and then sent it to the actuator.
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6 Experiments andevaluation
Ecient utilization of the available resources in the presence of constraints makes energy management challenging to
achieve. Therefore, evaluating the impact of applications and resource management policies on energy consumption
is vital in critical sectors such as military environments. This enables performance optimization and operational adapta-
tion of the systems.
Extensive simulations have been conducted to analyze the performance of proposed techniques. The simulations are
performed in the iFogSim simulator using the iFogSim module termed “DCNS” [29]. The application modules of iFogSim
are customized to accommodate the proposed scenarios. The number of investigated military IoT scenarios, termed
‘areas’, varies from 2 to 12, and in each simulation scenario, we assume a physical topology conguration having 2, 4,
6, 8, 10, and 12 areas of interest. Simulation parameters are summarized in Table2, which provides the maximum CPU
requirements in millions of instructions (MIs) for each task.
Conguration parameters of nodes used in simulation experiments are summarized in Table3.
Simulations are conducted in DCNS main class, which is meant for intelligent surveillance in the iFogSim simulator,
where the surveillance area varies from 2 devices (i.e., an intelligent camera) to 12. Furthermore, we assume 4 smart
nodes as monitoring entities for the deployed site in each surveillance area. These smart nodes are connected to a gate-
way through which the intelligent devices gain access to the edge and the centralized data center over the Internet, as
shown in Fig.4. The physical topology of the surveillance area is classied and carried out in dierent congurations in
each simulation scenario, i.e., cong 1 through cong 6.
6.1 Energy consumption
This section analyzes the variation in energy consumption at the mobile, edge, and datacenter levels by the given appli-
cation modules. Figure5 shows the comparison of the energy consumed at the data center for sequential and master-
worker modules. We make use of CPU capacity for each of the nodes in our employed modules as shown in Tables2
and 3. Simulation results show a slight variation between the energy consumption of the two modules. The sequential
module (SM) consumes more energy than the master-worker module (MWM).
Figure6 compares the result for energy consumption at the mobile level. Here, the master-worker module has lower
energy consumption than the sequential module. This is because the level of communication of the master-worker
module is less than the sequential module.
Figure7 shows energy consumption at the edge level. Based on the results observed, we can see that in some
instances, the energy consumption at the sequential module is less than that of the master-worker module. However,
the overall consumption of the two modules shows that the master-worker module achieves lower energy consumption
than the sequential module.
Figure8 provides an overview of the average energy consumed by the sequential module and master-worker module
in all three levels, i.e., the overall average energy consumption attained by the two scenarios in the entire system. The
results clearly show that the master-worker module achieves signicant savings in energy consumption. Experimental
results prove that employing a master-worker application module is more eective in handling smart devices. This is
because all the nodes in the sequential module are actively participating in the execution, unlike the master-worker
module, which does not require the engagement of all nodes within the module.
6.2 Network usage
Figure9 shows the network utilization in each of the evaluated scenarios—the network usage increases as the number
of devices connected to the application increases. The results show that the master-worker module exhibits higher
Table 3 The conguration
parameters Parameter Value
Tuple input 10,000 (MIs)
Tuple task 1 20,000 bytes
Interface tuple 2ms
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network utilization than the sequential module. In fact, in the master-worker module, the master node always receives
the resulting input from each worker module. Alternatively, in the case of a sequential application module, each module
forwards the resulting data to the next module. Module 1 and module 2, or worker 1 and worker 2 in the case of the
Master-Worker module, are placed at the edge of the node. This allows for a signicant decrease in how data is sent to
the cloud data center.
6.3 Tuple CPU execution delay
Figure10 shows the tuple CPU execution delay for the Sequential application and master-worker application modules.
Results reveal that the master-worker module has a lower delay in tuple execution while the sequential module is rela-
tively higher. Such behavior is that the sequential application module consumes more CPU time than the master-worker
application module. The reason for higher CPU time is that all nodes actively participate in the execution process in the
Fig. 4 The description of
surveillance areas
Fig. 5 The energy Consump-
tion at Data Centre
13.25
13.30
13.35
13.40
13.45
13.50
13.55
Config1 Config2 Config3 Config4 Config5 Config6
Energy (in Mega Joules)
Comparison of Energy Consumpon at Data Center Level
DC Energy SM DC Energy MWM
Fig. 6 The mobile level
energy consumption
0.00
10.00
20.00
30.00
40.00
50.00
Config1 Config2 Config3 Config4 Config5Config6
Energy (Mega Joules)
Comparison of Energy Consumpon at Mobile Level
Mobile Energy SM Mobile Energy MWM
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sequential module, unlike in the master-worker module, which involves only the concerned node at a time. Based on our
ndings, we can conclude that the master-worker module can handle tuple execution faster than the sequential module.
6.4 Simulation time
Figure11 shows the comparison of simulation time for the sequential module and master-worker module. The execu-
tion time of each scenario increases linearly as the number of devices and transmission rate increases. We also observed
that the simulation time of the master-worker module is comparatively lower than the sequential module because the
number of activities carried out by the sequential model is higher than the master-worker module.
6.5 Performance comparison
The performance of the proposed approaches in the two modules is evaluated, and the energy consumption is compared
with the techniques mentioned in [21–24]. In [21], a method was introduced to reduce the energy consumption and
the quality of service (QoS), whereas in [22], optimize battery usage and power consumption. Lutui etal. [23] presented
strategy for optimizing energy requirements in a smart house through device eciency. While their work considers the
physical components of the IoT, we augment the techniques by focusing on the logical components of the application
module to optimize further the energy consumed by the military-based IoT devices. The environments are dierent, but
it was shown that proper deployment of modules, signicant savings in energy consumption, about three times for the
edge devices making it more eective in handling smart devices.
IoT and related wireless network technologies nd their way into military elds and are critical for monitoring and
tracking. However, these technologies have drawbacks, including energy consumption, latency, complex infrastructure,
Fig. 7 The edge level energy
consumption
0
10
20
30
40
Config1 Config2 Config3 Config4 Config5 Config6
Energy (Mega Joules)
Comparison of Energy Consumpon at Edge Level
Edge Energy SM Edge Energy MWM
Fig. 8 The average energy
consumption on the
sequential and master-worker
modules
0.00
5.00
10.00
15.00
20.00
25.00
DE Energy Edge EnergyMobile Energy
ENERGY (MEGA JOULE)
PHYSICAL TOPOLOGY CONFIGURATION
AAvveerraaggeeEEnneerrggyyCCoonnssuummppttiioonn
SM MWM
Fig. 9 The network usage
on sequential module and
master-worker module
0
20
40
60
80
100
120
140
Config1 Config2 Config3 Config4 Config5 Config6
Network Usage
Physical Topology Configuraon
Network Usage in KB
Network Usage SM Network Usage MWM
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1 3
etc. The sensor cloud-based model for critical sectors constitutes applications characterized by a need for adapting to
variable and unpredictable operating environments. While our motivation is to study the approaches that will oer
energy-ecient processing of the request and data, the paper focused on the logical components of the application
modules. However, considering the physical and logical components of the IoT systems may provide further energy
management on these constrained devices with limited power sources while providing better latency. This should be
further research to bridge the gap between theoretical analysis and practical applicability.
In addition, the proposed work applies to fewer sensing devices, such as cameras, it could be extended to other smart
sensing devices as energy conservation is a key goal of all the devices deployed in a militarized eld.
Built based on the DDF model, complete characteristics of the application DAG have not been investigated, only the
energy of the data processing procedure presented. In future work, however, the other dynamic characteristics, energy
of the data transmission procedure, are aimed to be addressed while integrating network connectivity, failure of nodes,
etc., further dynamic characteristic of Fog and Cloud components. The future scope would also look into multiple task
processors’ transmission power allocation of an edge computing system with multiple independent tasks.
7 Conclusions
The emergence of the IoT allows sensors and devices to observe the surrounding environment and make decisions
autonomously. Innovative surveillance technologies have paved the way for the emergence of the military-based IoT.
However, these high-security environments are harsh and unpredictable, and the devices deployed in such environ-
ments operate continuously for a long. The New IoT-Fog architecture paradigms that include critical sectors are becom-
ing feasible for real-time decision making, and nding ecient energy conservation in the IoT-based application data
processing is vital. Hence, it is essential to choose an appropriate application design for operating that can conserve
energy throughout the lifetime of applications.
Towards these aims, two application modules were presented, and their performance was analyzed: the sequential
application module and the master-worker application module. It was found that the master-worker module has lower
energy consumption and communication overhead compared to the sequential application module. This enables the
creation of an energy-aware environment for the growing smart military devices by lowering the energy consumption
and extending the operation lifespan of the devices deployed within such a hostile military environment. The experimen-
tal results show that energy consumption within the military-based IoT can be reduced when an appropriate application
scenario is implemented.
Fig. 10 The tuple CPU execu-
tion delay for SM and MWM
0
2
4
6
8
10
12
14
Config1 Config2 Config3 Config4 Config5 Config6
Time (ms)
Tuple CPU execuon delay
Tuple (CPU) execuon delay SM Tuple CPU)execuon me MWM
Fig. 11 Execution time for SM
and MWM
0
5000
10000
15000
20000
25000
Config1 Config2 Config3 Config4 Config5 Config6
Execuon Time (ms)
Physical Topology Configuraon
Execuon Time of Module
Execuon Time SM Execuon Time MWM
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1 3
Author contributions Conceptualization, BYB. and SuI; methodology, BYB and SuI; software, BYB and AMK; validation, BYB, SuI, AMK, and IA;
formal analysis, BYB; investigation, SuI, IA; resources, AMK and IA; data curation, BYB; writing—original draft preparation, BYB, AMK; writing—
review and editing, IA and AMK, BYB; visualization, BYB; supervision, SuI and IA; project administration, SuI and IA; All authors have reviewed
the manuscript and agreed to publish this version of the manuscript. All authorshave read and approved the nal manuscript.
Data availability Not applicable; the study does not report any data.
Declarations
Competing interests Authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// cr ea t iv ec o mmons. org/ licen ses/ by/4. 0/.
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