Iraqi Journal of Information and communication technology

Dense IoT-WSNs are a subtype of IoT-WSNs in which a high density of deployed nodes and data
exchange is considered. Due to the capacity constraints of nodes in the IoT-WSN, the routing process requires a
unique design to accommodate a large amount of data while saving energy. The Internet Engineering Task Force
(IETF) develops RPL, a routing protocol for low-power lossy networks based on IPv6 that can handle a large
number of nodes. RPL nodes rely on an objective function mechanism for route selection. Each objective function
utilizes different metrics. The primary objective of this paper is to modify the RPL protocol by proposing two
objective functions to improve RPL performance over a dense network. The impact of the number of children
is also investigated. The first proposed protocol is called ENCRPL. It used three metrics: energy level, number
of neighbours, and a new metric depending on the number of children. The second protocol, called NCERPL, is
based on combining node and link metrics by using the numbers of neighbor nodes and children’s nodes as node
metrics and the expected transmission count (ETX) as a link metric. The proposed protocols are implemented and
evaluated using the Cooja simulator based on the Contiki OS. Regarding energy consumption, the results show that
the proposed protocols outperform RPL and the previously proposed protocol, called the MET protocol. ENCRPL
achieved a 93% improvement in energy savings over RPL with a density of 0.48 and 0.0075 nodes/m2. NCERPL
provides about a 14% improvement in energy savings over METRPL with a 0.48 density and a 44% improvement
with a 0.0075 density. Both ENCRPL and NCERPL achieved higher performance regarding the number of dead
nodes than RPL and METRPL in all scenarios. Considering packet delivery ratio and throughput, ENCRPL and
NCERPL perform better than RPL in high-density scenarios.

Large quantities of processing resources with strict latency specifications are needed for Internet of
Things (IoT) devices due to the rise of compute-intensive and delay-sensitive mobile apps. One promising solution
is to transfer resource-intensive computational tasks from IoT devices to either edge computing servers or cloud
computing servers. This paper aims to apply a simplified distributed ledger to an edge network to follow up the
offloaded data and maintain the response time as much as possible. The voting process is used as a consensus to
validate the new block, while the offloading decision is based on a fixed processing time offloading threshold value.
The proposed model has been programmed and the experimental evaluation of the proposed model shows that the
ledger did not significantly lengthen the response time and the offloaded task has been successfully tracked

Because of the Internet of Things (IoT) growing impact on many different uses and their expanding
computational and analytical capacities, they are a potential threat victim for malware intended to hack particular
IoT gadgets. Therefore, in this paper, a successful Recurrent Neural Network (RNN) is proposed for malware
detection. Multiple trials with varied hyper parameter values in this research are trained. When employing RNN
for malware categorization, thorough trials demonstrated that embedding size is more important than the input
size. RNN performance with two different feature vectors was assessed using hyper parameters to validate RNN
as an efficient solution for malware detection. Natural Language Processing (NLP) and feature selection are the
two feature vectors. A paired t-test was also employed in this paper to see if the findings were meaningful to one
another. Compared to the chi2 feature selection, RNN with NLP attained the maximum AUC value and a reasonable
variance. Based on the actual results. The proposed effective malware detection approach proves 99% detection
accuracy with NLP techniques and 89% with feature selection techniques, hitherto results with the RNN classifier

Face recognition has long attracted a lot of interest from the research and market communities due to
its many possibilities across numerous sectors, but it has proven to be exceedingly difficult to deploy in real-time
applications. Over the years, several face recognition algorithms and their variations have been created. In this paper,
an integrating STIP and SURF for a robust feature extraction approach is proposed. This approach consists of four
steps: In the first step, researchers are collecting the input images. In the next step, image preprocessing using a
Gaussian filter is used. Then, image segmentation is applied using Region of Interest (ROI). The Spatial-Temporal
Interest Point (STIP) is employed to extract the features related to facial behaviors from Facial Action Units
(FAUs). The most effective approach for object recognition in image processing that applies feature descriptors
is a Histogram of Oriented Gradients (HOG). In the last phase, use the feature selection process using SURF
(Speeded-up Robust Features). This proposed approach achieved (0.25 ms) better performance than the traditional
approach.

In this work, Emotion Recognition for Iraqi Autism Individuals (EmoReIQ) is presented, a dataset
of Electroencephalogram (EEG) signals recorded during various sessions for Autism Spectrum Disorder (ASD)
participants. Since individuals with ASD often have difficulty understanding and expressing their own emotions,
which leads to difficulties in social interactions, communication, and overall well-being; therefore, recognizing and
understanding emotions is crucial for them during therapy sessions to provide appropriate support and interventions.
Developing, being done for the first time in Iraq country, a dataset that is more specific to the cultural and linguistic
context of Iraqi ASD individuals will help treat and try to get them to safety. EEG signals from 28 ASD participants
were recorded while they were exposed to visual emotion-eliciting stimuli that evoked one of the five emotions (calm,
happiness, anger, fear, and sadness) in different experiment sessions. The classification algorithm, Artificial Neural
Network (ANN), is applied and analyzed for emotion recognition. EEG signals were recorded using BrainAccess, a
portable and wireless kit that allows the use of effective Brain-Computer Interface (BCI) techniques in everyday
applications. A dataset construction protocol is proposed, with emotional stimuli specifically designed to evoke the
emotional responses of ASD individuals. EEG data preprocessing and analysis framework is developed to select
and combine various EEG-based emotional-relevant features efficiently. With the proposed ANN classifier model,
the mean accuracy values are 78.86%, 83.32%, and 72.98% for valence, arousal, and dominance respectively. The
EmoReIQ dataset is validated and outperforms state-of-art datasets.

Cryptography, being an important science in securing communications networks, is continuously looking
for novel ways to strengthen traditional encryption techniques. This study explores the possibilities of improving the
Stream Cipher and fortifying it with a Modified Fisher Algorithm (MFA). Hence, the combination of Stream Cipher
with Modified Fisher Algorithm (SCwMFA) is more secure than each cipher alone. Where the Stream Cipher is
regarded as a powerful approach that uses binary form instead of characters with MFA to increase the strength
of the needed key. This combination offers a good level of security. Experimental results reveal that the suggested
combining algorithms based on the Index of Coincidence (IoC) metric outperforms the Stream Cipher with The
Traditional Fisher-Algorithm (SCwTFA) in terms of security with increasing cryptanalysis complexity.

In Wireless Sensor Network (WSNs) technology the proper congestion processing is the essential key to
prevent the parameters of Quality of Service (QoS) from facing intense loss such as likelihood of occurrence output
reduction and also it may lead to maximize the consumed energy and it also reflect on the increase of the end-to-end
time delivery side by side with a noticeable decrease in the percentage of transmitted packet arrival at the destination
end. Thus, evolving more superior technology is consider to be one of the most significant challenges in WSNs that
help to deal with issues like avidness, detection, and control in WSNs. This paper reviews most updated protocols
to mitigate the dilemmas of congestion in the WSNs based on the mechanism of congestionâs innate behavior. The
protocol categories can be divided into three divisions: traffic-based, resource-based and hybrid. The first protocol
category (traffic-based) is also witnessed more subdivided based on either the techniques of end-to-end transmission
or hop-by-hop. Both the establishment procedure of route and the exploit of active bandwidth techniques. It is also
important to indicate that inner operational process of the attenuation linked to congestion protocols are discussed
in this paper. Lastly, an intensive study is exploited in this paper to analyze congestion protocols in connection with
different performance standards in order to determine which a specific protocol category case is worth to develop.
The performance survey shows that the difficulties of accurate detection to the congestion for a particular network
if it is only considering the protocols class attitude when it alters with applicable platform and a metric on its own