Scopus Research — suha abdualhussien

The neurological disorder known as epilepsy has an ongoing negative impact on the brain. Identification of seizures is essential to the clinical care of individuals with epilepsy. Expert doctors frequently use visual electroencephalography (EEG) data analysis to detect epileptic seizures which is a method for observing the nonlinear electrical activity of the brain’s nerve cells. It is an epilepsy detection diagnostic tool. In this paper, we suggest an Internet of Things (IoT) framework for precise and effective seizure detection and monitoring for epileptic patients utilizing machine learning techniques. Three layers make up the proposed IoT framework: the things/devices, fog, and cloud tiers. The proposed method is summarized in transmitting the collected data from the thing layer to the FoG layer where a number of critical steps are carried out starting from segmenting the EEG data by converting it into 2-D table format and creating a Weighted Visibility Graph (WVG) from EEG data. Our suggested method extracts nine features from the WVG and an additional ten statistical features from the original EEG dataset. All these features are fed to the machine learning methods to classify the obtained signal as normal or abnormal. Two actions will be taken depending on the classification state either sending a notification to any predetermined caretaker in case of the occurrence of a seizure or reducing the data by using the threshold-based method in case of the absence of the seizure. As a result, in both cases, the data is uploaded to the cloud layer to be reviewed later by a specialized medical team. Four scenarios were used to evaluate our proposed method using performance evaluation metrics. The power of the provided methods is demonstrated by the proposed strategy, which yields a percentage of 100% in the fourth scenario which uses ML models with hyper-parameters, balanced EEG data, and extracted features. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

The Internet of Things (IoT) has developed into a new area of study that promises to elevate human culture to a higher level of sophistication. The network is essential in IoT since it is responsible for relaying information from sensors to the sink. In the IoT, where many devices share finite resources, extending the lifespan of the network is a difficult challenge. The lifespan of a network can be prolonged by the use of clustering. However, initial network nodes’ energy might be quickly depleted by incorrectly selecting cluster heads (CHs). This research aims to provide a solution by suggesting a fuzzy-based optimized nature-inspired clustering technique (FONIC) to choose the best CH to sustain the network over time. When dealing with unreliable network conditions, the precise solution provided by fuzzy logic (FL) is invaluable. Therefore, in order to calculate a fitness value, FL is used on network metrics such as energy, distance, degree, and centrality. In the end, the right CH is chosen with the help of the Penguin Search Optimization Algorithm (PeSOA). Python is utilized to do extensive simulations that confirm the effectiveness of the suggested FONIC protocol. Other protocols, including FIGWO, HMGWO, LEACH-PRO, FGWSTERP, and SSMOECHS, are contrasted with the proposed FONIC protocol. Compared to other top-tier protocols, the suggested FONIC protocol was shown to perform better than any of them, improving the ratio of packet transmission by 10% and network lifespans by 10–15%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation. © 2024 University of Bahrain. All rights reserved.

Wireless Sensor Networks (WSNs) are the main data collection tools used by Internet of Things (IoT) devices. The WSN-based IoT is a collection of several small, geographically dispersed, battery-powered sensors that are devoted to carrying out a certain activity in a collaborative manner. In a dense WSN-based IoT network, numerous sensors that are near to one another simultaneously collect the same data about the occurrence. Even though WSN-based IoT has opened up previously unimaginable possibilities in a variety of application areas, they are still susceptible to resource limitations. The energy of nodes, which is needed to run well for extended periods of time in many activities, is the most crucial resource in a given WSN-based IoT. Increasing the lifetime of the network is a major focus of research in the field of WSN-based IoT because it is impossible to replace or recharge batteries in remote, harsh or dangerous environments. In this article, an energy-efficient fuzzy-based unequal clustering with a sleep scheduling (EFUCSS) protocol for IoT that uses WSN is proposed. This protocol makes the network last longer and uses less energy. It does this by using clustering, scheduling, and data transmission. Unequal clusters based on Fuzzy C-Means are formed using this protocol to balance the energy used via reducing the distance that data travels. The selection of the cluster head is carried out using fuzzy logic system. The gateway's (GW) distance, remaining energy, and centrality are input variables. The output fuzzy variable is chance. Fuzzy inference is performed using the Mamdani technique. The sleep scheduling strategy is used between the coupled nodes to reduce the number of transmitted nodes. Extensive Python-based simulation experiments are run in order to evaluate the performance of the proposed EFUCSS protocol. While taking into account different WSN-based IoT scenarios and several criteria, such as network stability, network lifetime, and energy efficiency, a comparison is made between the proposed EFUCSS protocol and other well-known conventional protocols. The results show that the proposed EFUCSS improves remaining energy by 26.92%–213.4% and network lifespan by 39.58%–408.13%. The suggested EFUCSS also results in a greater improvement in network lifespan compared to other comparable algorithms. © 2023 Elsevier B.V.

In numerous Internet of Things contexts, there is an increasing interest to use wireless sensor technologies. One of the most difficult problems is gathering and analyzing commodity data, given the enormous rise of smart objects and their applications. Sensor nodes are battery-powered, and energy-efficient operations are important. To that end, before transmitting the final data to the central station, remove redundancy from the collected data by neighbouring nodes is beneficial for sensors. Data aggregation is one of the main strategies for reducing data redundancy and improving energy efficiency; it also extends the lifetime of wireless sensor networks. Moreover, network traffic can be minimized by an efficient data aggregation protocol. It may be sensed by more than one sensor when a particular target takes place in a particular area. This article provides an overview of different data aggregation methods and protocols, taking into account the key problems and facets of data aggregation in wireless sensor networks. The structures of data aggregation are grouped into four key classes, namely cluster-based, tree-based, chain-based and grid-based. The thorough comparison of the important approaches of each class often gives a suggestion for more research. © 2023 University of Bahrain. All rights reserved.

The Internet of things (IoT) is an omnipresent system that can be accessed from a long distance, linking a variety of devices (things), including wireless sensor networks (WSNs). Cyber-physical systems monitor things from a distance and control them. Because of its widespread usage in a variety of applications, WSN is among the most essential contributors to the IoT and plays a key part in the daily lives of people. The battery’s energy is a vital source in the sensor node, impacting the lifespan of the WSN. Energy scarcity is a serious concern in WSN, as a large volume of redundant data is gathered and transferred on a regular basis. As a result, efficient energy consumption is the fundamental approach to maximizing network lifetime. This article proposes a two-level data reduction approach for use at two network levels: sensor nodes and gateways (GWs). A novel Compression-Based Data Reduction (CBDR) technology and an effective transmitting data strategy derived from data correlation are being developed at the sensor node level. These strategies are designed to more efficiently compress data readings from IoT devices. CBDR compresses data in two stages: lossy SAX quantization and lossless LZW compression. The suggested approaches function as filtering at the GW level, allowing the GW to discover and subsequently delete groups of data that are duplicated and provided by surrounding nodes. At this level, two strategies are advised: the first is based on the data compression concept, and the second is to identify all couples of member nodes that produce duplicated sets so that redundancy may be eliminated before they are delivered to the sink. The proposed solutions are evaluated using extensive simulation tests made available by the network’s OMNeT++ simulator. The proposed methodologies’ efficiency is tested using four related works: the PFF protocol, the ATP protocol, the AVMDA protocol, and the PIP-DA protocol. The proposed solution uses up to 79%, 80%, 90%, and 6% less for each of the remaining data, transmitted data, energy, and data loss, respectively, depending on the results. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the sensor data readings, after which a lossless LZW compression to compress the loss quantization output. Quantizing the sensor node data readings down to the alphabet size of SAX results in lowering, to the advantage of the best compression sizes, which contributes to greater compression from the LZW end of things. Also, another improvement was suggested to the CBDR technique which is to add a Dynamic Transmission (DT-CBDR) to decrease both the total number of data sent to the gateway and the processing required. OMNeT++ simulator along with real sensory data gathered at Intel Lab is used to show the performance of the proposed technique. The simulation experiments illustrate that the proposed CBDR technique provides better performance than the other techniques in the literature. © 2021 University of Baghdad. All rights reserved.

Wireless Sensor Network is one of the most important contributors to IoT and performs significant role in people's lives due to its extensive use in many applications. Energy-saving is essential since sensor nodes are working by their restricted battery. In this article, data reduction method proposed to work at Gateway level of network. In GW, proposed method works as filtering via enabling GW to identify, then remove, sets of data that are redundant and produced by neighboring nodes. Principle idea of method recommended at this level is to exploit the advantage of spatial correlation between sensors to minimize energy depletion. © 2021 University of Kerbala.

Through developments in digital electronics and wireless technology, a variety of tiny devices have begun to be used in many aspects of everyday life. These devices can sense, compute and communicate. These typically consist of low-power radios, many smart sensors, and integrated CPUs. Such devices are utilized to establish a wireless sensor network (WSN) essential for the delivery of sensing services and monitoring of weather conditions. The concept of the Internet of things (IoT) is formed in conjunction with WSNs, where IoT can be described as an interconnection between recognizable devices in sensing and monitoring processes inside the internet networks. This paper offers a description of Periodic WSNs in general. It also offers an overview of the PWSN applications and challenges. © 2020 Published under licence by IOP Publishing Ltd.