Wireless Sensor Network Platform Research Articles

Application of IoT Android voice assistant based on sensor networks in higher education network mode

In order to make full use of the potential of wireless sensor networks in higher education, this study aims to provide convenient intelligent learning and management functions by constructing an iot Android voice assistant based on wireless sensor networks. The system structure of Android voice assistant based on wireless sensor network is researched, designed and implemented. Through the integration of wireless sensor network and school network platform, the data collection, transmission and analysis functions are realized. The results show that the Android voice assistant based on wireless sensor network effectively provides intelligent learning and management functions in the network mode of higher education. Students and teachers can realize course inquiry, resource acquisition, teaching evaluation and other operations through voice commands to improve learning and teaching efficiency. By combining the data acquisition capabilities of sensors and wireless transmission capabilities, the system provides students, teachers and school administrators with intelligent learning and management functions, which is expected to promote the further development of higher education network environment.

SVM‐SFL based malicious UAV detection in wireless sensor networks

SummaryIn the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore, the proposed study intends to develop an effective malicious UAV detection approach using a machine‐learning algorithm. Initially, the deployed sensor nodes in WSN are utilized to collect the environmental data. These sensor nodes transmit the collected data to the UAV. During data transmission, the sensor nodes generate a feed packet (authentication parameter) and forward it to the UAV along with the sensed information. The feedback packet is encrypted through a proxy re‐encryption scheme to secure the input data. These encrypted packets with the sensed input data are then transmitted to the base station. Finally, the feedback packet is decrypted and attains the actual input information. From the received data, the classification is performed using a proposed support vector machine with a shuffled frog leap (SVM‐SFL) approach. The proposed approach is implemented with the NS3 Python tool, and the results are analyzed by evaluating several performance matrices. Compared with other existing methods, the proposed study obtained improved results in terms of accuracy (98.61%), precision (98.5%), sensitivity (98.63%), and F‐measure (98.62%).

Towards the Structural Health Monitoring of Bridges Using Wireless Sensor Networks: A Systematic Study.

To perform a comprehensive assessment of important infrastructures (like bridges), the process of structural health monitoring (SHM) is employed. The development and implementation of SHM systems are generally based on wireless sensor networks (WSN) platforms. However, most of the WSN platforms are battery-powered, and therefore, have a limited battery lifetime. The power constraint is generally addressed by applying energy harvesting (EH) technologies. As a result, there exists a plethora of WSN platforms and EH techniques. The employment of a particular platform and technique are important factors during the development and implementation of SHM systems and depend upon various operating conditions. Therefore, there is a need to perform a systematic literature review (SLR) for WSN platforms and EH techniques in the context of SHM for bridges. Although state-of-the-art review articles present multiple angles of the field, there is a lack of an SLR presenting an in-depth comparative study of different WSN platforms and EH techniques. Moreover, a systematic analysis is also needed for the exploration of other design considerations such as inspection scale (global/local), response type (static/dynamic), and types of sensors. As a result, this SLR selects 46 articles (during 2007-2023), related to EH techniques and WSN platforms in SHM for bridges. The selected articles are classified into three groups: WSN platforms, energy harvesting techniques, and a combination of both. Subsequently, a comparative analysis of WSN platforms and EH techniques is made. Furthermore, the selected articles (total = 46) are also explored in terms of sensor type, inspection scale, and response type. As a result, 17 different sensor types are identified. This research is significant as it may facilitate the various stakeholders of the domain during the selection of appropriate WSN platforms, EH techniques, and related design issues.

Autonomous Electrical Current Monitoring System for Aircraft

Aircraft monitoring systems offer enhanced safety, reliability, reduced maintenance cost and improved overall flight efficiency. Advancements in wireless sensor networks (WSN) are enabling unprecedented data acquisition functionalities, but their applicability is restricted by power limitations, as batteries require replacement or recharging and wired power adds weight and detracts from the benefits of wireless technology. In this paper, an energy autonomous WSN is presented for monitoring the structural current in aircraft structures. A hybrid inductive/hall sensing concept is introduced demonstrating 0.5 A resolution, < 2% accuracy and frequency independence, for a 5 A – 100 A RMS, DC-800 Hz current and frequency range, with 35 mW active power consumption. An inductive energy harvesting power supply with magnetic flux funnelling, reactance compensation and supercapacitor storage is demonstrated to provide 0.16 mW of continuous power from the 65 μT RMS field of a 20 A RMS, 360 Hz structural current. A low-power sensor node platform with a custom multi-mode duty cycling network protocol is developed, offering cold starting network association and data acquisition/transmission functionality at 50 μW and 70 μW average power respectively. WSN level operation for 1 minute for every 8 minutes of energy harvesting is demonstrated. The proposed system offers a unique energy autonomous WSN platform for aircraft monitoring.

Research Progress of SHM System for Super High-Rise Buildings Based on Wireless Sensor Network and Cloud Platform

In recent years, the number of super high-rise buildings is increasing due to the rapid development of economy and construction technology. It is important to evaluate the health condition of super high-rise buildings to make them operate safely. However, conventional structural health monitoring (SHM) system requires a great number of wires to connect the sensors, power sources, and the data acquisition equipment, which is an extremely difficult process to plan the layout of all wires. Hence, one of the usually used compromising approaches is to limit the number of sensors to reduce the usage of wires. Recently, wireless sensor networks and cloud platform have been widely used in SHM system for super high-rise buildings because of their convenient installation, low maintenance cost, and flexible deployment. This paper presents a comprehensive review of the existing SHM system for super high-rise buildings based on wireless sensor network and cloud platform, which usually consists of sensing network subsystem, data acquisition subsystem, data transmission subsystem, and condition evaluation subsystem. This paper also reviews the crucial techniques and typical examples of SHM system used for famous super high-rise buildings. In addition, the existing difficulties in wireless sensor network and cloud platform based SHM system for super high-rise buildings and the future research directions are discussed and summarized.

Investigating the impact of network topologies on the iot-based wsn in smart home monitoring system

The object of this research is to present IoT WSN-based smart home monitoring system, which allows users to monitor and manage all of their appliances and home equipment via the Internet using established protocols. IoT is described as the connection of equipment and appliances to the Internet in order to monitor, report, and perform certain tasks. Wireless Sensor Networks (WSN) are considered as a key component in the IoT model's implementation. This research presented the IoT WSN platform using Riverbed Modeler Simulation Program in order to examine the network performance for different Wireless Sensor topologies (Star, Tree and Mesh). This platform consists of a number of scenarios with a number of sensors in each scenario. Each sensor is represented by the ZigBee end device, which sensed and collected data about the smart home and sent the collected data to the controller, which is represented by the ZigBee coordinator. The controller sends the data to the server to be monitored by the users through any gateway (Wi-Fi) after logging in using a specific application with three routing topologies on the controller. The results showed that IoT WSN tree topology is the best topology if the throughput is considered for improvement at the expense of data dropped with acceptable delay. Star topology improves the network performance in terms of data dropped and throughput when the number of sensors was increased. Mesh topology achieved the smallest data dropped with low throughput. Due to their features, these results were effective because they indicated that the selection of suitable routing topology played an important role in improving the degradation of IoT WSN performance due to the interference of Wi-Fi and ZigBee network since they utilized the same frequency band (2.4 GHz).

Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review.

In this paper, a detailed review of microcontroller unit (MCU)-based wireless sensor node platforms from recently published research articles is presented. Despite numerous research efforts in the fast-growing field of wireless sensor devices, energy consumption remains a challenge that limits the lifetime of wireless sensor networks (WSNs). The Internet-of-Things (IoT) technology utilizes WSNs for providing an efficient sensing and communication infrastructure. Thus, a comparison of the existing wireless sensor nodes is crucial. Of particular interest are the advances in the recent MCU-based wireless sensor node platforms, which have become diverse and fairly advanced in relation to the currently available commercial WSN platforms. The recent wireless sensor nodes are compared with commercially available motes. The commercially available motes are selected based on a number of criteria including popularity, published results, interesting characteristics and features. Of particular interest is to understand the trajectory of development of these devices and the technologies so as to inform the research and application directions. The comparison is mainly based on processing and memory specifications, communication capabilities, power supply and consumption, sensor support, potential applications, node programming and hardware security. This paper attempts to provide a clear picture of the progress being made towards the design of autonomous wireless sensor nodes to avoid redundancy in research by industry and academia. This paper is expected to assist developers of wireless sensor nodes to produce improved designs that outperform the existing motes. Besides, this paper will guide researchers and potential users to easily make the best choice of a mote that best suits their specific application scenarios. A discussion on the wireless sensor node platforms is provided, and challenges and future research directions are also outlined.

A Deep-DrpXML and IAG-GWO based CHST fostered blockchain technology for secured dynamic optimal routing for wireless sensor networks

In wireless sensor network (WSN), routing is one of the substantial maneuvers for distributing data packets to the base station. But malevolent node outbreaks will happen during routing process, which exaggerate the wireless sensor network operations. Therefore, a secure routing protocol is required, which safeguards the routing fortification and the wireless sensor network effectiveness. The existing routing protocol is dynamically volatile during real time instances, and it is very hard to recognize the unsecured routing node performances. In this manuscript, a Deep Dropout extreme Machine learning optimized Improved Alpha-Guided Grey Wolf based Crypto Hash Signature Token fostered Blockchain Technology is proposed for secure dynamic optimal routing in Wireless Sensor Networks (SDOR-DEML-IAgGWO-CHS-BWSN). In this, Crypto Hash signature (CHS) token are generated for flow accesses with a secret key owned by each routing sensor node and it also offers an optimal path for data transmission. Then the secured dynamic optimal routing information is delivered through the proposed Blockchain based wireless sensor network platform with the help of Deep Dropout Extreme Machine learning optimized Improved Alpha-Guided Grey Wolf routing algorithm. Then the proposed method is simulated using the NS-2 (Network Simulator) tool. The simulation performance of the proposed SDOR-DEML-IAgGWO-CHS-BWSN method provide 76.26%, 65.57%, 60.85%, 48.99% and 42.9% lower delay during 30% malicious routing environment, 73.06%, 63.82%, 59.25%, 44.79% and 38.84% lower delay during 60% malicious routing environment is compared with the existing methods.

Bi-level energy management model for the smart grid considering customer behavior in the wireless sensor network platform

Today, smart grids (SG) and its relationship to wireless sensor network (WSN) provides a suitable platform for establishing the two-way communication of the energy management system and network users. This issue can accurately plan the smart grid in real time with minimum cost. This paper proposes a bi-level energy management model for smart grids in uncertainty and demand-side management (DSM) in the Internet of things (IoT) platform based on WSN. In the upper level, for accurate planning in real-time and relationship with energy management system, the customers are modelled in wireless sensor network platform and clustered by fuzzy C-Means algorithm. Then, the energy consumption of the network sensors in the IoT platform is optimized by the genetic algorithm (GA). Moreover, the customer behavior optimization was performed utilizing the price-based demand response (PBDR) model and transferring demand load from the peak to the valley load times. The objective functions in the lower layer minimized the emission pollution, operation costs and energy not supplied (ENS) index using the multi-objective Moth–Flame optimization (MOMFO) algorithm. The Monte Carlo technique is used for modeling renewable energy sources (RES)'s uncertain output. Finally, the suggested approach is confirmed through the analysis of a case study. The proposed model's capability in planning a uniform smart grid operation is demonstrated by simulation study and comparison with multi-objective particle swarm optimization (MOPSO), epsilon-constraint and benders techniques.

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Wireless body sensor networks (WBSNs) are characterized by a large number of battery-powered wireless sensor nodes, and the most challenging aspects of WBSNs are sensor node energy consumption, delay, and security (communication and data) while maintaining regular wireless sensor network (WSN) capabilities. Data aggregation, as a common procedure in data gathering applications, can waste a lot of energy since sensor nodes may stay in the listen to state even when they are not receiving data during the data collection process. In this research work, introducing the Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) algorithm helps to improve the node energy consumption positively by turning nodes to the accessible state when not in use and waking them up when necessary. The algorithm is energy-based and uses a self-executing-based dynamic cross-propagation clustering system to send/receive scheduling data in the WSN platform. The energy level of the nodes is the most essential component in constructing network communication, in contrast to earlier clustering algorithms. The purpose of this proposed algorithm is to enhance the traditional notion of the clustering algorithm (location-based clustering), leading to the primary goal of enhancing the permanence of the wireless sensor network, which is to conserve network coverage, using self-executing DCPC clustering technology for location and power. The result of performance analysis of the SE-DCPC is achieved by simulation using two different communication processes of clustering and intelligence decision-making methods. The numerical results shows that SE-DCPC can effectively handle and maintain a high rate of network node energy consumption. The simulation result shows that the proposed approaches securely obtain the high throughput and very minimal delay at the client side, compared to existing clustering algorithm approaches. This SE-DCPC algorithm increased by 21.89% the communication medium lifetime and by 37% the energy consumption and reduced by 23.27% the overhead compared with existing clustering algorithms.

Fast and Intelligent Irrigation System Based on WSN.

In the agricultural industry, wireless sensor networks (WSNs) can be an important tool to promote economic growth. Using network devices in agriculture has the potential to enhance the production process. One of the key challenges WSN faces is energy efficiency. A model based on the water pipeline method is proposed in order to efficiently utilize sensor nodes in agricultural production and water distribution. A water pipeline serves as an important structure for transporting potable water across a distance for consumption or irrigation. In contrast, the biggest transportation problems of water pipelines are leaks. So, water resources may be lost as a result. These pipes need real-time monitoring to prevent such problems. The wireless sensor network technique, however, is considered one of the best solutions available today for monitoring water pipelines. A detailed analysis of agriculture is provided by the model. Aspects of WSN are discussed and their agricultural use is expounded. Moreover, this paper describes the various types of applicability of existing sensor networks in the field of agriculture, along with some technical perspectives. To achieve the best power consumption and communication for the two types of range, ZigBee wireless protocols are utilized. As such, high-performance information that provides a platform for WSNs to better support agricultural production is also included in our proposed model in order to address the shortcomings of existing WSNs regarding energy efficiency. This paper presents the improvements of the proposed solution compared to other techniques in the context of energy conservation in wireless sensor networks and in the monitoring water-saving process.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring

For the prediction of cardiovascular diseases, a wireless sensor network platform that employs a wireless sensor network-enabled electrocardiogram telemetry system will involve the following steps: the electrocardiogram signal’s acquisition, the electrocardiogram signal’s processing, and alerting the physician in case of any emergencies. This system will aid the physician in early as well as an accurate analysis of heart diseases. Currently, a wireless sensor network enabled electrocardiogram monitoring system is being developed for analyzing the electrocardiogram signal. In the past, the convolutional neural networks have been employed for resolving diverse artificial intelligence problems with good outcomes. Even so, designing the convolutional neural networks architecture continues to be a cumbersome as well as meticulous procedure that needs the field experts’ involvement. This work has explored the neuro-evolution application to the automatic design of convolutional neural networks topologies and also has devised a novel solution which is based on the Artificial Bee Colony as well as the Grey Wolf Optimizer. Moreover, this work has given the proposal of the hybrid Grey Wolf Optimizer-Artificial Bee Colony algorithm, in which the wolves will adopt the bees’ strategy of sharing information for promotion of their exploration capability whilst continuing with their original strategy of hunting strategy for maintenance of their exploitation capability. The experimental outcomes demonstrate the proposed algorithm’s superior performance over other existing algorithms.

A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications

The acquisition of physiological data are essential to efficiently predict and treat cardiac patients before a heart attack occurs and effectively expedite motor recovery after a stroke. This goal can be achieved by using wearable wireless sensor network platforms for real-time healthcare monitoring. In this paper, we present a wireless physiological signal acquisition device and a smartphone-based software platform for real-time data processing and monitor and cloud server access for everyday ECG/EMG signal monitoring. The device is implemented in a compact size (diameter: 30 mm, thickness: 4.5 mm) where the biopotential is measured and wirelessly transmitted to a smartphone or a laptop for real-time monitoring, data recording and analysis. Adaptive digital filtering is applied to eliminate any interference noise that can occur during a regular at-home environment, while minimizing the data process time. The accuracy of ECG and EMG signal coverage is assessed using Bland–Altman analysis by comparing with a reference physiological signal acquisition instrument (RHS2116 Stim/Recording System, Intan). Signal coverage of R-R peak intervals showed almost identical outcome between this proposed work and the RHS2116, showing a mean difference in heart rate of 0.15 ± 4.65 bpm and a Wilcoxon’s p value of 0.133. A 24 h continuous recording session of ECG and EMG is conducted to demonstrate the robustness and stability of the device based on extended time wearability on a daily routine.

Retraction Note: Design and simulation analysis of offshore tidal energy generating set system based on sensor network

Tidal energy refers to the potential energy and kinetic energy of tidal changes. It is a kind of energy that hides a huge amount of energy and can be regenerated endlessly. Tidal energy as an energy source is purely renewable energy. The tide rises and falls every day, and the energy is constant, so it will be endless. Power generation through tidal energy cannot only alleviate the energy shortage but also become the main additional energy source for people to carry out economic construction and development in areas near the sea. Because tidal power plants are usually built-in offshore areas with relatively small populations, they will not cause complicated problems such as flooding of farmland and immigration. In view of the shortage of energy and fuel in the world and the increasingly serious environmental and pollution problems, tidal energy has received widespread attention and influence all over the world due to its infinite reserves, lack of environmental pollution, and high predictability. Based on the submarine acoustic wireless sensor network platform, we have developed an experimental submarine acoustic ray sensor network platform, which provides an effective platform for testing and evaluating the submarine acoustic ray sensor network. This paper determines the capacity of TCF, the layout design of TCF in TCT, and the research on the collection system. Based on the underwater acoustic wireless sensor network platform, the underwater acoustic network experimental test system platform is being developed, and a feasible research plan is proposed for the detection and evaluation of the underwater acoustic wireless sensor network.

Dynamic power allocation and scheduling for MIMO RF energy harvesting wireless sensor platforms

Radio frequency (RF) energy harvesting systems are enabling new evolution towards charging low energy wireless devices, especially wireless sensor networks (WSN). This evolution is sparked by the development of low-energy micro-controller units (MCU). This article presents a practical multiple input multiple output (MIMO) RF energy-harvesting platform for WSN. The RF energy is sourced from a dedicated access point (AP). The sensor node is equipped with multiple antennas with diverse frequency responses. Moreover, the platform allows for simultaneous information and energy transfer without sacrificing system duplexity, unlike time-switching RF harvesting systems where data is transmitted only for a portion of the total transmission duty cycle, or power-splitting systems where the power difference between the information signal (IS) and energy signal (ES) is neglected. The proposed platform addresses the gap between those two. Furthermore, system simulation and two energy scheduling methods between AP and sensor node (SN) are presented, namely, Continuous power stream (CPS) and intermittent power stream (IPS).

Design and experiments of a thermoelectric-powered wireless sensor network platform for smart building envelope

A new thermoelectric-powered wireless sensor network platform is presented for the low-cost environmental sensing in building envelopes through thermoelectric energy harvesting and ultra-low power management. It is designed and prototyped entirely inside a window frame without compromising architectural aesthetics. This self-powered sensing platform is achieved by maximizing the harvested energy from building envelopes and optimizing the wireless sensing unit’s energy consumption. It harvests milli-watt level thermoelectric power from the temperature gradient across building envelopes through thermoelectric generators with an optimized thermal connector. The harvested energy is voltage-boosted and regulated through two integrated circuits that are tailored for ultra-low-power input. A low power system-on-chip is used to supervise the environmental sensing and wireless data communication. The energy consumption is tailored by adjusting the system sleep time to match the harvested energy. The proposed platform is prototyped in a window frame and thoroughly tested, where 1.5 mW of power is harvested from thermoelectric generators under 6 °C of temperature difference, and a 33.4% efficiency to the battery. In the meantime, 0.42 mW power is consumed by the wireless sensing unit under a sampling period of 2 h, which reaches the energy equilibrium state. The energy equilibrium algorithm can project the battery energy based on historical weather conditions, so as to achieve the self-powered condition given a geographic location. This smart building envelope systems include the unique innovations in self-powered system architecture, thermally optimized internal structure, and milli-watt level power management.

Energy Efficiency Analysis of Wireless Sensor Networks in Precision Agriculture Economy

Wireless sensor network (WSN) can play an important role during precision agriculture production to promote the growth of the agricultural economy. The application of WSN in agricultural production can achieve precision agriculture. WSN has the biggest challenge of energy efficiency. This paper proposes a model to efficiently utilize the energy of sensor nodes in precision agriculture production. The proposed model provides a comprehensive analysis of the precision agriculture. The model focuses on the characteristics of WSN and expands its application in precision agriculture. In addition, this paper also puts forward some technical prospects to provide a good reference for comprehensively and effectively improving the overall development level of precision agriculture. The paper analyzes the applicability and limitations of the existing sensor networks used for agricultural production technology. The ZigBee and Lora wireless protocols are utilized to have the best power consumption and communication in short distance and long distance. Our proposed model also suggests improvement measures for the shortcomings of existing WSN in the context of energy efficiency to provide an information platform for WSN to play a better role in agricultural production.

RETRACTED ARTICLE: Design and simulation analysis of offshore tidal energy generating set system based on sensor network

Tidal energy refers to the potential energy and kinetic energy of tidal changes. It is a kind of energy that hides a huge amount of energy and can be regenerated endlessly. Tidal energy as an energy source is purely renewable energy. The tide rises and falls every day, and the energy is constant, so it will be endless. Power generation through tidal energy cannot only alleviate the energy shortage but also become the main additional energy source for people to carry out economic construction and development in areas near the sea. Because tidal power plants are usually built-in offshore areas with relatively small populations, they will not cause complicated problems such as flooding of farmland and immigration. In view of the shortage of energy and fuel in the world and the increasingly serious environmental and pollution problems, tidal energy has received widespread attention and influence all over the world due to its infinite reserves, lack of environmental pollution, and high predictability. Based on the submarine acoustic wireless sensor network platform, we have developed an experimental submarine acoustic ray sensor network platform, which provides an effective platform for testing and evaluating the submarine acoustic ray sensor network. This paper determines the capacity of TCF, the layout design of TCF in TCT, and the research on the collection system. Based on the underwater acoustic wireless sensor network platform, the underwater acoustic network experimental test system platform is being developed, and a feasible research plan is proposed for the detection and evaluation of the underwater acoustic wireless sensor network.

Parallel Quadri-valent Quantum-Inspired Gravitational Search Algorithm on a heterogeneous platform for wireless sensor networks

Sensor nodes in a wireless sensor Network are assigned for different operational modes to perfume application-specific objectives. The decision to assign operational modes to nodes is a challenging problem in the presence of multiple criteria including energy-efficient, maintaining network connectivity, and fulfilling application goals. Several metaheuristic methods are introduced in the literature to address this NP-hard problem, however, these methods require further improvements in execution-time and finding the optimum solution. In this research, we propose an improved version of a metaheuristic method called Quadri-valent Quantum-Inspired Gravitational Search Algorithm (QQIGSA) to solve Quadri-valent problems by applying a Not Q-Gate and paralleling QQIGSA method on the graphics processing unit. The proposed method employs a heterogeneous platform and justifies its parameters. The experimental results show that the performance enhancement from 1.8 to 2.25 compared to the previous parallel implementations. Moreover, we achieve the speedup of 8 by using the proposed heterogeneous paralleling technique.