Optimal Sensor Network Research Articles

Memetic algorithm applied to topology control optimization of a wireless sensor network

Memetic algorithm applied to topology control optimization of a wireless sensor network

Performance Optimization of Surface Plasmon Resonance Imaging Sensor Network Based on the Multi-Objective Optimization Algorithm.

In this work, we report performance optimization of a wireless sensor network (WSN) based on the plain silver surface plasmon resonance imaging (SPRi) sensor. At the sensor node level, we established the refractive index-thickness models for both gold and silver in the sensor and calculated the depth-width ratio (DWR) and penetration depth (PD) values of the sensor of different gold and silver thicknesses by the Jones transfer matrix and Kriging interpolation. We optimized the DWR and PD simultaneously by using the multi-objective optimization genetic algorithm (MOGA). In the following performance optimization of WSN, we simultaneously optimized the transmission success rate and information dimension with the number of nodes and transmission failure rate of the sensor node as variables by the same algorithm. By calculating the information dimension and the transmission success rate of each Pareto optimal solution, we obtained the number of nodes and transmission failure probability of the node available for practical deployment of WSN. The above results indicate that the Pareto optimal solution set obtained from MOGA can help to provide the best solution for the optimization of some certain performance parameters and also assist us in making the trade-off decision in the structure design and network deployment if optimal values of all the performance parameters can be obtained simultaneously.

Dynamic Optimization Method of Wireless Network Routing Based on Deep Learning Strategy

Wireless sensor network is a network that integrates sensor technology, computer technology, information processing technology, and communication technology. This paper aims to study how to analyze and study the routing optimization of wireless sensor network based on deep learning and describe the neural network. This paper puts forward the problem of routing optimization, which is based on the dynamic programming of wireless sensor network, and then elaborates around its concept and related algorithms and designs and analyzes the case of wireless sensor network optimization. Through the comparative analysis of the five algorithms in computer simulation, although the average network delay performance of DPER reached 0.47 s, it could effectively prolong the life cycle of the network. The DPER algorithm not only improves the network life but also improves the network energy utilization rate, shortens the average path length of the network, and reduces the standard deviation of the remaining energy of the node.

Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements.

The world is advancing to a new era where a new concept is emerging that deals with “wirelessness”. As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN’s scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations.

Optimization and design of a diffuse optical wireless sensor network.

Wireless sensor networks (WSNs) are currently being deployed in everyday objects to collect and transmit information related to humidity, temperature, heartbeat, motion, etc. Such networks are part of the massive machine-type communication (mMTC) scenario within the fifth/sixth generation of wireless networks. In this paper, we consider the optimization and design of an optical WSN composed of multiple battery-powered sensor nodes based on light-emitting diode transmitters. Extending our previous work, we take into account both line-of-sight and diffuse-light propagation, and show that in indoor scenarios, diffuse radiation can improve link availability under shadowing/blocking and extend battery life. In order to optimize the optical wireless link parameters, we use a machine-learning approach based on a genetic algorithm to ascertain the performance limits of the system. The presented results indicate that the proposed system is a viable wireless option for WSNs within the context of mMTC.

An Efficient Data Acquisition and Processing Scheme for Wireless Multimedia Sensor Networks.

This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.

Coverage optimization of soil moisture wireless sensor networks based on adaptive Cauchy variant butterfly optimization algorithm

Soil moisture wireless sensor networks (SMWSNs) are used in the field of information monitoring for precision farm irrigation, which monitors the soil moisture content and changes during crop growth and development through sensor nodes at the end. The control terminal adjusts the irrigation water volume according to the transmitted information, which is significant for increasing the crop yield. One of the main challenges of SMWSNs in practical applications is to maximize the coverage area under certain conditions of monitoring area and to minimize the number of nodes used. Therefore, a new adaptive Cauchy variant butterfly optimization algorithm (ACBOA) has been designed to effectively improve the network coverage. More importantly, new Cauchy variants and adaptive factors for improving the global and local search ability of ACBOA, respectively, are designed. In addition, a new coverage optimization model for SMWSNs that integrates node coverage and network quality of service is developed. Subsequently, the proposed algorithm is compared with other swarm intelligence algorithms, namely, butterfly optimization algorithm (BOA), artificial bee colony algorithm (ABC), fruit fly optimization algorithm (FOA), and particle swarm optimization algorithm (PSO), under the conditions of a certain initial population size and number of iterations for the fairness and objectivity of simulation experiments. The simulation results show that the coverage rate of SMWSNs after ACBOA optimization increases by 9.09%, 13.78%, 2.57%, and 11.11% over BOA, ABC, FOA, and PSO optimization, respectively.

Artificial Immune Algorithm and Adamic Adar based Wireless Sensor Network Optimization

Artificial Immune Algorithm and Adamic Adar based Wireless Sensor Network Optimization

Energy-Saving Deployment Optimization and Resource Management for UAV-Assisted Wireless Sensor Networks With NOMA

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding.

A Framework for Wireless Sensor Network Optimization Using Fuzzy-Based Fractal Clustering to Enhance Energy Efficiency

Wireless sensor networks’ energy consumption is the major challenge to be handled. Clustering is one of the techniques majorly used for reducing energy consumption. During the course of time, many methodologies are being proposed and the existing ones are hybrid. Still, the energy can be reduced more. The scope of optimization is always there. Existing approaches either reduce energy consumption or work on routing or only on data gathering capabilities. But the technique proposed increases the lifetime of the wireless sensor network (WSN) by reducing energy consumption and improves routing efficiency. This paper proposes an approach based upon Fractal Clustering to improve the lifetime of the sensor nodes. The proposed approach named Enhanced Energy Efficient Fuzzy-based Fractal Clustering (EEFFC) algorithm optimizes the performance of WSN. First, fractal clustering is used on sensor nodes to find the location of the sensors. Then, a fuzzy inference system (FIS) is applied to results produced by fractal clustering. Applying FIS on cluster heads generated will optimize the results. As a result, the cost of data transmission will reduce, and hence, the lifetime of the network will improve. FIS generates multi-level clustering, which will result in a better routing path for sensor nodes. Hence, routing will also be improved. MATLAB 2020 is the simulation tool. The results of the simulation depict that EEFFC shows optimized results and it works better than LEACH, LEACH-SF, TEEN and DEEC. The energy consumption is being reduced by reducing the listening time of a node and by reducing the communication distance, for which clustering is optimized. The energy consumption has been reduced by 2% as compared to the algorithms it is compared with. Also, the node’s time of death has been delayed by 3% in total.

Combined optimal sensor network design and self-optimizing control with application in a typical sugarcane mill

The concept of self-optimizing control is applied in the context of sensor network design to support the simultaneous selection of optimal sensors and controlled variables. Optimality is defined as minimizing the capital cost of the sensor network as well as the revenue loss due to disturbances and measurement errors under a constant set-point policy. The method is applied to a sugarcane mill case study, where 41 controlled variables (CVs) are selected as linear combinations of the measured variables, and the optimal supporting sensor network is identified. The economically optimal sensor placement found in the present study reduced the average revenue loss due to disturbances and measurement errors by 39% compared to a mill operating with a typical sensor network. The substantial reduction in revenue loss for the optimal sensor placement is attributed to an improved precision in 19 CVs compared to the base case network. The case study demonstrates the natural extension of self-optimizing control to optimal sensor network design, as well as the potential benefits of the combined approach.

Secure Routing Optimization in Hierarchical Cluster-Based Wireless Sensor Networks

Popularity of wireless sensor networks (WSNs) is increasing continuously in different domains of daily life, as they provide efficient method of collecting valuable data from the surroundings for use in different applications. Routing in WSNs is the vital functionality that allows the flow of information generated by sensor nodes to the base station, while considering the severe energy constraint and the limitations of computational and storage resources. Indeed, this functionality may be vulnerable and must be in itself secured, since conventional routing protocols in WSNs provide efficient routing techniques with low power consumption, but they do not take into account the possible attacks. As sensor nodes may be easily captured and compromised, the classical cryptographic solutions become insufficient to provide optimal routing security, especially, for cluster-based WSNs, where cluster heads can be still among the compromised nodes. In this work, we propose a hierarchical, robust and well-adapted intrusion detection system, named THIDS, which is intended to be integrated into the secure hierarchical cluster-based routing protocols. We have chosen the protocol RLEACH to be equipped with the proposed IDS. The results of simulation performed under NS2 simulator show that the resulting protocol ORLEACH is much more resistant to compromised nodes exercising the most dangerous attacks.

Temperature Sensor Position Planning

One of the challenges in large volume metrology is that it is often difficult or impossible to control the ambient conditions in which the object is to be measured. Dimensional measurement results vary with those conditions and it becomes necessary to apply some form of compensation. Thermal compensation of dimensional measurement is primarily reliant on the ability to properly measure temperature across the volume, which can differ by several degrees, but conventionally a uniform scaling has been applied. This paper focuses upon temperature sensor network planning improvement to facilitate thermal compensation. Beyond assembly environments, data from sensor networks are increasingly used to make decisions, but appropriate design and testing of such networks can be limited. As the demand for production digital twins increase, appropriate methods to quantify and optimise uncertainty to improve confidence will be invaluable. A virtual test bed has been created for the design, test, and optimisation of temperature sensor networks supported by physical simulation. Sensor networks have been used to take virtual measurements from a known temperature distribution and used to reconstruct the temperature distribution. Random search optimisation on a subset of the sensor network was carried out to determine some initial rules for sensor network design. The positioning of the sensors within the measurement volume and the method of reconstructing the temperature field was found to be more important than the capability of the individual sensors. Two means of interpolating the ambient field have been investigated: polynomial fitting and kriging. Temperature sensor networks appeared more sensitive to changes at the spatial boundary and these positions seem to be most critical for accurate reconstruction. In the case of the barrel section assembly it was found that asymmetric sensor heights produced better results, for example. A polynomial interpolation model using a 16-sensor network with 0.1 °C (confidence interval, k = 2) uncertainty sensors could produce a consistent temperature reconstruction error of ~0.04 °C RMS, corresponding to a thermal expansion error of ~1.5 μm in aluminium over a 1.6 m-tall structure.

A Study on routing optimization of Wireless Sensor Networks using an Enhanced LEACH protocols

The LEACH routing protocol equalizes network power consumption by randomly selecting cluster headnodes in a loop, which will cause network malfunctions. Therefore, to solve this problem, it is necessary to reduce the data transmission energy consumption in the routing protocol and increase the network life cycle. However, there is also the problem that cluster headers have a wide range, and forwarding data from cluster headers consumes a lot of power in LEACH, which has not yet been resolved. In this article, we present an approach to optimize the routing protocol. First, the optimal number of cluster heads is calculated according to the total energy consumption per round to reduce the probability of excessive distribution of cluster heads. The cluster head is then used as the nucleus to construct the Voronoi diagram. Nodes in the same Voronoi diagram become a cluster, so the energy consumption of intra-cluster communication would be lower. Finally, to optimize the multi-hop routing protocol, an ant colony algorithm is added using a cluster header close to the BS to receive and forward it from a remote cluster header. Based on MATLAB simulation data, the protocol can significantly extend the lifetime of WSNs compared to the LEACH protocol and increase the energy efficiency per node unit per round. The energy consumption of the proposed approach is unique. The approach improved first node kill time (FND) by 127%, 22.2%, and 14.5% relative to LEACH, LEACH-C, and SEP, respectively.

Nodes clustering and multi-hop routing protocol optimization using hybrid chimp optimization and hunger games search algorithms for sustainable energy efficient underwater wireless sensor networks

Clustering and routing processes in underwater wireless sensor networks (UWSNs) are challenging tasks in the underwater environment due to the multiplicity of sensor nodes, transmission bandwidth, and limited energy resources. In order to address the shortcomings mentioned above, this paper proposes a novel hybrid Chimp Optimization and Hunger Games Search (ChOA-HGS) algorithms for clustering and multi-hop routing optimization in UWSNs. In this approach, first, the ChOA is used to choose cluster heads and efficiently structure clusters. Then, the HGS-based routing procedure is used to determine the network’s best pathways. The proposed approach combines the advantages of clustering and routing, resulting in optimal network lifetime and energy efficiency. The proposed ChOA-HGS is validated using a variety of measures after it is simulated using three different scenarios. In order to evaluate the performance of the ChOA-HGS, results are compared to PSO, MPSO, IPSO-GWO, TEEN, and LEACH. The results show that the ChOA-HGS outperformed other benchmarks in terms of lifetime and energy consumption.

Collection tree-oriented mesh routing optimization for extending the lifetime of wireless sensor networks

Routing optimization in wireless sensor networks facilitates to reduce the overhead of the maintaining of wireless sensor networks and extend the lifetime of wireless sensor networks. Collection tree-based routing protocol, which does not require route discovery, has been widely used for low overheads of calculation and storage. However, with collection tree-based routing protocol, some nodes easily become the bottleneck points and quickly run out of the energy. To deal with this drawback, this article proposes a collection tree-oriented mesh routing strategy with cooperatively consuming the residual energy among the neighboring sensor nodes. The collection tree-oriented mesh routing is formulated into a linear programming problem with the purpose to maximize the network lifetime. By solving the optimization problem, the optimal mesh routing and data forwarding scheme is derived. Experimental simulations show that the proposed collection tree-oriented mesh routing optimization strategy can extend the network lifetime by more than 20%.

Parallelized Active Information Gathering Using Multisensor Network for Environment Monitoring

Motivated by the application of environment monitoring, this article studies a novel algorithmic framework for solving, in a parallelized manner, the problem of multisensor active information gathering. Unlike the existing methods relying on the fully connected sensor network and sequential processing, our approach builds on the generic network topology and enables individual sensors to simultaneously make their decisions by communicating with the immediate neighbors. Leveraging the cooperation among the multisensor network, we show that the computational complexity of the proposed parallelized algorithm can be greatly reduced compared to the sequential updating schemes, and meanwhile, the suboptimality of obtained solutions is guaranteed. The interconnection between the algorithm complexity and solution quality is explicitly established with respect to the network topology. Based on such interconnection, we further provide the approach to design the optimal sensor network by the given time budget of the algorithm execution. Finally, numerical simulations on a methane emission monitoring scenario are presented to validate the effectiveness of our approach.

Learning Resource Allocation in Active-Passive Radar Sensor Networks

Recent advances in Passive Coherent Location (PCL) systems make combined active and passive radar sensor networks very attractive for both military and civilian air surveillance. PCL systems seem promising as cost-effective gap fillers of active radar coverage especially in alpine terrain and also as covert early warning sensors. However, PCL systems are sensitive to changes of Transmitters of Opportunity (ToO). Many approaches for energy-efficient target detection have been proposed for active radar sensor networks. However, energy-efficiency and topology optimization of combined active-passive radar sensor networks in realistic scenarios have been poorly studied until today. We here propose an unsupervised learning approach for topology optimization and energy-efficient detection in combined active-passive radar sensor networks. The interdependence of active and passive sensors in the network and the given target scenario is naturally accounted for by our approach. Optimal power budget and detection sectors of active radars and the most useful ToOs for each PCL sensor are simultaneously learned over time. This is a critical contribution for minimizing the need for active radar power budget and PCL computational resources. The power budget of active radars is minimized in a way that the added value of PCL sensors is fully exploited. We also demonstrate how our approach dynamically relearns to achieve robust performance when changes in the ToO of PCL sensors occur. We test our approach in a simulation suite for active-passive radar sensor networks using real-world air surveillance data and ToOs under real-world topographical conditions.

Wireless Sensor Networks Optimization with Localization-Based Clustering using Game Theory Algorithm

Wireless sensor networks, as remote monitoring system support or Internet of Things subsystems, require reliability and stability, which greatly influence sensor life spans. Optimization efforts were a breakthrough to obtain these requirements. T

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.