Optimal Sensor Network Research Articles

On-Demand Charging Management Model and Its Optimization for Wireless Renewable Sensor Networks.

Nowadays, wireless energy transfer (WET) is a new strategy that has the potential to essentially resolve energy and lifespan issues in a wireless sensor network (WSN). We investigate the process of a wireless energy transfer-based wireless sensor network via a wireless mobile charging device (WMCD) and develop a periodic charging scheme to keep the network operative. This paper aims to reduce the overall system energy consumption and total distance traveled, and increase the ratio of charging device vacation time. We propose an energy renewable management system based on particle swarm optimization (ERMS-PSO) to achieve energy savings based on an investigation of the total energy consumption. In this new strategy, we introduce two sets of energies called emin (minimum energy level) and ethresh (threshold energy level). When the first node reaches the emin, it will inform the base station, which will calculate all nodes that fall under ethresh and send a WMCD to charge them in one cycle. These settled energy levels help to manage when a sensor node needs to be charged before reaching the general minimum energy in the node and will help the network to operate for a long time without failing. In contrast to previous schemes in which the wireless mobile charging device visited and charged all nodes for each cycle, in our strategy, the charging device should visit only a few nodes that use more energy than others. Mathematical outcomes demonstrate that our proposed strategy can considerably reduce the total energy consumption and distance traveled by the charging device and increase its vacation time ratio while retaining performance, and ERMS-PSO is more practical for real-world networks because it can keep the network operational with less complexity than other schemes.

Research on Smart Fitness and Bodybuilding Based on Wireless Network Sensor Action Monitoring Device

Fitness and bodybuilding has been developed in China for decades and is deeply loved by the masses. Under the background of the national fitness program and the general environment of the sports industry, China's fitness and bodybuilding has developed in the long-term in terms of scope and breadth, but the path model is single, and it is difficult to adapt to the development of the times. With the widespread popularity of 5G+IOT, wireless network sensor motion monitoring devices have been widely used in various fields of life, and the effect is very significant. This research focuses on the congestion control, energy efficiency optimization and node positioning of wireless sensor networks. Taking the physical distance between nodes as the starting point, the ID3 algorithm and the C4.5 algorithm are proposed in a targeted manner, and verified through theoretical analysis and software simulation effectiveness of the proposed algorithm.

Scheduling-based Atom Search Bat Optimization for Energy-Constrained and Delay Sensitive Wireless Sensor Networks

Scheduling-based Atom Search Bat Optimization for Energy-Constrained and Delay Sensitive Wireless Sensor Networks

EC2STO: Enhanced Congestion Control approach for Throughput Optimization in Industrial Wireless Sensor Networks (IWSNs)

Congestion has become a major challenge in Wireless Sensor Networks (WSNs), specifically in industrial applications, due to the different processing capacities of sensor nodes within a network. Such ineffectiveness reduces the overall performance of the network. Thus, congestion needs to be controlled. A scalable approach to control the packet traffic in industrial WSNs (IWSNs) is proposed in this paper. Relay nodes are introduced to the IWSN to boost the packet transmission rate. Furthermore, the introduced sensor nodes are systematically arranged to form a tree-like topology for network reliability and flexibility. The communication between nodes is executed under the AN1066 MiWi protocol (protocol of the IEEE 802.15.4 family). Simulations using MATLAB showed that the proposed approach outperforms the existing congestion control algorithm. This is because the employed relay nodes and AN1066 MiWi protocol render the proposed approach more effective than the congestion control algorithm, which involves assigning a fixed capacity to every link between nodes, causing a wastage of the link capacity in case nodes that are not transmitting packets exist in the network.

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio, coverage ratio and sensing error. Data optimization was performed based on the availability of cloud resource, sensor energy, data flow volume and the centroid of each state. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

Dynamic Coordinator Management for Balancing Data Flow in the Wireless Sensor Network

This paper presents Wireless Sensor network data flow management methodology to collect the widest amount of information reducing data collisions and power consumption in network nodes. To provide simulation tasks stochastic based simulation of total network operation rules stability is observed. The objective of this research is to develop a stable, robust, and optimal wide sensor network harvested data-based management and control system operation tool for complex heating system that will minimize the use of energy reducing the impact on the system while satisfying the data harvesting rules. Author concentrates on the basic operation of such systems and present findings from the design process and initial tests.

Lifetime optimization of dense wireless sensor networks using continuous ring-sector model

Wireless sensor networks (WSNs) are becoming increasingly utilized in applications that require remote collection of data on environmental conditions. In particular dense WSNs are emerging as an important sensing platforms for the Internet of Things (IoT). WSNs are able to generate huge volumes of raw data, which require network structuring and efficient collaboration between nodes to ensure efficient transmission. In order to reduce the amount of data carried in the network, data aggregation is used in WSNs to define a policy of data fusion and compression. In this paper, we investigate a model for data aggregation in a dense WSN with a single sink. The model divides a circular coverage region centered at the sink into patches which are intersections of sectors of concentric rings, and data in each patch is aggregated at a single node before transmission. Nodes only communicate with other nodes in the same sector. Based on these assumptions, we formulate a linear programming problem to maximize system lifetime by minimizing the maximum proportionate energy consumption over all nodes. Under a wide variety of conditions, the optimal solution employs two transmissions mechanisms: direct transmission, in which nodes send information directly to the sink; and stepwise transmission, in which nodes transmit information to adjacent nodes. An exact formula is given for the proportionate energy consumption rate of the network. Asymptotic forms of this exact solution are also derived, and are verified to agree with the linear programming solution. We investigate three strategies for improving system lifetime: nonuniform energy and information density; iterated compression; and modifications of rings. We conclude that iterated compression brings the greatest increase in system lifetime, but at the cost of possible information loss.

A data transmission approach based on ant colony optimization and threshold proxy re-encryption in WSNs

Wireless sensor networks have become increasingly popular due to the rapid growth of the Internet of Things. As open wireless transmission mediums are easy to attack, security is one of the primary design concerns for wireless sensor networks. Current solutions consider routing and data encryption as two isolated issues, providing incomplete security. Therefore, in this paper we divide the WSN communication process into a data path selection phase and a data encryption phase. By proposing an improved transmission method based on ant colony optimization and threshold proxy re-encryption for wireless sensor networks,named as ACOTPRE, it resists internal and external attacks and ensures safe and efficient data transmission. In the data path selection stage, the ant colony optimization algorithm is used for network routing. The improvement of the pheromone concentration is proposed. In order to resist attacks from external attackers, proxy re-encryption is extended to WSN in the data encryption stage. The threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source node. We confirm the performance of our model via simulation studies.

A Local 3D Voronoi-Based Optimization Method for Sensor Network Deployment in Complex Indoor Environments.

Optimal sensor network deployment in built environments for tracking, surveillance, and monitoring of dynamic phenomena is one of the most challenging issues in sensor network design and applications (e.g., people movement). Most of the current methods for sensor network deployment and optimization are empirical and they often result in important coverage gaps in the monitored areas. To overcome these limitations, several optimization methods have been proposed in the recent years. However, most of these methods oversimplify the environment and do not consider the complexity of 3D architectural nature of the built environments specially for indoor applications (e.g., indoor navigation, evacuation, etc.). In this paper, we propose a novel local optimization algorithm based on a 3D Voronoi diagram, which allows a clear definition of the proximity relations between sensors in 3D indoor environments. This proposed structure is integrated with an IndoorGML model to efficiently manage indoor environment components and their relations as well as the sensors in the network. To evaluate the proposed method, we compared our results with the Genetic Algorithm (GA) and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithms. The results show that the proposed method achieved 98.86% coverage which is comparable to GA and CMA-ES algorithms, while also being about six times more efficient.

Pollination Inspired Clustering Model for Wireless Sensor Network Optimization

Remote and dangerous fields that are expensive, complex, and unreachable to reach human insights are examined with ease using the Wireless Sensor Network (WSN) applications. Due to the use of non-renewable sources of energy, challenges with respect to the network lifetime, fault tolerance and energy consumption are faced by the self-managed networks. An efficient fault tolerance technique has been provided in this paper as an effective management strategy. Using the network and communication nodes, revitalization and fault recognition techniques are used for handling diverse levels of faults in this framework. At the network nodes, the fault tolerance capability is increased by the proposed protocol model and management strategy. This enhances the corresponding data transmission in the network. When compared to the conventional techniques, the proposed model increases the network lifetime by five times. It is observed from the validation results that, with a 10% increase in the network lifetime, there is a 2% decrease in the fault tolerance proficiency of the network. The network lifetime and data transmission rate are improved while the network energy consumption is reduced significantly. The MATLAB environment is used for simulation purpose. In terms of energy consumption, network lifetime and fault tolerance, the proposed model offers optimal results.

An augmenting‐flow algorithm for a class of node‐capacitated maximum flow problems

AbstractWe consider a class of maximum flow problems (MFPs) having node‐ and arc‐capacity constraints, in which each unit of flow on arc consumes a positive amount of capacity at node i. This problem arises in wireless sensor network optimization applications, where node capacities refer to sensor energy limits. This version of the MFP is traditionally solved using linear programming due to the presence of the complicating node‐capacity constraints. As an alternative scheme, we prescribe an approach for this problem based on augmenting flows along paths and cycles, showing why sending flows on augmenting cycles becomes necessary in this class of problems. Although our augmenting flow algorithm ultimately requires the solution of auxiliary linear programs, we demonstrate the computational advantages of our approach on randomly generated test instances.

Optimal sensor placement for identifying multi-component failures in a wind turbine gearbox using integrated condition monitoring scheme

Wind turbine gearbox has a high failure frequency and downtime, and therefore, several sensors are installed to perform condition monitoring to reduce the operation and maintenance costs. A gearbox can have infinite sensor nodal positions, but, in reality, the positioning of sensors is limited to a finite number of locations. Also, sensor location influences the quality of the data captured by the sensors, which is of key importance in a condition monitoring system. Hence selection of optimal sensor placement (OSP) is a challenging task which needs to be addressed. When the sensor type changes, the measurement response changes, and hence the OSP methodologies based on the measured responses may not work well. For addressing this, an optimization method based on statistical features is proposed to find the optimum sensor placement (OSP). In order to evaluate the effectiveness of the proposed method, experiments are conducted on a laboratory scale model of wind turbine gearbox considering multi-component faults and an integrated condition monitoring scheme. Variational mode decomposition and the Spearmen correlation coefficient are used to processes raw acoustic and vibration signals. Feature extraction is performed to obtain nine statistical features, and a mathematical objective function is constructed as a function of these features. Grey wolf optimizer is employed to find the optimal values of the statistical features. Fault classification is performed using Random forest (RF) and deep multi-layer perceptron (MLP) algorithms. Optimal sensor network identified by the above method reported classification accuracy of 86.88% and 88.34% for RF and MLP, respectively. As a result, the number of sensors reduced from eight to five. The proposed method can be used as an effective technique for OSP problems.

Joint Power and Channel Optimization of Agricultural Wireless Sensor Networks Based on Hybrid Deep Reinforcement Learning

The reduction of maintenance costs in agricultural wireless sensor networks (WSNs) requires reducing energy consumption. At the same time, care should be taken not to affect communication quality and network lifetime. This paper studies a joint optimization algorithm for transmitted power and channel allocation based on deep reinforcement learning. First, an optimization model to measure network reward was established under the constraint of the signal-to-interference plus-noise-ratio (SINR) threshold, which includes continuous power variables and discrete channel variables. Secondly, considering the dynamic changes of agricultural WSNs, the network control is described as a Markov decision process with continuous state and action space. A deep deterministic policy gradient (DDPG) reinforcement learning scheme suitable for mixed variables was designed. This method could obtain a control scheme that maximizes network reward by means of black-box optimization for continuous transmitted power and discrete channel allocation. Experimental results indicated that the studied algorithm has stable convergence. Compared with traditional protocols, it can better control the transmitted power and allocate channels. The joint power and channel optimization provides a reference solution for constructing an energy-balanced network.

A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty

Structural health monitoring techniques for composite structures are dependent on the data acquired from sensors; thus, optimal sensor network design is important to provide adequate and reliable information. This paper presents a novel framework for optimizing the number of sensors and sensor placement under model uncertainty for vibration-based damage detection in composite structures. The number of target vibration modes to be identified is first determined, based on the sum of modal effective mass fractions, to sufficiently capture dynamic responses. Since any point on the structure can be a candidate sensor position (causing a large design space), a modal kinetic-energy-based index is proposed to narrow the design space. Design objectives simultaneously minimize the number of sensors and the mean and standard deviation values for the root mean square error of off-diagonal terms in the modal assurance criterion matrix. The nondominated sorting genetic algorithm II is adopted to solve this problem. Monte Carlo simulation (MCS) is applied to evaluate the latter two objective functions. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. To validate the optimized sensors, an optimization-based delamination detection process is applied. Case studies are presented to demonstrate the developed optimization framework.

On statistical Multi-Objective optimization of sensor networks and optimal detector derivation for structural health monitoring

Sensor placement and structural health classifiers are fundamental components of Structural Health Monitoring (SHM) systems, as they largely define system detection (or classification) performance. Optimal sensor placement strategies are designed to maximize the ability to detect damage or to minimize lifetime costs, given limited resource availability. However, usually choosing one strategy over the other and non-optimal detector implementation may provide poorly performing solutions in terms of detection performance or total cost, even though both are critical objectives for a cost-effective SHM system implementation.The work proposes a unique and coherent framework for optimal detector and sensing network design for SHM. After an optimal detector is defined based on the Neyman-Pearson likelihood ratio test, classification performance indexes are used in a multi-objective optimization paradigm for optimal sensor placement. Specifically, the optimization considers maximizing the classification performances and, simultaneously, minimizing a measure of total cost or risk in a Bayesian sense.Even though the approach is general for any structure and sensor measurement process, the method is numerically verified with a cracked plate under tension and monitored by measurements of local strain serving as the surrogate SHM system. The results are also validated by comparing the multi-objective optimal design to engineering judgment and single-objective-based solutions in terms of probability of detection and costs. The advantages of an optimization scheme are emphasized with respect to an engineering scheme and, above all, how a multi-objective optimization strategy reflects a conjunct saving in costs and improvement in detection performances.

Strain Sensor’s Network for Low-Velocity Impact Location Estimation on Carbon Reinforced Fiber Plastic Structures: Part-II

Identification of low velocity impact (LVI) location in composite aircraft structures is seamless need for safe, reliable operation and maintenance of aerospace industry. To locate the LVI’s an optimized sensor network has designed using the strain response from fiber Bragg grating (FBG) & resistance strain gauge (RSG) sensor bonded to the composite structure. Strain scan (SS) algorithm has been developed to locate such events reported as Part-I. In this work, we have developed a novel algorithm based on weighted energy (WE) of the sensor response. The LVI’s has been carried out on composite structures & the locations of LVI’s have estimated using SS, WE & previously developed machine learning base support vector machine (SVM) algorithms. The WE and SS algorithms are based on proximity of events (closer to the sensor, higher the response), whereas LS-SVR is a data-driven approach. Further, we have compared the performance of the developed algorithms and algorithms cited in the literature using the performance index (PI), a measure of estimation efficiency as a function of the number of sensors, dimension/area of the structure, error & number of test cases. It is established that WE algorithm shown suprema performance over the other algorithm with 34 mm mean Euclidian distance error & PI value of 5.5.

DACOR: A distributed ACO‐based routing protocol for mitigating the hot spot problem in fog‐enabled WSN architecture

SummaryAnt colony optimization (ACO) and unequal clustering algorithms in wireless sensor networks (WSNs) prove their efficiency in protracting the network lifetime. However, the existing ACO and unequal clustering algorithms, respectively, do not consider jointly energy efficiency and reliability and focus only on some normal parameters to adjust the cluster radius, then neglecting the cluster head (CH) neighborhood information as it is wise to reduce the cluster radius when there are more neighbor CHs in order to balance the load and energy consumption. To resolve these problems, we propose a fault‐tolerant distributed ACO‐based routing (DACOR) protocol for mitigating the hot spot problem in fog‐enabled WSN architecture. To improve the performance of the network, we propose a multiple fog nodes (FNs) and unequal clustering‐based network model. The proposed model is energy efficient as it avoids repetitive clustering and affects CHs to FNs based on distance. Also, unlike the existing works which use either single FN/sink‐based unequal clustering or multiple FNs/sinks to mitigate hot spot problem, we propose to distribute unequal clustering to multiple FNs (partitions). Additionally, we formulate a different rule to calculate the cluster radius based on significant parameters ensuring energy efficiency and balancing. To route data from source to destination, we devise a new probabilistic formula which considers not only energy efficiency but also reliability. The performance of the proposed DACOR protocol has been investigated under different scenarios through simulations. The results show that the proposed DACOR protocol outperforms the existing protocols in terms of various main metrics.

Reliability-Constrained Throughput Optimization of Industrial Wireless Sensor Networks With Energy Harvesting Relay

In industrial wireless sensor networks (IWSNs), a lot of energy is wasted in the form of electromagnetic radiations. It can be effectively utilized with energy harvesting (EH), which absorbs part of the energy in the transmission signal but reduces the throughput and reliability of IWSNs. In this article, we study the throughput optimization of IWSNs with EH from the interference radio-frequency (RF) signal considering the reliability constraint of the industrial information transmission. Under the premise of limited energy supply of EH relays, the throughput maximization of IWSNs is formulated as a nonconvex optimization problem. In order to transform the nonconvex problem to a convex optimization problem, the successive convex approximation (SCA) approach is adopted. Furthermore, a power allocation algorithm is designed to maximize the total transmission rate of the network. Simulation results demonstrate that the proposed algorithm can maximize the throughput under the primise of SINR reliability.

A Coverage Optimization Method for WSNs Based on the Improved Weed Algorithm.

Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed’s new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.

Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.