Optimal Sensor Network Research Articles

Coverage Optimization and Simulation of Wireless Sensor Networks Based on Particle Swarm Optimization

As a new type of measurement and control network, wireless sensor networks (WSNs) technology has the characteristics of diversity and wide practicability. Based on the characteristics of WSNs, wireless sensor networks are widely used in military and civil industries. However, due to the frequent changes in the basic topology of wireless sensor networks and the relatively small number of corresponding network nodes, the efficiency of the nodes is the reason for its further development. Therefore, the node distribution strategy and the corresponding network coverage optimization strategy of wireless sensor networks are of great significance to achieve energy saving and network life extension. In order to solve the above WSNs problems, this paper will analyze and simulate the coverage optimization of wireless sensor networks based on Improved Particle Swarm Optimization algorithm. Firstly, this paper will analyze the structure characteristics of wireless sensor networks, complete the WSNs network coverage model and get the corresponding functions. Then this paper innovatively proposes “external dispersion method” to solve the problem of local area overlap in WSNs network coverage. At the same time, it innovatively proposes “free-particle swarm optimization” to solve the local convergence of conventional particle swarm optimization. At the end of this paper, simulation experiments are carried out to compare the optimal particle swarm optimization algorithm with the traditional particle swarm optimization algorithm. The experiments show that the proposed algorithm has obvious advantages in convergence and coverage.

Uneven clustering routing algorithm based on glowworm swarm optimization

An uneven clustering routing algorithm based on glowworm swarm optimization for wireless sensor networks (UCRA-GSO) is proposed to solve the Wireless sensor network (WSN) energy balance problem. We introduce factors such as cluster head density, cluster head proximity distance, cluster head energy, and compactness of the cluster into a glowworm swarm optimization (GSO) method to find the optimal clustering method. The test case involves a forwarding-tree of two level gradients (single and multi-hop transmission) such that the distance between the sensor and the base station is determined by a cost function. The simulation results show that UCRA-GSO effectively extends the network's life cycle while balancing the network's energy dissipation.

IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks

In the past few years, research and development in Wireless Sensor networks (WSNs) have gained momentum due to its numerous applications in agriculture, industrial manufacturing, military surveillance, environmental monitoring, consumer electronics, medical & healthcare, disaster recovery operations etc. Dynamic WSNs offer a robust blend of distributed sensing, computing and communication. Dynamic sensor networks are characterized by large scale deployment, dynamic and unstructured topology, power limitations, less memory and limited computational capabilities. Sensor nodes deployed in real-time environment’s for sensing data have power-limitations which hampers the overall performance of WSNs. So, the only obvious solution is to propose an energy efficient routing protocol to optimize WSN real-time performance. Different specialists have proposed various directing conventions for WSNs dependent on Fuzzy Logic, Genetic Algorithms, Meta-Heuristics, and other improvement strategies. However, every solution suggested till date has its advantages and limitations. In this paper, our primary objective is to utilize Swarm-Intelligence based approach i.e. “Ant Colony Optimization (ACO)”, for routing protocol development. Ant colony optimization (ACO) based approach gives optimal solution in terms of efficient routing path determination, energy efficiency and delivering high performance in terms of packet delivery and throughput. In this paper, we propose a novel energy efficient ACO based multipath routing protocol for WSN i.e. IEEMARP (Improvised Energy Efficient Multipath ACO based Routing Protocol). The proposed protocol works in three phases (Neighbor Discovery via Link Knowledge, Packet Transmission via exponentially weighted moving average method and ACKR packet delivery for assuring end-to-end delivery. To validate the performance of the protocol proposed, extensive simulations were conducted using NS-2.35-allinone simulator on diverse parameters like (PDR), throughput, routing overhead, energy consumption and end-to-end delay. In addition to this, the performance of protocol is compared with traditional routing protocols like Basic ACO, DSDV and DSR and other ACO based WSN protocols like ACEAMR, AntChain, EMCBR, IACR, AntHQSeN, FACOR and ANTALG. Simulation based results, clearly states that as compared to Basic ACO, DSDV and DSR, the performance of WSN network is improvised to around 10% in all performance metrics via IEEMARP routing protocol. And as compared to ACEAMR, AntChain, EMCBR and IACR, IEEMARP performs 20% better in overall functionality and almost 10–12% better as compared to AntHQSeN, FACOR, ANTLAG routing protocols in varied WSN scenarios. It is also observed that IEEMARP protocol is highly efficient in TCP packet transmission from source to destination node.

Designing Robust, Cost‐Effective Field Measurement Sets using Universal Multiple Linear Regression

Core Ideas Computationally inexpensive, objective method for recommending sensor type/depth. Comprehensive information about potential measurement networks. Network design for user‐defined uncertainties before sensor installation. Limited monitoring budgets restrict the type and number of sensors that can be installed for field‐based studies. Therefore, sensor selection should be both informative and efficient. We propose a method to optimize sensor network design, prior to data collection, by combining multiple linear regression (MLR) and robust decision‐making (RDM). Multiple linear regression inherently considers the strength of the relationship between observations and predictions of interest and correlations among proposed observations. In our approach, we use universal Multiple Linear Regression (uMLR) to quantify the explanatory power of all possible combinations of model‐simulated candidate observations (of different sensor types and locations). A model‐ensemble approach allows for network design in the context of user‐defined uncertainties, including expected measurement error and parameter and structural uncertainty. Application of uMLR with RDM produces a comprehensive assessment of the likely value of many observation sets. These results can be used to design sensor networks to address specific experimental objectives and to balance the cost and effort of installing sensors to the expected value of the data for model testing and decision support.

Joint Source-Relay Optimization for MIMO Full-Duplex Bidirectional Wireless Sensor Networks with SWIPT.

The simultaneous wireless information and power transfer (SWIPT) technique has been considered as a promising approach to prolong the lifetime of energy-constraint wireless sensor networks (WSNs). In this paper, a multiple-input multiple-output (MIMO) full-duplex (FD) bidirectional wireless sensor network (BWSN) with SWIPT is investigated. Based on minimum total mean-square-error (total-MSE) criterion, a joint optimization problem for source and relay beamforming and source receiving subject to transmitting power and harvesting energy constraints is established. Since this problem is non-convex, an iterative algorithm based on feasible point pursuit-successive convex approximation (FPP-SCA) is derived to obtain a local optimum. Moreover, considering the scenarios in which source and relay nodes equipped with the same and different numbers of antennas, a low-complexity diagonalizing design-based scheme is employed to simplify each non-convex subproblem into convex problems and to reduce the computational complexity. Numerical results of the total-MSE and bit error rate (BER) are implemented to demonstrate the performance of the two different schemes.

Sensor placement optimization on complex and large metallic and composite structures

This study presents an effective solution for the optimization of piezoelectric (PZT) wafer placement in a network of convex and non-convex structures, toward the application in the field of structural health monitoring. The proposed objective function is to maximize the coverage of the monitored area, discretized by a set of control points, while minimizing the number of PZT wafers. In the optimum solution, each control point should be covered by a user-defined number of sensing paths, defined as the coverage level. The PZT locations were treated as continuous variables. Thus, during the optimization process, any location on the plate is considered as a potential position for a PZT wafer. The algorithm provides the flexibility of changing a wide range of parameters including the number of PZT wafers, the distance covered around the sensing path, the required coverage level, and the number of control points, in addition to identifying the most sensitive PZT wafer within the network. The tractability of the model proposed was improved by feeding the solver an initial solution. The model calculates the importance of each PZT wafer within the network, which allows for further reduction in the number of active PZT elements. The suggested model was solved using a genetic algorithm. Multiple sensor network configurations on composite and metallic structures were selected, including a large cargo door of an A330 airplane, and validated experimentally. The experimental validation was to evaluate the accuracy in damage localization within the optimized sensor networks. The results demonstrated the proficiency of the model developed in distributing the PZT wafers on non-convex structures and large metallic structures.

An Area Coverage and Energy Consumption Optimization Approach Based on Improved Adaptive Particle Swarm Optimization for Directional Sensor Networks.

Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.

3-D Deployment Optimization for Heterogeneous Wireless Directional Sensor Networks on Smart City

The development of smart cities and the emergence of three-dimensional (3-D) urban terrain data have introduced new requirements and issues to the research on the 3-D deployment of wireless sensor networks. We study the deployment issue of heterogeneous wireless directional sensor networks in 3-D smart cities. Traditionally, studies on the deployment problem of WSNs focus on omnidirectional sensors on a 2-D plane or in full 3-D space. Based on 3-D urban terrain data, we transform the deployment problem into a multiobjective optimization problem, in which objectives of Coverage, Connectivity Quality, and Lifetime, as well as the Connectivity and Reliability constraints, are simultaneously considered. A graph-based 3-D signal propagation model employing the line-of-sight concept is used to calculate the signal path loss. Novel distributed parallel multiobjective evolutionary algorithms (MOEAs) are also proposed. For verification, real-world and artificial urban terrains are utilized. In comparison with other state-of-the-art MOEAs, the novel algorithms could more effectively and more efficiently address the deployment problem in terms of optimization performance and operation time.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

WSN Optimization Algorithm for Traffic Environmental Monitoring

In accordance with the features of variety and disparity of traffic information, using wireless sensor network to monitor traffic environment, this paper proposes an optimization algorithm of applying wireless sensor to monitor traffic environment. In this paper, to optimize the coverage, we analyze the irregularity of network sensing areas based on environmental factors and propose a wireless sensor network optimization algorithm. According to the irregularity of network node sensing areas, we build an irregular network coverage model, divide the node monitoring area to improve the network coverage and make corrections to the coverage according to the network coverage connectivity and other features. The simulation test proves that the method proposed in this paper can avoid too many redundant nodes - it only requires a reasonable number of network nodes. This will reduce network energy costs and increase network connectivity without affecting the coverage, thus improving the network performance. the algorithm can satisfy the requirements of real-time acquisition, processing and remote supervision of traffic information.

Fuzzy Link Cost Estimation based Adaptive Tree Algorithm for Routing Optimization in Wireless Sensor Networks using Reinforcement Learning

Fuzzy Link Cost Estimation based Adaptive Tree Algorithm for Routing Optimization in Wireless Sensor Networks using Reinforcement Learning

Data fusion based coverage optimization in heterogeneous sensor networks: A survey

Sensor networks, as a promising network paradigm, have been widely applied in a great deal of critical real-world applications. A key challenge in sensor networks is how to improve and optimize coverage quality which is a fundamental metric to characterize how well a point or a region or a barrier can be sensed by the geographically deployed heterogeneous sensors. Because of the resource-limited, battery-powered and type-diverse features of the sensors, maintaining and optimizing coverage quality includes a significant amount of challenges in heterogeneous sensor networks. Many researchers from both academic and industrial communities have performed numerous significant works on coverage optimization problem in the past decades. Some of them also have surveyed the current models, theories and solutions on the problem of coverage optimization. However, most of the existing surveys and analytical studies ignore how to exploit data fusion and cooperation of the deployed sensors to enhance coverage performance. In this paper, we provide an insightful and comprehensive summarization and classification on the data fusion based coverage optimization problem and techniques. Aiming at overcoming the shortcomings existed in current solutions, we also discuss the future issues and challenges in this area and sketch a general research framework in the context of reinforcement learning.

Power, sensitivity, and response time optimization in TDM self-reference intensity sensor networks with ring resonators.

In this paper, we report on design rules for optimizing the response of intensity sensors embedded in ring resonators (RR) as part of a frequency based self-referencing method. Time division multiplexing sensor networks require a time response analysis for properly addressing the self-reference sensors. The selected measurement and reference frequencies are multiples of the RR Free Spectral Range to improve signal to noise ratio and avoid RR coupling coefficient (K) tolerance influence on the measurements. A weighting function that models the trade-off between high sensitivity and fast response for defining the operation point in terms of K and ring losses is also given.

Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm

The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinates, the new algorithm introduced the adaptive inertial weight coefficient, retained the advantage of strong local search ability of fruit fly optimization algorithm, improved the ability of global optimiza-tion, effectively solved the problem of three-dimensional location in intelligent workshop. The simulation results show that, the algorithm in this paper is applied to the location calculation of triangulation, which has smaller location error and shorter operation time, it improves the accuracy of the location data and meets the real-time location requirements of manufacturing resources such as intelligent workshop staff, materials, logistics vehicles etc. facilitate resource sensing and scheduling management, thereby improving management standards and product quality.

Research on sensor network optimization based on improved Apriori algorithm

Traditional node deployment strategies have problems such as slow deployment, small coverage, and poor service quality. Based on this, this study improves the traditional Apriori. This study uses the grid overlay model as the basic model and applies the improved Apriori algorithm to the WSN (wireless sensor networks) coverage optimization process of mobile nodes. This study simulates from different population sized SizePops, different search interval lengths L, and different maximum iterations Maxgen. At the same time, this paper selects the traditional Apriori algorithm and the improved Apriori optimization algorithm to compare and analyze. The research shows that the improved Apriori optimization algorithm can better combine the wireless sensor network model of the mobile node and can obtain higher network coverage under the same parameters, and the problem can be solved more effectively.

A novel PSO algorithm for dynamic wireless sensor network multiobjective optimization problem

AbstractAll real‐life multiobjective optimization problems (MOPs) are considered dynamic. It is occurring due to fluctuations in environmental or the global market instabilities that lead to the quick fluctuations of prices. Wireless sensor network optimization problem, by nature, is considered one of the dynamic MOP (DMOP) and needs to be solved by a special method to save time and effort. Thus, this paper proposed a novel particle swarm optimization (PSO) for dynamic wireless sensor network MOP to accelerate data transfer in networks and reduce energy losses. Generally, in DMOPs, the optimization period is broken into several equal subperiods. In each subperiod, there is a dynamic parameter that changes. In the proposed approach, PSO is used to handle DMOPs without any changing of its structure and has fast convergence properties. However, a new mechanism to choose the personal and global preferred particles is introduced, which based on the distance between the nondominated solutions obtained so far and the particles positions by using Euclidean metric. In addition, two types of archives are used to maintain the nondominated solutions. The first one is used to store the nondominated solutions obtained by each particle, whereas the second type is used to store the nondominated solutions achieved by all particles. The novel methodology performance is proved by applying it on three benchmark problems that were chosen from the literature and one design problem from the engineering domain. The simulation results mentioned that the proposed algorithm is active and efficacious in solving DMOPs.

Performance Optimization of Wireless Sensor network for Multimedia services

WSN multimedia services are currently being used by several types of applications. These services experiences performance related issues in the form of slow data rates, packet loss etc. due to stringent resources. Lots of solutions are contributed to develop a system that optimizes the resources utilization, performance and packet loss. Performance evaluation for enhancing QoS of WSN is also required where packets arrive and depart the service facility (sink) in burst. In this paper, novel analytical approach is developed for packets arrival from sensor node in burst and departure of services after getting the facility, in burst. The evaluation has been performed by shifting coefficient of variance for the time when services are provided in burst and when services are arrived on different scales of utilization of the sink node. At the end of this paper quantitative results are compared after applying priority to data packets to check credibility of proposed solution.

Optimal wireless sensor network configuration for structural monitoring using automatic-learning firefly algorithm

Wireless sensor networks are becoming attractive data communication patterns in structural health monitoring systems. Designing and applying effective wireless sensor network–based structural health monitoring systems for large-scale civil infrastructure require a great number of wireless sensors and the optimal wireless sensor networks configuration becomes critical for such spatially separated large structures. In this article, optimal wireless sensor network configuration for structural health monitoring is treated as a discrete optimization problem, where parameter identification and network performance are simultaneously addressed. To solve this rather complicated optimization problem, a novel swarm intelligence algorithm called the automatic-learning firefly algorithm is proposed by integrating the original firefly algorithm with the Lévy flight and the automatic-learning mechanism. In the proposed algorithm, the Lévy flight is adopted to maximize the searching capability in unknown solution space and avoid premature convergence and the automatic-learning mechanism is designed to drive fireflies to move toward better locations at high speed. Numerical experiments are performed on a long-span bridge to demonstrate the effectiveness of the proposed automatic-learning firefly algorithm. Results indicate that automatic-learning firefly algorithm can find satisfactory wireless sensor network configurations, which facilitate easy discrimination of identified mode vectors and long wireless sensor network lifetime, and the innovations in automatic-learning firefly algorithm make it superior to the simple discrete firefly algorithm as to solution quality and convergence speed.

How to design an optimal sensor network for the unfolded protein response.

Cellular protein homeostasis requires continuous monitoring of stress in the endoplasmic reticulum (ER). Stress-detection networks control protein homeostasis by mitigating the deleterious effects of protein accumulation, such as aggregation and misfolding, with precise modulation of chaperone production. Here, we develop a coarse model of the unfolded protein response in yeast and use multi-objective optimization to determine which sensing and activation strategies optimally balance the trade-off between unfolded protein accumulation and chaperone production. By comparing a stress-sensing mechanism that responds directly to the level of unfolded protein in the ER to a mechanism that is negatively regulated by unbound chaperones, we show that chaperone-mediated sensors are more efficient than sensors that detect unfolded proteins directly. This results from the chaperone-mediated sensor having separate thresholds for activation and deactivation. Finally, we demonstrate that a sensor responsive to both unfolded protein and unbound chaperone does not further optimize homeostatic control. Our results suggest a strategy for designing stress sensors and may explain why BiP-mitigated ER stress-sensing networks have evolved.

Optimization of piezoelectric wafer placement for structural health-monitoring applications

Optimization of the number and location of piezoelectric wafers used in sensor networks for continuous monitoring of automotive and aerospace structures is not yet fully developed. This article presents a novel approach for the optimization of piezoelectric wafer networks for application in the field of structural health monitoring. A mixed integer nonlinear program is formulated and validated for different geometric shapes. The proposed objective function is to maximize the coverage of the area under study, described by a set of control points. In the optimal solution, each control point should be covered by a user-defined number of sensing paths, where each sensing path is the line joining any two piezoelectric transducers within the monitored plate. During the optimization process, any place on the plate is a potential location of a piezoelectric wafer. A mathematical programming language is used to code the algorithm, and selected simulation cases are executed to demonstrate the efficiency of the proposed optimization algorithm. The algorithm introduces several optimization parameters, such as coverage Levels 2 and 3, number of paths passing through a control point, and areas of concentrated coverage. Furthermore, the algorithm features the flexibility to change a wide range of problem parameters, such as the number of piezoelectric wafers, their coverage range, and the number of control points. The tractability of the model proposed is improved by feeding the solver an initial solution that makes the branch-and-bound technique less extensive. Data fusion is conducted to determine the quality of the coverage provided by the optimized piezoelectric wafer locations and to scrutinize the efficiency of the proposed approach. Finally, two sensor network configurations are selected and validated experimentally. The validation demonstrates a high level of coverage within the network, as well as evaluating the accuracy of damage localization within the optimized sensor networks.