Wireless Sensor Network For Environmental Monitoring Research Articles

Environmental Monitoring with Wireless Sensor Network for Energy Aware Routing and Localization

This paper introduces a novel approach to enhance the efficiency of environmental monitoring in Wireless Sensor Networks (WSNs) through the application of the Centroid Direction of Arrival Routing (CDAR) protocol. WSNs play a pivotal role in collecting and transmitting data for environmental surveillance, yet energy constraints and dynamic deployment scenarios pose challenges to their sustained operation. The CDAR protocol addresses these challenges by optimizing energy consumption, improving data transmission efficiency, and extending network lifetimes. We present comprehensive performance evaluations across ten deployment scenarios, revealing CDAR’s adaptability to varying node densities and environmental conditions. The protocol consistently demonstrates high energy efficiency percentages, showcasing its ability to strike a balance between energy conservation and reliable data transmission. Security measures integrated into CDAR ensure the protection of transmitted environmental data. This paper provides valuable insights into the applicability of CDAR in achieving sustainable and effective WSNs for environmental monitoring, setting the stage for further research and real-world implementations.

Joint Light-Sensitive Balanced Butterfly Optimizer for Solving the NLO and NCO Problems of WSN for Environmental Monitoring.

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals' smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results.

Implementation of a WSN for Environmental Monitoring: From the Base Station to the Small Sensor Node.

In this paper, the implementation of a Wireless Sensor Network (WSN) for environmental monitoring (EM) is presented. It includes the design, implementation and experimental characterization of a multi-sector base station (BS) antenna composed of several microstrip Quasi-Yagi elements and the implementation and experimental characterization of a reduced form factor antenna for the sensor nodes (SN). Subsequently, it reports the implementation of a WSN based on Lopy4 transceivers, using the developed BS and SN antennas. Finally, experimental results obtained on the field to evaluate the performance of the network in terms of maximum coverage distance and coverage area are presented. According to the field tests, the connectivity between the sensor nodes and the developed WSN base station is confirmed at distances above 3.5 km and for all the antenna sectors of the multi-sector BS attaining a 360 of field of view.

Energy-efficient and balanced routing in low-power wireless sensor networks for data collection

Cost-based routing protocols are the main approach used in practical wireless sensor network (WSN) and Internet of Things (IoT) deployments for data collection applications with energy constraints; however, those routing protocols lead to the concentration of most of the data traffic on some specific nodes which provide the best available routes, thus significantly increasing their energy consumption. Consequently, nodes providing the best routes are potentially the first ones to deplete their batteries and stop working. In this paper, we introduce a novel routing strategy for energy efficient and balanced data collection in WSNs/IoT, which can be applied to any cost-based routing solution to exploit suboptimal network routing alternatives based on the parent set concept. While still taking advantage of the stable routing topologies built in cost-based routing protocols, our approach adds a random component into the process of packet forwarding to achieve a better network lifetime in WSNs. We evaluate the implementation of our approach against other state-of-the-art WSN routing protocols through thorough real-world testbed experiments and simulations, and demonstrate that our approach achieves a significant reduction in the energy consumption of the routing layer in the busiest nodes ranging from 11% to 59%, while maintaining over 99% reliability. Furthermore, we conduct the field deployment of our approach in a heterogeneous WSN for environmental monitoring in a forest area, report the experimental results and illustrate the effectiveness of our approach in detail. Our EER based routing protocol CTP+EER is made available as open source to the community for evaluation and adoption.

A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

In recent years, more and more researchers have paid attention to the three‐dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two‐dimensional plane is full of loopholes when applied in the real three‐dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three‐dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9% higher than that of GA and 5.4% higher than that of SA. The network energy consumption of BACSFLA is 36.0% lower than GA and 35.9% lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low‐power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time.

Scalable Grid-Based Data Gathering Algorithm for Environmental Monitoring Wireless Sensor Networks

Proper utilization of the available low-power is essential to extend the lifetime of the battery-operated wireless sensor networks (WSNs) for environmental monitoring applications. It is mandatory because the batteries cannot be replaced or recharged after deployment due to impracticality. To utilize the power properly, an appropriate cluster-based data gathering algorithm is needed which reduces the overall power consumption of the network significantly. So, in this paper, a grid-based data gathering algorithm called energy-efficient structured clustering algorithm with relay (EESCA-WR) is proposed. In this algorithm, the grids have a single grid leader (GL) and multiple grid relays (GRs). The count of GRs in a grid is variable based on the geographic location of the grid with respect to the destination sink (DS). By doing this, we ensure that the reduction in power consumption is achieved because of the multi-hop short-distance data communications. Also, the GLs are rotated in the right intervals in hybrid modes to minimize the usage of control messages considerably. A hybrid GL selection policy, a threshold-based GL rotation policy, and the policy of allotting dedicated relay-clusters in every grid make the proposed algorithm unique and better for homogeneous and heterogeneous wireless sensor networks. Performance evaluation of the proposed algorithm is carried out by varying the length of the field, the node-density, the grid-count, and the initial energy. Experimental results show that EESCA-WR is extremely scalable, energy-efficient with a minimum number of control messages, and can be used for large scale WSNs.

Integration of Compressive Sensing and Clustering in Wireless Sensor Networks using Block Tridiagonal Matrix Method

The most dominant applications of wireless sensor networks (WSNs) is Environmental monitoring, it generally needs long time to operate. Although, the energy of inherent restriction has the bottle neck in scale of each WSN applications. This articler demonstrates the framework for an integration of compressive sensing and blocks tri-diagonal matrices (BDMs) for the clustering in WSNs that can be used as the matrices of measurement by the combination of data prediction that is involved with the compression and retrieval to achieve data processing precision and effectiveness in clustered WSNs simultaneously. On basis of the analysis theoretically, this can be designed for the implementation in number of algorithms. The proposed framework furnishes the real world data demonstration which can be utilized to get the simulation results for a solution of cost effective for the applications on basis of cluster in WSNs for environmental monitoring.

Design and Implementation of Low-Power Analog-to-Information Conversion for Environmental Information Perception

Because sensing nodes typically have limited power resources, it is extremely important for signals to be acquired with high efficiency and low power consumption, especially in large-scale wireless sensor networks (WSNs) applications. An emerging signal acquisition and compression method called compressed sensing (CS) is a notable alternative to traditional signal processing methods and is a feasible solution for WSNs. In our previous work, we studied several data recovery algorithms and network models that use CS for compressive sampling and signal recovery. The results were validated on large data sets from actual environmental monitoring WSNs. In this paper, we focus on the hardware solution for signal acquisition and processing on separate end nodes. We propose the paradigm of an analog-to-information converter (AIC) based on CS theory. The system model consists of a modulation module, filtering module, and sampling module, and was simulated and analyzed in a MATLAB/Simulink 7.0 environment. Further, the hardware design and implementation of an improved digital AIC system is presented. We also study the performances of three different greedy data recovery algorithms and analyze the system power consumption. The experimental results show that, for normal environmental signals, the new system overcomes the Nyquist limit and exhibits good recovery performance with a low sampling frequency, which is suitable for environmental monitoring based on WSNs.

Simple Clustering for Wireless Sensor Networks

Abstract Traditional Wireless Sensor Networks (WSNs) are designed for connecting pure electronic or mechanical sensors through wireless communication. Nowadays, WSNs are combined with other technologies like big data and cloud for data management. While combining the sensors (low level devices) with a high end network like Internet, data communication should be taken care of in the gateway between these two networks. With this type of WSNs, if all the sensors are in direct communication with the high end network, then a lot of energy will be consumed, requiring complex protocols for making this communication possible. So, to reduce the energy wastage, the clustering concept is used in WSN. Although so many complex and innovative clustering techniques are available for WSN, this paper describes a very simple clustering technique for environmental monitoring WSN with proven results.

Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype

Software defined wireless networks (SDWNs) present an innovative framework for virtualized network control and flexible architecture design of wireless sensor networks (WSNs). However, the decoupled control and data planes and the logically centralized control in SDWNs may cause high energy consumption and resource waste during system operation, hindering their application in WSNs. In this paper, we propose a software defined WSN (SDWSN) prototype to improve the energy efficiency and adaptability of WSNs for environmental monitoring applications, taking into account the constraints of WSNs in terms of energy, radio resources, and computational capabilities, and the value redundancy and distributed nature of data flows in periodic transmissions for monitoring applications. Particularly, we design a reinforcement learning based mechanism to perform value-redundancy filtering and load-balancing routing according to the values and distribution of data flows, respectively, in order to improve the energy efficiency and self-adaptability to environmental changes for WSNs. The optimal matching rules in flow table are designed to curb the control signaling overhead and balance the distribution of data flows for achieving in-network fusion in data plane with guaranteed quality of service (QoS). Experiment results show that the proposed SDWSN prototype can effectively improve the energy efficiency and self-adaptability of environmental monitoring WSNs with QoS.

Performance comparison of data compression algorithms for environmental monitoring wireless sensor networks

Wireless sensor networks (WSNs) have serious resource limitations ranging from finite power supply, limited bandwidth for communication, limited processing speed, to limited memory and storage space. Data compression can help reduce memory and storage space requirements on sensor node. In WSNs, radio communication is the major consumer of energy. Therefore, applying data compression before transmission will significantly and directly help in reducing total power consumption of a sensor node thereby extending the network lifetime. In this article, we propose a simple lossless data compression algorithm designed specifically to be used by environmental monitoring sensor nodes for the compression of environmental data which are characterised by significant fluctuations in entropy. To verify the effectiveness of our proposed algorithm, we compare its compression performance with two existing WSNs compression algorithms using real-world environmental datasets. We show that our algorithm outperforms the other two algorithms when the entropy of the dataset is large.

A Differential Evolution-Based Routing Algorithm for Environmental Monitoring Wireless Sensor Networks

The traditional Low Energy Adaptive Cluster Hierarchy (LEACH) routing protocol is a clustering-based protocol. The uneven selection of cluster heads results in premature death of cluster heads and premature blind nodes inside the clusters, thus reducing the overall lifetime of the network. With a full consideration of information on energy and distance distribution of neighboring nodes inside the clusters, this paper proposes a new routing algorithm based on differential evolution (DE) to improve the LEACH routing protocol. To meet the requirements of monitoring applications in outdoor environments such as the meteorological, hydrological and wetland ecological environments, the proposed algorithm uses the simple and fast search features of DE to optimize the multi-objective selection of cluster heads and prevent blind nodes for improved energy efficiency and system stability. Simulation results show that the proposed new LEACH routing algorithm has better performance, effectively extends the working lifetime of the system, and improves the quality of the wireless sensor networks.

Minimizing energy consumptions in wireless sensor networks via two-modal transmission

We present a sophisticated framework to systematically explore the temporal correlation in environmental monitoring wireless sensor networks. The presented framework optimizes lossless data compression in communications given the resource constraint of sensor nodes. The insights and analyses obtained from the framework can directly lead to innovative and better design of data gathering protocols for wireless sensor networks operated in noisy environments to dramatically reduce the energy consumptions.

Development of an integrated wireless sensor network micro-environmental monitoring system

Wireless Sensor Network (WSN) is increasingly popular in the field of micro-environmental monitoring due to its promising capability. However, most systems using WSN for environmental monitoring reported in the literature are developed for specific applications without functions for exploiting user’s data processing methods. In this paper, a new system is designed in detail to perform micro-environmental monitoring taking the advantages of the WSN. The application-oriented hardware working style is designed, and the system platform for data acquisition, validation, processing and visualization is systematically presented. Several strategies are proposed to guarantee the system capability in terms of extracting useful information, visualizing events to their authentic time are also described. Moreover, a web-based surveillance subsystem is presented for remote control and monitoring. In addition, the system is extensible for engineers to carry their own data analysis algorithms. Experimental results are to show the path reliability and real-time characteristics, and to display the feasibility and applicability of the developed system into practical deployment.