Deployed Wireless Sensor Research Articles

Deployment Approach in WSNS Using Metaheuristic Algorithm: A Survey

Deploying wireless sensor networks (WSN) is one of the areas with the most significant research since the methods employed can have a big impact on the system's overall performance and the amount of energy the sensors in it consume.Thus, an effective deployment problem solution should not only "enhance its performance" but also save the energy needed to extend the lifetime of WSN. Finding an optimal solution for most deployment problems with limited computational resources is challenging, particularly for NP-hard optimization problems. By searching for a nearly optimal solution with constrained computational resources in a reasonable amount of time, metaheuristics present an alternative to exhaustive search and deterministic algorithms in solving these optimization problems. This paper classifies the deployment techniques of sensor networks and the metaheuristic algorithms.

Two-Level Clustering Algorithm for Cluster Head Selection in Randomly Deployed Wireless Sensor Networks

Two-Level Clustering Algorithm for Cluster Head Selection in Randomly Deployed Wireless Sensor Networks

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.

Challenges of ecocentric sustainable development in agriculture with special regard to the internet of things (IoT), an ICT perspective

Abstract“Feed the global population and regenerate the planet.”The conditions necessary for the implementation of the above commonly used slogan did not exist 10–15 years ago. We did not have access to the information and databases that would have allowed us to increase yields for the purpose of feeding the growing population. While increasingly meeting sustainability requirements and regenerating the Earth. Anthropocentrism, the belief that humans are superior to everything else, benefits humans by exploiting human greed and ignorance, which is a dead end for both individuals and societies. Only humans can ignore the dynamic equilibrium processes of nature and disregard the consequences that adversely affect future generations. Ecocentric agricultural practices have several prerequisites. It is important for the academic sphere to recognize its significance. Another fundamental challenge is the continuous monitoring of the production unit and its close and distant environment for the purpose of decision preparation using Big Data. The Internet of Things (IoT) is a global infrastructure that represents the network of physical (sensors) and virtual (reality) “things” through interoperable communication protocols. This allows devices to connect and communicate using cloud computing and artificial intelligence, contributing to the integrated optimization of the production system and its environment, considering ecocentric perspectives. This brings us closer to the self-decision-making capability of artificial intelligence, the practice of machine-to-machine (M2M) interaction, where human involvement in decision-making is increasingly marginalized. The IoT enables the fusion of information provided by deployed wireless sensors, data-gathering mobile robots, drones, and satellites to explore complex ecological relationships in local and global dimensions. Its significance lies, for example, in the prediction of plant protection. The paper introduces small smart data logger robots, including the Unmanned Ground Vehicles (robots) developed by the research team. These can replace sensors deployed in the Wireless Sensor Net (WSN).

Cluster head selection strategy of WSN based on binary multi-objective adaptive fish migration optimization algorithm

In deployed wireless sensor networks (WSNs), how to efficiently transmit information collected by sensor nodes with limited energy is a challenging problem. An appropriate cluster head selection strategy can efficiently solve this problem, but there are many factors to be considered, such as energy consumption, the coverage of cluster head nodes, and the number of cluster head nodes. Each factor has a profound impact on the performance of wireless sensor networks, and there are conflicts among them. In order to solve the conflict of multiple factors and obtain the optimal selection strategy of the cluster head node, this paper proposes a Binary Multi-Objective Adaptive Fish Migration Optimization (BMAFMO) algorithm. The algorithm introduces the Pareto optimal solution storage strategy to improve the global search ability of the optimization algorithm and transform the continuous solution into a binary solution according to the sigmoid transformation function to solve the problem of cluster head node selection. The new algorithm was comprehensively tested using eight test problems and four test metrics. At the same time, the reliability of the algorithm is tested by rank sum test. The test results show that the BMAFMO algorithm obtained the best results in 78.13% test problems compared with other algorithms. Finally, the BAMFMO algorithm is applied to solve the cluster head selection problem of WSN and the simulation results show the novel algorithm has better optimization ability than other heuristic algorithms.

Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs

Wireless sensor networks (W.S.N.s) are a critical research area with numerous practical applications. W.S.N.s are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As W.S.N.s advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. The proposed model uses bipartite graph properties to derive a controlled mobility model for the mobile sink. As a result, stationary nodes will be visited and planned to reduce routing overhead and enhance the network’s performance. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

Survey on Low-Cost Underwater Sensor Networks: From Niche Applications to Everyday Use

Traditionally, underwater acoustic modems and positioning systems were developed for military and Oil & Gas industries, that require deep water deployments and extremely reliable systems, focusing on high power expensive systems and leaving the use of low-cost devices only attractive for academic studies. Conversely, recent developments of low-cost unmanned vehicles, such as remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), suitable for shallow water coastal missions, and the need of sensors network deployments for measuring water quality and studying the effect of climate change in coastal areas, called to the need of low-cost and low-power acoustic modems and positioning systems that are gaining more and more momentum to date. The use of these devices can enable a wide set of applications, often based on low-cost AUV swarm formations, where an acoustic link between the vehicles is required to coordinate the mission, perform the maneuvers, and maintain the formation along the time. Moreover, they can make environmental wireless sensor deployment cost effective by substituting wired systems. Underwater positioning systems, usually used in large-scale operations, can be finally applied to small-scale application thanks to the reduction in costs, at the price of a lower transmission and positioning range and precision. While in open-sea application this performance reduction is a huge limitation, in river, lagoon, port and lake deployments this is not an issue, given that the extremely shallow water and the presence of many obstacles would deteriorate the acoustic signal anyway, not allowing long range transmissions even with expensive and sophisticated acoustic devices. In this paper, we review the recent developments of low-cost and low-power acoustic communication and positioning systems, both analyzing University prototypes and new commercial devices available in the market, identifying advantages and limitations of these devices, and we describe potential new applications that can be enabled by these systems.

Wireless Sensor Deployment Based on Multiobjective Adaptive Fish Migration Optimization

Wireless Sensor Network (WSN) is a powerful tool to help humans monitor a specific area, and the deployment strategy of sensors profoundly determines the performance of WSN. How to find the best deployment method has become the research topic for many scholars. The deployment strategy aims to expand the deployment scope, reduce energy consumption, and reduce duplicate coverage areas. Many multiobjective heuristic algorithms have been proposed to solve this problem. This paper proposes a multiobjective adaptive fish migration optimization (MAFMO) algorithm, which adds an adaptive‐based repository and crowding degree‐selection strategy for multiobjective optimization. The simulation results reveal that the MAFMO algorithm has more advantages in malleability and distribution than other famous algorithms. Finally, the algorithm is applied to the WSN deployment problem, and the simulation results are compared with other algorithms. The results show that a better solution can be found using MAFMO.

Wireless sensor deployment optimisation based on cost, coverage, connectivity, and load balancing

Wireless sensor deployment optimisation based on cost, coverage, connectivity, and load balancing

The method of intelligent wireless sensor to improve the water permeability of permeable asphalt concrete pavement

AbstractThis research aims to study the intelligent wireless sensor to improve the water permeability of permeable asphalt concrete pavement. The water permeability of pavement is extremely important for the traffic environment. This study first briefly introduces the background and significance analysis, and then exemplifies the deployment algorithm and model of wireless sensors on asphalt pavement. For example, the wireless sensor deployment strategy along the asphalt pavement based on linear dual sink nodes can be divided into linear wireless sensor node deployment, wireless sensor energy consumption model, and wireless sensor zone coverage principle. Another example is the wireless sensor network (WSN) deployment model along the asphalt road and the wireless sensor structure model, which are divided into WSN structure, sensor node structure, gateway interconnection of WSNs and other types of networks. Then this study conducts experiments and results analysis based on wireless sensors for improving the permeability of asphalt pavement and asphalt content. In this study, the factors affecting the water permeability of asphalt pavement are tested, and the application of sensors in testing its performance, and the optimal asphalt ratio is selected in the experiment to make its water permeability better. Finally, the study discusses and summarises the experiments, and it is concluded that the optimum asphalt content in the mixture is 5.48%–6.22%. Influencing factors include temperature and humidity, void ratio, cross slope gradient and so on. This experiment has a good reference prospect.

Optimizing Low-Complexity Analog Mappings for Low-Power Sensors With Energy Scheduling Capabilities

Power consumption is a major challenge for a massive deployment of wireless sensors in Internet of Things (IoT) networks. This article studies the use of analog joint source-channel coding (AJSCC) mappings in low-power sensing schemes. In particular, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">triangular</i> mapping geometry as a low-complexity dimension reduction mapping. The proposed triangular mapping is employed for analog compression of multiple sensor readings into one signal and, thus, limits the need for power-hungry analog-to-digital conversion and processing at the sensing nodes. A comprehensive performance analysis of the proposed triangular mapping in terms of the mean squared error (MSE) performance is provided analytically and verified numerically. The problem of mapping adaptation to different source distributions is also studied. Moreover, the proposed triangular mapping is adopted in an energy scheduling problem in which the sensing nodes schedule their use of the received powers at different time instants and adjust the mapping parameters accordingly with the goal of minimizing the sum distortion at the receiver. We present a fast low-complexity algorithm for optimal energy scheduling and verify its performance in comparison with commercial convex optimization solvers. It is shown that the proposed mapping provides a very good MSE performance compared to the AJSCC benchmarks despite having a much lower complexity circuit implementation.

Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO)

The problem of localization is one of most important issues in wireless sensor networks. Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy. For spiral deployed 2D wireless sensor networks, this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.

Wearable-User Authentication via Cross-Technology Interference in Heterogeneous Environments

The increasing deployment of wireless sensors enables a broad spectrum of health-related wearable applications. Due to the sensitivity of collected personal health information, these wearables should be authenticated together with their users as “wearable-user pairs” to ensure that they are attached to legitimate users. However, various devices are equipped with dedicated sensing abilities and wireless protocols corresponding to data characteristics in practice. Traditional authentication methodologies may not work in this heterogeneous environment because of protocol incompatibility. For example, how to verify a new ZigBee-enabled monitor when the existing trusted device is Wi-Fi-enabled? Therefore, to achieve authentication across protocols, in this article, we leverage the unique cross-technology interference (CTI), triggered by heterogeneous wireless transmissions, along with human physiological activity measurements (e.g., respiration patterns) to design an authentication scheme between wearables and users. Specifically, the authentication from an unknown ZigBee wearable to a trusted Wi-Fi device is achieved by monitoring the channel state information (CSI) changes according to human respiration. Our approach not only successfully recognizes a legitimate wearable-user pair but also blocks illegal access from adversaries. Extensive experiments have been conducted to demonstrate both the security and feasibility of the proposed scheme. The designed mechanism can achieve over 92% authentication accuracy with human subjects.

Inter-node compression with LDPC joint source–channel coding for highly correlated sources

This paper investigates a new communication system where two nodes want to disseminate highly correlated contents to a single destination and can be applied for densely deployed wireless sensors networks applications. Motivated by their capacity-achieving performance and existing practical implementations, the proposed communication scheme is fully based on Low-Density Parity-Check (LDPC) codes for data compression and channel coding. More specifically, we consider a network of two correlated binary sources with two orthogonal communication phases. Data are encoded at the first source with an LDPC channel code and broadcast in the first phase. Based on the first source received data, the second source computes the correlation vector and applies a Joint Source–Channel (JSC) LDPC code, which output is communicated in the second phase. At the receiver, the whole network is mapped on a joint factor graph over which an iterative message-passing joint decoder is proposed. The aim of the joint decoder is to exploit the residual correlation between the sources for better estimation. Simulation results are investigated and compared to the theoretical limits and to an LDPC-based distributed coding system where no inter-node compression is applied.

Smart Irrigation System Deploying PSoC and Wireless Sensor Network

Traditional agricultural systems require huge amount of power for field watering. This paper deals with designing of a smart irrigation system, that helps farmers water their agricultural fields using innovative technology. There is no need to frequently apply water across entire fields. Instead, they can use the minimum quantities required and target very specific areas. To increase the productivity, the newer technology is more helpful. The important factors of agricultural sectors are temperature, water and fertilizer management. On survey it is found that water management is an important issue, hence the research work is focused on the water and moisture management of soil. In last decades advancements in technology is tremendous. Programmable System-on-chip (PSoC) integrate configurable analog and digital blocks which makes it different from traditional microcontrollers and is a miniaturized device. It combines the architectures of FPGA and ASIC. Deploying the advanced features of PSoC it is proposed to design and develop the smart irrigation system based on PSoC and Wireless Communication Technology. To achieve the desired goal, the data from the soil moisture sensor is processed and the data is calibrated in real units. After that it is displayed on LCD and using wireless technology same is transmitted towards the base station. The smart NRF24L01 transceiver operates on IEEE 802.15.4 Industrial Scientific and Medical (ISM) Band. Moreover, the base station of Wireless Sensor Network is designed to demonstrate collected information in user friendly format.

Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications

Autonomous wireless sensor deployment with unmanned aerial vehicles for structural health monitoring applications

Wireless sensor deployment scheme for cost-effective smart farming using the ABC-TEEM algorithm

Wireless sensor deployment scheme for cost-effective smart farming using the ABC-TEEM algorithm

Detection and Avoidance of Clone Attack in IoT Based Smart Health Application

The deployment of wireless sensors in the hostile environment makes them susceptible to malicious attacks. One of the most harmful attacks is the clone attack in which a malicious node illegitimately claims the identity of a genuine node in the network and eventually tries to capture the entire network. This attack is also termed as node replication attack. The mobile nature of wireless sensor network (WSN) in smart health environment increases the vulnerability of node replication attack. Since the data involved in smart health system are highly sensitive data, preserving the system from the attack by malicious nodes is a crucial task. In this research, we proposed a new scheme called Routing Protocol for Energy Efficient Networks (RPEEN) for the detection of clone attack in IoT-based smart health application. The main advantage of this scheme is the increase in the energy efficiency as the energy efficiency is the most important constraint in WSN systems. The performance of the proposed scheme is highlighted using parameters like time delay, residual energy, throughput, energy efficiency and error rate. Further, to portray the efficacy of the proposed algorithm, this algorithm is compared with the existing Hybrid Multi-Level Clustering (HMLC) algorithm. It has been found that the proposed RPEEN scheme achieves a time delay of 0.63 and 0.6 ms with 0 dead nodes and by avoiding clone attack respectively. Furthermore, the proposed scheme attains the highest residual energy of 49.5 J for the 2500 rounds. In addition, the proposed algorithm attains the highest throughput of 99.2% for the 50 nodes.

The Management Mode of College Students' Mental Health Based on Wireless Sensor Network

In today's society, there are more and more psychological problems among college students, and the main source is no longer purely from academic pressure, society, parents and other aspects. As a result, more and more college students are facing mental health problems, and the lack of better mediation of mental health problems has exacerbated this phenomenon. The purpose of this article is to explore the management model of college students' mental health. For this reason, this paper proposes to monitor the mental health of college students through the use of wireless sensor networks. A trust model is established for analysis, and the sensor node structure is re-arranged to ensure the stable operation of the wireless sensor network and the reliability of the monitoring results. At the same time, experiments are designed to explore the mental health of college students. The experimental results of this paper show that the newly deployed wireless sensor network can increase the monitoring efficiency of the existing sensor network by 31%, which can greatly improve the monitoring of college students' mental health and help them solve related psychological problems.

Wireless Sensor Deployment Optimization Based on Cost, Coverage, Connectivity, and Load Balancing

Wireless Sensor Deployment Optimization Based on Cost, Coverage, Connectivity, and Load Balancing