Active Sensor Nodes Research Articles

Spatial Correlations Based Fault Tolerant Source Localization Using Wireless Sensor Networks

The source localization is a process to determine the location of a signal source based on measurements from sensor nodes or receivers that are spatially distributed in the area around the source. Localizing intruders and mobile users are some of the utilizations of wireless sensor networks (WSNs) based source localization problem. However, sensor faults and channel noise are the important issues that degrade the source localization performances of the systems. In this paper, we significantly study and improve our recent results; the representative fault tolerant hitting set and feature selection approaches for localizing a source in a decentralized WSN using the faulty sensor nodes and channel noise. First, we propose a technique to place some active sensor nodes in the sleep state such that the source localization performances of the hitting set and feature selection approaches do not significantly degrade. The proposed technique takes advantage of spatial correlations among sensor nodes, where nearby sensor nodes provide same decisions to the fusion center with a high probability. We observe that the performance gap for the results with and without sleeping sensor nodes decreases by increasing total sensor nodes. In addition, we improve our recent results by proposing a technique based on spatial correlations among sensor nodes to minimize the impact of channels’ noises, especially for a higher fault probability of sensor nodes. Finally, extensive simulations on synthetic and real-world data sets validate our theoretical results.

LEACH algorithm analysis and simulation using MATLAB

Research on Wireless Sensor Network (WSN) began to be carried out to meet various industrial needs including defense, health, environmental surveillance, and others. However, there are several obstacles in WSN, namely the problem of energy consumption which is the object of research by many researchers. The solution offered in this paper is to use the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a hierarchical protocol where this protocol focuses on saving energy use on WSN. This study used Matlab 2023 simulation software which used several measurement parameters to determine tissue life time, average residual energy, and throughput. The research scenario uses a homogeneous topology with three network sizes, namely 500 x 500, 750 x 750, and 1000 x 1000. Then also used three conditions for the number of sensor nodes, namely 100 nodes, 150 nodes, and 200 nodes. The results showed that the smaller the tissue size, the longer the life time and if the network size is wider, the network life time is shorter. The number of data packets transmitted depends on the number of active sensor nodes and sufficient energy to transmit.

Algorithm for Target Coverage Problem Based on Deep Learning in Wireless Sensor Networks

Aiming at the uncertain mechanism of node activation strategies and redundancy of feasible solution sets in the process of solving target coverage problem in wireless sensor networks, we proposed a deep learning based target coverage algorithm to learn the scheduling strategies of nodes in wireless sensor networks. Firstly , the algorithm abstracted the construction of feasible solution sets into Markov decision process, and intelligently selected activated sensor nodes as discrete actions according to the network environment. Secondly, a reward function evaluated the performance of the intelligent agent in selecting actions based on the coverage capacity and its residual energy of the active node. The simulation experiment result shows that the algorithm is effective in different network environments, and the network lifecycle is superior to the three greedy algorithms, the maximum lifetime coverage algorithm and the adaptive learning automaton algorithm.

Bayesian decision model based reliable route formation in internet of things

Security provisioning has become an important issue in wireless multimedia networks because of their crucial task of supporting several services. This paper presents Bayesian decision model based reliable route formation in internet of things (BDMI). The main objective of the BDMI approach is to distinguish unreliable sensor nodes and transmit the data efficiently. Active and passive attack recognition methods identify unreliable node sensor nodes. Remaining energy, node degree, and packet transmission rate parameters to observe their node possibilities for recognizing the passive unreliable nodes. In active recognition, the base station (BS) confirms every sensor node identity, remaining energy, supportive node rate, node location, and link efficiency parameters to detect active unreliable sensor nodes. The Bayesian decision model (BDM) efficiently isolates an unreliable sensor node in the multimedia network. Simulation outcomes illustrate that the BDMI approach can efficiently enhance unreliable node detection and minimize the packet loss ratio in the network.

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

An energy-efficient irregular hexagonal tessellation-based approach for connected k-coverage in planar wireless sensor networks

In the design of planar wireless sensor networks (PWSNs), the preliminary tasks are to achieve both coverage and connectivity, which are essential for the correct operation of this type of network. A PWSN is said to be in perfect operational condition only when it is capable of guaranteeing both coverage of the field and connectivity among all the active sensor nodes. In order to achieve both coverage and connectivity in PWSNs, we intend to solve the problem of connected k-coverage in PWSNs, where each point in a field of interest is covered (or sensed) by at least k sensor nodes (k > 1) simultaneously and all the participating sensor nodes in the k-coverage process are connected to each other. In our study, we found an irregular hexagon, denoted by IrHx(rs/n), as the best polygon for tessellating a field of interest and allowing to deploy sensor nodes, where n > 1 is a natural number and rs is the radius of the sensing range of the sensor nodes. First, we construct our proposed irregular hexagon, IrHx(rs/n), which forms a tile, using the regular hexagon. Second, we compute the minimum sensor density required to k-cover a planar field of interest using our proposed IrHx(rs/n) tile. Third, we establish a relationship between the sensing and communication radii of the sensor nodes to ensure network connectivity in k-covered PWSNs. Finally, we substantiate our theory with various simulation results.

An Improved Genetic Algorithm for Scheduling Sensor Nodes in WSN

The extended lifetime of Wireless Sensor Networks (WSN) is an attractive goal for various types of research. This can be achieved if not all sensor nodes in the network consume their energy; this is because the energy consumption of every sensor node is a vital resource for a WSN. The scheduling techniques have recently enticed the interest of the researchers’ community, as they provide the ability to adjust a set of nodes in sleep mode instead of activating all sensor nodes. However, we consider the sensors that were selected to be in sleep mode, which will not affect network coverage for any target or full connectivity. In this paper, the genetic algorithm has been used to build efficient scheduling for the sensor nodes, which were based on multiple objectives in the fitness function, and we have proposed improved mutation and crossover operations. Then, we evaluate our approaches in a target tracking application compared with previous GA approaches. Our simulations show that we have an optimal chromosome that contains a minimum number of active sensor nodes for scheduling within 3-5 iterations.

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic.

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

SALA-IoT: Self-Reduced Internet of Things With Learning Automaton Sleep Scheduling Algorithm

The extensive development of wireless communications has led to the popularity of the Self-Powered Internet of Things (SPIoT) networks. Even though significant advances made in keeping energy consumption at a low level, making such networks live longer are still one of the biggest challenges. In particular, a significant question is how to cover the maximum range of the environment by the sensor nodes with the lowest amount of energy. In this research, we have proposed a sleep Scheduling Algorithm based on Learning Automaton for IoT (SALA-IoT), utilizing machine learning approaches to find the optimal set of sensor nodes that can cover a wide range of the environment. This algorithm consists of learning and covering phases, in which the essential sensor nodes are activated, and the rest are turned off. Although the implementation of learning algorithms requires a high energy level, after the learning phase’s performance, the network gathers complete information about the status of the nodes. It can meet the needs of the network with a limited amount of energy. We have evaluated the proposed algorithm with several simulation scenarios and considered the coverage area and the number of active sensor nodes as the main evaluation metrics. The simulation results show that the sensors sensing and covering ranges are directly related to their transmitted power, and the transmitted power of the sensors can be adjusted to match the expected network requirements in terms of primary coverage factors; the proposed method improved by 6.743 for 50 nodes and 5.204 for 100 nodes, respectively, for distance and intervals in a different number of nodes.

Sistem Kendali Cerdas Penggunaan Daya Listrik Menggunakan Metode Eliminasi Nilai Tertinggi Berbasis IoT

The use of electrical equipment that exceeds the maximum power limit of the Miniature Circuit Breaker (MCB) determined by PLN so that there is a power outage is a problem for users of the household sector.The amount of initial power consumption when the electrical equipment is turned on again becomes a burden for the household sector. This can be prevented, one of which is by adjusting the electrical power load so that it does not exceed the threshold limit in the MCB. The intelligent control system in this study uses the IoT NodeMCU ESP8266 device as a sensor node that can measure the power used from electrical equipment connected to the sensor node. One of the sensor nodes calculates the total measured power of all active sensor nodes and determines the action for an overload condition. The overload condition is met if the total rated power exceeds the specified maximum power threshold. The action of this condition selects the highest power system at each sensor node and disconnects the electric current at the selected sensor node so that the electrical equipment with the highest power is off and the total power is corrected below the threshold limit. This algorithm is used to prevent disconnection in the MCB due to exceeding the electrical load or the maximum power allowed according to the provisions by PLN. The average power measurement accuracy at the sensor node is 85.9% or with a % error of 14.1% and the highest power elimination algorithm manages to keep the total power load below a predetermined threshold. As a result, electrical equipment with the highest power will always be compensated when conditions touch the maximum power threshold.

Multi-objective optimization for coverage aware energy consumption in wireless 3D video sensor network

This paper addresses the problem of minimizing energy consumption as well as maximizing the coverage area for 3D video sensor nodes deployed over a 2D target area in a wireless video sensor network. Reducing the number of active video sensor nodes helps decrease energy consumption, but decrease the coverage area as well as connectivity in the network. The problem is solved initially by a multi-objective integer linear programming based approach (APP_7) which provides an optimal solution but leads to intractability for a large-sized network due to the non-deterministic polynomial time-hard complexity of the problem. This fact motivates us to design a non-dominated sorting genetic algorithm-II based heuristic (APP_8), which generates a near-optimal solution and that is substantiated by comparing the simulation results of APP_7 with APP_8, and two existing greedy heuristics (EX_1, EX_11) for small-sized problem. Simulation experiments are conducted also to compare the results of APP_8 with EX_1 and EX_11 for large-sized network. Between two heuristics, EX_11/(EX_1) is better in terms of energy consumption/(area coverage). It has been observed that for the large-sized network (i.e. node density 100 on the target area of size 75 × 75 square meters) APP_8 is able to reduce energy consumption by 47.76% from EX_11, whereas area coverage is decreased by 0.51% from EX_1, which confirms the acceptability of APP_8 as the proper heuristic in such a scenario.

An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods.

Optimization of the Wake-Up Scheduling Using a Hybrid of Memetic and Tabu Search Algorithms for 3D-Wireless Sensor Networks

Computation of an optimal coverage and connectivity aware wake-up schedule of sensor nodes is a fundamental research issue in a 3D-Wireless Sensor Networks. Most of the existing metaheuristic-based wake-up scheduling schemes do not make sure optimal solution and occasionally smacked in local minima. This paper propose a hybrid metaheuristic-based wake-up scheduling scheme (Memtic-Tabu-based-WS) where best feature of memtic algorithm and Tabu Search algorithm is combined. The proposed scheme has considered four parameters such as energy consumption, coverage, connectivity, and optimal size of schedule list. Performance comparison of the proposed Memtic-Tabu-based-WS scheme is performed in different network scenario and compared with three well-known state-of-art schemes in terms of coverage ratio, active sensor nodes and fitness value. The result analysis validate the superiority of the proposed scheme over the existing schemes with better coverage ratio and derivation of the optimal wake-up schedule.

Mitigation of coverage and connectivity issues in wireless sensor network by multi-objective randomized grasshopper optimization based selective activation scheme

Connectivity and coverage play a major role in the proper functioning and performance of Wireless Sensor Network (WSN). Network connectivity indicates how well the sensory data can be communicated by the sensor nodes to the sink. Coverage specifies how well a Point of Interest (POI) is being monitored by the WSN. The sensors have limited energy source, sensing, and communication range, so proper activation of the sensors have a significant influence on WSN. Therefore, the main challenge is to ensure that a small number of sensors are activated to provide coverage and connectivity in the POI. To overcome these challenges, this paper proposes a multi-objective randomized Grasshopper Optimization Algorithm-based Selective Activation (MORGOA-SA) method to sustain the expected connectivity and coverage in the network. Initially, the fitness is evaluated with five main objective functions such as (i) maximizing the coverage, (ii) Ensuring connectivity, (iii) minimizing the coverage overlap, (iv) selection of nodes with higher residual energy, and (v) minimizing active sensor nodes. Then the nodes with the best fitness function are activated, whereas the rest undergo sleep mode. Multi-objective randomized GOA-SA reduces the energy consumption of the network and prevents the failure of sensor nodes. The simulations and comparative analysis justify that the proposed MORGOA-SA scheme has attained better results than the existing schemes in terms of minimum energy consumption, minimum activation of nodes, high throughput, high connectivity, quick convergence, and increased network lifetime.

A Hybrid Routing Protocol Based on Naïve Bayes and Improved Particle Swarm Optimization Algorithms

Clustering of sensor nodes is a prominent method applied to wireless sensor networks (WSNs). In a cluster-based WSN scenario, the sensor nodes are assembled to generate clusters. The sensor nodes also have limited battery power. Therefore, energy efficiency in WSNs is crucial. The load on the sensor node and its distance from the base station (BS) are the significant factors of energy consumption. Therefore, load balancing according to the transmission distance is necessary for WSNs. In this paper, we propose a hybrid routing algorithm based on Naïve Bayes and improved particle swarm optimization algorithms (HRA-NP). The cluster heads (CHs) are selected according to the CH conditional probability, which is estimated by the Naïve Bayes classifier. After the selection of the CHs, the multi-hop routing algorithm is applied to the CHs. The best routing path from each CH to the BS is obtained from an improved particle swarm optimization (PSO) algorithm. Simulations were conducted on evaluation factors such as energy consumption, active sensor nodes per round, the sustainability of the network, and the standard deviation of a load on the sensor node. It was observed that HRA-NP outperforms comparable algorithms, namely DUCF, ECRRS, and FC-RBAT, based on the evaluation factors.

An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

Underwater target search and tracking has become a technical hotspot in underwater sensor networks (UWSNs). Unfortunately, the complex and changeable marine environment creates many obstacles for localization and tracking. This paper proposes an automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network (ST-BPN). Firstly, the network contains active sensor nodes that can transmit detection signal. When analyzing the reflected signal spectrum, a modified convolutional neural network M-CNN is built to search the target. Then, based on the relationship between propagation delay and target location, a localization algorithm which can resist the influence of clock asynchrony LA-AIC is designed. Thirdly, a scheme based on consensus filtering TS-PSMCF is used to track the target. It is worth mentioning that a predictive switching mechanism, PSM, is added to the tracking process to adjust the working state of nodes. Simulation results show that the recognition accuracy of M-CNN is as high as 99.7%, the location accuracy of LA-AIC is 92.3% higher than that of traditional methods, and the tracking error of TS-PSMCF is kept between 0 m and 5 m.

SCHDRP: a 3D Wireless Sensor Networks Semi-Clustering and Hole Detection Routing Protocol

Wireless sensor Networks (WSNs) have proliferated in the past few years. Their power consumption and network lifetime has attracted researchers over the past decade. In addition, studying these networks in three-dimensional environment where nodes are randomly distributed in a three dimensional (3D) area in caves, underwater or underground reveals many more challenging tasks to prolong the network lifetime. One of the issues that these networks encounter is the disconnectivity or holes in the network. These holes prevent active sensor nodes from delivering their data to the sink or central node. In this work, an enhanced semi-clustering and hole detection multi-hops routing protocol (SCHDRP) is proposed to tackle the issue of disconnectivity and holes detection in the network. SCHDRP aggregates the sensed data from the sensor nodes and routes them without depleting the edge sensors nodes “located beside holes” power in the network. SCHDRP has been simulated and its results show that it detects the holes’ edge nodes with an accuracy of 86% utilizing the average number of neighbors in the network with a threshold value of 10%. Moreover, SCHDRP has enhanced the network lifetime 200% over ordinary multi-hop routing protocols.

Energy‐efficient routing protocol and optimized passive clustering in WSN for SMART grid applications

SummaryDue to low cost, ease of implementation and flexibility of wireless sensor networks (WSNs), WSNs are considered to be an essential technology to support the smart grid (SG) application. The prime concern is to increase the lifetime in order to find the active sensor node and thereby to find once the sensor node (SN) dies in any region. For this reason, an energy‐efficient Dynamic Source Routing (DSR) protocol needs to provide the right stability region with a prolonged network lifetime. This work is an effort to extend the network's existence by finding and correcting the considerable energy leveraging behaviors of WSN. We build a comprehensive model based on real measures of SG path loss for different conditions by using the characteristics of WSN nodes and channel characteristics. This method also establishes a hierarchical network structure of balanced clusters and an energy‐harvesting SN. The cluster heads (CHs) are chosen by these SN using a low overhead passive clustering strategy. The cluster formation method is focused on the use of passive clustering of the particle swarm optimization (PSO). For the sake of eliminating delayed output in the WSN, energy competent dynamic source routing protocol (EC‐DSR) is used. Chicken swarm optimization (CSO) in which optimum cluster path calculation shall be done where distance and residual energy should be regarded as limitation. Finally, the results are carried out with regard to the packet distribution ratio, throughput, overhead management, and average end‐to‐end delay to demonstrate the efficiency of the proposed system.

An Area Coverage Scheme Based on Fuzzy Logic and Shuffled Frog-Leaping Algorithm (SFLA) in Heterogeneous Wireless Sensor Networks

Coverage is a fundamental issue in wireless sensor networks (WSNs). It plays a important role in network efficiency and performance. When sensor nodes are randomly scattered in the network environment, an ON/OFF scheduling mechanism can be designed for these nodes to ensure network coverage and increase the network lifetime. In this paper, we propose an appropriate and optimal area coverage method. The proposed area coverage scheme includes four phases: (1) Calculating the overlap between the sensing ranges of sensor nodes in the network. In this phase, we present a novel, distributed, and efficient method based on the digital matrix so that each sensor node can estimate the overlap between its sensing range and other neighboring nodes. (2) Designing a fuzzy scheduling mechanism. In this phase, an ON/OFF scheduling mechanism is designed using fuzzy logic. In this fuzzy system, if a sensor node has a high energy level, a low distance to the base station, and a low overlap between its sensing range and other neighboring nodes, then this node will be in the ON state for more time. (3) Predicting the node replacement time. In this phase, we seek to provide a suitable method to estimate the death time of sensor nodes and prevent possible holes in the network, and thus the data transmission process is not disturbed. (4) Reconstructing and covering the holes created in the network. In this phase, the goal is to find the best replacement strategy of mobile nodes to maximize the coverage rate and minimize the number of mobile sensor nodes used for covering the hole. For this purpose, we apply the shuffled frog-leaping algorithm (SFLA) and propose an appropriate multi-objective fitness function. To evaluate the performance of the proposed scheme, we simulate it using NS2 simulator and compare our scheme with three methods, including CCM-RL, CCA, and PCLA. The simulation results show that our proposed scheme outperformed the other methods in terms of the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime.

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.