Energy-efficiency and coverage quality management for reliable diagnostics in wireless sensor networks

The rapid growth of the Internet and information technologies and also the diminution of the price of hardware components like wireless sensors leads to the fast growth in Wireless Sensor Network (WSN). The WSN is a bunch/group of sensors that are located across the area, in order to monitor and determine environmental conditions such as temperature, humidity, vibrations, wind, water levels, pollution levels. WSNs are susceptible to major attacks like Denial of Service (DOS), Wormhole attack, Sinkhole attack, etc. The main threat to the WSN is because of the broadcast nature of the nodes in the network. Therefore, the security of WSNs is the essential part that must be done. Hence, to overcome these problems or threats, we are trying to detect it using AI technology. With the expanding fields of Machine Learning and Deep Learning, we can apply various algorithms in order to classify different types of attacks. Once we detect the attack properly, we can prevent it accordingly. We are using WSN-DS. It has 4 classes of attacks which are Grayhole, Blackhole, TDMA(Scheduling), and Flooding which comes under the category of DOS attacks. In this chapter, we have analysed and compared the accuracies of 5 main machine learning classification algorithms. Also, we have analysed the 1 deep learning algorithm. The ANN (Artificial Neural Network) and 5 machine learning algorithms have been trained on the dataset. Furthermore, we have used K-fold cross-validation to get even more accurate predictions. After analysis of these algorithms, we came to the point that, the Machine Learning algorithms like Random Forest, Support Vector Machines and Deep Learning algorithm, namely, Artificial Neural Network can help us for detecting intrusions in the system or network. This will help researchers in designing their own machine learning model on top of our suggested model.

Nowadays, the network and the Internet applications have gained a substantial importance in the digital world, due to the great impact which it provides for health and community services. Among the most important services that have been provided are smart devices and vital factor measurement devices for patients, whether in a hospital or outside the hospital. Furthermore, sensors collect medical data or measurements of temperature and humidity, in various critical environments. The proper types of network that may be used in such difficult environment are wireless sensor networks that used to sense and process data. Additionally, the wireless sensor networks have been used in the environment of Internet of Things and smart cities in the general services and health fields. All these reasons have made researchers focus on wireless sensor networks and addressing the challenges that face them. The most important challenge facing this type of network is energy consumption and increase battery life. This paper discusses the methodologies used in energy conservation in wireless sensor networks, such as data reduction technology, shortest path selection and artificial intelligence algorithms used in smart routing and energy saving. Besides, we have introduced comparisons between the standard algorithms which are suggested by the researchers, to make a clear picture of the energy consumption problem and propose some effective solutions in wireless sensor networks field.

Energy is the most precious resource in a network of sensors since it directly influences the life of the microsensors, or even of the entire network. The energy consumed by a sensor node is essentially due to the following operations: capture, processing, and communication of data. Most of the energy of the nodes is spent in the communication process compared to the detection and processing of data. The main objective is to propose a communication protocol in aim to preserve energy consumption in wireless sensor networks. In this paper, we propose a modified version of LEACH protocol that is based on a multi-hop gateway in aim to optimize energy consumption in wireless sensor networks. The results of the simulation have allowed us to deduce that our contribution extends the overall lifetime of the network by the LEACH protocol.

Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

Wireless Sensor Networks (WSNs) is composed of sinks and sensor nodes, which can monitor large-scale environment. The sensor nodes have several advantages, such as low cost, small size, and capability of mobility, and they can do data sensing, data processing and wireless communication. However, the greatest challenge is the constraints of energy. The most critical issue is how to minimize the energy consumption and keep tracking the mobile target in WSNs. In this paper, we propose a tracking approach for tracking mobile target in hybrid wireless sensor network which consisting of static and mobile node. While static nodes are random distributed in the Field of Interested (FoI) and mobile nodes are moved to fill the coverage hole. We predict the area where the target may cross and construction the local barrier coverage to detect the target. In order to reduce the energy consumption, we wakeup the portion of constructed local barrier coverage to tracking the target. The simulation shows the proposed scheme can reduce the energy consumption and missing rate.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

A wireless sensor network (WSN) is defined as a set of spatially distributed and interconnected sensor nodes. WSNs allow one to monitor and recognize environmental phenomena such as soil moisture, air pollution, and health data. Because of the very limited resources available in sensors, the collected data from WSNs are often characterized as unreliable or uncertain. However, applications using WSNs demand precise readings, and uncertainty in data reading can cause serious damage (e.g., health monitoring data). Therefore, an efficient local/distributed data processing algorithm is needed to ensure: (1) the extraction of precise and reliable values from noisy readings; (2) the detection of anomalies from data reported by sensors; and (3) the identification of outlier sensors in a WSN. Several works have been conducted to achieve these objectives using several techniques such as machine learning algorithms, mathematical modeling, and clustering. The purpose of this paper is to conduct a systematic literature review to report the available works on outlier and anomaly detection in WSNs. The paper highlights works conducted from January 2004 to October 2018. A total of 3520 papers are reviewed in the initial search process. Later, these papers are filtered by title, abstract, and contents, and a total of 117 papers are selected. These papers are examined to answer the defined research questions. The current paper presents an improved taxonomy of outlier detection techniques. This will help researchers and practitioners to find the most relevant and recent studies related to outlier detection in WSNs. Finally, the paper identifies existing gaps that future studies can fill.

One of the remarkable challenges in the area of Wireless Sensor Networks (WSNs) is how to route the collected data and transfer them in the nodes. Due to the high rate of packet loss, guaranteeing reliable and efficient packet transfer and delivery is considered to be one of the hot issues in the area of WSNs. Indeed, due to energy limitation of nodes in WSNs, reliability and energy are regarded as critical parameters in these networks. In this paper, a method has been proposed for optimizing the parameters of reliability and energy consumption in multimedia WSNs. In the proposed protocol, Increasing the Delivery Ratio with Network Coding (IDRNC) a network coding mechanism has been used for transmitting data which enhances the reliability of the network. Furthermore, reducing the number of transmissions and receptions in network nodes results in the reduction of energy consumption. In the proposed protocol, the transmitted packets are first coded and transmitted to the sink through the shortest route. In the middle of the route, in case packets are lost, median nodes can detect and recover the lost packet through the coded packet. The simulation results indicated that the method proposed in this study operates better than the LIEMRO method with respect to the following features: system efficiency, average remaining energy of the network, the number of lost packets and reliability. Keywords: Multipath Routing, Network Coding, Reliability, Wireless Multimedia Sensor Networks (WMSNs)

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Wireless Sensor Networks (WSNs) is the vast area and developing very speedily now-a-days. This paper gives some of the major applications and critical limitations of WSNs like power and lifespan, communication bandwidth, memory size. Sensor nodes are working by node energy so the energy consumption is the essential issue in WSN. Routing in WSN consumes more energy than any other phase. Various routing techniques in WSNs are discussed and different routing protocols are illustrated. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the popular and feasible hierarchical clustering routing protocols in WSN. Working of LEACH is explained in brief. Furthermore, the descendants of LEACH e.g. LEACH-C (Centralized LEACH), LEACH-B (Balanced LEACH), LEACH-F, Multihop LEACH, MS-LEACH (Multihop and Single hop LEACH), MH-LEACH etc. are discussed in terms of the energy utilization. By analyzing the variants of LEACH, it is observed that the energy requirement in the cluster set up phase or data transmission phase is minimized to minimize the total energy consumption of the WSN. To elect the proper cluster head node, centralized approach is followed. Afterwards, data transmission will use the multihop communication technique. This will help to save some energy when base station is located far away from the cluster head node. Proposed protocol aims to decrease the energy overhead of the sensor nodes. The new approach is proposed entitled PV-LEACH which will save the energy consumption in cluster setup phase and also in data transmission phase of the conventional LEACH protocol. So, this will enhance the performance ratio in terms of energy consumption and life time of the sensor nodes.

The integration of Artificial Intelligence (AI) with Wireless Sensor Networks (WSNs) has substantially advanced the capabilities of these systems to collect, process, and respond to large volumes of sensor data. Machine learning (ML) algorithms, a key component of AI, enable WSNs to detect patterns, trends, and anomalies, thereby enhancing their adaptability and performance in dynamic environments. One particularly impactful application is in aging-in-place systems, where elderly individuals can be continuously monitored for vital signs and daily activities, maintaining independence while benefiting from timely health and safety interventions. As the global elderly population grows, there is a pressing need for intelligent monitoring solutions to support independent living. This review aims to consolidate and critically evaluate existing ML algorithms and sensor technologies used in WSNs for aging-in-place applications. It introduces a structured taxonomy of ML approaches—including supervised, unsupervised, hybrid, and reinforcement learning—and presents a comparative analysis of wearable and environmental sensors. Unlike prior reviews, this work integrates technical performance evaluation with application-specific relevance, offering a unified view of algorithmic capabilities, sensor trade-offs, and practical deployment challenges. Based on a systematic selection process, 124 peer-reviewed articles were analyzed. The review also identifies open research challenges, highlights limitations of current solutions, and proposes targeted future directions to guide the development of adaptive, reliable, and user-centered aging-in-place systems.

In the networking research community, Wireless Sensor Networks (WSN) has achieved considerable significance in the past years and it can be used in extensive variety of domains. In recent years, the tremendous growth of WSN does not meet all the necessities of environmental monitoring. Wireless Multimedia Sensor Network (WMSN) is required to fulfill the requirements of environment monitoring. In this paper, WMSN is used to monitor the agriculture soil environment. In order to ensure optimal use of the sensor nodes in WMSN and accurate access to the collected data,the routing protocols with both energy efficiency and quality of service ( QoS) properties are necessary for the transmission of photo, video and scalar data.This paper proposes a priority aware energy efficient QoS based data transmission for agriculture soil environment and also detects the pest. It utilizes the available bandwidth and data rate in the best way and in order to be more energy efficient during data transmission.For performance analysis, the proposed system is simulated using Java and the results of the experiments suggest that the proposed algorithm is more efficient compared to other methods in terms of delay and energy consumption.

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs.

New security approach in real-time wireless multimedia sensor networks

Curently, Wireless Sensor Network (WSN) is applied in many fields such as industrial plants, security, medical, as well as weather and environmental monitoring. The constraint of the use of WSN is the limited power of each sensor node, which means energy consumption efficiency is an important issue in WSN. Routing is a function in WSN which consumes a considerable amount of energy. One of the routing protocol that can increase the energy efficiency of WSN is Low Energy Adaptive Clustering Hierarchy (LEACH). In this research, energy consumption and the number of dead nodes of LEACH are evaluated using MATLAB simulation. The results show that elevation angle and number of clusters affect the energy consumption and number of dead nodes. HAP-WSN system with clustering topology will reduce energy consumption and minimize the number of dead nodes. From a set of simulations, it can be seen that energy consumption for 0° is greater than energy consumption for 10°. It can be also seen that the number of optimum cluster is 4 for both elevation angels.