Energy Consumption Of Sensors Research Articles

Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization

Despite the deployment of wireless sensor networks in diverse fields (health, environment, military applications, etc.) for tracking or monitoring, several challenges, such as extending the lifetime of the network under energy constraints, still need to be resolved. Lifetime is the operational time of the network during which it can perform dedicated tasks and satisfy the application requirements. The energy constraints dictate that the energy consumption of sensors should be minimized since in most cases the sensors are battery-powered. Various methods have been proposed to work around this problem using scheduling approaches. In this paper, particle swarm optimization-based scheduling was designed and implemented to maximize the lifetime of wireless sensor networks formulated as a Non-Disjoint Sets Cover (NDSC) problem. The experimental findings show that the proposed approach is extremely competitive to the state-of-the-art algorithms, as it is able to find the optimal and best-known solutions in the instances investigated.

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.

An improved deep Q-network approach for charging sequence scheduling with optimal mobile charging cost and charging efficiency in wireless rechargeable sensor networks

Mobile charging is a practical solution to overcome energy constraints in wireless rechargeable sensor networks (WRSNs). It utilizes a mobile charger (MC) to charge sensors based on the wireless power transfer technique via a charging scheduling scheme. However, how to schedule MC to achieve higher charging efficiency with less mobile charging cost, while ensuring network functionality remains a challenge. Specifically, the mobile charging cost is the total energy consumed by the MC during the charging task. This paper studies the charging sequence scheduling problem with optimal mobile charging cost and charging efficiency (CSCE), considering dynamic changes in sensor energy consumption. Moreover, we model the CSCE as a multi-objective mixed integer nonlinear programming. To address this issue, we propose an improved deep Q network approach for CSCE (IDQN-CSCE), where the MC acts as an agent to explore the WRSN and determine a charging policy based on the charging demand of sensors. IDQN-CSCE employs Q-learning and deep Q-networks (DQN) to train the Q-table and DQN network simultaneously. Charging actions are selected based on probability, and the probability of using Q-learning decreases as iteration steps increase. Additionally, we design a novel reward function consisting of a weighted sum of rewards and penalties fed back by the environment after performing a charging action. Simulation results show that IDQN-CSCE achieves higher charging efficiency than other baseline approaches with less mobile charging cost.

Towards Flexible and Low-Power Wireless Smart Sensors: Reconfigurable Analog-to-Feature Conversion for Healthcare Applications.

Analog-to-feature (A2F) conversion based on non-uniform wavelet sampling (NUWS) has demonstrated the ability to reduce energy consumption in wireless sensors while employed for electrocardiogram (ECG) anomaly detection. The technique involves extracting only relevant features for a given task directly from analog signals and conducting classification in the digital domain. Building on this approach, we extended the application of the proposed generic A2F converter to address a human activity recognition (HAR) task. The performed simulations include the training and evaluation of neural network (NN) classifiers built for each application. The corresponding results enabled the definition of valuable features and the hardware specifications for the ongoing complete circuit design. One of the principal elements constituting the developed converter, the integrator brought from the state-of-the-art design, was modified and simulated at the circuit level to meet our requirements. The revised value of its power consumption served to estimate the energy spent by the communication chain with the A2F converter. It consumes at least 20 and 5 times less than the chain employing the Nyquist approach in arrhythmia detection and HAR tasks, respectively. This fact highlights the potential of A2F conversion with NUWS in achieving flexible and energy-efficient sensor systems for diverse applications.

Lengthening battery life expectancy of sensors in WBANs: A multifactorial approach

Wireless Body Area Networks (WBANs) are emerging as a key component in healthcare within the Internet of Things (IoT) ecosystem, with sensor battery life being crucial for widespread adoption. To that end, the present work analyzes the different components of sensor’s energy consumption with the objective of deriving analytical expressions for the battery lifespan for an ETSI SmartBAN compliant network. Results have revealed that the sensing energy consumption, commonly considered negligible, cannot always be ignored. Moreover, stringent Quality of Service (QoS) requirements of the physiological sensed data, such as error rate and end-to-end delay, must be fulfilled. Therefore, our approach synergizes an energy-efficient and QoS-aware PHY/MAC configuration framework with sensor energy harvesting. To further increase the WBANs autonomy, the present proposal integrates adaptive sampling mechanisms at sensors. Additionally, this research incorporates the patient’s status information and the sensor’s battery level to regulate the behavior of the system. This novel multifactorial approach has allowed an in-depth and comprehensive investigation of the synergies and mutual influences among the different components that integrate this multipronged proposal, demonstrating a significant potential for lengthening the sensor’s battery life expectancy and to substantially extend the WBANs autonomy. Notably, adaptive sampling markedly improves battery lifespan, especially with higher harvestable power levels and shorter MAC frames. In scenarios with lower battery charge or improved patient conditions, the adaptive sampling framework notably enhances system performance and battery lifetime. An ‘average’ case study, considering a medium patient critical level and a 50% battery charge, shows that adaptive sampling can increase battery duration up to 840.94%, significantly boosting WBANs autonomy.

Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming

Measurement data from wireless sensors deployed in large agricultural areas could be used to help the automation of precision farming activities like irrigation management, fertilization, etc. The widespread use of sensors with limited battery capacity in precision farming largely depends on data collection methods that reduce the energy consumption of transmitting measurement data and prolong the battery run time. In this paper, we investigate joint clustering and multi-UAV-assisted data-gathering schemes to save the energy consumption of sensors. We establish a theoretical lower bound for the energy consumption of sensors to transport data to cluster heads and prove that the energy consumption of sensors approaches the theoretical lower bound if clusters are balanced regarding energy consumption. Therefore, the essential step of proposed heuristic multi-UAV schemes, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gathering data Assisted by Multi-UAV with a BAlanced Clustering</i> (GAMBAC), is to find balanced or near-balanced clusters concerning energy consumption. For sensor networks with a small number of nodes our heuristic algorithms give results close to the ones obtained by the reference solution. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Therefore, the GAMBAC algorithms could enhance the reliable data collection of sensor networks for precision farming.

Energy efficient greedy tree based algorithm for data aggregation in wireless sensor network

Secure data aggregation is essential in wireless sensor networks for lowering the amount of data transmitted and extending the lifetime of the network. The foundation underlying significantly greater industrial internet of things applications is primarily wireless sensor nodes. Sensors that have already been integrated within can be used to sense data in any form of real-time IoT application. In real-time physical surroundings, sensors utilize as little power as feasible to conduct operations including sensing, communicating, and processing data. Many investigations are being carried out to improve sensor node energy efficiency and network lifetime. To save energy, more attention must be paid to the clustering and routing aspects of communication. In this paper, we introduce Energy Efficient Greedy Tree based Data Aggregation (EE–GTDA) algorithm for efficient data aggregation with increased reliability and reduced energy consumption. It is a two-fold homogeneous technique that supervises safe energy-efficient connectivity and data aggregation with the greedy tree based solution that emphasizes multi-objective function. These methodologies are based on minimizing sensor energy consumption to maximize network lifetime simultaneously decreasing communication overhead. A trade-off between energy and safety is accomplished in order to increase efficient energy consumption with a higher packet delivery ratio.

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Fuzzy-Based Efficient Healthcare Data Collection and Analysis Mechanism Using Edge Nodes in the IoMT.

The Internet of Things (IoT) is an advanced technology that comprises numerous devices with carrying sensors to collect, send, and receive data. Due to its vast popularity and efficiency, it is employed in collecting crucial data for the health sector. As the sensors generate huge amounts of data, it is better for the data to be aggregated before being transmitting the data further. These sensors generate redundant data frequently and transmit the same values again and again unless there is no variation in the data. The base scheme has no mechanism to comprehend duplicate data. This problem has a negative effect on the performance of heterogeneous networks.It increases energy consumption; and requires high control overhead, and additional transmission slots are required to send data. To address the above-mentioned challenges posed by duplicate data in the IoT-based health sector, this paper presents a fuzzy data aggregation system (FDAS) that aggregates data proficiently and reduces the same range of normal data sizes to increase network performance and decrease energy consumption. The appropriate parent node is selected by implementing fuzzy logic, considering important input parameters that are crucial from the parent node selection perspective and share Boolean digit 0 for the redundant values to store in a repository for future use. This increases the network lifespan by reducing the energy consumption of sensors in heterogeneous environments. Therefore, when the complexity of the environment surges, the efficiency of FDAS remains stable. The performance of the proposed scheme has been validated using the network simulator and compared with base schemes. According to the findings, the proposed technique (FDAS) dominates in terms of reducing energy consumption in both phases, achieves better aggregation, reduces control overhead, and requires the fewest transmission slots.

A Novel Precomputed Optimal Procrastination Time Interval for Re-Clustering to Maximize Operation Time of Wireless Sensor Networks

In wireless sensor networks, the energy consumption of sensors is not uniform over the whole region of deployment. The uneven energy usage occurs because some sensors have to transmit data to farther distances or have to transmit more data packets than others. This leads to a shorter duration of operation because some sensors’ energy will deplete fast creating holes in the network. To alleviate this problem, we proposed an algorithm we named Procrastinated Clustering and Multi-Hop Routing (PCMR). To prolong the operation, it will optimally assign sensors with different precomputed procrastination periods to schedule the clustering and routing processes. In PCMR, sensors’ clustering and routing intervals depend on their locations in the network with respect to the sink. The algorithm could reduce and balance energy consumption for sensors distributed over a wide area. Procrastination periods are precomputed off-line before deployment. Therefore, it is easy to implement and is efficient, even for a large network for which real-time reorganization would involve transmitting a large number of signaling packets. The results from simulations show that the proposed PCMR algorithm could balance energy usage among sensors, and prolong the network lifetime compared to existing works based on techniques such as adjusting cluster size and/or multi-path transmission.

Heater Topology Influence on the Functional Characteristics of Thin-Film Gas Sensors Made by MEMS-Silicon Technology

The design of the heater plays a decisive role in the energy consumption, sensitivity, and speed of chemical sensors. The paper analyzes various options for the topology of meander-type platinum heaters in chemical sensors fabricated on thin dielectric membranes using MEMS-silicon technology. Comprehensive studies of the heater’s current–voltage characteristics have been carried out, heating rates have been measured at various currents, experimental temperature characteristics for various meander topologies have been obtained, heater options have been determined, and optimal heat transfer processes are ensured at a low power consumption of about 20–25 mW. Sensors with an optimal heater topology based on a double dielectric membrane were fabricated according to the described technological process, and sensory responses to 0.5 vol.% CH4 and 0.2% C3H8 were studied. The obtained results showed good results and confirmed the need to choose the optimal heater topology when designing sensors for recording the given type of gas mixtures in a certain temperature range.

Neural Network-based Dynamic Clustering Model for Energy Efficient Data Uploading and Downloading in Green Vehicular Ad-hoc Networks

Green VANET is an emerging field of research that spurs interest in energy consumption management for the development of smart cities. It also presents a unique variety of research problems for developing trustworthy and scalable routing protocols as vehicles are susceptible to the restrictions of road geometry and the barriers which limit networking capabilities in urban environments. Clustering is a process of assembling vehicle nodes to create a powerful and effective network infrastructure. According to recent studies, clustering-based routing algorithms in green VANET may significantly improve networking effectiveness and lower infrastructure costs. However, sometimes the vehicle nodes are unaware of their OBU energy consumption, which causes network execution issues and topological alterations. In such instances, the energy consumption of onboard sensors becomes a major problem in the clustering-based routing protocol. Hence, this paper proposes a self-organizing map neural network (SOMNN) based dynamic clustering model to identify energy-efficient nodes from each cluster for vehicular data uploading and downloading applications. The simulation results demonstrate that the proposed model solves network lifetime issues and provides superior network effectiveness with enhanced communication stability. The suggested dynamic clustering model reduces network energy consumption by 26% and 18% in comparison to k – means (KM) and fuzzy c – means (FCM) based clustering model.

Energy-efficient UAV-wireless networks for data collection

In this study, a singular unmanned aerial vehicle (UAV) is utilized to gather data from a set of ground sensors in wireless networks. The main objective is to minimize the total energy consumption of the ground sensors by finding the UAV’s optimal trajectory through an optimization problem. Two distinct channel models are implemented to represent the connection between the UAV and the sensors, which are compatible with fourth-generation (4G), fifth-generation (5G), and beyond 5G (B5G) systems. The optimization problem’s constraints involve ensuring the quality of service (QoS) and limiting the transmitted power of each ground sensor. To solve the intractable optimization problem, three different approaches are employed to obtain the UAV’s trajectory: (1) exhaustive search (ES) approach, (2) particle swarm optimization (PSO) approach, and (3) fixed placement approach. Finally, the proposed approaches’ effectiveness is validated using simulation results.

A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network

Nowadays, wireless sensor networks have played an important role in many applications. In these applications, a large number of wireless sensors are deployed in an environment to collect information and form network to transmit collected information to the base station or sink. Because wireless sensors have to work for a long period of time without maintenance, the energy consumption of wireless sensors has a great impact on the lifetime of wireless sensor network. To reduce and balance the energy consumption of wireless sensors, many information transmission routing approaches have been proposed. However, most of them do not consider all energy consumption factors of wireless sensors. To this end, an innovative information transmission routing approach based on Q-learning is proposed in this paper, which enables wireless sensors to adaptively select suitable neighboring sensors to achieve energy efficient information transmission in a decentralized manner. Based on the proposed approach, a wireless sensor first collects state and action information of its neighboring sensors, which includes information transmission direction and distance, the remaining energy, the energy consumption for information transmission and information transmission action. Then, all this information is used to update the Q-values of neighboring sensors, so as to enable the wireless sensor to select suitable neighboring sensor to transmit information according to Q-values. From simulation experiments, it can be seen that the proposed approach enables wireless sensors to reduce and balance the energy consumption of wireless sensors and extend the lifetime of the entire wireless sensor network.

JECP: Joint Energy Conservation and Collision Avoidance for Path Construction in Bluetooth Networks

With the growing elderly population of the world population, the number of elderly people who live alone is continuously growing. Providing them with high-quality healthcare has become a major concern nowadays. In order to detect the elderly people’s sudden danger of elderly living alone, this paper proposes a path construction mechanism, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> , which aims to conserve energy and avoid collision for the emergent sensors in Bluetooth mesh networks. In case that the emergent sensors are deployed in the same room, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> constructs disjoint paths to avoid collisions, which are triggered by the same emergent event at the same time. The other case is that the emergent sensors are deployed in different rooms. The probability that these emergent sensors are triggered by the same event is very small. Hence the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> constructs the best emergent path that shares the general sensors as much as possible to save energy consumption. The experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JECP</i> outperforms the existing mechanisms in terms of transmission delay and energy consumption for the emergent sensors in Bluetooth Low Energy (BLE) networks.

Energy-Efficient Relay Tracking and Predicting Movement Patterns with Multiple Mobile Camera Sensors

Camera sensor networks (CSN) have been widely used in different applications such as large building monitoring, social security, and target tracking. With advances in visual and actuator sensor technology in the last few years, deploying mobile cameras in CSN has become a possible and efficient solution for many CSN applications. However, mobile camera sensor networks still face several issues, such as limited sensing range, the optimal deployment of camera sensors, and the energy consumption of the camera sensors. Therefore, mobile cameras should cooperate in order to improve the overall performance in terms of enhancing the tracking quality, reducing the moving distance, and reducing the energy consumed. In this paper, we propose a movement prediction algorithm to trace the moving object based on a cooperative relay tracking mechanism. In the proposed approach, the future path of the target is predicted using a pattern recognition algorithm by applying data mining to the past movement records of the target. The efficiency of the proposed algorithms is validated and compared with another related algorithm. Simulation results have shown that the proposed algorithm guarantees the continuous tracking of the object, and its performance outperforms the other algorithms in terms of reducing the total moving distance of cameras and reducing energy consumption levels. For example, in terms of the total moving distance of the cameras, the proposed approach reduces the distance by 4.6% to 15.2% compared with the other protocols that do not use prediction.

Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN

Due to the limited and difficult access to sensor energy, energy conservation has always been an important issue in wireless body area network (WBAN). How to make full use of the limited energy of heterogeneous sensors in WBAN to achieve lightweight and high-reliable data transmission has also become key to the sustainable development of telemedicine services. This paper proposes a two-tier cooperation based high-reliable and lightweight forwarding (TTCF) mechanism via minimizing the amount of transmitted data and optimizing forwarding performance, so as to improve the efficiency and reliability of WBAN and reduce system energy consumption. In TTCF, an adaptive semi-tensor product compressed sensing evolution (STPCSE) model is first constructed to minimize the amount of data to be transmitted and extend the lifetime of sensors. Then, the important factors closely related to the energy consumption of human body sensors, including sampling frequency, residual energy and their importance in the network, are analyzed and redefined, and a high-reliable and lightweight forwarding model based on a multi-factor dynamic fusion is built. Finally, the performance and energy-saving effect of TTCF in a dynamic WBAN environment are compared and analyzed. Simulation results show that the system with our TTCF always performs the best in terms of data reconstruct accuracy, cumulative delivery rata, energy consumption and throughput. For example, its cumulative delivery rate is about 12% and 20.8% higher than that of UC-MPRP and CRPBA, and its residual energy and throughput are 1.22 times and 1.41 times, 1.35 times and 1.6 times of the latter two, respectively.

A sensing-based patient classification framework for efficient patient-nurse scheduling

Nowadays, hospitals and government departments are struggling to reduce the health costs and improve the service quality. Hospitals rely mainly on nurses who have many duties including caring of patients, communicating with doctors, administering medicine and checking vital signs. Wireless body sensor network (WBSN) is one of the main applications that helps the medical staff in electronic health surveillance with early detection of critical physiological symptoms. However, the rapid growth in the patient number poses a high pressure on hospitals and increases the nurse duties. In addition, WBSN suffers from big data collected by the medical sensors. Therefore, collecting data is one of the major reason behind the exhaustion of limited energy. In this paper, we propose a novel and efficient framework for nurse–patient intelligent task organization that treats the scheduling problem in hospitals. Typically, our framework consists of three phases: data collection and transmission, patient classification, and nurse scheduling. The first phase uses the early warning system (EWS) to update the medical staff when the patient situation is changed. This will help to minimize the energy consumption in sensor and control its data transmission rate. Once the patient data are collected, the second level introduces an efficient classification model that grouping patients according to the severity level of their vital signs. The last phase proposes a nurse-scheduling algorithm based on the classification results and some priority metrics to balance the workload of the stressed medical personnel. We performed our simulation on real health sensor data collected from 18 patients, including various vital signs, while assuming a certain number of nurses. The obtained results show the effectiveness of our framework in conserving the sensor energy up to 95%, and ensuring a balanced nurse workloads where each nurse serves, during each period of one hour, an average of 5 patients with corresponding cumulative criticality of 62%.

Structural optimization and simulation of piezoelectric- piezoresistive coupled MEMS steady-state electric field sensor

Abstract: In view of the problems of large volume, high energy consumption and difficult maintenance of electric field measurement sensors in existing power systems, non-contact miniature electric field sensors have become a hot topic in current research. In this paper, a MEMS miniature electric field measurement sensor model based on the principle of piezoelectric-piezoresistive coupling is constructed, and the sensor structure is optimized by analyzing the steady-state characteristics of the piezoelectric material and semiconductor membrane of the sensor. The input and output characteristics of the sensor were tested. The test results show that the sensor has excellent mechanical strain capacity, and the output voltage of the sensor has a linear relationship with the electric field strength, thus verifying the feasibility of the sensor measurement in the electric field. The research results will provide some reference for the development of contactless coupled sensors.

Sensors Scheduling for Remote State Estimation Over an Unslotted CSMA/CA Channel

This article investigates the sensor selection problem for remote state estimation. A set of sensors measures a process's state and sends the measurements to a remote estimator over an unslotted carrier sense multiple access/collision avoidance (CSMA/CA) channel in IEEE 802.15.4-based wireless body area networks. This article aims to answer which sensors should be selected according to probability so that the state estimation error covariance and the energy consumption of sensors can be optimized. A common assumption in most existing methods about sensor scheduling is that the packet loss process is unrelated to the channel input. This article analyzes the relationship between channel input and packet loss rate by modeling the communication process. A scheduling scheme is proposed to increase channel utilization from the perspective of sensor information fusion. Then the constraint on the performance metrics is presented. By transforming the sensor selection problem into a constrained optimization problem, the solution to the optimization problem is obtained by a numerical algorithm. Finally, the validity of the model and the feasibility of the sensor scheduling are demonstrated by a numerical simulation.