Remaining Energy Level Research Articles

An energy-aware protocol in wireless sensor networks using the scattered search algorithm and fuzzy logic.

Given the resource limitations of wireless sensor networks (WSNs), energy conservation is of utmost importance. Moreover, minimizing data collection delays is crucial to maintaining data freshness. Additionally, it is desirable to increase the number of collected data samples to enhance accuracy and robustness in data collection. For this purpose, this research article proposes a clustering-based routing protocol aimed at maximizing the delivery of data samples while minimizing energy consumption and data collection delays. The protocol employs a scattered search algorithm and fuzzy logic to cluster the sensor nodes. By considering the distance to the sink and the remaining energy level of the battery, the network is dynamically divided into clusters using a lightweight clustering approach. To evaluate the effectiveness of the proposed method, simulations were conducted in OPNET using the AFSRP protocol. The results demonstrate superior performance of the proposed method in terms of end-to-end delay by 13.44%, media access delay by 75.2%, throughput rate by 20.55%, energy consumption by 13.52%, signal-to-noise ratio by 43.40% and delivery rate of successfully sending data to the sink is 0.21% higher than the well-known AFSRP method.

Improved Discriminability of Severe Lung Injury and Atelectasis in Thoracic Trauma at Low keV Virtual Monoenergetic Images from Photon-Counting Detector CT

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials & Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p < 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p < 0.001) except for 50 keV (p > 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60–70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40–50 keV.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

An energy‐efficient method using PSO clustering and fuzzy routing to reduce energy consumption in IoT

SummaryIn “Internet of Things” (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

EVRP: A novel geometrical based energy efficient eye vision routing protocol for wireless sensor networks based on the k-means algorithm

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

Efficient Secure Communication in Zigbee Network Using the DNA Sequence Encryption Technique.

Zigbee IoT devices have limited computational resources, including processing power and memory capacity. Therefore, because of their complicated computational requirements, traditional encryption techniques are inappropriate for Zigbee devices. Because of this, we proposed a novel, "lightweight encryption" method (algorithm) is based on "DNA sequences" for Zigbee devices. In the proposed way, we took advantage of the randomness of "DNA sequences" to produce a full secret key that attackers cannot crack. The DNA key encrypts the data using two operations, "substitution" and "transposition", which are appropriate for Zigbee computation resources. Our suggested method uses the "signal-to-interference and noise ratio (SINR)", "congestion level", and "survival factor" for estimating the "cluster head selection factor" initially. The cluster head selection factor is used to group the network nodes using the "adaptive fuzzy c-means clustering technique". Data packets are then encrypted using the DNA encryption method. Our proposed technique gave the best results by comparing the experimental results to other encryption algorithms and the metrics for energy consumption, such as "node remaining energy level", key size, and encryption time.

A Data Transmission Scheme Using K-Means and Fuzzy Logic for IOT Sensor Based Forest Fire Detection System

Forest fires are natural disasters and effective mitigation of these fires require early warning systems. Deployment of sensors that are robust against environmental conditions, collection of sensor data in an energy efficient manner and at real time at the central server, and accurate detection of fire’s existence are all critical elements for implementing such systems. In this paper, we focus on second issue which is energy efficient routing of sensor data in a Internet-of-Things (IoT) sensor network that is deployed for forest fire detection. A Fuzzy-based Cluster Head selection technique for WSN in detecting forest fire is presented. In the proposed scheme, the nodes are divided into clusters using K-means clustering and then the cluster heads are determined by using a fuzzy logic scheme. Unlike traditional parameters, distance from the centroid and the remaining energy levels are used as parameters to select the cluster head. Simulation tool is used to implement the proposed technique. The simulation results suggest that the proposed cluster head selection approach outperforms the existing schemes

Glass eel (Anguilla anguilla L. 1758) feeding behaviour during upstream migration in an artificial waterway

The transition from marine to fresh water is a challenging task for juvenile eels. This critical step in the early eels' life is preceded by a metamorphosis from the oceanic larval to the continental glass eel stage, requiring major energy-demanding morphological, physiological and behavioural modifications during which time these animals do not feed. The success of the glass eels' inland migration after metamorphosis will largely depend on remaining energy levels, which can be supplemented only by resuming food uptake. Although it is crucial for their survival and the maintenance of the population, the feeding behaviour of glass eels is still an understudied aspect of the eels' complex life cycle. Many uncertainties about the phenology, diet, potential prey preferences and their relation with migration modus (migratory vs. sedentary) still remain. In this study, the authors analysed the stomach and gut contents of 458 European glass eels (Anguilla anguilla L. 1758) captured in a drainage canal connecting a small mesotidal estuary with an adjacent polder area during the spring migration seasons of 2016 and 2017. They demonstrated that although glass eels started feeding briefly upon arrival in the estuary, food uptake for early arrivals was restricted to a minority that sparsely feed on detritus and some worm-like benthic invertebrates. Along the season, food uptake intensified eventually engaging all glass eels and their dietary palette diversified including a wide array of planktonic and benthic organisms. Crustacean plankton (mainly cyclopoid copepods) was an important part of the glass eel diet, whereas benthic oligochaetes were less abundant as food source in spite of their high presence in the sediments. No clear differences in feeding behaviour could be observed between migratory and sedentary glass eels. This study showed that glass eels can use highly artificial and dynamic drainage canals as feeding ground during their critical marine/freshwater transition. This outcome is also a plea to improve the accessibility of alternative (unnatural) migration routes between the ocean and suitable freshwater growth habitats for the European eel.

Joint Power Allocation for Remote State Estimation With SWIPT

In this paper, we consider remote state estimation with simultaneous wireless information and power transfer (SWIPT). A remote estimator receives the state estimate transmitted by a sensor and sends an acknowledgment (ACK) signal to the sensor via a feedback wireless channel. The sensor has no other energy supply except that it can harvest wireless energy from the received ACK signal, which carries information and energy simultaneously. Since the symbol error rate of the state estimate transmitted by the sensor depends on the sensor’s transmission power allocation, there exists a tradeoff between the accuracy of the state estimate received by the remote estimator and the transmission power consumption of the sensor. Moreover, the sensor’s transmission power is restricted by the remaining energy level in its battery, which is decided by the amount of the harvested wireless energy. Therefore, we jointly optimize the transmission power allocation of the remote estimator and the sensor and formulate the optimization problem into an infinite time-horizon average cost Markov decision process (MDP). We first prove the existence of the optimal stationary power allocation policy, and then present the monotonic structures of the optimal policy. Simulation results are provided to verify and illustrate the main results.

An Energy-Friendly Scheduler for Edge Computing Systems.

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.

An energy‐efficient MAC protocol based on receiver initiation and multi‐priority backoff for wireless sensor networks

Wireless Sensor Network (WSN) has been widely used with its main characteristics of resilience, scalability, and mobility. However, the node battery capacity in WSN is limited. In addition, in scenes with emergencies such as forest ecological monitoring, data packets of wildfire emergencies have higher real-time requirements than periodic normal data. Therefore, this paper proposes an energy-efficient MAC protocol based on Receiver initiation and Multi-Priority Backoff (RMP-MAC). RMP-MAC adopts the wake-up matching mechanism initiated by the receiver. The sender adjusts its schedule according to the wake-up information of the receiver to match the receiver, which reduces the energy consumption of idle listening. In addition, RMP-MAC proposes a multi-priority factor adaptive backoff mechanism based on event priority, node remaining energy level, and the number of data packets waiting in the transmission queue, ensuring the transmission of emergency data in the actual situation of forest ecological monitoring. Simulation experiments show that compared with PB-MAC, AP-MAC, and MPQ-MAC, RMP-MAC can reduce energy consumption by 20.22%, 12.47%, and 6.62% respectively. In addition, it has improved in the average collisions, network throughput, and average delay of packets.

A Novel Energy-Safe Algorithm for Enhancing the Battery Life for IoT Sensors’ Applications

Energy safe is mandatory for all isolated IoT tools, such as in long way roads, mountains, or even in smart cities. If increasing the lifetime of these tools, the rentability of the global network loop becomes more efficient. Therefore, this paper presents a new approach for saving energy inside the source nodes by supervising the state of energy inside each source node and calculating the duty cycle factor. The relationship between these parameters is based on an optimization problem formulation. In this respect, the present paper is designed to propose a new approach that deals with increasing the lifetime of the wireless sensor network (WSN)-attached nodes, as fixed in the application. The newly devised design is based on implementing the IEEE 802.15.4 standard beacon-enabled mode, involving a cluster tree topology. Accordingly, every subgroup is allotted to apply a specifically different duty cycle, depending on the battery’s remaining energy level, which contributes to creating a wide range of functional modes. Hence, various thresholds are defined. Simulation results prove the efficiency of the proposed approach and show the energetic benefit. The proposed flowchart has minimized the consumed energy for the WSN, which improves the battery lifetime and enhances the IoT application’s robustness. Simulations and experiments have been carried out under different conditions and the results prove that the proposed method is a viable solution.

Design and Comparison of LEACH and Improved Centralized LEACH in Wireless Sensor Network

A WSN consists of a setup of sensor nodes/motes which perceives the environment under monitoring, and transfer this information through wireless links to the Base Station (BS) or sink. The sensor nodes can be heterogeneous or homogeneous and can be mobile or stationary. The data gathered is forwarded through single/multiple hops to the BS/sink. In this paper, propose improvements to LEACH routing protocol to reduce energy consumption and extend network life. LEACH Distance Energy (LEACH-DE) not only selects the cluster head node by considering that the remaining energy of the node is greater than the average remaining energy level of the nodes in the network, but also selects the cluster head node parameters based on the geometric distance between the candidate node and the BS. The simulation results show that the algorithm proposed in this work is superior to LEACH and LEACH-C (Centralized) in terms of energy saving and extending the lifetime of wireless sensor networks.

Energy-Aware Routing for Software-Defined Multihop Wireless Sensor Networks

In this paper, we propose an energy-aware routing algorithm and a control overhead reduction technique for prolonging the network lifetime of software-defined multihop wireless sensor networks (SDWSNs). This is an effort to optimize the energy consumption of WSNs that provide services to the Industrial Internet of Things (IIoT). A centralized controller grants a global view of the sensor network by introducing extra control overhead in the network, but this leads to extra energy costs. However, our new algorithm takes advantage of this global view and balances the network energy by selecting paths with the highest remaining energy level among multiple paths for each sensor node. We also identify key functions draining energy from the SDWSN and minimize their impact by implementing a data packet aggregation function, and minimizing the control overhead by keeping track of the sensor nodes' routing tables using a simple checksum function. We show that the proposed approach prolongs the network lifetime of the WSN by 6.5% on average compared to the standard shortest-path algorithm, and that the control overhead is reduced by approximately 12% while still maintaining a very high packet delivery ratio.

INERTIA PARTICLE SWARM OPTIMIZATION WITH DYNAMIC VELOCITY BASED CLUSTERING TECHNIQUE IN WIRELESS SENSOR NETWORKS

Wireless Sensor Networks (WSN) comprises a collection of nodes commonly employed to observe the physical environment. Different sensor nodes are linked to an inbuilt power unit to carry out necessary operations and data transmission between nearby nodes. The maximization of network lifetime and minimization of energy dissipation are considered as the major design issue of WSN. Clustering is a familiar energy efficient technique and the choice of optimal cluster heads (CHs) is considered as an NP hard problem. This paper presents an Inertia Particle Swarm Optimization algorithm with dynamic velocities (IPSO-DV) algorithm based clustering technique in WSN. The aim of the IPSO-DV technique is to select the CHs in such a way to maximize network lifetime. The IPSO-DV algorithm derives a fitness function (FF) to select CHs using distance to BS and remaining energy level. The application of dynamic velocities helps to improvise the effectiveness of the conventional PSO algorithm. To assure the performance of the presented IPSO-DV algorithm, a series of experiments were carried out and the results are investigated under several aspects. The experimentation outcome ensured the effective performance of the IPSO-DV algorithm over the compared clustering techniques.

Metaheuristic Clustering Protocol for Healthcare Data Collection in Mobile Wireless Multimedia Sensor Networks

Nowadays, healthcare applications necessitate maximum volume of medical data to be fed to help the physicians, academicians, pathologists, doctors and other healthcare professionals. Advancements in the domain of Wireless Sensor Networks (WSN) and Multimedia Wireless Sensor Networks (MWSN) are tremendous. M-WMSN is an advanced form of conventional Wireless Sensor Networks (WSN) to networks that use multimedia devices. When compared with traditional WSN, the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content. Hence, clustering techniques are deployed to achieve low amount of energy utilization. The current research work aims at introducing a new Density Based Clustering (DBC) technique to achieve energy efficiency in WMSN. The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level, distance, and node centrality. In addition, two static data collector points called Super Cluster Head (SCH) are placed, which collects the data from normal CHs and forwards it to the Base Station (BS) directly. SCH supports multi-hop data transmission that assists in effectively balancing the available energy. A detailed simulation analysis was conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects. The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds, which is significantly higher compared to existing methods.

An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks

In opportunistic networks, the requirement of QoS (quality of service) poses several major challenges to wireless mobile devices with limited cache and energy. This implies that energy and cache space are two significant cornerstones for the structure of a routing algorithm. However, most routing algorithms tackle the issue of limited network resources from the perspective of a deterministic approach, which lacks an adaptive data transmission mechanism. Meanwhile, these methods show a relatively low scalability because they are probably built up based on some special scenarios rather than general ones. To alleviate the problems, this paper proposes an adaptive delay-tolerant routing algorithm (DTCM) utilizing curve-trapezoid Mamdani fuzzy inference system (CMFI) for opportunistic social networks. DTCM evaluates both the remaining energy level and the remaining cache level of relay nodes (two-factor) in opportunistic networks and makes reasonable decisions on data transmission through CMFI. Different from the traditional fuzzy inference system, CMFI determines three levels of membership functions through the trichotomy law and evaluates the fuzzy mapping from two-factor fuzzy input to data transmission by curve-trapezoid membership functions. Our experimental results show that within the error interval of 0.05~0.1, DTCM improves delivery ratio by about 20% and decreases end-to-end delay by approximate 25% as compared with Epidemic, and the network overhead from DTCM is in the middle horizon.

EARPC – Energy Aware Routing Protocol for Cooperative MIMO Scheme in WSNs

Wireless sensor networks are typically operated on batteries. Therefore, in order to prolong network lifetime, an energy efficient routing algorithm is required. In this paper, an energy-aware routing protocol for the co-operative MIMO scheme in WSNs (EARPC) is presented. It is based on an improved cluster head selection method that considers the remaining energy level of a node and recent energy consumption of all nodes. This means that sensor nodes with lower energy levels are less likely to be chosen as cluster heads. Next, based on the cooperative node selection in each cluster, a virtual MIMO array is created, reducing uneven distribution of clusters. Simulation results show that the proposed routing protocol may reduce energy consumption and improve network lifetime compared with the LEACH protocol

Adaptive and Dynamic Mechanism for Round Length Determination in Cluster Based Wireless Sensor Networks

The resource-constrained nature of WSNs require efficient use of resources, especially energy, to prolong their lifetime. Clustering is one of the popular approaches to allocate the resources efficiently among the WSN nodes. In this work, we analyze the problem of round length determination in cluster based WSN which has severe impact on the energy efficiency. This problem is very important since round length determines how often the cluster head (CH) rotates or re-clustering process occurs. A longer round length will cause the CH nodes to operate for a long time and drain their energies faster than other nodes resulting in uneven energy consumption in the network, while a shorter round length results in considerable wastage of energy due to frequent running of the setup phase. Hence, we propose an adaptive and dynamic mechanism for round length determination in cluster based WSNs by adapting Behavior Curve Function modeled by quadratic Bezier curves, where we associate the remaining energy level of the cluster to its round operation length and to its assigned criticality which is defined based on network energy level. This helps to determine the number of frames in a round or how many times the data collection occurs in a cluster in a round and the criticality of the energy in the WSN. Simulation results reveal that the proposed mechanism has effectively reduced the energy consumption and improved the WSN lifetime in both homogeneous and heterogeneous network settings.

AN OPTIMUM ENERGY CONSUMPTION HYBRID ALGORITHM FOR XLN STRATEGIC DESIGN IN WSN'S

In this paper, X-Layer protocol is originated which executes mobility error prediction (MEP) algorithm to calculate the remaining energy level of each node. This X-Layer protocol structure employs the mobility aware protocol that senses the mobility concerned to each node with the utilization of Ad-hoc On-Demand Distance Vector (AODV), which shares the information or data specific to the distance among individual nodes. With the help of this theory, the neighbour list will be updated only to those nodes which are mobile resulting in less energy consumption when compared to all (static/mobile) other nodes in the network. Apart from the MEP algorithm, clustering head (CH) election algorithm has also been specified to identify the relevant clusters whether they exists within the network region or not. Also clustering multi-hop routing (CMHR) algorithm was implemented in which the node can identify the cluster to which it belongs depending upon the distance from each cluster surrounding the node. Finally comprising the AODV routing protocol with the Two-Ray Ground method, we implement X-Layer protocol structure by considering MAC protocol in accordance to IEEE 802.15.4 to obtain the best results in energy consumption and also by reducing the energy wastage with respect to each node. The effective results had been illustrated through Network Simulator-II platform.