Energy Consumption In Wireless Sensor Networks Research Articles

An improved target tracking scheme based on MC-MPMC method for mobile wireless sensor networks

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

An improved approach for energy consumption minimizing in WSN using Harris hawks optimization

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations.

A remote consultation system for sports injury based on wireless sensor network

INTRODUCTION: Although current research methods can realize the effective collection of human physiological signals in the health monitoring system, they cannot obtain the ideal detection effect due to the influence of the communication performance in the health monitoring system. OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed. METHODS: The wearable sensors is used in the body area network to collect human physiological signals. Through the wireless sensor network of the wireless communication module, the collected human physiological signals are transmitted to the remote consultation module. The wireless communication module selects CC2530 chip as the core chip of the wireless communication module. A fixed partition routing algorithm based on energy balance is used to stably transmit human physiological signals. RESULTS: The consultation personnel of the remote consultation module make a sports injury consultation judgment based on the received physiological signal results of the human body. The system test results show that the designed system can accurately monitor various physiological indicators of the human body. The wireless sensor network energy consumption of the system in this paper is all less than 500J, the energy consumption variance of the cluster head is less than 4×10-3, and the number of surviving nodes can be guaranteed to be higher than 130. It has high communication performance of wireless sensor network. CONCLUSION: The system can accurately judge whether there is a sports injury according to the monitoring results of physiological indicators, and realize the effective consultation of sports injury.

AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN

In recent years, optimizing sensor nodes energy appears to be a strong element in improving the performance of the Wireless Sensor Network (WSN). In the focus of these different approaches, substantial outputs regarding energy optimization are revealed by applying clustering-based object tracking techniques. Clustering is achieved through an enhanced adaptive LEACH algorithm, named Advanced Efficient Low-Energy Adaptive Clustering Hierarchy (AE-LEACH) algorithm. The cluster head (CH) is adopted through firm descriptors like residual energy and reserve distance from the Base Station (BS) in the clustering process. The selected CH predicts the target trajectory using Particle Filter (PA) algorithm and selectively activates from the next round of sensor nodes to continuously track the target. The target nodes’ predictable locations are routed to the base station through the backbone that lies in the cluster head. Gini index is used to assess the energy efficiency of the clustering algorithm . Thus, the proposed performance metrics stated on network timespan, residual energy, energy consumption, and Gini index for remaining energy and achieves significantly lower Gini index of upto 78.38%, 86.11%, and 85.92% as compared to K means clustering , HEED clustering, and LEACH clustering algorithm, respectively. Moreover, time accuracy is directly proportional to the network lifetime and energy consumption.

EVOLVING FRAMEWORK FOR IOT-BASED ENERGY EFFICIENT WIRELESS SENSOR NETWORKS FOR HEALTHCARE SERVICES

The Internet-of-Things (IoT) is a system of interrelated computing devices having unique identifiers that enables to transfer data over a network without requiring human-to-computer or human-to-human interaction. With the rapid growth in embedded wireless computing devices with high-speed internet connectivity, the Wireless Sensor Networks (WSNs) for healthcare applications comprises of interconnected several tinypowered, wearable, wireless bio-sensors to provide an effective way of collecting vital health-related data. The emerging paradigm of IoT in smart healthcare system requires a specialized secure framework in order to enable real-time health monitoring, reliable diagnostics, effective treatment processes, and many other related aspects of healthcare system. In this paper, various aspects of energy consumption in IoT-based framework of WSNs for healthcare services are discussed. The framework for IoTbased healthcare network cater to upgraded microcontrollers, IoT gateway devices, various wireless and web technologies for IoT, variety of bio-sensors and data collectors, and secure communication protocols. Techniques to optimize the energy consumption in WSNs have been presented leading to energy efficient framework for WSNs suitable for healthcare systems.

Manta Ray Foraging Optimization (MRFO)-Based Energy-Efficient Cluster Head Selection Algorithm for Wireless Sensor Networks

Wireless sensor network (WSN) has become a very popular technology with a wide range of applications. It consists of several spatially distributed sensors that work collaboratively to monitor a given region of interest (ROI). The limited energy available for each sensor node is a crucial restriction that affects the overall performance of the network. Therefore, energy efficiency is a major concern in WSNs. Over the years, many techniques have been developed and used to reduce energy consumption in WSNs. Clustering is one of the most effective energy-saving techniques that significantly can improve the efficiency of WSNs in terms of the network lifetime, energy consumption, and the number of received packets. In this paper, an energy-efficient algorithm for cluster head (CH) selection based on a newly formulated fitness function and using the manta ray foraging optimization (MRFO) is proposed. The objective function for the proposed formulation takes into account different network parameters such as the average distance between the CH and the sensors in its cluster, the distance between CHs and the base station (BS), residual energy, and CH balancing. The proposed algorithm is tested by running many simulations under a variety of conditions. The simulation results showed that the proposed algorithm has a better performance than that of some other algorithms reported in the literature in terms of energy consumption, networks lifetime, and the number of received packets.

A Reinforcement Learning-Based Dynamic Clustering Algorithm for Compressive Data Gathering in Wireless Sensor Networks

Compressive data gathering (CDG) is an effective technique to handle large amounts of data transmissions in resource-constrained wireless sensor networks (WSNs). However, CDG with static clustering cannot adapt to time-varying environments in WSNs. In this paper, a reinforcement learning-based dynamic clustering algorithm (RLDCA) for CDG in WSNs is proposed. It is a dynamic and adaptive clustering method aiming to further reduce data transmissions and energy consumption in WSNs. Sensor nodes act as reinforcement learning (RL) agents which can observe the environment and dynamically select a cluster to join in. These RL agents are instructed by a well-designed reward scheme to join a cluster with strong data correlation and proper distance. It is also a distributed and lightweight learning method. All agents are independent and operate in parallel. Additional overheads introduced by RL are lightweight. Computations of a linear reward function and a few comparison operations are needed. It is implementable in WSNs. Simulations performed in MATLAB validate the effectiveness of the proposed method and simulation results show that the proposed algorithm achieves the desired effect as well as fine convergence. It decreases data transmissions by 16.6% and 54.4% and energy consumption by 6% and 29%, respectively, compared to the two contrastive schemes.

Designing an Energy Efficient Network Using Integration of KSOM, ANN and Data Fusion Techniques

Energy in a wireless sensor network (WSN) is rendered as the major constraint that affects the overall feasibility and performance of a network. With the dynamic and demanding requirements of diverse applications, the need for an energy efficient network persists. Therefore, this paper proposes a mechanism for optimizing the energy consumption in WSN through the integration of artificial neural networks (ANN) and Kohonen self-organizing map (KSOM) techniques. The clusters are formed and re-located after iteration for effective distribution of energy and reduction of energy depletion at individual nodes. Furthermore, back propagation algorithm is used as a supervised learning method for optimizing the approach and reducing the loss function. The simulation results show the effectiveness of the proposed energy efficient network.

Survivability development of wireless sensor networks using neuro fuzzy-clonal selection optimization

Wireless Sensor Networks (WSN) are a kind of wireless network, transmitting data utilizing the multi-hop radio relaying technique, and with no fixed infrastructure. WSN has the ability to function as network host for transmission and reception of data. The unique characteristics of WSN include distributed routing; frequent path breaks due to mobility, packet switching, and self-organization. The energy restriction is an important aspect to consider while designing a sensor network in order to increase the network's survivability. Clustering is an essential approach for overcoming energy limits and increasing network longevity. In the LEACH-technique, simulated annealing optimization is employed to generate the optimal cluster construction. This approach consumes more energy in order to build the best clustering, and the sensor nodes quickly lose energy, reducing the network's survivability. So, Neuro Fuzzy-Clonal Selection Optimization (NF-CSO) model is constructed for minimizing the energy consumption, to identify the optimal position for multi-sink placement and to improve the network survivability. Initially Clonal Selection (CSO) algorithm does the task of determining the optimal position for placing the multi-sink and the Neuro Fuzzy algorithm were utilized to determine the best cluster head, resulting in less energy usage and an increase in network survivability. WSN's energy consumption has been decreased by about 58%, and the network life cycle has been increased by 65% compared to LEACH. The network simulator is used to implement the existing and proposed techniques. From the simulation analysis, it has been proven that the proposed NF- CSO algorithm performs better in selecting the optimal position of the multi-sink, and also reduces the energy consumption and increases network survivability. The proposed approach outperforms the existing LEACH, PSO and BFO techniques.

An Estimation Approach to Optimize Energy Consumption in Wireless Sensor Network: A Health-Care Application

Wireless Sensor Network (WSN) is gaining popularity day by day in a large area of applications. However, the operation of WSN is facing a multitude of challenges, mainly in terms of energy consumption since WSN nodes operate with battery power and changing the batteries is a complicated task, as networks may include hundreds to thousands of nodes. In this context, it is very crucial to know the remaining energy value in the battery of the sensor node to take required actions before losing sensor’s function. Sending these measurements is very expensive in terms of energy and reduces the battery lifetime of the sensor and thus of the entire network. In this paper, we are interested in defining a probabilistic approach which aims to estimate these monitoring energy values and optimize energy consumption in WSN. Our approach is based on hidden Markov chains and includes two phases namely a learning phase and a prediction phase. Our approach is implemented as a web service. We illustrate our approach with a sensor-based health-care monitoring case study for COVID-19 patients. To evaluate our approach, we carry out experimentations based on the AvroraZ a simulator with a test for different types of applications and for different energy models: μAMPS-specific model, Mica2-specific model, and Mica2-specific model with actual measurements. These experimentations demonstrate the accuracy and efficiency of our approach. Our results show that periodic WSN applications i.e. applications which send monitoring data periodically, tested with the μAMPS-specific model perform an accuracy of 98.65%. In addition, our approach can perform a gain up to 75% of the battery charge of the sensor with an estimation of three-quarters of the remaining energy values. a https://www.redcad.org/members/benhalima/azem/ .

A novel approach for clustering and routing in WSN using genetic algorithm and equilibrium optimizer

SummaryThe Internet of Things (IoT) is a new concept in the world of technology and information and has many applications in industry, communications, and various other fields. In the lowest layer of the IoT, wireless sensor networks (WSNs) play an important and pivotal role. WSN consists of a large number of sensors and is commonly used to monitor a target. It is important to reduce energy consumption in WSNs to extend network life, since it is usually impossible to replace sensor batteries. In this paper, a novel clustering and routing method is proposed. It is mainly based on genetic algorithms and equilibrium optimization. To improve scalability, the sensor nodes are clustered in the first phase using the genetic algorithm and then the best cluster heads are selected. It follows by the next step, where each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a multihop optimization routing algorithm by the new equilibrium optimization algorithm to send the data to the base station. Specifically, the main contribution of this work is to reduce energy consumption in WSN using a genetic algorithm for clustering and an equilibrium optimization algorithm for choosing the path between the cluster heads and the base station. The proposed method was simulated in MATLAB software, and it was compared with five other methods. It was observed that the proposed method outperforms other methods in terms of energy consumption, network lifespan, and the number of packages delivered.

Analytical Model for Underwater Wireless Sensor Network Energy Consumption Reduction

In an Underwater Wireless Sensor Network (UWSN), extreme energy loss is carried out by the early expiration of sensor nodes and causes a reduction in efficiency in the submerged acoustic sensor system. Systems based on clustering strategies, instead of each node sending information by itself, utilize cluster heads to collect information inside the clusters for forwarding collective information to sink. This can effectively minimize the total energy loss during transmission. The environment of UWSN is 3D architecture-based and follows a complex hierarchical clustering strategy involving its most effecting unique parameters such as propagation delay and limited transmission bandwidth. Round base clustering strategy works in rounds, where each round comprises three fundamental stages: cluster head selection, grouping or node association, and data aggregation followed by forwarding data to the sink. In UWSN, the energy consumed during the formation of clusters has been considered casually or completely evaded in the previous works. In this paper, the cluster head setup period has been considered the main contributor to extra energy utilizer. A numerical channel model is proposed to compute extra energy. It is performed by using a UWSN broad model. The results have shown that extra maximum energy consumption is approximately 12.9 percent of the system total energy consumed in information transmissions.

Aggregated PSO for Secure Data Transmission in WSN Using Fog Server

Privacy of data in Internet of Things (IoT) over fog networks is the biggest challenge in security of Wireless communication networks. In Wireless Sensor Network (WSN), current research on fog computing with IoT is gaining popularity among IoT devices over network. Moreover, the data aggregation will reduce the energy consumption in WSN. Due to the open and hostile nature of WSN, secure data aggregation is the major issue. The existing data aggregation methods in IoT and its associated approaches are lack of limited aggregation functions, heavyweight, issues related to the performance overhead. Besides, the overload on fog node will result in high latency, scalability, storage, degraded reliability and energy overhead. In order to overcome these issues, this proposed work has used two schemes for secure transmission of data over the network and reduce the energy consumption of the transmission. The secret data transferred between the IoT devices and the Fog server are transmitted through the aggregator node. If the aggregator node is placed far away from the Fog node, it may send the data to its neighbor aggregator. And it will append it with the current data and send it to the fog server through aggregator message receiving method. In addition to that, the fog server can extract the data through the fog message extractor method. In order to reduce the transmission cost and energy, Clustered Particle Swarm Optimization (CPSO) method is used to form the clusters. This proposed work can avoid the unnecessary energy consumption during the transmission and ensures secured aggregation so that the base station can know the origin of the sender and the validity of the received message. Therefore, the computation cost of the proposed work in authorization requires1MC+1H and the aggregation requires (n+2) MC+1H which is lesser than the existing methods.

Coyote Optimization Using Fuzzy System for Energy Efficiency in WSN

In recent days, internet of things is widely implemented in Wireless Sensor Network (WSN). It comprises of sensor hubs associated together through the WSNs. The WSN is generally affected by the power in battery due to the linked sensor nodes. In order to extend the lifespan of WSN, clustering techniques are used for the improvement of energy consumption. Clustering methods divide the nodes in WSN and form a cluster. Moreover, it consists of unique Cluster Head (CH) in each cluster. In the existing system, Soft-K means clustering techniques are used in energy consumption in WSN. The soft-k means algorithm does not work with the large –scale wireless sensor networks, therefore it causes reliability and energy consumption problems. To overcome this, the proposed Load-Balanced Clustering conjunction with Coyote Optimization with Fuzzy Logic (LBC-COFL) algorithm is used. The main objective is to perform the lifespan by balancing the gateways with the load of less energy. The proposed algorithm is evaluated using the metrics such as energy consumption, throughput, central tendency, network lifespan, and total energy utilization.

Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions, device monitoring, and collection of information. Due to limited energy resources available at SN, the primary issue is to present an energy-efficient framework and conserve the energy while constructing a route path along with each sensor node. However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routing with minimal energy consumption in WSN. This paper presents an energy-efficient routing system called Energy-aware Proportional Fairness Multi-user Routing (EPFMR) framework in WSN. EPFMR is deployed in the WSN environment using the instance time. The request time sent for the route discovery is the foremost step designed in the EPFMR framework to reduce the energy consumption rate. The proportional fairness routing in WSN selects the best route path for the packet flow based on the relationship between the periods of requests between different SN. Route path discovered for packet flow also measure energy on multi-user route path using the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR develops node dependent energy-efficient localized route path, improving the throughput. The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing, Throughput, RST, node density and average energy per packet in WSN. The Route Searching Time (RST) is reduced using the Boltzmann Distribution (BD), and as a result, the energy is minimized on multi-user WSN. Finally, GIFM applies an instance time difference-based route searching on WSN to attain an optimal energy minimization system. Experimental analysis shows that the EPFMR framework can reduce the RST by 23.47% and improve the throughput by 6.79% compared with the state-of-the-art works.

A Novel Approach for Energy-Efficient of WSN Using PEGASIS Protocol

Wireless Sensor Networks (WSNs) have hardware and software limitations and are deployed in hostile environments. The problem of energy consumption in WSNs has become a very important axis of research. To obtain good performance in terms of the lifetime of the WSN, several routing protocols have been proposed. We present in this article a new approach for the energy efficiency of WSN by using the PEGASIS (Power Efficient Gathering In Sensor Information Systems) routing protocol in order to reduce sensor energy consumption and to achieve good performance in terms of the lifetime of the network in WSN. The simulation results have shown that our proposed protocol excels regarding energy consumption in wireless sensor networks, resulting in an extension of a lifetime for the network. We simulated the proposed technique compared with traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol using MATLAB environment.

ERP-DDA: Event Reporting Protocol Based on Distributed Data Aggregation for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have a wide range of applications in human life. Accordingly, WSNs have beenthoroughly considered in research community to improve their performance and address their challenges. Eventreporting in WSNs has always been a major challenge in terms of energy consumption. Event reporting requiressensing the event and sending a reporting packet from the sensor to the centralized base station (BS). However,sensor’s energy is limited and stored in a non-rechargeable battery. Therefore, several event-reporting (or datacollection) protocols have been proposed to improve energy consumption in WSNs, and consequently, to extendtheir lifetime. In this paper, we propose an efficient event reporting protocol for WSNs called Event ReportingProtocol Based on Distributed Data Aggregation (ERP-DDA). This protocol mainly aims at reporting any event byno more than one sensor node such that energy saving is satisfied in the whole network. To achieve this goal, ERPDDAis mainly based on the following features: it is a cluster-based protocol, it is a multi-hop routing protocol, itapplies distributed data aggregation, and it employs variable clustering and cluster head selection. Simulationsshow that ERP-DDA significantly extends the lifetime of WSNs compared with other related protocols.

IPAWL: An integrated power aware Wireless sensor network and Location-Based social network for incidence reporting

One of the constraints in Wireless Sensor Network (WSN) is the limited power of distributed interconnected sensor nodes over the space. There are several proposed architectures for handling power consumption in a WSN. Traditional clustering and routing methods improve the Cluster Head (CH) selection of WSN internodes for balancing energy in the network. In such clustering methods, every message is transmitted from a source node to its CH, from the CH to other CHs, and finally to the BS (Base Station). Therefore, a high amount of energy should be consumed for routing when the source nodes are far from CHs or the BS. This paper proposes an integrated LBSN (Location-Based Social Network) and WSN routing and CH selection method for reducing routes from source nodes to BSs to reduce the energy consumption of WSN. In the integrated network, LBSN nodes can be added to the WSN as BS or WSN sensor nodes. In the first situation, the integrated network is called IPAWL I and the latter builds the IPAWL II network.To evaluate the method, we considered a scenario of smoke detection and reporting on the campus of the University of Calgary and generated a simulation dataset for the LBSN and WSN. Then, results of implementing IPAWL I, IPAWL II, and a stand-alone WSN on the dataset were compared in terms of network energy consumption and message transmission latency. Results demonstrated a 42% reduction in energy consumption in IPAWL I and 33% in IPAWL II. Moreover, a significant reduction of time latency was observed in bigger size networks.

Optimized fuzzy clustering in wireless sensor networks using improved squirrel search algorithm

Wireless sensor networks (WSNs) are important tools to monitor various events happening in specific environments. In WSNs, regarding the network size and number of sensor nodes, several mobile sink nodes can be used to collect the monitored data. In such cases, having a clustered WSN helps to get the sensed data more efficiently to the sink nodes. Besides, clustering is one of the well-known methods for mitigating the energy consumption of WSNs. This paper presents an efficient distributed multi-level clustering scheme using Sugeno-based Fuzzy Logic Controller (FLC) to organize the WSN into several multi-level clusters. These cluster heads (CHs) collect their members' data and forward them to one of the sinks. Besides, CHs cooperate in multi-hop routing of the data packets to the destination sinks. Also, an improved SSA algorithm is provided to optimize the clustering FLC. By optimizing the FLC its rule-base can be minimized and its effectiveness in the clustering process can be improved. Extensive simulations are conducted in the OMNET++ simulator to evaluate the proposed scheme regarding various metrics such FND (First Node Dead), HND (Half Nodes Dead), LND (Last Node Dead), the average energy of the WSN's nodes, data packet loss, and retransmission ratio. The achieved results exhibit that our presented approach outperforms several well-known clustering and multi-hop routing approaches.

Multilayer DS-MAC with game theory optimization

PurposeEnergy is the major concern in wireless sensor networks (WSNs) for most of the applications. There exist many factors for higher energy consumption in WSNs. The purpose of this work is to increase the coverage area maintaining the minimum possible nodes or sensors.Design/methodology/approachThis paper has proposed multilayer (ML) nodes deployment with distributed MAC (DS-MAC) in which nodes listen time is controlled based on communication of neighbors. Game theory optimization helps in addressing path loss constraints while selecting path toward base stations (BS).FindingsThe simulation is carried out using NS-2.35, and it shows better performance in ML DS-MAC compared to random topology in DS-MAC with same number of BS. The proposed method improves performance of network in terms of energy consumption, network lifetime and better throughput.Research limitations/implicationsEnergy consumption is the major problem in WSNs and for which there exist many reasons, and many approaches are being proposed by researchers based on application in which WSN is used. Node mobility, topology, multitier and ML deployment and path loss constraints are some of the concerns in WSNs.Practical implicationsGame theory is used in different situations like countries whose army race, business firms that are competing, animals generally fighting for prey, political parties competing for vote, penalty kicks for the players in football and so on.Social implicationsWSNs find applications in surveillance, monitoring, inspections for wild life, sea life, underground pipes and so on.Originality/valueGame theory optimization helps in addressing path loss constraints while selecting path toward BS.