Reduce Node Energy Consumption Research Articles

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

A Decentralized Dynamic Self-Triggered Control Approach to Consensus of Multiagent Systems

This article studies the self-triggered consensus control problem for linear multiagent systems (MASs) with the aim of improving resources utilization efficiency, e.g., saving communication bandwidth and reducing node energy consumption. First, a distributed control protocol is designed and a novel decentralized dynamic self-triggering mechanism (DDSTM) is proposed, with which, each agent can not only compute its own next triggering instant but also is able to compute the neighbors’ next triggering instant. Then, a hybrid system model is constructed to completely describe the close-loop system with jump dynamics. Based on the hybrid model, Lyapunov conditions are derived for MAS consensus analysis. The proposed DDSTM can overcome some limitations of the existing distributed STMs in the sense of synchronously acquiring consensus error or uniquely determining triggering instants, moreover, it allows transceivers to be turned on only at triggering instants and enter into sleep mode at the rest time, such that node energy is efficiently saved. Preadjustable minimum triggering interval is imposed between any consecutive triggering events; thus, a minimum sleep period for transceivers is always guaranteed. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.

BACSSOC: A novel clustering method for mobile forest protection using wireless sensor network with lower energy consumption and lower latency

The utilization of Mobile Forest Protection Using Wireless Sensor Network (Mobile-FPWSN) has exhibited notable advantages in mitigating economic losses in forestry and enhancing the efficiency of fire prevention, thereby leading to its widespread adoption in the forestry protection industry. However, in the current developmental stage of Mobile-FPWSN, the impact of sensor nodes on the system is often disregarded in forest fire prevention and control. Latest studies have indicated that clustering techniques exhibit noteworthy benefits, including a decrease in energy consumption and an extension of system life. However, current clustering methodologies do not comprehensively address the concerns of latency, energy consumption, and delay while operating under Mobile-FPWSN conditions. In order to tackle these challenges, a novel clustering method called Boltzmann adaptive chaotic salp swarm optimization clustering (BACSSOC) has been proposed for Mobile-FPWSN. This method aims to significantly prolong the system’s lifetime, efficiently reduce energy consumption, and minimize system latency to the greatest extent possible. BACSSOC dramatically reduces the energy consumption of cluster heads during the adaptive clustering phase of Mobile-FPWSN, thereby decelerating the depletion of node energy and improving the forest fire monitoring time in comparison with other state-of-the-art algorithms. To evaluate the efficacy of BACSSOC in comparison to ML-AEFA, PCFMO, and SOA-EACR algorithms in mobile FPWSNs, we have conducted a unique forest fire simulation experiment based on a realistic forest fire scenario. The results have unequivocally demonstrated that BACSSOC surpasses ML-AEFA, PCFMO, and SOA-EACR algorithms, exhibiting advantages of at least 15% in reducing node energy consumption, 7% in decreasing system latency, and 9% in increasing system lifespan. These findings highlight the remarkable potential of BACSSOC as an effective network clustering solution in mobile FPWSNs.

Energy-harvesting Q-learning secure routing algorithm with authenticated-encryption for WSN

Wireless sensor networks are susceptible to a variety of network attacks. Due to the limited energy of nodes and selfish nodes in the network, the packet delivery rate is lower. To address these issues, we innovatively propose an energy-harvesting Q-learning secure routing algorithm with authenticated-encryption. The algorithm uses physical unclonable functions and optimized Q-learning to ensure that the transmission path is reliable. Meanwhile, we combine the LSTM-based prediction model to predict the energy value that the nodes replenish. In addition, simulations are performed to compare the performances of the proposed algorithm with other algorithms under different attacks. The proposed algorithm has greater improvements in the packet delivery rate, filtering selfish nodes, and reducing node energy consumption.

Research on Hierarchical Clustering LEACH Protocol Optimization Algorithm Based on Chebyshev Distance

For the problems of premature death of cluster head nodes, short network life cycle and fast energy consumption in hierarchical clustering LEACH protocol in wireless sensor networks, an improved routing protocol K-means LEACH distance optimization algorithm based on LEACH is proposed in this paper. The improved routing algorithm takes into account the problems such as random selection of cluster heads and tedious calculation of distance formula in LEACH algorithm, so K-means clustering algorithm is introduced. The specific scheme is to first divide the data into K classes, then select the appropriate cluster heads, and introduce the Chebyshev distance formula on the basis of K-means clustering algorithm. From the comparative analysis of the simulation results, the K-means LEACH distance optimization algorithm compared to the original LEACH algorithm wireless network node life cycle by 64%, node energy consumption by 26%, which is conducive to improving the stability of WSN, reducing node energy consumption and extending the network survival time.

Improving the Transmission Efficiency of a WSN with the IACO Algorithm

The goal of this study is to reduce the energy consumption of the sensing network and enhance the overall life cycle of the network. This study proposes a data fusion algorithm for wireless sensor networks based on improved ant colony optimization (IACO) to reduce the amount of data transmitted by wireless sensor networks (WSN). This study updates pheromones for multiple optimal routes to improve the global optimal route in search function. The algorithm proposed in this study can reduce node energy consumption, improve network load balancing and prolong network life cycle. Through data fusion, regression analysis model and information processing of each node, this study uses an improved ant colony algorithm to identify the transferals avoid superfluous energy waste caused by long-span network transferal, set the shortest route and transmit data to the central node. The algorithm proposed in this study is conducive to improving the life cycle and stable network, that is, the most suitable and effective way to improve the energy consumption rate of the sensing nodes.

Traffic-driven ions motion optimization-based clustering routing protocol for cognitive radio sensor networks.

In cognitive radio sensor networks, single clustering protocol cannot simultaneously satisfy the various requirements of time-triggered and event-driven traffic, as a result, different kinds of clustering protocols are designed to serve them separately. In addition, for event-driven traffic, the long delay incurred by clustering and searching for available routes after events results in poor timeliness of information transmission. In order to solve above problems, a traffic-driven ions motion optimization-based clustering routing protocol (TD-IMOCRP) is proposed in this paper. For the first time, time-triggered and event-driven traffic can be served by a single clustering protocol. To be specific, ions motion optimization algorithm is leveraged to automatically determine the optimal number of clusters and form basic clustering structure. In this case, time-triggered traffic can be periodically served. Priority-based schedule and corresponding frame structure are designed to ensure priority delivery of event-driven information. The clustering architecture built for time-triggered traffic is leveraged, and there is no cluster construction and route selection after emergent events. Only the CRSNs nodes which discover emergent events and corresponding CHs participate in data transmission, which means that TD-IMOCRP covers fewer nodes, especially when the sink is located at the corner. Therefore, it can help reduce node energy consumption and delay. Simulation results demonstrate that compared with representative event-driven clustering protocols, TD-IMOCRP can decrease the average number of covered nodes and the total energy consumption by more than 66.3% and 25%, respectively. In addition, when serving time-triggered traffic, TD-IMOCRP can achieve almost the same performance as its basic version IMOCRP which is better than majority of current time-triggered clustering protocols. In a word, TD-IMOCRP can guarantee in-time delivery of event-driven information while guaranteeing its performance of serving time-triggered traffic.

The Compressed Sensing of Wireless Sensor Networks Based on Internet of Things

In the application of wireless sensor networks, poor computing power, limited storage space, and short duration have severely restricted the development of wireless sensor networks. Compressed sensing technology is an emerging signal front-end processing technology, which breaks the original use restrictions, saves storage space and energy consumption, and has become a research hotspot in the field of electronic technology. Based on the wireless network, this paper combines the big data P2P direct connection perception idea to expand the compression algorithm spatially. First, locate the data on different nodes, then perform task allocation on the data compression calculation in the cloud, and finally wait for the node to compress the data to be compressed. The results show that compressed sensing technology can accurately locate objects, overcome node defects in wireless sensor networks, reduce node energy consumption, improve data processing capabilities and calculation speed, and track targets. Specific references for more accurate investigation and positioning.

Design Of Energy Efficient and Secure Routing Protocol for Wsn in Iot


 
 
 
 Abstract
 This paper proposes an energy-efficient and secure routing system for wireless sensor networks (WSNs) in the Internet of Things (IoT). The proposed routing protocol improves packet delivery ratio, end-to-end latency, and network lifetime over existing protocols. The protocol under consideration reduces node energy consumption, increasing the network's lifetime, while ensuring data security. Simulation studies using several metrics evaluate the proposed procedure. The recommended protocol outperforms existing protocols in energy efficiency and security. The protocol under discussion has several IoT applications, including smart agriculture, healthcare monitoring, and environmental monitoring. It ensures reliable and secure data transfer. This paper introduces a novel routing system that addresses energy saving and security in wireless sensor networks and the Internet of Things (IoT).
 
 
 

An Improved Triggering Updating Method of Interest Message with Adaptive Threshold Determination for Directed Diffusion Routing Protocol

Although some progress has been made in studying the triggering updating methods of interest message for reducing node energy consumption in directed diffusion routing protocols, they do not consider the anisotropy of sensing areas of nodes and the requirement of diversity of interest message exchange rates within real-time performance, which makes these studies unable to accurately adapt to the characteristics of interest message update. In this work, we proposed an improved triggering updating method (ITUM) consisted of adaptive threshold determination based on the analytic hierarchy process and update judgement by similarity comparison of interest messages. Meanwhile, the network model and the sensing model are presented to describe actual network scenarios. We analyze the impact of critical parameters and evaluate the performance of ITUM with several interest message updating methods from the aspect of lowering the number of information exchanges. The simulation results prove that ITUM can improve the adaptability to scene changes while decreasing the number of information exchanges compared with the existing methods. Furthermore, it is shown that ITUM is a highly effective solution for determining the triggering updating conditions of various information exchange rates in directed diffusion routing protocol.

A Byzantine Sensing Network Based on Majority-Consensus Data Aggregation Mechanism.

In the current Internet of Things era, digital devices form complex interconnections. The statuses of objects of interest are monitored using sensors, and distributed wireless sensor networks are formed from numerous sensor nodes. Many Byzantine fault tolerance mechanisms in wireless sensor networks (WSNs) were proposed from Byzantine agreement which even with a few faulty nodes in a sensor network, most healthy nodes can reach a consensus, perform data transmission tasks, and maintain network operation. In this study, this mechanism was utilized together with the majority function technique; in particular, the proposed method uses original sensor signals to define a threshold to assert a binary value of one or zero, thereby performing data judgment and aggregation. This approach reduces node energy consumption and enables the nodes to quickly reach a consensus. Moreover, the operating performance of the network can be maintained even when problems such as node failure and faults occur within the fault tolerance range. Compared with existing algorithms, the proposed data aggregation mechanism exhibits a better network life cycle and can effectively extend the flexibility of network operations.

Research on dynamic performance regulation method of bidirectional relay network based on SDN architecture

In order to overcome the shortcomings of the traditional dynamic regulation methods of bidirectional relay network, such as low resource utilisation and high energy consumption of nodes, a dynamic regulation method of bidirectional relay network performance based on SDN architecture is proposed. SDN architecture is used to construct the bidirectional relay network model. The subcarrier pairing algorithm is used to pair the subcarriers, and then the model of two-way relay network is optimised. The optimal configuration of node power and subcarrier power is carried out respectively, and the joint energy consumption control model of two-way relay network is constructed by using game theory. In this way, the performance of bidirectional relay network can be dynamically adjusted. Through experiments, compared with the traditional method, the proposed method improves resource utilisation, reduces node energy consumption and has better performance.

Research on dynamic performance regulation method of bidirectional relay network based on SDN architecture

In order to overcome the shortcomings of the traditional dynamic regulation methods of bidirectional relay network, such as low resource utilisation and high energy consumption of nodes, a dynamic regulation method of bidirectional relay network performance based on SDN architecture is proposed. SDN architecture is used to construct the bidirectional relay network model. The subcarrier pairing algorithm is used to pair the subcarriers, and then the model of two-way relay network is optimised. The optimal configuration of node power and subcarrier power is carried out respectively, and the joint energy consumption control model of two-way relay network is constructed by using game theory. In this way, the performance of bidirectional relay network can be dynamically adjusted. Through experiments, compared with the traditional method, the proposed method improves resource utilisation, reduces node energy consumption and has better performance.

A distributed node deployment algorithm for underwater wireless sensor networks based on virtual forces

Abstract Smart ocean needs an excellent node deployment strategy to ensure complete and reliable network coverage of underwater wireless sensor networks. However, node mobility caused by ocean current results in ineffective node deployment such that network coverage cannot be guaranteed. Furthermore, a centralized method of node deployment to achieve effective ocean-monitoring is challenging because of the incapability to predict node locations. In addition, it is necessary to reduce the energy consumption by the process of node deployment because node energy is limited and the energy consumption of node mobility is high. To address these problems, this paper proposes DABVF, a distributed node deployment algorithm based on virtual forces, to increase the network coverage of underwater wireless sensor network. Prior to the design of this algorithm, the forces of nodes in a dynamic marine environment were investigated as part of the study. During the deployment process, a mobile benefit model considering node density, node residual energy, and node mobility effectiveness is established to improve the process of node mobility. In addition, aimed at applications in an underwater scenario where there are fault nodes with no mobile capacity, a mobile judgment mechanism for failure nodes is established to optimize the problem of low coverage caused by fault nodes. The simulation results verify that DABVF can improve network coverage, reduce node energy consumption, balance node residual energy, and optimize node distribution. Moreover, we compare DABVF with three available node deployment algorithms based on virtual forces and demonstrate that DABVF outperforms all of them.

Energy-Saving Algorithm and Simulation of Wireless Sensor Networks Based on Clustering Routing Protocol

An efficient and energy-saving algorithm, K-means and FAH (KAF), has been proposed to solve the problems of node energy constraints, short network cycle and low throughput in current wireless sensor networks. Network clustering is obtained by optimizing K-means clustering. Based on FAHP (Fuzzy Analytic Hierarchy Process) method, the cluster head selection is optimized considering the factors of node energy, distance from base station and energy efficiency of nodes. Based on the factors of transmission distance, energy and hop number, multi-hop routing is constructed to effectively reduce the energy consumption of nodes in data transmission. The simulation results show that compared with other protocols, KAF algorithm has obvious advantages in reducing node energy consumption, prolonging network life cycle and increasing network throughput. And under different routing protocol, the performances of the algorithm are verified. By adjusting the size of the candidate node set selection area, the reliability of data transmission of the long-distance node is increased, and the energy consumption load of the near-distance node is reduced. At the same time, the use of opportunistic transmission strategies increases the reliability of data transmission. The simulation results show that the proposed protocol can effectively reduce the energy consumption of nodes and prolong the network life cycle.

Link Reliability and Performance Optimization in Wireless Body Area Networks

Wireless body area networks (WBAN) require long life links and energy efficient system. Besides the increasing commercialization of WBAN, health monitoring applications calls for enhanced quality of service (QoS). The establishment of the reliable and energy efficient link is crucial to support the improvement of the WBAN performance parameters. In this article, we propose a cross-layer routing mechanism for WBAN quality of service enhancement. The protocol uses a cost function, which linearly combines node energy ratio, link reliability, and specific absorption rate functions. The proposed algorithm initially maximizes network lifetime longevity by reducing node energy consumption with nearly reasonable throughput and the packet delivery ratio whereas the enhancement of the QoS focused on improving network throughput and the packet delivery success rate for WBAN applications. The algorithm is implemented in two stages, firstly by designing the energy efficient and reliable link routing policy in the network layer and secondly the adjustment of the contention window for QoS performance enhancement in the data link layer using IEEE 802.11 medium access control (MAC) protocol. We conduct parametric modeling of the cost function to analyze network performance in different parametric combinations and contention window adjustments. Simulation results show the proposed protocol improves network performance indicators such as energy efficiency, lifetime longevity maximization, throughput, and packet success delivery ratio.

ResiDI: Towards a smarter smart home system for decision-making using wireless sensors and actuators

This article proposes ResiDI, an intelligent decision-making system for a residential distributed automation infrastructure based on wireless sensors and actuators. ResiDI transmits events using wireless technologies embedded in WSANs to reduce the wire load capacity of traditional systems. In addition, the nodes are equipped with batteries, as a backup system. These features allow the ResiDI to be installed anywhere in the house, without the need for drilling or changing any other pre-existing infrastructure. Furthermore, the roles and intelligence of ResiDI are distributed among the network nodes. Besides increasing precision in decision-making through a neural network, the ResiDI seeks to reduce node energy consumption by means of a temporal correlation mechanism. As proof of concept, a prototype was developed to integrate with ResiDI in order to demonstrate its viability. When compared with an approach in the literature, real and simulated results show that ResiDI makes three key contributions: (i) 22.03% increase in decision-making; (ii) 44.35% reduction in node energy consumption in a homogeneous way; and (iii) 95.24% efficiency in information transmission. Finally, ResiDI provides a gain in response time of 30.21%, so that the decision-making process is performed faster.

Opportunistic large array concentric routing algorithms with relay nodes for wireless sensor networks

This paper proposes two architectures, including an Opportunistic Large Array Concentric Routing Algorithm with Geographic Relay Nodes (OLACRA-GRN) architecture, and an Opportunistic Large Array Concentric Routing Algorithm with Relay Nodes (OLACRA-RN) architecture. First, the OLACRA-GRN architecture with the geographic information of relay nodes reduces node energy consumption and finds the optimum number of relay nodes to forward the data; an analysis of the characteristics of the energy model is also presented. Besides, the OLACRA-RN architecture without the geographic information of relay nodes is proposed, which can find the number layer of concentric circles in the sensing field. The optimal number layer of concentric circles is calculated according to the distance between the sink and field boundary. Simulation results show that our proposed OLACRA-GRN and OLACRA-RN architectures can effectively reduce node energy consumption more than Opportunistic Large Array Concentric Routing Algorithm (OLACRA) architecture.

Lifetime Optimization Algorithm with Mobile Sink Nodes for Wireless Sensor Networks Based on Location Information

In order to overcome the energy hole problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with mobile sink nodes for wireless sensor networks (LOA_MSN) is proposed. In LOA_MSN, hybrid positioning algorithm of satellite positioning and RSSI positioning is proposed to save energy. Based on location information, movement path constraints, flow constraint, energy consumption constraint, link transmission constraint, and other constraints are analyzed. Network optimization model is established and decomposed into movement path selection model and lifetime optimization model with known grid movement paths. Finally, the two models are solved by distributed method. Sink nodes gather data of sensor nodes along the calculated paths. Sensor nodes select father nodes and transmit data to corresponding sink node according to local information. Simulation results show that LOA_MSN makes full use of node energy to prolong network lifetime. LOA_MSN with multiple sink nodes also reduces node energy consumption and data gathering latency. Under certain conditions, it outperforms MCP, subgradient algorithm, EASR, and GRAND.

Energy analysis for the distributed receiver‐based cooperative medium access control for wireless sensor networks

This paper presents the energy analysis of a new energy-efficient distributed receiver-based cooperative medium access control (MAC) scheme for wireless sensor networks. The cooperative MAC scheme developed herein incorporates cooperation, overhearing avoidance, receiver-based relay node selection and a Markov-based channel state estimation onto the standard IEEE 802.15.4 scheme. The solution is developed based on a receiver oriented approach when selecting cooperative relay nodes and using a store and forward scheme to relay the packets to the destination node. This works’ main focus is in combining the cooperative packet relaying with overhearing avoidance in order to reduce node energy consumption hence enhancing the network lifetime. As the performance criteria, the energy consumed per node is investigated against packet arrival rate and average signal-to-noise ratio. The percentage of energy consumed and packet throughput are also investigated as the proposed MAC scheme is compared against the standard IEEE 802.15.4 MAC over the estimated channel conditions. The results show that the proposed MAC scheme with cooperation and overhearing avoidance resulted in both improved performance and improved energy saving patterns.