Residual Energy Of Sensor Nodes Research Articles

Fuzzy logic Based Seagull Optimization Algorithm for Efficiency and Security in Wireless Sensor Networks

Wireless sensor networks (WSN) find applications in diverse fields such as environmental monitoring, healthcare, and industrial control systems. The pivotal components of these networks are the sensor nodes, which, unfortunately, consume a substantial amount of energy when transmitting information directly to the base station (BS). To mitigate energy consumption associated with direct transmission, this paper proposes a two-phase approach utilizing hybrid clustering and routing algorithms. The proposed approach incorporates fuzzy and seagull techniques for clustering and adopts optimal CH (cluster head) selection, CBRP (Cluster-Based Routing Protocol), and AES (Advanced Encryption Standard) for secure routing. The system employs rule-based fuzzy logic to correlate input values in both clustering and routing algorithms. Decision-making is based on factors such as the residual energy of sensor nodes, distance from the BS, and the number of nodes within the communication range. Input variables' crisp values are transformed into diverse fuzzy values, and the fuzzy output values are converted back to crisp values using the centroid defuzzification method. Selection of cluster heads and routers is determined by the output values, with sensor nodes being allocated to respective cluster heads based on their load-handling capacity. The routing path is then generated considering the capacity of routers. Simulations are conducted to evaluate energy consumption, active sensor nodes per round, and the sustainability period of the network. This proposed hybrid clustering and routing system aim to enhance the overall efficiency of wireless sensor networks by optimizing energy consumption and ensuring secure data transmission. The optimization model identifies the most suitable nodes in the routing cycle, starting with chosen cluster heads. The overarching goal is to enhance network indicators, including network lifespan, power consumption per node, and packet delivery percentage. The proposed solution achieved a network lifetime of 100 hours and a data delivery rate of 98%. additionally, it consumed the least amount of energy, measuring at 95,000 joules.

Adaptive Threshold Residual Energy-Based Efficient Sensor Network Protocol (ATREEN)

Wireless sensor network (WSN) hierarchical (clustering) routing involves the gathering of sensor nodes to form clusters in which sensor node communicates to cluster head (CH). The CH is responsible for data aggregation and forwarding collected data toward base station (BS). Energy expenditure in usual protocols becomes unequal because of clusters having diverse lengths. In this paper, we propose another new enhanced clustering protocol called adaptive threshold residual energy-based efficient sensor network protocol (ATREEN) for adaptive selection of CH in WSNs. In ATREEN, CHs are chosen by comparing threshold of node value with respect to distance between cluster member and CH, residual energy of sensor nodes, and position of sensor node with respect to BS. Simulation results show that ATREEN protocol extends network lifetime and stability with decline in energy dissipation compared to clustering protocols such as low-energy adaptive clustering hierarchy (LEACH), power-efficient gathering in sensor information systems (PEGASIS), and distributed energy-efficient clustering (DEEC) scheme for heterogeneous WSNs.

Smart geographical routing protocol achieving high QoS and energy efficiency based for wireless multimedia sensor networks

Over the recent years, multimedia applications have begun to be used over wireless sensor networks (WSN), which hitherto has been primarily used to carry small amount of data. To meet the demand for higher throughput and real-time characteristics of multimedia contents, the routing protocol need to be simplified and optimized. In this paper, we present a routing algorithm that provides a low end-to-end packet delay and low packet loss, referred to as smart, greedy forwarding algorithm based on throughput and energy-awareness (SGFTEM). This algorithm routes multimedia packets across a WSN by choosing high- throughput paths rather than always choosing the shorter path to the sink. When it encounters network holes, a network void-bypass is applied, which enhances the reliability of the network. Energy management is also implemented to extend sensor node lifetime by way of reducing the radio transmission coverage to a suitable distance reachable by the sensor nodes The simulation results shows that, SGFTEM reduces the end-to-end delay by 40% and packet loss ratio by 35% and maintain energy consumption against other algorithms. With energy management, the residual energy of sensor nodes are made to be equally distributed across the entire network which results in load balancing. The performance of SGFTEM are also compared via simulation against AGEM, TPGF, GPSR, and AODV showing superior performance under similar scenarios i.e. low packet end-to-end delay, low packet loss and load balancing of routing paths.

Coverage Control for Underwater Sensor Networks Based on Residual Energy燩robability

Underwater sensor networks have important application value in the fields of water environment data collection, marine environment monitoring and so on. It has some characteristics such as low available bandwidth, large propagation delays and limited energy, which bring new challenges to the current researches. The research on coverage control of underwater sensor networks is the basis of other related researches. A good sensor node coverage control method can effectively improve the quality of water environment monitoring. Aiming at the problem of high dynamics and uncertainty of monitoring targets, the random events level are divided into serious events and general events. The sensors are set to sense different levels of events and make different responses. Then, an event-driven optimization algorithm for determining sensor target location based on self-organization map is proposed. Aiming at the problem of limited energy of underwater sensor nodes, considering the moving distance, coverage redundancy and residual energy of sensor nodes, an underwater sensor movement control algorithm based on residual energy probability is proposed. The simulation results show that compared with the simple movement algorithm, the proposed algorithm can effectively improve the coverage and life cycle of the sensor networks, and realize real-time monitoring of the water environment.

Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments

Underwater acoustic sensor networks (UASNs) provide temporary links, which is of great significance when it comes to dealing with abnormal situations or emergencies in Internet of underwater things (IoUT). However, UASNs are susceptible to changes and uncertainties in network topology, channel conditions, etc., which can easily lead to frequent link interruptions. In this paper, we introduce a link failure prediction mechanism and an autonomous underwater vehicle (AUV)-assisted routing holes repairing mechanism for routing design of UASNs in uncertain ocean environments, to save system energy consumption and improve network connectivity. The proposed link failure prediction mechanism takes into account residual energy of sensor nodes, node drifting information, and uncertain ocean ambient noise. When the energy of multiple sensor nodes is exhausted, the particle swarm optimization algorithm (PSO) is adopted to calculate the optimal repair location, and an AUV is used for fixed point repairing. The proposed method can effectively reduce the energy consumption of sensor nodes, increase the packet delivery ratio, and extend the life of entire network of UASNs.

Fuzzy rule-based system for energy efficiency in wireless sensor networks

The sensor nodes consume a large amount of energy to transfer the sensed information directly to the base station (BS). To reduce the energy consumption from the direct transmission, clustering and routing techniques are used. In this paper, we propose clustering and routing algorithms called energy-efficient two-phase approach using fuzzy logic (EETPF). Here, the rule-based fuzzy logic is used to associate the input values of clustering and routing algorithm. The fuzzy system makes decisions according to the residual energy of sensor nodes, distance of sensor nodes from the BS and the number of nodes in the communication range. The crisp values of the input variables are converted into different fuzzy values. The fuzzy output values are converted to crisp values using centroid defuzzification method. The cluster heads (CHs) and routers are selected with respect to the output values. The sensor nodes get allocated to respective CHs according to the load handling capacity of CHs. The routing path is generated according to the capacity of routers. The simulations are conducted on evaluation factors such as energy consumption, active sensor nodes per round and sustainability period of the network. It is observed that EETPF outperforms state-of-the-art algorithms under these evaluation factors.

Cluster Head Selection in Heterogeneous Wireless Sensor Network Using a New Evolutionary Algorithm

In wireless sensor network (WSN), limited energy resources with the nodes is a complex challenge as far as data routing, collecting and aggregating the data is concerned as all these processes are energy demanding. Network lifetime, stability period, and potential of the WSN are some of the parameters which are to be maximized subject to the constraints. The cluster head selection in the heterogeneous wireless sensor network has not been explored much and needs to be improved further to discover the potential of WSN in this area. In this study, optimal cluster head selection in heterogeneous wireless sensor network through Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation has been suggested. The efficacy of the proposed technique is tested on Classical Benchmark Functions, and obtained results are compared with the state of the art of algorithms. This algorithm is also validated on a heterogeneous wireless sensor network with cluster head selection as a multi-objective optimization problem. The residual energy of sensor node and distance travelled are to be optimized in order to minimize the fitness function. Simulation suggested that the proposed algorithm is found to be reliable and outperforms in terms of remaining energy of nodes, alive nodes versus round, dead nodes versus rounds, the lifespan of network, throughput, and stability period.

Load aware multipath data forwarding for enhanced lifetime of WSN

Modern Wireless sensor network (WSN) requires strict constraints on delay and energy consumption. These networks are internet of things (IoT), multimedia sensor network, and body area network. It requires huge amount of bandwidth to deliver the data. Since, time critical data must be reached at sink node without loss because sensor network has energy limitations and hardware constraints. In this paper, an efficient multipath routing algorithm is proposed to enhanced the lifetime of the network. The proposed scheme, rather than one route, discovered independent routes from source to destination. The Lagrangian relaxation method is used to model hop to hop distance for prolonging the network lifetime. The proposed routing technique selects the multiple paths by focusing on hop count and the residual energy of sensor nodes. Simulation results show that the proposed routing protocol improves the network performance in terms of packet delivery ratio, energy consumption, first dead node and average end to end delay.

Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN.

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

Fuzzy based multi-level multi-constraint multi-path reliable routing in wireless sensor network

In the direction of keeping up longer life time of network, there is a limitation on the life-span of Wireless Sensor Network. In numerous applications, data to be sensed should be transmitted commencing from sources towards a sink in a timely way. The low effectiveness of data transmission is capable of routing with low quality of service and needed to guarantee network transmission. The proposed scheme Fuzzy Based Multi-level Multi-constraint Multi-path Reliable Routing contains the energy, delay and transmission reliability. Clustering is utilized for efficient aggregation of residual energy. Super Cluster Head (SCH) selection algorithm among the CHs based fuzzy concept is proposed. The parameters are Attempt Rate (AR), Residual Energy of Sensor Nodes, and distance to the base station) (Dist). Further cost function (CF) for the average residual energy and average end-to-end (ENE) delay, average transmission reliability (AR) for multipath routing network is proposed. The sensed data from SCH to a sink by assures minimum end-to-end delay and maximum transmission reliability and maximum residual energy with assuring reliable routing. Also it is utilized an optimization technique to adjust the parameters used in fuzzy clustering levels to optimize the performance. The work also includes performance comparisons with some selected algorithms. The results of a simulation are shown in NS2 tool and reflect that proposed work performs better than other existing protocols considering different metric used for comparing delay, throughput, PDR, Packet Drop and control overhead.

A New Energy Efficient Multitier Deterministic Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) may be incorporated with thousands of small nodes. This gives them the capability to effectively sense, communicate, and compute parameters. However, the security and life span of a WSN node is a primary concern. This paper is focused on introducing a mathematical model of a modified Multitier Deterministic Energy-Efficient Clustering (DEC) based on novel election multi-tier random probability protocol for agricultural WSNs to enhance the life span of a WSN node along with a comparison of it with existing DEC protocol. In the proposed model, the selection of cluster heads, (CH), is done based on the energy drain pattern and location of the sensor nodes, which increased the lifespan of sensor nodes. In addition, several WSN probabilistic routing protocols to save energy throughout data transmissions like Low Energy Adaptive Clustering Hierarchy (LEACH), Power-Efficient Gathering in Sensor Information Systems (PEGASIS), DEC, and Stable Election Protocol (SEP) are explained. Moreover, it has been found that, after some mathematical modification in existing DEC routing, it will be capable to give a more positive result and reduce the energy drain in WSN nodes using a selective cluster head technique based on residual energy of sensor nodes. The DEC protocol is also compared with our proposed modified protocol for showing the energy-efficiency. The energy efficiency of clustering is associated with the field of energy sustainability of wireless sensor networks which is in the scope of Symmetry journal.

Extending the Lifetime of Wireless Sensor Networks using an Improved Clustering Protocol

Wireless Sensor Network (WSN) is one of the underlying mechanics of IoT (Internet-of-Things) which has recently seen a worldwide interest by its use in several domains as military, automation, agriculture, environment, underwater and etc. Energy efficiency and reliability of transmitted messages are two of the major requirements for the surveillance and wireless detection applications in WSN. For this reason, a growing number of research studies are carried out. Several routing protocols have been developed to furnish better performance for optimizing the network's energy consumption in WSN; most of them are based in clustering and hierarchical topology. However, most of these routing protocols cannot consider all required and important metrics to increase the lifespan of the network. This work introduces an enhanced algorithm of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol called Balanced Current Energy-LEACH (BCE-LEACH) which aims to equity the power consumption of sensor nodes of the network due to extend the lifetime of the network. The proposed protocol is based on the residual energy of sensor nodes to pick out cluster heads (CHs) that means only nodes with sufficient current energy can participate in CH selection, and then it is focused on both current energy and the distance toward the base station (BS) to select a parent CH which has current energy greater than the mean energy of CHs and the distance to the BS is fewer than the average. This parent CH groups all data from other CHs. The root CH after doing data aggregation sends compressed information to the BS using the multi-hop process between CHs. The improved algorithm is simulated in MATLAB R2016b simulation tool. Simulation results in this paper indicate that the introduced protocol works better than LEACH through extending the lifetime of the WSN.

Energy Efficient Routing Structure to Avoid Energy Hole Problem in Multi-Layer Network Model

Wireless sensor network (WSN) is a communication network with several sensor nodes connected without any wired connection. Sensor nodes in the WSN are battery operated; therefore, recharging or replacement of the sensor node is a crucial task. Efficient energy consumption of sensor nodes is one of the significant challenges in WSN. In cluster based WSN, sensor nodes are grouped to form clusters. Gateway collects data from sensor nodes, aggregates data and transmits to the base station directly or through other gateways using the routing path. This causes heavy traffic load at the sink and forms an energy hole around the sink. In this paper, an energy efficient routing structure using the multi-layer network model is proposed to avoid the energy hole around the sink. The routing structure considers the one-to-one connection for all gateways to balance the overall load on the network. In the designed network model, the number of gateways increases in each layer towards the sink to minimize the traffic load around the sink. The extensive simulations on four different scenarios are performed in terms of network parameters such as the first gateway dies, first sensor node dies, half of the sensor nodes alive over the number of rounds, energy consumption rate and residual energy of sensor nodes and gateways. The experimental results are significant and proven the competency of the proposed algorithm.

EDB‐CHS‐BOF: energy and distance‐based cluster head selection with balanced objective function protocol

In this research, two novel cluster head selection protocols are proposed, namely, Energy and Distance Based Cluster Head Selection (EDB-CHS) and EDB-CHS with Balanced Objective Function (EDB-CHS-BOF). They mainly aim at balancing energy consumption amongst all sensor nodes, prohibiting their precocious death, and extending the network lifetime. More specifically, in EDB-CHS protocol, a new cluster shape is proposed and consequently a tight closed-form expression for the optimal number of cluster heads (CHs) in the network is derived. Additionally, an efficacious CHS algorithm is presented, which is an expression of threshold probability that takes into account the residual energy of sensor nodes, how far they are located from the Base Station (BS), as well as the node's optimal probability of being a CH. On the other hand, the EDB-CHS-BOF protocol is proposed mainly to address the matter of having long-distance communications as a result of obtaining adjacent CHs. In EDB-CHS-BOF protocol, a new threshold probability for each sensor node to be a CH in any round is proposed. Not only to this extent, but rather a balanced objective function is proposed to ensure a well-proportioned distribution of CHs over the network. Simulation results demonstrate the superiority of the proposed protocols over other directly related works in terms of network lifetime and total data delivery.

Energy Map Generation in Wireless Sensor Network using Grey System Theory

Wireless sensor nodes are deployed in hostile environment as applications of wireless sensor network. Battery is the source of energy of these sensor nodes. Replacing their batteries is not feasible due to their deployment in hostile area. In the proposed research, main objective is to extend the lifetime of network by predicting the residual energy of sensor nodes. For enhancing the lifetime of the wireless sensor network, it is necessary to keep track of residual energy level. Tracking residual energy status of sensor nodes is helpful in creating the energy map for network. In this paper, an approach to predict the residual energy level and to generate energy map for wireless sensor network is proposed. Proposed algorithm is used with clustering algorithm. Simulation results show that proposed algorithm reduces number of the messages transmitted which intern increases network lifetime.

A Modified Energy-Efficient Clustering with Splitting and Merging for Wireless Sensor Networks using Cluster-Head Handover Mechanism

Energy efficiency is one of the most important challenges for Wireless Sensor Networks (WSNs). This is due to the fact that sensor nodes have limited energy capacity. Therefore, the energy of sensor nodes has to be efficiently managed to provide longer lifetime for the network. To reduce energy consumption in WSNs, a modified Energy Efficient Clustering with Splitting and Merging (EECSM) for WSNs using Cluster-Head Handover Mechanism was implemented in this research. The modified model used information of the residual energy of sensor nodes to select backup Cluster Heads (CHs) while maintaining a suitable CH handover threshold to minimize energy consumption in the network. The backup CHs take over the responsibilities of the CHs once the handover threshold is reached. The modified model was validated in terms of network lifetime and residual energy ratio with EECSM using MATLAB R2013a. Average improvements of 7.5% and 50.7% were achieved for the network lifetime and residual energy ratio respectively which indicates a significant reduction in energy consumption of the network nodes. Keywords— Clustering, Energy-Efficiency, Handover, Lifetime, Wireless Sensor Network

Energy-Efficient Harvested-Aware Clustering and Cooperative Routing Protocol for WBAN (E-HARP)

Wireless body area network (WBAN) is an interconnection of small bio-sensor nodes (BSNs) that are deployed in/on different parts of human body. It is used to sense health-related data, such as rate of heart beat, blood pressure, blood glucose level, electro-cardiogram (ECG), and electro-myography (EMG), of human body and pass these readings to real-time health monitoring systems. WBANs are the important research area and are used in different applications, such as medical field, sports, entertainment, and social welfare. BSNs or simply called sensor nodes (SNs) are the main backbone of WBANs. SNs normally have very limited resources due to its smaller size. Therefore, minimum consumption of energy is an essential design requirement of the WBAN schemes. In the proposed work, Energy-efficient Harvested-Aware clustering and cooperative Routing Protocol for WBAN (E-HARP) are presented. The presented protocol mainly proposes a novel multi-attribute-based technique for dynamic cluster head (CH) selection and cooperative routing. In the first phase of this two-phased technique, optimum CH is selected among the cluster members, based on calculated cost factor (CF). The parameters used for calculation of CF are residual energy of SN, required transmission power, communication link signal-to-noise ratio (SNR), and total network energy loss. In order to distribute load on one CH, E-HARP selects new CH in each data transmission round. In the second phase of E-HARP, data are routed with cooperative effort of the SN, which saves the node energy by prohibiting the transmission of redundant data packets. To evaluate the performance of the proposed technique, comprehensive experimentations using the NS-2 simulation tool has been conducted. The results are compared with some latest techniques, named EH-RCB, ELR-W, Co-LAEEBA, and EECBSR. The acquired results show a significant enhancement of E-HARP in terms of network stability, network life time, throughput, end-to-end delay, and packet delivery ratio.

A Green Routing Algorithm for IoT-Enabled Software Defined Wireless Sensor Network

Rapid growth in the domain of Internet of Things (IoT) leads to massive deployment of sensors and therefore the need to develop automatically reconfigurable complex wireless sensor network. Software defined networking (SDN) is a promising and widely adopted technique to automatic reconfigure the wireless sensor network. In SDN, control nodes are dynamically selected (to activate the functioning of the sensor network) in order to assign the tasks to other nodes and for routing data packets to control server. For residual energy of sensor nodes, and transmission distance among sensor nodes, the problem of control node selection has been formulated as an NP-hard problem. Usually, smart sensing devices of IoT suffers from low battery which are not frequently rechargeable. Therefore, an energy efficient routing mechanism is required to operate software defined wireless sensor network (SDWSN). In this paper, a green routing algorithm using fork and join adaptive particle swarm optimization (FJAPSO) is proposed to maximize the lifetime of sensor network. FJAPSO acts at two levels for auto optimization: optimal number of control nodes and optimal clustering of control nodes. Experimental results evidenced that FJAPSO outperforms other state of the art and significantly maximizes the lifetime of the sensor network.

Clustering Routing Protocol for Mobile Wireless Sensor Networks Based on Dynamic Network Partition

<p class="0abstract"><span lang="EN-US">To prolong the network life and improve the network performance, a new clustering routing protocol which uses dynamic network to divide and consider the residual energy of sensor nodes is proposed. The design and simulation of the clustering scheme for mobile wireless sensor networks are also studied. The results show that the protocol controls the number of cluster heads by region division of the network and balances its distribution in wireless sensor networks according to the latest location of the sink node. At the same time, the residual energy of nodes is considered to balance the energy consumption of the network when the cluster head is elected. The protocol effectively solves the problem of uneven distribution of cluster heads in time and space and energy consumption of each node in the LEACH protocol. Compared with the LEACH protocol, M-LEACH effectively prolongs the network life. From this, it is seen that the application of M-LEACH protocol in mobile wireless sensor network clustering routing is effective.</span></p>

A Novel Load Balancing Scheduling Algorithm for Wireless Sensor Networks

Clustering is a well-known approach to cope with large nodes density and efficiently conserving energy in wireless sensor networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Distributed Energy efficient Adaptive Clustering Protocol (DEACP) with data gathering and Load-balancing is proposed. The DEACP approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed DEACP protocol increases the profit of energy, and prolongs the network lifetime duration from 32 to 40% compared to DEEAC reference protocol and from 25 to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to fist node die, and 25.5% better than FEMCHRP with respect to last node die while maintaining the average data transmission delay. We found also that DEACP achieved 66.5% and 40.6% more rounds than DEEAC and FEMCHRP respectively.