Balanced Optimization Algorithm for Regional Distance in Wireless Sensor Networks

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Restricted battery life is a crucial constraint in wireless sensor networks (WSNs). Many routing protocols have been proposed to reduce the energy consumption by extending the network lifetime. So, a new design of energy efficient routing using clustering is proposed where we use heterogeneous nodes for data transmission through the optimal path between cluster heads (CHs) and the base station (BS). This paper discusses the algorithm called Advanced-Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with heterogeneous nodes. Multi-hop LEACH protocol changes the mode of communication to prolong the network lifetime from single hop to multi-hop between the CH and BS by reducing energy consumption. The paper tries to evaluate the performance of the network in terms of lifetime and stability by introducing the concept of optimum number of clusters and also considers the optimum number of hops in the network with heterogeneous nodes. This paper also provides the algorithm for the avoidance of the overlapping of the clusters and collisions in the network. For the evaluation and comparison of the performance of advanced single-hop and multi-hop energy efficient LEACH protocols with heterogeneous nodes, MATLAB simulations are carried out. Results show that advanced-multi-hop LEACH protocol outperforms the advanced single-hop LEACH protocol regarding energy efficiency, stability and network lifetime.

The present study compared the newly proposed fault tolerance multipath routing protocol (MRP-FT) under the particle swarm optimization-based fault tolerant routing (PSO-FT) technique to the existing low-energy adaptive clustering hierarchy (LEACH) protocol applied in different combinations of inter-node numbers (N ) ranging from 100 to 300 nodes (N100−300) in wireless sensor networks (WSNs) with different area sizes (Xm × Ym) of 100–1000 m2 (WSN100−1000) to enhance energy efficiency and reliability of WSNs. The implementation of MRP-FT protocol significantly decreased the packet overhead by 36.0–69.5%, delay by 40.4–52.9%, and energy consumption by 35.9–52.9%, while increasing reliability by 97.0–104.1%, compared to the existing LEACH protocol. For instance, at N100−200, the LEACH simulated packet overhead increased from 76-89 packets at t = 500 s with an increase in WSN size from 100 to 1000. At N300 + WSN100, the packet overhead showed the highest decrease of ca. 69.5% with MRP-FT over the LEACH protocol. The implementation of the MRP-FT protocol reduced energy consumption by 35–123 J over the LEACH protocol.

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

In recent literature surveys low energy adaptive clustering hierarchy (LEACH) protocol got more attention in Wireless Senor Network (WSN) for improving network lifetime in terms of energy consumption. Many versions of LEACH are coming with modifications with single hop communication to multi-hop communications. More work is done on LEACH protocol, and it is a good start for researchers in WSN. In this paper we surveyed various LEACH based routing protocols are discussed with their working models in WSN. This survey classified in two ways single hop communication and multihop communication on basis of transmission in cluster to base station and explains about hierarchical routing protocols with LEACH. This survey describes about various versions of LEACH with analysis of various characteristics like network lifetime, communication type, clustering mechanisms, and so on, of LEACH protocol. This survey describes about pros and cons of each version of LEACH. At last, the paper finishes up with ideas on future research in WSN.

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques.

Wireless Sensor Network (WSN) is the deployed randomly and on the far places to sense information. The security, quality of service and energy consumption is the major issues of WSN. To minimize the consumption of higher amount of energy in these networks, clustering is applied. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for the cluster head selection in the network. The cluster head is selected based on the energy consumption and distance to base station. A node is selected as Cluster Head (CH) if it has the highest amount of energy and the least distance from base station. In this research work, genetic algorithm is applied with the LEACH protocol for the cluster head selection. The proposed work is implemented in Matlab. The results of existing LEACH protocol is compared with proposed LEACH protocol in terms of certain parameters. The comparative analysis and achieved outcomes show that the proposed approach performs well in terms of energy consumption as it consumes lesser amount of energy.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

Due to the limitation of power and memory size for WSN (wireless sensor networks), the routing protocol of wireless sensor networks must maintain small routing information and reduce the power consumption as much as possible. LEACH (low-energy adaptive clustering hierarchy) protocol and PEGASIS (power-efficient gathering in sensor information systems) protocol are analyzed firstly in this paper. Use for reference of the ideas used in both of the two protocols of reducing power dissipation, a three-layered routing protocol for WSN based on LEACH (TL-LEACH) is given. Then, this improved LEACH protocol is simulated and the simulation results show that TL-LEACH protocol is with greatly improved WSN lifetime than LEACH protocol.

Generally, optimization is defined as the process of determining the minimum or maximum value that depends on the function of the goal, even now there are many problems regarding optimization. One of them is the problem regarding the selection of goods to be included in a limited storage medium called Knapsack problem. Knapsack problems have different types and variations. This study will solve the problem of bounded knapsack multiple constraints by implementing the Grey Wolf Optimizer (GWO) algorithm. The problem of bounded knapsack multiple constraints has more than one subject with the items that are inserted into the dimension storage media can be partially or completely inserted, but the number of objects is limited. The aim of this study is to determine the results of using the Grey Wolf Optimizer (GWO) algorithm for solving the problem of multiple constraints bounded knapsack and compare the optimal solutions obtained by the simplex method using the Solver Add-In in Microsoft Excel. The data used in this study is primary data. There are two parameters to be tested, namely population parameters and maximum iteration. The test results of the two parameters show that the population parameters and maximum iterations have the same effect, where the greater the value of the population parameters and the maximum iteration, the results obtained are also getting closer to the optimal value. In addition, based on the results of the final experiment it is known that the comparison of the results of the GWO algorithm and the simplex method has a fairly small percentage deviation which indicates that the GWO algorithm produces results that are close to the optimal value.
 Keywords: GWO algorithm, Knapsack, Multiple Constraints Bounded Knapsack.

Recently, special types of routing protocols are designed for sensor networks. Almost all of these routing protocols have considered the energy efficiency as the ultimate objective in order to maximize the lifetime of the whole network. The different types of routing protocols available in WSN are data centric, hierarchical, and location-based and on demand routing protocols. Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is one of the best hierarchical clustering protocols with probabilistic model to manage the energy consumption of WSN. However, LEACH uses global information without considering the local information i.e. the residual energy of each node for CH election process. Hence, Deterministic Energy-efficient Clustering protocol (DEC), a deterministic model is developed by using clustering scheme to manage and organize the WSN with mobile scenario. This protocol promises a better election of cluster-heads based on residual energy information and it is more energy efficient than LEACH protocols. Ad hoc on demand Distance Vector (AODV) is another routing protocol used in sensor network to support mobility. AODV is also on demand routing protocol. However, AODV do not provide multipath loop free and has link disjoint path. Hence, Ad hoc On-demand Multipath Distance Vector (AOMDV) is developed for WSN to guarantee loop freedom and disjointness of alternate paths. Frequent route discovery and route failure is occurred in AOMDV protocols due to the involvement of repeated mobile nodes without checking the energy levels of those nodes for packet transmission. To reduce the route failures occurred by the usage of nodes with reduced energy, energy aware ad hoc on-demand multipath distance vector routing protocol (E-AOMDV) is developed by incorporating energy aware concept in AOMDV protocol to increase the network performance. In this paper, the performance analysis namely residual energy of DEC protocol is examined for various packet length and nodes. Delivery ratio analysis of EAOMDV protocol is also done in this paper for various simulation times and nodes. The simulation results shows that DEC has better performance than LEACH and also E-AOMDV has better performance than AOMDV.

There are many existing clustering protocols that aim at making the sensor network stay functioning longer out of which Low-Energy Adaptive Clustering Hierarchy (LEACH) and Threshold-based LEACH (T-LEACH) protocols. T-LEACH protocol takes advantage of LEACH main deficiency, which is about having high control overhead. In other words, T-LEACH proposes that cluster heads do not have to turn over every round but rather every batch of rounds. Nodes will keep serving as cluster heads as long as their energy is higher than a threshold energy. This article imposes upon major drawbacks of T-LEACH and proposes a Modified Threshold-based Cluster Head Replacement (MT-CHR). In MT-CHR, a new probability of being a cluster head, for any node in any round, has been proposed which agrees fairly with the assumptions introduced in LEACH protocol.

Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. In this study, new methods based on the election algorithms (EA) and gray wolf optimizer (GWO) algorithms were used to determine the optimal cultivation pattern in Moghan plain during the statistical years of 2007-2016. The objective function in the agricultural sector was based on each product and its yield, net from each product and the cultivar. Then, maximization of the objective function was performed using GWO and EA algorithm. The results of using GWO algorithm in determining the optimal crop pattern in Moghan plain showed that using economic policies such as changing the cultivar pattern, we can obtain a better result compared to EA algorithm in the agricultural sector. In general, the results of GWO algorithm showed that in the Moghan plain with 0.9, 140 billion rials, that is, about 42 % will have economic growth. In sum, the results showed that GWO algorithm with high values of statistical criteria (R2=0.96, RMSE=0.022 and NSE=0.75) has a higher efficiency in optimizing the crop cultivation pattern of Moghan plain, which can be applied to the correct planning for other cultivation areas to be employed.

The requirement for operation with low energy consumption is one of the major constraints that guide the de- sign of routing protocol in Wireless Sensor Networks (WSN) because each node is powered by a limited and generally an irreplaceable source of energy. In this context, we would like to propose a new protocol which respects the energy constraints, nodes' life duration and packets reception. This work is based on the protocol LEACH-C (Low Energy Adaptive Clustering Hierarchy) which uses a centralized architecture to select clus- ter-heads while involving the base station and the sensors' local information. It addresses the isolation problem of remote nodes that fail to communicate their information (coordinates and residual energies) to the base station. This results in an ineffi- cient group formation that leads to a huge loss of data and there- fore the network loses its performance. For this, we propose a solution that takes into account the remote nodes during the formation of groups. This new protocol, called LEACH-CKM, uses the K-means classification method to group the nodes and Minimum Transmission Energy routing protocol (MTE) to route information from remote nodes. Keywords—WSN; Routing Protocol; Energy Optimization; Clustering.