Node localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

Vehicular Ad Hoc Networks (VANETs) is an emerging technology that can be applied in intelligent transportation systems. Routing protocols are essential for obtaining reliable VANET networks. This article proposes a novel framework, designated as Reliability Aware Multi-Objective Optimization Based VANETs Routing (RAMO). The framework includes three levels: the first is the simulation of the VANET system; the second is the routing criteria, based on reliability and geometrics; the third level is the routing algorithm. The actual network is the next stage. Furthermore, the framework includes an optimization block that controls the parameters of each of the reliability, geometrical and routing blocks. The optimization is presented under the multi-objective perspective and based on the development of a novel variant of multi-objective harmony searching. This has been designated as Enhanced Gaussian Mutation Harmony Searching (EGMHS), which includes Gaussian mutation, objective decomposition and a harmony memory extraction algorithm. The evaluation was performed based on two levels. The first was the EGMHS evaluation using nine benchmarking mathematical functions, while the second was the RAMO evaluation based on the network simulator. The metrics obtained, including set coverage, delta metric, hyper-volume, packet delivery ratio (PDR) and end-to-end (E2E) delay, demonstrate the superiority over the baseline approaches of both EGMHS and RAMO with EGMHS. 

Reliability-Aware Multi-Objective Optimization-Based Routing Protocol for VANETs Using Enhanced Gaussian Mutation Harmony Searching

In recent times, Wireless Sensor Networks (WSNs) are becoming more and more popular and are making significant advances in wireless communication thanks to low-cost and low-power sensors. However, since WSN nodes are battery-powered, they lose all of their autonomy after a certain time. This energy restriction impacts the network’s lifetime. Clustering can increase the lifetime of a network while also lowering energy use. Clustering will bring several similar sensors to one location for data collection and delivery to the Base Station (BS). The Cluster Head (CH) uses more energy when collecting and transferring data. The life of the WSNs can be extended, and efficient identification of CH can minimize energy consumption. Creating a routing algorithm that considers the key challenges of lowering energy usage and maximizing network lifetime is still challenging. This paper presents an energy-efficient clustering routing protocol based on a hybrid Mayfly-Aquila optimization (MFA-AOA) algorithm for solving these critical issues in WSNs. The Mayfly algorithm is employed to choose an optimal CH from a collection of nodes. The Aquila optimization algorithm identifies and selects the optimum route between CH and BS. The simulation results showed that the proposed methodology achieved better energy consumption by 10.22%, 11.26%, and 14.28%, and normalized energy by 9.56%, 11.78%, and 13.76% than the existing state-of-art approaches.

/pdf/a-hybrid-mayfly-aquila-optimization-algorithm-based-energy-3w1376c0.pdf

A Hybrid Mayfly-Aquila Optimization Algorithm Based Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are considered to be among the most important scientific domains. Yet, the exploitation of WSNs suffers from the severe energy restrictions of their electronic components. For this reason there are numerous scientific methods that have been proposed aiming to achieve the extension of the lifetime of WSNs, either by energy saving or energy harvesting or through energy transfer. This study aims to analytically examine all of the existing hardware-based and algorithm-based mechanisms of this kind. The operating principles of 48 approaches are studied, their relative advantages and weaknesses are highlighted, open research issues are discussed, and resultant concluding remarks are drawn.

/pdf/energy-sustainability-in-wireless-sensor-networks-an-28omb1qe.pdf

Energy Sustainability in Wireless Sensor Networks: An Analytical Survey

Vehicular Ad Hoc Networks (VANETs) is an emerging technology that can be applied in intelligent transportation systems. Routing protocols are essential for obtaining reliable VANET networks. This article proposes a novel framework, designated as Reliability Aware Multi-Objective Optimization Based VANETs Routing (RAMO). The framework includes three levels: the first is the simulation of the VANET system; the second is the routing criteria, based on reliability and geometrics; the third level is the routing algorithm. The actual network is the next stage. Furthermore, the framework includes an optimization block that controls the parameters of each of the reliability, geometrical and routing blocks. The optimization is presented under the multi-objective perspective and based on the development of a novel variant of multi-objective harmony searching. This has been designated as Enhanced Gaussian Mutation Harmony Searching (EGMHS), which includes Gaussian mutation, objective decomposition and a harmony memory extraction algorithm. The evaluation was performed based on two levels. The first was the EGMHS evaluation using nine benchmarking mathematical functions, while the second was the RAMO evaluation based on the network simulator. The metrics obtained, including set coverage, delta metric, hyper-volume, packet delivery ratio (PDR) and end-to-end (E2E) delay, demonstrate the superiority over the baseline approaches of both EGMHS and RAMO with EGMHS.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09723051.pdf

Reliability-Aware Multi-objective Optimization-based Routing Protocol for VANETs using Enhanced Gaussian Mutation Harmony Searching

Wireless sensor networks (WSNs) plays a significant role in the field of surveillance, monitoring the real time applications. Regardless its strong ability to handle such tasks, it is difficult to maintain a trade-off between the conflicting goals of network lifetime, transmission delay, high coverage and packet loss. Various solutions have been proposed by the researchers to address these issues comprising the solution in real-time network scenarios. This paper delivers a brief analysis of the solutions addressing recent research problems in WSN comprising conflicting goals, i.e. multi-objective optimization (MOO) technique. Firstly, an illustration of key optimization objective in WSNs is given which constitutes existing issues such as power control, rate control ant routing. Then, an elaboration of various objective functions used in MOO with its merits and demerits is also provided. Later, existing approaches for improving optimizing metric, applications performance of existing approaches and proposed architecture have been discussed.

Rashmi Chaudhry

Papers

Multi-Objective Optimization in WSN: Opportunities and Challenges