Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

Routing packets from the source node to the destination node in wireless sensor networks WSN is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed three (3) other common algorithms in static WSN environment in terms of throughput, energy consumption and energy efficiency which will result to reduction of packet loss rate during packet routing and increase of network lifetime

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance

Drone Base Station Trajectory Management for Optimal Scheduling in LTE-Based Sparse Delay-Sensitive M2M Networks

Providing connectivity in areas out of reach of the cellular infrastructure is a very active area of research. This connectivity is particularly needed in case of the deployment of machine type communication devices (MTCDs) for critical purposes such as homeland security. In such applications, MTCDs are deployed in areas that are hard to reach using regular communications infrastructure while the collected data is timely critical. Drone-supported communications constitute a new trend in complementing the reach of the terrestrial communication infrastructure. In this study, drones are used as base stations to provide real-time communication services to gather critical data out of a group of MTCDs that are sparsely deployed in a marine environment. Studying different communication technologies as LTE, WiFi, LPWAN and Free-Space Optical communication (FSOC) incorporated with the drone communications was important in the first phase of this research to identify the best candidate for addressing this need. We have determined the cellular technology, and particularly LTE, to be the most suitable candidate to support such applications. In this case, an LTE base station would be mounted on the drone which will help communicate with the different MTCDs to transmit their data to the network backhaul. We then formulate the problem model mathematically and devise the trajectory planning and scheduling algorithm that decides the drone path and the resulting scheduling. Based on this formulation, we decided to compare between an Ant Colony Optimization (ACO) based technique that optimizes the drone movement among the sparsely-deployed MTCDs and a Genetic Algorithm (GA) based solution that achieves the same purpose. This optimization is based on minimizing the energy cost of the drone movement while ensuring the data transmission deadline missing is minimized. We present the results of several simulation experiments that validate the different performance aspects of the technique

Research Challenges of Improved Cluster Chain Power-Efficient Routing Using Natural Computing Methods for Wireless Sensor Network

Wireless Sensor Networks (WSNs) primarily operate on batteries, making energy conservation crucial, especially in routing processes. Efficient routing in WSNs is challenging due to the network's distinct attributes. Among various routing protocols, CCPAR is noteworthy as it utilizes a chain between cluster heads to relay data to the base station. This research delves into nature-inspired techniques for energy-efficient routing in WSNs. It introduces the Moth-Dolphin Optimization Algorithm, capitalizing on the communication between moths to enhance routing performance. This innovative method combines the navigational skills of moths and the communicative attributes of dolphins. When benchmarked against other optimization methods, the Moth-Dolphin algorithm offers favorable results. The study then applies this algorithm to improve CCPAR routing, aiming for reduced energy consumption. The modified routing's effectiveness is evaluated against other top-tier algorithms, considering factors like energy consumption, throughput, network longevity, and delay

Efficient and optimal routing using ant colony optimization mechanism for wireless sensor networks

Recently more number of routing protocols is discovered for better data routing in Wireless Sensor Network (WSN). However link failures exist in the network due to appearance of low energy nodes, low link gap connectivity while routing, etc. To compute low complexity routes and to minimize the energy consumption a nature bio inspired algorithm Ant Colony Optimization (ACO) mechanism is applied in the sensor networks. An Efficient and Optimal Routing using ACO is proposed. The premium route is determined with sub-premier nodes having high link-gap connectivity factor. The best premier nodes are selected from the sub-premier nodes on basis of bandwidth integrity and eternal energy factors for determining the premium route. The proposed work is validated by comparing the results of other existing techniques. The performance metrics proves that the proposed mechanism exhibits better throughput and delivery rate with low loss rate

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks

Multipath Ant Colony Optimization Algorithm (MBEEACO) to Improve the Life Time of MANET

MANET selects a path with least number of intermediate nodes to reach the destination node. As the distance between each node increases, the quantity of transmission control increases. The power level of nodes affects the simplicity with which a route is constituted between a couple of nodes. This research paper utilizes the swarm intelligence technique through the artificial bee colony (ABC) algorithm to optimize the energy consumption in a dynamic source routing (DSR) protocol in MANET. The ABC algorithm is used to identify the optimal path from the source to the destination to overcome energy problems. The performance of the proposed MBEEACO algorithm is compared with DSR and bee-inspired protocols. The comparison was conducted based on average energy consumption, average throughput, average end-to-end delay, routing overhead, and packet delivery ratio performance metrics, varying the node speed and packet size. The proposed MBEEACO algorithm is superior in performance than other protocols in terms of energy conservation and delay degradation relating to node speed and packet size

Routing Based Ant Colony Optimization in Wireless Sensor Networks

Wireless Sensor Networks (WSN2019;s) have become an important and challenging issue in last year. Wireless Sensor Networks consist of nodes with limited power are deployed to gather useful information from the field and send the gathered data to the users. In WSNs, it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. In this paper, we introduce a heuristic way to reduce energy consumption in WSNs routing process using Ant Colony Optimization. We introduce three Ant Colony Optimization algorithms, the Ant System, Ant Colony System and improved AS and their application in WSN routing process. The simulation results show that ACO is an effective way to reduce energy consumption and maximize WSN lifetime