Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.Comment: To appear in Journal of Computer and System Science

Ant Colony Optimization for Network Aggregation in a Fully Connected Network

Ants Colony exhibit complex structure called ant streets. We were observed in several situations such as highway or may have placed some obstacle in the way and see ant’s reaction to such disturbances. One of the most surprising behavioral patterns by ants is the ability to find shortest path which is the challenge of today’s computer scientists. Ant Colony Optimization is a probabilistic technique for solving computational problems. This paper explains Ant Colony Optimization is an effective approach for finding a shortest path between two nodes on a network. DOI: 10.17762/ijritcc2321-8169.15067

Hybrid Swarm Intelligence Energy Efficient Clustered Routing Algorithm for Wireless Sensor Networks

Currently, wireless sensor networks (WSNs) are used in many applications, namely, environment monitoring, disaster management, industrial automation, and medical electronics. Sensor nodes carry many limitations like low battery life, small memory space, and limited computing capability. To create a wireless sensor network more energy efficient, swarm intelligence technique has been applied to resolve many optimization issues in WSNs. In many existing clustering techniques an artificial bee colony (ABC) algorithm is utilized to collect information from the field periodically. Nevertheless, in the event based applications, an ant colony optimization (ACO) is a good solution to enhance the network lifespan. In this paper, we combine both algorithms (i.e., ABC and ACO) and propose a new hybrid ABCACO algorithm to solve a Nondeterministic Polynomial (NP) hard and finite problem of WSNs. ABCACO algorithm is divided into three main parts: (i) selection of optimal number of subregions and further subregion parts, (ii) cluster head selection using ABC algorithm, and (iii) efficient data transmission using ACO algorithm. We use a hierarchical clustering technique for data transmission; the data is transmitted from member nodes to the subcluster heads and then from subcluster heads to the elected cluster heads based on some threshold value. Cluster heads use an ACO algorithm to discover the best route for data transmission to the base station (BS). The proposed approach is very useful in designing the framework for forest fire detection and monitoring. The simulation results show that the ABCACO algorithm enhances the stability period by 60% and also improves the goodput by 31% against LEACH and WSNCABC, respectively

Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering

Study on Different Topology Manipulation Algorithms in Wireless Sensor Network

Wireless sensor network (WSN) comprises of spatially distributed autonomous sensors to screen physical or environmental conditions and to agreeably go their information through the network to a principle area. One of the critical necessities of a WSN is the efficiency of vitality, which expands the life time of the network. At the same time there are some different variables like Load Balancing, congestion control, coverage, Energy Efficiency, mobility and so on. A few methods have been proposed via scientists to accomplish these objectives that can help in giving a decent topology control. In the piece, a few systems which are accessible by utilizing improvement and transformative strategies that give a multi target arrangement are examined. In this paper, we compare different algorithms' execution in view of a few parameters intended for every target and the outcomes are analyzed. DOI: 10.17762/ijritcc2321-8169.15029

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

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

Clustered chain founded on ant colony optimization energy efficient routing scheme for under-water wireless sensor networks

One challenge in under-water wireless sensor networks (UWSN) is to find ways to improve the life duration of networks, since it is difficult to replace or recharge batteries in sensors by the solar energy. Thus, designing an energy-efficient protocol remains as a critical task. Many cluster-based routing protocols have been suggested with the goal of reducing overall energy consumption through data aggregation and balancing energy through cluster-head rotation. However, the majority of current protocols are concerned with load balancing within each cluster. In this paper we propose a clustered chain-based energy efficient routing algorithm called CCRA that can combine fuzzy c-means (FCM) and ant colony optimization (ACO) create and manage the data transmission in the network. Our analysis and results of simulations show a better energy management in the network

Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs

Wireless Sensor Networks (WSNs) are a particular type of distributed self-managed network with limited energy supply and communication ability. The most significant challenge of a routing protocol is the energy consumption and the extension of the network lifetime. Many energy-efficient routing algorithms were inspired by the development of Ant Colony Optimisation (ACO). However, due to the inborn defects, ACO-based routing algorithms have a slow convergence behaviour and are prone to premature, stagnation phenomenon, which hinders further route discovery, especially in a large-scale network. This paper proposes a hybrid routing algorithm by combining the Artificial Fish Swarm Algorithm (AFSA) and ACO to address these issues. We utilise AFSA to perform the initial route discovery in order to find feasible routes quickly. In the route discovery algorithm, we present a hybrid algorithm by combining the crowd factor in AFSA and the pseudo-random route select strategy in ACO. Furthermore, this paper presents an improved pheromone update method by considering energy levels and path length. Simulation results demonstrate that the proposed algorithm avoids the routing algorithm falling into local optimisation and stagnation, whilst speeding up the routing convergence, which is more prominent in a large-scale network. Furthermore, simulation evaluation reports that the proposed algorithm exhibits a significant improvement in terms of network lifetime