Energy efficient chaotic whale optimization technique for data gathering in wireless sensor network

A Wireless Sensor Network includes the distributed sensor nodes using limited energy, to monitor the physical environments and forward to the sink node. Energy is the major resource in WSN for increasing the network lifetime. Several works have been done in this field but the energy efficient data gathering is still not improved. In order to amend the data gathering with minimal energy consumption, an efficient technique called chaotic whale metaheuristic energy optimized data gathering (CWMEODG) is introduced. The mathematical model called Chaotic tent map is applied to the parameters used in the CWMEODG technique for finding the global optimum solution and fast convergence rate. Simulation of the proposed CWMEODG technique is performed with different parameters such as energy consumption, data packet delivery ratio, data packet loss ratio and delay with deference to dedicated quantity of sensor nodes and number of packets. The consequences discussion shows that the CWMEODG technique progresses the data gathering and network lifetime with minimum delay as well as packet loss than the state-of-the-art methods

An Energy Driven Architecture for Wireless Sensor Networks

Most wireless sensor networks operate with very limited energy sources-their batteries, and hence their usefulness in real life applications is severely constrained. The challenging issues are how to optimize the use of their energy or to harvest their own energy in order to lengthen their lives for wider classes of application. Tackling these important issues requires a robust architecture that takes into account the energy consumption level of functional constituents and their interdependency. Without such architecture, it would be difficult to formulate and optimize the overall energy consumption of a wireless sensor network. Unlike most current researches that focus on a single energy constituent of WSNs independent from and regardless of other constituents, this paper presents an Energy Driven Architecture (EDA) as a new architecture and indicates a novel approach for minimising the total energy consumption of a WS

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Trust Score based Optimized Cluster Routing (TSOCR) approach for Enhancing the Lifetime of Wireless Sensor Networks

Energy efficiency is the most significant obstacle that Wireless Sensor Networks (WSN) must overcome, and the desire for solutions that maximize energy efficiency will never go away. There are a variety of methods that can be utilized to improve energy efficiency, with data transmission as the primary driver of maximum energy consumption. The transmission of data from the source to destination nodes uses more energy. When the transmission of data is handled better, the energy efficiency is improved and the lifetime of the network is increased. The purpose of this research is to propose an Trust Score based Optimized Cluster Routing (TSOCR)  scheme for WSNs, which is based on Whale Optimization Algorithm (WOA). A total trust score is derived by combining the results of computing three distinct trust scores, such as the direct, indirect, and the most recent trust score. The path that has the highest trust score is chosen as the route and employed for data transmission. The effectiveness of the work is evaluated by looking at factors such as the rate of packet delivery, the latency, the amount of energy consumed and the lifetime of the network

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

DESIGN OF A MINIMAL OVERHEAD CONTROL TRAFFIC TOPOLOGY DISCOVERY AND DATA FORWARDING PROTOCOL FOR SOFTWARE-DEFINED WIRELESS SENSOR NETWORKS

Software-defined networking is a novel concept that is ported into wireless sensor networks to make them more manageable and customizable. unfortunately, the topology discovery and maintenance processes generate high overhead control packet exchange between the sensor nodes and the central controller leading to a deterioration of the network's performance. In this paper, a novel minimal overhead control traffic topology discovery and data forwarding protocol is proposed and detailed. The proposed protocol requires some changes to the topology discovery protocol implemented in SDN-WISE to improve its performance. The proposed protocol has been implemented within the IT-SDN framework for evaluation. The results show reduced overhead control traffic and increase, of about 20%, data packet delivery rate over the protocol in SDN-WISE

Secure Clustering and Routing using Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm for Wireless Sensor Networks

Wireless Sensor Network (WSN) receives huge attention from various remote monitoring applications because of its self configuration, ease of maintenance and scalability features. But, the sensors of the WSNs vulnerable to malicious attackers due to the energy constraint, open deployment and lack of centralized administration. Therefore, the secure routing is established for achieving the secure and reliable data broadcasting in the WSN. In this paper, an Adaptive Decision and Levy Flight based Artificial Hummingbird Algorithm (ADLFAHA) is proposed for performing an effective secure routing under the blackhole and Denial of Service (DoS) attacks. The ADLFAHA is developed to perform Secure Cluster Head (SCH) selection and secure path identification according to the trust, energy, load and communication cost. An adaptive decision strategy and levy flight incorporated in the ADLFAHA is used to enhance exploration and achieves global optimization capacity that helps to enhance the searching process. Moreover, the developed ADLFAHA helps to avoid the congestion among the nodes by balancing the load in network. The ADLFAHA is analyzed using End to End Delay (EED), throughput, Packet Delivery Ratio (PDR) and overhead. The existing researches such as Firebug Optimized Modified Bee Colony (FOMBC) and Lightweight Secure Routing (LSR) are used to compare the ADLFAHA. The PDR of the ADLFAHA for the simulation time of 100 s is 98.21 that is high than the FOMBC and LSR

Energy Efficient Multi-hop routing scheme using Taylor based Gravitational Search Algorithm in Wireless Sensor Networks

A group of small sensors can participate in the wireless network infrastructure and make appropriate transmission and communication sensor networks. There are numerous uses for drones, including military, medical, agricultural, and atmospheric monitoring. The power sources available to nodes in WSNs are restricted. Furthermore, because of this, a diverse method of energy availability is required, primarily for communication over a vast distance, for which Multi-Hop (MH) systems are used. Obtaining the optimum routing path between nodes is still a significant problem, even when multi-hop systems reduce the cost of energy needed by every node along the way. As a result, the number of transmissions must be kept to a minimum to provide effective routing and extend the system\u27s lifetime. To solve the energy problem in WSN, Taylor based Gravitational Search Algorithm (TBGSA) is proposed, which combines the Taylor series with a Gravitational search algorithm to discover the best hops for multi-hop routing. Initially, the sensor nodes are categorised as groups or clusters and the maximum capable node can access the cluster head the next action is switching between multiple nodes via a multi-hop manner. Initially, the best (CH) Cluster Head is chosen using the Artificial Bee Colony (ABC) algorithm, and then the data is transmitted utilizing multi-hop routing. The comparison result shows out the extension of networks longevity of the proposed method with the existing EBMRS, MOGA, and DMEERP methods. The network lifetime of the proposed method increased by 13.2%, 21.9% and 29.2% better than DMEERP, MOGA, and EBMRS respectively

3R: a reliable multi-agent reinforcement learning based routing protocol for wireless medical sensor networks.

Interest in the Wireless Medical Sensor Network (WMSN) is rapidly gaining attention thanks to recent advances in semiconductors and wireless communication. However, by virtue of the sensitive medical applications and the stringent resource constraints, there is a need to develop a routing protocol to fulfill WMSN requirements in terms of delivery reliability, attack resiliency, computational overhead and energy efficiency. This paper proposes 3R, a reliable multi-agent reinforcement learning routing protocol for WMSN. 3R uses a novel resource-conservative Reinforcement Learning (RL) model to reduce the computational overhead, along with two updating methods to speed up the algorithm convergence. The reward function is re-defined as a punishment, combining the proposed trust management system to defend against well-known dropping attacks. Furthermore, an energy model is integrated with the reward function to enhance the network lifetime and balance energy consumption across the network. The proposed energy model uses only local information to avoid the resource burdens and the security concerns of exchanging energy information. Experimental results prove the lightweightness, attacks resiliency and energy efficiency of 3R, making it a potential routing candidate for WMSN

Wireless Communication Networks for Gas Turbine Engine Testing

A new trend in the field of Aeronautical Engine Health Monitoring is the implementation of wireless sensor networks (WSNs) for data acquisition and condition monitoring to partially replace heavy and complex wiring harnesses, which limit the versatility of the monitoring process as well as creating practical deployment issues. Using wireless technologies instead of fixed wiring will fuel opportunities for reduced cabling, faster sensor and network deployment, increased data acquisition flexibility and reduced cable maintenance costs. However, embedding wireless technology into an aero engine (even in the ground testing application considered here) presents some very significant challenges, e.g. a harsh environment with a complex RF transmission environment, high sensor density and high data-rate. In this paper we discuss the results of the Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) project, which aimed to design and simulate such a network to estimate network performance and de-risk the wireless techniques before the deployment