Fusion of cuckoo search and hill climbing techniques based optimal forwarder selection and detect the intrusion (original) (raw)
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Reinforcement Learning for Intrusion Detection and Improving Optimal Route by Cuckoo Search in WSN
Indian Journal of Computer Science and Engineering, 2021
Wireless Sensor Network (WSN) is a generally hopeful technology for several real-time applications due to its cost-effective, size, and distribution nature. WSN is a collection of sensor nodes spread in a great region such that the required information can be collected. However, sensor nodes are susceptible to attacks, for example, intrusion, hackers, defective hardware starting the physical incident, etc. Therefore, it is compulsory to defend a sensor node from an intrusion. If it brings attacked next, the information transmitted through the sensor may be wrong and lead to incorrect data analysis, leading to unnecessary outcomes. To solve these issues, Reinforcement Learning for Intrusion Detection (RLID) and Improving Optimal Route by Cuckoo Search is proposed. The Reinforcement Learning uses the repeating node classification for detecting the intrusion during the route discovery. Reinforcement learning evaluates the sensor node behaviour by the quality of the link, and it is computed by sensor node packet forward rate and node residual energy. Here, the repeating node classification method classified the intrusion sensor based on node-link quality. As a result, it can improve intrusion detection performance efficiently. Besides, the Cuckoo Search Technique (CST) is used to find the optimal forwarder for transmitting the data from sender to destination. The main objective of this work is to offer optimal routing and communicate the data via normal sensor nodes in WSN. The simulation platform and the obtained results are compared with the baseline protocol to prove the efficiency of our proposed approach.
Optimal Clustering and Routing for Wireless Sensor Network Based on Cuckoo Search
International Journal of Advanced Smart Sensor Network Systems, 2017
In this research work, the egg laying radius of cuckoo search algorithm is used to create a cluster and then search for the optimum node based on multiobjective genetic algorithm with pareto ranking, so that the data can be forwarded to the sink.The primary focus is onthe two performance metrics parameters,one is the maximization of network lifetime and other is the minimization of delay. For maximizing the network lifetime parameter, the overlapped target sensing by many sensors is wastage of energy by two or more sensors, where the same task can be done by one sensor. To overcome this problem, the sequence set cover methodology is used.For minimization of delay parameter, the sleep-wake scheduling mechanism will be considered, but substantial delays are introduced as transmitting node needs to wait for its next-hop relay node to wake up. These delays can be taken care by developing any cast based packet forwarding schemes where individual node forwards a packet to the first neighboring node that wakes up among multiple candidate nodes. This any cast forwarding schemes minimizes the expected packet-delivery delays from the sensor nodes to the sink node. The introduced work will perform energy proficient routing with an objective to improve the network life, packet loss ratio and overall network throughput. The proposed algorithm was simulated in MATLAB and compared with LEACH algorithm. The results show that our proposed algorithm issuperiorfor prolonging the network lifetime, minimizing the packet loss and increasing the throughput.
Wireless Sensor Network, 2014
Energy efficient routing is one of the major thrust areas in Wireless Sensor Communication Networks (WSCNs) and it attracts most of the researchers by its valuable applications and various challenges. Wireless sensor networks contain several nodes in its terrain region. Reducing the energy consumption over the WSCN has its significance since the nodes are battery powered. Various research methodologies were proposed by researchers in this area. One of the bio-inspired computing paradigms named Cuckoo search algorithm is used in this research work for finding the energy efficient path and routing is performed. Several performance metrics are taken into account for determining the performance of the proposed routing protocol such as throughput, packet delivery ratio, energy consumption and delay. Simulation is performed using NS2 and the results shows that the proposed routing protocol is better in terms of average throughput, and average energy consumption.
Improved Cuckoo Search based Sensor Deployment Scheme for Large-scale Wireless Sensor Networks
Wireless Sensor Network (WSN) consists of small number of low-cost sensor nodes, which are able to freely converse over short distances. One of the key important problems in Wireless Sensor Networks (WSNs) is how to proficiently position sensors to cover an area. In WSN, sensor deployment is considered as one of the major important issues, since it not only considers the network cost during network model creation in addition it also affects how well a region is examined by means of sensors. In this paper address the problem of sensor deployment in Large Scale Wireless Sensor Networks to minimize the usage of number of nodes. The local incidence rate information and an investigative sensor detection ability equation individual exploit an optimization problem designed for reducing the usage of number of sensor nodes is created. By solving the difficulty of sensor deployment, an optimal sensor deployment schema is introduced in this paper for Large Scale Wireless Sensor Networks (LSWSN). An effective Improved Cuckoo Search (ICS) based sensor deployment scheme is introduced in this work for large-area WSN where the event incidence rate differs over the sensor-deployed region. Proposed ICS deployment scheme determines the optimal number of sensors designed for a typical surveillance sensor network with the purpose of must be deployed in each local region with the purpose of minimizes the total number of sensors at the same time as satisfying the overall detection probability. Simulation results demonstrated that the proposed ICS schemes are efficient in terms of the usage of number of sensors and are distributed in nature to verify their effectiveness.
BSRS: Best Stable Route Selection Algorithm for Wireless Sensor Network Applications
Topological changes in sensor networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on the network ability to support QoS-driven services. Because of connectivity richness in sensor networks, there often exist multiple paths between a source and a destination. Since many applications require uninterrupted connectivity of a session, the ability to find long-living paths can be very useful. In this paper, we propose Best Stable Route Selection (BSRS) approach based on Artificial Bee Colony based search algorithm, ensures that contributes stable quality performance of network and to calculate the best stable path services randomly based on QoS parameter requirements and existing circulation load; so that efficient route selection can easily capture by designing of proposed BSRS approach. The implementation of the proposed BSRS technique is implemented using NS2 simulation environment and the AODV routing protocol is used to incorporate the proposed algorithm. The experimental results are measured in terms of end to end delay, throughput, packet delivery ratio, and energy consumption and routing overhead. The results show the proposed BSRS algorithm improves the flexibility of network node and performance of network when multiple inefficient paths exist.
Energy Efficient and Hybrid Bio-inspired Ant-Cuckoo Algorithm for Wireless Sensor Network
2021
To develop the energy efficient protocol hybrid bio-inspired Ant-Cuckoo algorithm is proposed here to balance the energy consumption between the nodes evenly. The clusters are formed in the network using ACO algorithm. This is done based on the distance measurement among the nodes. Once the cluster is formed then the cluster head is selected upon the node energy value. Here flexible and rigid threshold is set for processing successive rounds of transmission with the same cluster head to avoid the frequent cluster reformation. Data routing using hauler nodes are selected with the assistance of cluster heads is done by using cuckoo search algorithm. Thereby efficient routes with reliable nodes are selected for data transmission. Simulation results are shown for proving efficiency for the proposed method.
Energy efficient cluster formation in wireless sensor networks using cuckoo search
Swarm, Evolutionary, and …, 2011
Wireless Sensor Networks consist of wide range of applications to be discerned and researched nowadays. The foremost restraint of these Networks is to reduce energy consumption and to prolong the lifetime of the network. In this paper a meta-heuristic optimization technique, Cuckoo Search is used to aggregate data in the Sensor Network. In the proposed technique, the least energy nodes are formed as subordinate chains (or) clusters for sensing the data and high energy nodes as Cluster Head for communicating to the base station. The Cuckoo search is proposed to get enhanced network performance incorporating balanced energy dissipation and results in the formation of optimum number of clusters and minimal energy consumption. The feasibility of the scheme is manifested by the Simulation results on comparison with the traditional methods.
Wireless Personal Communications, 2017
Since sensor nodes make use of battery energy, energy consumption and limitation of sensor nodes is regarded as a fundamental challenge and problem in wireless sensor nodes. Recently, in wireless sensor networks (WSNs), clustering-based energyaware routing protocols divide neighboring nodes into separate clusters and select local cluster heads so as to combine and transmit information of each of the clusters to the central station. In this way, they attempt to maintain energy consumption balance by the network nodes. When compared with other methods, clustering methods have been able to achieve the best efficiency with regard to the enhancement of network lifetime. In this paper, using cuckoo optimization algorithm, an energy-aware clustering-based routing protocol was proposed in WSNs which is able to cluster the network and select optimal cluster heads. The proposed method considered four criteria with regard to selecting cluster heads in the targeted cuckoo algorithm, namely the remaining energy of nodes, distance to the base station, within-cluster distances and between cluster distances. The results of simulating the proposed method in Matlab environment indicated it is better than other algorithms such as low energy adaptive clustering hierarchical (LEACH), applicationspecific low power routing, LACH-EP and LEACH with distance-based threshold with regard to the first node die on average and packet delivery rate for six scenario.
Kitspress, 2024
Wireless Sensor Networks (WSNs) are increasingly the technology of choice due to their wide applicability in both military and civilian domains. The selective forwarding attack, one of the main attacks in WSNs, is the hardest denial-of-service attack to detect. The hostile nodes that initiate the selective forwarding attack will discard some or all of the data packets they receive. Numerous detection techniques for optional forwarding have been developed attacks are inaccurate or contain sophisticated algorithms, which is especially true when the attacker also uses other attacks like distributed denial of service, wormholes, and black holes to move through the network. To address these disadvantages, this research proposes a novel selective forwarding attack detection method based blue monkey-optimized ghost net (SAD-Ghost) method. To identify network threats, Blue Monkey optimization based on the hazard model is built in this case. A proposed technique to improve detection accuracy and minimize computation. The primary goal of the research is to develop a Selective Forwarding Attack Detection utilizing a blue Monkey optimized Ghost net to improve network lifetime. Initially, Blue Monkey optimization is used for optimal cluster head selection based on node degree and density. Moreover, cluster data are pre-processed using Tokenization, Normalization, and Reduction. The proposed method is utilized to detect intrusion in WSN and classify Normal, DDOS, Grey hole, and Blink litter. The experimental analysis demonstrates that the proposed method achieves a packet delivery rate of 97.6%, 95.3% and 90.50% and reduces energy consumption by 19.6%, 12.5% and 17.4% compared to existing clustering-based routing methods. Consequently, the proposed technique surpasses current methods in terms of network lifetime, energy efficiency, and packet delivery performance.