International Journal of Computer Networks and Applications

178 - 192

Authors: Rekha, Rajeev Gupta

Due to the advancement of wireless communication interchanges, electronic technology, and micro-electro-mechanical devices, Wireless Sensor Network (WSN) has got advanced as a promising zone of research. WSN consists of a collection of sensor nodes having a little calculative capability, limited memory, and constrained energy assets. Clusters are formed from the collection of sensor nodes whose leader node (Cluster head) can send the sensed information from hubs to the BS. To condense the power consumption and boost group longevity, the cluster head executes data accumulation. This paper discusses many algorithms based on deterministic, probabilistic, adaptive, fuzzy logic, and Multi-attribute decision-making techniques for clustering and cluster head election. Existing algorithms enhance the network lifetime and energy efficiency but fail to provide a better quality of service and security. So many issues and challenges have been laid down and it is concluded that when computational intelligence is combined with network intelligence then QoS and security both can be provided along with the network longevity and energy efficiency in homogeneous as well as a heterogeneous environment.

208 - 230

Authors: Denis R, Madhubala P

The exponential growth of communication technologies and related application environments has broadened the cloud computing ecosystem horizon to meet major communication needs. However, in-parallel upsurge in online attacks, security breaches or allied intrusion events has alarmed industries to ensure optimal data security. Unlike text data transmission, image, or other multimedia communication over the cloud requires computational efficiency, imperceptibility, etc. to meet attack-resilient transmission. Amongst the major available security systems, the combination of cryptosystems and steganography has been identified as an augmented security model for data transmission. However, it demands enhancement in both stages to meet cloud-specific communication efficiency. Considering it as motivation, in this paper an efficient Visually Imperceptible Hybrid Crypto-Steganography (VIHCS) model is developed using Hybrid Cryptosystems followed by Adaptive Genetic Algorithm assisted Least Significant Bit (LSB) embedding process. We developed a novel Hybrid Cryptosystem by strategically applying Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) algorithms to secure secret data to be embedded in a cover image. In addition, the use of the Adaptive Genetic Algorithm based Optimal Pixel Adjustment (AGA-OPAP) strengthened the Least Significant Bit embedding while retaining the best possible image quality and visual imperceptibility. The proposed model achieved higher security, high embedding capacity as well as image quality, which is vital for cloud communication. To perform LSB embedding we applied 2D-Discrete Wavelet Transform (2D-DWT-2L) method with 8×8 dimensional block-wise embedding. It helps to achieve better embedding efficiency in conjunction with the AGA-OPAP model. Simulation results and respective visio-statistical assessment revealed that the proposed VIHCS model can accomplish better performance and reliable secure data communication over the cloud environment. Thus, the VIHCS model achieves maximum possible imperceptibility and hence can avoid attacks- such as steganalysis based attack or RS-attacks.

137 - 145

Authors: G. Kiruthiga, S. Mary Vennila

With the advancement in internet technology, everyone can able to utilize resources with low cost using cloud resources. There will be numerous requests for task scheduling to share resources in the cloud environment. When the task request is received by the cloud technology it should have the ability to distribute the workload among sharable resources in a balanced manner and effective utilization of resources. Machine learning and metaheuristic algorithms provide a dynamic part in balanced task assignments in the cloud paradigm. Existing unsupervised models-based load balancing, centroid selection is done randomly and imprecise job requests are not well handled by them. This paper aims to develop a clustering model-based task scheduling with the knowledge of behavioural inspired optimization algorithm in a highly balanced manner. A robust Intuitionistic Fuzzy C-means empowered grasshopper optimization has been anticipated in this work, which utilizes the merits of the Intuitionistic fuzzy and Grass Hopper algorithm for prominent task scheduling among virtual servers in a cloud environment. The results proved that IFCM-GOA reduces the makespan, execution time and, high balance load scheduling with improved cloud resource utilization.

157 - 166

Authors: Gurpreet Singh Shahi, Ranbir Singh Batth, Simon Egerton

With the rise in the economy and population of various developing and developed nations leads to an increase in the number of vehicles on the road to manifolds recently. In the smart vehicular networks, there are numerous vehicle transportation problems (VTP) exists. The main or the most common problem is the congestion occurrence due to traffic jams and road accidents, especially in urban areas. So route planning to find the optimal route for a vehicle that is moving from source to destination is an important and challenging task as the route conditions change with the traffic conditions, hence the shortest and optimal route needs to be re-evaluated. This manuscript presents a study and comparison of various pathfinding algorithms which include Dijkstra, A Star, CH, and Floyd Warshall algorithms which are available as optimal route finding algorithms. The simulation of working algorithms with comparison has been performed using SUMO with TRACI and using python coding. The simulation is performed on real maps of urban areas. The parameters used for the comparative study are travel time, distance travelled, and speeds of vehicles.

103 - 115

Authors: D. Somashekhara Reddy, C. Chandrasekhar

In a Wireless Sensor Network (WSN), seamless mobility management can change the current mobile node’s location point to another network devoid of any link failure during communication. The seamless mobility system is very useful to detect the nearest base station over the wireless network without any distinct interference. In this paper, a novel technique called Support Vector Regressive Dragonfly Optimization based Shift Invariant Deep Neural Learning (SVRDO-SIDNL) is introduced for improving the seamless data transmission with minimum delay. The Shift Invariant Deep Neural Learning comprises of many layers to learn the series of input. For each layer, the different processes are carried out to accomplish the traffic optimized seamless data delivery. The input layer of the deep neural learning receives mobile nodes with coverage region and then is sent to the hidden layer. The mobile nodes' signal strength is analyzed by applying the support vector regression at the hidden layer. Then, the node with weak signal strength is identified and performs the handover. Through oppositional learned multi-objective dragonfly optimization technique, recognition of nearby attachment points with greater bandwidth availability is performed for the handover process. Then, the mobile node connection is altered from the existing attachment point to a new attachment point without losing connectivity. The simulation results reveal that the SVRDO-SIDNL technique offers a greater delivery rate, throughput with lesser packet loss at less delay.

82 - 92

Authors: G. Kiruthiga, S. Mary Vennila

Scheduling of tasks in a cloud environment has larger influence on time and energy depletion. Different heuristic models were developed to solve the NP-hard task scheduling problem based on time. However, ideal task scheduling algorithms must also maximize energy efficiency with good load balancing and ensure better Quality-of-Service (QoS). An innovative multi-objective Chaotic Darwinian Chicken Swarm Optimization (CDCSO) system is suggested in this article to provide energy efficient QoS and load balancing aware task scheduling. The multi-objective CDCSO algorithm incorporates the chaotic and Darwinian Theory to the standard Chicken Swarm Optimization to increase its global exploration and maximize the convergence rate. This performance enhanced CDCSO algorithm models the cloud task scheduling problem as NP-hard and utilizes the optimization principles to solve them based on multiple objective parameters. The multi-objective fitness function used in CDCSO is modelled based on the objective parameters namely energy, cost, task completion time, response time, throughput and load balancing index. Based on this multi-objective function, the CDCSO effectively allocates the tasks to the suitable energy efficient, cost and time minimized Virtual machines (VMs) which are also optimally load balanced. CloudSim simulations were conducted and the obtained results illustrated that the proposed multi-objective CDCSO has provided better task scheduling with minimized energy, cost, time and optimal load balancing.

28 - 42

Authors: M. Usha, J. Sathiamoorthy, R. Ashween, Bhagavath Nishanth Ramakrishnan

Underwater communication is a newer addition to the ad hoc network arena as it opens several avenues of research that can be carried out under water. It has a varied range of applications which it carries out with the help of Autonomous Underwater Vehicles (AUV).Acoustic waves are used for sending out the network signals because they tend to have a lower attenuation under water. Underwater Acoustic Communication (UWAC) is used to send and receive signals underwater. Since UWAC is a relatively new field, there are still a lot of challenges to overcome. Unlike terrestrial communication, UWAC has to encounter factors like limited bandwidth, longer propagation delay, Doppler Effect, and channel availability problems. All of these factors have a direct effect on the throughput. Clustering the network can effectively help in managing the network efficiently. We have proposed an Energy Efficient Minimum Cost Cluster routing Protocol (EEMCCP), which routes packets with the help of clusters. The clustering of the network is efficiently achieved by using the Chaotic Algae Algorithm (CAA).After clustering the network, one node from each cluster acts as the Cluster Head (CH). The proposed protocol does not require any node position information and also ensures that, only a minimal number of nodes are involved in the end to end routing process. Some of the Cluster Heads are selected as gateway nodes based on their RSSI (Received Signal Strength Indictor) values of the Hello packets. Selected CHs are appointed as gateway nodes that are responsible for supplying the data to the AUV nodes that are nearer to them. When these gateway CH nodes exhaust their energy, back up nodes act as gateway nodes thus making the protocol a flexible one. In fact, the node location and position are not taken into consideration. The AUV nodes transfer the acquired information to a communication link which in turn transfers the data to a terrestrial destination. The EEMCCP focuses towards a cost effective solution for transmitting data. The performance of EEMCCP is compared with the existing protocols employed for underwater communication. From the analysis, we find that EEMCCP achieves a good throughput and PDR than the existing protocols.