Narendran Rajagopalan

Optimization of QoS Parameters for Channel Allocation in Cellular Networks Using Soft Computing Techniques

The usage of mobile communications systems has grown exponentially. But the bandwidth available for mobile communications is finite. Hence there is a desperate attempt to optimize the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls and priority are considered. Genetic Algorithm and Artificial Neural Networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that Genetic Algorithm performs better than Heuristic Method. But application of Artificial Neural Networks outperforms Genetic Algorithm and Heuristic method by a considerable margin. Channel allocation can be optimized using these soft computing techniques resulting in better throughput.

An efficient and dynamic backoff algorithm for IEEE 802.11 networks

Performance of Wireless LAN can be improved at each layer of the protocol stack with respect to energy efficiency. The Media Access Control layer is responsible for the key functions like access control and flow control. During contention, Backoff algorithm is used to gain access to the medium with minimum probability of collision. After studying different variations of backoff algorithms, a new variant called History based Probabilistic Backoff Algorithm is proposed. Mathematical analysis and simulation results using NS-2 with parameters like retransmission counts, saturated throughput, transmission delay, different data sizes and different traffic patterns was carried out. It is seen that proposed History based Probabilistic Backoff algorithm outperforms the other backoff algorithms.

Analysis and Comparative Study of Different Backoff Algorithms with Probability Based Backoff Algorithm

Data Link Layer is the most important Layer in any type of Local Area Network. The main functions of the Data Link Layer is access control and flow control. The efficient implementation of access control protocol, decides the optimal usage of network resources. The backoff algorithm is a very important aspect in access control protocol implementation. Backoff algorithm is used to reduce the probability of frequent collisions when stations try to access the medium simultaneously. The basic Binary Exponential Backoff algorithm, Modified Binary Exponential Backoff algorithm and their drawbacks are analyzed and a new variation called Probability Based Backoff algorithm is proposed, which takes network traffic also into consideration.

Internet of Things: A Survey on IoT Protocol Standards

In the coming years, resource constrained devices which can be uniquely identified as an object and communicate seamlessly, often called as the Internet of Things (IoT) needs a standard technology to evolve in this real world. The emerging IoT technology will lead to the advancement in various fields like smart city, communication, healthcare, industry, transportation, security, education, research work and environmental services. And several industry bodies and standard forums are researching to develop a protocol which will satisfy all the special requirements and security, needed by constrained devices with limited processing power and resources. This paper surveys some of the main IoT protocol standards with its limitations, and solutions are proposed for future research works.

Route discovery in cellular networks using soft computing techniques

A novel method for topology discovery of computer network using ant colony optimisation ACO is proposed. In this approach, the base station BS simulates the way ants forage for food to find out the routes to other BSs. The route discovered by each ant is associated with pheromone strength which in turn decides whether the route is the best or not. ACO applied to CN gives all the existing routes between the various BSs in a CN. Genetic algorithm GA-based optimisation is further applied to get the optimal path satisfying multiple constraints viz., number of hops, Poisson traffic distribution, buffer capacity, link delay, queuing delay, and residual bandwidth from the set of paths given by ACO. Our simulation results show that different network models viz., random model, unidirectional ring, bidirectional ring, star, and tree are explored faster using ACO and our scheme using ACO-GA outperforms the scheme without GA.

Optimized Channel Allocation Using Genetic Algorithm and Artificial Neural Networks

As the spectrum for wireless transmission gets crowded due to the increase in the users and applications, the efficient use of the spectrum is a major challenge in today’s world. A major affecting factor is the inefficient usage of the frequency bands. Interference in the neighboring cells affects the reuse of the frequency bands. Some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls and priority are considered for optimized channel allocation. Genetic Algorithm and Artificial Neural Networks are applied to determine the optimal channel allocation considering the quality of service parameters. The simulation results show that using Genetic algorithm betters heuristic method and artificial neural networks performs better than Genetic Algorithm by a comfortable margin.

A Comparative Study of Different Queuing Models Used in Network Routers for Congestion Avoidance

As the network traffic is increasing exponentially, the queuing model used in a network decides the degree of congestion that is possible in the network infrastructure. Hence using a suitable queuing model based upon the network infrastructure like buffer size and the type of data traffic flowing through the network will help in better utilization of system resources by minimizing congestion to the best. In this paper, we study the different queuing models that have evolved over time and how they suit the application.

Channel allocation scheme for cellular networks using evolutionary computing

The usage of mobile communications systems has grown exponentially. But, the bandwidth available for mobile communications is finite. Hence, there is a desperate attempt to optimise the channel assignment schemes. In this work, some of the quality of service parameters such as residual bandwidth, number of users, duration of calls, frequency of calls, priority, time of calls and mean opinion score are considered. Genetic algorithm and artificial neural networks is used to determine the optimal channel assignment considering the quality of service parameters. The simulation results show that genetic algorithm performs better than frequency assignment at random, a heuristic method. But application of artificial neural networks outperforms genetic algorithm and frequency assignment at random method by a considerable margin. Channel allocation can be optimised using these soft computing techniques resulting in better throughput.

An ACO-GA Optimization Scheme for Route Discovery in Cellular Networks

The recent advancements in mobile communication has triggered mankind in every part of the world to use wireless devices viz., cell phones, laptops, PDAs, etc., To provide excellent services to the mobile users, every service provider expands his network to cover a wide coverage area. To reduce the establishment cost of service providers, infrastructure sharing among service providers is becoming popular. This requires existing connectivity/ topology in a Cellular Network (CN) to be explored and discovered. In this paper, a novel method for topology discovery of CN using Ant Colony Optimization (ACO) is proposed. In this approach, the Base Station(BS)s simulate the way ants forage for food to find out the routes to other BSs. The route discovered by each ant is associated with trail (pheromone) strength which in turn decides whether the route is the best or not. ACO applied to CN gives all the existing routes between the various BSs in a CN. Genetic Algorithm (GA) based optimization is further applied to get the optimal path satisfying multiple constraints viz., number of hops, Poisson traffic distribution, buffer capacity, link delay, queuing delay, and residual bandwidth from the set of paths given by ACO. Our simulation results show that different network models viz., Random model, unidirectional ring, bidirectional ring, star, and tree are explored faster using ACO and our scheme using ACO-GA outperforms the scheme without GA with respect to Call Service Rate and Call Dropping Rate.

A novel approach to protein substructure matching

The rapidly increasing volumes of structural data of proteins has led to need of algorithms which can rapidly predict functions for proteins based on structure. Similarity between protein structures can provide evidence of possible functional similarity. In this paper, an attempt is made to efficiently recognize similar protein structures in the protein database contain thousands of proteins. This paper gives an efficient heuristic algorithm for finding protein 3D substructures in a 3D protein structure that are similar to a given query 3D protein substructure. This algorithm can be used for searching a database of protein 3D structures. Our approach is to divide the protein structure into sub-structures of size of query structure and compare each sub structure with the query protein using Procrustes algorithm which is based on the root mean square distance between the structures. The division involves constructing a bounding box over both the query and protein structure and dividing the bigger box into sizes of the smaller box. The above algorithm is implemented in parallel using message passing interface. Experiments show that our algorithm can find similar 3D substructures in reasonable time. This paper also presents various statistics as how our algorithm performs against a sequential algorithm and how the algorithm performs with varying sizes of the query structure.