Balwinder Singh Sohi

Modified Grey Wolf Optimizer for Global Engineering Optimization

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.

Noise Reduction in MR brain image via various transform domain schemes

Despite the phenomenal progress in the field of image denoising it continues to be an active area of research and still holds margin in improving the standard of the denoising techniques. Image denoising has emerged as a significant tool in medical imaging specifically. In this article we have compared and evaluated three transform domain techniques on an MRI test image subjectively and objectively. The performance of Curvelet, Shearlet, and Tetrolet transform with a selective thresholding is evaluated. Shearlet is able to yield the best quality of image denoising. The study aims at analysing the performance of transform domain methods on MRI image at low and high levels of noise.

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Dual Way Residue Noise Thresholding along with feature preservation

A unique method for calculating residue noise has been given.The performance of Weighted bilateral filter on image denoising has been improvised.PSNR values higher than state of art denoising techniques have been achieved.The concept of adding the residue calculated via two way denoising can lead to significant improvement in preservation of feature details. It is extensively endorsed that preserving the intrinsic geometrical features of an image is essential while denoising it. With an aim to achieve this several directional image representations have been given in the recent literature. In this paper an efficient denoising scheme using an innovative method of calculating the residue image is being proposed. The residue image is further thresholded to remove excessive noise while recovering fine features and details. The recovered features are added to first stage of denoised image to enhance the information content and visual quality of denoised image. The proposed methodology DWRNT (Dual Way Residue Noise Thresholding) is a combination of various spatial and transforms domain methods. Extensive experimental results and investigations reveal that our method can depict far better performance in terms of both subjective evaluation and objective evaluation than various other state-of-the-art image denoising techniques. In this way the proposed methodology is able to recover feature details of an image thereby reducing information loss along with efficient noise removal. Display Omitted

A Novel Energy Efficient Stable Clustering Approach for Wireless Sensor Networks

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, differential evolution based clustering algorithm for WSNs named threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP), is proposed to prolong network lifetime. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TEDRP named stable TEDRP (STEDRP) with an intend to extend the stability period of the network. In STEDRP, energy aware heuristics is applied for CH selection in order to improve the stability period. The results demonstrate that the proposed protocols significantly outperform existing protocols in terms of energy consumption, system lifetime and stability period.

An Optimal Tree-Based Routing Protocol Using Particle Swarm Optimization

Wireless sensor networks (WSNs) contain many sensor nodes which are deployed in the various geographical areas to perform various tasks like monitoring, data aggregation and data processing. For performing all these operations, energy is highly consumed, thus sensor nodes begin to die soon and also creates energy holes in some of the geographical locations. All the sensor nodes are powered by battery, and it is quite difficult to replace the battery, and so energy consumption is prime objective to increase the network lifetime. Clustering and tree-based routing like LEACH, PEDAP, TBC and TREEPSI solves most of the energy consumption problem as it saves energy during a lot of operations in WSNs. In this paper, we propose an optimal tree-based routing protocol (OTBRP) that is efficient in terms of stability period (time period before first node dead) and therefore offers good network lifetime. The parameters like first node dead, half node dead and last node dead are considered for the measurement of network lifetime. In order to evaluate the performance of OTBRP, the comparison is made with the GSTEB and PEGASIS. Simulation results show that there is a gain of approx. 200 and 150% in stability period in comparison with PEGASIS and GSTEB, respectively.

Two-dimensional gray scale image denoising via morphological operations in NSST domain & bitonic filtering

Abstract The denoising of an image is one of the most classical and basic step in image processing. The most challenging task is to design a feature preserving denoising algorithm. This article presents an efficient denoising method derived from morphological filtering in NSST domain and Bitonic filtering. In the first stage the noisy components are processed by morphological circular disc operators i.e. Top Hat/ Bottom Hat filtering in NSST domain, as Shearlet is a powerful multi-scale and multi-directional image representation tool. The resultant image is then decomposed into 8 bit planes and each bit plane is passed through bitonic filter separately. These filtered images are assembled to obtain the final denoised image. Experimental results on standard test images substantiate that the proposed method achieves reasonable and consistent denoising performance, especially in preserving fine structure information as compared with existing algorithms specifically at high noise levels.

Off-line analysis of internet traffic for accurate identification of P2P applications using neural networks

P2P applications supposedly constitute a substantial proportion of todays Internet traffic. The ability to accurately identify different P2P applications in internet traffic is important to a broad range of network operations including application-specific traffic engineering, capacity planning, resource provisioning, service differentiation, etc. However, current P2P applications use several obfuscation techniques, including dynamic port numbers, port hopping, and encrypted payloads. As P2P applications continue to evolve, robust and effective methods are needed for identification of P2P applications. In this paper, we compare two neural network approaches (Radial Basis Function Network and Multi-Layer Perceptron) that precisely identify the P2P traffic. We find out that RBFN outperforms MLP neural network, but owing to the large time taken for model building, RBF network is found suitable for off-line identification of P2P applications in the internet traffic.

An energy-aware cluster-based stable protocol for wireless sensor networks

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user-friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing techniques to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation technique in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, cluster-head (CH) has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long-run operation of WSN. In this paper, genetic algorithm (GA)-based threshold-sensitive energy-efficient routing protocol (TERP) is proposed to prolong network lifetime. Multi-hop communication between CHs and base station (BS) is utilized using GA to achieve optimal link cost for load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TERP named stable TERP (STERP) so as to extend the stability period (time interval from initial time to the death of first node) of the network. In STERP, energy-aware heuristics is applied for CH selection in order to improve the stability period. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.