Abstract We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on... We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on a probabilistic genetic program for automatically searching the design space for good network topologies. The experimental results for synthesizing and optimizing 3- level enterprise network (65 user nodes) for an edgeserver placement has demonstrated the effectiveness of our methodology in finding good solutions with a static workload in less than five minutes. Read More Read Less

Abstract This paper describes a palette-based colored image segmentation system. In this paper we analyzed the properties of Hue, Saturation, and Luminance (HSL) color space with emphasis on the visual perception of the variation in Hue, Saturation and Luminance values of palette colors. The proposed algorithm based on the concept of re-coloring the palette colors of 256-color images using the properties... This paper describes a palette-based colored image segmentation system. In this paper we analyzed the properties of Hue, Saturation, and Luminance (HSL) color space with emphasis on the visual perception of the variation in Hue, Saturation and Luminance values of palette colors. The proposed algorithm based on the concept of re-coloring the palette colors of 256-color images using the properties of HSL color space and segmenting true type images by taking part of the image, generating a palette to it then apply the method to segment the selected region. After re-coloring of the palette colors the colors in the image will be re-colored according to its new palette, and as a result the image will be segmented to different segments. Read More Read Less

Abstract In-networks, Data Aggregation is usually warranted for distributed wireless sensor networks, owing to reliability and energy efficiency reasons. Sensor nodes are usually deployed in unattended and unsafe environments and hence are vulnerable to intentional or unintentional damages. Individual nodes are prone to different type of faults such as hardware faults, crash faults etc and other security vulnerabilities wherein one or... In-networks, Data Aggregation is usually warranted for distributed wireless sensor networks, owing to reliability and energy efficiency reasons. Sensor nodes are usually deployed in unattended and unsafe environments and hence are vulnerable to intentional or unintentional damages. Individual nodes are prone to different type of faults such as hardware faults, crash faults etc and other security vulnerabilities wherein one or more nodes are compromised to produce bogus data so as to confuse the rest of the network in collaborative sensing applications. The availability of constrained resources and the presence of faulty nodes make designing fault tolerant information aggregation mechanisms in large sensor networks particularly challenging. In our work, we consider Byzantine type of faults, which encompasses most of the common sensor node faults [9]. Faulty nodes are assumed to send inconsistent and arbitrary values to other nodes during information exchange process. These values are termed as outliers and we use a statistical test called Modified Z-score method to reliably detect and remove outliers. We show by simulation that the proposed strategy works well for 2 major classes of collaborative sensor network applications viz. (i) Target/ Event detection and (ii) Continuous data gathering. Read More Read Less

Abstract A Mobile Ad-Hoc Network (MANET) is a selfconfiguring network of mobile nodes connected by wireless links to form an arbitrary topology without the use of existing infrastructure. In this paper, we have studied the effects of various mobility models on the performance of two routing protocols Dynamic Source Routing (DSRReactive Protocol) and Destination-Sequenced Distance- Vector (DSDV-Proactive Protocol). For experiment purposes,... A Mobile Ad-Hoc Network (MANET) is a selfconfiguring network of mobile nodes connected by wireless links to form an arbitrary topology without the use of existing infrastructure. In this paper, we have studied the effects of various mobility models on the performance of two routing protocols Dynamic Source Routing (DSRReactive Protocol) and Destination-Sequenced Distance- Vector (DSDV-Proactive Protocol). For experiment purposes, we have considered four mobility scenarios: Random Waypoint, Group Mobility, Freeway and Manhattan models. These four Mobility Models are selected to represent possibility of practical application in future. Performance comparison has also been conducted across varying node densities and number of hops. Experiment results illustrate that performance of the routing protocol varies across different mobility models, node densities and length of data paths. Read More Read Less