Today an application is secured using invitro perimeter security. This is the reason for security being considered as nonfunctional requirement in software development life cycle (SDLC). In next generation Internet (NGI), where all applications will be networked, security needs to be in-vivo; security must be functions within the application. Applications running on any device, be it on a mobile or on a fixed platform - need to be security-aware using security aware software development life cycle (SaSDLC), which is the focus of this paper. We also present a tool called Suraksha that comprises of security designers' workbench and Security Testers' Workbench that helps a developer to build security-aware applications.

In wireless sensor networks (WSNs), mobile sink-driven data acquisition can mitigate hotspot issues, which further increases WSN efficiency, such as throughput, lifetime, and energy efficiency, while reducing delay and packet loss. Recently, most mobile sink algorithms have focused on efficient paths, and few consider obstacles in the network environment. Nevertheless, constructing an obstacle-aware trajectory in a WSN is challenging. In this context, this paper proposes a bug algorithm based on an obstacle-aware intelligent trajectory (CSOBUG) for a mobile sink to acquire data from sensor nodes in WSNs efficiently with the help of cat swarm optimization (CSO). The proposed CSOBUG algorithm has two phases: selecting visiting points and constructing a trajectory. A CSO-based clustering approach is used to select visiting points, and a bug algorithm is used to select a trajectory. Comparing CSOBUG with existing techniques, it is found that CSOBUG is less computationally intensive than the existing techniques. As well as outperforming traditional methods based on multiple performance metrics, the CSOBUG achieves superior results in a variety of scenarios.

Modeling mobility in a large scale unorganized disasters using existing mobility models is a tedious and cumbersome task because they are designed for small scale and well organized disasters. In large scale unexpected disasters obstacles play a key role in determining the path of the nodes. Moreover, complete path from source to destination cannot be determined at the initial step. It is to be determined dynamically step by step instead. We use ant colony optimization to select the path dynamically. We propose a mobility model which facilitates simple and realistic placement of obstacles. The disaster area is divided into small cells to model the obstacles in a realistic way. As there are no mobility traces available for the disaster scenarios, we generate synthetic traces and compare them with the existing mobility models for disaster scenarios. We observed significant differences in the mobility parameters. This can be due to the dynamic path selection in our model.