Group Search Optimizer for the Mobile Location Management Problem

We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach

Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Mobile agent location management in global networks

Mobility management is a necessity in highly dynamic and large-scale Mobile Agents (MAs) networks, especially in a multi-region environment in order to control and communicate with agents after launching. Existing mechanisms for locating MAs are not efficient as these do not consider the effect of location updates on migration time and produce network overload. This paper presents a hierarchical model for location management of MAs in global networks. Three protocols are developed, namely search, update and search-update. The location management technique uses one combination of search, update and search-update protocols throughout execution. Three cases are considered for Update and Search-Update Protocols. Thus, nine combinations of location management protocols are generated, from which an agent can dynamically select one as per requirement, to communicate with other agents on the global network. We have implemented these protocols on the system developed at IIT Roorkee, to evaluate the performance. Results indicate that overhead generated by these protocols does not affect the actual agent response and migration time[1].