Distributed Video Coding and Content Analysis for Resource Constraint Multimedia Applications

Video coding and analysis for low power and low bandwidth multimedia applications has always been a great challenge. The limited computational resources on ubiquitous multimedia devices like cameras along with low and varying bandwidth over wireless network lead to serious bottlenecks in delivering real-time streaming of videos for such applications. This work presents a Content-based Network-adaptive Video-transmission (CbNaVt) framework which can waive off the requirements of low bandwidth. This is done by transmitting important content only to the end user. The framework is illustrated with the example of video streaming in the context of remote laboratory setup. A framework for distributed processing using mobile agents is discussed with the example of Distributed Video Surveillance (DVS). In this regard, the increased computational costs due to video processing tasks like object segmentation and tracking are shared by the cameras and a local base station called as Processing Proxy Server (PPS).However, in a distributed scenario like traffic surveillance, where moving objects is tracked using multiple cameras, the processing tasks needs to be dynamically distributed. This is done intelligently using mobile agents by migrating from one PPS to another for tracking an individual case object and transmitting required information to the end users. Although the authors propose a specific implementation for CbNaVt and DVS systems, the general ideas in design of such systems exemplify the way information can be intelligently transmitted in any ubiquitous multimedia applications along with the use of mobile agents for real-time processing and retrieval of video signal.

Adaptive Content Delivery in E-Learning Systems using Mobile Agents

The e-Learning refers to the use of networking technologies to create, foster, deliver and facilitate learning anytime, anywhere. This chapter discusses our research on personalization of e-Learning content based on the learner’s profile. After justifying the feasibility of using mobile agents in distributed computing systems for information retrieval, processing and mining, the authors deal with the relevance of mobile agents in e-Learning domain. The chapter discusses the proposed Case-Based Reasoning (CBR) as an approach to context-aware adaptive content delivery. Different parameters like technological, cultural and educational background of a learner are taken as the basis for forming the case-base that determines the type of content to be delivered. Along with the CBR, a diagnostic assessment to gauge an insight into the student’s current skills is done to determine the type of content to deliver. The implementation observations of such implementation vis-à-vis traditional e-Learning are also documented.

Mobile Agent-Based Collaborative Computing Framework for Handling Constraint Resources

Information processing and collaborative computing using agents over a distributed network of heterogeneous platforms are important for many defense and civil applications. In this chapter, a mobile agent based collaborative and distributed computing framework for network centric information processing is presented using a military application. In this environment, the challenge is to continue processing efficiently while satisfying multiple constraints like computational cost, communication bandwidth, and energy in a distributed network. The authors use mobile agent technology for distributed computing to speed up data processing using the available systems resources in the network. The proposed framework provides a mechanism to bridge the gap between computation resources and dispersed data sources under variable bandwidth constraints. For every computation task raised in the network, a viable system that has resources and data to compute the task is identified and sent to the viable system for completion. Experimental evaluation under the real platform is reported. It shows that in spite of an increase of the communication load in comparison with other solutions the proposed framework leads to a decrease of the computation time.

Mobile Agent-Based Multimedia Content Discovery

Content discovery is an important aspect in the context of ever-growing internet based information services and management. Especially multimedia content discovery is an essential aspect because it is an effective and efficient way to deliver the concept to the people or making them understands easily. For this multimedia content discovery, a well known search engine Google is using different kind of linking techniques. But the challenge arises, in case the multimedia content location is not available with the link, then it is not able to discover the content location. To overcome the above limitation, we propose this mobile agent based multimedia content discovery model. Here, mobile agent would be send to the location of the server where the content is available and bring it to the real world environment. Also with the advantage of the mobile agent, we can reduce the network traffic and also we can discover all the contents location.

Algorithms for Secure Multimedia Delivery over Mobile Devices and Mobile Agents

Rapid advances in embedded systems and mobile communications have flooded the market with a large volume of multimedia data. In this chapter, the authors present a summary of multimedia compression and encryption schemes, the way they have evolved over the decades. They first discuss the traditional approach to data encryption and their extension to video encryption. Next, they present the next generation algorithms for secure multimedia delivery, namely the Joint Video Compression and Encryption (JVCE) approach and give the reader an introduction to these approaches, the underlying assumption, advantages and limitations. The authors discuss the implementation of JVCE algorithms in light of requirements of mobile devices and propose how mobile agents can facilitate such an implementation.

Recovery of Ubiquitous Multimedia Content Discovery Mobile Agent

Multimedia content is ubiquitous; therefore it is very difficult to bring all the hidden contents to the every one of universe. Mobile agent technology is the efficient technique to discover and bring the multimedia content to the universe with the help of dynamic itinerary movement. While mobile agent is roaming to discover the ubiquitous content, it has to go and visit multiple servers with different character in nature (that is server may be legitimate or hostile; hostile intention is to disturb the agent functionalities either by killing the agent or modifying the agent functionalities). Whenever the agent is disturbed (agent is altered or killed) by the hostile servers while roaming to discover the content, we should have the recovery mechanism to rollback the agent. This chapter adopts the K-response recovery model to rollback the original agent even then it is cracked or killed by the malicious servers while discovering the multimedia content.

Personalized Content Representation through Hybridization of Mobile Agent and Interface Agent

Mobile agent has an ability to co-operate with heterogeneous network environment. There are specific predefined techniques to impart mobility to an agent. As a result, the agent behaves only in predefined way. To impart other features beside mobility that helps in interfacing the destination network to complete the intended job, a mobile agent need to be incorporated with additional functionalities. One of such functionalities is ability to access local user profiles, preferences, and other resources as well as other local agents to present information in user’s context. To meet this demand, hybridization of mobile and interface agent that facilitates development of customized application is discussed in this chapter. The multi-agent architecture, described in this chapter, encompasses this hybrid agent to access user profile and fuzzy indicator matrix. Both the profile and matrix are further utilized to construct content preference list according to users’ perspectives. The indicator matrix enlists typical interest and preferences of a group, such as purpose of surfing/using the system (research, teaching, learning, problem solving, etc.); level information needed (highly technical, conceptual, mixed, etc.), media preference (type of document such as text, code, video, etc.). The system is designed as multi-tier structure called resource tier, service tier, and application tier to provide resources, third party services, and application support to learners, instructors, and administrator groups. The chapter utilizes the proposed generic multi tier architecture for a personalized learning (p-Learning) system and discusses its design in detail including working of different agents, mobility and ticket management, user profile structure, and risk management policies. The chapter concludes with discussion on results and future research directions.

Workflow Management and Mobile Agents

Service composition is an extremely powerful and versatile way to aggregate and reuse distributed services and software components into richer and complex scenarios. Workflow Management Systems have emerged as one of the leading technologies to execute service compositions but typically fail to support distributed scenarios, where distributed services should be invoked in a scalable and effective way. Mobile Agent platforms propose a suitable framework to distribute the execution of complex service compositions, and therefore to enable scalability and improve performance. However, current proposals for MA-based WFMSs still target rather static and poorly distributed scenarios and exploit agent migration benefits only in a partial and insufficient way. The authors’ model proposes to overcome these problems via a richer and more effective agent delegation strategy that can also cope with dynamic scenarios where services can move and replicate, in order to achieve a better integration by taking advantage of both technologies.

Security Management in Heterogeneous Distributed Sensor Networks

A Heterogeneous Distributed Sensor Network (HDSN) is a type of distributed sensor network where sensors with different functional types participate at the same time. In this sensor network model, the sensors are associated with different deployment groups but they cooperate with each other within and out of their respective groups. The heterogeneity of HDSN refers to the functional heterogeneity of the sensors participating in the network unlike the heterogeneity considered (e.g., considering transmission range, energy level, computation ability, sensing range) for traditional heterogeneous sensor networks. Taking this model into consideration, in this chapter the authors present a secure group association authentication mechanism using one-way accumulator which ensures that; before collaborating for a particular task, any pair of nodes in the same deployment group can verify the legitimacy of group association of each other. Secure addition and deletion of sensors are supported in this approach. In addition, a policy-based sensor addition procedure is also suggested. For secure handling of disconnected nodes of a group, the authors use an efficient pairwise key derivation scheme. Side by side proposing their mechanisms, they also discuss the characteristics of HDSN, its scopes, applicability, efficiency, challenges, and future. Before concluding the chapter, the authors also talk about the applicability of our security management framework for secure mobile multimedia delivery over sensor networks.

An Agent-Based Approach to Adapt Multimedia Web Content in Ubiquitous Environment

Nowadays, Ubiquitous Computing allows a high number of multimedia contents to be accessible anywhere and anytime by using several devices, also characterized from limited computational and storage resources. To support users in multimedia choices, different recommender systems have been proposed in the past, but any of them considers the effects of the exploited devices, even though users show different behaviours in presence of different devices. This chaptertries to give a contribution in this setting, proposing a new agent-based recommender system in which each device is provided with a client agent able to monitor the user’s behaviour performed on that device. A unique server agent associated with that user collects from his/her devices this information to build a global profile, periodically returned to the client agents. Finally, recommendations of multimedia resources are generated from the collaboration among a recommender agent, associated with a Web site, and the client agent running on the device currently exploited by the user. Some experiments confirm the high quality of the recommendations generated by the proposed approach.