Delay Tolerant Networks

Opportunistic networks are one of the emerging evolutions of the network system. In opportunistic networks, nodes are able to communicate with each other even if the route between source to destination does not already exist. Opportunistic networks have to be delay tolerant in nature (i.e., able to tolerate larger delays). Delay tolerant network (DTNs) uses the concept of “store-carry-forward” of data packets. DTNs are able to transfer data or establish communication in remote area or crisis environment where there is no network established. DTNs have many applications like to provide low-cost internet provision in remote areas, in vehicular networks, noise monitoring, extreme terrestrial environments, etc. It is therefore very promising to identify aspects for integration and inculcation of opportunistic network methodologies and technologies into delay tolerant networking. In this chapter, the authors emphasize delay tolerant networks by considering its architectural, routing, congestion, and security issues.

A Perspective on Using Blockchain for Ensuring Security in Smart Card Systems

Due to the momentous growth in the field of Internet of Things (IoT), various commercial and government organizations are exploring possibilities of mass issuance of smart cards in different applications. Widespread deployment of smart card-based systems in heterogeneous environment would facilitate card holders to participate in these applications in a personalized manner. Despite the security features, valuable data and access to decisive services make these systems prime target for attackers. These systems can be subjected to a range of security attacks – from hardware exploitation to exploitation of software bugs, from unauthorized data access to social engineering, and so forth. In the future, where many sectors will be trying to adopt the concept of Blockchain, it will create new opportunities for benefiting citizens with enhanced security over their data. In this chapter, the author performs in-depth analysis over the role of Blockchain in securing the smart card ecosystem.

Enabling Technologies for IoT

The internet of things (IoT) is a fast-growing paradigm gaining position in the modern scenario of communication. It targets to provide interconnection among different objects at any time, and anywhere on the earth, under the leverage of internet. IoT has exhibited the promising enhancement in almost all dimensions of the everyday human life scenarios. The IoT applications are smart energy systems, manufacturing services, industrial automation, healthcare, education, smart city, transportation, and security and surveillance. However, there are several issues associated with IoT objectives such as data traffic, security and privacy, data analytics, device localization, and scalability. In addition to this, the IoT objects are resource constrained in terms of memory, computing power, energy, storage, and networking capability. Hence, IoT systems need enabling technologies to overcome these challenges by means of cloud computing, big data, cyber physical systems, and block chain. This chapter discusses how these enabling technologies can be integrated with IoT and its challenges.

An API for Development of User-Defined Scheduling Algorithms in Aneka PaaS Cloud Software

Cloud computing provides resources using multitenant architecture where infrastructure is created from one or more distributed datacenters. Scheduling of applications in cloud infrastructures is one of the main research area in cloud computing. Researchers have developed many scheduling algorithms and evaluated them using simulators such as CloudSim. Their performance needs to be validated in real-time cloud environments to improve their usefulness. Aneka is one of the prominent PaaS software which allows users to develop cloud application using various programming models and underline infrastructure. This chapter presents a scheduling API developed for the Aneka software platform. Users can develop their own scheduling algorithms using this API and integrate it with Aneka to test their scheduling algorithms in real cloud environments. The proposed API provides all the required functionalities to integrate and schedule private, public, or hybrid cloud with the Aneka software.

Big Data Analytics

The world is increasingly driven by huge amounts of data. Big data refers to data sets that are so large or complex that traditional data processing application software are inadequate to deal with them. Healthcare analytics is a prominent area of big data analytics. It has led to significant reduction in morbidity and mortality associated with a disease. In order to harness full potential of big data, various tools like Apache Sentry, BigQuery, NoSQL databases, Hadoop, JethroData, etc. are available for its processing. However, with such enormous amounts of information comes the complexity of data management, other big data challenges occur during data capture, storage, analysis, search, transfer, information privacy, visualization, querying, and update. The chapter focuses on understanding the meaning and concept of big data, analytics of big data, its role in healthcare, various application areas, trends and tools used to process big data along with open problem challenges.

On the Use of System-Level Benchmarks for Comparing Public Cloud Environments

The proliferation of public cloud providers and services offered necessitate that end users have benchmarking-related information that help compare the properties of the cloud computing environment being provided. System-level benchmarks are used to measure the performance of overall system or subsystem. This chapter surveys the system-level benchmarks that are used for traditional computing environments that can also be used to compare cloud computing environments. Amazon's EC2 Service is one of the leading public cloud service providers and offers many different levels of service. The research in this chapter focuses on system-level benchmarks and looks into evaluating the memory, CPU, and I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes.

Survey on Various MapReduce Scheduling Algorithms

The advent of social networking and internet of things (IoT) has resulted in exponential growth of data in the last few years. This, in turn, has increased the need to process and analyze such data for optimal decision making. In order to achieve better results, there is an emergence of newly-built architectures for parallel processing. Hadoop MapReduce (MR) is a programming model that is considered as one of the most powerful computation tools for processing the data on a given cluster of commodity nodes. However, the management of clusters along with various quality requirements necessitates the use of efficient MR scheduling. The chapter discusses the classification of MR scheduling algorithms based on their applicability with required parameters of quality of service (QoS). After classification, a detailed study of MR schedulers has been presented along with their comparison on various parameters.

Survey on Industrial Internet of Things (IoT) Threats and Security

Analyzing the evolution of new generation peripherals can affirm that the next decade will be characterized by the exponential increase in the number of “objects” interconnected to the internet that will be more able to communicate with each other independently and will lead to the affirmation of the paradigm internet of things (IoT), which will revolutionize everyday life on a global level. This evolution will concern not only the business realities, interested in the development of applications and systems necessary to emerge and be competitive on the market but also the ordinary citizens who will be surrounded by interconnected objects able to facilitate their everyday life. This aspect implies particular attention to the implementation of solutions oriented to cyber security necessary to guarantee an efficient and effective level of protection against the threats coming from the “world” internet, known by the term cyber space.

Fog Computing

Fog computing is an extension to cloud computing that inhibits its limitations and enhances its amenities. Being similar to cloud computing, it has some more fascinating features that escalate the overall performance of the system. It faces many new disputes besides those already inherited from cloud computing. Fog computing is actually a paradigm that provides services at the network's edge as it serves the end-users with data, applications, storing, and computing capabilities. Fog computing is a new breed in services and applications to the end-users by enabling the above features, hence making its security and privacy aspects much more challenging then the cloud computing. Further, in this chapter, the basic concepts of fog computing are discussed with its applications as a high lighting feature. In addition, discussion about the attacks that could setback the advantages of fog computing and some defense mechanisms to overcome the effects of these attack have been discussed, giving a comprehensive study of fog computing.

Achieving Secure and Privacy-Preserving in Mobile Social Networks

This chapter proposes a security framework for achieving secure and privacy-preserving mobile social networks named ASPP. Based on the cooperative neighbor, reactive routing protocol, and short signatures technique, the proposed scheme can not only detect and avoid but also can preserve the message privacy against elemental attacks and compound attacks. In addition, ASPP is robust against eavesdropping attack, wormhole attack, packet analysis attack, packet tracing attack, and replay attack. Extensive analyses and experiments are given to demonstrate its high detection rate and practicability under various scenarios.