Quality-aware Tasking in Mobile Opportunistic Networks - Distributed Information Retrieval and Processing utilizing Opportunistic Heterogeneous Resources.

Advances in wireless technology have facilitated direct communication among mobile devices in recent years, enabling opportunistic networks. Opportunistic networking among mobile devices is often utilized to offload and save cellular network traffic and to maintain communication in case of impaired communication infrastructure, such as in emergency situations. With a plethora of built-in capabilities, such as built-in sensors and the ability to perform even intensive operations, mobile devices in such networks can be used to provide distributed applications for other devices upon opportunistic contact. However, ensuring quality requirements for such type of distributed applications is still challenging due to uncontrolled mobility and resource constraints of devices. Addressing this problem, in this thesis, we propose a tasking methodology, which allows for assigning tasks to capable mobile devices, considering quality requirements. To this end, we tackle two fundamental types of tasks required in a distributed application, i.e., information retrieval and distributed processing. Our first contribution is a decentralized tasking concept to obtain crowd collected data through built-in sensors of participating mobile devices. Based on the Named Data Networking paradigm, we propose a naming scheme to specify the quality requirements for crowd sensing tasks. With the proposed naming scheme, we design an adaptive self-organizing approach, in which the sensing tasks will be forwarded to the right devices, satisfying specified quality requirements for requested information. In our second contribution, we develop a tasking model for distributed processing in opportunistic networks. We design a task-oriented message template, which enhances the definition of a complex processing task, which requires multiple processing stages to accomplish a predefined goal. Our tasking concept enables distributed coordination and an autonomous decision of participating device to counter uncertainty caused by the mobility of devices in the network. Based on this proposed model, we develop computation handover strategies among mobile devices for achieving quality requirements of the distributed processing. Finally, as the third contribution and to enhance information retrieval, we integrate our proposed tasking concept for distributed processing into information retrieval. Thereby, the crowd-collected data can be processed by the devices during the forwarding process in the network. As a result, relevant information can be extracted from the crowd-collected data directly within the network without being offloaded to any remote computation entity. We show that the obtained information can be disseminated to the right information consumers, without over-utilizing the resource of participating devices in the network. Overall, we demonstrate that our contributions comprise a tasking methodology for leveraging resources of participating devices to ensure quality requirement of applications built upon an opportunistic network

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

Privacy-Aware and Reliable Complex Event Processing in the Internet of Things - Trust-Based and Flexible Execution of Event Processing Operators in Dynamic Distributed Environments

The Internet of Things (IoT) promises to be an enhanced platform for supporting a heterogeneous range of context-aware applications in the fields of traffic monitoring, healthcare, and home automation, to name a few. The essence of the IoT is in the inter-networking of distributed information sources and the analysis of their data to understand the interactions between the physical objects, their users, and their environment. Complex Event Processing (CEP) is a cogent paradigm to infer higher-level information from atomic event streams (e.g., sensor data in the IoT). Using functional computing modules called operators (e.g., filters, aggregates, sequencers), CEP provides for an efficient and low-latency processing environment. Privacy and mobility support for context processing is gaining immense importance in the age of the IoT. However, new mobile communication paradigms - like Device-to-Device (D2D) communication - that are inherent to the IoT, must be enhanced to support a privacy-aware and reliable execution of CEP operators on mobile devices. It is crucial to preserve the differing privacy constraints of mobile users, while allowing for flexible and collaborative processing. Distributed mobile environments are also susceptible to adversary attacks, given the lack of sufficient control over the processing environment. Lastly, ensuring reliable and accurate CEP becomes a serious challenge due to the resource-constrained and dynamic nature of the IoT. In this thesis, we design and implement a privacy-aware and reliable CEP system that supports distributed processing of context data, by flexibly adapting to the dynamic conditions of a D2D environment. To this end, the main contributions, which form the key components of the proposed system, are three-fold: 1) We develop a method to analyze the communication characteristics of the users and derive the type and strength of their relationships. By doing so, we utilize the behavioral aspects of user relationships to automatically derive differing privacy constraints of the individual users. 2) We employ the derived privacy constraints as trust relations between users to execute CEP operators on mobile devices in a privacy-aware manner. In turn, we develop a trust management model called TrustCEP that incorporates a robust trust recommendation scheme to prevent adversary attacks and allow for trust evolution. 3) Finally, to account for reliability, we propose FlexCEP, a fine-grained flexible approach for CEP operator migration, such that the CEP system adapts to the dynamic nature of the environment. By extracting intermediate operator state and by leveraging device mobility and instantaneous characteristics, FlexCEP provides a flexible CEP execution model under varying network conditions. Overall, with the help of thorough evaluations of the above three contributions, we show how the proposed distributed CEP system can satisfy the requirements established above for a privacy-aware and reliable IoT environment

Transition in Monitoring and Network Offloading - Handling Dynamic Mobile Applications and Environments

Communication demands increased significantly in recent years, as evidenced in studies by Cisco and Ericsson. Users demand connectivity anytime and anywhere, while new application domains such as the Internet of Things and vehicular networking, amplify heterogeneity and dynamics of the resource-constrained environment of mobile networks. These developments pose major challenges to an efficient utilization of existing communication infrastructure. To reduce the burden on the communication infrastructure, mechanisms for network offloading can be utilized. However, to deal with the dynamics of new application scenarios, these mechanisms need to be highly adaptive. Gathering information about the current status of the network is a fundamental requirement for meaningful adaptation. This requires network monitoring mechanisms that are able to operate under the same highly dynamic environmental conditions and changing requirements. In this thesis, we design and realize a concept for transitions within network offloading to handle the former challenges, which constitutes our first contribution. We enable adaptive offloading by introducing a methodology for the identification and encapsulation of gateway selection and clustering mechanisms in the transition-enabled service AssignMe.KOM. To handle the dynamics of environmental conditions, we allow for centralized and decentralized offloading. We generalize and show the significant impact of our concept of transitions within offloading in various, heterogeneous applications domains such as vehicular networking or publish/subscribe. We extend the methodology of identification and encapsulation to the domain of network monitoring in our second contribution. Our concept of a transition-enabled monitoring service AdaptMon.KOM enables adaptive network state observation by executing transitions between monitoring mechanisms. We introduce extensive transition coordination concepts for reconfiguration in both of our contributions. To prevent data loss during complex transition plans that cover multiple coexisting transition-enabled mechanisms, we develop the methodology of inter-proxy state transfer. We target the coexistence of our contributions for the use case of collaborative location retrieval on the example of location-based services. Based on our prototypes of AssignMe.KOM and AdaptMon.KOM, we conduct an extensive evaluation of our contributions in the Simonstrator.KOM platform. We show that our proposed inter-proxy state transfer prevents information loss, enabling seamless execution of complex transition plans that cover multiple coexisting transition-enabled mechanisms. Additionally, we demonstrate the influence of transition coordination and spreading on the success of the network adaptation. We manifest a cost-efficient and reliable methodology for location retrieval by combining our transition-enabled contributions. We show that our contributions allow for adaption on dynamic environmental conditions and requirements in network offloading and monitoring