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

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

Enabling In-Network Processing utilizing Nearby Device-to-Device Communication

In disaster situations, relief work can be enhanced and facilitated by acquiring and processing distributed informa- tion. However, the communication and computation infrastruc- tures might be impaired or inaccessible in emergency response scenarios. Consequently, approaches for coordination, and re- source utilization are still challenging. To this end, we proposed the concept of an adaptive task-oriented message template (ATMT), that bundles the control information and the payload data, re- quired to process and extract information, into a single message. Thus, an ATMT enables distributed in-network processing of complex tasks, allows to leverage the idle resources of mobile devices of the first responders. In this paper, we demonstrate the use of the ATMT concept in an example of face detection, which can be used to offer Person Finder similar services in an emergency ad hoc network. We utilize Google Nearby peer- to-peer networking API, standard and available on Android- based devices, to realize the handover of an ATMT message between mobile devices. This successful integration underpins the prospective adoption of the ATMT concept

TrustCEP : Adopting a Trust-Based Approach for Distributed Complex Event Processing

The advent of the Internet of Things (IoT), with modern sensors and sensor-based devices, will significantly stimulate the development of context-aware applications. An effective means to extract higher-level contextual information from sensor data is distributed complex event processing (CEP), which facilitates the analysis of real-time data streams coming from heterogeneous and distributed sources. Considering that user context is inherently sensitive information, the preservation of privacy is critical once the processing of user context takes place over several (possibly malicious) devices, especially in collaborative scenarios. In this paper, we tackle this issue by introducing a trust-based approach for the placement and execution of CEP operators in a distributed environment. We propose a trust management model based on communication interactions among the users. Furthermore, we incorporate trust recommendations using a cosine-based similarity check in order to overcome collusion and on-off attacks. We developed a smartphone-based distributed CEP system called TrustCEP to evaluate our approach for trust management. Based on the evaluation of TrustCEP, we observe that our approach induces a minimal increase in average battery consumption compared to privacy-negligent approaches