Optimal grouping-of-pictures in IoT video streams

We study a dynamic video encoder that detects scene changes and tunes the synthesis of Groups-of-Pictures accordingly. Such dynamic encoding can be applied to infrastructures with restricted resources, like IoT facilities where multimedia streams are of use. In such facilities the scarcity of resources (energy, bandwidth, etc.) is a dominant solution design factor. In the domain of video capturing/transmission content-driven approaches should be adopted to improve efficiency while maintaining quality at acceptable levels. We propose a time-optimized decision making model that yields different sizes of groups-of-pictures (frames) to meet the previously discussed objectives i.e., transmit video sequences in acceptable quality with rational use of the wireless resources. Our quantitative findings show that the propose scheme performs quite efficiently while dispatching video sequences with different characteristics

Time-Optimized Contextual Information Flow on Unmanned Vehicles

Nowadays, the domain of robotics experiences a significant growth. We focus on Unmanned Vehicles intended for the air, sea and ground (UxV). Such devices are typically equipped with numerous sensors that detect contextual parameters from the broader environment, e.g., obstacles, temperature. Sensors report their findings (telemetry) to other systems, e.g., back-end systems, that further process the captured information while the UxV receives control inputs, such as navigation commands from other systems, e.g., commanding stations. We investigate a framework that monitors network condition parameters including signal strength and prioritizes the transmission of control messages and telemetry. This framework relies on the Theory of Optimal Stopping to assess in real-time the trade-off between the delivery of the messages and the network quality statistics and optimally schedules critical information delivery to back-end systems

Adaptive epidemic dissemination as a finite-horizon optimal stopping problem

Wireless ad hoc networks are characterized by their limited capabilities and their routine deployment in unfavorable environments. This creates the strong requirement to regulate energy expenditure. We present a scheme to regulate energy cost through optimized transmission scheduling in a noisy epidemic dissemination environment. Building on the intrinsically cross-layer nature of the adaptive epidemic dissemination process, we strive to deliver an optimized mechanism, where energy cost is regulated without compromising the network infection. Improvement of data freshness and applicability in routing are also investigated. Extensive simulations are used to support our proposal

Advanced fuzzy inference engines in situation aware computing

We focus on the very important family of context-aware applications. Context-aware computing relies on tasks like capturing/sensing environmental parameters (e.g., lightness, location), classifying context, and inferring further knowledge about that context (determine the situation the user is currently in). However, the relevant applications have to deal with the inherent imperfection of context sensing for decision making. We propose an extension of context representation and inference for situation-aware applications. Our model relies on Fuzzy Set Theory to accommodate the imperfection of sensed context. Based on this model, we have developed three fuzzy inference engines, which rely on advanced semantics (specialization, mereological and compatibility relations). We have evaluated the proposed engines through a series of experiments involving real users. Our findings indicate the strong points of the proposed context classification and inference processes. © 2009 Elsevier B.V. All rights reserved

Context discovery in mobile environments: A Particle Swarm Optimization approach

We introduce a novel application of Particle Swarm Optimization in the mobile computing domain. We focus on context aware applications and investigate the context discovery problem in dynamic environments. Specifically, we investigate those scenarios where nodes with context aware applications are trying to (physically) locate up-to-date context, captured by other nodes. We establish the concept of context quality (an ageing framework deprecates contextual information thus leading to low quality). Nodes with low quality context cannot capture such information by themselves but are in need for "fresh" context in order to feed their application. We assess the performance of the proposed algorithm through simulations. Our findings are quite promising for the mobile computing domain and context awareness in specific. We assess two different strategies for the PSO-based context discovery framework. © Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering 2010

Optimal, quality-aware scheduling of data consumption in mobile ad hoc networks

In this paper we study the delivery of quality contextual information in mobile ad-hoc networks. We consider that information has a certain quality level that fades over time. Mobile context-aware applications receive and process disseminated information given that the corresponding quality is above the lowest level. The necessity for optimally scheduling information delivery arises from the dynamic nature of the network, e.g., probabilistic spreading, caching, deferred delivery, and mobility of nodes. We propose two policies for optimal scheduling information delivery consumption based on the Optimal Stopping Theory. The mobile nodes delay the reporting of information to mobile context-aware applications in search for better quality. The proposed policies efficiently deal with the delivery of quality information in mobile ad-hoc networks

Enhancing situation-aware systems through imprecise reasoning

Context awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. One important problem that arises in such systems is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current context of the user. Hence, the knowledge upon which the context / situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed ontologies in order to approximately infer the current situation of the user. Finally, we present a series of experimental results that provide evidence of the flexible, efficient nature of the proposed situation awareness architecture