Aly Syed

Power Control in Wireless Sensor Networks with Variable Interference

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counterproductive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency and interference mitigation and, thus, have beneficial effects on network performance. Through simulations, we analyze the performance of sensor nodes in an environment with variable interference. Then we study the relation between transmission power and communication efficiency, particularly in the context of Adaptive and Robust Topology (ART) control, showing how appropriate power reduction can benefit both energy and spectrum efficiency. We also identify critical limitations in ART, discussing the potential of more cooperative power control approaches.

Power-managed smart lighting using a semantic interoperability architecture

We present a power-managed smart lighting system that allows collaboration of Consumer Electronics (CE) lighting-devices and corresponding system architectures provided by different CE suppliers. In the example scenario, the rooms of a building are categorized as low- and high-priority, each category utilizing a different system architecture. The rooms collaborate through a semantic interoperability platform. The overall smart lighting system conforms to a power quota regime and maintains a target power consumption level by automatically adjusting power consumed by luminaries in the building. Experiments with CE devices of different suppliers operating on different networks show that the semantic interoperability architecture allows device collaboration that can lead to lower power cost.

Dynamic performance of IEEE 802.15.4 devices under persistent WiFi traffic

Recent studies have provided coexistence and interaction models between IEEE 802.15.4 and IEEE 802.11 standards. However, the performance of IEEE 802.15.4 devices under WiFi interference are evaluated based on limit parameters i.e. Packet Reception Rate, which does not exhibit the dynamic interactions in the wireless channel. In this paper, we conduct a series of experiments to demonstrate the dynamic interactions between the IEEE 802.15.4 and IEEE 802.11 bgn standards on relevant devices. The performance of four existing Link Quality Estimators (LQEs) of IEEE 802.15.4 nodes under the IEEE 802.11 bgn interference is analyzed. We show that IEEE 802.15.4 transmission failures are largely due to channel access failures rather than corrupted data packets. Based on the analysis, we propose a new LQE - Packet Reception Rate with Clear Channel Assessment - by merging the Clear Channel Assessment count with the Packet Reception Rate. In comparison to existing LQEs, results show that the new estimator distinguishes persistent IEEE 802.11 bgn traffic more robustly.

Increasing reliability and availability in smart spaces: A novel architecture for resource and service management

Smart spaces are physical spaces where services provided by Consumer Electronics (CE) devices with varying resource availabilities work together to realize user-specific automated scenarios. These scenarios may be interrupted in case one of the services making up the scenario stops working, e.g. due to lack of resources, node failure or leave. Therefore, the user experience is highly dependent on the availability and reliability of smart space resources and services. This paper proposes an architecture for resource and service management in smart spaces. We present mechanisms for automatic node entry and exit, service discovery and matching, failure detection and recovery, and resource monitoring. The proposed architecture is evaluated in the context of a simple use case scenario.

An architecture for self-organization in pervasive systems

Pervasive computing environments consist of many independent collaborating electronic devices, including sensors and actuators. Ad-Hoc extendibility of such systems is desirable but the current network technologies use the concept of a central coordinator device in the network or define application profiles which are not easy to extend and maintain. The distributed architecture proposed in this paper allows these devices to organize themselves automatically to execute some pervasive system application without the intervention of a central controlling device. The knowledge that defines interactions between these devices is derived from an ontological model of a particular domain. This knowledge is distributed over the devices such that every device only has information about its own interactions and operations. A simple demonstration of this architecture is presented.

Interference Mitigation through Adaptive Power Control in Wireless Sensor Networks

Adaptive transmission power control schemes have been introduced in wireless sensor networks to adjust energy consumption under different network conditions. This is a crucial goal, given the constraints under which sensor communications operate. Power reduction may however have counter-productive effects to network performance. Yet, indiscriminate power boosting may detrimentally affect interference. We are interested in understanding the conditions under which coordinated power reduction may lead to better spectrum efficiency, interference mitigation and, thus, have beneficial effects on network performance. Through a combination of measurements and simulations, we study the relation between transmission power and communication efficiency with the technique of Adaptive and Robust Topology control (ART), showing how power reduction can benefit energy and spectrum efficiency. We identify critical limitations in ART (in terms of stability and adaptivity), discussing the potential of more cooperative power-control approaches.

Predictive Power Control in Wireless Sensor Networks

Communications in Wireless Sensor Networks (WSNs) are affected by dynamic environments, variable signal fluctuations and interference. Thus, prompt actions are necessary to achieve dependable communications and meet quality of service requirements. To this end, the reactive algorithms used in literature and standards, both centralized and distributed ones, are too slow and prone to cascading failures, instability and sub-optimality. We explore the predictive power of machine learning to better exploit the local information available in the WSN nodes and make sense of global trends. We aim at predicting the configuration values that lead to network stability. In this work, we adopt the Q-learning algorithm to train WSNs to proactively start adapting in face of changing network conditions, acting on the available transmission power levels. Our aim is to prove that smart nodes lead to better network performance with the aid of simple machine learning.

RDF recipes for context-aware interoperability in pervasive systems

Nowadays home automation systems integrate many devices from security system, heating, ventilation and air conditioning system, lighting system or audio-video systems. Every time a new device is installed, problems with connecting it to other devices and synchronization may appear. The technology trends are to build more powerful and functional new autonomous devices, rather than improve device synchronization, data and functional distribution and adaptation to changing environment. This paper highlights interoperability problems in pervasive computing and presents a solution for devices with limited processing capabilities by use of an ontology for knowledge formulation and semantic interoperability.

Runtime Validation Framework

Testing large-scale complex systems at runtime is of paramount importance. This is particularly true for dynamical systems, such as distributed adaptive network embedded systems (ANES), which exhibit adaptive capabilities aiming at autonomously reconfiguring and adjusting their behavior based on the changing environmental conditions. In such cases, it is not feasible, during the development stages, to anticipate all the possible operating conditions that the system may face in a real environment. This is because some information about the execution context and the system itself can be available only once the system has been deployed. Thus, in order to correctly assess the effectiveness, efficiency and robustness of ANES, it is required to verify that the system correctly adopts the proper adaptation mechanisms in response to the context changes as well as to check the quality of such adaptations. The focus of the chapter is to discuss about the needs for employing runtime verification and validation of ANES and the main challenges and requirements for its implementation. In addition, it presents a reference framework that supports developers in testing adaptive systems at runtime. One of its key feature is the capability to emulate certain realistic conditions through synthetic data, which is useful to check the system’s behavior under specific and controlled situations.