Smart home gateways have to forward multi-sourced network traffic generated with different distributions and with different quality-of-service (QoS) requirements. The state-of-the-art QoS-aware scheduling methods consider only the conventional priority metrics based on the IP type of service (ToS) field to make a decision for bandwidth allocation. Such priority-based scheduling methods are not optimal to provide both QoS and quality of experience (QoE), since higher priority traffic may not require lower delay than lower priority traffic (for example, traffic generated from medical sensors has a higher priority than traffic from streaming devices, but the latter one requires lower maximum delay). To solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in the smart home network (QP-SH) to make dynamic QoS-aware scheduling decisions meeting delay requirements of all traffic while preserving their degrees of criticality. A new metric that combines the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay is defined. Our experiments show that the proposed solution increases 15% of packets that meet their priorities and 40% of packets that meet their maximum delays as well as 25% of the total number of packets in the system. INDEX TERMS Quality of service, quality of experience, smart home, traffic scheduling optimization.

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As small-scale embedded systems such as Smartphones rapidly evolve, mobile malwares grow increasingly more sophisticated and dangerous. An important attack vector targeting Android Smartphone is repackaging legitimate applications to inject malicious activities, where such repackaging can be performed before or after the installation of applications on the Smartphone. To detect the behaviour deviation of applications caused by the injected malicious activities, complex anomaly detection algorithms are usually applied, however they require a system resources budget that is beyond the capacities of these small-scale devices. This paper focuses on the usability of on-device anomaly detection algorithms and proposes a detection framework for Android-based devices. The proposed solution allows using a remote server without relying entirely on it. The experimental results allow building resources consumption profiles of the studied anomaly detections algorithms and thus, provide reliable measurements that help define trade-offs between detection accuracy and resource consumption.

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