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With the proliferation of portable and mobile IoT devices and their increasing processing capability, we witness that the edge of network is moving to the IoT gateways and smart devices. To avoid Big Data issues (e.g. high latency of cloud based IoT), the processing of the captured data is starting from the IoT edge node. However, the available processing capabilities and energy resources are still limited and do not allow to fully process the data on-board. It calls for offloading some portions of computation to the gateway or servers. Due to the limited bandwidth of the IoT gateways, choosing the offloading levels of connected devices and allocating bandwidth to them is a challenging problem. This paper proposes a technique for managing computation offloading in a local IoT network under bandwidth constraints. The existing bandwidth allocation and computation offloading management techniques underutilize the gateway's resources (e.g. bandwidth) due to the fragmentation issue. This issue stems from the discrete coarse-grained choices (i.e. offloading levels) on the IoT end nodes. Our proposed technique addresses this issue, and utilizes the available resources of the gateway effectively. The experimental results show on average 1 hour (up to 1.5 hour) improvement in battery life of edge devices. The utilization of gateway's bandwidth increased by 40%. 1

Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. Current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, an IoT gateway is shared among several IoT devices. However, given the limited available bandwidth and processing capabilities of an IoT gateway, the quality of service (QoS) of IoT devices must be adjusted over time not only to fulfill the needs of individual IoT device users, but also to tolerate the QoS needs of the other IoT devices sharing the same gateway.In this paper, we address the problem of QoS management for IoT devices under bandwidth, battery, and processing constraints. We first formulate the problem of resourceaware QoS tailored to the IoT paradigm and then propose an efficient problem decomposition that enables the adoption of a recurrent dynamic programming approach with reduced execution time overhead. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding to the number and type of the employed IoTdevices. Experiments show that our solution improves the overall QoS by 50% compared to an unsupervised system while both meet the constraints.

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