Distributed QoS management for internet of things under resource constraints

Abstract

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.

Bandit Convex Optimization for Scalable and Dynamic IoT Management

“…(1)), including [13], [15], and the recent variant [14] incorporating with adaptive stepsizes. Consider now the per-slot problem (1), which contains the current objective f t (x), the current constraint g t (x) ≤ 0, and a time-invariant feasible set X . With λ ∈ R N + denoting the Lagrange multiplier associated with the time-varying constraint, the online Lagrangian of (1) can be expressed as…”

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

“…Table 6 summarizes the researches reviewed in this section. Other resource allocation [222,233,255] Energy conservation is one of the main contributors to better system performance and to enhance life quality. Several reviewed ECG monitoring systems proposed solutions for lower energy consumption by reducing signal transmission, processing, and supporting signal compression.…”

“…However, the use of the aforementioned approaches implicate some challenges regarding the lack of interoperability of different wireless devices, short battery life, and some security problems. Another way of reducing energy consumption is optimizing processing techniques and proposing new enhanced algorithms for signal processing [222,233,236,237]. Alternatively, signal compression is used for lower energy consumption in [134,135,[239][240][241][242][243].…”

“…In this system, the ECG data are stored as model parameters rather than data samples to reduce the storage space, and only the abnormal sensed ECG signals are transmitted to reduce energy [232]. Other resource allocation approaches were addressed in [222,233].…”

On Maximizing Task Throughput in IoT-Enabled 5G Networks Under Latency and Bandwidth Constraints

“…Related Work: A few attempts have been made to address similar problems in recent literature. For example, Samie et al have proposed a novel resource management scheme for IoT devices in [2], where they have reasoned out the need for a discrete number of resources at different stages of operation in the context of a smart health application. Further, authors have proposed a QoS based resource allocation approach for smarthealth care application in IoT-enabled networks.…”