Design of Event-Triggered Asynchronous H∞ Filter for Switched Systems Using the Sampled-Data Approach

The design of networked switched systems with event-based communication is attractive due to its potential to save bandwidth and energy. However, ensuring the stability and performance of networked systems with event-triggered communication and asynchronous switching is challenging due to their time-varying nature. This paper presents a novel sampled-data approach to design event-triggered asynchronous H∞ filters for networked switched systems. Unlike most existing event-based filtering results, which either design the event-triggering scheme only or co-design the event-triggering condition and the filter, we consider that the event-triggering policy is predefined and synthesize the filter. We model the estimation error system as an event-triggered switched system with time delay and non-uniform sampling. By implementing a delay-dependent multiple Lyapunov method, we derive sufficient conditions to ensure the global asymptotic stability of the filtering error system and an H∞ performance level. The efficacy of the proposed design technique and the superiority of the filter performance is illustrated by numerical examples and by comparing the performance with a recent result

Diabetes insipidus: the basic and clinical review

Diabetes insipidus (DI) is a complex disease. DI is inability of the body to conserve water. Polydipsia and polyuria are the major manifestations of DI. DI has various variants including central diabetes insipidus (due to defect in ADH secretion), nephrogenic diabetes insipidus (due to defect in ADH receptors or urea receptors), gestational diabetes insipidus (due to catabolism of ADH by placental vasopressinase) and primary polydipsia (due to massive fluid intake). The cause of various variants of DI is either acquired or congenital. High plasma osmolality due to hypotonic urine excretion can be fatal because it can cause psychosis, lethargy, seizures, coma or even death. Polyuria and polydipsia help in the diagnosis of DI. Differential diagnosis of various variants of DI can be carried out on the basis of water deprivation test, MRI and other radiological techniques. The proper management of DI is the replenishment of water loss and correction of clinical presentations produced as a result of DI, major is hypernatremia. The best management for primary polydipsia is fluid restriction while fluid intake is used for adipsic diabetes insipidus. ADH replacement therapy is widely used to treat DI. DDAVP or desmopressin is mostly preferred ADH analogue because it has less side effects and resistant to placental vasoprssinase

Spectrum on demand : a competitive open market model for spectrum sharing for UAV-assisted communications

Unmanned aerial vehicles (UAVs)-assisted communication has gathered significant interest of the industry, especially with regards to the vision of providing ubiquitous connectivity for beyond 5G (B5G) networks. In this article, we motivate the need for utilizing licensed spectrum for UAV-assisted communication and discuss its advantages such as reliability and security. Moreover, we explore a new dimension to spectrum sharing by proposing a decentralized competitive open market approach based model, where the different mobile network operators (MNOs) have the opportunity to lease the spectrum to UAV base stations (UAV-BSs), leading to new revenue generation opportunities. The proposed spectrum sharing mechanism is based on the logarithmic utility function and willingness to pay of each UAV-BS. We provide a tradeoff analysis between spectrum sharing and price offered by the MNOs, highlighting the impact of the willingness to pay on the spectrum sharing. The results also highlight the behaviour of price and spectrum shared w.r.t. time, thereby providing an insight into different performance regions until the algorithm converges to it’s optimal value. In addition, we also present future directions that could lead to interesting analyses, especially with regards to incentive-based spectrum sharing and security

Energy-aware AI-driven Framework for Edge Computing-based IoT Applications

The significant growth in the number of Internetof- things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying stateof-the-art convolutional neural networks (CNNs) on such energy constrained devices is not feasible due to their computational cost. Existing model compression methods such as network pruning and quantization can reduce complexity, but these methods only work for fixed computational or energy requirements, which is not the case for edge devices with an intermittent energy source. In this work, we propose a pruning scheme based on deep reinforcement learning (DRL), which can compress the CNN model adaptively according to the energy dictated by the energy management policy and accuracy requirements for IoT applications. The proposed energy policy uses predictions of energy to be harvested and dictates the amount of energy that can be used by the edge device for deep learning inference. We compare the performance of our proposed approach with existing state-of-the-art CNNs and datasets using different filter-ranking criteria and pruning ratios.We observe that by using DRL driven pruning, the convolutional layers that consume relatively higher energy are pruned more as compared to their counterparts. Thereby, our approach outperforms existing approaches by reducing energy consumption and maintaining accuracy

Analysis and Design of Secure Sampled-Data Control Subject to Denial-of-Service Attacks

This study addresses the issue of secure control design for cyber-physical systems (CPS) against denial of service (DoS) attacks. We take into account a continuous-time linear system with a convex quadratic performance measure and a sampled linear state feedback control. DoS attacks impose constraints on the CPS, where packets may be jammed between the sensor and controller by a malicious entity, potentially resulting in system instability and performance degradation. We assume that the attacker can perform DoS attacks with a limited time and frequency due to energy restrictions. We devise an efficient procedure using the linear matrix inequality approach to compute an upper bound on the performance degradation brought on by the DoS attack. We also propose a redesign of the controller to minimize this performance degradation. Finally, a simulation example illustrates the computation of the performance degradation under a bounded DoS attack and the design of a secure controller. Simulation results show that the designed controller effectively keeps the feedback loop’s performance and stability under attack

Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things Magazin

Energy management in harvesting enabled sensing nodes: prediction and control

Energy efficient transmission rate regulation of wireless sensing nodes, is a critical issue when operating in an energy harvesting (EH) enabled environment. In this work, we view the energy management problem as a queue control problem where the objective is to regulate transmission such that the energy level converges to a reference value. We employ a validated non-linear queuing model to derive two non-linear robust throughput controllers. A notable feature of the proposed scheme is its capability of predicting harvest-able energy. The predictions are generated using the proposed Accurate Solar Irradiance prediction Model (ASIM) whose effectiveness in generating accurate both long and short term predictions is demonstrated using real world data. The stability of the proposed controllers is established analytically and the effectiveness of the proposed strategies is demonstrated using simulations conducted on the Network Simulator (NS-3). The proposed policy is successful in guiding the energy level to the reference value, and outperforms the Throughput Optimal (TO) policy in terms of the throughput achieved

Intelligent Beam Steering for Wireless Communication Using Programmable Metasurfaces

Reconfigurable Intelligent Surfaces (RIS) are well established as a promising solution to the blockage problem in millimeter-wave (mm-wave) and terahertz (THz) communications, envisioned to serve demanding networking applications, such as 6G and vehicular. HyperSurfaces (HSF) is a revolutionary enabling technology for RIS, complementing Software Defined Metasurfaces (SDM) with an embedded network of controllers to enhance intelligence and autonomous operation in wireless networks. In this work, we consider feedback-based autonomous reconfiguration of the HSF controller states to establish a reliable communication channel between a transmitter and a receiver via programmable reflection on the HSF when Line-of-sight (LoS) between them is absent. The problem is to regulate the angle of reflection on the metasurface such that the power at the receiver is maximized. Extremum Seeking Control (ESC) is employed with the control signals generated mapped into appropriate metasurface coding signals which are communicated to the controllers via the embedded controller network (CN). This information dissemination process incurs delays which can compromise the stability of the feedback system and are thus accounted for in the performance evaluation. Extensive simulation results demonstrate the effectiveness of the proposed method to maximize the power at the receiver within a reasonable time even when the latter is mobile. The spatiotemporal nature of the traffic for different sampling periods is also characterized