Internet Technology Letters最新文献

Wireless sensors are important nowadays to collect real-time data in hospital wards. Small sensors can be deployed either on/in patients' bodies or at fixed locations in wards. We propose an autonomous wireless system, where sensors can harvest energy from radio frequency signals, monitor and transmit data using the harvested energy. By properly modeling energy statuses and stochastic distributions of sensors, we analyze the coverage performance using Campbell's theorem, probability generating functional (PGFL), and Gil-Pelaez inversion theorem. The nonlinear energy harvesting model and the general Nakagami-m fading channels are considered in the analysis. Numerical results show that the coverage performance degrades when sensors are distributed more densely, the energy and data links are prolonged, or the transmission rate gets larger, while it improves when higher power is used for the energy transfer, or the energy and data links undergo lighter fading. Our proposed scheme can well facilitate the long-time monitoring tasks in hospital wards, which can greatly alleviate the burdens of medical staff.

The above article, first published online on October 23, 2020 Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/itl2.252) has been retracted by agreement between the journal Editor-in-Chief, Luigi Alfredo Grieco, Department of Electrical and Information Engineering (DEI) Politecnico di Bari, Bari, Italy and Wiley Periodicals, LLC. The retraction has been agreed due to pertinent evidence indicating that the peer review process of this paper was compromised. The accepted letter has gone through an additional evaluation by scientific experts who judge that the quality of those contributions was not in line with the standards of the ITL journal. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.

Unmanned aerial vehicle (UAV) assisted wireless communication has been recently proposed as an effective solution to provide wireless connectivity to ground users which can improve the network coverage area and spectral efficiency. In this paper, we consider the post-disaster network scenario where users are isolated from the network infrastructure. We investigate the best positioning of the UAV in terms of the optimal trajectory as well as UAV vertical location to cover as many ground users as possible to improve the spectral efficiency. When the trajectory is optimized, the optimal transmit power within the available power budget is then obtained. The simulation results demonstrate that the proposed UAV-enabled wireless communication technique improves the overall spectral and energy efficiency.

Fog computing computes and stores data locally based upon distributed architectures that extend cloud to the edge of the network. This enables fog paradigms to be suitable for delay-sensitive applications that demand various network functions (NFs). These NFs here are provided using network function virtualization (NFV) to allow elastic NF deployment on the fog nodes, that is, virtual network functions (VNFs). Now a major challenge for NFV-based fog architectures is the limited resources, which vulnerate them to premature saturation and load imbalance. Hence resource management schemes are necessary for fog networks, in particular load migration and redistribution. Along this, an efficient migration scheme is proposed here using the coordinated generalized pattern search (CGPS) heuristic method. The scheme migrates excess load from heavily-loaded (HL) nodes to lightly-loaded (LL) nodes in proximity. The scheme adopts two criteria, termed as exclusive and common use of VNF instances. Results show that the proposed scheme achieves high migration success at reduced delays, as well as alleviating resources with low migration frequency.

In this letter, we consider the privacy-preserving strategy of the distributed state estimator over wireless sensor networks. Aiming at an eavesdropper who can intercept the data transmitted on the communication channel, we design a simple preserving scheme to encode the transmitted data by using system dynamics and history estimates. To analyze the privacy-preserving performance, we use the gap between the values deciphered by the eavesdropper and decoded by the sensor as the privacy index to evaluate the degree of robust privacy protection. Finally, we provide some simulations to illustrate the effectiveness of the proposed preserving schemes.

Internet technology and smart medical equipment have been rapidly developed and widely popularized. Telemedicine can improve the efficiency of patients' medical treatment and can facilitate patients to receive doctor's diagnosis and treatment, which saves medical resources and facilitate the monitoring of patients suffering from geriatric diseases and chronic diseases. In this work, we adopt the deep learning model to form a medical-oriented English translation mechanism, based on which we can improve diagnostic efficiency and promote the development of smart telemedicine systems. We combine the recurrent neural network and attention mechanism to design an English translation model, which can solve the language barrier in the cross-domain medical diagnosis process and improve the diagnosis efficiency. Specifically, first, we design a RNN translation model based on the encoder-decoder structure. Second, we incorporate an attention mechanism into the designed translation model to improve the translation performance of the model. The experimental results show that the proposed mechanism is efficient.