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The Routing Protocol for Low-Power and Lossy Networks (RPL) is a novel routing protocol standardized for constrained environments such as 6LoWPAN networks. Providing security in IPv6/RPL connected 6LoWPANs is challenging because the devices are connected to the untrusted Internet and are resource constrained, the communication links are lossy, and the devices use a set of novel IoT technologies such as RPL, 6LoWPAN, and CoAP/CoAPs. In this paper we provide a comprehensive analysis of IoT technologies and their new security capabilities that can be exploited by attackers or IDSs. One of the major contributions in this paper is our implementation and demonstration of well-known routing attacks against 6LoWPAN networks running RPL as a routing protocol. We implement these attacks in the RPL implementation in the Contiki operating system and demonstrate these attacks in the Cooja simulator. Furthermore, we highlight novel security features in the IPv6 protocol and exemplify the use of these features for intrusion detection in the IoT by implementing a lightweight heartbeat protocol.

This article introduces an innovative approach for creating a circular polarization (CP) antenna-based rotation-insensitive implantable wireless power transfer (WPT) system for medical devices. The system is constructed to work in the industrial, scientific, and medical (ISM) frequency band of 902–928 MHz. Initially, a flexible, wide-band, and bio-compatible open-ended CP slot antenna is designed within a single-layer human skin tissue model to serve as the receiving (Rx) element. To form the implantable WPT link, a circular patch antenna is also constructed in the free-space to use as a transmitting (Tx) source. Further, a new metamaterial-polarization converter (MTM-PC) structure is developed and incorporated into the proposed system to enhance the power transfer efficiency (PTE). Furthermore, the rotational phenomenon of the Rx implant has been studied to show how the rotation affects the system’s performance. Moreover, a numerical analysis of the specific absorption rate (SAR) is conducted to confirm compliance with safety regulations and prioritize human safety from electromagnetic exposure. Finally, to validate the introduced concept, prototypes of the different elements of the proposed WPT system were fabricated and tested using skin-mimicking gel and porcine tissue. The measured results confirm the feasibility of the introduced approach, exhibiting improved efficiency due to use of the MTM-PC. The amplitude of the transmission coefficient ( | S 21 | ) has improved by 6.94 dB in the simulation, whereas the enhancement of 7.04 dB and 6.76 dB is obtained from the experimental study due to the integration of MTM-PC. As a result, the PTE of the proposed MTM-PC integrated implantable WPT system is increased significantly compared to the system without MTM-PC.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7–2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of ∼0.7 dB and ∼1.9 dB per cm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.

The human body can act as a medium for the transmission of electromagnetic waves in the wireless body sensor networks context. However, there are transmission losses in biological tissues due to the presence of water and salts. This Letter focuses on lateral intra-body microwave communication through different biological tissue layers and demonstrates the effect of the tissue thicknesses by comparing signal coupling in the channel. For this work, the authors utilise the R-band frequencies since it overlaps the industrial, scientific and medical radio (ISM) band. The channel model in human tissues is proposed based on electromagnetic simulations, validated using equivalent phantom and ex-vivo measurements. The phantom and ex-vivo measurements are compared with simulation modelling. The results show that electromagnetic communication is feasible in the adipose tissue layer with a low attenuation of ∼2 dB per 20 mm for phantom measurements and 4 dB per 20 mm for ex-vivo measurements at 2 GHz. Since the dielectric losses of human adipose tissues are almost half of ex-vivo tissue, an attenuation of around 3 dB per 20 mm is expected. The results show that human adipose tissue can be used as an intra-body communication channel.

Background The combination of an aging population and nursing staff shortages implies the need for more advanced systems in the healthcare industry. Many key enablers for the optimization of healthcare systems require provisioning of location awareness for patients (e.g. with dementia), nurses, doctors, assets, etc. Therefore, many Indoor Positioning Systems (IPSs) will be indispensable in healthcare systems. However, although many IPSs have been proposed in literature, most of these have been evaluated in non-representative environments such as office buildings rather than in a hospital. Methods To remedy this, the paper evaluates the performance of existing IPSs in an operational modern healthcare environment: the “Sint-Jozefs kliniek Izegem” hospital in Belgium. The evaluation (data-collecting and data-processing) is executed using a standardized methodology and evaluates the point accuracy, room accuracy and latency of multiple IPSs. To evaluate the solutions, the position of a stationary device was requested at 73 evaluation locations. By using the same evaluation locations for all IPSs the performance of all systems could objectively be compared. Results Several trends can be identified such as the fact that Wi-Fi based fingerprinting solutions have the best accuracy result (point accuracy of 1.21 m and room accuracy of 98 %) however it requires calibration before use and needs 5.43 s to estimate the location. On the other hand, proximity based solutions (based on sensor nodes) are significantly cheaper to install, do not require calibration and still obtain acceptable room accuracy results. Conclusion As a conclusion of this paper, Wi-Fi based solutions have the most potential for an indoor positioning service in case when accuracy is the most important metric. Applying the fingerprinting approach with an anchor installed in every two rooms is the preferred solution for a hospital environment.

As LED lighting becomes increasingly ubiquitous, Visible Light Communication is attracting the interest of academia and industry as a complement to RF as the physical layer for the Internet of Things. Aside from its much greater spectral availability compared to RF, visible light has several attractive properties that may promote its uptake: its lack of health risks, its opportunities for spatial reuse, its relative immunity to multipath fading, its lack of electromagnetic interference, and its inherently secure nature: differently from RF, light does not penetrate through walls. In this article, the authors outline the security implications of Visible Light Communication, review the existing contributions to this under-explored space, and survey the research opportunities that they envision for the near future.

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed the design and development of various intelligent systems over wide application domains. While most existing machine learning models require large memory and computing power, efforts have been made to deploy some models on resource-constrained devices as well. A majority of the early application systems focused on exploiting the inference capabilities of ML and DL models, where data captured from different mobile and embedded sensing components are processed through these models for application goals such as classification and segmentation. More recently, the concept of exploiting the mobile and embedded computing resources for ML/DL model training has gained attention, as such capabilities allow (i) the training of models via local data without the need to share data over wireless links, thus enabling privacy-preserving computation by design, (ii) model personalization and environment adaptation, and (ii) deployment of accurate models in remote and hardly accessible locations without stable internet connectivity. This work targets to summarize and analyze state-of-the-art systems research that allows such on-device model training capabilities and provide a survey of on-device training from a systems perspective.

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their data in local clients. However, standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In addition, it is often prohibited for service providers to verify the quality of data samples due to the increasing concern of user data privacy. In this paper, we address this challenge by proposing Auto-weighted Robust Federated Learning (arfl), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected risk with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. The weights are allocated by comparing the empirical loss of a client with the average loss of the best p clients (p-average), thus we can downweight the clients with significantly high losses, thereby lower their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST and Shakespeare, considering different deep neural network models. The results show that our solution is robust against different scenarios including label shuffling, label flipping and noisy features, and outperforms the state-of-the-art methods in most scenarios.