Wireless Smart Devices Research Articles

Factors of Weight Loss for Telemedically Supported Metabolic Syndrome Patients in a Controlled Trial

Metabolic syndrome (MetS) is a complex of interrelated risk factors, associated with several serious chronic diseases like diabetes. The goal of this study was to find dietary factors of successful weight loss for MetS outpatients. We performed a 90-day dietary intervention in a telemedically supported, pre- and post-test, controlled trial in Hungary involving 132 MetS patients; 67 were in the intervention, and 65 were in the control group. Patients in the intervention group used wireless smart devices, a dietary logger, and a lifestyle app. During the trial, we recorded the patients’ weight loss and diet composition. For analysis, t-tests were used, and the temporal trends of diet composition in the intervention group were analyzed between two sub-groups according to weight loss success. Correlation and regression models were used to find predictors of success. The intervention group achieved more weight loss, and the success in this group was linked with more consumption of raw fruits/vegetables, poultry and potato dishes, while age had a negative effect. We conclude that telemedically supported dietary coaching is an efficient alternative for interventions directed at weight loss. Future trials should investigate the therapeutic application of diets rich in raw fruits, especially apples, and vegetables, as well as poultry dishes.

Delay-sensitive task offloading and efficient resource allocation in intelligent edge–cloud environments: A discretized differential evolution-based approach

The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resource-intensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay-aware resource-constrained offloading problem for edge–cloud systems is mathematically formulated as a 0–1 integer linear programming, and it is shown to be NP-complete. Then, a delay-aware resource-constrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests—analysis of variance, post-hoc, and Friedman tests—are also performed.

HANNA: Human-friendly provisioning and configuration of smart devices

Today, there are billions of connected IoT devices and their number continues to grow as they contribute to the digitalization of infrastructures. However, the deployment process of these smart wireless devices when delivered to customer premises is slow and error prone as each of them needs to be provisioned with authentication credentials to access the corporate network. In this paper, we propose HANNA, a human-friendly provisioning and configuration framework for smart devices, that extends the zero-touch paradigm to large IoT deployments by introducing voice assisted configuration in combination with large scale ad-hoc communications to overcome the initial installation effort of IoT deployments. The most prominent role in HANNA is played by the assisting device, which includes a voice assistant capable of correctly understanding a minimum number of keywords required for initial provisioning and configuration of the devices. The device’s role is to interact with the user and ensure that all provisioning details are received. These are then converted into appropriate machine instructions for further use by the mass provisioning mechanism. We provide an example prototype implementation of HANNA and evaluate the performance of the assisting device in the human-to-machine communication phase and the performance of the selected communication technique in the machine-to-machine communication phase. Our results show the potential of existing speech-to-text engines for this application area and also reveal shortcomings with respect to the robustness of the engines in office-like working environments as well as with respect to user’s gender and language proficiency level. Additionally we show that the proposed machine-to-machine provisioning approach is always faster compared to manual provisioning for cases with more than ten devices.

Towards Enabling Fault Tolerance and Reliable Green Communications in Next-Generation Wireless Systems

Green communications have witnessed significant attention being paid to the next generation of wireless systems research and development. This is due to growing use of sensor- and battery-oriented smart wireless devices. The related literature in green communications for next-generation wireless systems majorly relies on transmission and sensing power management, but lacks a fault-tolerant centric approach. In this context, this paper presents a fault-tolerant and reliable green communications framework for next-generation wireless systems (FRGNWS). Firstly, maximum node-disjoint routes from all source nodes to the base station are identified based on the hybrid adapted grey wolf sine cosine optimizer. Secondly, a fault-tolerant and reliable route is selected from the maximum disjoint routes for each sensor node to the base station based on the hybrid adapted grey wolf whale optimizer. The performance of our proposed green communications framework is assessed by simulation experiments considering a realistic implementation scenario and different metrics. Simulation results clearly validate the efficacy of the proposed green communications framework as compared to the state-of-the-art techniques.

A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus.

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value.

Secure Data Aggregation Aided by Privacy Preserving in Internet of Things

With the rapid development of Internet of Things (IoT), more and more wireless smart devices are widely deployed in its typical applications. These devices acquire numerous data, and their security transmission is a challenge issue due to the openness and broadcast nature of wireless network. Security data aggregation (SDA) plays a significant role in IoT which can not only protect the data privacy but also reduce the network traffic among smart devices. In this paper, a multiple application SDA (MASDA) mechanism is proposed and it can ensure the data confidentiality without losing the integrity of data transmission. Firstly, we discuss an improved homomorphic message authentication code (iHMAC) which can verify the integrity of sensing data and eliminate the injection of false data. Secondly, a multiple application elliptic curve cryptography mechanism (iECC) is described. We introduce multiple types of data simultaneous encryption into an elliptic curve encryption system. This encryption mechanism aggregates the encrypted sensing data and prevents the sensing from being tampered and leaked in relay nodes. Finally, MASDA is a multiple application mechanism and provide both the confidentiality and integrity compared with other SDA schemes. It fuses the sensing data of different applications in a single data packet, and the base station can recover them one-by-one. The security analysis and the simulation experiments verify that MASDA has better performances in terms of the security, the computation cost, the communication overhead, and the data accuracy.

Multi-Modal Context Propagation for Person Re-Identification With Wireless Positioning

Existing person re-identification methods mainly rely on thevisualappearance captured by camerasfor identity matching.However, due to the sensitivity of visual data to occlusion, blur, clothing change, etc.,existing methodsstruggleto distinguish pedestriansin challenging scenarios. Inspired by the fact that most pedestrians carry around smart wireless devices, e.g. mobile phones that can be sensed by WiFi or cellular networks as wireless positioning signals, we propose to exploit the free yet informative wireless signalsto assist person re-identification. It is well recognized thatwireless signals are robust to visual noisesmentioned above, which perform as a good complement to the visual data. To make full use of these multi-modal clues for person re-identification, we propose a multi-modal context propagation framework MCPF that contains a recurrent context propagation module RCPM and an unsupervised multi-modal cross-domain method UMM-ReID. RCPM enables context information to be continuously propagatedand fusedbetween visual data and wireless data. UMM-ReID utilizes wireless signals to constrain the estimation of pseudo labels. To evaluate our approach, we contribute a new wireless positioning person re-identification (WP-ReID) dataset. Extensive experimentsdemonstrate the effectiveness of the proposed method. Benefitingfrom the collaboration of RCPM and UMM-ReID, the proposed framework MCPF achieves a significant performance improvement over existing methods.

Graphene-based wide band semi-flexible array antenna with parasitic patch for smart wireless devices

AbstractIn this paper, a semi-flexible 2 × 1 array antenna is proposed with epoxy glass fiber and graphene as patch and ground, respectively. Microstrip patch antenna with a center parasitic patch of half-wavelength and slot in the radiating patch have been incorporated for the bandwidth enhancement in order of 79.56% (2.21–5.13 GHz). The antenna has an overall size of 0.30λ × 0.24λ at a lower frequency of operation (2.45 GHz). The incorporation of slotted Graphene in radiating element leads to a wideband regime with satisfactory gain values of 2.73 and 3.744 dBi at 2.40 and 4.0 GHz, respectively. Antenna radiation efficiency in the range of 78% with linear polarization makes the antenna appropriate for WLAN band and smart wireless devices application.

Optimized Triple-Band h-Shaped Slot Microstrip Antenna Array Based Wireless Mobile Charger

This paper proposes a novel design of a triple frequency band passive communication receiver for wireless chargers for smart wireless devices, wearables and other wireless devices powered with lithium-ion batteries. The design of this receiver includes triple frequency band h-shaped slot microstrip two-element antenna array; Radio Frequency (RF) matching circuit; and passive Voltage Doubler Rectifier (VDR) circuits. The said antenna design uses 900 MHz, 1800 MHz, and 2400 MHz frequency bands. This design aims to have comparatively similar charging rate as existing wireless chargers. Therefore, the proposed antenna is the major part of the design, because it should receive more power from the three RF signal sources to achieve target charging rate. Also, in the receiver design, a (Duroid 6010) substrate with a relative permittivity 10.7 is used. Such a receiver consists of an RF matching circuit, RF multistage VDR, and a DC load circuit that would produce a 3.7 V at the output. In order to minimize the power dissipation in the rectifier circuit and consequently increase efficiency, a zero bias Schottky diode is used. Finally, the simulation results indicated that the number of VDR stages and the value of load resistance are the two major factors affecting the output of the proposed wireless charger.

Multiple High-Order Cumulants-Based Spectrum Sensing in Full-Duplex-Enabled Cognitive IoT Networks

With unprecedented progress on Internet of Things (IoT), spectrum scarcity becomes even severe with the explosive growth of wireless smart devices. To deal with spectrum scarcity issues, cognitive radio (CR)-enabled IoT has been emerged as a promising solution, which allows IoT devices reusing the underutilized spectrum bands. In this article, we investigate spectrum sensing in CR-IoT, in which full-duplex CR-IoT node can perform spectrum sensing and data transmission concurrently for reducing sensing delay. Two sensing methods are proposed based on multiple high-order cumulants for excavating rich information of the non-Gaussian transmitted signals. Specifically, for the scenarios with a single sensing antenna, we first propose a multiple high-order cumulants-based sensing method (MCS) derived from the likelihood ratio test, which is assumed to be near optimum. The test statistics are derived, respectively, in two cases, i.e., the case only performing sensing and the case performing sensing and transmission simultaneously. Interestingly, the derived two test statistics have same expression, while the corresponding sensing thresholds are different from each other. For the scenarios with multiple sensing antennas, we propose a multiantenna-assisted multiple high-order cumulants-based sensing method (MMCS), which can provide a tradeoff between the computational complexity and sensing performance. We conduct the hypothesis test with Hotelling's T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -statistic and derive the corresponding sensing threshold. Theoretical performance evaluated by detection probability and computational complexity of the proposed methods are analyzed. Additionally, extensive simulations are provided, which show both the proposed methods can counter the adverse effects of noise uncertainty, and MCS has superiority over MMCS in terms of sensing accuracy.

Wireless Resource Management and Resilience Optimization of the M2M‐Oriented Mobile Communication System

Machine‐to‐machine (M2M) communication technology is an emerging technology that can connect smart wireless devices. The most obvious feature of M2M is that the communication between devices does not require human intervention. Therefore, ensuring the low‐energy consumption of M2M devices is a necessary condition for prolonging the survival time of their devices. This paper first considers the coexistence of M2M and H2H scenarios. Aimed at the energy efficiency of M2M equipment and the channel capacity of H2H equipment, a multiobjective problem is constructed for joint spectrum and power resource management, and a weighted Chebyshev algorithm is proposed to solve this problem. Secondly, in view of the additional interference problems caused by the introduction of M2M communication, the intercell cooperative link selection algorithm is used to optimize its resilience. The effectiveness of the algorithm is proven by simulation results.

Machine Learning Techniques and A Case Study for Intelligent Wireless Networks

With the widespread deployment of wireless technologies and IoT, 5G wireless networks will support various communication connectivity and services for the huge number of wireless smart/ intelligent devices and machines. The challenge lies in assisting wireless networks to intelligently learn experience, autonomously optimize network configurations and smartly make decisions to support massive wireless smart devices with minimum human intervention, so the diverse and colorful service requirements can be satisfied with the optimum performance. Machine learning, as one of the powerful artificial intelligence tools, is capable of efficiently supporting wireless smart devices by assisting them to smartly observe the environment, analyze data and make decisions with the intelligence. Hence, in this article, we briefly review the major concepts of common machine learning techniques and present their potential applications in intelligent wireless networks, including spectrum sensing, channel estimation, device clustering, behavior prediction, position tracking, data demission reduction, adaptive routing, energy harvesting/efficiency, resource management, and so on. Furthermore, we propose deep reinforcement learning for intelligent resource management in intelligent wireless networks in an exemplary case study. Simulation results demonstrate the effectiveness and advance of machine learning in intelligent wireless networks.

Abstract WP193: A Pilot Study of Expanded Home-Based Telerehabilitation After Stroke

Introduction: High doses of activity-based rehabilitation therapy help but many patients do not receive this, e.g., due to access, cost, and low compliance. Home-based telerehabilitation (TR) can address these issues. A prior study found 6 weeks of TR targeting arm motor deficits after stroke comparably efficacious vs. therapy delivered in-clinic. Here, we evaluated a program expanded in TR dose and scope. Methods: Adults with stroke and arm motor deficits saw a licensed OT/PT who performed a live exam then supervised home-based TR (6 days/week, 1 hour/day) through games, exercises, and education. New features examined herein included (a) extending therapy to 12 weeks, (b) treating both arm and leg motor deficits, (c) augmented reality games, (d) wireless smart devices, (e) ingesting a daily experimental (placebo) pill, (f) using functional objects, (g) evaluating social networks, and (h) automated actionable reports. Results: Patients (n=13) were median age 61 [IQR=52-65.5], and 129 [52-486] days post-stroke. Patients initiated therapy on 79.9% of the 72 assigned days and completed &gt;30 min on 65.7% of days, for a 12-week total therapy dose of 50.4 [33.3 - 56.7] hours. Non-compliant days during weeks 7-12 were double those of weeks 1-6. Modified Rankin scores improved by 1 level in 6/13 patients, 3 of whom were &gt; 3month post-stroke. Fugl-Meyer motor scores increased by 6 [2.5-12.5] points in the arm and 1 [-0.5 - 5] point in the leg, mainly in weeks 1-6. Geriatric Depression Scale scores fell from 3 [1-5] at baseline (3/13 with depression) to 1 [0-4] (0/13 with depression) at week 12. Augmented reality gaming and functional objects were well received. Communication with smart devices was challenging. The experimental pill was taken, with photo verification, on 90.9% of days. Enrollees had large social networks. Automatic reports reliably notified study personnel when compliance was low or behavioral scores were concerning. Conclusions: High doses of home-based TR targeting arm and leg motor deficits are feasible and improve functional outcomes, motor deficits, and mood. The current system automates experimental pill ingestion and actionable reports to clinicians. Compliance declined over time, suggesting the need for novel approaches to extended periods of TR.

Towards a software-based mobility management for 5G: An experimental approach for flattened network architectures

The number of mobile subscribers, as well as the data traffic generated by them, is increasing exponentially with the growth of wireless smart devices and the number of network services that they can support. This significant growth is pushing mobile network operators towards new solutions to improve their network performance and efficiency. Thus, the appearance of Software Defined Networking (SDN) can overcome the limitations of current deployments through decoupling the network control plane from the data plane, allowing higher flexibility and programmability to the network. In this context, the process of handling user mobility becomes an essential part of future mobile networks. Taking advantage of the benefits that SDN brings, in this article we present a novel mobility management solution. This proposal avoids the use of IP-IP tunnels and it adds the dynamic flow management capability provided by SDN. In order to analyse performance, an analytical model is developed to compare it with NB-DMM (Network-based DMM), one of the main DMM (Distributed Mobility Management) solutions. Additionally, performance is also evaluated with an experimental testbed. The results allow handover latency in real scenarios and numerical investigations to be measured, and also show that SR-DMM achieves better efficiency in terms of signaling and routing cost than NB-DMM solution.

Proactive caching with content‐based pricing for cooperative femtocells in two‐tier heterogeneous networks

SummaryWith increase in the number of smart wireless devices, the demand for higher data rates also grows which puts immense pressure to the network. A vast majority of this demand comes from video files, and it is observed that only a few popular video files are requested more frequently during any specified time interval. Recent studies have shown that caching provides a better performance as it minimizes the network load by avoiding the fetching of same files multiple times from the server. In this paper, we propose to combine two ideas; proactive caching of files and content‐based pricing in macro‐femto heterogeneous networks. The femtocell access point (FAP) is allowed to manipulate its users' demand through content‐based pricing and serve the users' requests by proactively downloading suitable content into its cache memory which reduces the load of the femtocell. In addition, an incentive mechanism is also proposed which encourages the FAP to help macrocell users under its coverage zone by allowing access to its cached content and thereby reduces the macrocell load. The proposed content‐based pricing and proactive caching scheme for femtocells is modeled as a Stackelberg game among the macrocell base station and the FAP to jointly maximize both of their utilities. Performance analysis of the scheme is presented for a single femtocell scenario and compared with the conventional flat pricing‐based scheme via numerical examples. The results demonstrate significant reduction in network load using our proposed scheme.

A Decentralized Damage Detection System for Wireless Sensor and Actuator Networks

The unprecedented capabilities of monitoring and responding to stimuli in the physical world of wireless sensor and actuator networks (WSAN) enable these networks to provide the underpinning for several Smart City applications, such as structural health monitoring (SHM). In such applications, civil structures, endowed with wireless smart devices, are able to self-monitor and autonomously respond to situations using computational intelligence. This work presents a decentralized algorithm for detecting damage in structures by using a WSAN. As key characteristics, beyond presenting a fully decentralized (in-network) and collaborative approach for detecting damage in structures, our algorithm makes use of cooperative information fusion for calculating a damage coefficient. We conducted experiments for evaluating the algorithm in terms of its accuracy and efficient use of the constrained WSAN resources. We found that our collaborative and information fusion-based approach ensures the accuracy of our algorithm and that it can answer promptly to stimuli (1.091 s), triggering actuators. Moreover, for 100 nodes or less in the WSAN, the communication overhead of our algorithm is tolerable and the WSAN running our algorithm, operating system and protocols can last as long as 468 days.

A Novel Energy Management Approach for Smart Homes Using Bluetooth Low Energy

Smart grids are an evolution of the existing electric distribution systems due to the growing demand of energy, the expansion in the use of renewable energy sources, and the development of novel and innovative information and communication technologies (ICT). The installation of systems based on wireless networks can play a key role in the extension of the smart grid toward smart homes, that can be deemed as one of the most important components of smart grids. In fact, monitoring and control applications, energy harvesting, and innovative metering methodologies through smart wireless devices are becoming increasingly important. This paper proposes a novel energy management approach for smart homes that combines a wireless network, based on bluetooth low energy (BLE), for communication among home appliances, with a home energy management (HEM) scheme. The proposed approach addresses the impact of standby appliances and high-power rating loads in peak hours to the energy consumption charges of consumers. Simulation results show that the proposed approach is efficient in terms of reducing peak load demand and electricity consumption charges with an increase in the comfort level of consumers.

Service Environment for Smart Wireless Devices: An M2M Gateway Selection Scheme

Wireless standardization activities, such as the Bluetooth V4.x (Bluetooth smart), have led to an explosive growth in innovative short-range wireless devices capable of providing various services to assist everyday life. Allowing machine-to-machine (M2M) communication between these M2M devices also presents new commercial service opportunities. M2M service providers, including telecom carriers, may need to establish new networks to accommodate M2M interactions while identifying new business opportunities. Typically, M2M devices need another capable device, such as a smartphone, to act as a gateway to connect to the Internet to operate at their full potential. We propose a novel scheme in which any Internet-enabled device in the vicinity can act as an M2M gateway for M2M devices. This eliminates the need for the M2M device user to also own a smartphone or an Internet-enabled device to obtain services such as online-to-offline. This scheme faces new challenges, such as how to identify the best M2M gateway for a required service and the QoS. This paper proposes a method to select an M2M gateway from a large number of possibilities in order to increase service availability while also obtaining better signal strength for higher QoS. The methods presented are evaluated in terms of their performance, including energy consumption, and a service deployment guideline is derived using real-world data collected at an exhibition, which gave encouraging results.

An advanced Wi-Fi data service platform coupled with a cellular network for future wireless access

Wireless LAN devices are now everywhere because of the rapid spread of smart wireless devices. Demand for far richer content services is also driving the expansion of mobile traffic. Converging the cellular network with Wi-Fi is a reasonable way to support the increasing mobile traffic because most mobile user terminals already have Wi-Fi interfaces. More opportunities for Wi-Fi use require further enhancement of system capacity and manageability, especially in the high-density Wi-Fi network. This is because the chronic depletion of system resources is becoming a significant problem in the Wi-Fi network given the increases in Wi-Fi density and traffic. This article introduces a Wi- Fi data service platform coupled with cellular networks, which strengthens the synergy of two networks. Enhanced monitoring and performance prediction are essential to provide a highgrade user experience in high-density Wi-Fi environments.

Development of a Mini-Mobile Digital Radiography System by Using Wireless Smart Devices

The current technologies that trend in digital radiology (DR) are toward systems using portable smart mobile as patient-centered care. We aimed to develop a mini-mobile DR system by using smart devices for wireless connection into medical information systems. We developed a mini-mobile DR system consisting of an X-ray source and a Complementary Metal-Oxide Semiconductor (CMOS) sensor based on a flat panel detector for small-field diagnostics in patients. It is used instead of the systems that are difficult to perform with a fixed traditional device. We also designed a method for embedded systems in the development of portable DR systems. The external interface used the fast and stable IEEE 802.11n wireless protocol, and we adapted the device for connections with Picture Archiving and Communication System (PACS) and smart devices. The smart device could display images on an external monitor other than the monitor in the DR system. The communication modules, main control board, and external interface supporting smart devices were implemented. Further, a smart viewer based on the external interface was developed to display image files on various smart devices. In addition, the advantage of operators is to reduce radiation dose when using remote smart devices. It is integrated with smart devices that can provide X-ray imaging services anywhere. With this technology, it can permit image observation on a smart device from a remote location by connecting to the external interface. We evaluated the response time of the mini-mobile DR system to compare to mobile PACS. The experimental results show that our system outperforms conventional mobile PACS in this regard.