Low Battery Consumption Research Articles

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Globus pallidus internus versus subthalamic nucleus deep brain stimulation for isolated dystonia: A 3-year follow-up.

Bilateral deep brain stimulation (DBS) surgery targeting the globus pallidus internus (GPi) or the subthalamic nucleus (STN) is widely used in medication-refractory dystonia. However, evidence regarding target selection considering various symptoms remains limited. This study aimed to compare the effectiveness of these two targets in patients with isolated dystonia. This retrospective study evaluated 71 consecutive patients (GPi-DBS group, n = 32; STN-DBS group, n = 39) with isolated dystonia. Burke-Fahn-Marsden Dystonia Rating Scale scores and quality of life were evaluated preoperatively and at 1, 6, 12, and 36 months postoperatively. Cognition and mental status were assessed preoperatively and at 36 months postoperatively. Targeting the STN (STN-DBS) yielded effects within 1 month (65% vs. 44%; p = 0.0076) and was superior at 1 year (70% vs. 51%; p = 0.0112) and 3 years (74% vs. 59%; p = 0.0138). For individual symptoms, STN-DBS was preferable for eye involvement (81% vs. 56%; p = 0.0255), whereas targeting the GPi (GPi-DBS) was better for axis symptoms, especially for the trunk (82% vs. 94%; p = 0.015). STN-DBS was also favorable for generalized dystonia at 36-month follow-up (p = 0.04) and required less electrical energy (p < 0.0001). Disability, quality of life, and depression and anxiety measures were also improved. Neither target influenced cognition. We demonstrated that the GPi and STN are safe and effective targets for isolated dystonia. The STN has the benefits of fast action and low battery consumption, and is superior for ocular dystonia and generalized dystonia, while the GPi is better for trunk involvement. These findings may offer guidance for future DBS target selection for different types of dystonia.

Hybrid solution for smart rural applications in areas without internet coverage

Agriculture and livestock farming are one of the main economic bases of rural areas in Southern Europe, with many classified as 'sparsely populated'. This article proposes and tests a technical solution to support different applications that require a large range of coverage and tracking assets, as can be found for instance in smart farming, or environmental monitoring. LoRaWAN, which provides several kilometres of coverage, low battery consumption and robust communications, is selected as the solution to overcome the lack of cellular network connectivity in those areas. Then, the design of a hybrid solution for rural areas, that requires the use of a LoRaWAN mobile gateway to extend the range of LoRaWAN coverage, is presented. In addition, to ensure quality of service (QoS) and avoid data loss, a 5G internet connection is used to provide connectivity to the LoRaWAN gateway, combined with the store and forward communications mechanism to avoid data loss in areas without internet connection.

Quality Requirements for Implementing Augmented Reality in Heritage Spaces: Teachers’ Perspective

As a consequence of the scarcity of studies of augmented reality (AR) in Spain, this study developed a questionnaire to evaluate teachers’ perceptions on the use of AR for heritage teaching (n = 347 teachers: n = 131 in-service/n = 216 in-training). The objectives were to: (1) identify teachers’ existing knowledge about AR; (2) evaluate educational strategies teachers value most in AR apps for teaching; (3) evaluate necessary AR functions; (4) determine desired technical and functional characteristics; and (5) compare any significant differences between the two groups. The results provide a contribution to the increasing implementation of AR apps in heritage education, which promotes the understanding, enjoyment, experience and knowledge of heritage. Heritage education is increasingly present in Spanish classrooms due to awareness of the country’s rich heritage, and AR is a good tool for understanding and linking society with its heritage. From the results, it is clear that, despite their desire for implementation, there is a lack of teacher training in applying AR; both subsamples pointed out the importance of humanising the explanations in AR apps with experts or actors who mediate heritage and value the ease of use of these apps, multifunctionality, low battery consumption, and use in the background. Significant differences suggest potentially greater educational commitment among active teachers who seek deep and meaningful learning, above the superficiality and enjoyment reported by teachers in training and, on the contrary, the value placed on technical and visual aspects, related to the high use of mobile devices.

Continuous observations of forest canopy structure using low-cost digital camera traps

Assessing forest canopy dynamics is crucial for understanding the response of vegetation to environmental variability and change. While digital repeat photography is gaining increased attention for obtaining field phenology observations, colour indices derived from this method are often affected by leaf colour and actual canopy structure, complicating the physical interpretation of results. In addition, repeated photography requires power, storage capacity and remote data transfer, which are often limited in forest conditions. As an alternative, we tested a simple, cheap and fast solution to derive daily canopy structure observation from digital camera traps (CTs). Formerly deployed for wildlife monitoring, CTs are low-cost digital cameras designed for outdoor conditions and have low battery consumption, enable repeat acquisition, and often feature remote data transfer protocols.The trial was performed in a deciduous oak stand, where continuous images were acquired over a 1-year period using the time-lapse feature of the CT. Daily time series of canopy structure attributes were derived from the collected images using simple and automated procedures. Results were validated against reference manual cover photography measurements. The daily time series of foliage cover and leaf area index were then used to derive phenological transition dates, which were compared against phenological observations obtained from satellite Sentinel-2 data. Results indicated that field and satellite data provided comparable accuracy in determining the start of season (SOS). Larger discrepancies were found in determining the end of season (EOS), which can be attributed to the low number of good quality autumn images available from the satellite data. We concluded that CT is a robust method, which is ideally suited for routine, continuous field monitoring of canopy attributes and phenology. While this method can be used for evaluating remote sensing observations, the combination of CTs with satellite data holds great potential for greater spatiotemporal coverage, from field to landscape scales.

Lightweight Cryptography Algorithms for IOT Devices

Lightweight cryptography is a new concept for securing data more effectively while using fewer resources and providing greater throughput, conservatism, and low battery consumption. Every fraction second, the Internet of Things (IoT), which connects billions of objects, generates massive amounts of data. As the number of devices grows, so does the amount of data generated, and the security of that data becomes a concern. In IoT architecture, gadgets are essentially smaller and low-powered. Because of their complexity, traditional encryption methods are computationally expensive and take many rounds to encrypt, basically wasting the limited energy of IoT devices. However, a less sophisticated method may jeopardise the intended fidelity. There are various lightweight cryptography techniques available, and we choose one of the symmetric encryption techniques known as Advanced Encryption Standard (AES). The speed of this algorithm is six times that of triple DES.

Safety and Tolerability of Burst-Cycling Deep Brain Stimulation for Freezing of Gait in Parkinson's Disease.

Background: Freezing of gait (FOG) is a common symptom in Parkinson’s disease (PD) and can be difficult to treat with dopaminergic medications or with deep brain stimulation (DBS). Novel stimulation paradigms have been proposed to address suboptimal responses to conventional DBS programming methods. Burst-cycling deep brain stimulation (BCDBS) delivers current in various frequencies of bursts (e.g., 4, 10, or 15 Hz), while maintaining an intra-burst frequency identical to conventional DBS.Objective: To evaluate the safety and tolerability of BCDBS in PD patients with FOG.Methods: Ten PD subjects with STN or GPi DBS and complaints of FOG were recruited for this single center, single blinded within-subject crossover study. For each subject, we compared 4, 10, and 15 Hz BCDBS to conventional DBS during the PD medication-OFF state.Results: There were no serious adverse events with BCDBS. It was feasible and straightforward to program BCDBS in the clinic setting. The benefit was comparable to conventional DBS in measures of FOG, functional mobility and in PD motor symptoms. BCDBS had lower battery consumption when compared to conventional DBS.Conclusions: BCDBS was feasible, safe and well tolerated and it has potential to be a viable future DBS programming strategy.

Measuring pH in low ionic strength glacial meltwaters using ion selective field effect transistor (ISFET) technology

AbstractMeasuring pH in glacial meltwaters is challenging, because they are cold, remote, subject to freeze‐thaw cycles and have low ionic strength. Traditional methods often perform poorly there; glass electrodes have high drift and long response times, and spectrophotometric techniques are unpractical in cold, remote environments. Ion selective field effect transistor (ISFET) sensors are a promising alternative, proven in marine and industrial applications. We assess the suitability of two models of ISFET, the Honeywell Durafet and Campbell Scientific Sentron, for use in glacial melt through a series of lab and field experiments. The sensors have excellent tolerance of freeze‐thaw and minimal long‐term drift, with the Durafet experiencing less drift than the Sentron model. They have predictable response to temperature, although the Durafet housing causes some lag during rapid cycling, and the impact of stirring is an order of magnitude less than that of glass electrodes. At low ionic strength (&lt; 1 mmol L−1), there is measurable error, but this is quantifiable, and less than glass electrodes. Field tests demonstrated low battery consumption, excellent longevity and resistance to extreme conditions, and revealed biogeochemical processes that were unlikely to be recorded by standard methods. Meltwater pH in two glacial catchments in Greenland remained &gt; 7 with consistent diurnal cycles from the very first meltwater flows. We recommend that ISFET sensors are used to assess the pH of glacial meltwater, since their tolerance is significantly better than alternative methods: the Durafet is accurate to ± 0.2 pH when waters are &gt; 1 mmol L−1 ionic strength, and ± 0.3 pH at &lt; 1 mmol L−1.

SARDO: An Automated Search-and-Rescue Drone-Based Solution for Victims Localization

Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper we present SARDO, a drone-based search and rescue solution that exploits the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.

IoT Network Design Using Open-Source LoRa Coverage Emulator

Many wireless Internet-of-Things applications require extended battery life ranging from a few months to a few years. Such applications have motivated the recent developments in low power wide area networks, including the rise of Long Range (LoRa) technology. LoRa has a simple modulation scheme designed for extended converge, low battery consumption, and resistance to high interference levels. Thus LoRa is primarily targeted for shared spectrum applications where interference levels are typically higher than controlled spectrum applications where a single operator usually has a dominant control on the quality of service. As a result, it is of paramount importance to carefully design IoT networks while taking into account the impending impacts of interference and propagation environments. This paper presents a novel LoRa network design framework that utilizes a developed open-source emulator to provide a reliable network coverage estimation. The framework is tested in one of the largest open-access IoT network designs in Australia, which enabled the deployment of 294 sensors and 48 gateways. Both the framework and the emulator are implemented using MATLAB scripting, enabling integration with built-in and external radio planning tools. The framework leverages real interference measurements captured using software defined radio that records the spectrotemporal behavior of the existing traffic in the shared band.

Offloading Computations to Mobile Devices and Cloudlets via an Upgraded NFC Communication Protocol

The increasing complexity of smartphone applications and services necessitate high battery consumption, but the growth of smartphones’ battery capacity is not keeping pace with these increasing power demands. To overcome this problem, researchers gave birth to the Mobile Cloud Computing (MCC) research area. In this paper, we advance on previous ideas, proposing and implementing a Near Field Communication (NFC)-based computation offloading framework. This research is motivated by the advantages of NFC's short distance communication, its better security, and its low battery consumption characteristics. We design a new NFC communication protocol that overcomes the limitations of the default NFC protocol; removing the need for constant user interaction, the one–way communication restraint, and the limit on low data size transfer. Via the implemented framework, parts of mobile applications can be offloaded to other mobile devices or cloudlets equipped with an NFC reader. We present experimental results of the energy consumption and the time duration of computationally and data intensive representative applications: (i) RSA key generation and encryption, (ii) gaming/puzzles, (iii) face detection, (iv) media download from the Internet, and (v) data transferring between the mobile and the cloudlet. We show that when the helper device is more powerful than the device offloading the computations, the execution time of the tasks is reduced. Finally, we show that devices that offload application parts considerably reduce their energy consumption due to the low–power NFC interface and the benefits of offloading.

Battery draining attacks against edge computing nodes in IoT networks

ABSTRACTMany IoT devices, especially those deployed at the network edge have limited power resources. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Specifically, we implemented hello flooding, packet flooding, selective forwarding, rank attack, and versioning attack in ContikiOS and simulated them in the Cooja simulator. We consider a number of relevant metrics, such as CPU time, low power mode time, TX/RX time, and battery consumption. Besides, we test the stretch attack with three different batteries as an extreme scenario. Our results show that versioning attack is the most severe in terms of draining the power resources of the network, followed by packet flooding and hello flooding attacks. Furthermore, we find that selective forwarding and rank attacks are not able to considerably increase the power resource usage in our scenarios. By quantifying the effects of these attacks, we demonstrate that under specific scenarios, versioning attack can be three to four times as effective as packet flooding and hello flooding attacks in wasting network resources. At the same time, packet flooding is generally comparable to hello flooding in CPU and TX time usage increase but twice as powerful in draining device batteries.

Peace: Privacy-Preserving and Cost-Efficient Task Offloading for Mobile-Edge Computing

The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.

Performance Analysis of VLC Based on 802.11ac Frame Structure

Visible light communication (VLC) has gained tremendous attention recently as it possesses prominent features to address the limitations imposed by current wireless technologies, such as high capacity, high spectral efficiency, high energy efficiency, low battery consumption, and low latency. In this letter, we have designed a packet-based VLC protocol based on the IEEE 802.11ac frame structure, which can be utilized for the simulation, analysis, and testing of a wireless VLC communication system. Our evaluation is based on the channel impulse responses (CIRs) provided by the IEEE task group for the 802.15.7r1 standard, where we estimate the channel based on the legacy fields and then compare the performance of wireless VLC with a radio frequency (RF) 802.11ac-based wireless local area network (WLAN). We demonstrate in this letter that the VLC can be comfortably incorporated with the already existing communication standards and is also capable of providing equal performance as that of the 802.11ac.

Parkinsonian Tremor Detection from Subthalamic Nucleus Local Field Potentials for Closed-Loop Deep Brain Stimulation.

Deep Brain Stimulation (DBS) is a widely used therapy to ameliorate symptoms experienced by patients with Parkinson's Disease (PD). Conventional DBS is continuously ON even though PD symptoms fluctuate over time leading to undesirable side-effects and high energy requirements. This study investigates the use of a Iogistic regression-based classifier to identify periods when PD patients have rest tremor exploiting Local Field Potentials (LFPs) recorded with DBS electrodes implanted in the Subthalamic Nucleus in 7 PD patients (8 hemispheres). Analyzing 36.1 minutes of data with a 512 milliseconds non-overlapping window, the classification accuracy was well above chance-level for all patients, with Area Under the Curve (AUC) ranging from 0.67 to 0.93. The features with the most discriminative ability were, in descending order, power in the 31-45 Hz, 5-7 Hz, 21-30 Hz, 46-55 Hz, and 56-95 Hz frequency bands. These results suggest that using a machine learning-based classifier, such as the one proposed in this study, can form the basis for on-demand DBS therapy for PD tremor, with the potential to reduce side-effects and lower battery consumption.

Energy Management for EV Charging in Software-Defined Green Vehicle-to-Grid Network

Vehicle-to-grid (V2G) networks are expected to balance the supply and demand in smart grid by reducing the peak-to-average ratio of power grid load curve. We are entering the era of wireless communication, where we can enjoy various advantages such as lower cost, lower battery consumption, and lower access latency. We believe advanced wireless communication techniques have great potential to further promote the economical deployment of V2G network and lower energy consumption. In this article, we address the green V2G network for efficient energy management. However, we still face many challenging issues even if we exploit the promising wireless communication technique in green V2G networks. For example, it becomes more and more challenging to achieve the efficiency and economy of renewable energy resource allocation due to the increasing number of electric vehicles and limited capacity of local aggregators (LAGs). To address the issues, we consider a software-defined green V2G network for energy management, which consists of three planes: management plane, control plane, and data plane. Specifically, the control plane is aimed at guiding both data flow and energy flow to implement an efficient and economic strategy for energy scheduling, while the data plane collects the information through LAGs for the customized services in the management plane. Additionally, we present an energy management scheme of charging stations as a case study. Simulation results reveal that our proposals could achieve delightful performance on global optimization in the software-defined green V2G network.

Hybrid 5G optical‐wireless SDN‐based networks, challenges and open issues

The fifth-generation (5G) mobile networks are expected to bring higher capacity, higher density of mobile devices, lower battery consumption and improved coverage. 5G entails the convergence of wireless and wired communications in a unified and efficient architecture. Mobile nodes, as defined in fourth-generation era, are transformed in heterogeneous networks to make the front-haul wireless domains flexible and intelligent. This work highlights a set of critical challenges in advancing 5G networks, fuelled by the utilisation of the network function virtualisation, the software defined radio and the software defined networks techniques. Furthermore, a novel conceptual model is presented in terms of control and management planes, where the inner architectural components are introduced in detail.

Upcoming 5-G Wireless technology and its security concept

Abstract: The abbreviation of 5G technology is fifth generation technology. Today globalization in telecommunication has seen lots of improvements like 0G, 1G to 2.5G and from 3G to 5G. 5G is a major phase of mobile telecommunication beyond 4G standard. 5G technology is not currently commercially used for any country of the world. 5G supported voice over IP (VOIP) enabled devices. The advantages of 5G technology are that users can simultaneously connect to the multiple wireless devices. The aim of 5G planning is to provide the higher density of mobile broadband users and supporting device-to-device ultra-reliable and massive machine communications and lower latency than 4G and lower battery consumption for better implementation of the internet of things.5G wireless technology is depend on IEEE 802.11 wireless technology. Wireless local area network (WLAN) wireless metropolitan area (WLAN) and AD-hoc wireless technology are included in 5G wireless technology. This paper cover the security of 5G wireless technology. The main aim is to provide protection of 5G wireless technology

Applying VLC in 5G Networks: Architectures and Key Technologies

Visible light communication has gained tremendous attention recently and has become a favorable complementary technology to millimeter- wave communication in short-range communication scenarios for future 5G networks. VLC possesses a number of prominent features to address the highly demanding 5G system requirements for high capacity, high data rate, high spectral efficiency, high energy efficiency, low battery consumption, and low latency. These prominent features include but are not limited to abundant license-free spectrum, the ability to provide multiple gigabit-per-second data rates, low energy consumption, and low implementation costs. In this article a heterogeneous multi-layer 5G cellular architecture with a control plane and user plane separation scheme is presented. Three layers — the macrocell layer operating below 3 GHz, the picocell layer operating in the mmWave spectrum, and the optical attocell layer operating at the visible spectrum — are considered in this architecture, with each layer targeting a different coverage and communication need. We further address potential solutions for uplink communications, backhaul connections, and downlink communications in an optical attocell. Several key technologies employed in VLC downlink communications, including modulation, multiple access, and error control schemes, are also discussed.

A Method for Stable Indoor Positioning using Beacons

Indoor navigation finds its usage in indoor shopping, conferences, or evacuation in case of emergency case. The fundamental technology for indoor navigation is indoor positioning. The main problem in indoor positioning is how to calculate an accurate position from the wireless signals emitted by indoor access points. Since the GPS cannot be used in indoor environments, other devices that can be used as access points have to be utilized. One of those devices, which have gained a lot of popularity, recently, due to its low battery consumption and wide signal range is the Beacon. However, the Beacon contains also much error in the estimation of the distance. Therefore, if used with the conventional trigonometric method to estimate the position, the position will contain much error. Therefore, in this paper, we propose a stable indoor positioning method using multiple sets of Beacons. The proposed method alleviates the position error by a signal processing technique. Experimental results verify the validness of the proposed method.