Low-power Internet Of Things Research Articles

A Novel Real-Time Deep Learning Approach for Indoor Localization Based on RF Environment Identification

Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly enhanced by identifying the type of the surrounding indoor environment. This letter develops a highly accurate and computationally efficient method for indoor localization based on a novel two-stage deep learning approach. In the first stage, a convolutional neural network (CNN) is designed to perform indoor environment identification by extracting the inherent features based on a real dataset obtained from radio-frequency (RF) measurements. Then, in the second stage, another CNN is designed to perform indoor localization taking into account knowledge of the environment type as obtained from the first stage. Numerical investigations demonstrate that the proposed two-stage CNN deep learning approach outperforms benchmark methods, with significant enhancements in localization accuracy and computational efficiency. The findings of this letter make the proposed CNN approach a key element for facilitating real-time deployment of efficient low-power IoT sensor networks.

Kinerja jaringan saraf berbasis backpropagation dan LVQ sebagai algoritme fingerprint RSS LoRa untuk penentuan posisi pada ruang terbuka

Outdoor positioning is one of the important applications in the Internet of things (IoT). The usage of GPS is unsuitable for low-power IoT devices. Alternatively, it can use the LoRa devices. This research aims to find a better method as the fingerprint algorithm for determining the outdoor position using RSS LoRa. The methods used as the fingerprint algorithm were two artificial neural network models, i.e. backpropagation (BP) with four types of training methods and learning vector quantization (LVQ) with two types of training methods. The experiment results show the performance of LVQ1 better than those of LVQ2. Besides, the LVQ1 was also better than the BP method. However, both BP and LVQ2 have a performance that is almost similar to about 70 %. Both of the artificial neural network models, BP and LVQ, can be used as a fingerprint algorithm to determine quite accurate the outdoor object position.

ITLS: Lightweight Transport-Layer Security Protocol for IoT With Minimal Latency and Perfect Forward Secrecy

Enabling end-to-end secure communication is essential for many Internet-of-Things (IoT) application scenarios. Transport-layer security (TLS) and datagram TLS (DTLS) are the de-facto protocols for communication security in the IP-based IoT. However, the current authentication approaches of TLS are confronted with the heavy overhead and security issues in the resource-constrained IoT scenario. On the other hand, the identity-based cryptography (IBC) becomes an attractive cryptographic solution for the IoT. Unfortunately, the current IBC-based proposals exist the problem of either high communication latency or low level of security. In this article, we propose the first lightweight secure transport protocol called iTLS, which delivers protected data in the first flight with perfect forward secrecy, and provides implicit mutual authentication without certificates. iTLS dynamically generates identity-based early keys before receiving a server response, allowing clients to send the encrypted data without additional round trips. Furthermore, it employs the ephemeral secret ticket to obtain an ephemeral server key in the previous connection. Therefore, the early key established afterward can provide full forward secrecy. Design and implementation in the form of extension make iTLS fully compatible with TLS 1.3 and easy to be converted to a DTLS version. Our evaluation shows that iTLS reduces the network traffic overhead by at least 61.2%, and handshake latency on an ideal network by at least 60% compared to the certificate-based TLS. The results demonstrate iTLS achieves strong adaptability on the low-power and lossy IoT networks.

Multi-Target Adaptive Reconfigurable Acceleration for Low-Power IoT Processing

Low-power processors for the Internet-of-Things (IoT) demand a high degree of adaptability to efficiently execute applications with different resource requirements under varying scenarios. Current single-ISA heterogeneous Chip Multiprocessors (CMPs), such as ARM's big.LITTLE, provide multiple cores and voltage/frequency levels to address this challenge. However, finding the best possible type of core and the corresponding voltage/frequency level for all the execution scenarios, which involve different applications and phases, remains far from being reached. In this article, we propose extending such a single-ISA heterogeneous CMP with a Coarse-Grained Reconfigurable Array (CGRA) and a hardware-based dynamic binary translation (DBT) module that transparently maps application code onto the CGRA for acceleration. To achieve low-energy levels and efficiently manage the power consumption of the CGRA, we introduce an additional voltage rail that enables operation in the Near-Threshold Voltage (NTV) regime when needed, leveraging key features of the CGRA's structure to address the implementation challenges of NTV computing. For less than 35 percent area overhead to the baseline CMP, performance and energy consumption are improved as follows. Compared to: (a) power-efficient execution in the LITTLE core, MuTARe achieves 29 percent reduction in energy consumption, and $2\times$ 2 × speedup; (b) performance-efficient execution in the big core, a speedup of $1.6\times$ 1 . 6 × with an energy reduction of 41 percent is achieved.

UAV-enabled data acquisition scheme with directional wireless energy transfer for Internet of Things

Low power Internet of Things (IoT) is suffering from two limitations: battery-power limitation of IoT nodes and inflexibility of infrastructure-node deployment. In this paper, we propose an Unmanned Aerial Vehicle (UAV)-enabled data acquisition scheme with directional wireless energy transfer (WET) to overcome the limitations of low power IoT. The main idea of the proposed scheme is to employ a UAV to serve as both a data collector and an energy supplier. The UAV first transfers directional wireless energy to an IoT node which then sends back the data packets to the UAV by using the harvested energy. Meanwhile, we minimize the overall energy consumption under conditions of balanced energy supply and limited overall time. Moreover, we derive the optimal values of WET time and data transmission power. After analysing the feasibility of the optimal WET time and data transmission, we design an allocation scheme based on the feasible ranges of data size level and channel-fading degree. The numerical results show the feasibility and adaptability of our allocation scheme against the varied values of multiple system parameters. We further extend our scheme to the multi-node scenario by re-designing energy beamforming and adopting multi-access mechanisms. Moreover, we also analyse the mobility of UAVs in the proposed scheme.

LPWAN Technologies: Emerging Application Characteristics, Requirements, and Design Considerations

Low power wide area network (LPWAN) is a promising solution for long range and low power Internet of Things (IoT) and machine to machine (M2M) communication applications. This paper focuses on defining a systematic and powerful approach of identifying the key characteristics of such applications, translating them into explicit requirements, and then deriving the associated design considerations. LPWANs are resource-constrained networks and are primarily characterized by long battery life operation, extended coverage, high capacity, and low device and deployment costs. These characteristics translate into a key set of requirements including M2M traffic management, massive capacity, energy efficiency, low power operations, extended coverage, security, and interworking. The set of corresponding design considerations is identified in terms of two categories, desired or expected ones and enhanced ones, which reflect the wide range of characteristics associated with LPWAN-based applications. Prominent design constructs include admission and user traffic management, interference management, energy saving modes of operation, lightweight media access control (MAC) protocols, accurate location identification, security coverage techniques, and flexible software re-configurability. Topological and architectural options for interconnecting LPWAN entities are discussed. The major proprietary and standards-based LPWAN technology solutions available in the marketplace are presented. These include Sigfox, LoRaWAN, Narrowband IoT (NB-IoT), and long term evolution (LTE)-M, among others. The relevance of upcoming cellular 5G technology and its complementary relationship with LPWAN technology are also discussed.

SDN Controller Placement and Switch Assignment for Low Power IoT

Software defined networking (SDN) complements low power Internet of Things (IoT), since the former offers dynamicity and the latter is susceptible to environmental changes. The SDN controller placement refers to the selection of the IoT sensors running the controllers, while the switch assignment is the process of mapping each sensor to a controller. Both choices affect the volume of the control traffic, a significant metric in low power wireless IoT networks where bandwidth is scarce or energy consumption is important. In this paper, we model an optimization problem for minimum control traffic, assess its complexity and devise a set of heuristic algorithms for expediting its solution. We initially present a fast and simple heuristic algorithm, which is then extended to two iterative algorithms with even better performance at the cost of time complexity. Our simulations and testbed experimentation reveal close to optimal performance of all heuristic solutions with significantly less computation time than explicitly solving the optimization problem. At the end, we provide insights for further enhancements of these heuristics with a bias for minimum control delay.

A Hybrid Energy Harvesting Design for On-Body Internet-of-Things (IoT) Networks

The Internet-of-things (IoT) has been gradually paving the way for the pervasive connectivity of wireless networks. Due to the ability to connect a number of devices to the Internet, many applications of IoT networks have recently been proposed. Though these applications range from industrial automation to smart homes, healthcare applications are the most critical. Providing reliable connectivity among wearables and other monitoring devices is one of the major tasks of such healthcare networks. The main source of power for such low-powered IoT devices is the batteries, which have a limited lifetime and need to be replaced or recharged periodically. In order to improve their lifecycle, one of the most promising proposals is to harvest energy from the ambient resources in the environment. For this purpose, we designed an energy harvesting protocol that harvests energy from two ambient energy sources, namely radio frequency (RF) at 2.4 GHz and thermal energy. A rectenna is used to harvest RF energy, while the thermoelectric generator (TEG) is employed to harvest human thermal energy. To verify the proposed design, extensive simulations are performed in Green Castalia, which is a framework that is used with the Castalia simulator in OMNeT++. The results show significant improvements in terms of the harvested energy and lifecycle improvement of IoT devices.

System Assessment of WUSN Using NB-IoT UAV-Aided Networks in Potato Crops

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors’ battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor’s density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance.

Partial Offloading in Energy Harvested Mobile Edge Computing: A Direct Search Approach

In the next generation wireless communication paradigm, the number of devices are expected to increase exponentially after the concept of Internet of Things (IoT). These devices are power constrained, with limited processing capability. Therefore, in order to get the maximum advantage from these low power IoT sensing devices, it is of utmost need to empower them. Similarly, the devices are not able to process the computationally intensive applications. In this work, Wireless Power Mobile Edge Cloud (WPMEC) is considered, which is an integration of Wireless Power Transfer (WPT) and Mobile Edge Cloud (MEC) to address low power devices' battery and computational capabilities. The WPMEC is charging the devices in the first phase using the WPT and in the second phase, the devices are offloading their computational intensive data to the MEC. Partial offloading scheme is first time introduced and analyzed with WPMEC. Performance of proposed solution is evaluated in terms of overall network computational energy efficiency. Extensive simulations have been carried out to validate the proposed solution. It is shown that the proposed partial offloading scheme with WPMEC outperforms the binary and local computational schemes.

Timestamp Scheme to Mitigate Replay Attacks in Secure ZigBee Networks

ZigBee is one of the communication protocols used in the Internet of Things (IoT) applications. In typical deployment scenarios involving low-cost and low-power IoT devices, many communication features are disabled, consequently affecting the security offered by ZigBee. The ZigBee specification assumes that deployment of frame counters is sufficient to mitigate replay attacks in secure ZigBee networks. However, we demonstrate that it is insufficient in this paper (i.e., the network is no longer secure after the coordinator restarts). As a countermeasure, we present a timestamp-based scheme to mitigate replay attacks. Our mitigation strategy does not consume power significantly, and fully powered devices will be responsible for providing power-constrained devices with the current timestamp. The proposed scheme is designed for all ZigBee topologies and different states of ZigBee End Devices (ZEDs). Findings from our evaluation show that the proposed scheme can successfully mitigate replay attacks, with no significant network performance degradation even assuming a worst-case scenario (i.e., many devices are sending data simultaneously).

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource management techniques in wireless IoT networks and the techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

IEEE 802.11ba Wake-Up Radio: Performance Evaluation and Practical Designs

Featuring a long operation time to avoid frequent battery replacements, highly energy-efficient radios have shown their pivotal functions for low-power and battery-driven devices in supporting manifold Internet-of-Things (IoT) services. To this end, prolific systems for highly energy-efficient radios have been widely deployed, but they impose a critical challenge of sacrificed data rates to preclude the applications that both require high energy efficiency and high data rates. Recently, this challenge has driven the concept of equipping two radio-chains on a low-power device, known as the primary connectivity radio (PCR) and companion connectivity radio (CCR). The PCR supports high data rate transmissions, and switches to the sleeping mode when no data should be received. In this case, the CCR stays awake to monitor traffic, and wakes up the PCR when data requiring to be received arrives. However, to practice such a concept, sophisticated operations in the physical (PHY) layer and medium access control (MAC) layers are required. Consequently, the IEEE 802.11 Task Group “ba” (TGba) has been formed to launch the normative works since 2017. In the normative development, the most challenging issue lies in the design of a new frame structure especially the preamble sequence, by which the CCR in a station (STA) can efficiently synchronize with an access point (AP) and the PCR can be promptly waken up when an AP wishes to transmit data to an STA. To comprehend such a crucial foundation for the next generation low-power IoT devices, this paper provides comprehensive knowledge of state-of-the-art PHY/MAC operations of IEEE 802.11ba. Most importantly, a preamble sequence design for synchronization is further proposed for the new frames supported in IEEE 802.11ba. Through comprehensively evaluating the performance of different designs (in terms of data rate configuration and synchronization sequences), we justify the outstanding performance of the proposed design in terms of the synchronization error rate and packet error rate, to satisfy the urgent demands in the normative works of IEEE 802.11ba.

GROLL: Geographic Routing for Low Power and Lossy IoT Networks

Routing in low-power lossy networks (LLNs) remains an important research problem for Internet of Things (IoT), despite the recent standardization in RFC 6550 of RPL as an IPv6 Routing Protocol [1]. Recent deployment experiences [2] have emphasized the challenges faced by a complex design in real world deployments. One of the key challenges is versatile routing protocol design that can handle efficiently and seamlessly various combinations of routing primitives, namely unicast, multicast, and convergecast. Although RPL supports these traffic types, due to its design principle oriented around data collection, its unicast and multicast functionalities are limited. In this article, we present the design and system implementation of the first Geographic Routing framework for Low Power and Lossy Networks (GROLL). GROLL’s routing engine effectively integrates routing for unicast, multicast, and convergecast and takes into account Complex Network Topologies (CNT), i.e., network holes and cuts. GROLL detects CNTs and abstracts the boundary information of detected CNTs with significantly reduced memory size. The proposed unified routing framework was implemented on real hardware and it was evaluated extensively on a testbed of 42 TelosB motes.

A Hardware Approach of a Low-Power IoT Communication Interface by NXP FlexIO Module

This article describes implementation possibilities of specialized microcontroller peripherals, as hardware solution for Internet of Things (IoT) low-power communication, interfaces. In this contribution, authors use the NXP FlexIO periphery. Meanwhile, RFC1662 is used as a reference communication standard. Implementation of RFC1662 is performed by software and hardware approaches. The total power consumption is measured during experiments. In the result section, authors evaluate a time-consumption trade-off between the software approach running in Central Processing Unit (CPU) and hardware implementation using NXP FlexIO periphery. The results confirm that the hardware-based approach is effective in terms of power consumption. This method is applicable in IoT embedded devices.

RF Near-Field Coupling System Using Reverse PDN and Considering Electromagnetic Effects

One of the more intriguing applications of wireless power transfer is power delivery for low-power Internet of Things (IoT) devices. By utilizing RF near-field coupling (NFC), an improved combination of small receiver coil area and system efficiency can be achieved, as is ideal for wireless charging for low-power IoT platforms. However, utilizing high efficiency RF NFC comes with a cost—various electromagnetic effects can lead to detuning of the resonant frequency if not properly modeled. In addition, integrating power regulation with high frequency ac–dc conversion can create large ripple voltage supply to a power distribution unit (PDU). This paper investigates the system level issues that arise in efficient, regulated RF NFC systems for milliwatt-scale output power. A method for modeling the various electromagnetic, frequency detuning effects is presented, as well as a selection process for the circuit component values in the design. Furthermore, a reverse power distribution network is designed to integrate the rectifier with a PDU, introducing a target impedance and phase considerations for an RF rectifier. The modeling and integration solutions are experimentally verified through two different RF NFC systems. The systems have measured peak efficiency of 68.4% at 3.3 V output and 58.6% for 1 V output with less than 100 mm $^2$ receiver coil area, demonstrating a unique combination of small coil area and high efficiency compared to other NFC systems to better support low-power IoT devices.

Seamless connectivity architecture and methods for IoT and wearable devices

Wearable and Internet of Things (IoT) devices have the potential to improve lifestyle, personalize receiving treatments or introduce assisted living for elderly people. However, service delivery depends on maintaining and troubleshooting device connectivity to smartphones, where user engagement and technology proficiency represent a possible barrier that prevents a wider adoption, especially in the elderly and disabled population. Low-cost and low-power wearable and IoT devices face challenges when operating out of range of known home networks or pared devices. We propose an architecture and methods to provide seamless connectivity (Se-Co) between devices and wireless networks while maintaining low-power, low-cost and standards compatibility. Through Se-Co, the devices connect without user interaction both in home and in unknown roaming networks while maintaining anonymity, privacy and security. Roaming networks approve data limited connectivity to unknown devices that are able to provide a valid anonymized certificate of compliance and no harm through a home provider. Se-Co enables shifting data processing, such as pattern processing using artificial intelligence, from a wearable device or smartphone towards the cloud. The proposed Se-Co architecture could provide solutions to increase usability of wearable devices and improve their wider adoption, while keeping low the costs of devices, development and services.

Multi-User Offloading for Edge Computing Networks: A Dependency-Aware and Latency-Optimal Approach

Driven by the tremendous application demands, the Internet of Things (IoT) systems are expected to fulfill computation-intensive and latency-sensitive sensing and computational tasks, which pose a significant challenge for the IoT devices with limited computational ability and battery capacity. To address this problem, edge computing is a promising architecture where the IoT devices can offload their tasks to the edge servers. Current works on task offloading often overlook the unique task topologies and schedules from the IoT devices, leading to degraded performance and underutilization of the edge resources. In this article, we investigate the problem of fine-grained task offloading in edge computing for low-power IoT systems. By explicitly considering: 1) the topology/schedules of the IoT tasks; 2) the heterogeneous resources on edge servers; and 3) the wireless interference in the multiaccess edge networks, we propose a lightweight yet efficient offloading scheme for multiuser edge systems, which offloads the most appropriate IoT tasks/subtasks to edge servers such that the expected execution time is minimized. To support the multiuser offloading, we also propose a distributed consensus algorithm for low-power IoT devices. We conduct extensive simulation experiments and the results show that the proposed offloading algorithms can effectively reduce the end-to-end task execution time and improve the resource utilization of the edge servers.

Low power consumption Internet of Things and its core sensor package technology in underground pipe gallery

Underground pipeline corridor is a part of the construction of Smart City. There are many kinds of pipelines in it. Because of the complex air composition and special space structure, it is impossible to carry out high-frequency manual maintenance. Therefore, in order to ensure the safe operation of pipeline corridor, it is necessary to monitor its working environment in real time. In this paper, a low-power Internet of Things (IOT) system is designed for the special environment of underground pipeline gallery. The system’s network is built by low-power ZigBee module. Cooperated with micro-sensors, it can monitor the environment of pipeline gallery in real time, and the low-power of the system can make up for the long-term unattended situation of pipeline gallery. This paper also designs a sensor packaging technology for this environment, which protects the sensor from corrosion in the corridor environment without affecting the smooth network. Finally, the material is tested.

RAMTEL: Robust Acoustic Motion Tracking Using Extreme Learning Machine for Smart Cities

Motion tracking is attractive in what concerns a smart city environment, where citizens have to interact with Internet of Things (IoT) infrastructures spread all around one particular city. Motion tracking is important for smart services and location-based services in smart cities, since it provides natural ways for users to interact with the IoT infrastructures, such as the ability to recognize of a wide range of hand motion in real-time. Compared with dedicated hardware devices, ubiquitous devices with reliable speakers and microphones can be developed to achieve cheap acoustic-based motion tracking, which is appropriate for low-power and low-cost IoT applications. However, for complex urban environments, it is very difficult for acoustic-based methods to achieve accurate motion tracking due to multipath fading and limited sampling rate at mobile devices. In this paper, a new parameter called multipath dispersion vector (MDV) is proposed to estimate and mitigate the impact of multipath fading on received signals using extreme learning machine. Based on MDV, a robust acoustic motion tracking (RAMTEL) method is proposed to calculate the moving distance based on the phase change of acoustic signals, and track the corresponding motion in 2-D plane by using multiple speakers. The method is then proposed and implemented on standard Android smartphones. Experiment results show, without any specialized hardware, RAMTEL can achieve an impressive millimeter-level accuracy for localization and motion tracking applications in multipath fading environments. Specifically, the measurement errors are less than 2 and 4 mm in 1-D and 2-D scenarios, respectively.