Low-power Internet Of Things Networks Research Articles

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks.

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.

Unified Simultaneous Wireless Information and Power Transfer for IoT: Signaling and Architecture With Deep Learning Adaptive Control

In this article, we propose a unified simultaneous wireless information and power transfer (SWIPT) signaling and architecture to take advantage of both single tone and multitone signaling by adjusting only the power allocation ratio of a unified signal. Toward this, we design a unified and integrated receiver architecture for the proposed unified SWIPT signaling via an envelope detection with low power consumption. To lower the computational complexity of the receiver, we propose an adaptive control algorithm where the transmitter adjusts the communication mode through temporal convolutional network (TCN)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">asymmetric processing</i> . To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition at low-power Internet of Things (IoT) devices. Consequently we will facilitate the effective deployment of low-power IoT networks that concurrently supply both information and energy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wirelessly</i> to the devices by using the proposed unified SWIPT and adaptive control algorithm at the transmitter side.

Self-Energy Recycling for Low-Power Reliable Networks: Half-Duplex or Full-Duplex?

Self-energyrecycling (sER), which allows transmit energy reutilization, has emerged as a viable option for improving the energy efficiency (EE) in low-power Internet of Things networks. In this article, we investigate its benefits also in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multiantenna techniques in a communication system. We analyze the tradeoffs when considering not only the energy spent on transmission but also the circuitry power consumption, thus making the analysis of much more practical interest. In addition to the well-known spectral efficiency improvements, results show that FD also outperforms HD in terms of reliability. We show that sER introduces not only benefits in EE matters but also some modifications on how to achieve maximum reliability fairness between uplink and downlink transmissions, which is the main goal in this article. In order to achieve this objective, we propose the use of a dynamic FD scheme where the small base station determines the optimal allocation of antennas for transmission and reception. We show the significant improvement gains of this strategy for the system outage probability when compared to the simple HD and FD schemes.

RSSI study of wireless Internet of Things technologies

The Internet of Things (IoT) idea now entails a dispersed collection of different sensor networks with several functions that collect data, which is then analysed and used in applications such as smart cities. These networks are capable of transmitting massive volumes of data in a relatively efficient, yet unsecure wireless environment. These applications will only succeed if is developed a dependable a low-cost real-time method for pinpointing accurate location. Power consumption is another consideration for indoor localisation. Recent wireless technologies like ZigBee, Bluetooth Low Energy (BLE) and Long Range (LoRa), use less resources, making them ideal for interior positioning. When IoT devices are utilised, these technologies are compared in terms of precision of localisation and power consumption. Tracking the Received Signal Strength Indicator (RSSI) values can be used to locate mobile sensor nodes for low-power IoT networks. The RSSI values of sensor nodes in the BLE, ZigBee and LoRa networks for IoT were explore in this study.

Minimization of the Worst Case Average Energy Consumption in UAV-Assisted IoT Networks

The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of unmanned aerial vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-signal-to-average-interference-plus-noise ratio ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\bar {\text {S}}\overline {\text {IN}}\text {R}$ </tex-math></inline-formula> ) Quality-of-Service (QoS) constraints. We minimize the worst case average energy consumption of the latter, thus targeting the fairest allocation of the energy resources. The problem is nonconvex and highly nonlinear; therefore, we reformulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst case average energy consumption. Furthermore, we show that the target <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\bar {\text {S}}\overline {\text {IN}}\text {R}$ </tex-math></inline-formula> is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.

Cell-Free Massive MIMO for Massive Low-Power Internet of Things Networks

In this article, we consider the application of a cell-free (CF) massive multiple-input–multiple-output (MIMO) system to low-power Internet of Things (IoT) networks. CF Massive MIMO distributes many access points (APs) in a given area and supports many IoT devices simultaneously using the same time and frequency resources. To support many IoT devices that have more than the number of service antennas within limited coherence time/frequency, the reference signal (RS) should be reused. Generally, CF Massive MIMO with massive connectivity requires a lot of power and, thus, it is difficult to use in low-power IoT networks. In this regard, we propose an energy-efficient (EE) power control scheme that can significantly reduce the radiation power of IoT devices. We note that with many IoT devices, there is a quite large amount of interference, and if we choose the radiation power based on this information, the signal-to-interference-plus-noise ratio (SINR) becomes approximately independent of the radiation power. We show that our scheme can reduce the radiation power more than 90% compared to the normal operation. We also show that with many IoT devices, there is a spectral efficiency hardening effect, and maximum ratio (MR) processing and minimum mean square error (MMSE) processing present similar performance and, thus, MR processing could be sufficient for the use in CF Massive MIMO with massive IoT connectivity.

Energy efficient scheduling and power control of massive MIMO in massive IoT networks

We present various energy efficient (EE) scheduling and power control schemes for Massive MIMO with massive Internet of Things (IoT) connectivity. To support massive IoT devices simultaneously with low latency, the reference signal (RS) is heavily reused. To increase the performance, we reorganize the distributed IoT devices in ascending and in descending orders. We show that the spectral efficiency (SE) is improved based on water-filling effect. Next, we show that a generalized power control scheme is quite effective in increasing the fairness. Nevertheless, it requires a lot of power which is not fitted to low power IoT networks. Based on these facts, we propose sigmoid function based power allocation schemes to compromise between SE/EE performance and the fairness. Numerical results verify that the proposed scheme can achieve arbitrary SE/EE performance tradeoff with satisfactory fairness.

Intrusion Prevention Scheme Against Rank Attacks for Software-Defined Low Power IoT Networks

The 6LoWPAN (IPv6 over low-power wireless personal area networks) standard enables resource-constrained devices to connect to the IPv6 network, blending an IPv6 header compression protocol. For this network technology, a new routing protocol called Routing Protocol for Low Power Lossy network (RPL) has been designed. The latter is a lightweight protocol that determines the route across the nodes based on rank values. This protocol is known to be non-resilient against Rank attacks, which aim at creating non-optimized routes for packet forwarding, hence overwhelming the constrained 6LoWPAN. With 5G, Software-Defined Networks (SDNs) have been developed to facilitate simple programmable control plane, Quality of Service (QoS) provisioning, and route configuration services for 6LoWPAN. However, there is still a lack of optimization mechanisms to protect 6LoWPAN against Rank attacks in SDN-based deployment. To this end, in this paper, a Reinforcement-Learning (RL) agent is leveraged to assist and complement an SDN controller in achieving cost-efficient route optimization, and QoS provisioning packet forwarding to prevent rank attacks. Experimental results confirm that our approach effectively prevents Rank attacks while providing an adequate delay and radio duty cycle. Meanwhile, it maximizes the packet delivery ratio, facilitating practical implementations in software-defined Low Power Internet of Things (IoT) networks.

An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study

Abstract Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.

Network Blueprint for Maximizing the Lifetime of Smart Devices in Low Power IoT Networks

In the modern communication and computation era, internet of things (IoT) is developing as the key technology that satisfies the requirements of various applications. Prolonging device lifetime and maintaining network reliability is the evident objective for IoT network. Therefore, the authors come up with the network architecture that integrates node placement technique and routing technique. In the architecture, node placement is implemented by varying the density of nodes, by varying battery level of nodes, and by varying transmission range of nodes. Energy efficient and reliable path computation is addressed by routing technique. Therefore, enhancing the features of routing and node placement technique and integrating them together in network architecture can efficiently prolong the network lifetime. From the results, the authors observed that the proposed network architecture prolongs the network lifetime two times better than the standard model and also outperforms EQSR protocol and maintains the reliable data transfer.

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.

Evaluating Security of Low-Power Internet of Things Networks

Evaluating Security of Low-Power Internet of Things Networks

Atomic-SDN: Is Synchronous Flooding the Solution to Software-Defined Networking in IoT?

The adoption of Software Defined Networking (SDN) within traditional networks has provided operators the ability to manage diverse resources and easily reconfigure networks as requirements change. Recent research has extended this concept to IEEE 802.15.4 low-power wireless networks, which form a key component of the Internet of Things (IoT). However, the multiple traffic patterns necessary for SDN control makes it difficult to apply this approach to these highly challenging environments. This paper presents Atomic-SDN, a highly reliable and low-latency solution for SDN in low-power wireless. Atomic-SDN introduces a novel Synchronous Flooding (SF) architecture capable of dynamically configuring SF protocols to satisfy complex SDN control requirements, and draws from the authors' previous experiences in the IEEE EWSN Dependability Competition: where SF solutions have consistently outperformed other entries. Using this approach, Atomic-SDN presents considerable performance gains over other SDN implementations for low-power IoT networks. We evaluate Atomic-SDN through simulation and experimentation, and show how utilizing SF techniques provides latency and reliability guarantees to SDN control operations as the local mesh scales. We compare Atomic-SDN against other SDN implementations based on the IEEE 802.15.4 network stack, and establish that Atomic-SDN improves SDN control by orders-of-magnitude across latency, reliability, and energy-efficiency metrics.

Effective Integration of Reliable Routing Mechanism and Energy Efficient Node Placement Technique for Low Power IoT Networks

Internet of Things (IoT) is the emerging technology that links physical devices (sensor devices) with cyber systems and allows global sharing of information. In IoT applications, devices are operated by battery power and low power radio links, which are constrained by energy. In this paper, node placement technique and routing mechanism are effectively integrated in single network architecture to prolong the lifetime of IoT network. In proposed network architecture, sensor node and relay node are deployed, sensor nodes are responsible for collecting the environmental data and relay nodes are responsible for data aggregation and path computation. In node placement technique, densities of relay nodes are varied based on traffic area, to prevent energy hole problem. In routing technique, energy efficient and reliable path computation is done to reduce number of re transmissions. To adopt IoT scenario, we included IEEE 802.15.4 PHY/MAC radio and IPv6 packet structure in proposed network architecture. Proposed work result shows, proposed architecture prolongs network lifetime.