Wireless Internet Of Things Networks Research Articles

PLUG-N-HARVEST Architecture for Secure and Intelligent Management of Near-Zero Energy Buildings.

Building Automation (BA) is key to encourage the growth of more sustainable cities and smart homes. However, current BA systems are not able to manage new constructions based on Adaptable/Dynamic Building Envelopes (ADBE) achieving near-zero energy-efficiency. The ADBE buildings integrate Renewable Energy Sources (RES) and Envelope Retrofitting (ER) that must be managed by new BA systems based on Artificial Intelligence (AI) and Internet of Things (IoT) through secure protocols. This paper presents the PLUG-N-HARVEST architecture based on cloud AI systems and security-by-design IoT networks to manage near-zero ADBE constructions in both residential and commercial buildings. To demonstrate the PLUG-N-HARVEST architecture, three different real-world pilots have been considered in Germany, Greece and Spain. The paper describes the Spain pilot of residential buildings including the deployment of IoT wireless networks (i.e., sensors and actuators) based on Zwave technology to enable plug-and-play installations. The real-world tests showed the high efficiency of security-by-design Internet communications between building equipment and cloud management systems. Moreover, the results of cloud intelligent management demonstrate the improvements in both energy consumption and comfort conditions.

An Efficient Cooperative Medium Access Control Protocol for Wireless IoT networks in Smart World System

In recent years, the Internet of Things (IoT) has become the key enabler for Smart World System (SWS). In SWS environment, the IoT is empowering wireless devices/objects with abilities of sensation and communication with each other via Internet and providing simplified, more efficient, and enjoyable lifestyle. However, efficient communication between various wireless devices in IoT network is very important, especially in long distant wireless transmission case. Cooperative Medium Access Control (MAC) protocols for wireless networks supporting the IoT is becoming popular to handle this issue. It considers several metrics, e.g., SNR (Signal to Noise Ratio), delay and residual energy. Nowadays, distance metric has appended a new dimension in the relay selection metric list for Cooperative MAC. Howsoever, protocols that have used distance metric can be optimized further in selecting a relay node by considering reliability. Hence, targeting to optimize relay selection for designing a cooperative MAC protocol, we have proposed a Reliability-based Distance-aware Data-rate-adaptive Cooperative MAC for wireless IoT networks. For the assurance of unfailing and cooperative data transmission, this work strengthens the most suitable relay node selection using a cooperative reliability factor. Distance and residual energy are also used to ascertain that the position of optimal relay possess superior rates. Extensive simulation results demonstrate that proposed reliability-based optimal relay selection as well as MAC operation significantly improves the performance over the existing ones.

Dynamic CoAP Mode Control in Real Time Wireless IoT Networks

IEEE 802.15.4 is one of the preferred mechanisms to provide modulation and media access control in wireless Internet of Things networks. With the goal of preserving battery life, IEEE 802.15.4 is characterized by low power consumption that results in low signal-to-noise ratios responsible of low transmission rates and high bit error rates. In these low rate lossy networks, and from the application layer perspective, the constrained application protocol provides lightweight session management and control with two modes of operation: 1) a reliable mode where retransmissions guarantee packet delivery at a cost of increased latency, power consumption, and throughput and 2) an unreliable mode that provides packet loss-prone delivery of packets with lower battery use. In this paper, we present a dynamic mode selection mechanism that extends battery life while minimizing latency as well as transmission rates and improving overall quality of service.

Optimizing Resources Allocation for Fog Computing-Based Internet of Things Networks

In the wireless Internet of Things (IoT) networks with resource-constrained devices, fog computing has been introduced to deal with the computation-intensive applications at the edges of the networks. While fog computing decreases the computation delay and fronthaul traffic data, it also brings the severe challenge on complex resource allocation of the available computation and communication resources under the stringent quality of service (QoS) requirements. In this paper, we investigate the problem of tasks scheduling and heterogeneous resource allocation for multiple devices in the wireless IoT networks. The IoT devices that collect a massive amount of data need to make proper offloading decision to transfer the data to the fog computing nodes (FNs). Moreover, to support a massive number of device connections and transfer a huge amount of data with low latency and limited resource, we consider the deployment of non-orthogonal multiple access (NOMA) in IoT networks, which enables multiple IoT devices to simultaneously transmit data to the same FN at the same time, frequency, and code domain. We jointly optimize the allocation of resource blocks and transmit power of multiple IoT devices, subject to the respective QoS requirements. Furthermore, the optimization problem is formulated as a mixed-integer nonlinear programming problem to minimize the system energy consumption. Since it is an NP-hard problem, we introduce an improved genetic algorithm (IGA) to solve it. The simulation results show that the proposed scheme achieves good performance in throughput, delay, outage probability, and energy consumption.

Fast Decentralized Data Analytics in IoT Wireless Networks

This paper presents an innovative mechanism for decentralized data analytics in the Internet of Things (IoT) networks. In contrast to traditional centralized (eg. Cloud) based solution, our proposed decentralized framework can avoid the problems raised in data collection such as the constraint in high latency, and the associated data breach and privacy issues. The invented mechanism enables each IoT node in the network to independently obtain the global optimal analytics with local computation and communication. The proposed framework has been tested in seismic imaging and machine learning applications. The evaluation results validate its fast convergence to the optimal model, and demonstrates that it allows (near) real-time data analytics in IoT networks under bandwidth and energy limitation, particularly in uncertain and dynamic environments.

Active Learning for Wireless IoT Intrusion Detection

Internet of Things (IoT) is becoming truly ubiquitous in our everyday life, but it also faces unique security challenges. Intrusion detection is critical for the security and safety of a wireless IoT network. This paper discusses the human-in-the-loop active learning approach for wireless intrusion detection. We first present the fundamental challenges against the design of a successful Intrusion Detection System (IDS) for wireless IoT network. We then briefly review the rudimentary concepts of active learning and propose its employment in the diverse applications of wireless intrusion detection. Experimental example is also presented to show the significant performance improvement of the active learning method over traditional supervised learning approach. While machine learning techniques have been widely employed for intrusion detection, the application of human-in-the-loop machine learning that leverages both machine and human intelligence to intrusion detection of IoT is still in its infancy. We hope this article can assist the readers in understanding the key concepts of active learning and spur further research in this area.

Mechano-Magnetic Telemetry for Underground Water Infrastructure Monitoring

This study reports on the theory of operation, design principles, and results from laboratory and field tests of a magnetic telemetry system for communication with underground infrastructure sensors using rotating permanent magnets as the sources and compact magnetometers as the receivers. Many cities seek ways to monitor underground water pipes with centrally managed Internet of Things (IoT) systems. This requires the development of numerous reliable low-cost wireless sensors, such as moisture sensors and flow meters, which can transmit information from subterranean pipes to surface-mounted receivers. Traditional megahertz radio communication systems are often unable to penetrate through multiple feet of earthen and manmade materials and have impractically large energy requirements which preclude the use of long-life batteries, require complex (and expensive) built-in energy harvesting systems, or long leads that run antennas near to the surface. Low-power magnetic signaling systems do not suffer from this drawback: low-frequency electromagnetic waves readily penetrate through several feet of earth and water. Traditional magnetic telemetry systems that use energy-inefficient large induction coils and antennas as sources and receivers are not practical for underground IoT-type sensing applications. However, rotating a permanent magnet creates a completely reversing oscillating magnetic field. The recent proliferation of strong rare-earth permanent magnets and high-sensitivity magnetometers enables alternative magnetic telemetry system concepts with significantly more compact formats and lower energy consumption. The system used in this study represents a novel combination of megahertz radio and magnetic signaling techniques for the purposes of underground infrastructure monitoring. In this study, two subterranean infrastructure sensors exploit this phenomenon to transmit information to an aboveground radio-networked magnetometer receiver. A flow meter uses a propeller to directly rotate a diametrically magnetized neodymium magnet. A moisture sensor rotates a magnet with a low-power electric motor. Laboratory performance and field tests establish the capabilities of magnetic telemetry for IoT-linked leak-detection sensors. Remote datalogging with encryption demonstrates the viability of integrating sensors and surface receivers into a LoRa wireless IoT network.

Joint power control and channel assignment in uplink IoT Networks: A non-cooperative game and auction based approach

Internet of Things (IoT) is a network of disparate objects enabled to exchange data with each other to implement smart applications. In near future, the possibility of a truly global IoT is expected to be covered by connecting billions of devices through emerging wireless systems such as the fifth generation of cellular networks (5G). Employing enhanced multiple access technologies and providing effective resource allocation schemes to maximize the number of supported devices is a highly important factor to make future networks practical. Most of the current researches consider Orthogonal Multiple Access (OMA) techniques and try to improve Quality of Service (QoS) metrics like the data rate. In this work, we investigate the problem of allocating resources in an uplink MCCDMA wireless IoT network, including channel assignment and power control, to satisfy the signal-to-interference ratio (SIR) requirement of all devices in a scalable manner. The problem is primarily formulated as a Mixed Integer Linear Programming (MILP) problem with the goal of maximizing the number of supported devices while the sum of transmit powers is minimized. We prove that the problem is NP-Hard in general, and use the Lagrangian relaxation and decomposition methods to provide a subgradient-based approach and find at least a near-optimal solution; yet in a homogeneous case where a same feasible SIR requirement and a same path gain are assumed for all devices, a closed-form solution is obtained. Towards an efficient solution for the general case, a centralized heuristic algorithm and an evolutionary distributed algorithm are developed. The distributed scheme consists of a Vickrey auction based channel assignment phase and a non-cooperative target SIR tracking power control phase to allocate resources in an iterative and decentralized manner. In the distributed scheme, the base station is not needed to know path gains. Numerical results show that the difference between the optimal solution and our proposed algorithms are negligible, so they can be efficiently employed in real-world scenarios.

Analysis of End-to-End Packet Delay for Internet of Things in Wireless Communications

Accurate and efficient estimators for End to End delay (E2EPD) plays a significant and critical role in Quality of Service (QoS) provisioning in Internet of Things (IoT) wireless communications. The purpose of this paper, on one hand, is to propose a novel real-time evaluation metrics, on the other hand, addresses the effects of varying packet payload (PP) size. These two objectives rely on the analysis of E2EPD for QoS provisioning in multi-hop wireless IoT networks through multiple hops count from source to destination. The results of this study show the critical effect of PP size, hops count and interface speed on the improving E2EPD use of applications requiring real-time IoT communications.

Printed Motes for IoT Wireless Networks: State of the Art, Challenges, and Outlooks

Although wireless sensor networks (WSNs) have been an active field of research for many years, the modules incorporated by WSN nodes have been mainly manufactured utilizing conventional fabrication techniques that are mostly subtractive, requiring significant amounts of materials and increased chemical waste. The new era of the Internet of Things (IoT) will see the fabrication of numerous small form factor devices for wireless sensing for a plurality of applications, including security, health, and environmental monitoring. The large volume of these devices will require new directions in terms of manufacturing cost and energy efficiency, which will be achieved with redesigned, energy-aware modules. This paper presents the state of the art of printed passives, sensors, energy harvesting modules, actives, and communication front ends, and summarizes the challenges of implementing modules that feature low power consumptions without compromising the low fabrication cost. The plethora of the modules presented herein will facilitate the implementation of low cost, additively manufactured, energy-aware IoT nodes that can be fabricated in large volumes with green processes.

Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.

Long-Term Monitoring of the Sierra Nevada Snowpack Using Wireless Sensor Networks

Historically, the study of mountain hydrology and the water cycle has been largely observational, with meteorological forcing and hydrological variables extrapolated from a few infrequent manual measurements. Recent developments in the Internet of Things (IoT) technology are revolutionizing the field of mountain hydrology. Low-power wireless sensor networks can now generate denser data in real time and for a fraction of the cost of labor-intensive manual measurement campaigns. The American River Hydrological Observatory (ARHO) project has deployed 13 low-power wireless IoT networks throughout the American River basin to monitor California’s snowpack. The networks feature a total of 945 environmental sensors, each reporting a reading every 15 min. The data reported is made available to the scientific community minutes after it is generated. This article provides an in-depth technical description of the ARHO project. It details the requirements and different technical options, describes the technology deployed today, and discusses the challenges associated with large-scale environmental monitoring in extreme conditions.

Optimal algorithm for Internet-of-Things service composition based on response time

In the near future, the Internet-of-Things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware infrastructure, and Service-Oriented Architecture (SOA) is a valuable software system that allows heterogeneous devices to interoperate each other. Even though IoT researchers have tackled a number of challenges for service composition, the orchestration techniques on IoT are rarely studied yet. Moreover, it is hard to automatically compose IoT services with an optimal aggregate response time since we have to compare all the combinations of parallel/serial structures in a composite IoT service. In the SOA literature, many researches on QoS-aware composition consider parallel work-flows, but they employ service selection models that require users to manually or semi-automatically predefine a top-level work process. Accordingly, they cannot guarantee the optimal solution. In this paper, we hence propose a novel IoT service composition algorithm which automatically produces a composite IoT service with a minimal response time.