Number Of End Devices Research Articles

Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies.

The number of end devices that use the last-mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and requires reliable functioning to support smooth and efficient business processes. To efficiently manage such massive wireless networks, more advanced and accurate network monitoring and malfunction detection solutions are required. In this article, we perform a first-time analysis of image-based representation techniques for wireless anomaly detection using recurrence plots (RPs) and Gramian angular fields and propose a new deep learning architecture enabling accurate anomaly detection. We elaborate on the design considerations for developing a resource-aware architecture and propose a new model using time series to image transformation using RPs. We show that the proposed model: 1) outperforms the one based on Gramian angular fields by up to 14% points; 2) outperforms classical ML models using dynamic time warping by up to 24% points; 3) outperforms or performs on par with mainstream architectures, such as AlexNet and VGG11 while having <10x their weights and up to ≈ 8% of their computational complexity; and d) outperforms the state of the art in the respective application area by up to 55% points. Finally, we also explain on randomly chosen examples how the classifier takes decisions.

A Novel Approach to Improving Distributed Deep Neural Networks over Cloud Computing

In recent years, deep distributed neural networks (DDNNs) and neural networks (NN) have excelled in an extensive list of applications. For example, deep convolutional neural networks (DCNNs) are constantly gaining new features in various tasks in computer vision. At the same time, the number of end devices, including Internet of Things (IoT) devices has increased prominently. These devices are attractive targets for machine learning applications because they are often directly connected to sensors. For example (cameras, microphones, and gyroscopes) that record large amounts of input data in a stream mode. This study presents the design of a DDNN with end devices, edges, and clouds that spans computer hierarchies. The idea presented is considered one of the new ideas because it depends on two layers to distinguish, namely the convolutional layer and the pooling layer. The main objective behind using these two layers in one proposal is to provide and obtain the best results. Finally, we discovered that the proposed technique produced the best results in terms of accuracy and cost, with the precision of the definition reaching 99 % and the cost being quite affordable at 25. As a result, we conclude that these results are far superior to those achieved by the researchers in their ideas provided in previous recent literature.

Digital Object Architecture for IoT Networks

The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications. In this regard, one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks. Many unique software and hardware solutions are used as a unique global identifier; however, such solutions have many limitations. This article proposes a novel solution, based on the Digital Object Architecture (DOA), that meets the requirements of identifying devices and applications of the IoT. This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks. We propose a model of an identification system based on the architecture of digital objects, which differs from the well-known ones. The proposed model ensures an acceptable quality of service (QoS) in the common architecture of the existing public communication networks. A novel interaction architecture is developed by introducing a Middle Handle Register (MHR) between the global register, i.e., Global Handle Register (GHR), and local register, i.e., Local Handle Register (LHR). The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented. The developed model is simulated for a wide-area network with allocated registers, and the results are introduced and discussed.

SPMOO: A Multi-Objective Offloading Algorithm for Dependent Tasks in IoT Cloud-Edge-End Collaboration

With the rapid development of the internet of things, there are more and more end devices, such as wearable devices, USVs and intelligent automobiles, connected to the internet. These devices tend to require large amounts of computing resources with stringent latency requirements, which inevitably increases the burden on edge server nodes. Therefore, in order to alleviate the problem that the computing capacity of edge server nodes is limited and cannot meet the computing service requirements of a large number of end devices in the internet of things scenario, we combined the characteristics of rich computing resources of cloud servers and low transmission delay of edge servers to build a hybrid computing task-offloading architecture of cloud-edge-end collaboration. Then, we study offloading based on this architecture for complex dependent tasks generated on end devices. We introduce a two-dimensional offloading decision factor to model latency and energy consumption, and formalize the model as a multi-objective optimization problem with the optimization objective of minimizing the average latency and average energy consumption of the task’s computation offloading. Based on this, we propose a multi-objective offloading (SPMOO) algorithm based on an improved strength Pareto evolutionary algorithm (SPEA2) for solving the problem. A large number of experimental results show that the algorithm proposed in this paper has good performance.

Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network

Long Range Wide Area Network (LoRaWAN) is the one of the promising low power wide area network (LPWAN) technologies at present and is expected to grow in the foreseeable future as a tool to provide connectivity among small things. In this paper, we present a simple analytical model to compute the throughput and packet loss probability of Medium Access Control (MAC) for Class-A of LoRaWAN. This analysis results can be used as a reference for deploying the appropriate number of end-devices (EDs) that can be accepted in a gateway (GW) while maximizing network throughput or guaranteeing the packet loss rate of EDs.

The Fog Development Kit: A Platform for the Development and Management of Fog Systems

With the rise of the Internet of Things (IoT), fog computing has emerged to help traditional cloud computing in meeting scalability demands. Fog computing makes it possible to fulfill real-time requirements of applications by bringing more processing, storage, and control power geographically closer to end devices. However, since fog computing is a relatively new field, there is no standard platform for research and development in a realistic environment, and this dramatically inhibits innovation and development of fog-based applications. In response to these challenges, we propose the Fog Development Kit (FDK). By providing high-level interfaces for allocating computing and networking resources, the FDK abstracts the complexities of fog computing from developers and enables the rapid development of fog systems. In addition to supporting application development on a physical deployment, the FDK supports the use of emulation tools (e.g., GNS3 and Mininet) to create realistic environments, allowing fog application prototypes to be built with zero additional costs and enabling seamless portability to a physical infrastructure. Using a physical testbed and various kinds of applications running on it, we verify the operation and study the performance of the FDK. Specifically, we demonstrate that resource allocations are appropriately enforced and guaranteed, even amidst extreme network congestion. We also present simulation-based scalability analysis of the FDK versus the number of switches, the number of end devices, and the number of fog devices.

Delay and Energy Consumption Analysis of Frame Slotted ALOHA variants for Massive Data Collection in Internet-of-Things Scenarios

This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements.

Capacity Planning of LoRa Networks With Joint Noise-Limited and Interference-Limited Coverage Considerations

LoRa networks are emerging low power wide area networks (LP-WAN) that are configured as star-of-star networks. Within each underlying star network, potentially thousands of LoRa end-devices utilize the patented chirp spread spectrum-based LoRa modulation to communicate directly with a LoRa gateway over long distances at the scale of kilometers. Typically, each LoRa gateway supports six quasi-orthogonal logical star networks that correspond to the available LoRa modulation spreading factors. The aggregate capacity of a LoRa gateway is evaluated as the number of end-devices that could be supported within the corresponding cell area by all six available spreading factor networks while fulfilling some QoS coverage requirement. Successful coverage of LoRa signals could be mainly attributed to noise-limited and interference-limited considerations. In the LoRa literature, these two factors have been treated in an isolated manner, where independent SNR and SIR thresholds have been set to signify coverage. However, in reality, the SNR threshold for successful coverage depicts a decreasing function of the SIR experienced by the end-device of interest. In this paper, the capacity of LoRa networks is evaluated with joint noise and interference considerations. The presented framework for capacity evaluation has the advantage that it derives the cumulative capacity distribution that could be supported from the cell-edge towards the cell-center of an individual LoRa gateway. It is then demonstrated that the capacity distribution curves could be employed in the LoRa network planning of an example IoT home security application.

Features of designing IoT systems

The development of IoT systems, especially in the field of Industrial Internet of things with a large number of end devices is a complex scientific and technical problem. Inability to take into account the number of factors at the early stage leads to a negative result. This document demonstrates the scientific approach to the creation of the above systems. The IoT protocols, the IoT system architecture and its components are described in this article. The problems arising in the design of IoT systems are described. The basic characteristics of the most common protocols are systematized. The method for solution of the actual scientific and applied problem, allowing to design the IoT system, taking into account various fundamental parameters including the frequency range, bandwidth, data transfer rate, radiated power, range, work intensity, as well as the fundamental constraints, is proposed. As a result of the application of the described method, the optimal IoT protocol can be chosen to build a system, or to determine and optimize the parameters of the proprietary protocol. That makes it possible to build a reliable and scalable IoT system for the solution of the relevant experimental problems.

Safeguarding 5G-and-Beyond Networks with Physical Layer Security

The eight articles in this special section provide the latest survey of physical layer security research on various promising techniques for 5G era and beyond networks. The number of mobile-connected wireless devices is quickly growing and will reach 11 billion by 2020, exceeding the world’s projected population at that time (7.8 billion). Meanwhile, the demand for wireless data transmission in futuristic wireless networks is growing exponentially. For example, a mobile user is expected to download approximately 1 terabyte of data on average annually by 2020. Cutting-edge wireless-enabled applications, such as e-healthcare, augmented reality, Tactile Internet, and the Internet of Vehicles (IoV), are becoming a reality, which is triggering an explosion of mobile data traffic, a rapid increase in the number of end-devices, massive instantaneous end-to-end connections, and high-reliability low-latency services. It will be an extremely daunting task for fourth generation (4G) networks to meet the ever-increasing communication needs, and the aforementioned features have been recognized as essential requirements for the fifth generation (5G) era and beyond.

Contention Tree-Based Access for Wireless Machine-to-Machine Networks With Energy Harvesting

In this paper, we consider a wireless machine-to-machine network composed of end-devices with energy harvesters that periodically transmit data to a gateway. While energy harvesting allows for perpetual operation, the uncertain amount of harvested energy may not guarantee fully continuous operation due to temporary energy shortages. This fact needs to be addressed at the medium access control layer. We thus investigate the performance of an energy harvesting-aware contention tree-based access (EH-CTA) protocol, which uses a tree-splitting algorithm to resolve collisions and takes energy availability into account. We derive a theoretical model to compute the probability of delivery and the time efficiency . In addition, we conduct a performance comparison of EH-CTA using an EH-aware dynamic frame slotted-ALOHA (EH-DFSA) as a benchmark. We determine the parameters that maximize performance and analyze how it is influenced by the amount of harvested energy and the number of end-devices. Results reveal the superior performance of EH-CTA over EH-DFSA. While EH-DFSA requires an estimate of the number of contending end-devices per frame to adapt the frame length, EH-CTA uses short and fixed frame lengths, which enables scalability and facilitates synchronization as the network density increases.

A performance investigation on IoT enabled intra-vehicular wireless sensor networks

The concept of Internet of Things (IoT) can be utilised in vehicles, since the number of sensor nodes in vehicles is rising tremendously because of the uplifting demand of applications for security, safety and convenience. In order to establish the communication among these nodes inside a vehicle, a controller area network with wired architecture provides a prominent solution. However, this solution is not flexible because of the architectural complexity and the demand for a large number of sensors inside the vehicle; hence wired architectures are replaced by wireless ones. Moreover, scalability will be an important issue while introducing the IoT concept in Intra-Vehicular Wireless Sensor Networks (IVWSNs). In this paper, a comprehensive performance investigation on the IoT enabled IVWSNs (IoT-IVWSNs) to be carried out in order to address this issue. The overview of the IoT-IVWSNs with a comparative study of the existing technologies and the design challenges for such network are provided. The link design between an end-device and the control unit is analysed, and the performance of the network has been investigated and some open research issues are addressed. It reveals that the delay in packet transmission increases due to higher traffic loads and the number of end-devices. This result demonstrates that the existing MAC protocol works well for a small network (i.e., a network with a maximum number of 50 nodes) but is not suitable for a large network (i.e., a network with more than 50 nodes). The outcome of this research helps to design a smart car system.

Modeling and analysis of reservation frame slotted-ALOHA in wireless machine-to-machine area networks for data collection.

Reservation frame slotted-ALOHA (RFSA) was proposed in the past to manage the access to the wireless channel when devices generate long messages fragmented into small packets. In this paper, we consider an M2M area network composed of end-devices that periodically respond to the requests from a gateway with the transmission of fragmented messages. The idle network is suddenly set into saturation, having all end-devices attempting to get access to the channel simultaneously. This has been referred to as delta traffic. While previous works analyze the throughput of RFSA in steady-state conditions, assuming that traffic is generated following random distributions, the performance of RFSA under delta traffic has never received attention. In this paper, we propose a theoretical model to calculate the average delay and energy consumption required to resolve the contention under delta traffic using RFSA. We have carried out computer-based simulations to validate the accuracy of the theoretical model and to compare the performance for RFSA and FSA. Results show that there is an optimal frame length that minimizes delay and energy consumption and which depends on the number of end-devices. In addition, it is shown that RFSA reduces the energy consumed per end-device by more than 50% with respect to FSA under delta traffic.