Energy-Aware Security Adaptation for Low-Power IoT Applications

The constant evolution in communication infrastructures will enable new Internet of Things (IoT) applications, particularly in areas that, up to today, have been mostly enabled by closed or proprietary technologies. Such applications will be enabled by a myriad of wireless communication technologies designed for all types of IoT devices, among which are the Long-Range Wide-Area Network (LoRaWAN) or other Low-power and Wide-Area Networks (LPWAN) communication technologies. This applies to many critical environments, such as industrial control and healthcare, where wireless communications are yet to be broadly adopted. Two fundamental requirements to effectively support upcoming critical IoT applications are those of energy management and security. We may note that those are, in fact, contradictory goals. On the one hand, many IoT devices depend on the usage of batteries while, on the other hand, adequate security mechanisms need to be in place to protect devices and communications from threats against their stability and security. With thismotivation in mind, we propose a solution to address the management, in tandem, of security and energy in LoRaWAN IoT communication environments. We propose and evaluate an architecture in the context of which adaptation logic is used to manage security and energy dynamically, with the goal of guaranteeing appropriate security, while promoting the lifetime of constrained sensing devices. The proposed solution was implemented and experimentally evaluated and was observed to successfully manage security and energy. Security and energy are managed in line with the requirements of the application at hand, the characteristics of the constrained sensing devices employed and the detection, as well as the threat, of particular types of attacks.

BloodChain: A Blood Donation Network Managed by Blockchain Technologies

Due to the rapid change of population structure, leading to lower birth rates and quick aging rates, the demand for blood supply is increasing significantly. In most countries, blood quality and origin are managed by blood management information systems, such as national authorities. Nevertheless, the traditional system has limitations in this field, such as a lack of detailed blood information, making it challenging to manage blood quality, supply, and demand. Hence, to solve these issues, this paper proposes a blockchain-based system called BloodChain, an improved system to support blood information management, providing more detailed information about blood, such as blood consumption and disposal. BloodChain exploits private blockchain techniques with a limited number of relatively fast and reliable participants, making them suitable for B2B (Business to Business) transactions. In this paper, we also develop a proposed system based on the architecture of Hyperledger Fabric. The evaluation of BloodChain is performed in several scenarios to prove our proposed model’s effectiveness.

Unmanned Agricultural Tractors in Private Mobile Networks

The need for high-quality communications networks is urgent in data-based farming. A particular challenge is how to achieve reliable, cost-efficient, secure, and broadband last-mile data transfer to enable agricultural machine control. The trialed ad hoc private communications networks built and interconnected with different alternative wireless technologies, including 4G, 5G, satellite and tactical networks, provide interesting practical solutions for connectivity. A remotely controlled tractor is exemplified as a use case of machine control in the demonstrated private communication network. This paper describes the results of a comparative technology analysis and a field trial in a realistic environment. The study includes the practical implementation of video monitoring and the optimization of the control channel for remote-controlled unmanned agricultural tractors. The findings from this study verify and consolidate the requirements for network technologies and for cybersecurity enablers. They highlight insights into the suitability of different wireless technologies for smart farming and tractor scenarios and identify potential paths for future research.

A Study of 5G Edge-Central Core Network Split Options

With the wide adoption of edge compute infrastructures, an opportunity has arisen to deploy part of the functionality at the edge of the network to enable a localized connectivity service. This development is also supported by the adoption of “on-premises” local 5G networks addressing the needs of different vertical industries and by new standardized infrastructure services such as Mobile Edge Computing (MEC). This article introduces a comprehensive set of deployment options for the 5G network and its network management, complementing MEC with the connectivity service and addressing different classes of use cases and applications. We have also practically implemented and tested the newly introduced options in the form of slices within a standard-based testbed. Our performed validation proved their feasibility and gave a realistic perspective on their impact. The qualitative assessment of the connectivity service gives a comprehensive overview on which solution would be viable to be deployed for each vertical market and for each large-scale operator situation, making a step forward towards automated distributed 5G deployments.

Large Geographical Area Aerial Surveillance Systems Data Network Infrastructure Managed by Artificial Intelligence and Certified over Blockchain: A Review

This paper proposes an aerial data network infrastructure for Large Geographical Area Surveillance Systems. The work presents a review of previous works from the authors, existing technologies in the market, and other scientific work, with the goal of creating a data network supported by Autonomous Tethered Aerostat Airships used for sensor fixing, a drones deployment base, and meshed data network nodes installation. The proposed approach for data network infrastructure supports several independent and heterogeneous services from independent, private, and public companies. The presented solution employs Edge Artificial Intelligence (AI) systems for autonomous infrastructure management. The Edge AI used in the presented solution enables the AI management solution to work without the need for a permanent connection to cloud services and is constantly fed by the locally generated sensor data. These systems interact with other network AI services to accomplish coordinated tasks. Blockchain technology services are deployed to ensure secure and auditable decisions and operations, which are validated by the different involved ledgers.

Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications

Bluetooth low energy (BLE)-based location service technology has become one of the fastest growing applications for Bluetooth. Received signal strength (RSS) is often used in localization techniques for ranging or location fingerprinting. However, RSS-based localization solutions have poor performance in multipath environments. In this paper, we present a measurement system designed using multiple ESP32 BLE modules and the Bluetooth mesh networking technology, which is capable of exploiting the space, time, and frequency diversities in measurements. To enable channel-aware multi-device RSS measurements, we also designed a communication protocol to associate channel ID information to advertising messages. Based on channel measurement and analysis, we demonstrate that with a six-receiver configuration and space-time-frequency diversity combining, we can significantly reduce the residual linear regression fitting errors in path loss models. Such a reduction leads to more accurately correlating RSS measurements to the distance between the transmitter and receiver devices and thus to achieving improved performance with the RSS-based localization techniques. More importantly, the reduction in the fitting errors is achieved without differentiating the three advertising channels, making it possible to conveniently implement the proposed six-receiver configuration using commercially available BLE devices and the standard Bluetooth mesh networking protocol stack.

Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey

The last decade has witnessed the rise of the proliferation of Internet-enabled devices. The Internet of Things (IoT) is becoming ever more pervasive in everyday life, connecting an ever-greater array of diverse physical objects. The key vision of the IoT is to bring a massive number of smart devices together in integrated and interconnected heterogeneous networks, making the Internet even more useful. Therefore, this paper introduces a brief introduction to the history and evolution of the Internet. Then, it presents the IoT, which is followed by a list of application domains and enabling technologies. The wireless sensor network (WSN) is revealed as one of the important elements in IoT applications, and the paper describes the relationship between WSNs and the IoT. This research is concerned with developing energy-efficiency techniques for WSNs that enable the IoT. After having identified sources of energy wastage, this paper reviews the literature that discusses the most relevant methods to minimizing the energy exhaustion of IoT and WSNs. We also identify the gaps in the existing literature in terms of energy preservation measures that could be researched and it can be considered in future works. The survey gives a near-complete and up-to-date view of the IoT in the energy field. It provides a summary and recommendations of a large range of energy-efficiency methods proposed in the literature that will help and support future researchers. Please note that the manuscript is an extended version and based on the summary of the Ph.D. thesis. This paper will give to the researchers an introduction to what they need to know and understand about the networks, WSNs, and IoT applications from scratch. Thus, the fundamental purpose of this paper is to introduce research trends and recent work on the use of IoT technology and the conclusion that has been reached as a result of undertaking the Ph.D. study.

An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication

Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.

Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.

Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.