Exploring Computing Continuum in IoT Systems: Sensing, Communicating and Processing at the Network Edge

As Internet of Things (IoT), originally comprising of only a few simple sensing devices, reaches 34 billion units by the end of 2020, they cannot be defined as merely monitoring sensors anymore. IoT capabilities have been improved in recent years as relatively large internal computation and storage capacity are becoming a commodity. In the early days of IoT, processing and storage were typically performed in cloud. New IoT architectures are able to perform complex tasks directly on-device, thus enabling the concept of an extended computational continuum. Real-time critical scenarios e.g. autonomous vehicles sensing, area surveying or disaster rescue and recovery require all the actors involved to be coordinated and collaborate without human interaction to a common goal, sharing data and resources, even in intermittent networks covered areas. This poses new problems in distributed systems, resource management, device orchestration,as well as data processing. This work proposes a new orchestration and communication framework, namely CContinuum, designed to manage resources in heterogeneous IoT architectures across multiple application scenarios. This work focuses on two key sustainability macroscenarios: (a) environmental sensing and awareness, and (b) electric mobility support. In the first case a mechanism to measure air quality over a long period of time for different applications at global scale (3 continents 4 countries) is introduced. The system has been developed in-house from the sensor design to the mist-computing operations performed by the nodes. In the second scenario, a technique to transmit large amounts of fine-time granularity battery data from a moving vehicle to a control center is proposed jointly with the ability of allocating tasks on demand within the computing continuum

Data aggregator implemented through industrial gateway IOT 2050 for smart microgrid

[EN] This paper describes the operation and implementation of a Data Aggregator based on an Industrial Gateway (DAIG) for the storage and transmission of information from an experimental Smart microgrid hybridised with green hydrogen. This DAIG consists of a Siemens IOT 2050, a commercial device that combines physical characteristics such as robustness and open-source software. To achieve this objective, each subsystem involved in the microgrid has a set of sensors and a Data Acquisition Device (DAD) to obtain the relevant logical magnitudes of its operation. The IOT 2050 serves as a centralised system, communicating via Modbus TCP/IP with each DAD and storing the data read in a local database by means of a Python script. Finally, as an example of the application of the designed infrastructure, an IoT software is implemented to visualise the data stored in the DAIG

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. Traffic analysis using traditional methods (e.g., through classical machine-learning models) is much less effective under those settings, as the features picked manually are not distinctive any more. In this work, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue

NexGen D-TCP: Next generation dynamic TCP congestion control algorithm

With the advancement of wireless access networks and mmWave New Radio (NR), new applications emerged, which requires a high data rate. The random packet loss due to mobility and channel conditions in a wireless network is not negligible, which degrades the significant performance of the Transmission Control Protocol (TCP). The TCP has been extensively deployed for congestion control in the communication network during the last two decades. Different variants are proposed to improve the performance of TCP in various scenarios, specifically in lossy and high bandwidth-delay product (high- BDP) networks. Implementing a new TCP congestion control algorithm whose performance is applicable over a broad range of network conditions is still a challenge. In this article, we introduce and analyze a Dynamic TCP (D-TCP) congestion control algorithm overmmWave NR and LTE-A networks. The proposed D-TCP algorithm copes up with the mmWave channel fluctuations by estimating the available channel bandwidth. The estimated bandwidth is used to derive the congestion control factor N. The congestion window is increased/decreased adaptively based on the calculated congestion control factor. We evaluated the performance of D-TCP in terms of congestion window growth, goodput, fairness and compared it with legacy and existing TCP algorithms. We performed simulations of mmWave NR during LOS \u3c-\u3e NLOS transitions and showed that D-TCP curtails the impact of under-utilization during mobility. The simulation results and live air experiment points out that D-TCP achieves 32:9% gain in goodput as compared to TCPReno and attains 118:9% gain in throughput as compared to TCP-Cubic

Handoff Characterization of Multipath Video Streaming

Video streaming has become the major source of Internet traffic nowadays. Considering that content delivery network providers utilize Video over Hypertext Transfer Protocol/ Transmission Control Protocol (HTTP/TCP) as the preferred protocol stack for video streaming, understanding TCP performance in transporting video streams has become paramount. Recently, multipath transport protocols have allowed streaming of video over multiple paths. In this paper, we analyze the impact of handoffs on multipath video streaming and network performance on WiFi and cellular paths. We utilize network performance measures, as well as video quality metrics, to characterize the performance and interaction between network and application layers of video data for various network scenarios.Twelfth International Conference on Evolving Internet (INTERNET 2020), October 18-22, 2020, Porto, Portuga

Robust, Resilient and Reliable Architecture for V2X Communication

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive these further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug- in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything- to-everything (X2X) communications

A Pervasive Computational Intelligence based Cognitive Security Co-design Framework for Hype-connected Embedded Industrial IoT

The amplified connectivity of routine IoT entities can expose various security trajectories for cybercriminals to execute malevolent attacks. These dangers are even amplified by the source limitations and heterogeneity of low-budget IoT/IIoT nodes, which create existing multitude-centered and fixed perimeter-oriented security tools inappropriate for vibrant IoT settings. The offered emulation assessment exemplifies the remunerations of implementing context aware co-design oriented cognitive security method in assimilated IIoT settings and delivers exciting understandings in the strategy execution to drive forthcoming study. The innovative features of our system is in its capability to get by with irregular system connectivity as well as node limitations in terms of scares computational ability, limited buffer (at edge node), and finite energy. Based on real-time analytical data, projected scheme select the paramount probable end-to-end security system possibility that ties with an agreed set of node constraints. The paper achieves its goals by recognizing some gaps in the security explicit to node subclass that is vital to our system’s operations