A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT

Internet of Things (IoT) forms the foundation of next generation infrastructures, enabling development of future cities that are inherently sustainable. Intrusion detection for such paradigms is a non-trivial challenge which has attracted further significance due to extraordinary growth in the volume and variety of security threats for such systems. However, due to unique characteristics of such systems i.e., battery power, bandwidth and processor overheads and network dynamics, intrusion detection for IoT is a challenge, which requires taking into account the trade-off between detection accuracy and performance overheads. In~this context, we are focused at highlighting this trade-off and its significance to achieve effective intrusion detection for IoT. Specifically, this paper presents a comprehensive study of existing intrusion detection systems for IoT systems in three aspects: computational overhead, energy consumption and privacy implications. Through extensive study of existing intrusion detection approaches, we have identified open challenges to achieve effective intrusion detection for IoT infrastructures. These include resource constraints, attack complexity, experimentation rigor and unavailability of relevant security data. Further, this paper is envisaged to highlight contributions and limitations of the state-of-the-art within intrusion detection for IoT, and~aid the research community to advance it by identifying significant research directions

Middleware platform for distributed applications incorporating robots, sensors and the cloud

Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity

Smart Traffic Light based on IoT and mBaaS using High Priority Vehicles Method

An increase of the number of vehicles which is not followed by the number of roads can lead to the increase of congestion, especially in big cities. Regulation of law no 22 Year 2009 explains that there are seven types of vehicles prioritized on the road. This research aims to build a Smart Traffic Light as a solution with the goal of making the prioritized vehicle journey smooth when crossing the road with Smart Traffic Light. The proposed system is "Smart Traffic Light on IoT and mBaaS (Mobile Backend As a Service) using High Priority Vehicles Method". The Smart Traffic Light has three important parts, including: (1) Smart Traffic Application; (2) Smart Traffic Controller; and (3) mBaaS. Prioritized vehicle drivers cross the road using the Smart Traffic Application when they are in an emergency situation. Smart Traffic Application and Smart Traffic Controller communicate using mBaaS. Smart Traffic Application has a vehicle track search facility as well as identification of traffic light location. A few meters before crossing, Smart Traffic Application will send the location to mBaaS and continue to be read by Smart Traffic Controller using internet. If it meets the criteria of High Priority Vehicle, then Traffic Light will be changed to green in the same path. The results show that when testing the data rate from Smart Traffic Application to Smart Traffic Controller, it takes no later than 8.15 seconds and 1.2 seconds (the fastest) with the average data transmission time of 3.39 seconds. Smart Traffic Light is able to identify the direction of the vehicle before passing through the Smart Traffic Application

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field