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Networked Embedded Systems: An Overview
Published in Richard Zurawski, Networked Embedded Systems, 2017
At the device and embedded level, the limited computing, memory, and communication bandwidth resources of controllers embedded in the field devices pose considerable challenge for the implementation of effective security policies which, in general, are resource demanding. This limits the applicability of the mainstream cryptographic protocols, even vendor-tailored versions. The operating systems running on small footprint controllers tend to implement essential services only, and do not provide authentication or access control to protect mission and safety-critical field devices. In applications restricted to the Hypertext Transfer Protocol (HTTP), such as embedded Web servers, Digest Access Authentication [49], a security extension to HTTP, may offer an alternative and viable solution. In case of the denial of service (DoS) attack, the processor is preoccupied with handling communication interrupts potentially compromising the (hard) real-time requirements and operational safety as a result—clever interrupt priority allocation and/or selection are needed. Interrupts handling outside the normal operational conditions can cause with time battery draining on battery powered devices, making embedded controllers to become unavailable—a serious problem in wireless sensor networks deployed on the factory floor where the function of the unavailable node cannot be taken over by other nodes. In general, an embedded controller is expected to withstand autonomously many security attacks; the “buffer overflow” attack, for instance, which has the potential to crash the system—proper error and exception handling is required here.
Drivers
Published in Rick Bitter, Taqi Mohiuddin, Matt Nawrocki, LabVIEW™ Advanced Programming Techniques, 2017
Rick Bitter, Taqi Mohiuddin, Matt Nawrocki
The File Logging selection in the NI Spy options allows the program to record all calls to a log file. File logging is useful when debugging an application that causes the system to crash. If file logging in the Fail-Safe Logging mode, you can view the API calls that were captured prior to the system crash by opening the saved log file. In order to use this function, a file name must be provided to store the logged API calls. There are two modes of file logging available. The first is FailSafe Logging. Fail-Safe Logging is a method of guaranteeing that the log file will not be corrupted if the system crashes. The logging is accomplished by opening the log file, writing the data, and closing the log file after each API call. It should be obvious that this method of logging the data is slow. If performance and time are an issue, Fast Logging is available. This method of logging opens the file at the start. The data from each call is written to the log file when the call is captured. The file is not closed until the capture is stopped or logging is disabled. The Fast Logging method of file logging is much faster than Fail-Safe Logging, but if your system crashes, data will be lost.
Bluetooth: State of the Art, Taxonomy, and Open Issues for Managing Security Services in Heterogeneous Networks
Published in N. Jeyanthi, Kun Ma, Thinagaran Perumal, R. Thandeeswaran, Managing Security Services in Heterogenous Networks, 2020
The vulnerability arising from the Bluetooth is quite alarming and it does not serve the purpose for which it is designed. There are many applications like home automation products from cloud cameras like Google's Nest Cam to Bluetooth-enabled pressure cookers. Say, for example, the vulnerability in the security cameras are quite alarming. Dropcam, Dropcam Pro, Nest Cam Outdoor, and Nest Cam Indoor running version 5.2.1 of Nest’s firmware can be wirelessly attacked via Bluetooth to clatter and sojourn recording footage. There are three vulnerabilities are in camera firmware version 5.2.1, and no solution is publicly availableAn attacker can trigger a buffer overflow in the camera by pinging it an overlong Wi-Fi SSID parameter via Bluetooth Low Energy (BLE). This triggers a buffer overflow condition, which causes the cameras to stop recording, crash and reboot.The miscreant sends a long Wi-Fi password parameter to the camera. This triggers a buffer overflow condition, which causes the cameras to stop recording, crash and reboot.It bashes the camera from its linked Wi-Fi network entirely. Attackers can blast the camera with a new SSID connect to, which blows it off its network; as it is linking the new SSID, which apparently doesn’t exist, it rejoins the previous wireless network about 90 seconds later. From then on, the device stops recording footage to its cloud-connected backend.
An IoT-based Framework of Vehicle Accident Detection for Smart City
Published in IETE Journal of Research, 2023
Pankaj P. Tasgaonkar, Rahul Dev Garg, Pradeep Kumar Garg
An Intelligent Transportation System consists of advanced sensors, high-speed communication, and information technology for the pedestrians, drivers of the road vehicles, and the monitoring stations [37]. The sensors like cameras, RFID readers have been installed either on the roadside, inductive loops and magnetic sensors, etc., under the road or they are present on vehicles like accelerometers, GPS, ultrasonic sensors, etc. Environmental sensors also give information related to rain, cyclone, landslide with large LCDs showing the warnings. The sensor nodes obtain the power from an external battery or a solar cell. Routing protocols in WSN can handle more complex processing and computations requirements. There is a chance that a third party can influence the sensor data and wrong information is passed to the end-users, to avoid such circumstances, security is assigned with encrypted algorithms. Neural network was used to detect crash events from computing the coefficients as a feature vector. Acoustic signals were considered and database of road vehicle crashes, braking, and traffic were stored. [38]. In the crash-sensing algorithm [39], this change of velocity, v(t), is compared to the threshold value, Vth, to provide the effective crash detection decision. The change of velocity, v(t), is the integration of the acceleration signal.
Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data
Published in Transportation Letters, 2023
Sumit Mishra, Vishal Singh, Ankit Gupta, Devanjan Bhattacharya, Abhisek Mudgal
To deploy an AIMD principle-based algorithm for adjusting the signal timings, microcontrollers (as slaves) are needed for each lane. These slaves are connected to the central microcontroller unit server (MCU), which acts as the master. In general, various ready-to-use traffic-light hardware boards (master and slave) are available and are deployed at various traffic-lights. The proposed method would use these microcontrollers to deploy the AIMD algorithm to control signal timings based on the level of congestion. Further, the central MCU is wirelessly connected to the traffic control center where processing may be done. The MCU at the intersection receives the timing according to the congestion level and then communicates it to every traffic light via a small network as shown in Figure 4. If a standalone solution is needed for each intersection, then the central MCU has to be powerful. Otherwise, a Single Board Computer (SBC) like Intel NUC can independently perform the functionality of a server desktop PC housed at the traffic control center. Further, traffic light control hardware implementations are susceptible to threats like security, system crash due to bad internet connection or overheating. For dealing with these threats a reliable operating system crash susceptible architecture must be used (Jin, Ma, and Kosonen 2017).
The impact factors on the competence of big data processing
Published in International Journal of Computers and Applications, 2022
Wei Li, William W. Guo, Michael Li
Churn refers to the following dynamic and opportunistic characteristics. Computing nodes are voluntary for a computing task. They can randomly join for computing and are allowed to leave before completing the task.Computing nodes are unreliable and can crash at any time, leaving the computing task unfinished.Maintaining a reliable overlay on the unreliable computing nodes or volunteers can be achieved through a stabilization protocol, but incurs the cost for looking up an available task or looking up a storage node to upload a result. The reason for such a cost is that, when the entire overlay is ensured reliable, nothing can be pre-located or determined, due to the unreliability of the nodes that form the overlay.