In this paper presented method for reducing the amount of data transmitted and stored in IoT systems. Instead of expensive and complex network devices, developers can apply cheap and proven low-speed solutions (ZigBee, NB IoT, BLE). This approach is focused on on-the-sensor data processing. Correlation and autocorrelation methods for detecting events, depending on shape of a wave, were described in details and proposed endpoint architecture implementation.
Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware.
The top-level of this data-set has two folders corresponding to the two algorithms studied (exploration and task allocation). Each folder contains a simulation and field deployment sub-folder, which in turn contains the data files. In the simulation folder, there are additional sub-folders labeled based on the experimental configuration used during the experiment. In particular, the simulation folders are labeled as PacketDropRate_NumberOfRobots_RepetitionNumber (for example, a folder with 0.0_6_1 denotes an experimental setting with a packet drop of 0.0, 6 robots and the first repetition). Each simulation folder for the exploration algorithm contains a ROS bag file, a CSV file (one for each robot, containing the Voronoi tessellations as computed by the robot), a log file (one for each robot, containing the logs generated by ROSBuzz) and a folder containing the CSV files generated from the ROS topics using the corresponding bag file. The field deployment sub-folders for the task allocation algorithm contain six experimental trials using field robots. Each of these experimental folders contains the ROSBuzz log files generated by the field robots, one for each robot. Similarly, the field deployment sub-folder within the Semi-autonomous exploration folder contains the CSV files generated from the ROS bag files during the three field deployment experiments. Only the CSV files used to generate the trajectories for the user-centric study (exploration) are provided in this data-set to keep the anonymity of the user. The python notebooks used to parse the data provided in this data-set and generate the plots are also attached.
This file contains the experimental results for the research paper "Optimization-Based Offloading and Routing Strategies for Sensor-Enabled Video Surveillance Networks". Just open the excel file! then you can view the data.
IoT Gateway Architecture
Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems.
The generation of the dataset containing OPC UA traffic was possible due to the setup and execution of a laboratory CPPS testbed. This CPPS uses OPC UA standard for horizontal and vertical communications.Regarding the CPPS testbed setup, it consists on seven nodes in the network.Each network node consist on a Raspberry Pi device, running the Python FreeOpcUa implementation. In this configuration, there are two production units, each one containing three devices, and one node representing a Manufacturing Execution System (MES). Each device implements both OPC UA server and client, where the server publish to a OPC UA variable updates regarding sensor readings and the client subscribes all OPC UA variables from all other devices in the same production unit. On the other side, the MES only implements the OPC UA client, which subscribes all OPC UA variables from all devices in both production units. Also, connected to this network, is an attack node as it is assumed that the attacker already gained access to the CPPS network.After setting up the CPPS testbed, a python implementation that implements Tshark was used to capture OPC UA packets and export this traffic to a csv file format dataset. This traffic includes both normal and anomalous behaviour. Anomalous behaviour is achieved with the malicious node, which injects attacks into the CPPS network, targeting one or more device nodes and the MES. The attacks selected for the malicious activities are:
- Denial of Service(DoS);
- Eavesdropping or Man-in-the-middle (MITM) attacks;
- Impersonation or Spoofing attacks.
To perform the attacks mentioned, a python script is used, which implements the Scapy module for packet sniffing, injection and modification. Regarding the dataset generation, another python script, that implements Tshark (in this case Pyshark) was used to capture only OPC UA packets and export this traffic to a csv file format dataset. Actually, the OPC UA packets are converted to bidirectional communication flows, which are characterized by the following 32 features:
- src_ip: Source IP address;
- src_port: Source port;
- dst_ip: Destination IP address;
- dst_port: Destination port;
- flags: TCP flag status;
- pktTotalCount: Total packet count;
- octetTotalCount: Total packet size;
- avg_ps: Average packet size;
- proto: Protocol;
- service: OPC UA service call type;
- service_errors: Number of service errors in OPC UA request responses;
- status_errors: Number of status errors in OPC UA request responses;
- msg_size: OPC UA message transport size;
- min_msg_size: minimum OPC UA message size;
- flowStart: Timestamp of flow start;
- flowEnd: Timestamp of flow end;
- flowDuration: Flow duration in seconds;
- avg_flowDuration: Average flow duration in seconds;
- flowInterval: Time interval between flows in seconds;
- count: Number of connections to the same destination host as the current connection in the past two seconds;
- srv_count: Number of connections to the same port number as the current connection in the past two seconds;
- same_srv_rate: The percentage of connections that were to the same port number, among the connections aggregated in Count;
- dst_host_same_src_port_rate: The percentage of connections that were to the same source port, among the connections having the same port number;
- f_pktTotalCount: Total forward packets count;
- f_octetTotalCount: Total forward packets size;
- f_flowStart: Timestamp of first forward packet start;
- f_rate: Rate at which forward packets are transmitted;
- b_pktTotalCount: Total backwards packets count;
- b_octetTotalCount: Total backwards packets size;
- b_flowStart: Timestamp of first backwards packet start;
- label: Binary label classification;
- multi_label: Multi classification labeling.
The generated dataset has 33.567 normal instances, 74.013 DoS attack instances, 50 impersonation attack instances, and 7 MITM attack instances. This gives a total of 107.634 instances. Also, all attacks were grouped into one class (anomaly - 1) and the rest of the instances belong to the normal class (0).
For more information, please contact the author: Rui Pinto (firstname.lastname@example.org).
A simple dataset that gives the processing cost (in cycles) for verifying multiple messages signed with ECDSA and implicitly certified public keys. It considers two implicit certification models: ECQV and SIMPL.
This dataset is used in article "Schnorr-based implicit certification: improving the security and efficiency of vehicular communications", submitted to IEEE Transactions on Computers. Namely, it is used as basis for building that article's Figure 2.
Master data has played a significant role in improving operational efficiencies and has attracted the attention of many large businesses over the decade. Recent professional searches have also proved a significant growth in the practice and research of managing these master data assets.