This paper focuses on the performance evaluation of consensus algorithms used in a blockchain system for Internet of Things (IoT). In such systems the time necessary to achieve consensus should be small. Three most used consensus algorithms (modified proof of work, practical byzantine fault tolerance and binary consensus) are evaluated in different situations - type of motes, number of nodes participating in consensus algorithm and radio propagation model. An integrated solution is proposed to adapt an IoT node to different consensus algorithm. The simulations in Contiki IoT operating system show good performance (time to achieve consensus less than seconds).

The paper has the purpose to evaluate the performance of the lightweight TCP/IP protocol stack using the digital signal processor Blackfin BF537. The applications of such embedded system are in sensor networking, voice over IP (VoIP) communications and process control in industrial environments. The performance of LwIP stack has been evaluated using the evaluation board EZ-KIT. In this paper we demonstrated that the LwIP stack has good performance, comparable with the TCP/IP stack implementation in operating systems like Linux or Windows. Furthermore, a framework for developing embedded networking applications is provided.

This paper proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. To traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. Also, the paper illustrates a general framework to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases are made and the influence of filtering are evaluated.

The paper presents a framework for integrating the Analog Devices wireless sensors network (ADI-WSN) developing and evaluation boards in the WSN operating system Contiki. The proprietary software used by ADI-WSN is replaced by new proposed software with the following advantages: multitasking environment to create a flexible and more efficient applications by using the operating system primitives, active loops elimination for sensors, battery life improvements, application portability and easier ADI-WSN node connectivity with other type nodes already integrated in Contiki operating system. The obtained results show the full functionality of ADI-WSN node in Contiki environment and the extending the battery life with a factor of ten.