Different architectures of interactions between the objects in the Internet of Things have been proposed, based on direct exchange of data between the wireless devices or based on synchronization with fixed network devices or cloud infrastructure. We evaluated how the selection of the communication architecture influences the performance of the data exchange using discrete event simulation. The distribution of time needed to pass information between smart objects is calculated for three popular network management protocols: CoAP, SNMP and NETCONF. The evaluation is executed for 3 types of communication proposed in the fog computing paradigm: direct synchronization between devices, synchronization through the local gateway or synchronization via the cloud servers. The analysis shows that the communication via the cloud infrastructure requires up to the three times longer time than the data exchange through a local gateway and up to 6 times longer than the direct communication between the network nodes. Transmission through CoAP and SNMP introduces similar delay, considerably lower than NETCONF.

The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.