This paper proposes an advanced routing method in the purpose of increasing IoT routing device’s power-efficiency, which allows to centralize routing tables computing as well as to push loading, related to routing tables computation, towards the Cloud environment at all. We introduced a phased solution for the formulated task. Generally, next steps were performed: stated requirements for the system with Cloud routing, proposed possible solution, and developed the whole system’s structure. For a proper study of the efficiency, the experiment was conducted using the developed system’s prototype for real-life cases, each represents own cluster size (several topologies by each size), used sizes are: 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27 and 29. Expectable results for this research – decrease the time of cluster’s reaction on topology changes (delay, needed to renew routing tables), which improves system’s adaptivity.

Covering a wide area by a large number of WiFi networks is anticipated to become very popular with Internet-of-things (IoT) and initiatives such as smart cities. Such network configuration is normally realized through deploying a large number of access points (APs) with overlapped coverage. However, the imbalanced traffic load distribution among different APs affects the energy consumption of a WiFi device if it is associated to a loaded AP. This research work aims at predicting the communication-related energy that shall be consumed by a WiFi device if it transferred some amount of data through a certain selected AP. In this paper, a forecast of the energy consumption is proposed to be obtained using an algorithm that is supported by a mathematical model. Consequently, the proposed algorithm can automatically select the best WiFi network (best AP) that the WiFi device can connect to in order to minimize energy consumption. The proposed algorithm is experimentally validated in a realistic lab setting. The observed performance indicates that the algorithm can provide an accurate forecast to the energy that shall be consumed by a WiFi transceiver in sending some amount of data via a specific AP.

The Bluetooth Low Energy (BLE) MESH network technology gains popularity in low duty IoT systems. Its advantage is a low energy consumption that enables long lifetime of IoT systems. The paper proposes and evaluates new MRT management methods, i.e. exact and heuristic, that improves energy efficiency of BLE MESH network by minimizing the number of active relay nodes. The performed experiments confirm efficiency of the MRT methods resulting in significantly lower energy consumption of BLE MESH network.

Waste management is a challenging problem for most of the countries. The current waste segregation and the collection method are not efficient and cost-effective. In this paper, a prototype is presented for smart waste management. It is also capable of waste segregation at the ground level and providing real-time data to the administrator. Impact and cost analysis of the deployment of smartbin is also presented considering one ward of Ahmedabad Municipal Corporation. It is clear from that deployment of this smartbin will save about 40% of the current expenditure for that ward.

The subject of the article is the design and practical implementation of the wireless mesh network. IQRF radio modules were used for the network design. The IQRF® technique has enabled the construction of a mesh network with the possibility of reconfiguration. The theoretical part contains a description of the IQRF® hardware solutions used. The practical scope includes the design part, where the configuration of the radio modules was carried out and the parameters of the radio network were set to allow its implementation in various topologies. Then, a wireless network consisting of 10 IQRF modules was launched in the P3 building of the Opole University of Technology. At this stage, configured radio modules were placed in selected rooms on all five floors of the building in order to carry out tests of the radio network constructed in this way. The tests included measuring the packet transmission delay time as well as the received signal strength. Research was carried out for several network topologies.

The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.

One of the ways to improve calculations related to determining the position of a node in the IoT measurement system is to use artificial neural networks (ANN) to calculate coordinates. The method described in the article is based on the measurement of the RSSI (Received Signal Strength Indicator), which value is then processed by the neural network. Hence, the proposed system works in two stages. In the first stage, RSSI coefficient samples are taken, and then the node location is determined on an ongoing basis. Coordinates anchor nodes (i.e. sensors with fixed and previously known positions) and the matrix of RSSI coefficients are used in the learning process of the neural network. Then the RSSI matrix determined for the system in which the nodes with unknown positions are located is fed into the neural network inputs. The result of the work is a system and algorithm that allows determining the location of the object without processing data separately in nodes with low computational performance.

Nowadays, the technological innovations affect all human activities; also the agriculture field heavily benefits of technologies as informatics, electronic, telecommunication, allowing huge improvements of productivity and resources exploitation. This manuscript presents an innovative low cost fertigation system for assisting the cultures by using dataprocessing electronic boards and wireless sensors network (WSN) connected to a remote software platform. The proposed system receives information related to air and soil parameters, by a custom solar-powered WSN. A control unit elaborates the acquired data by using dynamic agronomic models implemented on a cloud platform, for optimizing the amount and typology of fertilizers as well as the irrigations frequency, as function also of weather forecasts got by on-line weather service.

An important element of Internet of Things systems (IoT) is wireless data transmission. Narrowband Internet of Things (NB-IoT) and LTE Cat M1 (LTE-M) are the new standards for such transmission intended for LTE cellular networks. Cellular network operators has recently launched such transmission. The article presents the results of measurements of NB-IoT transmission parameters in this network, inside the building and in open urban areas. The main features of the NBIoT system and measuring equipment are briefly discussed.