Wireless Sensor Networks (WSNs) have existed for many years and had assimilated many interesting innovations. Advances in electronics, radio transceivers, processes of IC manufacturing and development of algorithms for operation of such networks now enable creating energy-efficient devices that provide practical levels of performance and a sufficient number of features. Environmental monitoring is one of the areas in which WSNs can be successfully used. At the same time this is a field where devices must either bring their own power reservoir, such as a battery, or scavenge energy locally from some natural phenomena. Improving the efficiency of energy harvesting methods reduces complexity of WSN structures. This survey is based on practical examples from the real world and provides an overview of state-of-the-art methods and techniques that are used to create energyefficient WSNs with energy harvesting.
The data aggregation process of wireless sensor networks faces serious security problems. In order to defend the internal attacks launched by captured nodes and ensure the reliability of data aggregation, a secure data aggregation mechanism based on constrained supervision is proposed for wireless sensor network, which uses the advanced LEACH clustering method to select cluster heads. Then the cluster heads supervise the behaviors of cluster members and evaluate the trust values of nodes according to the communication behavior, data quality and residual energy. Then the node with the highest trust value is selected as the supervisor node to audit the cluster head and reject nodes with low trust values. Results show that the proposed mechanism can effectively identify the unreliable nodes, guarantee the system security and prolong the network lifetime.
The paper presents a circuit structure that can be used for powering an IoT (Internet of Things) sensor node and that can use energy just from its surroundings. The main advantage of the presented solution is its very low cost that allows mass applicability e.g. in the IoT smart grids and ubiquitous sensors. It is intended for energy sources that can provide enough voltage but that can provide only low currents such as piezoelectric transducers or small photovoltaic panels (PV) under indoor light conditions. The circuit is able to accumulate energy in a capacitor until a certain level and then to pass it to the load. The presented circuit exhibits similar functionality to a commercially available EH300 energy harvester (EH). The paper compares electrical properties of the presented circuit and the EH300 device, their form factors and costs. The EH circuit’s performance is tested together with an LTC3531 buck-boost DC/DC converter which can provide constant voltage for the following electronics. The paper provides guidelines for selecting an optimal capacity of the storage capacitor. The functionality of the solution presented is demonstrated in a sensor node that periodically transmits measured data to the base station using just the power from the PV panel or the piezoelectric generator. The presented harvester and powering circuit are compact part of the sensor node’s electronics but they can be also realized as an external powering module to be added to existing solutions.
This paper details a hardware implementation of a distributed Θ(1) time algorithm allows to select dynamically the master device in ad-hoc or cluster-based networks in a constant time regardless the number of devices in the same cluster. The algorithm allows each device to automatically detect its own status; master or slave; based on identifier without adding extra overheads or exchanging packets that slow down the network. We propose a baseband design that implements algorithm functions and we detail the hardware implementation using Matlab/Simulink and Ettus B210 USRP. Tests held in laboratory prove that algorithm works as expected.
The 6TiSCH communication stack enables IPv6 networking over the TSCH (Time Slotted Channel Hopping) mode of operation defined in IEEE 802.15.4. Lately it became an attractive solution for Low power and Lossy Networks (LLNs), suitable for Industrial Internet of Things (IIoT) applications. This article introduces a credible energy consumption model for the 6TiSCH network nodes, operating in the 863-870 MHz band. It presents the analysis leading to the construction of the model as well as verification through experimental measurements which showed 98% accuracy in determining power consumption for two different network topologies. The article includes reliable battery lifetime predictions for transit and leaf nodes along with other parametric study results.