The advantage of a Wireless Sensor Network (WSN) compared to a centric approach is the distribution of sensing suites. However, in order for such a system of distributed resources to work in a reliable and effective way a smart cooperation between nodes is needed. In this paper we propose a middleware approach for a highly reliable data storage that helps to assure data availability despite the well known WSN resource problems and disappearing or inactive nodes by providing a reasonable data redundancy in the system. Such a solution helps to ease the design and optimization of the data exchange between nodes as well. Our solution is configurable in order to satisfy the needs of the application on top regarding performance/requirements trade-off. The options specify the quantity and quality of the data replication. Additional features like event mechanism that monitors the data and the possibility to issue database like queries increase the applicability of our middleware. In this paper we focus on the evaluation of its capabilities regarding reliability, the consistency of replicates and the costs of the data management. The simulation results for a reasonable set-up show that the CPU load caused by the data replication is low (below 3 percent) and the average inconsistency time is as small as about 0,06 seconds for a single hop and about 0,15 seconds for a two hops replication area. There is still room for improvements, but a clear definition of problems helps to find ways to cope with them in order to achieve the chosen goals.
Currently, ensuring the correct functioning of the electrical grid is an important issue in terms of maintaining the normative voltage parameters and local line overloads. The unpredictability of Renewable Energy Sources (RES), the occurrence of the phenomenon of peak demand, as well as exceeding the voltage level above the nominal values in a smart grid makes it justifiable to conduct further research in this field. The article presents the results of simulation tests and experimental laboratory tests of an electricity management system in order to reduce excessively high grid load or reduce excessively high grid voltage values resulting from increased production of prosumer RES. The research is based on the Elastic Energy Management (EEM) algorithm for smart appliances (SA) using IoT (Internet of Things) technology. The data for the algorithm was obtained from a message broker that implements the Message Queue Telemetry Transport (MQTT) protocol. The complexity of selecting power settings for SA in the EEM algorithm required the use of a solution that is applied to the NP difficult problem class. For this purpose, the Greedy Randomized Adaptive Search Procedure (GRASP) was used in the EEM algorithm. The presented results of the simulation and experiment confirmed the possibility of regulating the network voltage by the Elastic Energy Management algorithm in the event of voltage fluctuations related to excessive load or local generation.
Public key cryptography (PKC) has been considered for a long time to be computationally too expensive for small battery powered devices. However, PKC turned out to be very beneficial for issues such as key distribution, authentication etc. In the recent years first research groups started to cope with the challenges applying PKC in resource-constrained environments. One result is that in particular ECC seems to be very suitable for such environments, because it provides the same level of security as RSA does while requiring much shorter keys. In this paper we evaluate the power consumption resulting from using various PKC approaches with respect to calculations and transmission of signatures etc. Our findings here clearly indicate that software realisations of PKC lead to relatively long duty cycles (operating intervals) which in turn require significant amount of energy. In contrast, the energy required for computation is negligible if the PKC is performed by power efficient hardware accelerators. In such cases the corresponding transmission power becomes much more significant. So we argue for dedicated hardware for elliptic curve cryptography in order to reduce energy consumption and to prolong life time of sensor nodes. Since additional hardware equals to additional cost, we are focussing on hardware accelerators that are optimised with respect to silicon area consumption. Our solution that supports an ECC key length of 163 bit takes about 1.02 mm 2 cell area in a 0.25µm technology and needs about 12.8 µWs per point multiplication. Due to its small size the accelerator can be manufactured for about 0.05 USD in mass production.
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