A Collaborative Framework for Avoiding Interference Between Zigbee and WiFi for Effective Smart Metering Applications

Energy management is one of the foremost priorities of research in many countries across the world. The introduction of modern information and communication technologies (ICT) are transforming the existing power grid, towards a more distributed and flexible “Smart Grid” (SG). The wireless sensor networks (WSN) are considered for data communication and are generally, incorporated with actuators to implement the control actions remotely. The wireless technologies like ZigBee (for automation), WiFi (for internet) and Bluetooth (entertainment) work in the 2.4GHz band. The coexistence of different wireless technologies working in the common area is unavoidable. Hence, this phenomenon degrades the performance of each other, due to the interference phenomenon. The wireless nodes with high energy had a great influence on the performance of the nodes working with low energy. Under the influence of interference, the low-power nodes experience the uncertain sleep-wake scheduling and increased delays in channel occupation. Interference also results in, high packet error rates (PER), decreased throughput, and high energy consumption. Hence for overcoming the above problems, A collaborative framework for an effective interference management and its avoidance is proposed in this paper. The framework proposed assures the effective ZigBee communication by systematic channel scheduling operating even under the influence of Wi-Fi. The work proposed performs better even under extreme interference conditions and the results obtained shows enriched performance

Energy-efficient provenance transmission in large-scale wireless sensor networks

Large-scale sensor-based decision support systems are being widely deployed. Assessing the trustworthiness of sensor data and the owners of this data is critical for quality assurance of decision making in these systems. Trust evaluation frameworks use data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, which, in turn, results in high energy dissipation with extended period of radio communication, making trust systems unusable. We propose an energy-efficient provenance transmission and construction scheme, which we refer to as Probabilistic Provenance Flow (PPF). To the best of our knowledge, ours is the first approach to make the Probabilistic Packet Marking (PPM) approach of IP traceback feasible for sensor networks. We propose two bit-efficient complementary provenance encoding and construction methods, and combine them to handle topological changes in the network. Our TOSSIM simulations demonstrate that PPF requires at least 33% fewer packets and consumes 30% less energy than PPM-based approaches to construct provenance, yet still provides high accuracy in trust score calculation