<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Wireless Local Area Networking standard (Wi-Fi) and the WPAN standard (Bluetooth and Zigbee) products utilize the same unlicensed 2.4 GHz ISM band. Co-existence between such wireless technologies within the same frequency spectrum is crucial to ensure that each wireless technology maintains and provides its desired performance requirements. This paper provides a brief description of the newly introduced Zigbee standards including the Physical (PHY) and media access control (MAC) layer. It focuses on developing MatLab/Simulink models for the Zigbee protocol and the performance evaluation of these models. Several simulations were run and the results were analyzed for the different scenarios. The results showed how the relationship between the signal Bit Error Rate (BER) and Signal to Noise Ratio (SNR) was affected when varying the data rate and power. Furthermore, this paper investigated the co-existence of WLAN (IEEE 802.11g) with Zigbee (IEEE 802.15.4 by quantifying potential interferences and examining the impact on the throughput performance of IEEE 802.11g and Zigbee devices when co-existing within a particular environment. The effect of Zigbee on IEEE 802.11g was compared with the effect of Bluetooth under the same operating conditions.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

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In delay tolerant WSNs mobile ferries can be used for collecting data from sensor nodes, especially in large-scale networks. Unlike data collection via multi-hop forwarding among the nodes, ferries travel across the sensing field and collect data from sensors. The advantage of using a ferry-based approach is that, it eliminates the need for multi-hop forwarding of data, and as a result energy consumption at the nodes is significantly reduced. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection algorithm using a ferry node is proposed while considering the overall ferry roundtrip travel time and the overall consumed energy in the network. To minimize the overall roundtrip travel time, we divided the sensing field area into virtual grids based on the assumed sensing range and assigned a checkpoint in each one. A Genetic Algorithm with weight metrics to solve the Travel Sales Man Problem (TSP) and decide on an optimum path for the ferry to collect data is then used. We utilized our previously published node ranking clustering algorithm (NRCA) in each virtual grid and in choosing the location for placing the ferry’s checkpoints. In NRCA the decision of selecting cluster heads is based on their residual energy and their distance from their associated checkpoint which acts as a temporary sink. We simulated the proposed algorithm in MATLAB and showed its performance in terms of the network lifetime, total energy consumption and the total travel time. Moreover, we showed through simulation that nonlinear trajectory achieves a better optimization in term of network lifetime, overall energy consumed and the roundtrip travel time of the ferry compared to linear predetermined trajectory. In additional to that, we compared the performance of your algorithm to other recent algorithms in terms of the network lifetime using same and different initial energy values.

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Depending on the application, mobile ferries can be used for collecting data in a WSN especially those at a large-scale with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry based approach eliminates or minimizes the need for multi-hop forwarding of data, and as a result energy consumption at the nodes will be significantly reduced especially nodes that are near the base station as they are used by other nodes to forward data to the base station. However, this increases data delivery latency and as such might not be suitable for all applications. In this paper an efficient data collection scheme using a ferry node is proposed with emphasis of the effect of ferry's path. In this scheme the decision of selecting cluster heads is based on their residual energy and their distance from the ferry path. We simulated the proposed scheme in MATLAB using different scenarios to show their performance in terms of the network lifetime and total energy consumption in the network. We found that the centered and the diagonal fitted paths performed better than the diagonal path in terms of the network lifetime and energy consumed. We also found that increasing the check points increases the lifetime of the network