Traditional beaconless geographical routing (BGR) protocols perform poorly in a heterogeneous environment due to the consideration of location solely with an assumption of homogeneous transmission. Therefore, we propose a novel BGR with consideration of the locations of candidate forwarders and different transmission ranges. Our proposed protocol, namely, the heterogeneity-aware BGR (HA-BGR), is designed for a heterogeneous mobile sensor network (MSN), where the transmission ranges of mobile sensors are different. In HA-BGR, a node takes its distance, transmission range, and progress to the destination into consideration before setting itself to be a candidate next hop and remains active. Examined by analytical and extensive simulation results, HA-BGR not only needs fewer next-hop candidates but also achieves better performance in the end-to-end delivery ratio and latency as compared to the recently proposed convex-lense decision region BGR (CDR-BGR).

Installing a mobile sink on the public vehicle, which is a path-constrained vehicle, to collect data in a mobile sensor network can lower maintenance cost. However, immediately sending the data packet to the mobile sink when it arrives does not guarantee the shortest path, leading to high energy consumption. Meanwhile, immediately sending the data packet to the closest future position of the mobile sink can guarantee the shortest path. However, the mobile sensor node, which buffers the data packet while waiting for the mobile sink to arrive, may move away from the trajectory, resulting in reduction of delivery ratio. Therefore, we shall propose to delay sending the data packet until the mobile sink approaches the position with the shortest path. It not only guarantees the shortest path but also exhibits robustness with dynamic node mobility. Via extensive simulations, we show that our proposed scheme can effectively reduce energy consumption and increase delivery ratio even for the high-speed mobile sink.

ABSTRACT The amount of Short Message Service (SMS) spam is increasing. Various solutions to filter SMS spam on mobile phones have been proposed. Most of these use Text Classification techniques that consist of training, filtering, and updating processes. However, they require a computer or a large amount of SMS data in advance to filter SMS spam, especially for the training. This increases hardware maintenance and communication costs. Thus, we propose to filter SMS spam on independent mobile phones using Text Classification techniques. The training, filtering, and updating processes are performed on an independent mobile phone. The mobile phone has storage, memory and CPU limitations compared with a computer. As such, we apply a probabilistic Naïve Bayes classifier using word occurrences for screening because of its simplicity and fast performance. Our experiment on an Android mobile phone shows that it can filter SMS spam with reasonable accuracy, minimum storage consumption, and acceptable processing time without support from a computer or using a large amount of SMS data for training. Thus, we conclude that filtering SMS spam can be performed on independent mobile phones. We can reduce the number of word attributes by almost 50% without reducing accuracy significantly, using our usability‐based approach. Copyright © 2012 John Wiley & Sons, Ltd.