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The low‐power short‐range networks have gained prevalence in research fields such as machine‐to‐machine communications and the Internet of things. Given the large number of devices that may exist, the IPv6 over low‐power wireless personal area networks (6LoWPAN) standard is expected to be widely used. Although Contiki, NS‐3, and OMNeT++ have developed the 6LoWPAN model, it is still not available in optimized network engineering tools (OPNET) because it is extremely difficult to interface the built‐in IP module with the customized modules in OPNET. To bridge this gap, we first developed a preliminary 6LoWPAN model and implemented the header compression and header restoration functions to handle the incoming and outgoing packets in OPNET. To further improve the system performance, we also proposed an algorithm that aggregates packets, reduces intranetwork collisions and increases the packet delivery ratio. The model and algorithm in this work are free to download, so researchers from the OPNET community can easily extend this work for their own research in the future. [ABSTRACT FROM AUTHOR]

Some M2M applications such as event monitoring involve a group of devices in a vicinity that act in a co-ordinated manner. An LTE network can exploit the correlated traffic characteristics of such devices by proactively assigning resources to devices based upon the activity of neighboring devices in the same group. This can reduce latency compared to waiting for each device in the group to request resources reactively per the standard LTE protocol. In this paper, we specify a new low complexity predictive resource allocation algorithm, known as the one way algorithm, for use with delay sensitive event based M2M applications in the LTE uplink. This algorithm requires minimal incremental processing power and memory resources at the eNodeB, yet can reduce the mean uplink latency below the minimum possible value for a non-predictive resource allocation algorithm. We develop mathematical models for the probability of a prediction, the probability of a successful prediction, the probability of an unsuccessful prediction, resource usage/wastage probabilities, and mean uplink latency. The validity of these models is demonstrated by comparison with the results from a simulation. The models can be used offline by network operators or online in real time by the eNodeB scheduler to optimize performance. [ABSTRACT FROM PUBLISHER]