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IEEE 802.15.4 standard has a limited capacity to ensure low latency for time-critical industrial applications. The IEEE 802.15.4e LLDN mode has been specifically designed for industrial applications requiring low latency and round trip time. One of the main characteristics of LLDN regards the support of device mobility. The mobile devices aiming at association with the PAN coordinator are discovered and configured during discovery and configuration states. These states are isolated from the online state during which transmission of regular readings occurs. As discovery state, configuration state and online state utilize a shared single channel, a long association time at discovery and configuration states leads to a long delay for data transmission in online state, and vice versa. In this paper, we propose a modified superframe structure for fast discovery of mobile devices. Our goal is to minimize association latency for new devices by reducing the latency to enter into the online state. We show the efficiency of our proposed superframe structure compared with the default LLDN via our analysis.

We propose a decentralized downlink scheduling protocol for wireless cellular networks that allows each base station to control its transmission power. On the beginning of each scheduling period, neighbor base stations exchange their local system states, i.e., channel, traffic and position statistics of their local mobile users. But on each slot of the current scheduling period, the base stations may not be able to receive full information from neighbor cells. Therefore, to enable the base stations to make optimal decisions, for user scheduling and transmission power control, we develop an analytical optimization framework, based on the theory of Partially Observable Markov Decision Process. To help a base station, this decision-theoretic approach uses the history of observations about neighbor base stations system statistics and their scheduling policies. Simulations show that our approach is more efficient than existing centralized scheduling approaches which due to the high complexity they do not allow base stations to communicate with each other.We have conducted our experiments, using a two-tier 19-cell network. We have compared the results of our contribution with the techniques proposed in [1,2], referred to as Baseline 1, 2 respectively. These approaches define a set of patterns for on-off BSs. These on/off patterns are designed such that a BS scheduled to be on can expect the interference mitigation from one of its neighbors. In order to mitigate high overhead of information exchange and complexity, Baselines 1, 2 drop the

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