To efficiently support and manage massive number of Internet of Things (IoT) short and bursty sessions, current long-term evolution (LTE) system needs to reduce signal load generated by IoT session setup/synchronization, while balancing the system performance, such as UE power consumption and delays to time-sensitive traffic. In LTE, radio resource control (RRC) and discontinuous reception (DRX) affect power consumption, signal load, and delay. We provide a session management methodology suitable for IoT traffic over LTE. Our analysis starts with a Markov chain analysis of the impact of DRX parameters. This is followed by an optimal uplink scheduler design and an IoT-aware adaptive DRX algorithm at the client, both of which modulate the tradeoff among signal load, delay, and power consumption. Scalability is also considered by providing a high-priority clustering-based adaptive DRX algorithm at eNB. Simulation results show that for packets with 0.1 s delay, our scheduler outperforms “Tx now” (and “Wait Till Deadline”) by 50% (and 30%) in power saving and by 60% (and 15%) in signal saving. With knowledge of the traffic pattern, IoT-aware adaptive DRX can further reduce signal load by 25%, especially for delay-sensitive traffic.

Rapid increases in the demand for broadband data are increasingly causing a growth in costs for communication service providers (CSPs). Yet under the current pricing plans, CSPs' revenue has not kept pace with these costs. Thus, many CSPs are considering Smart Data Pricing (SDP) as a way to reduce cost or increase revenue. Before offering such novel data plans, however, CSPs must conduct trials of the specific data plans proposed. Due to the complexity of necessary changes in network equipment and a need to carefully design the trial in order to understand customer behavior, planning such trials is not only a critical precursor to SDP deployment, but also a nontrivial undertaking in itself. This paper discusses general principles of trial design and proposes two methods for estimating their effectiveness. We first give an introduction to the goals of SDP research and review three possible SDP approaches. We then discuss the importance of pre-trial participant surveys and some technical considerations of implementing the trial infrastructure for a particular SDP algorithm. Finally, we show how the CSP may extrapolate from the trial results to estimate the SDP trial's benefits, in terms of changes in traffic patterns and a reduction in spectrum requirements. We conclude with some remarks about future work.

A reconfigurable mesh, R-mesh for short, is a two-dimensional array of processors connected by a grid-shaped reconfigurable bus system. Each processor has four I/O ports that can be locally connected during execution of algorithms. This paper considers the d-dimensional Euclidean minimum spanning tree (EMST) and the all nearest neighbors (ANN) problem. Two results are reported. First, we show that a minimum spanning tree of n points in a fixed d-dimensional space can be constructed in O(1) time on a /spl radic/(n/sup 3/)/spl times//spl radic/(n/sup 3/) R-mesh. Second, all nearest neighbors of n points in a fixed d-dimensional space can be constructed in O(1) time on an n/spl times/n R-mesh. There is no previous O(1) time algorithm for the EMST problem; ours is the first such algorithm. A previous R-mesh algorithm exists for the two-dimensional ANN problem; we extend it to any d-dimensional space. Both of the proposed algorithms have a time complexity independent of n but growing with d. The time complexity is O(1) if d is a constant.