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In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (CoM) and quality-oriented machine-type communication (QoM), which take into account the stochastic geometry-based deployment and the random active/inactive status of MTCDs. Specifically, in the proposed schemes, the active Type-I MTCDs operate using a novel Bernoulli random process-based simultaneous wireless information and power transfer (SWIPT) architecture. Next, utilizing multi-user power-domain non-orthogonal multiple access (PD-NOMA), each active Type-I MTCD can simultaneously communicate with another Type-I MTCD and a scalable number of Type-II MTCDs. In the performance analysis of the proposed schemes, we prove that the true distribution of the received power at a Type-II MTCD in the QoM scheme can be approximated by the Singh-Maddala distribution. Exploiting this unique statistical finding, we derive approximate closed-form expressions for the outage probability (OP) and sum-throughput of massive MTC (mMTC) networks. Through numerical results, we show that the proposed schemes provide a considerable sum-throughput gain over conventional mMTC networks. [ABSTRACT FROM AUTHOR]

In this paper, we consider a Hybrid Access Point (HAP) that supplies energy to sensor devices tasked with monitoring one or more mobile targets with a known trajectory. The HAP's goal is to maximize a Quality of Monitoring (QoM) metric that is a ratio of the following quantities: (i) distance between a sensor device and a target, and (ii) duration in which a target is monitored by a sensor device. We formulate a Mixed Integer Linear Program (MILP) and use it to determine the subset of sensor devices to be charged in each time slot, their activation time, and the transmission or charging power used by the HAP. We also propose a Cross-Entropy (CE) approach and a heuristic algorithm called Energy Reallocation Linear Programming Approximation (ERLPA) to select sensor devices for charging in large-scale networks. Our results show that (i) QoM is affected by the energy requirement of sensor devices, energy storage capacity, number of channels available to the HAP, sensor sensing radius and energy conversion efficiency of sensor devices, and (ii) both the CE method and ERLPA are capable of producing schedules that are near optimal. [ABSTRACT FROM AUTHOR]