In this paper, we analyze the secrecy and throughput of multiple-input single-output (MISO) energy harvesting (EH) Internet of Things (IoT) systems, in which a multi-antenna base station (BS) transmits signals to IoT devices (IoTDs) with the help of relays. Specifically, the communication process is separated into two phases. In the first phase, the BS applies transmit antenna selection (TAS) to broadcast the signal to the relays and IoTDs by using non-orthogonal multiple access (NOMA). Here, the relays use power-splitting-based relaying (PSR) for EH and information processing. In the second phase, the selected relay employs the amplify-and-forward (AF) technique to forward the received signal to the IoTDs using NOMA. The information transmitted from the BS to the IoTD risks leakage by the relay, which is able to act as an eavesdropper (EAV) (i.e., an untrusted relay). To analyze the secrecy performance, we investigate three schemes: random-BS-best-relay (RBBR), best-BS-random-relay (BBRR), and best-BS-best-relay (BBBR). The physical layer secrecy (PLS) performance is characterized by deriving closed-form expressions of secrecy outage probability (SOP) for the IoTDs. A BS transmit power optimization algorithm is also proposed to achieve the best secrecy performance. Based on this, we then evaluate the system performance of the considered system, i.e., the outage probability and throughput. In addition, the impacts of the EH time, the power-splitting ratio, the numbers of BS antennas, and the numbers of untrusted relays on the SOP and throughput are investigated. The Monte Carlo approach is applied to verify our analytical results. Finally, the numerical examples indicate that the system performance of BBBR is greater than that of RBBR and BBRR.INDEX TERMS Energy harvesting, Internet of Things, physical layer secrecy, throughput, NOMA, MISO, untrusted relay.

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Network congestion is a key challenge in resource-constrained networks, particularly those with limited bandwidth to accommodate high-volume data transmission, which causes unfavorable quality of service, including effects such as packet loss and low throughput. This challenge is crucial in wireless sensor networks (WSNs) with restrictions and constraints, including limited computing power, memory, and transmission due to self-contained batteries, which limit sensor node lifetime. Determining a path to avoid congested routes can prolong the network. Thus, we present a path determination architecture for WSNs that takes congestion into account. The architecture is divided into 3 stages, excluding the final criteria for path determination: (1) initial path construction in a top-down hierarchical structure, (2) path derivation with energy-aware assisted routing, and (3) congestion prediction using exponential smoothing. With several factors, such as hop count, remaining energy, buffer occupancy, and forwarding rate, we apply fuzzy logic systems to determine proper weights among those factors in addition to optimizing the weight over the membership functions using a bat algorithm. The simulation results indicate the superior performance of the proposed method in terms of high throughput, low packet loss, balancing the overall energy consumption, and prolonging the network lifetime compared to state-of-the-art protocols.

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