LoRalLoRaWAN is one of the most popular technologies for IoT use cases requiring wide coverage and energy efficiency. In an IoT network, devices are often powered by unreliable energy sources, and replacing them can be difficult and rapidly increase network operating costs. For these reasons, it is essential to optimize the energy consumed by an IoT sensor, and this optimization must be based on a refined energy consumption model. In this work, an energy consumption model is proposed that takes into account the complexity of the sensor environment, the LoRalLoRaWAN specifications, and the gateway input saturation. Simulation results put the energy consumption and the transmission success probability into perspective to show the limit number of sensors per gateway.

This paper addresses the spectrum sensing challenges in Cognitive Radio-based Internet of Things (CR-IoT) networks, which are characterized by intermittent primary user (PU) activities. Unlike conventional approaches relying on fixed detection thresholds, we propose and evaluate an adaptive thresholding method within an Energy Detection (ED) framework, dynamically optimized based on real-time noise and signal characteristics. The PU activity is realistically modeled as a two-state Markov chain, with signals transmitted via Binary Phase Shift Keying (BPSK) modulation over an Additive White Gaussian Noise (AWGN) channel. Simulation results demonstrate that our adaptive threshold approach significantly enhances detection accuracy while reducing total error probability at low SNR conditions (from -25 dB to 0 dB), thus effectively increasing idle channel utilization compared to traditional fixed threshold techniques.

The deployment of an IoT network is subject to energy consumption constraints. To minimize service costs, it is essential to optimize the lifetime of objects, which are often powered by unreliable energy sources. This optimization must be based on finely-tuned energy consumption models and those generally proposed in the literature do not take into account all the specificities of the transmission of a connected object. In this article, a refined power consumption model based on in-situ measurements is proposed for an 802.11ah station. This improved model takes into account the number of nodes in the network, the number of retransmission attempts and the various exchanges linked to the higher communication layers. The results show the influence of the number of stations in an 802.11ah network on a station’s energy consumption, and demonstrate that the maximum number is limited by the probability of successful transmission.

One of the key issues with the Internet of Things (IoT) is energy consumption. This type of network is mainly composed of connected objects supplied by an unreliable power source. Wi-Fi HaLow is one of the telecommunication technologies that proposes to connect IoT objects by providing energy optimization mechanisms. It allows the integration of a full IP stack and the implementation of recent security mechanisms. In this article, we introduce power consumption measurements performed on the first device available on the market. In our study, we integrate the current consumed by the power amplifier in transmission phase, and we qualify the power-saving mechanisms. We then propose an analytical model to determine the energy consumed by a sensor node depending on the carried payload. Finally, we validate the use of this technology in IoT application in terms of energy consumption.

Q-routing is inspired by Q-learning, a reinforcement learning algorithm. Originally, it uses latency as routing metric. But, latency can be difficult to estimate especially in a noisy wireless environment. In this paper, we propose to filter the latency measure with a moving average, in order to improve the quality of service metrics such as packet delivery ratio and average delay. We compare our modification to the original Q-routing and use OLSRv2 as reference routing protocol. We observe an improvement of the average delivery time on a wireless grid of 3 % compared to the original Q-routing. On our mobility scenario, the number of routes changes is at least twice lower (from 6 to 3 route changes between the two approaches in this scenario). The gain on QoS metrics depends mainly on the speed of the nodes. These improvements are obtained without making Q-routing more complex as a moving average is added. We provide all the materials to conduct reproducible research on our public git repository.

Solar energy harvesting constitutes an attractive solution to provide energy for communicating objects. The advantage of this energy over other forms of renewable energy is that the available solar power can be predicted with reasonable accuracy, which allows the implementation of efficient power management techniques. Nevertheless, harvested solar energy comes from a non‐controllable source. Therefore, the prediction of the solar radiation and energy availability is a critical issue, as the amount of the harvested energy may vary over time. In this study, a novel solar radiation and energy predictor (Solar Energy Predictor for Communicating Sensors: SEPCS) is proposed. This prediction model uses past energy observations to forecast future energy availability in short term. To assess the performance of the proposed algorithm, authors used database providing the solar radiation evolution for one year. Then, a comparative performance evaluation shows that the SEPCS predictor significantly outperforms the state‐of‐the‐art energy predictors, by decreasing the average prediction error from 28 to 6.5%.

Device-to-device (D2D) communications are announced as a cornerstone of the next generation of wireless communications (5G). They offer the promise to drastically reduce the energy consumption of devices (phones, tablets, etc.), and increase network coverage areas. In this paper, we propose to study the influence of a relay device in a D2D communication. We aim to show the impact of relaying on the energy consumption. Our work provides a new perspective on the D2D communications by integrating innovative topology in these communications. Indeed, D2D is a local-area-based type of communications. Then, we assume that the data transmitted by different users have a certain correlation between each other. Therefore, a new approach is to use the common entropy between each D2D devices to reduce the amount of data relayed by the relay device. This minimization leads inevitably to a reduction of the energy consumed by the relay device, and the network. We analyze the optimal case (in terms of position of the relay node and correlation coefficient between different data) to validate the proposed topology, based on conditional entropy.

The main objective of this paper is to present realistic energy consumption models to evaluate the energy consumption including both the transmission energy and the circuit energy at the transmitter when using Minimum Energy coding (ME-coding) On-Off Keying (OOK) scheme, we consider a simple communication link between sensor nodes. We present two energy models with a specific circuit (MAX1472) and we consider also the limitations, introduced by a practical circuit, in terms of symbol bit rate and output power. The results with the first model demonstrate that the total energy consumption of ME- Coding OOK is lower than that of the uncoded OOK, but the optimal choice of the code must take into account the limitations of the circuit. The second model with MAX1472 shutdown mode brings a reduction of the total energy consumption compared with the first model but is limited in the symbol rate. Due to this limitation, the optimal choice is finally the first model with a larger bit rate.

Cooperative transmission using space time codes can be used to reduce transmission energy consumption for long range communication in wireless sensor networks (WSN). However, differing from traditional (multi-input-multi-output) MIMO systems, distributed cooperative communication systems in WSN usually lack of perfect synchronization. For the cooperative systems using Alamouti code, the effect of synchronization error on the receive bit error rate (BER) performance over Rayleigh fading channel is investigated in this paper. Taking also path loss effect and the propagation delay into account, the optimal transmit power allocation and the optimal reception areas are presented. The analytical and simulation results show that, for a small range of synchronization error and using the optimal transmit power allocation, the degradation of receive BER performance is negligible for some reception area.

In this paper, we evaluate through analytical modeling, the performance of ad hoc networks when the nodes are endowed with M-element antenna array. We first derive the loss probability due to fading when performing a simple beamforming strategy and we present an accurate analytical model for the performance of IEEE 802.11 MAC protocol. We then extend the latter model, by using the calculated probability, to derive the saturation throughput when beamforming techniques are used in real multipath fading environments where ad hoc networks are deployed.

Maximizing the system mutual information for MIMO ad hoc networks is considered in this paper. We first give a brief overview of the non-linear optimization methods and systematically we compare their performances. Then we propose a fast and distributed algorithm based on the quasi Newton methods to give a lower bound of the system capacity of MIMO ad hoc networks. Our proposed algorithm solves the maximization problem while diminishing the amount of information in the feedback links needed in the cooperative optimization. Finally, an extensive set of simulations shows that our algorithm enjoys a provable convergence and presents the highest convergence rate.

In cellular mobile radio systems, transmit beamforming can be used to reduce the interference in the downlink. In a time division duplex (TDD) system with sufficiently short duplex period, the uplink channel estimation can be used to design the downlink beamformer. The purpose of this paper is to analyse the sensitivity of several transmit techniques to the channel variations between uplink and downlink time-slots. This analysis is done using experimental data obtained from real environments.