In dynamically changing Mobile Ad Hoc Networks (MANETs), the size of control overhead directly affects the rate of network information updates, which in turn impacts the Packet Delivery Ratio (PDR). In scenarios with dynamic topology changes, a fixed-interval Hello broadcasting scheme struggles to meet the varying broadcast demands in different mobility scenarios. However, some adaptive schemes are designed too conservatively and other schemes are overly aggressive. To address this issue, this paper proposes an optimized adaptive Hello scheme based on statistical distribution of link duration to balances PDR and control overhead. Thanks to a more advanced mechanism, simulation results demonstrate the proposed scheme can effectively adjust control overhead to ensure delivery reliability in both low and high mobility scenarios. Compared with existing schemes, proposed scheme improves the PDR per unit overhead from 2.64% to 41.38% in low mobility scenarios. In high mobility scenarios, the PDR of the proposed scheme is always higher than 55%, and increased from 2.92% to 34.43% compared with the low overhead schemes. Moreover, the PDR per unit control overhead of the proposed scheme is increased from 0.91% to 36.26% compared with high overhead scheme in high mobility scenarios. The proposed scheme demonstrates strong adaptability across varying mobility scenarios in MANETs and offers a novel approach to setting Hello message interval based on optimal link duration thresholds.

The introduction of unmanned aerial vehicle (UAV) will facilitate data collection of wireless sensor networks effectively. However, since the communication range setting of existing data collection methods does not take into account the difference between air-to-ground (AG) and ground-to-ground (GG) links, the same communication range is usually used. This results in unsatisfactory performance of existing methods in minimizing the task completion time. In this paper, by considering the different characteristics of AG and GG links, a more efficient data collection method is proposed, in which clustering of the ground nodes, UAV trajectory and data collection mode are jointly optimized. Simulation results show that the difference between AG and GG links will affect the task completion time significantly. Thanks to the more efficient data transmission and shorter trajectory length, task completion time of the proposed method reduces by 8.86%~74.38% in different scenarios compared with existing methods.

Accurate State-of-Charge (SOC) estimation is crucial for Internet-of-Things (IoT) devices to use their battery capacity efficiently. Considering the limitations of computation and measurement capabilities, IoT devices typically employ lightweight SOC estimation methods. However, existing lightweight methods are not accurate enough, mainly because they rely on some battery parameters that are easily affected by load changes, such as the available capacity and terminal voltage. By analyzing the characteristics of electromotive force (EMF) under various discharge conditions experimentally, this paper proposes a SOC estimation method based on approximated EMF prediction. Firstly, by utilizing the regular variation of EMF with terminal voltage and current, EMF is approximated as a polynomial function of the latter two, which effectively reduces the complexity of online EMF prediction. Then, the predicted EMF is mapped to SOC using the stable relationship between SOC and EMF. To evaluate the proposed method more realistically, actual IoT devices powered by two commonly used primary batteries were employed for SOC estimation. Experimental results show that both accuracy and stability of the proposed method are much better than existing lightweight methods while maintaining comparable computation complexity.

Accurate open circuit voltage (OCV) prediction is crucial for battery modeling and state evaluation. However, existing OCV prediction methods are not online implementable for battery powered Internet-of-Things (IoT) devices, as these methods typically require disconnecting battery from load and have high computational complexity. To solve this problem, this paper proposes a lightweight OCV prediction method that can be implemented online. Firstly, by obtaining discharge data within the limited current range of IoT devices and fully utilizing the intrinsic relationship between OCV, terminal voltage and current, the dataset used for training is significantly reduced. Then, utilizing the advantages of support vector regression in handling nonlinear data with small size, a nonlinear model for OCV with terminal voltage and current is established. The inference model obtained in this way requires very few support vectors while achieving relatively accurate OCV prediction. Experimental results show that the probability that the prediction error is less than 6mV exceeds 80% for two commonly used primary batteries under both constant and variable current conditions. Meanwhile, the inference time overhead is less than 20ms. Compared with existing fast OCV prediction methods, the proposed method can achieve prediction errors of the same order of magnitude while reducing the time overhead by at least two orders of magnitude. More importantly, the proposed method does not require disconnecting battery from load, which makes it online implementable for IoT devices.

Accurate and reliable channel modeling is crucial for UAV-assisted low-power wireless networks. However, existing researches have shown that existing channel models are not accurate in this scenario. This paper evaluates existing channel models and analyzes the factors impacting the accuracy of channel modeling. The results show that path loss exponent and antenna irregularity will affect the path loss under different flight modes. Based on the above finding, by considering the path loss exponent and antenna irregularity, this paper proposes a channel modeling method based on the relative position of the UAV and ground node. Experimental results show that the proposed model is suitable for UAV-assisted low-power wireless networks. The proposed method greatly reduces the estimation error of path loss under the premise of low computation overhead. Compared with existing channel models, thanks to more comprehensive considerations of the proposed method, the estimation error of the model established by the proposed method is reduced by 4.42% to 92.78% under the ground-to-air channel and 13.23% to 82.12% under the air-to-ground channel.

Data driven unmanned aerial vehicle (UAV) position prediction methods have been widely used in flying ad hoc networks, which are mainly based on classical and machine learning based algorithms. These prediction methods use historical position series to predict the future positions. However, effectiveness of these methods in actual scenarios has not been fully verified, especially for high speed UAVs. This paper evaluates typical position prediction methods using real flight trajectories of fixed wing UAVs. The results show that accuracy of position prediction depends on the update speed of historical positions. The higher the update speed, the smaller the prediction error. However, the impact of update speed on different methods varies. Some methods have advantages at low update speeds, while others are superior at high update speeds. Moreover, characteristics of flight trajectory also affect the prediction performance, no matter which method is used.

Estimating the connected region is essential for node deployment and trajectory planning in UAV assisted wireless sensor networks (WSNs). However, existing estimation models are constructed for traditional ground based WSNs, which does not consider the impact of UAV mobility in the three-dimensional (3D) space. In this paper, the reason for the failure of traditional models in the 3D space is analyzed firstly and then an estimation model applicable to the 3D space is proposed by introducing the impact of radio irregularity that is common for sensor nodes. Experimental results under different flight modes clearly show that the proposed model can estimate the connected region in the 3D space much more accurately. Although this model gives a somewhat pessimistic estimate, it is effective for almost all directions in the 3D space. Its application in UAV trajectory planning for data collection is also evaluated through simulation, which shows that using the proposed model could reduce the flight time and improve the transmission efficiency simultaneously.

Implementing an resilient, efficient, and reliable network structure is crucial for highly dynamic unmanned aerial vehicle (UAV) swarms, for which flying ad hoc network (FANET) is the most suitable form. Similar to traditional ad hoc networks, the performance of FANET largely depends on the efficiency, reliability, and stability of routing schemes. However, unique characteristics of UAV make the routing design of FANET face more challenges. In order to better understand the development of FANET routing schemes, this paper attempts to clarify the current research status and grasp the future development trend of FANET routing by reviewing and analyzing relevant literatures in the past decade. Results show that geographic routing, delay tolerant network, and opportunity forward are possible countermeasures to the challenges of FANET routing.

Existing works on UAV network protocols generally use software simulators for performance evaluation, which makes the analysis results often differ significantly from the test results in actual environments. In order to make the analysis of UAV network protocols more realistic, it is necessary to introduce actual UAV nodes into the simulation. By drawing on the idea of hardware-in-the-loop (HIL) simulation, this paper proposes a simulation framework with real UAV nodes in the loop. To show the potentials of the proposed simulation framework, an instance for HIL simulation of routing protocols is implemented. Preliminary results indicate that using a small number of actual flying UAV nodes as an organic component of simulation could reduce the gap between simulation results and actual situations.

The introduction of unmanned aerial vehicle (UAV) makes low-power links face many new challenges in UAV-assisted wireless-sensor network (WSN). In this article, the spatial characteristics of these low-power links are analyzed comprehensively, and the applicability of traditional link quality metrics and models is evaluated. In particular, the differences between link characteristics of UAV-assisted WSN and those of traditional WSN are discussed, and possible reasons for these differences are analyzed. To achieve this goal, two UAVs working on different bands are used and three different flight modes are designed. Experimental results show that link characteristics of UAV-assisted WSN become more complicated due to the 3-D movement of UAV. Link quality fluctuates significantly when the altitude or horizontal distance changes, and it is difficult to demarcate a clear bound between the connected and transitional region. Traditional models for spatial characteristic are only applicable when there is no UAV communication interference and the direction between the air node and ground node remains unchanged. Meanwhile, existing models between physical-layer metrics and packet reception ratio are only applicable when there is no UAV communication interference. For links with the same quality, stability of UAV-assisted WSN is not significantly different from that of traditional WSN. Finally, there is more obvious overall asymmetry between uplinks and downlinks in UAV-assisted WSN, and the quality of downlinks is significantly better than that of uplinks. The discovery of these new features would have great significance on the design of UAV-assisted WSN.

Parameters of low power radio transceiver such as transmit power are essential for the performance of link quality estimation, channel modeling, and node localization. In this paper, characteristics of channel frequency, transmit power, and receive power of a typical radio transceiver are evaluated through measurements. The results show that changes of transmit power and receive power are both different from ideal characteristics, especially the transmit power. The change of receive power is piece-wise linear, and there are obvious non-linear regions. Although the change of transmit power is approximately linear, there are obvious offsets among different channels of a single node and among the same channels of different nodes. It means that the calibration model of transmit power is device dependent. Two calibration models are proposed for both transmit power and receive power, respectively. Lookup table model produces better accuracy but more additional memory overhead for both the calibration of transmit power and receive power. Linear fitting model for receive power calibration can significantly reduce the memory overhead. However, its average error is 0.49dBm higher than that of lookup table model. Although some work believes that the calibration for receive power is device independent, our measurement results show that this assumption will bring significant errors. Lookup table with offset model for transmit power calibration could reduce the memory overhead obviously while only having an average error of 0.08dBm.

Most existing unmanned aerial vehicle (UAV) assisted wireless sensor network (WSN) studies usually assume that using a large SNR or a short distance can ensure good link quality. However, they don't consider the actual characteristics of UAV assisted WSN. In this paper, spatial characteristics of wireless links, link quality indicating capabilities of physical layer metrics and symmetry of uplink and downlink are analyzed by comparing the measured data of UAV assisted WSN and traditional WSN. The results show that link characteristics of UAV assisted WSN have changed significantly compared with those of traditional WSN. Firstly, connected region of the three traditional communication regions (connected region, transitional region, and disconnected region) is significantly compressed and even no longer exists, which makes that short distance can no longer guarantee reliable communication. Secondly, traditional physical layer metrics such as RSSI, SNR and LQI are no longer valid and even become deceptive, which may lead to wrong judgment of link quality. Finally, there is obvious overall asymmetry between uplink and downlink of UAV assisted WSN, and the quality of downlink is significantly higher than that of the uplink. The discovery of these new features would have great significance on guiding the design of UAV assisted WSN.

Cross-Technology Interference (CTI) greatly affects the performance of low power sensor networks, especially under severe WiFi interference. Fast and accurate packet reception rate estimation under CTI is crucial for improving network efficiency and reducing packet retransmissions. However, there are many drawbacks in existing approaches such as low accuracy, high overhead, and requirement of offline training. In this paper, a theoretical packet reception rate estimation approach is proposed, which combines bit error rate model of IEEE 802.15.4 with noise distribution ingeniously. This approach is fully distributed that could effectively eliminate the overheads caused by transmitting measurement packets or collecting statistics of data packets. In addition, offline data collection and training are no longer needed. More importantly, the proposed approach is almost unaffected by the CTI level. Compared with state-of-the-art approaches, estimate error of the proposed one is reduced by at least 4.83%~79.54% under different CTI levels.

Real-time and accurate packet reception rate estimation is crucial for wireless sensor networks. However, existing approaches usually rely on offline data collection and training, which limits their generality. Specifically, models fitted by the test data acquired under specific conditions cannot be used in all environments and for arbitrary packet sizes. In this paper, a simplification method for the theoretical bit error rate model of IEEE 802.15.4 2.4 GHz physical layer is proposed, which is about 18 to 38 times faster than the original one. Then, with the simplified model, a lightweight packet reception rate estimation approach is designed, which is self-adaptive to different environments and arbitrary packet sizes. With the proposed approach, offline data collection and training are no longer needed, which will reduce deployment cost effectively. Compared with state-of-the-art approaches, estimate error of the proposed one is reduced by 2.46%~74.97% in different environments, and by 2.46%~62.00% for different packet sizes.

The following topics are dealt with: MIMO communication; cellular radio; wireless channels; error statistics; radiofrequency interference; 5G mobile communication; optimisation; learning (artificial intelligence); probability; OFDM modulation.