Thanks to the smart device revolution, modern wireless devices have increased computational/storage capabilities and can also support for multiple network interfaces such as cellular and WiFi interfaces. Intelligent utilization of multiple network interfaces can address the problem of cellular traffic congestion and it can also increase the frequency resources of cellular networks. Cooperative content distribution (CCD) is one such technique that can be performed by using multiple wireless interfaces. In CCD, a device receives content from a base station on its cellular interface and distributes it to other devices in its vicinity through another wireless interface such as WiFi. However, due to the broadcast nature of the secondary links such as WiFi, even a single bad link can serve as a bottleneck in terms of the CCD performance. To address this problem, in this paper, we propose a device selection method for CCD that takes into account both the primary (cellular) and secondary link (WiFi/short-range) network interfaces. The proposed method incurs little overhead as it utilizes information such as acknowledgement of data packets, that already exists in the network. We evaluate and compare (with the other content delivery methods) the performance of the proposed method in terms of: 1) Number of carriers utilized by a cellular base station (BS); 2) Average bits-per-Joule performance; and 3) The average time required to deliver a content file. Moreover, we also take into account the impact of the presence of independent competing/interfering links (such as competing users in the unlicensed band) on the performance of the proposed method.Index Terms-Cooperative content delivery, cellular networks, device to device communications, multiple network interfaces, and intelligent selection method.

In a cooperative content distribution (CCD) using multiple interfaces, a smart wireless device receives content from a base station (BS) on its cellular interface, and it broadcasts the same content through another wireless interface, such as WiFi. However, different users can experience different link qualities, and users with slow wireless links can be a bottleneck in terms of CCD performance. To address this problem, we propose a device selection method which leverages multiple interfaces of the selected devices to perform CCD. Our proposed method takes into account the link quality of both primary (cellular) and secondary (WiFi/short-range) interfaces of the devices, and selects the devices with the best link quality for CCD. To analyze the stability of the proposed CCD method against selfish deviators, we model the problem as a repeated CCD game. We show that although the proposed method yields significant gains in terms of energy and frequency carrier savings, it is vulnerable to selfish deviating users. To address this challenge, we propose a carrier aggregation based incentive mechanism. The analytical and simulation results show that the proposed mechanism maximizes individual and network payoffs, and is an equilibrium against unilateral selfish deviations.Index Terms-Cooperative content delivery, multiple wireless interfaces, efficient off-loading, incentive mechanism, carrier aggregation and game theory.

Accurate and timely monitoring is imperative to the resilience of forests for economic growth and climate regulation. In the UK, forest management depends on citizen science to perform tedious and time-consuming data collection tasks. In this study, an unmanned aerial vehicle (UAV) equipped with a light sensor and positioning capabilities is deployed to perform aerial surveying and to observe a series of forest health indicators (FHIs) which are inaccessible from the ground. However, many FHIs such as burrows and deadwood can only be observed from under the tree canopy. Hence, we take the initiative of employing a quadruped robot with an integrated camera as well as an external sensing platform (ESP) equipped with light and infrared cameras, computing, communication and power modules to observe these FHIs from the ground. The forest-monitoring time can be extended by reducing computation and conserving energy. Therefore, we analysed different versions of the YOLO object-detection algorithm in terms of accuracy, deployment and usability by the EXP to accomplish an extensive low-latency detection. In addition, we constructed a series of new datasets to train the YOLOv5x and YOLOv5s for recognising FHIs. Our results reveal that YOLOv5s is lightweight and easy to train for FHI detection while performing close to real-time, cost-effective and autonomous forest monitoring.

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