Cube Satellites, aka CubeSats, are a class of nano satellites that have gained popularity recently, especially for those that consider CubeSats as an emerging alternative to conventional satellites for space programs. This is because they are cost-effective, and they can be built using commercial off-the-shelf components. Moreover, CubeSats can communicate with each other in space and ground stations to carry out many functions such as remote sensing (e.g., land imaging, education), space research, wide area measurements and deep space communications. Consequently, communications between CubeSats and ground stations is critical. Any antenna design for a CubeSat needs to meet size and weight restrictions while yielding good antenna radiation performance. To date, a limited number of works have surveyed, compared and categorised the proposed antenna designs for CubeSats based on their operating frequency bands. To this end, this paper contributes to the literature by focusing on different antenna types with different operating frequency bands that are proposed for CubeSat applications. This paper reviews 48 antenna designs, which include 18 patch antennas, 5 slot antennas, 4 dipole and monopole antennas, 3 reflector antennas, 3 reflectarray antennas, 5 helical antennas, 2 metasurface antennas and 3 millimeter and sub-millimeter wave antennas. The current CubeSat antenna design challenges and design techniques to address these challenges are discussed. In addition, we classify these antennas according to their operating frequency bands, e.g., VHF, UHF, L, S, C, X, Ku, K/Ka, W and mm/sub-mm wave bands and provide an extensive qualitative comparison in terms of their size, -10 dB bandwidths, gains, reflection coefficients, and deployability. The suitability of different antenna types for different applications as well as the future trends for CubeSat antennas are also presented.

Edge computing leverages computing resources closer to the end-users at the edge of the network, rather than distant cloud servers in the centralized IoT architecture. Edge computing nodes (ECNs), experience less transmission latency and usually save on energy while network overheads are mitigated. The ECNs can be fixed or mobile in their positions. We will focus on mobile ECNs in this survey. This paper presents a comprehensive survey on mobile ECNs and identifies some open research questions. In particular, mobile ECNs are classified into four categories, namely aerial, ground vehicular, spatial, and maritime nodes. For each specific group, any mutual basic terms used in the state-of-the-art are described, different types of nodes employed in the group are reviewed, the general network architecture is introduced, the existing methods and algorithms are studied, and the challenges that the group is scrimmaging against are explored. Moreover, the integrated architectures are reviewed, wherein two different categories of the aforementioned nodes jointly play the role of ECNs in the network. Finally, the research gaps, that are yet to be filled in the area of mobile ECNs, are discussed along with directions for future research and investigation in this promising area.

CubeSats are a class of miniaturized satellites that have become increasingly popular in academia and among hobbyists due to their short development time and low fabrication cost. Their compact size, lightweight characteristics, and ability to form a swarm enables them to communicate directly with one another to inspire new ideas on space exploration, space-based measurements, and implementation of the latest technology. CubeSat missions require specific antenna designs in order to achieve optimal performance and ensure mission success. Over the past two decades, a plethora of antenna designs have been proposed and implemented on CubeSat missions. Several challenges arise when designing CubeSat antennas such as gain, polarization, frequency selection, pointing accuracy, coverage, and deployment mechanisms. While these challenges are strongly related to the restrictions posed by the CubeSat standards, recently, researchers have turned their attention from the reliable and proven whip antenna to more sophisticated antenna designs such as antenna arrays to allow for higher gain and reconfigurable and steerable radiation patterns. This paper provides a comprehensive survey of the antennas used in 120 CubeSat missions from 2003 to 2022 as well as a collection of single-element antennas and antenna arrays that have been proposed in the literature. In addition, we propose a pictorial representation of how to select an antenna for different types of CubeSat missions. To this end, this paper aims is to serve both as an introductory guide on CubeSats antennas for CubeSat enthusiasts and a state of the art for CubeSat designers in this ever-growing field.

Drones have become prevalent for the delivery of goods by many retail companies such as Amazon and Dominos. Amazon has an issued patent that describes how drones scan and collect data on their fly-overs while dropping off packages [1]. In this context, we propose a path optimization algorithm for a drone multi-hop communications network that can carry and forward data in addition to its primary function of parcel deliveries. We argue that traditional Delay Tolerant Networking (DTN) based protocols may not be efficient for this purpose. Therefore, this paper proposes a new DTN-based algorithm that optimizes drone flight paths in conjunction with optimized routing to deliver both parcels and data in a power efficient way and within the shortest possible time. We propose a heuristic algorithm called Weighted Flight Path Planning (WFPP) that priorities the data packets in an exchange pool in order to create an optimized path for the drones. Our approach is to determine a weight for each packet based on the packet’s remaining time to live, priority, size, and location of the packet’s destination. When two drones meet each other, they exchange the high weighted packets. Simulation studies show that WFPP delivers up to 25% more packets compared with EBR, EPIDEMIC, and a similar path planning method [2]. Also, WFPP reduces the data delivery delays by up to 66% while the overhead ratio is low.

Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are emerging alternative energy technologies that have the potential to provide wireless energy delivery in the future. A key component of RFEH or WPT systems is the receiving antenna, which significantly impacts the power delivery capability of the system. This survey extensively examines rectennas designed for multi-directional reception (or wide-angle coverage) of radio frequency (RF) signals with high gain. These rectennas perform better than other types of rectennas when the exact positions of RF sources are unknown or when the sources change location over time. This paper classifies rectennas into three categories based on their power combining approach: (i) DC power combining, (ii) RF power combining, and (iii) hybrid power combining. These rectennas will also be analysed in terms of angular coverage, size and profile, and gain, as well as polarization, broadband, and multiband performance. The various approaches adopted in the literature to address these challenges are critically analysed. To this end, based on the gaps in the literature and the lessons learnt, we propose the potential open research questions that the researcher can investigate in their research.

The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification. This paper analyses VGG-16, InceptionResNetV2, DenseNet121, Inception V3, and MobileNetV2 models to classify real-life waste when trained on pristine and unadulterated materials, versus samples collected at a landfill site. When training on DiversionNet, the unadulterated material dataset with labels required for landfill modelling, classification accuracy was limited to 49.69% in the real environment. Using real-world samples in the newly formed RealWaste dataset showed that practical applications for deep learning in waste classification are possible, with Inception V3 reaching 89.19% classification accuracy on the full spectrum of labels required for accurate modelling.