Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

As mobile devices evolve to be powerful and pervasive computing tools, their usage also continues to increase rapidly. However, mobile device users frequently experience problems when running intensive applications on the device itself, or offloading to remote clouds, due to resource shortage and connectivity issues. Ironically, most users’ environments are saturated with devices with significant computational resources. This paper argues that nearby mobile devices can efficiently be utilised as a crowd-powered resource cloud to complement the remote clouds. Node heterogeneity, unknown worker capability, and dynamism are identified as essential challenges to be addressed when scheduling work among nearby mobile devices. We present a work-sharing model, called Honeybee, using an adaptation of the well-known work stealing method to load balance independent jobs among heterogeneous mobile nodes, able to accommodate nodes randomly leaving and joining the system. The overall strategy of Honeybee is to focus on short-term goals, taking advantage of opportunities as they arise, based on the concepts of proactive workers and opportunistic delegator. We evaluate our model using a prototype framework built using Android and implement two applications. We report speedups of up to four with seven devices and energy savings up to 71 percent witheight devices.

With the proliferation of IoT applications, more and more smart, connected devices will be required to communicate with one another, operating in situations that involve diverse levels of range and cost requirements, user interactions, mobility, and energy constraints. Wireless technologies that can satisfy the aforementioned requirements will be vital to realise emerging market opportunities in the IoT sector. Bluetooth Mesh is a new wireless protocol that extends the core Bluetooth low energy (BLE) stack and promises to support reliable and scalable IoT systems where thousands of devices such as sensors, smartphones, wearables, robots, and everyday appliances operate together. In this article, we present a comprehensive discussion on current research directions and existing use cases for Bluetooth Mesh, with recommendations for best practices so that researchers and practitioners can better understand how they can use Bluetooth Mesh in IoT scenarios.

With the proliferation of IoT devices, there is a demand for technologies to support high-velocity, dynamic resource provisioning to provide secure, cost-efficient and real-time IoT services in resource-constrained environments. Conventional fog computing by itself cannot address such requirements and needs to be complemented with opportunistic fog computing, by providing mobile fog resources on-demand. In this paper, we discuss key issues in this area, and investigate potential solutions from existing work. We conclude the paper with a summary of gaps, and propose an opportunistic architecture for future work.

Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and cloud computing to harness their collective strengths, facilitating optimised data processing across these tiers. Our approach optimises performance, reducing energy consumption, and lowers costs. We evaluate our architecture through a series of experiments conducted on a purpose-built testbed. The results demonstrate significant improvements, with speedups of up to 7.5 times and energy savings reaching 80%, underlining the effectiveness and practical benefits of our cooperative edge-fog-cloud model in supporting the dynamic computational needs of IoT ecosystems. We argue that a multi-tier (e.g., edge-fog-cloud) dynamic task offloading and management of heterogeneous devices will be key to flexible edge computing, and that the advantage of task relocation and offloading is not straightforward but depends on the configuration of devices and relative device capabilities.