Sensor-cloud originates from extensive recent applications of wireless sensor networks and cloud computing. To draw a roadmap of the current research activities of the sensor-cloud community, we first investigate the state-of-the-art sensor-cloud literature reviews published since the late 2010s and discovered that these surveys have primarily studied the sensor-cloud in specific aspects, security-enabled solutions, efficient management mechanisms, and architectural challenges. While the existing surveys have reviewed the sensor-cloud from various perspectives, they are inadequate for the three key issues that require urgent attention in the sensor-cloud: reliability , energy , and heterogeneity . To fill this gap, we perform a thorough survey by examining the origins of the sensor-cloud and providing an in-depth and comprehensive discussion of these three key challenges. We summarize initial designs of the new edge-based schemes to address these challenges and identify several open issues and promising future research directions.

Intelligent networking and big data analytics are two important pillars for the operation of systems. Edge computing is frequently used in smart IoT systems, particularly in those which cannot be served efficiently through cloud computing due to the limitations in bandwidth, latency and Internet connectivity. However, applications always generate a large amount of data, which are pre-programmed and predefined to run on the cloud or edge platform and can't be changed at run time. The applications may gain better performance if they synergistically run on the cloud and edge platform. In this study, a novel algorithm called Dynamic Switching Algorithm is proposed to ensure intelligent dynamics where all tasks are either offloaded on cloud or on edge according to the system's real-time conditions. We further divide applications into four types based on their real-time requirements. Each type of application is set to a reasonable latency to make sure the system to have less processing time. The results demonstrate that our method outperforms two state-of-the-art methods, decreasing both the average delay and energy consumption of offloading by 8.17%~66.90% and 3.76%~78.60% respectively. The experimental evaluations show that the performance of the proposed method could effectively offload tasks in smart IoT systems.

The use of Wireless Charging Vehicles (WCVs) to wirelessly recharge sensors is a promising but not yet fully developed technology. The existing WCV trajectory planning schemes either fail to consider the directional characteristics of wireless recharging or fail to jointly optimise the mobile feature and the one-to-many feature. The objective of this paper is to find the optimal recharging scheme to minimise the energy costs of WCVs (recharging cost and mobile cost) in rechargeable networks. While recharging, the recharging efficiency has a trade-off between the distance and recharging angle. For the recharging distance, if the WCV comes close to the sensors, the recharging efficiency is high, but the entire recharging trajectory of the WCV will be long and vice versa. For the recharging angle, if the recharging deflection angle is small, the recharging efficiency is high, but the calculation complexity will be high and vice versa. To solve this problem, we prove that the recharging group can reduce mobile costs. Based on this idea, we optimise the recharging trajectory of the WCV to reduce the mobile energy consumption. The optimal recharging group generation problem is discussed, and the problem is proven to be NP-hard. We propose a greedy recharging group generation algorithm with an approximate ratio of $\text{ln} \, N$ , where $N$ is the number of sensors. On this basis, we propose a linear programming equation to minimise the recharging delay of the recharging group to reduce the energy cost during recharging. Experiments show that the number of recharging groups generated by this scheme is 24.5% less than the circle coverage baseline method on average. The energy consumption is reduced by 49.8% on average compared with one-to-one recharging.

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O($\ln m$), where $m$ represents the number of users' tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios.

Immersive systems, which offer immersive interactive experiences by tightly coupling the real and virtual worlds, encompassing Augmented Reality (AR) and Virtual Reality (VR), have undergone rapid development in recent years. The inherent latency issue of immersive systems poses considerable challenges for AR/VR applications that are highly sensitive to delay. To address this challenge, we propose utilizing edge collaboration as an efficient means to schedule AR/VR requests, owing to the inherent ability of edge nodes to seamlessly coordinate within the same geographical region and optimally allocate request resources to minimize latency. However, when it comes to edge nodes across different regions, a significant hurdle emerges. These nodes often lack real-time knowledge about cached resources across other regions, making cross-regional edge collaboration for AR/VR request scheduling an extremely challenging problem. We formally define and prove the NP-hardness of this problem. To tackle this complexity, we introduce a Federated Digital Twin Model Construction (FedDT), which establishes a global digital twin network connecting different edge nodes. This network simulates and reflects the resource status and availability of each edge node in real time. Subsequently, we devise an extended Dijkstra algorithm to determine the shortest path between AR/VR requests and edge nodes, taking into account current network conditions to optimize service delay. Both theoretical analysis and experimental results confirm that compared with baseline methods, our proposed solution consistently achieves an average delay reduction of 36.78% across various scenarios, effectively demonstrating its superior performance.