Given the environmental impact and cost-efficiency challenges of the conventional central District Heating (DH) systems, there is a shift towards hybrid solutions. The demand for small-scale Heat Pumps (HPs), integral components of these systems, has surged due to their electrically driven, cost-effective operation, and potential to meet environmental goals. This paper conducts a systematic literature review by investigating and highlighting hybrid heating solutions and their role in decarbonizing the built environment. It compares and discusses the potential benefits and challenges of various hybrid HP-DH systems against conventional DH-only heating approaches. The study evaluates these systems based on economic, environmental, and energy efficiency aspects, and it explores the use of intelligent and AI-based algorithms. The results indicate that, from an economic perspective, the hybrid approach can potentially offer cost savings over the long term, considering factors such as initial investment and operating expenses. The findings of the reviewed works suggest that in a DH-HP configuration, an operational cost saving between 5% and 27%, and a CO2 reduction of up to 32.3% can be achieved without additional resources. Additionally, the environmental impact analysis indicates a significant decrease in greenhouse gas emissions, aligning with global efforts to mitigate global warming.

Edge computing has low transmission delay and unites more agile interconnected devices spread across geographies, which enables cloud-edge storage more suitable for distributed data sharing. This paper proposes a trustworthy and reliable multi-keyword search (TRMS) in blockchain-assisted cloud-edge storage, where data users can choose a faster search based on edge servers or a wider search based on cloud servers. To acquire trustworthy search results and find reliable servers, the blockchain-based smart contract is introduced in our scheme, which will execute the search algorithm and update the score-based trust management model. In this way, search results and trust scores will be published and recorded on the blockchain. By checking search results, data users can judge whether the returned documents are top-k documents. Based on the trust management model, we can punish the malicious behavior of search servers, while data users can choose more reliable servers based on trust scores. To improve efficiency, we design a threshold-based depth-first search algorithm. Extensive experiments are simulated on Hyperledger Fabric v2.4.1, which demonstrate our scheme (with 16 threads) can reduce the time cost of index construction by 92% and the time cost of search by 82%, approximately. Security analysis and extensive experiments can prove the security and efficiency of the proposed scheme.

Due to the high mobility of Unmanned Aerial Vehicle (UAV), it can be an effective method for pollution detection of vessels on the sea. How to optimize the flight path of the UAV so that the visited energy consumption is minimized is a problem that remains to be solved. In this paper, the Lin-Kernighan-Helsgaun-based trajectory planning method (LKH-TPM) is used to solve the UAV scheduling problem to minimize the UAV visit path length and compare it with the ant colony (ACO) algorithm, simulated annealing (SA) algorithm and tabu search (TS) algorithm. The experiments are carried out under different ship numbers, different sea areas, and different base station numbers, and it is verified that LKH-TPM is a more effective solution for the problem under study.

“Request Triggered Recharging” has been a flexible type of scheduling schemes to allow the Mobile Charging Vehicle (MCV) to supply energy for sensor nodes on demand. However, in most existing works, MCV always passively waits for the arrival of the unpredictable requests that may cause it missing the best departure time to serve nodes. To solve this problem, we propose a Balanced Distribution strategy for the number of Recharging Requests based on dynamic dual-thresholds (BDRR). Firstly, the adjustable Double Recharging Request Thresholds (DRRTs) are set for each node to ensure that all the requesting nodes can be successfully charged. Then, the Method for Setting the Energy Replenishment Value (MSERV) is proposed to enable the distribution of the moments at which nodes send out their recharging requests being concentrated within each period. Furthermore, an efficient traversal path for the MCV is constructed by safe or dangerous scheduling strategy, and the Charging Capacity Reduction Scheme (CCRS) is also executed to help survive more nodes in need. Finally, a Passer-by Recharging Scheme (PRS) is introduced to further improve the energy efficiency of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the energy efficiency of MCV with different network scales.

This article provides an overview of recent research on edge-cloud architectures in hybrid energy management systems (HEMSs). It delves into the typical structure of an IoT system, consisting of three key layers: the perception layer, the network layer, and the application layer. The edge-cloud architecture adds two more layers: the middleware layer and the business layer. This article also addresses challenges in the proposed architecture, including standardization, scalability, security, privacy, regulatory compliance, and infrastructure maintenance. Privacy concerns can hinder the adoption of HEMS. Therefore, we also provide an overview of these concerns and recent research on edge-cloud solutions for HEMS that addresses them. This article concludes by discussing the future trends of edge-cloud architectures for HEMS. These trends include increased use of artificial intelligence on an edge level to improve the performance and reliability of HEMS and the use of blockchain to improve the security and privacy of edge-cloud computing systems.

Odometry estimation plays a key role in facilitating autonomous navigation systems. While significant consideration has been devoted to research on monocular odometry estimation, sensor fusion techniques for Stereo Visual Odometry (SVO) have been relatively neglected due to their demanding computational requirements, posing practical challenges. However, recent advancements in hardware, particularly the integration of CPUs with dedicated artificial intelligence units, have alleviated these concerns. In this paper, we investigate the efficacy of attention mechanisms and the incorporation of stereo input in comparison to monocular odometry, aiming to enhance the performance of SVO. We tested two different types of attention mechanisms, i.e., Triplet Attention (TA) and Convolutional Block Attention Module (CBAM), and their fusion in two stages Early and Late. Our results show that the fusion of the second camera improves the performance of the model, as well as early fusion with TA provided the best results.

Visual odometry is a key component of autonomous vehicle navigation due to its cost-effectiveness and efficiency. However, it faces challenges in low-light conditions because it relies solely on visual features. Tomitigate this issue, various methods have been proposed, including sensor fusion with LiDAR, multi-camerasystems, and deep learning models based on optical flow and geometric bundle adjustment. While theseapproaches show potential, they are often computationally intensive, perform inconsistently under differentlighting conditions, and require extensive parameter tuning. This paper evaluates the impact of image enhancement models on visual odometry estimation in low-light scenarios. We assess odometry performance onimages processed with gamma transformation and four deep learning models: RetinexFormer, MAXIM, MIRNet, and KinD++. These enhanced images were tested using two odometry estimation techniques: TartanVOand Selective VIO. Our findings highlight the importance of models that enhance odometry-specific featuresrather than merely increasing image brightness. Additionally, the results suggest that improving odometryaccuracy requires image-processing models tailored to the specific needs of odometry estimation. Furthermore, since different odometry models operate on distinct principles, the same image-processing techniquemay yield varying results across different models.

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. This study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. Data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. The methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. The findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps.

The advent of intertwined technology, conjoined with powerful centralized machine algorithms, spawns the need for privacy. The efficiency and accuracy of any Machine Learning (ML) algorithm are proportional to the quantity and quality of data collected for training, which could often compromise the data subject’s privacy. Federated Learning (FL) or collaborative learning is a branch of Artificial Intelligence (AI) that decentralizes ML algorithms across edge devices or local servers. This chapter discusses privacy threat models in ML and expounds on FL as a Privacy-preserving Machine Learning (PPML) system by distinguishing FL from other decentralized ML algorithms. We elucidate the comprehensive secure FL framework with Horizontal FL, Vertical FL, and Federated Transfer Learning that mitigates privacy issues. For privacy preservation, FL extends its capacity to incorporate Differential Privacy (DP) techniques to provide quantifiable measures on data anonymization. We have also discussed the concepts in FL that comprehend Local Differential Privacy (LDP) and Global Differential Privacy (GDP). The chapter concludes with 184open research problems and challenges of FL as PPML with implications, limitations, and future scope. 

The research delves into intelligent hybrid heat management Systems, exploring the challenges faced and solutions for enhancing efficiency. Hybrid heating systems are complex cyber-technical systems that combine city heating networks with renewable energy sources, such as heat pumps and solar panels. Traditional heating systems often lack adaptability to internal and external conditions, leading to suboptimal performance and user expectations. This paper proposes a new approach by integrating smart technologies, the Internet of Things, Artificial Intelligence, Machine Learning, optimization techniques, and trade-offs into the management of hybrid heat systems. The emphasis is also placed on the fact that the introduction of smart technologies makes it possible to make hybrid heating systems human-oriented and meet individual needs. Energy efficiency improvement is achievable by combining solutions, such as actual forecasting, with intelligent management that adapts to changing climates and user behaviors. The challenges addressed include inadequate responsiveness to load changes, inaccurate heat consumption forecasting, and inefficient data management. The paper emphasizes the need for intelligent systems that comply with the current standards, providing cost optimization, socializing and ensuring resilience, customer orientation, reliability, safety, and trustworthiness. This exploration of intelligent hybrid heat management systems seeks to overcome existing challenges and pave the way for a sustainable, digitally optimized future in district heating systems.