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.

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.

Autonomous vehicles (AVs) are being enhanced by introducing wireless communication to improve their intelligence, reliability and efficiency. Despite all of these distinct advantages, the open wireless communication links and connectivity make the AVs' vulnerability to cyber-attacks. This paper proposes an L-2 -gain-based resilient path following control strategy for AVs under time-constrained denial-of-service (DoS) attacks and external interference. A switching-like path following control model of AVs is first built in the presence of DoS attacks, which is characterized by the lower and upper bounds of the sleeping period and active period of the DoS attacker. Then, the exponential stability and L-2 -gain performance of the resulting switched system are analyzed by using a time-varying Lyapunov function method. On the basis of the obtained analysis results, L-2 -gain-based resilient controllers are designed to achieve an acceptable path-following performance despite the presence of such DoS attacks. Finally, the effectiveness of the proposed L-2 -gain-based resilient path following control method is confirmed by the simulation results obtained for the considered AVs model with different DoS attack parameters.

Buildings are rapidly becoming more digitized, largely due to developments in the internet of things (IoT). This provides both opportunities and challenges. One of the central challenges in the process of digitizing buildings is the ability to monitor these buildings' status effectively. This monitoring is essential for services that rely on information about the presence and activities of individuals within different areas of these buildings. Occupancy information (including people counting, occupancy detection, location tracking, and activity detection) plays a vital role in the management of smart buildings. In this article, we primarily focus on the use of passive infrared (PIR) sensors for gathering occupancy information. PIR sensors are among the most widely used sensors for this purpose due to their consideration of privacy concerns, cost-effectiveness, and low processing complexity compared to other sensors. Despite numerous literature reviews in the field of occupancy information, there is currently no literature review dedicated to occupancy information derived specifically from PIR sensors. Therefore, this review analyzes articles that specifically explore the application of PIR sensors for obtaining occupancy information. It provides a comprehensive literature review of PIR sensor technology from 2015 to 2023, focusing on applications in people counting, activity detection, and localization (tracking and location). It consolidates findings from articles that have explored and enhanced the capabilities of PIR sensors in these interconnected domains. This review thoroughly examines the application of various techniques, machine learning algorithms, and configurations for PIR sensors in indoor building environments, emphasizing not only the data processing aspects but also their advantages, limitations, and efficacy in producing accurate occupancy information. These developments are crucial for improving building management systems in terms of energy efficiency, security, and user comfort, among other operational aspects. The article seeks to offer a thorough analysis of the present state and potential future advancements of PIR sensor technology in efficiently monitoring and understanding occupancy information by classifying and analyzing improvements in these domains.

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users’ sensitive data. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover’s Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

Segmenting images is an intricate and exceptionally demanding field within computer vision. Instance Segmentation is one of the subfields of image segmentation that segments objects on a given image or video. It categorizes the class labels according to individual instances, ensuring that distinct instance markers are assigned to each occurrence of the same object class, even if multiple instances exist. With the development of computer systems, segmentation studies have increased very rapidly. One of the state-of-the-art algorithms recently published by Meta AI, which segments everything on a given image, is called the Segment Anything Model (SAM). Its impressive zero-shot performance encourages us to use it for diverse tasks. Therefore, we would like to leverage the SAM for an effective instance segmentation model. Accordingly, in this paper, we propose a hybrid instance segmentation method in which Object Detection algorithms extract bounding boxes of detected objects and load SAM to produce segmentation, called Box Prompted SAM (BP-SAM). Experimental evaluation of the COCO2017 Validation dataset provided us with promising performance.