Software technology is undergoing a paradigm shift driven by two converging trends. First, the scope of software responsibility has expanded significantly: as “software-defined everything” becomes a reality, software has evolved into the integration core of sociocyber-physical systems (SCPSs). Second, the capabilities and development methods of software are being greatly enhanced by recent breakthroughs in artificial intelligence (AI). This article presents perspectives and observations on software engineering in this era of rapid progress. We aim to outline a set of foundational challenges in engineering SCPSs and highlight the need for innovative software solutions that extend beyond AI technologies alone. Specifically, we examine the need for a new paradigm that can address the complexities introduced by SCPSs, which challenge conventional paradigms through the blurring of system boundaries, continuous lifecycle evolution, and the embracing of inherent uncertainty. We highlight new engineering principles of socio-technical co-design, cyber-physical integration, and development-operation convergence, and a knowledge- and data-driven approach to taming uncertainty. Emerging proposals, including digital humanism, agentic SCPS, ubiquitous operating system, and continuous quality assurance, are discussed alongside possible extensions to existing technologies. We then outline key research directions for both runtime support and quality assurance. On the runtime side, we argue for a new generation of software infrastructure for SCPSs, including unified hardware abstractions, scalable and resilient runtime systems, AI-enabled system management, and human-centric operating system primitives. On the assurance side, we highlight the need for new approaches combining unified socio-cyber-physical modeling, specification of both technical and non-technical properties, data-driven simulation and testing, continuous verification, and runtime monitoring under uncertainty. Furthermore, we present specific challenges within key application domains, including intelligent vehicles, smart manufacturing, and smart cities. In doing so, we aim to stimulate discussion within the software engineering community and encourage support from industry and government to address the critical engineering and governance issues inherent to this new generation of systems.

Crowdsourcing and Mobile Crowd Sensing (MCS) platforms have revolutionized data collection, harnessing the collective intelligence of crowdsourced sensing. Accurately classifying and extracting core information such as heterogeneous sensors in MCS tasks plays a key role in the platform execution efficiency. However, existing methods struggle with extracting pivotal information from task descriptions that are open-domain, implicitly expressed, and linguistically diverse, ultimately hindering the efficiency of task assignment and execution. To overcome these challenges, we propose LKG-MF, a Label Knowledge Graph-powered Multi-task Framework, to achieve better core information mining performance in crowdsourcing and mobile crowd sensing tasks. Specifically, we first construct an MCS task dataset comprising over 10,000 real tasks from 7 platforms. Then we devise a label knowledge graph to capture heterogeneous semantics and relationships among labels and enhance label representation. Further, we present a multi-granularity feature extraction network to capture precise task-specific features. To optimize performance across disparate tasks, we incorporate a task- adaptive loss function that adeptly balances their optimization rates. Experimental results show that LKG-MF outperforms baselines average by 2.3%, significantly improving multi-task classification accuracy. Notably, when we integrate the LKG-MF model into MCS platforms, the task assignment efficiency is improved by 38.6% and the task completion time is reduced by 45.1%, which demonstrates the practical impact and effectiveness of our model in improving the performance of MCS platforms.

In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this paper, we propose a blockchain-based self-supervision model for vehicle violations that utilizes inter-vehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles.

In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services, such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this article, we propose a blockchain-based self-supervision model for vehicle violations that utilizes intervehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles.

With the advent of the services computing era, challenges in educating capable future software services engineers and researchers have become more pressing than ever. Software services engineers are professionals whose training cuts across computer science, software engineering, services computing, as well as relevant educational elements in management science and engineering, social science, serviceology, and service science and engineering [1]. We foresee an urgent need in this fast-emerging multidisciplinary field "Software Services Engineering" (SSE) [2] [2.1] for a comprehensive collection of education and training artifacts including well-defined body of knowledge (BOK), model curricula, open-source platforms, certification requirements, accreditation criteria, articulation directives, exemplary professional course modules, among other related components.

This paper introduces population digital health (PDH)-the use of digital health information sourced from health internet of things (IoT) and wearable devices for population health modeling-as an emerging research domain that offers an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. PDH combines health data sourced from health IoT devices, machine learning, and ubiquitous computing or networking infrastructure to increase the scale, coverage, equity, and cost-effectiveness of population health. This contrasts with the traditional population health approach, which relies on data from structured clinical records (eg, electronic health records) or health surveys. We present the overall PDH approach and highlight its key research challenges, provide solutions to key research challenges, and demonstrate the potential of PDH through three case studies that address (1) data inadequacy, (2) inaccuracy of the health IoT devices' sensor measurements, and (3) the spatiotemporal sparsity in the available digital health information. Finally, we discuss the conditions, prerequisites, and barriers for adopting PDH drawing on from real-world examples from different geographic regions.

This special issue introduces the concept of “venture scientists” and the role of technology entrepreneurship in driving innovation across various fields. It discusses challenges posed by recent advances in emerging technologies and efforts to adapt.

Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity of medical data. These challenges make it difficult for traditional AI models to extract rare disease features for disease prediction. In this paper, we propose a Dynamic Federated Meta-Learning (DFML) approach to improve rare disease prediction. We design an Inaccuracy-Focused Meta-Learning (IFML) approach that dynamically adjusts the attention to different tasks according to the accuracy of base learners. Additionally, a dynamic weight-based fusion strategy is proposed to further improve federated learning, which dynamically selects clients based on the accuracy of each local model. Experiments on two public datasets show that our approach outperforms the original federated meta-learning algorithm in accuracy and speed with as few as five shots. The average prediction accuracy of the proposed model is improved by 13.28% compared with each hospital's local model.

A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

Healthcare systems are capable of collecting a significant number of patient health-related parameters.Analyzing them to find the reasons that cause a given disease is challenging.Feature Selection techniques have been used to address this issue-reducing these parameters to a smaller set with the most "determinant" information.However, existing proposals usually focus on classification problems-aimed to detect whether a person is or is not suffering from an illness or from a finite set of illnesses.However, there are many situations in which health professionals need a numerical assessment to quantify the severity of an illness, thus dealing with a regression problem instead.Proposals using Feature Selection here are very limited.This paper examines several Feature Selection techniques to gauge their applicability to the regression-type problems, comparing these techniques by applying them to a real-life scenario on the functional profiles of older adults.Data from 829 functional profiles assessments in 49 residential homes were used in this study.The number of features was reduced from 31 to 25-with a correlation between inputs and outputs of 0.99 according to the R 2 score and a Mean Square Error (MSE) of 0.11-or to 14 features-with a correlation of 0.98 and MSE of 5.73.

Graphic-pattern-based implicit authentication has been successfully exploited to elevate the security of smartphones. On-screen pressure is one of the key features in such an approach since it can reveal users' touch pattern. However, state-of-the-art approaches rely on a system API to obtain on-screen pressure, which is not adequately accurate and cannot meet the demands of robust implicit authentication. To bridge this gap, we propose PresSafe, a novel implicit authentication system that utilizes the smartphone's built-in barometer sensor to measure pressure during the unlocking process, and to utilize the pressure data in authentication. A key technical challenge in utilizing barometer sensing, however, is to understand the user activity through measured pressure. To overcome this challenge, PresSafe leverages barometer data along with data from other conventional but heterogeneous ambient sensors to produce accurate and robust user activity descriptions. PresSafe utilizes a transfer-learning-based hybrid workflow to integrate user activity representation learning with a lightweight classical authentication algorithm to obtain a unified model. This approach offloads the computational cost from the terminal and addresses privacy concerns. To ensure applicability of our approach despite data heterogeneity and insufficient training data, we utilize a channel-adaptive data processing mechanism. Extensive experiments utilizing more than 70000 records from 23 volunteers in six different locations show that PresSafe achieves an FAR of 0.45%, an FRR of 0.49%, and an EER of 0.47%, which clearly demonstrate its superiority over several existing solutions.

The proliferation of Internet of Things (IoT) rapidly increases the possiblities of Simple Service Discovery Protocol (SSDP) reflection attacks. Most DDoS attack defence strategies deploy only to a certain type of devices in the attack chain,and need to detect attacks in advance, and the detection of DDoS attacks often uses heavy algorithms consuming lots of computing resources. This paper proposes a comprehensive DDoS attack defence approach which combines broad learning and a set of defence strategies against SSDP attacks, called Broad Learning based Comprehensive Defence (BLCD). The defence strategies work along the attack chain, starting from attack sources to victims. It defends against attacks without detecting attacks or identifying the roles of IoT devices in SSDP reflection attacks. BLCD also detects suspicious traffic at bots, service providers and victims by using broad learning, and the detection results are used as the basis for automatically deploying defence strategies which can significantly reduce DDoS packets. For evaluations, we thoroughly analyze attack traffic when deploying BLCD to different defence locations. Experiments show that BLCD can reduce the number of packets received at the victim to 39 without affecting the standard SSDP service, and detect malicious packets with an accuracy of 99.99%.

As important indicators of urban public safety, public safety and environmental security (PSES) is related to residents’ living security and greatly affects their quality of life and happiness index. Due to PSES characteristics, such as diverse forms, wide distribution, and unpredictable occurrence times, traditional solutions consume huge manpower, and time in the implementation process. Although some professional software and hardware systems have emerged to assist in solving the problems, there are still challenges, such as limited sensing coverage and monotonous sensing modes, lack of interaction and understanding between systems and tasks, and scarcity of effective system architecture and functional modules. To meet these challenges, we design a PSES multiterminal fusion system (SafeCity) based on the idea and technology of heterogeneous mobile crowd sensing. With collaboration among humans, machines, and things (H-M–T), the proposed system makes full use of the idle mobility, sensing, and computing resources in the city, and systematically provides a solution to the various PSES issues. Apart from the system architecture, functions, core mechanism, and algorithm libraries, the task execution flow is explained in depth through the description of several cases. We implement a prototype to verify the rationality and effectiveness of SafeCity. And, comprehensive comparison and evaluation show that SafeCity is far superior to other solutions in terms of function, performance, and stability.

Healthcare delivery transformations and the use of connected health devices are paving the way to a paradigm shift from current healthcare systems towards patient-centered systems. Many proposals successfully reorient health information systems so that data are still distributed among the institutions and services that generate them, while being accessed jointly from a single point of view per patient. However, this means that control over the operations involving this data is lost. Mechanisms to maintain the traceability of health data are needed. This will enable the verification of the integrity of the records and will provide assurances that they have not been compromised. This problem has already been addressed in other domains such as food supply chains, where traceability allows to know all interactions with a food supply from the time it is produced until it is consumed. This paper proposes a blockchain solution to achieve the traceability of health data in patient-centered distributed environments. To validate this proposal, a case study involving 50 sociosanitary institutions in Portugal have been chosen. Different performance tests have been conducted to demonstrate the suitability, feasibility and scalability of our proposal.

World Health Organization and local governments recommended that older adults self-isolate due to the elevated risk for adverse health outcomes faced when contracting COVD-19. Technology offers better access to virtual communications for social connections and healthcare. Yet, the barriers and facilitators of older adults' use of technology during this world-changing event are, for the most part, unknown. The purpose of this paper is to synthesize using inductive thematic analysis the literature on broader health and social impacts on older adults from lockdown-related measures caused by the pandemic. The findings consisted of three dichotomous themes regarding older adults' barriers and facilitators to technology. The first theme centers on personal belief and perception of oneself. The second theme explores the digital literacy continuum. The third theme focuses on older adults' barriers and facilitators when adopting technology. The practical significance of these findings is to better inform the design and delivery of accessible technology to older adults.

Blockchain is a Byzantine fault tolerant (BFT) system wherein decentralized nodes execute consensus protocols to drive the agreement process on new blocks added to a distributed ledger. Generally, two-round communications among 3 f + 1 nodes are required to tolerate up to f faults in BFT-based consensus networks. This communication pattern corresponds to the worse-case scenario of consensus achievement, even under asynchronous network conditions. Nevertheless, it is not uncommon for a network to operate under better conditions, where a consensus can be reached with a lower communication cost. Hence, with the addition of a faster optimistic path toward an agreement, the idea of dual-mode consensus has been proposed as a promising approach to enhance the performance of asynchronous BFT protocols. However, this opportunity is not completely exploited by existing dual-mode protocols as the fast path can be followed only in a nonfaulty and synchronous network. This article presents a novel dual-mode protocol consisting of fast and backup subprotocols. To create different consensus committees for fast and backup-mode operations, the network contains both active and passive nodes. A consensus can be expedited through a fast-mode operation when majority of the active nodes can communicate synchronously. Under non-ideal conditions, the backup protocol takes over the agreement process from its fast-mode counterpart without starting over the suspended round. The safety and liveness of the proposed protocol are guaranteed with lower communication costs, which balance the trade-off between protocol efficiency and availability.

In recent years, the Internet of Things (IoT) has gained tremendous attention and exponential growth in every domain of life. However, these devices face many challenges due to the limited resources in terms of storage and computation. Collaborative Edge Computing (CEC) is an emerging paradigm that solves these issues where multiple edge devices share computational resources to collaborate and satisfy user requirements. The fundamental issues in CEC are to make an offloading decision while considering the flow scheduling and balancing the load over multiple edge devices. Moreover, the efficient resource allocation of edge nodes is challenging, particularly when IoT devices are more vulnerable and become resource-hungry. The combination of Software Defined Networking (SDN) and Blockchain (BC) can play a vital role in solving the issues mentioned above. This paper presents a token-based resource management mechanism as SDBlockEdge by integrating these two technologies. The programming abstractions and global view of the SDN can help offload decision and flow scheduling, whereas the smart contract mechanism of BC can help control the abnormal behavior of IoT devices. We design the Resource Management Controller (RMC), which collaborates with the SDN controller to keep the record of available resources. It helps for offloading decisions and balancing the load over edge servers, whereas the SDN controller helps consider the less loaded path, while offloading reduces the task completion time. Moreover, the resources are allocated against tokens, and smart contracts are used to pay the cost of these resources. The proposed approach is implemented in Mininet_WiFi and Containernet, where docker hosts act as edge nodes with different IoT devices. The results after the extensive simulation show the effectiveness of the proposed approach.