The rapid expansion of the Internet of Things (IoT) marketplace requires intelligent and adaptable infrastructures capable of efficient resource allocation and management among diverse entities, such as electric vehicle charging stations and energy providers. As the IoT marketplace increasingly intersects with critical electricity and transportation infrastructures, the need for coordinated, intelligent decision-making becomes more urgent. Despite its promising potential, the IoT marketplace currently faces critical challenges, including inefficient resource allocation, system congestion, and unpredictable patterns of energy demand, all of which hinder its seamless operation. In response, this paper proposes an innovative AI-driven framework for the IoT marketplaces that integrates context-aware techniques with Q-learning to transform resource allocation and matchmaking processes. Using EV charging as the primary case study, we implement context-aware similarity matching to accurately pair EVs with optimal charging stations, while Q-learning algorithms dynamically enhance matchmaking decisions. Our experimental results clearly demonstrate that this integrated approach effectively reduces charging delays, optimizes energy allocation, and substantially improves overall system efficiency. This research marks a significant advancement toward an intelligent and agile IoT marketplace infrastructure, which addresses critical challenges and supports the sustainable evolution of future electricity and transportation infrastructures.

We conduct a survey on drones used as a service, denoted as drone-as-a-service (DaaS). We develop a novel taxonomy based on DaaS functions, research tasks, and application domains. We provide a discussion on drones and their associated capabilities based on their type of use. We propose a three-layered DaaS system architecture that vertically integratescloudcomputing,drones, andservicesas a reference framework to compare existing drone service implementations. Additionally, we propose a representative uncertainty-aware DaaS model for delivery scenarios, illustrating how service definitions can incorporate both functional and nonfunctional attributes under dynamic environmental conditions. Finally, we identify and discuss future research directions and open problems related to the use of drones for service delivery.

In recent years, drone delivery has drawn significant attention for its promising potential in solving the last-mile delivery problem. As a novel service paradigm, Drone-as-a-Service (DaaS) is emerging as an effective way to address service provisioning problems within complex delivery networks. However, existing DaaS composition frameworks often fail to consider the sharing of delivery services and are not applicable to multi-package drone delivery tasks. Meanwhile, as one of the most critical real-world environmental factors for drones, the impact of dynamic wind conditions is not adequately considered by existing studies. This may lead to Quality of Service (QoS) degradation of delivery services. To address the above issues, in this paper, we propose a wind-aware service provisioning strategy for multi-package drone delivery. Given the advantages of Edge Computing (EC) in handling such dynamic factors due to its low latency and high reliability, we first establish a spatio-temporal DaaS model based on service sharing according to the edge-based drone delivery system. Then, we propose a novel wind-aware drone delivery service provisioning strategy for multi-package delivery to minimize energy consumption of drones. The proposed strategy consists of two phases: service sharing and service composition. In the service sharing phase, the service sharing plan is generated by an improved genetic algorithm. In the service composition phase, the edge server dynamically generates the service composition plan through our proposed policy iteration based DaaS composition method. Experimental results using real delivery network and wind data demonstrate that our strategy is able to reduce delivery energy consumption of drone by about 10.1%.

Mobile Crowd Computing (MCC) leverages the collaborative power of nearby devices to solve resource-intensive tasks, offering transformative potential across various fields. However, ensuring device reliability—which directly impacts the trustworthiness of devices in MCC environments—poses a significant challenge. Balancing validation accuracy with performance and energy efficiency is particularly difficult due to MCC’s dynamic and resource-constrained decentralized nature. Existing validation methods are not feasible in MCC, as they can negatively affect speed and energy consumption. This paper introduces the Honeybee-RS framework, a novel approach for validating offloaded computational results in MCC environments. Honeybee-RS provides delegator-based validation mechanisms and derives reliability scores from the validation process. Experiments demonstrate the effectiveness of these mechanisms, significantly enhancing reliability in MCC while maintaining performance.

With the widespread use of Internet of Things (IoT) technology, an enormous number of end devices that request various kinds of cloud services have been connected to the Internet. Multi-access edge computing (MEC) can reduce the service response time by selecting the required edge computing resources closer to the end device. However, MEC-based smart systems require heterogeneous and diverse services support. Taking unmanned aerial vehicle (UAV) last-mile delivery system as an example, there are two types of services required: delivery and computational services. The edge services in MEC environments are distributed and limited. Inefficient service selection plans will affect the quality of services of such smart systems. Therefore, how to design a suitable service selection strategy is a crucial issue for MEC-based smart systems. To address this issue, we propose a service selection framework and a holistic and hybrid service selection ($H^{2}S^{2}$) strategy for MEC-based UAV last-mile delivery systems in real-world UAV last-mile delivery scenarios. This framework considers three important characteristics of UAV delivery systems: diverse service requirements, service availability, and service mobility. The$H^{2}S^{2}$strategy focuses on selecting the optimal delivery and computational services and provides an integrated approach with a static service selection algorithm and a dynamic service re-selection algorithm. The$H^{2}S^{2}$strategy determines the optimal delivery and computational service selection plans with the lowest UAV energy consumption and shortest service response time. We assess the effectiveness and efficiency of the$H^{2}S^{2}$strategy through ablation studies and comparative analyses with diverse representative strategies. The experimental results show that the$H^{2}S^{2}$strategy improves the effectiveness and efficiency of the UAV delivery system by significantly reducing UAV's energy consumption and service response time.

We introduce the concept ofadaptive trustin crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers.Usage patternsof IoT consumers are exploited to provide an accurate trust value for service providers. A noveladaptive trust management frameworkis proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtaintrust indicatorsthat are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to buildservice-to-indicatormodel to evaluate a service’strust at each indicator. Similarly, ausage-to-indicatormodel is built to obtain theimportance of each trust indicatorfor a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.

Mobile devices are ubiquitous, heterogeneous and resource constrained. Execution of complex tasks in mobile devices are resource demanding and time-consuming, forcing developers to offload portions of the complex task to cloud or edge computing resources. Task offloading becomes increasingly challenging due to intermittent Internet connectivity, remote resource unavailability, high costs, latency, and limited energy of the mobile device. A mobile device user is typically surrounded by other mobile devices, which can be leveraged to collaboratively compute a resource-intensive task. With the help of a work sharing framework, it is feasible for devices to communicate and collaborate. However, some mobile devices are incapable of computing complex portions of the task, and some can compute in accelerated mode. In this demonstration, we introduce Honeybee-T a collaborative mobile crowd computing framework that uses a work-stealing algorithm. The algorithm allows work sharing with collaborating devices based on devices' computational ability and task-dependencies. The experiments show that by employing Honeybee-T framework, when compared to monolithic execution of a large compute-intensive task, there is a considerable performance gain, as well as energy savings.

We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits amulti-perspective trust modelthat captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set ofattributesthat contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate atrust modelfor crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.

We propose an uncertainty-aware service approach to provide drone-based delivery services called Drone-as-a-Service (DaaS) effectively. Specifically, we propose a service model of DaaS based on the dynamic spatiotemporal features of drones and their in-flight contexts. The proposed DaaS service approach consists of three components: scheduling, route-planning, and composition. First, we develop a DaaS scheduling model to generate DaaS itineraries through a Skyway network. Second, we propose anuncertainty-aware DaaS route-planning algorithmthat selects the optimal Skyways under weather uncertainties. Third, we develop two DaaS composition techniques to select an optimal DaaS composition at each station of the planned route. Aspatiotemporal DaaS composerfirst selects the optimal DaaSs based on their spatiotemporal availability and drone capabilities. Apredictive DaaS composerthen utilises the outcome of the first composer to enable fast and accurate DaaS composition using several Machine Learning classification methods. We train the classifiers using a new set of spatiotemporal features which are in addition to other DaaS QoS properties. Our experiments results show the effectiveness and efficiency of the proposed approach.

We propose a novel framework for composing crowdsourced wireless energy services to satisfy users’ energy requirements in a crowdsourced Internet of Things (IoT) environment. A new energy service model is designed to transform the harvested energy from IoT devices into crowdsourced services. We propose a new energy service composability model that considers the spatio-temporal aspects and the usage patterns of the IoT devices. A multiple local knapsack-based approach is developed to select an optimal set of partial energy services based on the deliverable energy capacity of IoT devices. We propose a heuristic-based composition approach using the temporal and energy capacity distributions of services. Experimental results demonstrate the effectiveness and efficiency of the proposed approach.

We develop a novel framework for efficiently and effectively discovering crowdsourced services thatmovein close proximity to a user over a period of time. We introduce a moving crowdsourced service model which is modelled as a moving region. We propose a deep reinforcement learning-based composition approach to select and compose moving IoT services considering quality parameters. Additionally, we develop a parallel flock-based service discovery algorithm as a ground-truth to measure the accuracy of the proposed approach. The experiments on two real-world datasets verify the effectiveness and efficiency of the deep reinforcement learning-based approach.

With the rapid growth of Internet of Things (IoT), Mobile Edge Computing (MEC) is becoming the major platform for many smart systems such as smart logistics, smart healthcare, and smart transportation, given its lower latency and higher reliability compared with centralized cloud computing. There is a growing interest in Drone-as-a-Service in recent years which enables the MEC-based smart UAV delivery system. However, most existing works on Drone-as-a-Service focus on the static service composition or the dynamic service provisioning, rather than a holistic service provisioning strategy for the entire UAV delivery process. In this paper, we propose a holistic service provisioning strategy for Drone-as-a-Service in MEC-based UAV delivery to address such an issue. Specifically, a MEC-based UAV relay delivery system framework (RDS) is designed, which considers both the static stage for provisioning delivery services and the dynamic stage for provisioning computing services. Based on the service models for both static and dynamic stages, an energy-efficient service provision strategy (ESP-GA) for MEC-based UAV last-mile delivery is proposed, which aims to minimize the overall energy consumption under deadline constraints. Through the simulation experiments based on a prototype UAV delivery system, the experimental results have successfully demonstrated the superior performance of the proposed holistic strategy in comparison with several representative service provisioning strategies.

We propose just-in-time memoryless trust for crowdsourced IoT services. We leverage the characteristics of the IoT service environment to evaluate their trustworthiness. A novel framework is devised to assess a service's trust without relying on previous knowledge, i.e., memoryless trust. The framework exploits service-session-related data to offer a trust value valid only during the current session, i.e., just-in-time trust. Several experiments are conducted to assess the efficiency of the proposed framework.

We propose a novel type of service composition, called elastic composition which provides a reliable framework in a highly fluctuating IoT energy provisioning settings. We rely on crowdsourcing IoT energy (e.g., wearables) to provide wireless energy to nearby devices. We introduce the concepts of soft deadline and hard deadline as key criteria to cater for an elastic composition framework. We conduct a set of experiments on real-world datasets to assess the efficiency of the proposed approach.

We introduce the notion of Adaptive Trust in crowdsourced IoT services; a usage-based trust that represents a service's trustworthiness based on consumers' uses. A novel four-stage framework is proposed to assess the dynamic service trust by leveraging how the service is being used. The first stage uses an algorithm to predict different trust factors that affect the overall trustworthiness of an IoT service. Trust factors are fed to the second stage to build a service-to-factor model that predicts the trustworthiness of a service at each given trust factor. A usage-to-factor model is built at the third stage, which detects the importance of each factor for a specific usage scenario. The last stage utilizes the two models to compute a trust value specifically tailored for a particular usage. Several experiments have been conducted using real dataset to ensure the efficiency of the proposed approach.

We propose a novel composition framework for drone-based package delivery services termed as Drone-as-a-Service (DaaS). The proposed framework includes a spatio-temporal service model and a quality model for DaaS. A drone service selection algorithm is designed using 3D Rtree. We develop a Dijkstra-based and a heuristic-based drone service composition approach to meet users' delivery requirements, i.e., expected delivery time and cost. Experimental results on a real-world dataset demonstrate the efficiency of our proposed approach.

We present a new spatio-temporal incentive-based approach to achieve a geographically balanced coverage of crowdsourced services. The proposed approach is based on a new spatio-temporal incentive model that considers multiple parameters including location entropy, time of day, and spatio-temporal density to encourage the participation of crowdsourced service providers. We present a greedy network flow algorithm that offers incentives to redistribute crowdsourced service providers to improve the crowdsourced coverage balance within an area. A novel participation probability model is also introduced to estimate the expected number of crowdsourced service providers’ movement based on spatio-temporal features. Experimental results validate the efficiency and effectiveness of the proposed approach.

We propose a framework to discover proximate IoT service relationships based on spatio-temporal features. We introduce a spatio-temporal proximity model in terms of spatial-proximity and temporal-proximity to discard insignificant IoT service relationships. The proximity model focuses on quantifying the correlation strength among IoT services from time and location aspects. A new algorithm is proposed to discover proximate spatio-temporal IoT service relationships. We also present preliminary experimental results.

We present a new two-level composition model for crowdsourced Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. The proposed approach is defined based on a formal Sensor-Cloud service model that abstracts the functionality and non-functional aspects of sensor data on the cloud in terms of spatio-temporal features. A spatio-temporal indexing technique based on the 3D R-tree to enable fast identification of appropriate Sensor-Cloud services is proposed. A novel quality model is introduced that considers dynamic features of sensors to select and compose Sensor-Cloud services. The quality model defines Coverage as a Service which is formulated as a composition of crowdsourced Sensor-Cloud services. We present two new QoS-aware spatio-temporal composition algorithms to select the optimal composition plan. Experimental results validate the performance of the proposed algorithms.

We propose a new framework for composing Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate spatio-temporal Sensor-Cloud services, two new quality attributes are introduced. We present a heuristic algorithm based on A* to compose Sensor-Cloud services in terms of spatio-temporal aspects. In addition, a new spatio-temporal technique based on 3D R-tree to access Sensor-Cloud services is proposed. Analytical and simulation results are presented to show the performance of the proposed approach.

Clustering is currently one of the most crucial techniques for dealing with massive amount of heterogeneous information on the web, which is beyond human beingpsilas capacity to digest. Recent studies have shown that the most commonly used partitioning-based clustering algorithm, the K-means algorithm, is more suitable for large datasets. However, the K-means algorithm can generate a local optimal solution. In this paper we present novel harmony search clustering algorithms that deal with documents clustering based on harmony search optimization method. By modeling clustering as an optimization problem, first, we propose a pure harmony search based clustering algorithm that finds near global optimal clusters within a reasonable time. Contrary to the localized searching of the K-means algorithm, the harmony search clustering algorithm performs a globalized search in the entire solution space. Then harmony clustering is integrated with the K-means algorithm in three ways to achieve better clustering. The proposed algorithms improve the K-means algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, hence more stable. In the experiments we conducted, we applied the proposed algorithms, K-means clustering algorithm on five different document datasets. Experimental results reveal that the proposed algorithms can find better clusters when compared to K-means and the quality of clusters is comparable and converge to the best known optimum faster than it.