Context-Aware Trustworthy IoT Energy Services Provisioning

We propose an IoT energy service provisioning framework to ensure consumers' Quality of Experience (QoE). A novel context-aware trust assessment model is proposed to evaluate the trustworthiness of providers. Our model adapts to the dynamic nature of energy service providers to maintain QoE by selecting trustworthy providers. The proposed model evaluates providers' trustworthiness in various contexts, considering their behavior and energy provisioning history. Additionally, a trust-adaptive composition technique is presented for optimal energy allocation. Experimental results demonstrate the effectiveness and efficiency of the proposed approaches.Comment: 15 pages, 12 figures, This paper is accepted in the 21th International Conference on Service Oriented Computing (ICSOC 2023

Service-Based Wireless Energy Crowdsourcing

We propose a novel service-based ecosystem to crowdsource wireless energy to charge IoT devices. We leverage the service paradigm to abstract wireless energy crowdsourcing from nearby IoT devices as energy services. The proposed energy services ecosystem offers convenient, ubiquitous, and cost-effective power access to charge IoT devices. We discuss the impact of a crowdsourced wireless energy services ecosystem, the building components of the ecosystem, the energy services composition framework, the challenges, and proposed solutions.Comment: 15 pages, 7 figures, This is an invited paper and it will appear in the proceedings of the 20th International Conference on Service Oriented Computing (ICSOC

Failure-Sentient Composition For Swarm-Based Drone Services

We propose a novel failure-sentient framework for swarm-based drone delivery services. The framework ensures that those drones that experience a noticeable degradation in their performance (called soft failure) and which are part of a swarm, do not disrupt the successful delivery of packages to a consumer. The framework composes a weighted continual federated learning prediction module to accurately predict the time of failures of individual drones and uptime after failures. These predictions are used to determine the severity of failures at both the drone and swarm levels. We propose a speed-based heuristic algorithm with lookahead optimization to generate an optimal set of services considering failures. Experimental results on real datasets prove the efficiency of our proposed approach in terms of prediction accuracy, delivery times, and execution times.Comment: 11 pages, 14 figures, This paper is accepted in the 2023 IEEE International Conference on Web Services (ICWS 2023

Detecting Changes in Crowdsourced Social Media Images

We propose a novel service framework to detect changes in crowdsourced images. We use a service-oriented approach to model and represent crowdsourced images as image services. Non-functional attributes of an image service are leveraged to detect changes in an image. The changes are reported in form of a version tree. The version tree is constructed in a way that it reflects the extent of changes introduced in different versions. Afterwards, we find semantic differences in between different versions to determine the extent of changes introduced in a specific version. Preliminary experimental results demonstrate the effectiveness of the proposed approach.Comment: Accepted Paper in ICSO

Positional Encoding-based Resident Identification in Multi-resident Smart Homes

We propose a novel resident identification framework to identify residents in a multi-occupant smart environment. The proposed framework employs a feature extraction model based on the concepts of positional encoding. The feature extraction model considers the locations of homes as a graph. We design a novel algorithm to build such graphs from layout maps of smart environments. The Node2Vec algorithm is used to transform the graph into high-dimensional node embeddings. A Long Short-Term Memory (LSTM) model is introduced to predict the identities of residents using temporal sequences of sensor events with the node embeddings. Extensive experiments show that our proposed scheme effectively identifies residents in a multi-occupant environment. Evaluation results on two real-world datasets demonstrate that our proposed approach achieves 94.5% and 87.9% accuracy, respectively.Comment: 27 pages, 11 figures, 2 table

Monitoring Efficiency of IoT Wireless Charging

Crowdsourcing wireless energy is a novel and convenient solution to charge nearby IoT devices. Several applications have been proposed to enable peer-to-peer wireless energy charging. However, none of them considered the energy efficiency of the wireless transfer of energy. In this paper, we propose an energy estimation framework that predicts the actual received energy. Our framework uses two machine learning algorithms, namely XGBoost and Neural Network, to estimate the received energy. The result shows that the Neural Network model is better than XGBoost at predicting the received energy. We train and evaluate our models by collecting a real wireless energy dataset.Comment: 3 pages, 4 figures. This is an accepted demo paper and it will appear in The 21st International Conference on Pervasive Computing and Communications (PerCom 2023

Towards peer-to-peer sharing of wireless energy services

Crowdsourcing wireless energy services is a novel convenient alternative to charge IoT devices. We demonstrate peer-to-peer wireless energy services sharing between smartphones over a distance. Our demo leverages (1) a service-based technique to share energy services, (2) state-of-the-art power transfer technology over a distance, and (3) a mobile application to enable communication between energy providers and consumers. In addition, our application monitors the charging process between IoT devices to collect a dataset for further analysis. Moreover, in this demo, we compare the peer-to-peer energy transfer between two smartphones using different charging technologies, i.e., cable charging, reveres charging, and wireless charging over a distance. A set of preliminary experiments has been conducted on a real collected dataset to analyze and demonstrate the behavior of the current wireless and traditional charging technologies.Comment: 4 pages, 4 figures. This is an accepted demo paper and it will appear in the 20th International Conference on Service Oriented Computing (ICSOC 2022

Crowdsourcing IoT Energy Services

The proliferation of the Internet of things (IoT) may give rise to a self-sustained crowdsourced IoT ecosystem. The augmented capabilities of IoT devices, such as sensing and computing resources, may be leveraged for peer-to-peer sharing. People can exchange a wide range of IoT services, such as computing offloading, hotspot proxies, energy sharing, etc. These crowdsourced IoT services present a convenient, cost-effective, and sometimes the only possible solution fora resource-constrained device. The concept of wireless energy crowdsourcing has been recently introduced to provide IoT users with power access anywhere, anytime, through crowdsourcing. We leverage the service paradigm to unlock the full potential of IoT energy crowdsourcing. We define an IoT Energy Service as the abstraction of wireless energy delivery from an IoT device (i.e., provider) to another device (i.e., consumer). Crowdsourcing IoT energy services has the potential to create a green service exchange environment by recycling unused IoT energy or relying on renewable energy sources. An IoT device may share its spare energy with another IoT device in its vicinity. The composition of IoT energy services is expected to play an essential role in the crowdsourced IoT environment. A single energy service may not satisfy the consumer’s requirement. The preferred solution is to select and compose an optimal set of services according to the consumer’s requirements. We introduce a novel composition framework to crowdsource wireless energy services from IoT devices. We design a novel composability model considering IoT devices’ energy usage behavior and spatio-temporal aspects. We formulate the composition problem as a multi-objective optimization of meeting users’ energy requirements in the earliest and shortest time possible. In a crowdsourced IoT environment, IoT energy services may differ from their advertisement due to the fluctuation of service providers’ behavior. We propose an elastic composition framework that anticipates the fluctuation of crowdsourced IoT energy services and selects the most reliable services to ensure the minimum waiting time. We also leverage the mobility patterns of the crowd in confined areas to capture the disconnections between energy providers and consumers. We then model the intermittent behavior of energy services based on their mobility patterns to propose a fluid composition framework. The fluid composition selects and composes an optimal set of dynamic energy services according to the consumers’ requirements. Additionally, energy service providers and consumers may have different spatio-temporal preferences. These preferences’ incongruity may severely impact the balance between energy services and existing energy requests. We leverage the mobility patterns and the energy usage behavior of energy consumers and providers to propose a proactive composition approach. The novelty of the proactive composition is that energy services are composed seamlessly without affecting the usage behavior and the mobility patterns of service providers and consumers. First, the proposed composition framework anticipates the next required energy based on the energy usage behavior. The proactive composition, then, plans the location, time, and required amount of one or multiple energy requests ahead of time according to the consumer’s mobility pattern. Sometimes, in a crowdsourced IoT environment, the available energy services may not satisfy all existing requests. The under-provision of energy requests may demotivate consumers to participate in the crowdsourced IoT energy market. It is challenging to satisfy consumers by fulfilling only parts of their energy requirements. We introduce the notion of fairness in provisioning IoT energy services to satisfy the maximum number of energy requests. We propose a fairness-aware service provisioning framework to cater for multiple energy requests