Crowdsourcing IoT Energy Services

Abstract

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.