Reliability Model for Incentive-Driven IoT Energy Services

Abstract

We propose a novel reliability model for composing energy service requests. The proposed model is based on consumers' behavior and history of energy requests. The reliability model ensures the maximum incentives to providers. Incentives are used as a green solution to increase IoT users' participation in a crowdsourced energy sharing environment. Additionally, adaptive and priority scheduling compositions are proposed to compose the most reliable energy requests while maximizing providers' incentives. A set of experiments is conducted to evaluate the proposed approaches. Experimental results prove the efficiency of the proposed approaches.

Conflict Detection and Resolution in IoT Systems: A Survey

“…Chaki et al [24], Abusafia et al [25], and Lakhdari et al [26] discusses various types of conflicts of available IoT services. Chaki et al [24] proposed a novel IoT conflict model that encompasses both functional and nonfunctional properties of IoT services in a multiresident environment where residents may have different habits, resulting in IoT service conflicts.…”

In-Flight Energy-Driven Composition of Drone Swarm Services

“…Mobility pattern impact on IoT energy services was addressed in [46]. Other energy sharing compositions were proposed to address charging mobile IoT devices [47] [48] [46]. To the best of our knowledge, none of the previous work studied the use of drones as wireless chargers to reduce the delivery time in drone delivery services.…”

Quality of Experience Optimization in IoT Energy Services

“…For instance, in a particular location at a specific time, an energy consumer might not find the required energy to fulfill their request. Another challenge is the limited availability of energy in the crowdsourced IoT environment, which leads energy providers to resist offering their energy [15] [16]. Furthermore, the business model of the super provider is context-sensitive, i.e., the revenue and foot traffic may differ over time depending on the time, and the type of the business [17].…”

“…We assume that the IoT coordinator offers effective incentives in the form of credits to encourage providers' participation. The credits may be used to receive energy when the providers act as consumers in the future [14] [15]. The IoT coordinator is assumed to be deployed one hop away from the energy providers and consumers (e.g., router at the edge) to minimize the communication overhead and latency while advertising energy services and requests.…”

“…Existing energy service composition frameworks mainly focus on accommodating a single request [3][15] or catering a single service [17] [14]. These techniques do not consider leveraging multiple services to fulfill multiple requests simultaneously [15][3].…”