Recent years have seen a widespread use of information and communication technology (ICT) in the implementation of smart city applications. A key enabler in the smart city paradigm is the Internet-of-Things (IoT), which facilitates automated real-time sensing, communication, and actuation, assisting in unmanned monitoring of physical phenomenon and supports intelligent decision making. Nevertheless, designing a smart and energy-efficient IoT network for sustainability and near-perfect device actuation is a major challenge. To address this, our preliminary work (Roy et al., 2019) proposed a gene regulatory network (GRN)-based distributed event sensing and data collection framework called bioSmartSense. It attempted to make sensing and reporting tasks energy-efficient through bio-inspired self-modulation of IoT device energy levels. In this paper we extend it, under the name bioSmartSense+, to conceive realistic sensing and reporting mechanisms by incorporating device heterogeneity, probabilistic sensing, and priority-based event reporting. For experimental study, we used both simulated and real data to evaluate energy and coverage-related performances. Experimental results establish the efficacy of our framework in terms of energy-efficiency and event reporting rate compared to a state-of-the-art data collection approach.

近年来，信息和通信技术（ICT）在实施智能城市应用程序中得到了广泛使用。物联网（IoT）是智慧城市范例中的关键推动力，它促进了自动实时感测，通信和驱动，有助于对物理现象进行无人监控，并支持智能决策。尽管如此，为可持续性和近乎完美的设备致动而设计一个智能且节能的物联网网络仍然是一项重大挑战。为了解决这个问题，我们的初步工作（Roy等人，2019）提出了一种基于基因调控网络（GRN）的分布式事件感知和数据收集框架，称为bioSmartSense。它试图通过生物启发式的IoT设备能级自调节来提高传感和报告任务的能源效率。在本文中，我们将其扩展为bioSmartSense +，以通过结合设备异构性，概率感知和基于优先级的事件报告来构想现实的感知和报告机制。对于实验研究，我们同时使用模拟和真实数据来评估能源和覆盖范围相关的性能。与最新的数据收集方法相比，实验结果在能效和事件报告率方面确立了我们框架的有效性。