# An Aspect-Oriented Approach to Time-Constrained Strategies in Smart City IoT Applications
## 1 Introduction  
The Internet of Things (IoT) is rapidly gaining traction in various problem domains, including smart cities applications [1; 2; 3]. From an engineering perspective, IoT networks can be modelled as heterogeneous multi-agent systems (MASs) [4; 5], often with memory, time, power, bandwidth, and processing power constraints [6; 7]. The smart city is \"derived from the adoption and application of mobile computing systems through practical data management networks amongst all components and layers of the city itself\" [8]. A smart city is an \"urbanization region that collects data using several digital and physical devices...to manage revenues, resources and assets...utilized to boost performance throughout the city\" [3]. In [9], the authors identify social, environmental, and economic sustainable development as prerequisites for smart cities. IoT-based and related disruptive technologies are proposed as a way of achieving these goals [9]. They consider smart cities in terms of dimensions, digital transformation, sustainability, and resiliency [9]. However, they note that existing research has been focused on \"megacities in developed nations [9]\" and that there is room for broader studies which consider smart city applications in a wider range of contexts [9]. In a survey of smart city IoT applications [2], the authors enumerate several components of smart cities in which IoT technologies take part: smart transportation, smart agriculture, smart energy, smart infrastructure, smart city services, smart homes, smart health, and smart industry. Each of these smart city components is supported by a complex infrastructure, from the sensing layer, comprising resource-constrained edge devices, through to the business layer, comprising data analytics supported by applications and cloud services [2]. To achieve these goals, the temporal aspect of data and agent behavior in such systems is critical: smartcity applications must respond to changes in the environment and dynamically reconfigure themselves to provide optimal service. In [10], the authors propose to use enterprise architecture (EA) and service-oriented architecture to support the development of smart cities. The EA approach considers four perspectives: the \"business view, IT view, governance view, and security view [10]\". Service-oriented architecture (SOA) \"offers flexible integration and service reusability [11]\" through a service-based modular architecture [11]. Other research has considered the potential for applying machine-learning strategies to improve smart city outcomes [12], the relationship between smart city policies and sustainability [13] and security and privacy risks [14]. Each of these considerations can be applied in a smart city in the form of a strategy, implemented in an executable form and disseminated to the IoT nodes in the smart city. The distributed and real-time nature of the data in such IoT applications means that, often, these data are transient [15]. Some of the data are superfluous in a given time period, while other data \"may remain useful for only a specific period of time after... originally queried and cached\" [16]. In [16], we proposed that, generally, data in the IoT have a window of validity (WoV) which can be defined according to the relevance of the data at a particular time and whether an acceptable copy of the data is available. In this paper, we consider the research question of how the problem of time-constrained strategy dissemination can be resolved with minimal resource impact. We propose that, in smart cities applications, besides the WoV of data, the strategies employed in the decision-making algorithms themselves may be time-constrained by a WoV. Our paper proposes a method by which the software engineering problem of how to efficiently manage the dissemination and evolution of such strategies in the IoT/smart cities context can be resolved, ensuring the timeliness and applicability of both the data and the decision-making strategies acting on them.