Ambient Intelligence Context-Based Cross-Layer Design in Wireless Sensor Networks
By exchanging information directly between non-adjacent protocol layers, cross-layer (CL) interaction can significantly improve and optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing CL schemes mainly exploit information exchange between physical, medium access control (MAC), and routing layers, with only a
... dful involving application layer. For the first time, we proposed a framework for CL optimization based on user context of ambient intelligence (AmI) application and an ontology-based context modeling and reasoning mechanism. We applied the proposed framework to jointly optimize MAC and network (NET) layer protocols for WSNs. Extensive evaluations show that the resulting optimization through context awareness and CL interaction for both MAC and NET layer protocols can yield substantial improvements in terms of throughput, packet delivery, delay, and energy performances. Keywords: wireless sensor networks; cross-layer optimization; context; ambient intelligence context-adaptive AmI applications. The term context refers to information that describes the current state or situation of an entity, which can be a person (e.g., user context), place, or object. The high-level user context information is a necessity in AmI applications to deliver personalized services to the users in an intuitive and intelligent way to support their everyday activities. Moreover, we envision such user context information could be harnessed for optimizing the performance of the underlying WSNs through cross-layer (CL) interactions. By allowing direct information exchange between non-adjacent protocol layers via CL interaction, network performances such as energy efficiency and delay can be optimized . This is particularly important for WSNs where sensor devices are energy-constrained and deployed for real-time monitoring applications. In the current literature, most research on CL optimization for WSNs have focused on interactions between lower layers of the protocol stack, i.e., physical, medium access control (MAC), and network layers  . There was also research on CL optimization that considered application requirements, e.g., quality-of-service (QoS) requirements of multimedia applications  . Unlike these previous works that either were not concerned with the application layer or used the application to only define the requirements of CL optimization, this paper focuses on how application derived information, i.e., the user context information derived from AmI application, can optimize the underlying WSN performance through CL interactions. At the system level, an AmI system can adapt its intelligent services to user-related context information. However, its underlying WSN rarely considers AmI context information. If user context information can influence how an intelligent system responds, there is also a possibility for the underlying network to use the user context information. This allows the network protocols to become smart by adapting their functionality to user situations. For the first time, we proposed a generic and customizable CL framework that utilizes AmI context information from application layer for optimizing protocol performance in WSNs . This framework can be applied to any layer of the protocol stack and is sufficiently generic to be customized to different AmI applications. In this paper, we present the framework's architecture in more detail and apply the proposed framework to jointly optimize the backoff behavior of a contention-based MAC protocol, and the path selection of an ad hoc On-demand Distance Vector (AODV) based routing protocol for WSNs by adapting their protocol functions in real-time to the user context information inferred from an AmI application. The rest of the paper is organized as follows. Section 2 outlines related work. Section 3 presents the motivating scenario. Section 4 describes the proposed framework. Section 5 illustrates a use case of the proposed framework by implementing it to optimize two existing protocols at the MAC and NET layers. Section 6 presents and discusses the evaluation results. Finally, Section 7 concludes the paper. Related Work Many existing cross-layer protocols have been designed. However, they often ignore some important information such as context information, which can be relevant for network optimization. This section reviews a number of representative context-aware cross-layer designs in WSNs.