No abstract

Wireless sensor networks (WSNs) are an enabling technology of context-aware systems. The Internet of Things (IoT), which has attracted much attention in recent years, is an emerging paradigm where everyday objects and spaces are made context-aware and interconnected through heterogeneous networks on a global scale. However, the IoT system can suffer from poor performances when its underlying networks are not optimized. In this paper, an ontology model for representing and facilitating context sharing between network entities in WSNs is proposed for the first time. The context model aims to enable optimal context-aware management of WSNs in IoT, which will also harness the rich context knowledge of IoT systems

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 handful 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.

No abstract

No abstract

Direct information exchange between non-adjacent protocol layers or "cross-layer" (CL) interaction can 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 handful involving application layer. In this paper, we focus on CL optimization for WSNs in ambient intelligence (AmI) applications, where low-level sensor data on users and their surroundings are collected and processed to infer higher-level user context information for context-adaptive AmI applications. For the first time, a framework for CL optimization based on user context of AmI application and an ontology-based context modeling and reasoning mechanism, is proposed in this paper. We apply the proposed framework to a contention-based MAC protocol for WSNs to adapt its backoff behavior to the user context. Results show that the modified MAC protocol with CL interactions can yield appreciable performance improvement in terms of throughput, frame delay, and energy efficiency.

No abstract