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We propose a Context-aware Smallworld routing protocol for wireless ad-hoc networks which finds efficient routes to the destination nodes often through a very short chain of intermediate nodes, without using any routing table. This protocol exploits the “small-world” phenomenon of social networks where source-destination pairs typically get connected through few intermediate friends and the individuals can collectively discover such short paths. The key idea behind our routing protocol is that for forwarding an incoming packet to the destination, a node picks the next hop from among the one-hop neighbor from its one-hop neighborhood that is closest to the destination, and multiple randomly selected long-distance neighbor(s), i.e., from outside of its one-hop neighborhood, based on which of these neighbors is closest to the destination. The long-distance neighbors are selected by using their contextual information through a probabilistic mapping. We provide the theoretical foundations behind our protocol. This simple but effective algorithm can meet performance objectives for most scenarios by reducing loss and latency. Simulation results, using synthetic network topologies, have demonstrated that the routing performance of context-aware Smallworld is better than geographical (distance-based) Smallworld and other standard proactive routing protocols (e.g., OLSRv2) when metrics, such as packet losses, end-to-end delays, hop-counts, connectivity drops, and control traffic generated, are used as the key performance indicators. We also show that the proposed protocol is resilient to dynamic topology changes. [ABSTRACT FROM AUTHOR]

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy- efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course. [ABSTRACT FROM AUTHOR]