Building efficient wireless sensor networks with low-level naming

Abstract

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational testbed.

A case for ICN usage in IoT environments

“…Pentikousis et al [14] present several ICN baseline scenarios and discuss relevant issues towards the application of this approach to IoT. By making the information visible and identifiable at the network layer, named data can be used for a more efficient coordination and collaboration management within IoT, reinforcing the beneficial effect of named data on IoT environments [15].…”

RideSharing: Fault Tolerant Aggregation in Sensor Networks Using Corrective Actions

“…Each sensor could send its id, value pair to the data sink, but this would increase the data being transmitted in the network. In-network aggregation can reduce this communication overhead substantially [14], [18]. Sensors are arranged in a spanning tree rooted at the sink, each intermediate node receives messages from its children, computes an aggregate (e.g., SUM) of its children's and own values, and then forwards to its parent only a single message with the aggregate value.…”

PINCO: a pipelined in-network compression scheme for data collection in wireless sensor networks

“…But schemes for pushing the query into the network, processing the data in the network, and compression for data reduction at the source are proposed for wireless sensor networks in general [9] [12] [13] [14] [16]. Research on in-network data processing for sensor networks is mainly focused on data aggregation and will be summarized and then compared with our approach in Section III.…”

“…Researchers have proposed different ad-hoc routing algorithms [9] [12] [18] for sensor networks. These algorithms take into consideration the data-centric and many-to-one nature of sensor data flows created by propagating sensor measurements of the same phenomenon from many nodes to the basenode.…”

“…sending a single bit of data can consume the energy of executing a thousand instructions) [15]. The third one is that, sensor data communicated throughout the network are likely to have both spatial and temporal redundancy being activated by the same phenomenon of interest [12] [9]. With these three features that set sensor networks apart from traditional networks, sensor applications have the main goal of collecting data from the network.…”