Situation awareness via Internet of things and in-network data processing

Abstract

Computing on the edge of the Internet of things comprises among other tasks in-sensor signal processing and performing distributed data fusion and aggregation at network nodes. This poses a challenge to distributed sensor networks of low computing power devices that have to do complex fusion, aggregation and signal processing in situ. One of the difficulties lies in ensuring validity of data collected from heterogeneous sources. Ensuring data validity, for example, the temporal and spatial correctness of data, is crucial for correct in-network data fusion and aggregation. The article considers wireless sensor technology in military domain with the aim of improving situation awareness for military operations. Requirements for contemporary intelligence, surveillance and reconnaissance applications are explored and an experimental wireless sensor network, designed to enhance situation awareness to both in-the-field units and remote intelligence operatives, is described. The sensor nodes have the capability to perform in-sensor signal processing and distributed in-network data aggregation and fusion complying with edge computing paradigm. In-network data processing is supported by service-oriented middleware which facilitates run-time sensor discovery and tasking and ad hoc (re)configuration of the network links. The article describes two experiments demonstrating the ability of the wireless sensor network to meet intelligence, surveillance and reconnaissance requirements. The efficiency of distributed data fusion is evaluated and the importance and effect of establishing data validity is shown.

A multisensor data fusion strategy for path selection in Internet‐of‐Things oriented wireless sensor network (WSN)

“…In the work of Ehala et al, the in‐network data processing technique (IDP) has been proposed using the multisensor model. In this paper, the author stated that the IDP is supported by service‐oriented middleware, which facilitates run‐time sensor discovery and tasking and ad hoc (re)configuration of the network links.…”

Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

“…This section discusses the importance of temporal validity intervals of input data for fusion node and how the value of validity interval of sensor readings affects the selection of temporally compatible inputs for the data fusion in WSN. The necessity of checking and ensuring the sensor data validity has been discussed in our earlier papers, 4,35 where it has been explained how every sensor reading has temporal and spatial validity intervals associated with it. These intervals depend on several aspects, for example, the validity area depends on the location of the WSN and on the properties of the phenomenon being observed, while the temporal validity interval depends both on the properties of the environment where the node is located and on the phenomenon being observed.…”

“…The more detailed overview of the hardware is given in our previous work. 4 Sensor node placement for the experiment is depicted in Figure 6. Two fusion nodes A and B were used, with node A receiving messages from the four sensors on the left and node B receiving messages from four sensors on the right.…”

“…Sensor sampling speed for each microphone was 20 kHz and measurement frame length, used in AoA processing, was 136.5 ms. As a result, approximately seven AoA calculations were done per second by a single node. Before transmitting the results, the sensor node was able to check the temporal validity of readings (described in Ehala et al 4 ) and to transmit only the valid results to fusion nodes at an interval determined by the data subscription agreement between sensor and fusion nodes.…”

“…We suggest that only temporally and spatially compatible data should be fused and/or combined to infer new synthesized readings, and we propose a strategy for selection of temporally suitable data for improving the temporal consistency of input data for in-network processing. The strategy, implemented in the WSN fusion nodes' middleware component as a default service, combines the use of temporal constraints 4 with a temporal alignment and selection algorithm and is customized for processing multiple streams of sensor data, which can be intermittent and arrive out of order. The mechanisms for data alignment, selection of suitable input data and verifying them against validity and simultaneity constraints are described using Q-modelling formalism.…”