Sensors are increasingly being deployed to monitor critical infrastructure. However, as the number of sensors being deployed increases, so does the amount of sensor data that must be transmitted, stored, and analyzed. Thus, a significant number of methods have been proposed to improve sensor data acquisition and analytics. However, the proposed strategies and methods generally focus exclusively on either sensor data acquisition or analytics, thus ignoring the possible optimization that can be performed by taking a holistic view. To explore this opportunity, this paper provides an overview of sensor data acquisition and analytics and an analysis of two very different use cases, specifically monitoring wind turbines and measuring utility consumption using smart meters. Based on this analysis, the Framework for joint Sensory Data Acquisition and Analytics (SENDAI) is proposed, an integrated framework that models sensor data acquisition and analytics together, thus enabling holistic reasoning about sensor data acquisition and analytics. To demonstrate how the information in SENDAI can be used to reason about sensor data acquisition and analytics together, we show how sensor data acquisition can be optimized to respond efficiently to query workloads.

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest inpragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

With the increasing use of Internet-of-Things–enabled sensors, it is important to have effective methods to query the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) only a subset of the sensors at any given time, since the values of the non-sampled sensors can be estimated from the sampled values. If we can divide the set of sensors into disjoint so-calledrepresentative sampling subsets, in which each represents all the other sensors sufficiently well, then we can alternate between the sampling subsets and, thus, increase the battery life significantly of the sensor network. In this article, we formulate the problem of finding representative sampling subsets as a graph problem on a so-calledsensor graphwith the sensors as nodes. Our proposed solution,SubGraphSample, consists of two phases. In Phase-I, we create edges in thesimilarity graphbased on the similarities between the time-series of sensor values, analyzing six different techniques based on proven time-series similarity metrics. In Phase-II, we propose six different sampling techniques to find the maximum number ofrepresentative sampling subsets. Finally, we proposeAutoSubGraphSample, which auto-selects the best technique for Phase-I and Phase-II for a given dataset. Our extensive experimental evaluation shows thatAutoSubGraphSamplecan yield significant battery-life improvements within realistic error bounds.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case: the instants at which information is collected and used may be dependent on a certain query process, and resource-constrained environments such as most Internet of Things (IoT) use cases require precise timing to fully exploit the limited available transmissions. In this work, we consider apull-based communication modelin which the freshness of information is only important when the receiver generates a query: if the monitoring process is not using the value, the age of the last update is irrelevant. We optimize the Age of Information at Query (QAoI), a metric that samples the AoI at relevant instants, better fitting the pull-based resource-constrained scenario, and show how this can lead to very different choices. Our results show that QAoI-aware optimization can significantly reduce the average and worst-case perceived age for both periodic and stochastic queries.

Age of Information (AoI) has become an important concept in communications, as it allows system designers to measure the freshness of the information available to remote monitoring or control processes. However, its definition tacitly assumes that new information is used at any time, which is not always the case. Instead instants at which information is collected and used are dependent on a certain query process. We propose a model that accounts for the discrete time nature of many monitoring processes, by considering a pull-based communication model in which the freshness of information is only important when the receiver generates a query. We then define the Age of Information at Query (QAoI), a more general metric that fits the pull-based scenario, and show how its optimization can lead to very different choices from traditional push-based AoI optimization when using a Packet Erasure Channel (PEC).