In the 2.4 GHz unlicensed spectrum, the coexistence of WiFi, Bluetooth and IEEE 802.15.4 devices generates increased channel contention. Notably, low-power wireless networks experience packet loss and delays due to interference. To improve the performance of low-power wireless networks under interference, we propose a data driven proactive approach based on interference modeling for white space prediction. We leverage statistical analysis of real-world traces from two indoor environments characterized by varying channel conditions to identify interference patterns. We characterize interference in terms of Inter-Arrival Time (IAT) and number of interfering signals and use a Gaussian Mixture Model (GMM) to accurately estimate the interference distribution as observed by the low-power wireless nodes. Then, we use a Hidden Markov Model (HMM) for white space prediction. Our validation w.r.t. real-world traces from two environments show that our GMM model can estimate interference with an accuracy higher than 94:7%. Moreover, the white space prediction evaluation shows an average accuracy of 97:7% and 89:5% across the two environments.

In shared wireless sensor networks (WSNs), multiple users request access to sensing resources, often with varying sampling rates and QoS requirements. To accommodate a request, appropriate sensing, computing and communication resources need to be allocated across the network. Traditionally, each request is mapped to a dedicated set of resources, even when the requests are similar. The need to reduce resource usage has led to data virtualization techniques that focus primarily on merging the requests ignoring the QoS requirements. In this paper, we present a QoS-aware resource allocation approach, JudiShare, that merges requests, where possible, if they are compatible in their requirements, providing judicious reuse of both sensing and communication resources through a mixture of data virtualization and Virtual Network Embedding (VNE). We show that JudiShare respects QoS requirements and reduces resource usage to up to 60%. This, in turn, allows up to 50% more requests to be accommodated onto the network, even when the network resources are fully utilized.

Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks causing a significant degradation of the overall performance. We propose here a proactive approach based on WiFi interference modeling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channeloccupancy prediction. We evaluated the performance of: i) the MMPP(2) traffic model w. r. t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.

We present a technique to identify transmission timing for IEEE 802.15.4 based Wireless Sensor Networks (WSNs) in the presence of WiFi interference. Our technique is based on modeling WiFi traffic with a Modulated Markov Poisson Process (MMPP) model in order to enable us to predict when WiFi transmissions take place and avoid them. We have evaluated the accuracy of our model in a small test-bed. Results are promising and suggest that our approach can increase the reliability of IEEE802.15.4 transmissions.

Previous research has shown that centralized network control in Wireless Sensor Networks (WSNs) can lead to improved network lifetime, benefit reliability, help to diagnose and localize network failures, assist network recovery, and lead to optimal routing and transmission scheduling. A stepping stone to centralized network control is to build and maintain a complete network topology model that scales and reacts to the network dynamics that occur in low-power wireless networks. We propose SMOG as a mechanism to build and maintain a centralized full network topology model using probabilistic data structures. Extensive analysis of the proposed approach in both simulation and two testbeds shows that SMOG can build a complete model of a WSN of over 100 nodes with 98% accuracy in less than four minutes. Our approach also offers fast recovery from heavy network interference, recovering model accuracy to 98% in less than two and a half minutes.

How do the characteristics of the surrounding environment affect the ability of the nodes of a wireless sensor network (WSN) to communicate? Partial answers to this question can be found in the literature, but always with a focus on the short-term, small-scale behavior of individual links, as this directly informs the design of WSN protocols. In this paper, we are instead concerned with the large scale behavior of the overall network, observed over a longer time scale, as our primary interest is to support the deployment of WSNs by characterizing the impact of the target environment. Motivated by a real-world wildlife monitoring application, we report about experimental campaigns in three outdoor environments characterized by varying degrees of vegetation. Experiments are repeated in summer and winter, to account for seasonal variations, and span multiple days, allowing us to assess variations induced by the succession of day and night. Our experiments focus primarily on characterizing the impact of the environment on the physical layer, but we also investigate how this is mirrored at higher layers. We analyze the experimental data along multiple dimensions, yielding quantitative answers to the aforementioned question, and eliciting trends and findings previously not reported in the literature. We argue that this type of study may inspire new methods to better estimate the performance of a WSN in its target deployment environment.

We study what happens when the same wireless sensor network (WSN) is "immersed" in different outdoor environments, namely, two types of forests and an open field. Qualitative answers can be derived from existing results. Here, we aim to quantify the extent of changes, both at the physical and application layer, based on the combined effect of environmental parameters over daily and seasonal time-scales.

Assessing the connectivity of Wireless Sensor Networks in the specific environment in which they are deployed is crucial to develop reliable system services and understand their behavior. In this demo, we introduce Trident, a tool that measures communication with an untethered infrastructure. It enables the execution of connectivity experiments "in the wild", supporting also the sharing of the gathered results.