Design Patterns in Beeping Algorithms

We consider networks of processes which interact with beeps. In the basic model defined by Cornejo and Kuhn, which we refer to as the BL variant, processes can choose in each round either to beep or to listen. Those who beep are unable to detect simultaneous beeps. Those who listen can only distinguish between silence and the presence of at least one beep. Stronger variants exist where the nodes can also detect collision while they are beeping (B_{cd}L) or listening (BL_{cd}), or both (B_{cd}L_{cd}). Beeping models are weak in essence and even simple tasks are difficult or unfeasible with them. This paper starts with a discussion on generic building blocks (design patterns) which seem to occur frequently in the design of beeping algorithms. They include multi-slot phases: the fact of dividing the main loop into a number of specialised slots; exclusive beeps: having a single node beep at a time in a neighbourhood (within one or two hops); adaptive probability: increasing or decreasing the probability of beeping to produce more exclusive beeps; internal (resp. peripheral) collision detection: for detecting collision while beeping (resp. listening); and emulation of collision detection: for enabling this feature when it is not available as a primitive. We then provide algorithms for a number of basic problems, including colouring, 2-hop colouring, degree computation, 2-hop MIS, and collision detection (in BL). Using the patterns, we formulate these algorithms in a rather concise and elegant way. Their analyses (in the full version) are more technical, e.g. one of them relies on a Martingale technique with non-independent variables; another improves that of the MIS algorithm (P. Jeavons et al.) by getting rid of a gigantic constant (the asymptotic order was already optimal). Finally, we study the relative power of several variants of beeping models. In particular, we explain how every Las Vegas algorithm with collision detection can be converted, through emulation, into a Monte Carlo algorithm without, at the cost of a logarithmic slowdown. We prove that this slowdown is optimal up to a constant factor by giving a matching lower bound

Election algorithms with random delays in trees

The election is a classical problem in distributed algorithmic. It aims to design and to analyze a distributed algorithm choosing a node in a graph, here, in a tree. In this paper, a class of randomized algorithms for the election is studied. The election amounts to removing leaves one by one until the tree is reduced to a unique node which is then elected. The algorithm assigns to each leaf a probability distribution (that may depends on the information transmitted by the eliminated nodes) used by the leaf to generate its remaining random lifetime. In the general case, the probability of each node to be elected is given. For two categories of algorithms, close formulas are provided

Whac-A-Mole: Smart Node Positioning in Clone Attack in Wireless Sensor Networks

Wireless sensor networks are often deployed in unattended environments and, thus, an adversary can physically capture some of the sensors, build clones with the same identity as the captured sensors, and place these clones at strategic positions in the network for further malicious activities. Such attacks, called clone attacks, are a very serious threat against the usefulness of wireless networks. Researchers proposed different techniques to detect such attacks. The most promising detection techniques are the distributed ones that scale for large networks and distribute the task of detecting the presence of clones among all sensors, thus, making it hard for a smart attacker to position the clones in such a way as to disrupt the detection process. However, even when the distributed algorithms work normally, their ability to discover an attack may vary greatly with the position of the clones. We believe this aspect has been greatly underestimated in the literature. In this paper, we present a thorough and novel study of the relation between the position of clones and the probability that the clones are detected. To the best of our knowledge, this is the first such study. In particular, we consider four algorithms that are representatives of the distributed approach. We evaluate for them whether their capability of detecting clone attacks is influenced by the positions of the clones. Since wireless sensor networks may be deployed in different situations, our study considers several possible scenarios: a uniform scenario in which the sensors are deployed uniformly, and also not uniform scenarios, in which there are one or more large areas with no sensor (we call such areas “holes”) that force communications to flow around these areas. We show that the different scenarios greatly influence the performance of the algorithms. For instance, we show that, when holes are present, there are some clone positions that make the attacks much harder to be detected. We believe that our work is key to better understand the actual security risk of the clone attack in the presence of a smart adversary and also with respect to different deployment scenarios. Moreover, our work suggests, for the different scenarios, effective clone detection solutions even when a smart adversary is part of the game

Data analytics for smart buildings: a classification method for anomaly detection for measured data

Abstract Data generated by the increasingly frequent use of sensors in housing provide the opportunity to monitor, manage and optimize the energy consumption of a building and the user comfort. These data are often strewn with rare or anomalous events, considered as anomalies (or outliers), that must be detected and ultimately corrected in order to improve the data quality. However, many approaches are used or might be used (for the most recent ones) to achieve this purpose. This paper proposes a classification methodology of anomaly detection techniques applied to building measurements. This classification methodology uses a well-suited anomaly typology and measurement typology in order to provide, in the future, a classification of the most adapted anomaly detection techniques for different types of building measurements, anomalies and needs

Deep learning models for building window-openings detection in heating season

The increasing use of monitoring systems such as Building Management System (BMS) or connected devices bring the opportunity to better evaluate, model or control both occupants’ comfort and energy consumed by an operated building thanks to the consequent amount of data provided (e.g., air temperature, CO2 concentration, electricity consumption). Occupants’ behavior and more specifically window-openings affect both occupants’ thermal comfort and building energy consumption and are therefore key components to consider. This paper presents a comparison of machine learning models applied on window-openings detection during the heating season such as: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Random Forest Classifier (RFC) and two Recurrent Neural Network (RNN), namely, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). While some applications of Artificial Intelligence (AI) methods applied on window-openings detection exist in the literature, this Submitted to Building and Environment January 2023 study proposes a detailed comparison of the main methods and focuses on the impact of feature engineering process considering four different data transformations based on field expertise and more than 800 different combinations built on six indoor and outdoor measurements. Results show that some of the proposed transformations and combinations positively impact all models performances. The best performances on window-openings detection are attained by using indoor temperature and CO2 concentration on RNN models with an average F1-score of 0.78 while LDA, SVM and RFC models tend to provide satisfying but lower performance around 0.70-72. In addition, by using the right transformation, significant results can be achieved by detecting up to 84-88 % of window-opening times with the sole use of indoor air temperature measurements

On Handshakes in Random Graphs

International audienceIn [Y. Métivier, N. Saheb, A. Zemmari, Analysis of a randomized rendezvous algorithm, Inform. and Comput. 184 (2003) 109–128], the authors introduce and analyze a randomized procedure to implement handshakes in graphs. In this paper, we investigate the same problem in random graphs. We prove results on the probability of success and we study the distribution of the random variable which counts the number of handshakes in the graph