The empirical Shannon entropy is a popular metric for anomaly detection in network traffic. However, computing its exact value in real time requires fast access to a large number of counters, which is unfeasible in high-speed networks. Approximate approaches using sketches can estimate the entropy with low memory usage. However, achieving good estimation accuracy still requires large data structures, making their implementation difficult in dedicated hardware and programable switches. In this paper, we present an entropy-estimation algorithm and its implementation in a programmable switch, which achieves good accuracy for large traffic traces with low memory usage. The algorithm uses sketches to track the packet count of only the most-frequent flows and models the rest of the traffic with a uniform distribution. The implementation operates within the restrictions imposed by the P4 switch programming language, achieving a 1.72% average estimation error on 12 real-world large traces from public repositories.

Bluetooth Low Energy (BLE) is a wireless protocol optimized for low-power communication. To design energy-efficient devices, the protocol provides a number of parameters that need to be optimized within an energy, latency, and throughput design space. Therefore, an energy model that can predict the energy consumption of a BLE-based wireless device for different parameter value settings is needed. As BLE differs from the well-known Bluetooth Basic Rate (BR) significantly, models for Bluetooth BR cannot be easily applied to the BLE protocol. In past years, there have been a couple of proposals on energy models for BLE. However, none of them can model all the operating modes of the protocol. This article presents an energy model of the BLE protocol, which allows the computation of a device’s power consumption in all possible operating modes. To the best of our knowledge, our proposed model is not only one of the most accurate ones known so far (because it accounts for all protocol parameters), but it is also the only one that models all the operating modes of BLE. Based on this model, guidelines for system designers are presented that help choose the right parameters for optimizing the energy consumption. The model is publicly available as a software library for download.

The process of two wireless devices meeting over-the-air for the first time is referred to as neighbor discovery. In mobile ad-hoc networks, battery powered devices duty-cycle their radios during neighbor discovery. As a result, they transmit and receive for very short durations of time and sleep at other times. Energy-efficient protocols, which guarantee short, bounded latencies while achieving low energy-consumptions are highly important for long battery lifetimes. In the past, neighbor discovery has been carried out mostly using slotted protocols, which subdivide time into multiple, equal length periods, called slots. An alternative are slotless protocols, which decouple beaconing from listening and can potentially achieve lower latency-duty-cycle-relations. As in slotted protocols, they also guarantee bounded latencies. However, understanding the mechanisms that ensure these deterministic bounds is more complex than for slotted protocols, since they rely on less intuitive concepts. In this demo, we propose a setup that visualizes the operation of two radios with slotless protocols in real-time, thereby providing insights that help in understanding slotless neighbor discovery. This demo is supposed to accompany the paper entitled "Griassdi: Mutually Assisted Slotless Neighbor Discovery Protocols", which appeared at IPSN 2017 as a regular paper.

Recent results show that slotless, purely-interval based neighbor discovery protocols, in which time is assumed to be continuous, achieve significantly lower worst-case discovery latencies than time-slotted protocols. In slotted protocols, the discovery of device A by B and vice-versa occurs within the same slot, and hence the latencies for one-way and two-way discovery are identical. However, in purely interval-based protocols, these latencies are independent from each other, leading to longer mean latencies for two-way discovery. In this paper, we propose a cooperative approach to reduce this two-way discovery latency. In particular, each side broadcasts information on the time-period until its next reception phase takes place. The remote device adjusts its beacon schedule accordingly once a first packet is received. Compared to non-cooperative slotless protocols, this technique can reduce the two-way discovery latency by up to 43 %. We propose a theory to model such protocols and show that with an optimized schedule, our proposed protocol achieves considerably shorter mean latencies than all known protocols, while still guaranteeing worst-case latencies that are similar to the best known solutions. For example, compared to Searchlight-Striped, our proposed protocol achieves by up to 89 % lower mean latencies and by up to 86 % lower worst-case latencies.

With the Internet of Things (IoT) becoming a reality, power-efficient techniques are crucial to achieve sufficient battery lifetimes. Whereas current medical IoT devices typically acquire data with a constant quality, we propose an architecture that dynamically adjusts the data quality adaptively based on the current medical condition of the subject being monitored. Since transmission and processing make up a large fraction of the energy consumption, the reduction of the link traffic and processing effort caused by such an adjustment results in a decreased energy consumption of the devices. For example, if anomalies in the monitored data are detected, the monitoring is performed with an increased granularity and more exhaustive processing. Further, not all data generated by the medical sensors needs to be transmitted during all times. Only if certain events are detected, the transmission of the complete data needs to be activated. In this paper, we present a novel approach for body-worn medical IoT devices. In particular, a generic, distributed architecture for the power-management of the whole system, which is based on dynamically switching services, is presented. We show that such an architecture can reduce the energy-consumption of medical sensors by up to 80 % in real-world measurements.

Bluetooth Low Energy is a time-slotted wireless protocol aimed towards low power communication for battery-driven devices. As a power-management capability, whenever there is less data to send, the slave is allowed to remain in a low power mode during a given number of time-slots in a row. However, since the master does not know the exact sleep behavior of the slave, it has to wake-up at every time-slot and repeat its packets until the slave is awake. As a result, applications with variable throughput lead to many energy-consuming idle-slots at the master. In such applications, usually the connection parameters are chosen considering the worst case at design time and remain constant during operation. In this paper, we propose a novel power-management framework for BLE. Rather than skipping slots at the slave side, the proposed system updates the interval between two consecutive time-slots during runtime by applying online algorithms. To avoid data-loss or high delays, the framework guarantees that constraints on latency are met and buffers never overflow. Energy measurements of three different test-cases show that up to 42 percent of the energy consumption of a BLE master can be saved with our power management system.

Today high-end cars have extremely complex E/E architectures -- with 50--100 electronic control units (ECUs), connected by communication buses like CAN, FlexRay and Ethernet. They are used to run several (control) applications with many million lines of code. We propose a radically new architecture where all these applications are instead run on a mobile phone being carried by the driver. The car now has a considerably simpler architecture with few or no ECUs, using RF links to connect sensors and actuators to the mobile phone with a powerful multicore processor. We discuss the advantages and challenges and describe a small prototype implementation with an adaptive cruise control application.