Nils Aschenbruck

Selective and Secure Over-The-Air Programming for Wireless Sensor Networks

The growing range of Wireless Sensor Network (WSN) applications, their long-life and large-scale design, as well as various deployment fields necessitate the feasibility of remote maintenance and reprogramming of in-situ sensor nodes. The network-wide dissemination of program code is not appropriate in every WSN due to the heterogeneity of sensor hardware, the diversity of sensing tasks, and the event and location dependency of software. Thus, a flexible and group-wise selective Over-The-Air Programming (OTAP) is required in these scenarios. Furthermore, securing the OTAP protocol is imperative in order to prevent unauthorized and malicious reprogramming attempts. In this paper, we introduce SenSeOP, a selective and secure OTAP protocol for WSNs. For this purpose, the proposed protocol uses multicast transfer supported by asymmetric cryptography. We evaluate the performance of our approach in real testbeds, compare it with state-of-the-art protocols, and show that this approach enables efficient and reliable wireless reprogramming.

Introducing Geographic Restrictions to the SLAW Human Mobility Model

Among other statistical features, the analysis of fine-grained GPS traces from different outdoor scenarios has shown that human mobility statistically resembles Lévy Walks and led to the design of the Self-similar Least-Action Walk (SLAW) mobility model. It was concluded that human mobility is scale-free and that this feature is invariant irrespective of any geographic constraints. These constraints were considered too scenario-specific and were omitted in SLAW. However, we argue that geographic constraints should not be considered as an unnecessary detail, but as an important feature of a realistic mobility model for the simulative performance evaluation of mobile networks. Therefore, we introduce geographic restrictions to SLAW in the form of maps. Our evaluation of the extended model (called MSLAW) shows that the introduced restrictions have a significant impact on several performance metrics relevant for opportunistic networks.

A security architecture and modular intrusion detection system for WSNs

Wireless Sensor Networks (WSNs) are deployed in a wide range of application scenarios. These typically involve monitoring or surveillance of animals or humans, infrastructure, or territories. Since security as well as privacy play an increasingly important role in these contexts, sensor nodes and sensor networks need to be protected from spurious environmental effects and malicious attacks. In addition to attacks known from conventional wireless networks, the specific properties of WSNs lead to new kinds of attack. Moreover, countermeasures are subject to strict resource limitations of the motes and, therefore, have to be light-weight and effective at the same time. In this paper, we first present a comprehensive security architecture for WSNs, consisting of different attack types (including WSN-specific attacks) and countermeasures. Second, we propose a modular Intrusion Detection System (IDS) as a framework for this architecture. Finally, we give details on selected modules and discuss practical implementation issues.

Identification of contamination zones for sinkhole detection in MANETs

This paper presents a novel approach intended to detect the well-known sinkhole attack in MANETs, one of the most representative route poisoning attacks aimed at exploiting multi-hop source-destination routes to seize communications. Malicious sinkhole nodes try to attract most of the surrounding network traffic by providing fake routes, invalidating alternative legitimate routes and disrupting the normal network operation.Our detection proposal leverages on the existence of contamination borders, formed by legitimate nodes under the influence of the sinkhole attack and, at the same time, neighbors of other non-contaminated legitimate nodes. We show that analyzing the routing information of their vicinity, these border nodes are more likely to properly detect sinkholes. We evaluate our approach in a simulation framework and the results show the promising nature of the proposal in terms of detection capabilities and performance in comparison with other solutions in the literature.

A Novel Collaborative Approach for Sinkhole Detection in MANETs

This paper presents a novel approach intended to detect sinkholes in MANETs running AODV. The study focuses on the detection of the well-known sinkhole attack, devoted to attract most of the surrounding network traffic by providing fake routes, and thus, invalidating alternative legitimate routes and disrupting the normal network operation. Our detection approach relies on the existence of “contamination borders”, formed by legitimate nodes under the influence of the sinkhole attack and, at the same time, neighbors of non-contaminated legitimate nodes. Thus, by collecting the routing information of the neighbors, these nodes are likely to be able to properly detect sinkholes. We evaluate our approach in a simulation framework and the experimental results show the promising nature of this approach in terms of detection capabilities.

On the potential of Wireless Sensor Networks for the in-situ assessment of crop leaf area index

Design of a novel low-cost sensor modification for non-destructive LAI assessment.Maize field campaigns including a comparative analysis with a standard instrument.An impact evaluation showing high accuracy and robustness of our approach. A precise and continuous in-situ monitoring of bio-physical crop parameters is crucial for the efficiency and sustainability in modern agriculture. The leaf area index (LAI) is an important key parameter allowing to derive vital crop information. As it serves as a valuable indicator for yield-limiting processes, it contributes to situational awareness ranging from agricultural optimization to global economy. This paper presents a feasible, robust, and low-cost modification of commercial off-the-shelf photosynthetically active radiation (PAR) sensors, which significantly enhances the potential of Wireless Sensor Network (WSN) technology for non-destructive in-situ LAI assessment. In order to minimize environmental influences such as direct solar radiation and scattering effects, we upgrade such a sensor with a specific diffuser combined with an appropriate optical band-pass filter. We propose an implementation of a distributed WSN application based on a simplified model of light transmittance through the canopy and validate our approach in various field campaigns exemplarily conducted in maize cultivars. Since a ground truth LAI is very difficult to obtain, we use the LAI-2200, one of the most widely established standard instruments, as a reference. We evaluate the accuracy of LAI estimates derived from the analysis of PAR sensor data and the robustness of our sensor modification. As a result, an extensive comparative analysis emphasizes a strong linear correlation ( r 2 = 0.88 , RMSE=0.28) between both approaches. Hence, the proposed WSN-based approach enables a promising alternative for a flexible and continuous LAI monitoring.

An UHF RFID performance evaluation architecture based on traces from a software defined transceiver

Software Defined Radio (SDR) based approaches are frequently used to study the performance of EPC Global Class 1 Generation 2 compliant ultra-high frequency (UHF) Radio Frequency IDentification (RFID) systems and their enhancements. To perform credible evaluations, either an anechoic chamber or parallel processing of different reader software at the SDR level are used. Both approaches are challenging as an anechoic chamber does not reflect environmental influences and parallel processing at SDR level lacks reproducibility. In this paper, we present a trace based evaluation architecture. We measure traces in real world scenarios using a software defined RFID transceiver. Based on these traces credible and reproducible evaluations can be performed. We show the value of our architecture by evaluating different clock recoveries approaches.

On the potential of Wireless Sensor Networks for the in-field assessment of bio-physical crop parameters

The exploration of bio-physical crop parameters is fundamental for the efficiency of smart agriculture. The leaf area index (LAI) is one of the most important crop parameters and serves as a valuable indicator for yield-limiting processes. It contributes to situational awareness ranging from agricultural optimization to global economy. In this paper, we investigate the potential of Wireless Sensor Networks (WSNs) for the in-field assessment of bio-physical crop parameters. Our experiences using commercial off-the-shelf (COTS) sensor nodes for the indirect and nondestructive LAI estimation are described. Furthermore, we present the design of our measurement architecture and results of various in-field measurements. By directly comparing the results achieved by WSN technology with those of a conventional approach, represented by a widely used standard instrument, we analyze whether bio-physical crop characteristics can be derived from WSN data with a desired accuracy. Moreover, we propose a simple approach to significantly enhance the accuracy of COTS sensor nodes for LAI estimation while, at the same time, reveal open challenges.

RaLaNS — A ray launching based propagation loss model for ns-3

Although a realistic simulation of wireless networks mainly depends on a proper model of radio wave propagation, it is common to simply assume a fixed, circular range of communication. Many protocols and algorithms have been designed based on this assumption, risking that they might not be applicable to real scenarios. We assume that researchers mainly fall back to simple propagation models because more realistic models are rare and difficult to use. Therefore, we present RaLaNS, a propagation loss model for ns-3 that is based on ray launching. The generated signal strength distribution takes into account reflection and diffraction at buildings, and thus, greatly differs from the simple models in ns-3. Therefore, RaLaNS allows for a more realistic evaluation of new algorithms. We demonstrate this by showing a case study of node placement strategies in urban areas in the second part of the paper.

On Modeling and Impact of Geographic Restrictions for Human Mobility in Opportunistic Networks

Human mobility has been shown to be of significant impact on the performance of Opportunistic Networks (OppNets). As shown in earlier work, the integration of geographic restrictions is not only a further step to more credibility of OppNet simulative performance evaluation, but also influences relevant metrics, such as contact duration and number of contacts. In this paper, we discuss and propose solutions to modeling issues related to geographic restrictions and investigate the impact of the road network underlying the scenarios map extract. Inter-Contact Times (ICTs) are not significantly impacted by the diversity in road network structure but considerably shorter and less contacts are reported for Tokyos dense road network, making it an especially challenging urban scenario for forwarding algorithm performance evaluation.