Markus Wälchli

BLR: beacon-less routing algorithm for mobile ad hoc networks

Routing of packets in mobile ad hoc networks with a large number of nodes or with high mobility is a very difficult task and current routing protocols do not really scale well with these scenarios. The beacon-less routing algorithm (BLR) presented in this paper is a routing protocol that makes use of location information to reduce routing overhead. However, unlike other position-based routing protocols, BLR does not require nodes to periodically broadcast Hello-messages (called beaconing), and thus avoids drawbacks such as extensive use of scarce battery-power, interferences with regular data transmission, and performance degradation. BLR selects a forwarding node in a distributed manner among all its neighboring nodes with having information neither about their positions nor even about their existence. Data packets are broadcasted and the protocol takes care that just one of the receiving nodes forwards the packet. Optimized forwarding is achieved by applying a concept of dynamic forwarding delay. Consequently, the node that computes the shortest forwarding delay relays the packet first. This forwarding is detected by the other nodes and suppresses them to relay the same packet any further. Analytical results and simulation experiments indicate that BLR provides efficient and robust routing in highly dynamic mobile ad hoc networks.

Optimized Stateless Broadcasting in Wireless Multi-Hop Networks

In this paper we present a simple and stateless broadcasting protocol called Dynamic Delayed Broadcasting (DDB) which allows locally optimal broadcasting without any prior knowledge of the neighborhood. As DDB does not require any transmissions of control messages, it conserves critical network resources such as battery power and bandwidth. Local optimality is achieved by applying a principle of Dynamic Forwarding Delay (DFD) which delays the transmissions dynamically and in a completely distributed way at the receiving nodes ensuring nodes with a higher probability to reach new nodes transmit first. An optimized performance of DDB over other stateless protocols is shown by analytical results. Furthermore, simulation results show that, unlike stateful broadcasting protocols, the performance of DDB does not suffer in dynamic topologies caused by mobility and sleep cycles of nodes. These results together with its simplicity and the conservation of network resources, as no control message transmissions are required, make DDB especially suited for sensor and vehicular ad-hoc networks.

Evaluating the limitations of and alternatives in beaconing

In position-based routing protocols, each node periodically transmits a short hello message (called beacon) to announce its presence and position. Receiving nodes list all known neighbor nodes with their position in the neighbor table and remove entries after they have failed to receive a beacon for a certain time from the corresponding node. In highly dynamic networks, the information stored in the neighbor table is often outdated and does no longer reflect the actual topology of the network causing retransmissions and rerouting that consume bandwidth and increase latency. An analysis on the possible impact of beacons due outdated and inaccurate neighbor tables is needed. We quantify by analytical and simulation means the possible performance loss and explore the limitations of position-based routing protocols which use beaconing. In highly mobile ad-hoc networks, the delay can increase by a factor of 20. The neighbor table inaccuracy is the main source of packet loss in uncongested networks. We propose and evaluate several concrete mechanisms to improve the accuracy of neighborhood information, e.g., by dynamic adaptation of the timer values when beacons are broadcasted, and show their effectiveness by extensive simulation.

Distributed Event Localization and Tracking with Wireless Sensors

In this paper we present the distributed event localization and tracking algorithm DELTA that solely depends on light measurements. Based on this information and the positions of the sensors, DELTA is able to track a moving person equipped with a flashlight by dynamically building groups and electing well located nodes as group leaders. Moreover, DELTA supports object localization. The gathered data is sent to a monitoring entity in a fixed network which can apply pattern recognition techniques to determine the legitimacy of the moving person. DELTA enables object tracking with minimal constraints on both sensor hardware and the moving object. We show the feasibility of the algorithm running on the limited hardware of an existing sensor platform.

Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks

In this paper the node-level decision unit of a self-learning anomaly detection mechanism for office monitoring with wireless sensor nodes is presented. The node-level decision unit is based on Adaptive Resonance Theory (ART), which is a simple kind of neural networks. The Fuzzy ART neural network used in this work is an ART neural network that accepts analog inputs. A Fuzzy ART neural network represents an adaptive memory that can store a predefined number of prototypes. Any observed input is compared and classified in respect to a maximum number of M online learned prototypes. Considering M prototypes and an input vector size of N , the algorithmic complexity, both in time and memory, is in the order of O (MN ). The presented Fuzzy ART neural network is used to process, classify and compress time series of event observations on sensor node level. The mechanism is lightweight and efficient. Based on simple computations, each node is able to report locally suspicious behavior. A system-wide decision is subsequently performed at a base station.

Building Intrusion Detection with a Wireless Sensor Network

This paper addresses the detection and reporting of abnormal building access with a wireless sensor network. A common office room, offering space for two working persons, has been monitored with ten sensor nodes and a base station. The task of the system is to report suspicious office occupation such as office searching by thieves. On the other hand, normal office occupation should not throw alarms. In order to save energy for communication, the system provides all nodes with some adaptive short-term memory. Thus, a set of sensor activation patterns can be temporarily learned. The local memory is implemented as an Adaptive Resonance Theory (ART) neural network. Unknown event patterns detected on sensor node level are reported to the base station, where the system-wide anomaly detection is performed. The anomaly detector is lightweight and completely self-learning. The system can be run autonomously or it could be used as a triggering system to turn on an additional high-resolution system on demand. Our building monitoring system has proven to work reliably in different evaluated scenarios. Communication costs of up to 90% could be saved compared to a threshold-based approach without local memory.

Backbone MAC for energy-constrained wireless sensor networks

In this paper we propose a routing backbone construction mechanism that exploits and uses the synchronization messages exchanged by synchronized contention-based MAC protocols. Due to the usage of synchronization messages no additional control traffic is required to setup the routing backbone. Every node running a synchronized contention-based MAC protocol follows a given listen/sleep cycle. Because routing is supported by the backbone, non-backbone nodes can temporarily turn off their radios for multiple listen/sleep cycles. Thus, additional energy can be saved. Accordingly, non-backbone nodes do not have to wake up in every listen/sleep cycle to synchronize with other nodes, but wake up only if required, i.e., if they have to report some sensor readings to a base station. In this case, they synchronize to the backbone, send their data, and go back to sleep after successful transmission. Our approach is applicable to rather static networks with mainly source-to-sink traffic. Most monitoring applications are of this kind.

Event Classification and Filtering of False Alarms in Wireless Sensor Networks

In this paper the classification of discrete events, computed on tiny wireless sensor nodes, is investigated. Three different classifiers are evaluated: a Bayesian classifier, a fuzzy logic controller (FLC), and a neural network approach. The target applications pose several requirements on the classifiers. No a priori knowledge about the event classes is available. Events are only observable as collections of raw sensor data. Accordingly, event classes need to be learned from that raw (training) data. As a consequence, pre-labeling of the events is not possible either. In our work, event classes are learned by a k-means clustering algorithm. Any subsequent classifier training is based on these extracted event classes. Thus, the resulting classifiers are completely self-learning. Event classes are learned from emitted signal strength estimations, which are collected and processed by dynamically established tracking groups. The resulting event estimates are reported to a base station, where the classifiers are trained. The learned classifier parameters are then downloaded onto the sensor nodes, where any subsequent classification and filtering is performed.

Gravity-Based Local Clock Synchronization in Wireless Sensor Networks

Contention-based MAC protocols follow periodic listen/sleep cycles. These protocols face the problem of virtual clustering if different unsynchronized listen/sleep schedules occur in the network, which has been shown to happen in wireless sensor networks. To interconnect these virtual clusters, border nodes maintaining all respective listen/sleep schedules are required. However, this is a waste of energy, if locally a common schedule can be determined. We propose to achieve local synchronization with a mechanism that is similar to gravitation. Clusters represent the mass, whereas synchronization messages sent by each cluster represent the gravitation force of the according cluster. Due to the mutual attraction caused by the clusters, all clusters merge finally. The exchange of synchronization messages itself is not altered by LACAS. Accordingly, LACAS introduces no overhead. Only a not yet used property of synchronization mechanisms is exploited.