Faruk Bagci

SecSens - Security Architecture for Wireless Sensor Networks

In recent years, the potential range of applications for sensor networks is expanding. Their use has been considered for safety critical areas such as: hospitals or power plants. The security comes more to the fore. This paper presents SecSens, an architecture that provides basic security components for wireless sensor networks. Since robust and strong security features require powerful nodes, SecSens uses a heterogeneous sensor network. In addition to a large number of simple (cheap) sensor nodes providing the actual sensor tasks, there are a few powerful nodes (cluster nodes) that implement the required security features. The basic component of SecSens offers authenticated broadcasts to allow recipients to authenticate the sender of a message. To protect the sensor network against routing attacks, SecSens includes a probabilistic multi-path routing protocol, which supports the key management and the authenticated broadcasts. SecSens also provides functions to detect forged sensor data by verifying data reports en-route. SecSens is successfully evaluated in a real test environment with two different kinds of sensor boards.

Prediction of indoor movements using bayesian networks

This paper investigates the efficiency of in-door next location prediction by comparing several prediction methods. The scenario concerns people in an office building visiting offices in a regular fashion over some period of time. We model the scenario by a dynamic Bayesian network and evaluate accuracy of next room prediction and of duration of stay, training and retraining performance, as well as memory and performance requirements of a Bayesian network predictor. The results are compared with further context predictor approaches – a state predictor and a multi-layer perceptron predictor using exactly the same evaluation set-up and benchmarks. The publicly available Augsburg Indoor Location Tracking Benchmarks are applied as predictor loads. Our results show that the Bayesian network predictor reaches a next location prediction accuracy of up to 90% and a duration prediction accuracy of up to 87% with variations depending on the person and specific predictor set-up. The Bayesian network predictor performs in the same accuracy range as the neural network and the state predictor.

Comparison of different methods for next location prediction

Next location prediction anticipates a persons movement based on the history of previous sojourns. It is useful for proactive actions taken to assist the person in an ubiquitous environment. This paper evaluates next location prediction methods: dynamic Bayesian network, multi-layer perceptron, Elman net, Markov predictor, and state predictor. For the Markov and state predictor we use additionally an optimization, the confidence counter. The criterions for the comparison are the prediction accuracy, the quantity of useful predictions, the stability, the learning, the relearning, the memory and computing costs, the modelling costs, the expandability, and the ability to predict the time of entering the next location. For evaluation we use the same benchmarks containing movement sequences of real persons within an office building.

AMUN - autonomic middleware for ubiquitous environments applied to the smart doorplate project

We envision future office buildings that partly or fully implement a flexible office organization. These organizational principles save office space, but require a sophisticated software system that is highly dynamic, scalable, context-aware, self-configuring, self-optimizing and self-healing. We therefore propose an autonomic middleware approach for ubiquitous in-door environments.

Smart doorplate

AbstractThis paper introduces the vision of smart doorplates within an office building. The doorplates are able to display current situational information about the office owner, to act instead of the office owner in case of absence, and to direct visitors to the current location of the office owner based on a location-tracking system. Different scenarios are proposed and a prototype implementation is presented.

Communication and security extensions for a ubiquitous mobile agent system (UbiMAS)

Future computers will be integrated in objects of everyday life. The number of processors in the environment will increase and data will be distributed over different nodes. New classes of information and devices will appear, i.e. data will be catched from environmental sensors and will be used for context extraction. The amount of new devices and services makes an efficient use by centralized systems very difficult.Mobile agents provide an eminent method by virtualizing the user and performing actions on her behalf. They offer a possibility to encapsulate information of a person and her preferences, likings, and habits and perform location-based services of the ubiquitous system in the name of the user. This paper presents a mobile agent system application, where a mobile user-agent follows the user while she moves in the physical space. The user-agent knows personal information and communicates with service-agents in the name of its user to perform special tasks. Because of the personal data security and privacy are major concerns of such an agent system. The paper describes the communication of agents and agent nodes and focuses on the security extensions required by this approach

AMUN: an autonomic middleware for the Smart Doorplate Project

We envision future office buildings that partly or fully implement a flexible office organization where office rooms are dynamically assigned to currently present employees. Such organizational principles save required office space, therefore decrease costs, but require a sophisticated software system that is highly dynamic, scalable, context-aware, self-configuring, self-optimizing and self-healing. We propose an autonomic/organic middleware approach for such ubiquitous indoor environments with an extensive monitoring at different system levels and demonstrate the software by using our Smart Doorplate Project that is designed to support a flexible office organization.

AMUN-autonomic middleware for ubiquitous environments applied to the smart doorplate project

We envision future office buildings that partly or fully implement a flexible office organization. These organizational principles save office space, but require a sophisticated software system that is highly dynamic, scalable, context-aware, self-configuring, self-optimizing and self-healing. We therefore propose an autonomic middleware approach for ubiquitous in-door environments.

The reflective mobile agent paradigm implemented in a smart office environment

Ubiquitous systems will integrate computers invisibly and unobtrusively in everyday objects. Data will be catched from single or multi-sensor devices and will be used for context extraction. New location-based services will be adapted to user preferences. For this the ubiquitous system needs to know user profiles, likings, and habits. As the user moves, these information must be made available at the new location of the user. Either the user carries the data on wearable or portable computers or the smart environment takes responsibility for transporting them. The amount of new devices and services makes an efficient use by centralized systems very difficult. The idea presented in this paper is that a virtual reflection of the user represented by a mobile agent accompanying in the smart environment. Mobile agents offer a possibility to encapsulate information of a person and the person’s preferences and perform location-based services of the ubiquitous system in the name of the user. Security and privacy are major concerns of such an agent system. This paper describes a ubiquitous mobile agent system named UbiMAS which has security extensions to provide high protection of agents and significant personal data. UbiMAS is applied in the smart doorplate project as part of a smart office environment.

Optimisations for LocSens – an indoor location tracking system using wireless sensors

Ubiquitous and pervasive computing envisions context-aware systems that gather real-world information from many fixed and mobile microchips and sensors integrated in everyday objects. To provide valuable services, it is necessary to estimate the location of users or objects. Outdoor location tracking is achieved by Global Positioning System (GPS), but due to its poor indoor coverage, there is a need for alternative technologies in buildings. Since multiple wireless sensors may be situated in the environment, they can be used for location estimation and tracking. This paper presents LocSens, a cost-effective location tracking system based on sensor nodes with wireless connectivity. LocSens works with a minimum number of sensor nodes. It is established and tested in a real indoor scenario over multiple rooms. LocSens could be improved by optimising algorithms and using more precise sensor boards. Results confirm gained accuracy in location estimation and tracking of moving objects.