System identification is the art and science of building mathematical models of dynamic systems from observed input-output data. It can be seen as the interface between the real world of applications and the mathematical world of control theory and model abstractions. As such, it is an ubiquitous necessity for successful applications. System identification is a very large topic, with different techniques that depend on the character of the models to be estimated: linear, nonlinear, hybrid, nonparametric etc. At the same time, the area can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. The area has many facets and there are many approaches and methods. A tutorial or a survey in a few pages is not quite possible. Instead, this presentation aims at giving an overview of the “science ” side, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem. 1.

The imminent mass deployment of pervasive computing technologies such as sensor networks and RFID tags, together with the increasing participation of the Web community in feeding geo-located information within tools such as Google Earth, will soon make available an incredible amount of information about the physical and social worlds and their processes. This opens up the possibility of exploiting all such information for the provisioning of pervasive context-aware services for “browsing the world”, i.e., for facilitating users in gathering information about the world, interacting with it, and understanding it. However, for this to occur, proper models and infrastructures must be developed. In this paper we propose a simple model for the representation of contextual information, the design and implementation of a general infrastructure for browsing the world, as well as some exemplar services we have implemented over it. Keywords: Context-awareness, Location-dependent Services, Middleware, Sensor Networks, RFID Tags.

Self-organization is a great concept for building scalable systems consisting of huge numbers of subsystems. The primary objectives are coordination and collaboration on a global goal. Until now, many self-organization methods have been developed for communication networks in general and ad hoc networks in particular. Nevertheless, the term self-organization is still often misunderstood or misused. This paper contributes to the ad hoc community by providing a better understanding of self-organization in ad hoc networks. Primarily, solutions for the medium access contol and the network layer are analyzed and discussed. The main contribution of this paper is a categorization of self-organization methodologies. Additionally, well-known methods in ad hoc networks are classified and some case studies are provided.

Network lifetime has become the key characteristic to be used for evaluating sensor networks in an application specific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network – based on the particularly selected definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models – focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. We also demonstrate the applicability of our definition based on the surveyed lifetime definitions found in the literature as well as using an example to explain the various aspects influencing sensor network lifetime. sensor networks, lifetime, connectivity, coverage, longevity Index Terms I.

Abstract. Distributed sensor networks offer many new capabilities for contextually monitoring environments. By making such systems mobile, we increase the application-space for the distributed network mainly by providing dynamic context-dependent deployment, continual relocatability, automatic node recovery, and a larger area of coverage. In existing models, the addition of actuation to the nodes has exacerbated three of the main problems with distributed systems: power usage, node size, and node complexity. In this paper we propose a solution to these problems in the form of parasitically actuated nodes that harvest their mobility and local navigational intelligence by selectively engaging and disengaging from mobile hosts in their environment. We analyze the performance of parasitically mobile distributed networks through software simulations and design, implement, and demonstrate hardware prototypes.

Sensor networks offer a powerful combination of distributed sensing, computing and communication. They lend themselves to countless applications and, at the same time, offer numerous challenges due to their peculiarities, primarily the stringent energy constraints to which sensing nodes are typically subjected. The distinguishing traits of sensor networks have a direct impact on the hardware design of the nodes at at least four levels: power source, processor, communication hardware, and sensors. Various hardware platforms have already been designed to test the many ideas spawned by the research community and to implement applications to virtually all fields of science and technology. We are convinced that CAS will be able to provide a substantial contribution to the development of this exciting field.

Abstract — We propose a low overhead scheme for detecting a network partition or cut in a sensor network. Consider a network S of n sensors, modeled as points in a two-dimensional plane. An ε-cut, for any 0 < ε < 1, is a linear separation of εn nodes in S from a distinguished node, the base station. We show that the base station can detect whenever an ε-cut occurs by monitoring the status of just O ( 1) nodes in the network. ε Our scheme is deterministic and it is free of false positives: no reported cut has size smaller than 1 εn. Besides this combinatorial result, we also 2 propose efficient algorithms for finding the O ( 1) nodes that should act ε as sentinels, and report on our simulation results, comparing the sentinel algorithm with two natural schemes based on sampling. I.

In this paper, we consider energy-e#cient gathering of correlated data in sensor networks. We focus on single-input coding strategies in order to aggregate correlated data. For foreign coding we propose the MEGA algorithm which yields a minimum-energy data gathering topology in O time. We also consider self-coding for which the problem of finding an optimal data gathering tree was recently shown to be NP-complete; with LEGA, we present the first approximation algorithm for this problem with approximation ratio 2(1 + # 2) and running time O(m + n log n).

Abstract—This paper addresses the maximal lifetime scheduling problem in sensor surveillance systems. Given a set of sensors and targets in an area, a sensor can watch only one target at a time, our task is to schedule sensors to watch targets and forward the sensed data to the base station, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched and all active sensors are connected to the base station. We propose an optimal solution to find the target-watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using the linear programming technique; 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime; and 3) determining the sensor surveillance trees based on the above obtained schedule matrices, which specify the active sensors

{liujn, micah,