The traditional approach of providing network security has been to borrow tools from cryptography and authentication. However, we argue that the conventional view of security based on cryptography alone is not sufficient for the unique characteristics and novel misbehaviors encountered in sensor networks. Fundamental to this is the observation that cryptography cannot prevent malicious or non-malicious insertion of data from internal adversaries or faulty nodes. We believe that in general tools from different domains such as economics, statistics and data analysis will have to be combined with cryptography for the development of trustworthy sensor networks. Following this approach, we propose a reputation-based framework for sensor networks where nodes maintain reputation for other nodes and use it to evaluate their trustworthiness. We will show that this framework provides a scalable, diverse and a generalized approach for countering all types of misbehavior resulting from malicious and faulty nodes. We are currently developing a system within this framework where we employ a Bayesian formulation, specifically a beta reputation system, for reputation representation, updates and integration. We will explain the reasoning behind our design choices, analyzing their pros & cons. We conclude the paper by verifying the efficacy of this system through some preliminary simulation results.

We propose using application specific virtual machines (ASVMs) to reprogram deployed wireless sensor networks. ASVMs provide

Sensor networks are being deployed at massive scales, containing a range of platforms. Programming paradigms for sensor networks should meet the attendant challenges of scale and heterogeneity. Researchers have considered virtual machines as a means to address these challenges. However, in order to satisfy the resource limitations of sensor nodes, they export only a minimal set of services to the application programmer. This makes applications of even moderate complexity difficult to implement. We present VM --- a framework for building resource-efficient virtual machines that scale and export comprehensive service suites on a per-application basis. We advocate the use of fine-grained software synthesis to build resource-efficient system software, and facilitate both application changes and system software upgrades at runtime through an efficient incremental update scheme. We have used our framework to build virtual machines on the Mica platform and describe how virtual machines are effective in meeting the difficult demands of heterogeneity and reprogrammability.

We present empirical measurements of the packet delivery performance of the Telos and MicaZ sensor platforms. At a high level, their behavior is similar to that of earlier platforms. They exhibit link asymmetry, a reception “grey region, ” and temporal variations in packet loss. Looking more deeply, however, there are subtle differences, and looking deeper still, the patterns behind these complexities become clear. Packet losses are highly correlated over short time periods, but are independent over longer periods. Environmental noise (802.11b) has high spatial correlation. Packet loss occurs when a receiver operating near its noise floor experiences a small decrease in received signal strength, rather than an increase in environmental noise. These variations cause the reception “grey region. ” While short-term link asymmetries are not uncommon, long-term asymmetries are rare. Based on these findings, we suggest several ways in which current practices could be easily changed that would greatly improve the efficiency, performance, and lifetime of sensor networks. 1

The development of a reliable large-scale wireless sensor networks (WSNs) is very difficult because of their stringent resource constraints, harsh energy budget, and demanding application requirements. We identify that three OS features – OS protection, virtual memory, and preemptive scheduling – will significantly improve the reliability of WSN systems and facilitate developing complex WSN software. However, due to the limitation of hardware, it is impossible to implement these features with traditional OS design techniques. To solve this problem, we design a new OS kernel, the tkernel, to perform extensive load-time code modification and enhance the system abstraction visible to programmers. After the modification, the application and OS work in a collaborative way supporting the aforementioned features. Having implemented the t-kernel on MICA2 motes with an 8-bit processor and 4KB RAM, we evaluate its performance by measuring the overhead and execution speed. We analyze the CPU utilization in sensor network applications, and verify that, though CPU-bound computation tasks may slow down 0.5–4 times, the performance of applications under typical workloads does not degrade. The t-kernel significantly enhances developers ’ ability to design sophisticated applications and protects WSNs from accidental programming errors. To the authors ’ best knowledge, the t-kernel is unique in the follow ways: it performs efficient binary translation on highly resource constrained sensor nodes with only 4KB RAM, it provides software based virtual memory without repeatedly writable swapping devices, and it protects OS from application error without memory protection or privileged execution hardware. 1

Abstract — Camera sensors constitute an information rich sensing modality with many potential applications in sensor networks. Their effectiveness in a sensor network setting however greatly relies on their ability to calibrate with respect to each other, and other sensors in the field. This paper examines node localization and camera calibration using the shared field of view of camera pairs. Using a new distributed camera sensor network we compare two approaches from computer vision and propose an algorithm that combines a sparse set of distance measurements with image information to accurately localize nodes in 3D. Our algorithms are evaluated using a network of iMote2 nodes equipped with COTS camera modules. The sensor nodes identify themselves to cameras using modulated LED emissions. Our indoor experiments yielded a 2-7cm error in a 6x6m room. Our outdoor experiments in a 30x30m field resulted in errors 20-80cm, depending on the method used. I.

Wireless sensor networks need an efficient and reliable reprogramming service to facilitate management and maintenance tasks. In this article we first outline a framework to examine different functions in reprogramming, followed by an analysis of reprogramming challenges. We then provide a comprehensive survey of the state-of-the-art reprogramming systems, and discuss different approaches to address these challenges. Finally we explore performance, protocol behavior, and the impact of several design factors. A typical wireless sensor network (WSN) consists of a large number of small-sized battery-powered sensor nodes that integrate sensing, computing, and communication capabilities. WSN applications include geophysical/structural/habitat monitoring, security surveillance, disaster area or battlefield information collection,

Sensor networks have a significant potential in diverse applications some of which are already beginning to be deployed in areas such as environmental monitoring. As the application logic becomes more complex, programming difficulties are becoming a barrier to adoption of these networks. The difficulty in programming sensor networks is not only due to their inherently distributed nature but also the need for mechanisms to address their harsh operating conditions such as unreliable communications, faulty nodes and extremely constrained resources. Researchers have proposed different programming models to overcome these difficulties with the ultimate goal of making programming easy while making full use of available resources. In this paper, we first explore the requirements for programming models for sensor networks. Then we present a taxonomy of the programming models, classified according to the level of abstractions they provide. We present an evaluation of various programming models for their responsiveness to the requirements. Our results point to promising efforts in the area and a discussion of the future directions of research in this area.

This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a run-time, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low level sensor measurements into high level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lens that observe people’s locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at run-time by using more than 1.3 million location measurements acquired through our real home deployment.

Abstract — Digital implementations of feedback laws commonly consider periodic execution of control tasks. In this paper we go beyond the periodic model by developing self-triggered schedules for the execution of control tasks. These schedules guarantee asymptotic stability under sample-and-hold implementations while drastically reducing processor usage when compared with the more traditional periodic implementations. At the technical level the results rely on a homogeneity assumption on the continuous dynamics and extend to the selftriggered framework some of the advantages of event-triggered implementations recently studied by the authors. The results presented in this paper can be seen as an effort towards understanding the real-time scheduling requirements of control tasks. I.