Abstract. In this position paper, we address the issue of durable maintenance of a wireless sensor network, which will be crucial if the vision of large, long-lived sensornets is to become reality. Durable maintenance requires tools for diagnosing and fixing occurring problems, which can range from internode connectivity losses, to time synchronization problems, to software bugs. While there are solutions for fixing problems, an appropriate diagnostic infrastructure is essentially still lacking. We argue that diagnosing a sensornet application requires the ability to dynamically and temporarily extend the application on a selected group of nodes with virtually any functionality. We motivate this claim based on deployment experiences to date and propose a highly nonintrusive solution to dynamically extending a running application on a resource-constrained sensor node. “During the spring of 2004, 80 mica2dot sensor network nodes were placed into two 60 meter tall redwood trees in Sonoma, California. [...]Onemonthlater,initial examination of the gathered data showed that the nodes in one tree had been entirely unable to contact the base station. Of the 33 remaining nodes, 15 % returned no data. Of the 80 deployed nodes, 65 % returned no data at all, from the very beginning. [...]One week into the Sonoma deployment, another 15 % of the nodes died [...]andnorecords exist oftheeventsthatmayhavecausedthisfailure.[...]” G. Tolle and D. Culler [2]. “[...]In2004, [Dutch] researchers [...]teamedupinanambitious project to use 150 wireless sensor nodes in a three-month pilot deployment on precision agriculture. [...] Out of 97 nodes running for [only] three weeks generating 1 message per 10 minutes, we received only 5874 messages, which amounts to 2%. [...]” 1

If the vision of large, long-lived wireless sensor networks serving the society is to become reality, deployment and durable maintenance need much more attention. Both these issues require the ability to first diagnose, and then fix occurring problems. While there are solutions for fixing problems, an appropriate diagnostic infrastructure is essentially still lacking. Our position is that diagnosing a sensornet application requires the ability to dynamically and temporarily extend the application on a selected group of nodes with virtually any functionality. We propose a solution to this problem that is highly nonintrusive.

Abstract—Wireless reprogramming of sensor nodes is a requirement for long-lived networks due to changes in the functionality of the software running on the nodes. The amount of information that needs to be wirelessly transmitted during reprogramming should be minimized to reduce reprogramming time and energy. In this paper, we present a multi-hop incremental reprogramming protocol called Hermes that transfers over the network the delta between the old and new software and lets the sensor nodes rebuild the new software using the received delta and the old software. It reduces the delta by using techniques to mitigate the effects of function and global variable shifts caused by the software modifications. Then it compares the binary images at the byte level with a method to create small delta. For a wide range of software change scenarios that we experimented with, we find that Hermes transfers up to 201 times less information than Deluge, the standard reprogramming protocol for TinyOS and 64 times less than an existing incremental reprogramming protocol by Jeong and Culler. I.

Distributed sensor applications emerge as a promising solution to be utilized for complex business scenarios. However, the development and deployment of these applications remains a complex challenge. In this survey we present a two-dimensional classification of middleware technologies needed to realize these complex enduser business scenarios. Subsequently, we discuss currently existing middleware approaches and place them in the classification framework we developed. Finally, based on this classification overview, we identify two issues for which middleware support leaps behind: cross-layer integration and end-to-end integration.

hitoshi atu-aizu.ac.jp Virtual Machines (VMs) have been proposed as an efficient programming model for Wireless Sensor Network (WSN) devices. However, the processing overhead required for VM execution has a significant impact on the power consumption and battery lifetime of these devices. This paper analyses the sources of power consumption in the Maté VM for WSNs. The paper proposes a generalised processor architecture allowing for hardware acceleration of VM execution. The paper proposes a number of hardware accelerators for Maté VM execution and assesses their effectiveness. 1.

Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a runtime environment used to realize this scenario, based on semantics of the Scheme programming language. SensorScheme is a general purpose WSN platform, providing dynamic reprogramming, memory safety (sandboxing), blocking I/O, marshalled communication, compact code transport. It improves on the state of the art by making better use of the little available memory, thereby providing greater capability in terms of program size and complexity. We illustrate the use of our platform with some application examples, and provide experimental results to show its compactness, speed of operation and energy efficiency. 1

Computer Science Sensor networks are being deployed at massive scales, containing a range of sensors and underlying computing platforms. Programming paradigms for sensor networks should meet the attendant challenges of scale and heterogeneity. Furthermore, with sensor networks expected to be deployed for long periods of time, the ability to reprogram them remotely is necessary in order to provide new services, fix bugs, and enhance applications and system software. The operating system and whole system reprogramming approach in use today will have limited use in the future, given the envisioned scales of sensor network deployments. In order to address this challenge, researchers have treated virtual machines as system software. However, to satisfy the resource limitations of sensor nodes, they usually export only a minimal set of services to the application programmer. This makes applications of even moderate complexity difficult to implement. In this dissertation, methods to implement virtual machines that scale and export com-prehensive service-suites on a per-application basis are presented. The use of fine-grained software synthesis and scaling to build resource-efficient system software is advocated, and a new incre-mental linking technique to reduce the cost of application evolution to facilitate both application

Abstract—Image processing applications introduce new challenges to the design of sensor network systems via non-trivial in-network computation. As embedded processing becomes more complex, in-situ reconfiguration is seen as the key enabling technology to maintain and manage such systems. In dynamic event-driven heterogeneous sensor networks, reconfiguration also encompasses autonomous re-partitioning of applications across multiple tiers to provide a low-power responsive system by efficiently coping with variations in run-time resource usage and availability. Hence, we aim to provide an efficient lowpower macro-programming environment that supports multidimensional software reconfiguration of heterogeneous imaging networks. Working towards this initiative, we present the ViRe framework for mote-class devices based on data-centric application composition and execution. Applications, modeled as dataflow graphs, are composed from a library of pre-defined and reusable image processing elements. Concise scripts capture the wiring information and are used to install applications in the network, while execution on the nodes is performed via processor native code to minimize overhead. A lean run-time engine tightly monitors application execution to provide an efficient, robust and scalable support for complex reconfigurable embedded image processing. Thus, the system is able to lower application repartitioning overhead and minimize loss of work during software reconfiguration.

With sensor networks starting to get mainstream acceptance, programmability is of increasing importance. Customers and field engineers will need to reprogram existing deployments and software developers will need to test and debug software in network testbeds. Script languages, which are a popular mechanism for reprogramming in general-purpose computing, have not been considered for wireless sensor networks because of the perceived overhead of interpreting a script language on tiny sensor nodes. In this paper we show that a structured script language is both feasible and efficient for programming tiny sensor nodes. We present a structured script language, SCript, and develop an interpreter for the language. To reduce program distribution energy the SCript interpreter stores a tokenized representation of the scripts which is distributed through the wireless network. The ROM and RAM footprint of the interpreter is similar to that of existing virtual machines for sensor networks. We show that the interpretation overhead of our language is on par with that of existing virtual machines. Thus script languages, previously considered as too expensive for tiny sensor nodes, are a viable alternative to virtual machines. 1

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogramming lifetime by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120–389 times less information than Deluge, and 18–42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%–53.87% in terms of beacon packets, and 56.83 % in terms of unsynchronized nodes. In addition, Elon prolongs the reprogramming lifetime by a factor of 2.3.