Simulation can be an important step in the development of software for wireless sensor networks and has been the subject of intense research in the past decade. While most previous efforts in simulating wireless sensor networks have focused on protocol-level issues utilizing models of the software implementation, a significant challenge remains in precisely measuring time-dependent properties such as radio channel utilization. One promising approach, first demonstrated by ATEMU, is to simulate the behavior of sensor network programs at the machine code level with cycle-accuracy, but poor performance has so far limited its scalability. In this paper we present Avrora, a cycle-accurate instructionlevel sensor network simulator which scales to networks of up to 10,000 nodes and performs as much as 20 times faster than previous simulators with equivalent accuracy, handling as many as 25 nodes in real-time. We show how an event queue can enable efficient instruction-level simulation of microcontroller programs and allow the hidden parallelism in finegrained sensor network simulations to be extracted, once two core synchronization problems are identified and solved. Avrora's ability to measure detailed time-critical phenomena can shed new light on design issues for large-scale sensor networks.

Energy is of primary importance in wireless sensor networks. By being able to estimate the energy consumption of the sensor nodes, applications and routing protocols are able to make informed decisions that increase the lifetime of the sensor network. However, it is in general not possible to measure the energy consumption on popular sensor node platforms. In this paper, we present and evaluate a softwarebased on-line energy estimation mechanism that estimates the energy consumption of a sensor node. We evaluate the mechanism by comparing the estimated energy consumption with the lifetime of capacitor-powered sensor nodes. By implementing and evaluating the X-MAC protocol, we show how software-based on-line energy estimation can be used to empirically evaluate the energy efficiency of sensor network protocols. 1.

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.

In this paper we present Levels, a programming abstraction for energy-aware sensor network applications. Unlike most previous work it does not try to maximize network lifetime but rather helps to meet user-defined lifetime goals while maximizing application quality. Levels is targeted to applications where there is no redundancy and no node should fail early. With our programming abstraction the application developer defines so-called energy levels. These energy levels form a stack and can be deactivated from top to bottom if the lifetime goal cannot be met otherwise. Each code block within an energy level contains information about its energy consumption, which can be obtained from simulation tools without much effort. The runtime system then uses the data about the energy consumption of the different levels to compute an optimal level assignment for the time remaining. As we show in the evaluation, applications using Levels can accurately meet given lifetime goals and offer good application quality. In addition, the runtime overhead of our system is almost negligible.

Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make trade-offs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) – a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributed-memory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (16-64 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in sub-realtime speed. To our knowledge, DiSenS is the first full-system sensor network simulator with such scalability.

Wireless sensor networks (WSN) are composed of battery-driven communication entities performing multiple, usually different tasks. In order to complete a given task, all sensor nodes, which are deployed in an ad-hoc fashion, have to collaborate by exchanging and forwarding measurement data. We define the behavior of the overall sensor network based on the parameters lifetime and functional density. The functional density describes the distribution of all necessary tasks in a given geographical area. The lifetime is primarily given by the time each task is successfully performed by at least one node, i.e. the functional density of all necessary tasks. Nodes can become unavailable due to insufficient remaining energy. We assume that sensor nodes can be reconfigured or reprogrammed by a mobile robot system. There are various reasons for considering robots for this reconfiguration, e.g. reliability, security, and deployment issues. In this paper, we evaluate the advantages of exploiting reconfiguration and reprogramming schemes WSN using mobile robots. The primary objective is to increase the lifetime of the overall network. This goal is achieved by optimizing the functional density of heterogeneous tasks. Based on a developed simulation model, we discuss the advantages and performance characteristics. 1.

Sensor network computing can be characterized as resource-constrained distributed computing using unreliable, low bandwidth communication. This combination of characteristics poses significant software development and maintenance challenges. Effective and efficient debugging tools for sensor network are thus critical. Existent development tools, such as TOSSIM, EmStar, ATEMU and Avrora, provide useful debugging support, but not with the fidelity, scale and functionality that we believe are sufficient to meet the needs of the next generation of applications. In this paper, we propose a debugger, called S 2 DB, based on a distributed full system sensor network simulator with high fidelity and scalable performance, DiSenS. By exploiting the potential of DiSenS as a scalable full system simulator, S 2 DB extends conventional debugging methods by adding novel device level, program source level, group level, and network level debugging abstractions. The performance evaluation shows that all these debugging features introduce overhead that is generally less than 10 % into the simulator and thus making S 2 DB an efficient and effective debugging tool for sensor networks.

Abstract — The focus of the sensor network community on energy-efficiency has produced a string of novel MAC, routing, and data-aggregation protocols. Their power consumption has mainly been assessed through simulations, i.e. by counting the fraction of time spent in sending, receiving, and computing, and multiplying that by figures taken from data sheets or isolated (single-node) power measurements. In contrast we present PowerBench, a 24-node testbed capable of recording the power consumption of all nodes in parallel with a 5 kHz sampling rate and 30 µA resolution. This is accomplished by means of a low-cost interface board featuring a shunt resistor and an A/D converter, whose output is collectively sampled by an embedded Linux platform (Linksys NSLU2). The experience with the PowerBench testbed so far is twofold. First, we have determined that –much to our surprise – timerbased estimations can match true, measured power consumption values within a few percent. Second, we have experienced that a graphical display of the power traces is an effective means to study (and debug) protocol behavior; in particular, inter-node related timing issues can be easily viewed from the state (IDLE/COMPUTE/RX/TX) changes embodied in the power data. I.

Abstract — We describe an approach for the formal modeling and analysis of ad-hoc sensor networks, at various levels of abstraction. It is global because it takes into account all the following aspects: a precise modeling of the hardware that implements a model of the physical environment as viewed by the sensors. The global model is executable, to enable validation by simulations, but we also aim at analyzing the global model with various formal validation tools (automatic test, runtime verification techniques, model-checking and abstract interpretations). Each technique or tool may need particular abstractions of the model. In this paper, we illustrate the whole approach with a simple model, and show what formal analysis can be performed on the model. A. Ad-hoc Sensor Networks I.

Abstract. Focusing on the assessment of environmental noise pollution in urban areas, we provide qualitative considerations and experimental results to show the feasibility of wireless sensor networks to be used in this context. To select the most suitable data collection protocol for the speci c noise monitoring application scenario, we evaluated the energy consumption performances of the CTP (Collection Tree Protocol) and DMAC protocols. Our results show that CTP, if used enabling the LPL (Low Power Listening) option, provides the better performances trade-o for noise monitoring applications. 1 Environmental noise monitoring Conservative estimations give in about 300 millions the number of citizens within the European Community that are exposed to alarming levels of noise pollution [1]. Raising the public's awareness of this problem, the Directive 2002/49/EC of the European Parliament has made the avoidance, prevention, and reduction of environmental noise a prime issue in European policy. To better assess the