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 develop tractable mathematical models and approximate solution algorithms for a class of integer optimization problems with probabilistic and deterministic constraints, with applications to the design of distributed sensor networks that have limited connectivity. For a given deployment region size, we calculate the Pareto frontier of the sensor network utility at the desired probabilities for d-connectivity and k-coverage. As a result of our analysis, we determine (i) the number of sensors of different types to deploy from a sensor pool, which offers a cost vs. performance trade-off for each type of sensor, (ii) the minimum required radio transmission ranges of the sensors to ensure connectivity, and (iii) the lifetime of the sensor network. For generality, we consider randomly deployed sensor networks and formulate constrained optimization techniques to obtain the localization performance. The approach is guided and validated using an unattended acoustic sensor network design. Finally, approximations of the complete statistical characterization of the acoustic sensor networks are given, which enable average network performance predictions of any combination of acoustic sensors. Categories and Subject Descriptors: C.2.1 [Computer-Communication Networks]: Distributed networks; G.1.6 [Numerical Analysis]: Optimization—Constrained optimization, convex programming, integer programming, nonlinear programming; G.3 [Probability and Statistics]: Experimental design

We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints. 1

Key distribution refers to the problem of establishing shared secrets on sensor nodes such that secret symmetric keys for communication privacy, integrity and authenticity can be generated. In a wireless sensor network, pre-distribution of secret keys is possibly the most practical approach to protect network communications but it is difficult due to the ad hoc nature, intermittent connectivity, and resource limitations of the sensor networks. In this paper, we propose a key distribution scheme based on random key pre-distribution for heterogeneous sensor network (HSN) to achieve better performance and security as compared to homogeneous network which suffer from high communication overhead, computation overhead, and/or high storage requirements. In a key generation process, instead of generating a large pool of random keys, a key pool is represented by a small number of generation keys. For a given generation key and publicly known seed value, a one-way hash function generates a key chain, and these key chains collectively make a key pool. Each sensor node is assigned a small number of randomly selected generation keys. The proposed scheme reduces the storage requirements while maintaining the same security strength.

Abstract. Software-intensive systems become more and more important in our everyday lives. But their increasing complexity makes it difficult to develop and maintain them. This chapter gives an overview of the state of the art of building software-intensive systems and outlines research challenges that have been identified by the InterLink working group “software-intensive systems and new computing paradigms”. 1

Abstract—Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus are expected to have different sensing models. Even for the same sensor type, the sensing model may need to be changed in different environments. Designing and testing a different coverage protocol for each sensing model is indeed a costly task. To address this challenging task, we propose a new probabilistic coverage protocol (denoted by PCP) that could employ different sensing models. We show that PCP works with the common disk sensing model as well as probabilistic sensing models, with minimal changes. We analyze the complexity of PCP and prove its correctness. In addition, we conduct an extensive simulation study of large-scale sensor networks to rigorously evaluate PCP and compare it against other deterministic and probabilistic protocols in the literature. Our simulation demonstrates that PCP is robust, and it can function correctly in presence of random node failures, inaccuracies in node locations, and imperfect time synchronization of nodes. Our comparisons with other protocols indicate that PCP outperforms them in several aspects, including number of activated sensors, total energy consumed, and network lifetime.

Let P: = {Xi}i≥1 be a stationary point process in ℜ d, {Ci}i≥1 be a sequence of i.i.d random sets in ℜ d, and {Y t i; t ≥ 0}i≥1 be i.i.d. {0, 1}-valued continuous time stationary Markov chains. We define the Markov-Boolean model Ct: = {Y t i (Xi + Ci), i ≥ 1}. Ct represents the coverage process at time t. We first obtain limit laws for k-coverage of an area at an arbitrary instant. We then derive limit laws for the k-coverage induced on a one-dimensional path at an arbitrary instant. Finally, we obtain the limit laws for the k-coverage seen by a particle as it moves along a one-dimensional path.

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