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In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general χ2 distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation. [ABSTRACT FROM AUTHOR]

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Cramé Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems. [ABSTRACT FROM AUTHOR]

RFID and Wireless Sensor Networks exemplify computationally constrained environments, where the compact nature of the components cannot support complex computations or high communication overhead. On the other hand, such components should support security applications such as message integrity, authentication, and time stamping. The latter are efficiently implemented by Hash Message Authentication Codes (HMAC). As clearly stated in the literature, current approved implementations of HMAC require resources that cannot be supported in constrained components. An approach to implement a compact HMAC by the use of stream ciphering is presented in this paper. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks featuring direct sink access have been studied as an efficient architecture to gather and process data for numerous applications. In this paper, we focus on the joint effect of clustering and data correlation on the performance of such networks. More specifically, we propose a novel cluster-based data collection scheme for sensor networks with direct sink access (CDC-DSA) and provide an analytical framework to evaluate its performance in terms of energy consumption, latency, and robustness. In our scheme, cluster heads use a low-overhead and simple medium access control (MAC) conceptually similar to ALOHA to contend for the reachback channel to the data sink. Since, in our model, data is collected periodically, packet arrival is not modeled by a continuous random process, and therefore, we base our framework on a transient analysis rather than a steady-state analysis. Using random geometry tools, we study how the optimal average cluster size and energy savings, under the proposed MAC, vary according to the level of data correlation in the network. Extensive simulations for various protocol parameters show that our analysis is fairly accurate for a wide range of parameters. Our results suggest that, despite the trade-off between energy consumption and latency, both can be substantially reduced by a proper clustering design. [ABSTRACT FROM AUTHOR]

In this paper, we present Esense, a new paradigm of communication between devices that have fundamentally different physical layers. Esense is based on sensing and interpreting energy profiles. While our ideas are generic enough to be applicable in a variety of contexts, we illustrate the usefulness of our ideas by presenting novel solutions to existing problems in three distinct research domains. As part of these solutions, we demonstrate the ability to communicate between devices that follow two different standards: IEEE 802.11 and 802.15.4. We consider two scenarios here: 1) where there is no background traffic and 2) where there is background 802.11 traffic. In each case, we build an "alphabet setâ: a set of signature packet sizes that can be used for Esense communication. Specifically for the second case, we take a measurement-based alphabet set construction by considering WiFi traces from actual deployments. Based on practical observations and experiments, we theoretically quantify the maximum achievable transmission rate when using Esense. With background traffic, we could potentially construct an alphabet of size as high as 100. Such a large alphabet size promises efficient Esense communication. We show via a prototype implementation that effective communication is indeed feasible even when both sides use different physical layers. [ABSTRACT FROM AUTHOR]

This paper considers the coverage problem for hybrid networks which comprise both static and mobile sensors. The mobile sensors in our network only have limited mobility, i.e., they can move only once over a short distance. In random static sensor networks, sensor density should increase as O(log L + k log log L) to provide k-coverage in a network with a size of L. As an alternative, an all-mobile network can provide k-coverage with a constant density of O(k), independent of network size L. We show that the maximum distance for mobile sensors is O(1/√k log3/4(kL)). We then propose a hybrid network structure, comprising static sensors and a small fraction of O(1/√k) of mobile sensors. For this network structure, we prove that k-coverage is also achievable with a constant sensor density of O(k). Furthermore, for this hybrid structure, we prove that the maximum distance which any mobile sensor has to move is bounded as O(log3/4 L). We then propose a distributed relocation algorithm, where each mobile sensor only requires local information in order to optimally relocate itself. We verify our, analysis via extensive numerical evaluations and show an implementation of the mobility algorithm on real mobile sensor platforms. [ABSTRACT FROM AUTHOR]

Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting an intruder in a battlefield. The intrusion detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers. For this purpose, it is a fundamental issue to characterize the WSN parameters such as node density and sensing-range in terms of a desirable detection probability. In this paper, we consider this issue according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, we derive the detection probability by considering two sensing models: single-sensing detection and multiple-sensing detection. In addition, we discuss the network connectivity and broadcast reachability, which are necessary conditions to ensure the corresponding detection probability in a WSN. Our simulation results validate the analytical values for both homogeneous and heterogeneous WSNs. [ABSTRACT FROM AUTHOR]

Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called Congestion-Aware Routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-Enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed. [ABSTRACT FROM AUTHOR]

Recent research shows that significant energy saving can be achieved in wireless sensor networks by using mobile elements (MEs) capable of carrying data mechanically. However, the low movement speed of MEs hinders their use in data-intensive sensing applications with temporal constraints. To address this issue, we propose a rendezvous-based approach in which a subset of nodes serves as the rendezvous points (RPs) that buffer data originated from sources and transfer to MEs when they arrive. RPs enable MEs to collect a large volume of data at a time without traveling long distances, which can achieve a desirable balance between network energy saving and data collection delay. We develop two rendezvous planning algorithms: RP-CP and RP-UG. RP-CP finds the optimal RPs when MEs move along the data routing tree, while RP-UG greedily chooses the RPs with maximum energy saving to travel distance ratios. We design the Rendezvous-based Data Collection protocol that facilitates reliable data transfers from RPs to MEs in presence of significant unexpected delays in ME movement and network communication. Our approach is validated through extensive simulations. [ABSTRACT FROM AUTHOR]

We provide a better sufficient condition for the connectivity of cluster heads asymptotically almost surely (a.a.s.) and a tighter bound on the number of cluster heads in HEED [1]. [ABSTRACT FROM AUTHOR]