Acoustic source localization has many important applications. Convex relaxation provides a viable approach of obtaining good estimates very efficiently. There are two popular convex relaxation methods using either semi-definite programming or second order cone programming. However, the performances of the methods have not been studied properly in the literature and there is no comparison in terms of accuracy and performance. The aims of this paper are twofold. First of all, we study and compare several convex relaxation methods. We demonstrate by numerical examples that most of the convex relaxation methods cannot localize the source exactly even in the performance limit when the time difference of arrival (TDOA) information is exact. In addressing this problem, we propose a novel mixed SDP-SOCP relaxation model and study the characteristics of the optimal solutions and its localizable region. Furthermore, an error correction scheme for the proposed SDP-SOCP model is developed so that exact localization can be achieved in the performance limit. Experimental data have been collected in a room with two different array configurations to demonstrate our proposed approach.
Provenance records the history of data acquisition and transmission. In wireless sensor networks (WSNs), provenance is critical for many different purposes, including assessing the trustworthiness of data acquired and forwarded by sensors, supporting situation awareness, and detecting early signs of attacks. However, a major drawback in provenance for WSNs is its size. It is thus critical to develop efficient techniques for provenance encoding. A major issue of previously proposed provenance encoding techniques is that the size of the provenance either expands too fast with increases in the number of packet transmission hops or is very sensitive to the WSN's topology, i.e., the size of the provenance expands drastically with changes in the WSN's topology. In this paper we propose a novel provenance encoding technique, based on dynamic Bayesian network and overlapped arithmetic coding scheme, that addresses such issue. Through theoretical analysis, simulation, and testbed experiments, we show that our scheme outperforms other WSN lightweight provenance schemes with respect to provenance size and energy consumption.
Water is essential for human survival. While approximately 71% of the world is covered in water, only 2.5% of this is fresh water; hence fresh water is a valuable resource that must be carefully monitored and maintained. According to the U.S. Environmental Protection Agency (EPA), monitoring water is essential to track water quality changes over time and identify existing or emerging problems, to design effective programs and control actions to prevent or remedy water pollution, and to respond to emergencies, such as spills and floods. Given the importance of water quality monitoring (WQM), a number of solutions, from gathering samples intermittently to continuous monitoring, using both human collectors and automatic technology-based solutions, have been proposed and implemented. In this paper, we provide a review of the traditional manual methods for WQM and then overview a particular class of in-situ WQM systems, those employing Wireless Sensor Networks (WSNs). In particular, we highlight recent developments in the sensor devices, data acquisition procedures, communication and network architectures, and power management schemes to maintain a long-lived operational WQM system. Finally, we discuss open issues that need to be addressed to further advance automatic WQM using WSNs.
Force-directed algorithms such as the Kamada-Kawai algorithm have shown promising results for solving the boundary detection problem in a mobile ad hoc network. However, the classical Kamada-Kawai algorithm does not scale well when it is used in networks with large numbers of nodes. It also produces poor results in non-convex networks. To address these problems, this paper proposes an improved version of the Kamada-Kawai algorithm. The proposed extension includes novel heuristics and algorithms that achieve a faster energy level reduction. Our experimental results show that the improved algorithm can significantly shorten the processing time and detect boundary nodes with an acceptable level of accuracy.
Resource constraints, unattended operating environment and communication phenomena make Wireless Sensor Networks (WSNs) susceptible to operational failures and security attacks. However, applications often impose stringent requirements on data reliability and service availability, due to the deployment of sensor networks in various critical infrastructures. Given the failure- and attack-prone nature of sensor networks, enabling sensor networks to continuously provide their services as well as to effectively recover from attacks is a crucial requirement. We present Kinesis, a security incident response system for WSNs designed to keep WSNs functional despite anomalies or attacks and to recover from attacks without significant interruption. Kinesis is quick and effective in responding to incidents, distributed in nature, dynamic in selecting response actions based on the context, and lightweight in terms of response policy specification and communication and energy overhead. A per-node single timer-based distributed strategy to select the most effective response executor in a neighborhood makes the system simple and scalable, while achieving load balancing and redundant action optimization. We implement Kinesis in TinyOS and measure its performance for various application and network layer incidents. Extensive TOSSIM simulations and testbed experiments show that Kinesis successfully counteracts anomalies/attacks and behaves consistently under various attack scenarios and rates.
Temporal Lossless and Lossy Compression in Wireless Sensor Networks
Recent advances in smartphone processing power have opened the possibilities for them to act as the processing component of software defined radios (SDRs). For low-power sensor networks using various communication protocols, this means that SDR equipped smartphones can be their system management devices, (potentially) without the need for external modules. Nevertheless, the high processor and energy usage overhead of SDRs remains as a technical barrier that blocks the practical adoption of smartphone-based SDRs. In this work, we show that software implementation flexibility can relax this overhead. Specifically, we show using an implementation of the low-power listening (LPL) MAC that software improvements have the potential to significantly reduce the operational overhead of SDRs. Moreover, we show that packet reception filters can help further reduce the overhead without sacrificing application-level message exchange qualities. Empirical results with a smartphone-based SDR suggest that by combining LPL with packet reception filters, the processing and energy overhead can be reduced by multiple orders of magnitude. We not only see this as a chance to practically realize smartphones as a sensor network controller, but also believe that experiences with smartphone-based SDRs can provide guidelines for future wireless protocol and low-power radio designs that well-suit mobile computing environments.