Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

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A survey on localization for mobile wireless sensor networks

A new cooperative received signal strength-based localization algorithm is proposed which employs relative error estimation and semidefinite programming (SDP). First, the log-normal shadowing RSS measurement model is transformed into an equivalent multiplicative model. Then, a relative error estimation criterion is used with this model to develop a nonconvex estimator to approximate the maximum likelihood solution. Finally, semidefinite relaxation is applied to the nonconvex estimator to obtain an SDP estimator. The proposed algorithm is first derived for noncooperative RSS-based localization and then extended to the cooperative case. The Cramer–Rao lower bound is derived for cooperative RSS-based localization. Performance results are presented, which demonstrate that the proposed SDP estimator provides a significant improvement over existing localization methods.

Cooperative RSS-Based Localization in Wireless Sensor Networks Using Relative Error Estimation and Semidefinite Programming

This paper describes a novel sniper localization method for a network of acoustic sensors. This approach relies only on the time-difference-of-arrival (TDOA) between the muzzle blast and shock wave from multiple single acoustic sensor nodes, as opposed to an array of microphones, relaxing the need for precise time synchronization across the network. This method is also best suited where an array of sensors, on a per-node basis, is not feasible. Results of the sniper localization algorithm using data collected in a field are presented here. Derivation of the Cramer-Rao bound is presented, and the results of the algorithm are compared with the bound.

Sniper Localization Using Acoustic Asynchronous Sensors

In this paper, we study the challenging problem of node replication detection. Although defending against node replication attacks demands immediate attention, only a few solu- tions were proposed. In this paper, an Efficient and Distributed Detection (EDD) scheme and its variant, SEDD, are proposed to resist against node replication attacks in mobile sensor net- works. The characteristics possessed by EDD and SEDD include (1) Distributed Detection; (2) Efficiency and Effectiveness; (3) Individual Detection; (4) Network-Wide Revocation Avoidance. Performance comparison with known methods are provided to demonstrate the efficiency of the EDD and SEDD schemes.

/pdf/efficient-and-distributed-detection-of-node-replication-4mjwzwdx66.pdf

Efficient and Distributed Detection of Node Replication Attacks in Mobile Sensor Networks

Distributed acoustic sensing underlies an increasingly important class of sensor network applications, from habitat monitoring and bioacoustic census to security applications and virtual fences. VoxNet is a complete hardware and software platform for distributed acoustic monitoring applications that focuses on three key goals: (1) rapid deployment in realistic environments; (2) a high level programming language that abstracts the user from platform and network details and compiles into a high performance distributed application; and (3) an interactive usage model based on run-time installable programs, with the ability to run the same high level program seamlessly over live or stored data. The VoxNet hardware is self-contained and weather-resistant, and supports a four-channel microphone array with automated time synchronization, localization, and network coordination. Using VoxNet, an investigator can visualize phenomena in real-time, develop and tune online analysis, and record raw data for off-line analysis and archival. This paper describes both the hardware and software elements of the platform, as well as the architecture required to support distributed programs running over a heterogeneous network. We characterize the performance of the platform, using both microbenchmarks that evaluate specific aspects of the platform and a real application running in the field.

/pdf/voxnet-an-interactive-rapidly-deployable-acoustic-monitoring-rmu649xwcn.pdf

VoxNet: An Interactive, Rapidly-Deployable Acoustic Monitoring Platform

The paper presents a wireless sensor network-based mobilecountersniper system. A sensor node consists of a helmetmountedmicrophone array, a COTS MICAz mote for internodecommunication and a custom sensorboard that implementsthe acoustic detection and Time of Arrival (ToA) estimationalgorithms on an FPGA. A 3-axis compass providesself orientation and Bluetooth is used for communicationwith the soldier's PDA running the data fusion and the userinterface. The heterogeneous sensor fusion algorithm canwork with data from a single sensor or it can fuse ToA orAngle of Arrival (AoA) observations of muzzle blasts andballistic shockwaves from multiple sensors. The system estimatesthe trajectory, the range, the caliber and the weapontype. The paper presents the system design and the resultsfrom an independent evaluation at the US Army AberdeenTest Center. The system performance is characterized by 1-degree trajectory precision and over 95% caliber estimationaccuracy for all shots, and close to 100% weapon estimationaccuracy for 4 out of 6 guns tested.

https://www.isis.vanderbilt.edu/sites/default/files/sys5090-volgyesi.pdf

Christopher B. Nash

Papers

Shooter localization and weapon classification with soldier-wearable networked sensors