Two efficient solutions are proposed for the localization problem of an object in multiple-input multiple-output (MIMO) radar systems, when the transmitter positions and offsets are unknown. The localization problem is first recast into a convex form by applying the semidefinite relaxation (SDR) technique, the solution of which converges to global optimum. We also propose a closed-form solution warranting global convergence, in which the object position is estimated by two stages. In the stage-one solution, the auxiliary variables are introduced to transform the nonlinear problem into a linear form. The stage-two solution is further designed to refine the estimates obtained from the stage-one solution. The minimum number of receivers and the complexity are also analyzed for the proposed solutions. The simulated results show that the SDR and closed-form solutions provide good estimates for the object position and transmitter positions. Their performance can sufficiently approach the Cramér-Rao Lower Bound (CRLB) accuracy at the high signal-to-noise ratio (SNR).

Security over mobile Internet-of-Things (IoT) devices is critical due to the open nature of distributed wireless communication. To efficiently establish a secure connection between two communication parties, a fast mobile key extraction protocol, KEEP, is proposed. KEEP fastly generates similar bit sequences from two communication parties’ measurements of channel-state information (CSI) of different subcarriers. Then, a distributed “verification-recombination” mechanism is introduced to generate the same encryption key from bit sequences without the public-key authentication, digital signature, or key distribution center of the other party. We implemented real-world experiments using commercial off-the-shelf 802.11n devices to evaluate the performance of KEEP in various scenarios. Theoretical analysis and experimental verification show that KEEP is more secure, effective, and reliable than the state-of-the-art methods.

Routing dynamics are intrinsic characteristics of operational wireless sensor networks (WSNs). We present the measurement and analysis results for routing dynamics in a large-scale WSN. We seek to answer several fundamental questions: How dynamically are current routing protocols performing? What causes routing dynamics? What is the impact of routing dynamics? Answers to the above questions are critical to understanding the interactions among multiple network elements, evaluating protocol design strategies, and improving system performances. However, measurements in large-scale WSNs are challenging due to the lack of dedicated log information (be analogous to configuration files, syslog messages used in Internet). We propose an approach to identify the routing dynamics based on limited information and correlate them with system events to find out the root causes. The key findings of our study include: parent change events mainly affect local nodes, i.e. They do not cause routing instability on far-away nodes, environment dynamics and routing loops have large impact on routing, small portion of parent changes might not be necessary, while a large portion of parent changes are effective in improving network performance.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e., require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper, we propose LISTEN, a noninteractive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. Both the analytical and experimental results demonstrate that it is accurate, cost-efficient, and especially suitable for WCSNs that consist of low-end camera sensors.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

Diagnosis for wireless sensor networks is difficult, due to the limited resources and the ad hoc manner of networks. The existing approaches mainly focus on collecting diagnosis metrics, which bring heavy communication overhead to the network. We present a new model called DSD for network diagnosis which deduce the root causes for failures using the sensing data traces. We discover that the characteristics of the sensing data reflect the network status in some way, according to considerable experiments in the GreenOrbs project. We mine the relationships between the sensing data and the failures in the sensor networks, and record them in a failure knowledge library. Through this diagnosis mechanism, we deduce the root cause of the failures without adding any additional network burden. Moreover, the failure knowledge library can be used to improve the efficiency of diagnosis. We analyze the three months sensing data from the GreenOrbs project, and experimental results show that the proposed scheme can improve the diagnosis performance with low energy cost.

As a large scale real sensor network system, GreenOrbs reveals that locating sensor nodes in the forest still faces great challenges because of volatile and fluctuating environmental factors. In this paper, we present a novel localization scheme, EARL, which provides accurate reference nodes and good ranging quality. We exam the range quality along routing paths by taking complex environmental factors into account, such as forest density, temperature and humidity. To improve localization accuracy, we use power scanning technique to judge the accuracy reference nodes and further calibrate the bad nodes through reverse-localization. To overcome the error propagation, we assign different weights to the range measurement according to the ranging quality. We implemented our localization scheme in GreenOrbs testbed, and evaluate through extensive experiments. The results demonstrate that EARL outperforms the current localization approaches with better accuracy. The localization accuracy achieved by our method is around 20% higher than best existing methods.

In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large scale sensor networks. (1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. (2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. (3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained optimal and thus better adapt to the wild environment when building a large scale sensor network.

Although GPS is deemed as ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. Though a number of techniques are proposed for indoor localization, they are separated efforts that are way from a real ubiquitous localization system. Our real-world experience from InSpace, a pervasive computing system with wireless devices to provide intelligent services to users, shows that locating mobile users remains very challenging due to various interfering factors. We analyze real traces of mobile phones carried by users and find that mobile users exhibit temporal-spatial stability and neighborhood relativity. Motivated by this observation, we develop a Mobile Boundary Localization approach, MBL, to exploit the associated information to locate mobile users. This localization approach uses different treatment in different conditions and lets each mobile phone try to estimate its possible location range. We have implemented and evaluated MBL by extensive real-world experiments in InSpace and simulations. The results demonstrate that MBL significantly outperforms state-of-the-art localization approaches with more accurate, efficient, and consistent performance.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e. require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper we propose LISTEN, a non-interactive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. The experimental results demonstrate that it is accurate, efficient, and suitable for WCSNs that consist of low-end camera sensors.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. The real-world experience from GreenOrbs, a sensor network system in the forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper we propose CDL, a Combined and Differentiated Localization approach. The central idea is that ranging quality is the key that determines the overall localization accuracy. In its unremitting pursuit of better ranging quality, CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented CDL and evaluated it by extensive experiments and simulations. The results demonstrate that CDL outperforms current state-of-art approaches with better accuracy, efficiency and consistent performance.

Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.