To ensure sustainable operations of wireless sensor systems, environmental energy harvesting has been regarded as the right solution for long-term applications. In energy-dynamic environments, energy conservation is no longer considered necessarily beneficial, because energy storage units (e.g., batteries or capacitors) are limited in capacity and leakageprone. In contrast to legacy energy conservation approaches, we aim at energy synchronization for wireless sensor devices. The starting point of this work is TwinStar, which uses ultra-capacitor as the only energy storage unit. To efficiently use the harvested energy, we design and implement leakage-aware feedback control techniques to match local and network-wide activity of sensor nodes with the dynamic energy supply from environments. We conduct system evaluation under three typical real-world settingsindoor, outdoor, and mobile backpack under a wide range of system settings. Results indicate our leakage-aware control can effectively utilize energy that could otherwise leak away. Nodes running leakage-aware control can enjoy 70% more energy than the ones running non-leakage-aware control and application performance (e.g., event detection) can be improved significantly.

Wireless sensor networks have been proposed for many location-dependent applications. In such applications, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free localization depending only on connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to the above limitations, this paper presents a range-free approach to capturing a relative distance between 1-hop neighboring nodes from their neighborhood orderings that serve as unique high-dimensional location signatures for nodes in the network. With little overhead, the proposed design can be conveniently applied as a transparent supporting layer for many state-of-the-art connectivity-based localization solutions to achieve better positioning accuracy. We implemented our design with three well-known localization algorithms and tested it in two types of outdoor test-bed experiments: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with sub-hop resolution, and reduces localization errors by as much as 35%. In addition, extensive simulations reveal an interesting feature of robustness for our design under unevenly distributed radio propagation path loss, and confirm its effectiveness for large-scale networks.

Abstract-Tracking mobile targets using sensor networks is a challenging task because of the impacts of in-the-filed factors such as environment noise, sensing irregularity and etc. This paper proposes a robust tracking framework using node sequences, an ordered list extracted from unreliable sensor readings. Instead of estimating each position point separately in a movement trace, we convert the original tracking problem to the problem of finding the shortest path in a graph, which is equivalent to optimal matching of a series of node sequences. In addition to the basic design, multidimensional smoothing is developed to enhance tracking accuracy. Practical system deployment related issues are discussed in the paper, and the design is evaluated with both simulation and a system implementation using Pioneer III Robot and MICAz sensor nodes. In fact, tracking with node sequences provides a useful layer of abstraction, making the design framework generic and compatible with different physical sensing modalities.

Time synchronization remains as a challenging task in wireless sensor networks that face severe resource constraints. Unlike previous work's aiming at pure clock accuracy, this paper proposes On-Demand Synchronization (ODS), a design to achieve efficient clock synchronization with customized performance. By carefully modeling the error uncertainty of skew detection and its propagation over time, ODS develops a novel uncertainty-driven mechanism to adaptively adjust each clock calibration interval individually rather than traditional periodic synchronization, for minimum communication overhead while satisfying the desired accuracy. Besides, ODS provides a nice feature of predictable accuracy, allowing nodes to acquire the useful information about real-time qualities of their synchronization. We implemented ODS on the MICAz mote platform, and evaluated it through testbed experiments with 33 nodes as well as simulations obeying real world conditions. Results show that ODS is practical, flexible, and quickly adapts to varying accuracy requirements and different traffic load in the network for improved system efficiency. I. INTRODUCTIONTime synchronization is one of the most fundamental and widely employed middle-ware services in wireless sensor networks (WSN) [10]. It allows nodes in the network to have a common notion of time, either respect to a global reference or among themselves [9]. Accurate time synchronization is critical for saving communication energy [26][28], promoting localization accuracy [1][24], optimizing surveillance coverage [18][34], extending network lifetime [14][15] and improving system security [4]. However, due to extremely limited resources at each low-cost sensor node (e.g., poor clock quality, limited computation and communication capabilities, ultra tight energy budgets, etc), time synchronization remains as a challenging task in the WSN community. Previous research in this field mostly focused on how to do time synchronization for better clock accuracy (e.g., AD [33], TSS [19], RBS [20], TPSN [21], FTSP [22], VHT [5], etc). In spite of microsecond (µs) level accuracy achieved [5][7][22], they are not designed to provide a precise and convenient trade-off between service performance and energy efficiency, which complicates their deployment for energy sensitive applications with different timing requirements. In practice, systems depend on diverse synchronization qualities. For example, the bridge surveillance project [18] demands tens-of-µs clock accuracy for effective data acquisition; to detect jamming attacks in the network [4], the deviation of a couple of mil-

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