Malignant brain tumors have become an aggressive and dangerous disease that leads to death worldwide. Multi-modal MRI data is crucial for accurate brain tumor segmentation, but missing modalities common in clinical practice can severely degrade the segmentation performance. While incomplete multi-modal learning methods attempt to address this, learning robust and discriminative features from arbitrary missing modalities remains challenging. To address this challenge, we propose a novel Semantic-guided Masked Mutual Learning (SMML) approach to distill robust and discriminative knowledge across diverse missing modality scenarios. Specifically, we propose a novel dual-branch masked mutual learning scheme guided by Hierarchical Consistency Constraints (HCC) to ensure multi-level consistency, thereby enhancing mutual learning in incomplete multi-modal scenarios. The HCC framework comprises a pixel-level constraint that selects and exchanges reliable knowledge to guide the mutual learning process. Additionally, it includes a feature-level constraint that uncovers robust inter-sample and inter-class relational knowledge within the latent feature space. To further enhance multi-modal learning from missing modality data, we integrate a refinement network into each student branch. This network leverages semantic priors from the Segment Anything Model (SAM) to provide supplementary information, effectively complementing the masked mutual learning strategy in capturing auxiliary discriminative knowledge. Extensive experiments on three challenging brain tumor segmentation datasets demonstrate that our method significantly improves performance over state-of-the-art methods in diverse missing modality settings.

In this paper, we present our effort towards comprehensive traffic forecasting for big data applications using external, light-weighted file system monitoring. Our idea is motivated by the key observations that rich traffic demand information already exists in the log and meta-data files of many big data applications, and that such information can be readily extracted through run-time file system monitoring. As the first step, we use Hadoop as a concrete example to explore our methodology and develop a system called HadoopWatch to predict traffic demands of Hadoop applications. We further implement HadoopWatch in a small-scale testbed with 10 physical servers and 30 virtual machines. Our experiments over a series of MapReduce applications demonstrate that HadoopWatch can forecast the traffic demand with almost 100% accuracy and time advance. Furthermore, it makes no modification on the Hadoop framework, and introduces little overhead to the application performance. Finally, to showcase the utility of accurate traffic prediction made by HadoopWatch, we design and implement a simple HadoopWatch-enabled network optimization module into the HadoopWatch controller, and with realistic Hadoop job benchmarks we find that even a simple algorithm can leverage the forecasting results provided by HadoopWatch to significantly improve the Hadoop job completion time by up to 14.72%.

Extensive empirical studies presented in this article confirm that the quality of radio communication between low-power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This article presents ATPC, a lightweight algorithm for Adaptive Transmission Power Control in wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations over a long period of time. The results from the real-world experiments demonstrate that (1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability, and (2) with online control, ATPC is robust even with environmental changes over time.

This paper presents our effort towards comprehensive traffic forecasting for big data applications using external, light-weighted file system monitoring. Our idea is motivated by the key observations that rich traffic demand information already exists in the log and meta-data files of many big data applications, and that such information can be readily extracted through run-time file system monitoring. As the first step, we use Hadoop1 as a concrete example to explore our methodology and develop a system called HadoopWatch to predict traffic demand of Hadoop applications. We further implement HadoopWatch in our real small-scale testbed with 10 physical servers and 30 virtual machines. Our experiments over a series of MapReduce applications demonstrate that HadoopWatch can forecast the traffic demand with almost 100% accuracy and time advance. Furthermore, it makes no modification of the Hadoop framework, and introduces little overhead to the application performance.

As cloud-based computation grows to be an increasingly important paradigm, providing a general computational interface to support datacenter-scale programming has become an imperative research agenda. Many cloud systems use existing virtual machine monitor (VMM) technologies, such as Xen, VMware, and Windows Hypervisor, to multiplex a physical host into multiple virtual hosts and isolate computation on the shared cluster platform. However, traditional multiplexing VMMs do not scale beyond one single physical host, and it alone cannot provide the programming interface and cluster-wide computation that a datacenter system requires. We design a new instruction set architecture, DISA, to unify myriads of compute nodes to form a big virtual machine called DVM and present programmers the view of a single computer, where thousands of tasks run concurrently in a large, unified, and snapshotted memory space. The DVM provides a simple yet scalable programming model and mitigates the scalability bottleneck of traditional distributed shared memory systems. Along with an efficient execution engine, the capacity of a DVM can scale up to support large clusters. We have implemented and tested DVM on four platforms, and our evaluation shows that DVM has excellent performance and scalability. On one physical host, the system overhead of DVM is comparable to that of traditional VMMs. On 16 physical hosts, the DVM runs 10 times faster than MapReduce/Hadoop and X10. On 160 compute nodes in the TH-1/GZ supercomputer, the DVM delivers a$\bf{12.99\times}$speedup over the computation on 10 compute nodes. The implementation of DVM also allows it to run above traditional VMMs, and we verify that DVM shows linear speedup on a parallelizable workload on 256 large EC2 instances.

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogrammable lifetime (i.e., the time period during which the sensor nodes can be reprogrammed) by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120--389 times less information than Deluge, and 18--42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%--53.87% in terms of beacon packets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogrammable lifetime by a factor of 3.3.

As Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to understand behavior patterns of end-hosts and network applications. This paper presents a novel approach based on behavioral graph analysis to study the behavior similarity of Internet end-hosts. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of end-hosts. By applying simple and efficient clustering algorithms on the similarity matrices and clustering coefficient of one-mode projection graphs, we perform network-aware clustering of end-hosts in the same network prefixes into different end-host behavior clusters and discover inherent clustered groups of Internet applications. Our experiment results based on real datasets show that end-host and application behavior clusters exhibit distinct traffic characteristics that provide improved interpretations on Internet traffic. Finally, we demonstrate the practical benefits of exploring behavior similarity in profiling network behaviors, discovering emerging network applications, and detecting anomalous traffic patterns.

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

Traditional cloud computing technologies, such as MapReduce, use file systems as the system-wide substrate for data storage and sharing. A distributed file system provides a global name space and stores data persistently, but it also introduces significant overhead. Several recent systems use DRAM to store data and tremendously improve the performance of cloud computing systems. However, both our own experience and related work indicate that a simple substitution of distributed DRAM for the file system does not provide a solid and viable foundation for data storage and processing in the data center environment, and the capacity of such systems is limited by the amount of physical memory in the cluster. To overcome the challenge, we construct VOLUME (Virtual On-Line Unified Memory Environment), a distributed virtual memory to unify the physical memory and disk resources on many compute nodes, to form a system-wide data substrate. The new substrate provides a general memory based abstraction, takes advantage of DRAM in the system to accelerate computation, and, transparent to programmers, scales the system to handle large datasets by swapping data to disks and remote servers. The evaluation results show that VOLUME is much faster than Hadoop/HDFS, and delivers 6-11x speedups on the adjacency list workload. VOLUME is faster than both Hadoop/HDFS and Spark/RDD for in-memory sorting. For kmeans clustering, VOLUME scales linearly to 160 compute nodes on the TH-1/GZ supercomputer.

The explosive growth of Internet-connected consumer devices in the digital home has made home networks one of the important emerging topics in networking research. A rich body of research efforts have been made to study broadband performance and home network management, little is known about network traffic that are exchanged within home networks or between Internet-connected devices in home networks and end hosts on the Internet. In this paper we design and implement a built-in traffic monitoring system on programmable home routers to collect and analyze incoming, outgoing and internal traffic for residential home networks. To illustrate the applications of the proposed built-in traffic monitoring system, we deploy the system in two real home networks, and demonstrate its capabilities of detecting unwanted traffic towards home networks as well as those suspicious traffic originating from compromised devices in home networks. In addition, our correlation analysis on unwanted traffic towards distributed home networks reveals aggressive scanners on the Internet and sheds lights on traffic characteristics of these scanners.

In recent years, data centers have emerged to be an increasingly important computing infrastructure. It is shown that wireless sensor networks (sensor nets) can provide fine-grained measurements in data centers, and achieve better control of the data center platform for energy efficiency. However, the usage of sensor nets has so far been limited to auxiliary functions, such as sensory data collection across a data center. We argue that the combined computational and networking capability of a sensor network enables it to interact with the clusters in a much more sophisticated way and enhance essential functions in a data center. We have designed a Cluster-Area Sensor Network (CASN) to improve the cluster management and operational security in the system. Implemented with TelosB motes, CASN can be easily deployed in a cluster, with sensor nodes attached in an ad hoc manner to servers, and provides key system functions including cluster-wide command dissemination and verification of physical presence. Experimental results show that CASN has 85% success rate in verifying physical locations of servers with coarse-grained localization when the threshold is 3 meters, and incurs small latency in cluster-wide command dissemination.

802.11-based wireless LANs (WLANs) have become an important communication infrastructure for today's pervasive computing applications. Nevertheless, WLAN users often experience various performance issues such as highly variable signal quality. To diagnose such transient service degradations and plan for future network upgrades, it is essential to closely monitor the performance of a WLAN and collect user statistics. This paper proposes a new WLAN performance monitoring approach motivated by the fact that many low-power wireless technologies such as ZigBee and Bluetooth co-exist with WLAN in the same open radio spectrum and are capable of sensing Received Signal Strength (RSS) of 802.11 transmissions. We have developed a ZigBee-based WLAN monitoring system called WizNet. Powered by batteries, ZigBee sensors of WizNet can be deployed in large quantities to monitor the spatial performance of a WLAN in long periods of time. By adopting digital signal processing techniques, WizNet automatically identifies 802.11 signals from ZigBee RSS measurements and associates them with wireless access points. To ensure the monitoring fidelity, WizNet accounts for the significant differences in ZigBee and WLAN radios, such as bandwidth and susceptibility to multipath and frequency-selective fading. A simple yet accurate linear estimator derived from a signal propagation model is used to infer the access points' signal to noise ratio (SNR). Moreover, WizNet can measure the congestion level of the channel and detect rogue APs. WizNet can also collect WLAN client statistics and classify device models based on RSS signatures of 802.11 access point scans. We have implemented WizNet in TinyOS 2.x and extensively evaluated its performance on a wireless testbed. Our results over a period of 140 hours show that WizNet can accurately capture the spatial and temporal performance variability of a large-scale production WLAN.

The networked application environment has motivated the development of multitasking operating systems for sensor networks and other low-power electronic devices, but their multitasking capability is severely limited because traditional stack management techniques perform poorly on small-memory systems without virtual memory support. In this paper, we show that combining binary translation and a new kernel runtime can lead to efficient OS designs on resource constrained platforms. We introduce SenSmart, a multitasking OS for sensor networks, and present new OS design techniques for supporting preemptive multitask scheduling, memory isolation, and adaptive stack management. Our solution provides memory isolation and automatic stack relocation on usual sensornet platforms. The adaptive stack management frees programmers from the burden of estimating tasks' stack usage, yet it enables SenSmart to schedule and run more tasks than other multitasking OSes for sensor networks. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart has a significantly better capability in managing concurrent tasks than other sensornet operating systems.

As cloud-based computation becomes increasingly important, providing a general computational interface to support datacenter-scale programming has become an imperative research agenda. Many cloud systems use existing virtual machine monitor (VMM) technologies, such as Xen, VMware, and Windows Hypervisor, to multiplex a physical host into multiple virtual hosts and isolate computation on the shared cluster platform. However, traditional multiplexing VMMs do not scale beyond one single physical host, and it alone cannot provide the programming interface and cluster-wide computation that a datacenter system requires. We design a new instruction set architecture, DISA, to unify myriads of compute nodes to form a big virtual machine called DVM, and present programmers the view of a single computer where thousands of tasks run concurrently in a large, unified, and snapshotted memory space. The DVM provides a simple yet scalable programming model and mitigates the scalability bottleneck of traditional distributed shared memory systems. Along with an efficient execution engine, the capacity of a DVM can scale up to support large clusters. We have implemented and tested DVM on three platforms, and our evaluation shows that DVM has excellent performance in terms of execution time and speedup. On one physical host, the system overhead of DVM is comparable to that of traditional VMMs. On 16 physical hosts, the DVM runs 10 times faster than MapReduce/Hadoop and X10. On 256 EC2 instances, DVM shows linear speedup on a parallelizable workload.

Surveillance is a critical problem for harbor protection, border control or the security of commercial facilities. The effective protection of vast near-coast sea surfaces and busy harbor areas from intrusions of unauthorized marine vessels, such as pirates smugglers or, illegal fishermen is particularly challenging. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental Wireless Sensor Network (WSN) on the sea's surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect any passing ships by distinguishing the ship-generated waves from the ocean waves. We design a three-tier intrusion detection system with which we propose to exploit spatial and temporal correlations of an intrusion to increase detection reliability. We conduct evaluations with real data collected in our initial experiments, and provide quantitative analysis of the detection system, such as the successful detection ratio, detection latency, and an estimation of an intruding vessel's velocity.

Surveillance is a vital problem for harbor protection, border control or the security of other commercial facilities. It is particularly challenging to protect the vast near-coast sea surface and busy harbor areas from intrusions of unauthorized marine vessels, such as trespassing boats and ships. In this paper, we present an innovative solution for ship intrusion detection. Equipped with three-axis accelerometer sensors, we deploy an experimental wireless sensor network on the sea surface to detect ships. Using signal processing techniques and cooperative signal processing, we can detect the passing ships by distinguishing the ship-generated waves and the ocean waves. We design an intrusion detection system in which we propose to exploit spatial and temporal correlations of the intrusion to increase detection reliability. We conduct evaluations with real data collected by our initial experiments, and provide quantitative analysis on the detection system, such as the successful detection ratio and the estimation of the intruding ship velocity.

This paper explores the behavior similarity of Internet end hosts in the same network prefixes. We use bipartite graphs to model network traffic, and then construct one-mode projection graphs for capturing social-behavior similarity of end hosts. By applying a simple and efficient spectral clustering algorithm, we perform network-aware clustering of end hosts in the same prefixes into different behavior clusters. Based on information-theoretical measures, we find that the clusters exhibit distinct traffic characteristics which provides improved interpretations of the separated traffic compared with the aggregated traffic of the prefixes. Finally, we demonstrate the applications of exploring behavior similarity in profiling network behaviors and detecting anomalous behaviors through synthetic traffic that combines Internet backbone traffic and packet traces from real scenarios of worm propagations and denial of service attacks.

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (i.e., duty cycling) sensors could reduce energy consumption, however at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques have studied how to collaboratively reduce the sensing gap in space and time, however none of them provides a rigorous approach to confine sensing error within desirable bounds. In this work, we propose a collaborative scheme called CIES, based on the novel concept of error inference between collaborative sensor pairs. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceed the tolerance. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. We further validated the simulation and algorithms by constructing a lab test-bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in highly dynamic environments.

Recent years have witnessed the wide adoption of the RFID technology in many important application domains including logistics, inventory, retailing, public transportation, and security. Though RFID tags (transponders) can be passive, the high power consumption of RFID readers (interrogators) has become a critical issue as handheld and mobile readers are increasingly available in pervasive computing environments. Moreover, high transmission power aggravates interference, complicating the deployment and operation of RFID systems. In this paper, we present an energy-efficient RFID inventory algorithm called Automatic Power Stepping (APS). The design of APS is based on extensive empirical study on passive tags, and takes into consideration several important details such as tag response states and variable slot lengths. APS dynamically estimates the number of tags to be read, incrementally adjusts the transmission power level to use sufficient but not excessive power for communication, and consequently reduces both the energy consumption for reading a set of tags and the possibility of collisions. We design APS to be compatible with the current Class-1 Generation-2 RFID standards so that a reader running APS can interact with existing commercial tags without modification. We have implemented APS both on an NI RFID testing platform and in a high-fidelity simulator. The evaluation shows that APS can save more than 60% energy used by RFID readers while maintaining comparable performance on the read rate.

Recent spates of cyber attacks towards cloud computing services running in large data centers have made it imperative to develop effective techniques to detect anomalous behaviors in the "clouds". In this paper, we propose to use the distributions of IP address octets and centroid based measures to characterize the inherent IP structure in high-volume data center traffic, and subsequently design a simple yet effective algorithm to detect abnormal traffic patterns caused by network attacks such as worms, virus, and denial of service attacks. We evaluate the effectiveness and efficiency of this algorithm with synthetic traffic that combines real data center traffic collected from a large Internet content provider with worm traces and denial of service attacks. The experiment results show that our algorithm consistently diagnoses the abnormal traffic from normal ones, and does so in a short time with a low false alarm rate. We believe that the proposed approach could be potentially deployed in real-time data center environments to enhance the security and high availability of cloud computing.

The networked application environment has motivated the development of multitasking operating systems for sensor networks and other low-power electronic devices, but their multitasking capability is severely limited because traditional stack management techniques perform poorly on small memory systems. In this paper, we show that combining binary translation and a new kernel runtime can lead to efficient OS designs on resource-constrained platforms. We introduce SenSmart, a multitasking OS for sensor networks, and present new OS design techniques for supporting preemptive multi-task scheduling, memory isolation, and versatile stack management. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart performs efficient binary translation and demonstrates a significantly better capability in managing concurrent tasks than other sensor net operating systems.

Recent years have witnessed the wide adoption of the RFID technology in many important application domains including logistics, inventory, retailing, public transportation, and security. Though RFID tags (transponders) can be passive, the high power consumption of RFID readers (interrogators) has become a critical issue as handheld and mobile readers are increasingly available in pervasive computing environments. Moreover, high transmission power aggravates interference, complicating the deployment and operation of RFID systems. In this paper, we present an energy-efficient RFID inventory algorithm called Automatic Power Stepping (APS). The design of APS is based on extensive empirical study on passive tags, and takes into consideration several important details such as tag response states and variable slot lengths. APS dynamically estimates the number of tags to be read, incrementally adjusts power level to use sufficient but not excessive power for communication, and consequently reduces both the energy consumption for reading a set of tags and the possibility of collisions. We design APS to be compatible with the current Class-1 Generation-2 RFID standards and hence a reader running APS can interact with existing commercial tags without modification. We have implemented APS both on the NI RFID testing platform and in a high-fidelity simulator. The evaluation shows that APS can save more than 60% energy used by RFID readers.

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogramming lifetime by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120--389 times less information than Deluge, and 18-42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%-53.87% in terms of beacon packets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogramming lifetime by a factor of 2.3.

After decades of engineering development and infrastructural investment, Internet connections have become a commodity product in many countries, and Internetscale "cloud computing" has started to compete with traditional software business through its technological advantages and economy of scale. Cloud computing is a promising enabling technology of Internetware. One distinct characteristic of cloud computing is the global integration of data, logic, and users, but such integration magnifies a sharp concern about privacy, which is one of the most frequently cited reasons by enterprises for not migrating to cloud-based solutions. We argue that cloud-based systems should include privacy as a fundamental design goal, and that privacy in a cloud environment is bidirectional, covering both end users and application providers. End users need privacy-aware software services that prevent their private data from being exposed to other users or the cloud providers. Application providers need a privacy-protected testing methodology to prevent the companies' internal activities and product features from leaking to external users. Focusing on privacy protection, we discuss the research challenges in this unique design space, and explore potential solutions for enhancing privacy protection in several important components of the system.

Energy efficiency is a fundamental issue for outdoor sensor network systems. This article presents the design and implementation of multidimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. A novel tripwire service is integrated with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. The tripwire service partitions a network into distinct, nonoverlapping sections and allows each section to be scheduled independently. Sentry scheduling selects a subset of nodes, the sentries, which are turned on while the remaining nodes save energy. Duty cycle scheduling allows the active sentries themselves to be turned on and off, further lowering the average power draw. The multidimensional power management strategies proposed in this article were fully implemented within a real sensor network system using the XSM platform. We evaluate key system parameters using a network of 200 XSM nodes in an outdoor environment, and an analytical probabilistic model. We evaluate network lifetime using a simulation of a 10,000-node network that uses measured XSM power values. These evaluations demonstrate the effectiveness of our integrated approach and identify a set of lessons and guidelines, useful for the future development of energy-efficient sensor systems. One of the key results indicates that the combination of the three presented power management techniques is able to increase the lifetime of a realistic network from 4 days to 200 days.

Abstract — Energy efficiency is a fundamental issue for out-door sensor network systems. This paper presents the design and implementation of multi-dimensional power management strategies in VigilNet, a major recent effort to support long-term surveillance using power-constrained sensor devices. We integrate a novel tripwire service with an effective sentry and duty cycle scheduling in order to increase the system lifetime, collaboratively. Through extensive system implementation, we demonstrate the feasibility to achieve high surveillance perfor-mance and energy efficiency, simultaneously. We invest a fair amount of effort to evaluate our architecture with a network of 200 XSM motes in an outdoor environment, an extensive simulation with 10,000 nodes, as well as an analytical probabilistic model. These evaluations demonstrate the effectiveness of our integrated approach and identify many interesting lessons and guidelines, useful for the future development of energy-efficient sensor systems. I.

Abstract — Sensing events from dynamic environments are normally asynchronous and non-repeatable. This lack of repeatability makes it particularly difficult to statistically evaluate the performance of sensor network applications. Hence, it is essential to have the capability to capture and replay sensing events, providing a basis not only for system evaluation, but also for realistic protocol comparison and parameter tuning. To achieve that, we design and implement EnviroLog, a distributed service that improves repeatability of experimental testing of sensor networks via asynchronous event recording and replay. To use EnviroLog, an application programmer needs only to specify two types of simple annotations to the source code. Automatically, the preprocessor embeds EnviroLog into any desired level of an event-driven architecture. It records all events generated by lower layers and can replay them later to upper layers on demand. We validate the accuracy and performance of recording and replay through a set of microbenchmarks, using the latest XSM platforms. We further demonstrate the strength of EnviroLog in system tuning and performance evaluation for sensor network applications in an outdoor environment with 37 XSMs. I.

Since sensor nodes normally have limited resources in terms of energy, bandwidth and computation capability, efficiency is a key design goal in sensor network research. As one of techniques to achieve efficiency, data aggregation has been extensively investigated in recent literature. Previous research on data aggregation has demonstrated its effectiveness in reducing traffic, easing congestion and decreasing the energy consumption. However few are actually designed for a real-world application and implemented in a running system. This paper describes our design and implementation of a physical tracking system, using an aggressive data aggregation architecture as one of building blocks. This architecture can be generally applied to other sensor systems, where communication efficiency is a paramount concern and networking resources are limited.

The development of a reliable large-scale wireless sensor network (WSN) is very difficult because of resource constraints, energy budget, and demanding application requirements. Three OS features-OS protection, virtual memory, and preemptive scheduling-can significantly improve the reliability of WSN systems and facilitate developing complex WSN software. However, due to the lack of hardware support for privileged execution and address translation, it is impossible to implement these features with traditional OS design techniques. To solve this problem, we design a new OS kernel, the t-kernel, to perform extensive code modification at load time. The modified code and the OS work in a collaborative way supporting the aforementioned features. Having implemented the t-kernel on MICA2 motes, we evaluate its performance by measuring the overhead and execution speed. We analyze the CPU utilization of sensor network applications, and verify that, though CPU-bound tasks execute 1.5-3 times as long as in native mode, application performance under typical workloads does not noticeably degrade. The t-kernel significantly enhances developers' ability to design reliable and sophisticated sensor networks, and includes several new design techniques, such as efficient binary translation on highly constrained sensor nodes, differentiated virtual memory without repeatedly writable swapping devices, and the protection of the OS from application errors without privileged execution hardware.

No abstract available.

Extensive empirical studies presented in this paper confirm that the quality of radio communication between low power sensor devices varies significantly with time and environment. This phenomenon indicates that the previous topology control solutions, which use static transmission power, transmission range, and link quality, might not be effective in the physical world. To address this issue, online transmission power control that adapts to external changes is necessary. This paper presents ATPC, a lightweight algorithm of Adaptive Transmission Power Control for wireless sensor networks. In ATPC, each node builds a model for each of its neighbors, describing the correlation between transmission power and link quality. With this model, we employ a feedback-based transmission power control algorithm to dynamically maintain individual link quality over time. The intellectual contribution of this work lies in a novel pairwise transmission power control, which is significantly different from existing node-level or network-level power control methods. Also different from most existing simulation work, the ATPC design is guided by extensive field experiments of link quality dynamics at various locations and over a long period of time. The results from the real-world experiments demonstrate that 1) with pairwise adjustment, ATPC achieves more energy savings with a finer tuning capability and 2) with online control, ATPC is robust even with environmental changes over time.

This article describes one of the major efforts in the sensor network community to build an integrated sensor network system for surveillance missions. The focus of this effort is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice, and energy consumption is narrowly accounted for within a single protocol. In this article, we describe the design and implementation of a complete running system, called VigilNet, for energy-efficient surveillance. The VigilNet allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We evaluate VigilNet middleware components and integrated system extensively on a network of 70 MICA2 motes. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such an integrated sensor system.

Battlefield surveillance often involves a high element of risk for military operators. Hence, it is very important for the military to execute unmanned surveillance by using large-scale wireless sensor systems. This invited paper summarizes the architecture of the VigilNet system - a long-term real-time networked sensor system for military surveillance. Specifically, we review the design of several major subsystems within VigilNet including sensing and classification, localization, tracking, networking, power management, reconfiguration, graphic user interface, and the debugging subsystem. High-level programming abstractions are also presented. This is a balanced design to achieve realtime response, high confidence detection, accurate tracking and energy efficiency simultaneously.

A wide variety of sensors have been incorporated into a spectrum of wireless sensor network (WSN) platforms, providing flexible sensing capability over a large number of low-power and inexpensive nodes. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification algorithms that achieve reliable sensing performance on energy-and-cost effective hardware without special powerful nodes in a continuously changing physical environment. We present the detection and classification system in a cutting-edge surveillance sensor network, which classifies vehicles, persons, and persons carrying ferrous objects, and tracks these targets with a maximum error in velocity of 15%. Considering the demanding requirements and strict resource constraints, we design a hierarchical classification architecture that naturally distributes sensing and computation tasks at different levels of the system. Such a distribution allows multiple sensors to collaborate on a sensor node, and the detection and classification results to be continuously refined at different levels of the WSN. This design enables reliable detection and classification without involving high-complexity computation, reduces network traffic, and emphasizes resilience and adaptation to the realistic environment. We evaluate the system with performance data collected from outdoor experiments and field assessments. Based on the experience acquired and lessons learned when developing this system, we abstract common issues and introduce several guidelines which can direct future development of detection and classification solutions based on WSNs.

Low-power embedded systems traditionally employ a "thin" OS because of resource constraints, hardware variety, and cost efficiency. This results in two problems - First, the embedded system programmers are limited to professionals with sufficient knowledge on hardware; Second, it is much slower for progress in programming languages and software engineering to find their ways to the systems with embedded microcontrollers, which is 98% of the microprocessors market. When low-power embedded processors are used in wireless sensor networks (WSNs), the thin OS approach, if followed, leads to another serious problem - The OS services cannot meet applications' ever-growing requirements. If these three problems are not solved, the transformation of the prosperous research on WSNs into a technology and market success has to be slow.

Distributed sensor networks are quickly gaining recognition as viable embedded computing platforms. Current techniques for programming sensor networks are cumbersome, inflexible, and low-level. We introduce EnviroTrack, an object-based distributed middleware system that raises the level of programming abstraction by providing a convenient and powerful interface to the application developer geared towards tracking the physical environment. EnviroTrack is novel in its seamless integration of objects that live in physical time and space into the computational environment of the application. Performance results demonstrate the ability of the middleware to track realistic targets.

The focus of surveillance missions is to acquire and verify information about enemy capabilities and positions of hostile targets. Such missions often involve a high element of risk for human personnel and require a high degree of stealthiness. Hence, the ability to deploy unmanned surveillance missions, by using wireless sensor networks, is of great practical importance for the military. Because of the energy constraints of sensor devices, such systems necessitate an energy-aware design to ensure the longevity of surveillance missions. Solutions proposed recently for this type of system show promising results through simulations. However, the simplified assumptions they make about the system in the simulator often do not hold well in practice and energy consumption is narrowly accounted for within a single protocol. In this paper, we describe the design and implementation of a running system for energy-efficient surveillance. The system allows a group of cooperating sensor devices to detect and track the positions of moving vehicles in an energy-efficient and stealthy manner. We can trade off energy-awareness and surveillance performance by adaptively adjusting the sensitivity of the system. We evaluate the performance on a network of 70 MICA2 motes equipped with dual-axis magnetometers. Our results show that our surveillance strategy is adaptable and achieves a significant extension of network lifetime. Finally, we share lessons learned in building such a complete running system.

Power management is an important technique to prolong the lifespan of sensor networks. Many power-management protocols employ wake-up/sleep schedules, which are often complicated and inefficient. We present power management schemes that eliminate such wake-up periods unless the node indeed needs to wake up. This type of wake-up capability is enabled by a new radio-triggered hardware component. We evaluate the potential power saving in terms of the lifespan of a sensor network application, using experiment data and SPICE circuit simulations. Comparing the result with always-on and rotation-based power management schemes, we find the radio-triggered scheme saves 98% of the energy used in the always-on scheme, and saves over 70% of the energy used in the rotation-based scheme. Consequently, the lifespan increases from 3.3 days (always-on) or 49.5 days (rotation-based) to 178 days (radio-triggered). Furthermore, a store-energy technique can extend operating distance from 10 feet to 22 feet, or even longer if longer latency is acceptable. We also present amplification and radio-triggered IDs which can further enhance performance.

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