Low Earth Orbit (LEO) satellites provide ubiquitous, high-resolution imagery, making them well-suited for Earth observation applications. However, existing LEO Earth observation systems often experience significant latency in time-sensitive applications such as event detection and monitoring. Prior work has aimed to accelerate event detection by (i) optimizing event-capture latency using historical event distributions [17], and (ii) minimizing end-to-end query latency based on pre-stored images [58]. Instead of reducing event detection delays, we explore a new direction: continuous monitoring and dynamic tracking. To pursue this direction, we investigate the use of increasingly dense satellite constellations and propose LENS, a LEO-satellite Earth-observation Network Scheduling system. The key challenge of our work is to enable timely monitoring of regions of interest both before and after events occur, ensuring continuous and low-latency situational awareness. We address this challenge with two key techniques: (i) static scanning to proactively cover regions before events occur, and (ii) dynamic tracking to adapt observations after events are detected. We evaluate LENS through real-world hardware experiments and large-scale trace-driven emulations using orbital traces from over 6,000 active Starlink satellites. Our results show that LENS achieves up to a 90.6% reduction in observation latency for complete state-level coverage (e.g., California), while delivering more than a 4 × improvement in event-region monitoring coverage, compared with state-of-the-art strategies. To the best of our knowledge, this is the first work that leverages LEO satellites to achieve continuous monitoring and dynamic tracking.

Earth observation (EO) constellations operated by organizations such as Planet, Google, and Amazon generate hundreds of terabytes of imagery every day. However, limited downlink bandwidth prevents immediate data transmission, forcing satellites to store large volumes of imagery onboard and often overwrite valuable data before it can be downlinked. Existing EO pipelines treat each capture independently and fail to exploit the substantial redundancy naturally present in EO constellations. In practice, two major redundancies dominate: (i) temporal stability, where consecutive images of the same area change minimally over time, and (ii) spatial overlap, where neighboring satellites capture largely identical ground regions. To address these inefficiencies, we present CoOrbit, a collaborative EO system that conserves onboard storage by retaining only changed and non-overlapping regions. CoOrbit combines lightweight onboard embedding differencing, TLE-based overlap inference, and adaptive reference embedding planning. We further extend CoOrbit with an application-driven design, allowing satellites to downlink only application-relevant tiles for even greater efficiency. Evaluations on satellite-grade GPUs and imagery demonstrate that CoOrbit achieves over 108.6× reduction in storage cost and 41.6× reduction in communication size compared to existing EO pipelines. Our evaluation also shows an additional order-of-magnitude reduction when the wildfire-driven design is applied.

Smart buildings are supposed to be able to send alerts and localize threats. Despite traditional smart security devices such as fire alarms, entrance guards, and cameras, modern smart buildings also need to identify and track hostiles who hide potentially harmful metal objects under their clothes. We introduce Magneto, the first metal-sensing and biometric-based tracking system that makes use of existing power cables and WiFi infrastructures. Magneto tracks and identifies individuals' gait signatures while simultaneously sensing and discriminating metal objects. By leveraging existing power line infrastructure and WiFi networks, Magneto turns normal buildings into automated secure smart buildings. We built a prototype that fused both magnetic and RF sensing networks and evaluated it with individuals carrying 10 different metal objects. To show the robustness of our system, the volunteers have also sat in a metal wheelchair in the process of evaluation. Our extensive evaluation in a real-life environment shows that Magneto achieves a metal detection accuracy of 91.4% and a localization accuracy above 97%.

Recent advances in cross-technology communication can support direct communication among heterogeneous IoT devices (i.e., WiFi, ZigBee, and BLE) without requiring any modifications to the hardware, which has significantly improved the communication efficiency and shown great advantages for supporting smart applications. However, until now a key establishment protocol to support secure and reliable asymmetric cross- technology communication (CTC) is missing, which introduces severe privacy and security issues. Existing solutions are not designed for CTC, since they mainly focus on the symmetric communication among homogeneous IoT devices. In this work, we present a Key Establishment Protocol (KEP), which explores and lever- ages the unique feature of CTC - Possibility PN Sequence Reception (PSR) to not only perform key establishment between heterogeneous IoT devices with different physical layers (i.e., WiFi and ZigBee) but also improve the communication reliability at the same time. Our extensive real-world experiments show that KEP can finish the key establishment in seconds and effectively defend against multiple types of attacks. Furthermore, KEP doubles the packet reception ratio compared to the state-of-the-art solutions.

Researchers have demonstrated the feasibility of detecting human motion behind the wall with radio frequency (RF) sensing techniques. With these techniques, an eavesdropper can monitor people's behavior from outside of the room without the need to access the room. This introduces a severe privacy-leakage issue. To address this issue, we propose Aegis, an interference-negligible RF sensing shield that i) incapacitates the RF sensing of eavesdroppers that work on any WiFi frequency bands and at any unknown locations outside of the protected area; ii) has minimum interference to the on-going WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, the accuracy of legitimate sensing system only decreases by 0.08, while the accuracy of the illegitimate sensing system is as low as 0.04. Moreover, the on-going data communication throughput is even increased by$\text{10}\;\text{MB/s}$on$\text{2.4}\;\;\text{GHz}$WiFi band and$\text{5}\;\text{MB/s}$on$\text{5}\;\;\text{GHz}$WiFi band.

Orthogonal frequency-division multiplexing (OFDM) has been widely used in WiFi, LTE, and adopted in 5G. Recently, researchers have proposed multiple OFDM-based WiFi backscatter systems that use the same underlying design principle (i.e., codeword translation) at the OFDM symbol-level to transmit the tag data. However, since the phase error correction in WiFi receivers can eliminate the phase offset created by a tag, the codeword translation requires specific WiFi receivers that can disable the phase error correction. As a result, phase error is introduced into the decoding procedure of the codeword translation, which significantly increases the tag data decoding error. To address this issue, we designed a novel OFDM backscatter called TScatter, which uses high-granularity sample-level modulation to avoid the phase offset created by a tag being eliminated by phase error correction. Moreover, by taking advantage of the phase error correction, our system is able to work in more dynamic environments. Our design also has two advantages: much lower bit error rate (BER) and higher throughput. We conducted extensive evaluations under different scenarios. The experimental results show that TScatter has i) three to four orders of magnitude lower BER when its throughput is similar to the latest OFDM backscatter system MOXcatter; or ii) more than 212 times higher throughput when its BER is similar to MOXcatter. Our design is generic and has the potential to be applied to backscatter other OFDM signals (e.g., LTE and 5G).

The battery-powered Unmanned Aerial Vehicle (UAV) is a promising alternative to traditional logistics trucks. Using UAVs can achieve much more speedy, cost-effective, and environment-friendly delivery on an urban scale. However, UAVs suffer from insufficient delivery range and battery aging. This paper presents an innovative logistics UAV scheduling framework using public buses, in which logistics UAVs Land and Recharge its battery on Buses (ULRB) to extend its delivery range and decelerate its fading battery capacity. This work correlates physical layer parameters such as the energy consumption rate, the parcels weight, UAV velocity, the battery temperature to the UAVs path planning, the battery discharging, and the capacity fading models. Specifically, the ULRB framework consists of a single-UAV scheduling module and a multi-UAV dispatching module. In the single-UAV module, a Markov-based algorithm is utilized to plan the UAVs flying path to land and dynamically get recharged on the bus. The latter module optimized the delivery progress in a multi-UAV, multi-parcel, and multi-bus scenario. Finally, using a large-scale real-world bus trajectory dataset, extensive evaluations are conducted to verify ULRB. The results show that ULRB can extend the UAVs delivery range by 5.54 and decelerate the battery aging by 3.26 on average.

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

Convergecast is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. This problem further causes not only wasted packet retransmissions, but also a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose anincast-collision-free convergecast protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique, establishes a non-conflicting schedule for efficient convergecast, and improves the channel utilization by allowing senders to opportunistically transmit packets once detecting unused slots. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the baseline protocol, iCore effectively minimizes the end-to-end delay by 25% ~ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

The camera is one of the most important sensors for an autonomous vehicle (AV) to perform Environment Perception and Simultaneous Localization and Mapping (SLAM). To secure the camera, current autonomous vehicles not only utilize the data gathered from multiple sensors (e.g., Camera, Ultrasonic Sensor, Radar, or LiDAR) for environment perception and SLAM but also require the human driver to always realize the driving situation, which can effectively defend against previous attack approaches (i.e., creating visible fake objects or introducing perturbations to the camera by using advanced deep learning techniques). Different from their work, in this paper, we in-depth investigate the features of Infrared light and introduce a new security challenge called I-Can-See-the-Light- Attack (ICSL Attack) that can alter environment perception results and introduce SLAM errors to the AV. Specifically, we found that the invisible infrared lights (IR light) can successfully trigger the image sensor while human eyes cannot perceive IR lights. Moreover, the IR light appears magenta color in the camera, which triggers different pixels from the ambient visible light and can be selected as key points during the AV's SLAM process. By leveraging these features, we explore to i) generate invisible traffic lights, ii) create fake invisible objects, iii) ruin the in-car user experience, and iv) introduce SLAM errors to the AV. We implement the ICSL Attack by using off-the-shelf IR light sources and conduct an extensive evaluation on Tesla Model 3 and an enterprise-level autonomous driving platform under various environments and settings. We demonstrate the effectiveness of the ICSL Attack and prove that current autonomous vehicle companies have not yet considered the ICSL Attack, which introduces severe security issues. To secure the AV, by exploring unique features of the IR light, we propose a software-based detection module to defend against the ICSL Attack.

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, to enable the communication from ZigBee to WiFi, we leverage WiFi's fine-grained channel state information to extract the concurrently transmitted ZigBee-to-WiFi message from time overlapped ZigBee and WiFi packet. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating control messages) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400x) and reduces the latency.

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim’s transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim’s throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).

To support ubiquitous computing for various applications (such as smart health, smart homes, and smart cities), the communication system requires to be ubiquitously available, ultra-low-power, high throughput, and low-latency. A passive communication system such as backscatter is desirable. However, existing backscatter systems cannot achieve all of the above requirements. In this paper, we present the first LTE backscatter (LScatter) system that leverages the continuous LTE ambient traffic for ubiquitous, high throughput and low latency backscatter communication. Our design is motivated by our observation that LTE ambient traffic is continuous (v.s. bursty and intermittent WiFi/LoRa traffic), which makes LTE ambient traffic a perfect signal source of a backscatter system. Our design addresses practical issues such as time synchronization, phase modulation, as well as phase offset elimination. We extensively evaluated our design using a testbed of backscatter hardware and USRPs in multiple real-world scenarios. Results show that our LScatter's performance is consistently orders of magnitude better than WiFi backscatter in all the above scenarios. For example, LScatter's throughput is 13.63Mbps, which is 368 times higher than the latest ambient WiFi backscatter system [54]. We also demonstrate the effectiveness of our system using two real-world applications.

Rapidly evolving wireless technologies enable devices to directly exchange information without infrastructural support. In these device-to-device (D2D) communication scenarios, it is often difficult to setup cryptographic keys to initialize the secure communication, especially when the D2D connections are mobile and dynamic. This paper proposes an application layer solution scheme to bootstrap secure communications using the inherent randomness in the wireless transmissions. The proposed scheme is lightweight, easy to deploy and compatible with many physical layer wireless technologies. This paper contains security analysis to the scheme and conducts experiments to demonstrate its practicality.

Internet-of-things (IoT) devices are sharing the radio frequency band (e.g., 2.4 GHz ISM band). The exponentially increasing number of IoT devices introduces potential security issues at the gateway in IoT networks. In this paper, we introduce a set of new attacks through concealed jamming - an adversary pretends to be (or compromises) a legitimate WiFi device, then sends out WiFi packets to prevent ZigBee devices' communication or collide with ZigBee's packets. By doing this, concealed jamming has the potential to severely delay the reception of ZigBee packets that may contain important information (e.g., critical health data from wearables, fire alarms, and intrusion alarms). To defend against these attacks, we designed a novel ZigBee data extraction technique that can recover ZigBee data from the ZigBee packets that were collided with WiFi packets. We extensively evaluated our design in different real-world settings. The results show that ZigBee devices (protected by our proposed methods) achieve similar performance as those that are not under the concealed jamming attack. Moreover, compared with unprotected devices, their throughput is more than 15 times higher than the unprotected one that is under concealed jamming attacks.

The password has become today's dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context-free grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for cross-site attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.

Many chatbots have been developed that provide a multitude of services through a wide range of methods. A chatbot is a brand-new conversational agent in the highspeed changing technology world. With the advance of Artificial Intelligence and machine learning, chatbots are becoming more and more popular. A chatbot is the extension of human interface mediums such as the phone and social platforms. Similarly, Cryptocurrency is a new extension of digital or virtual currency designed to work as a medium of exchange. In the current digital exchanging world, investors and interested parties are eager to know more information about, and the capabilites of, this new type of currency. One of the potential paths to retrieve the info automatically and quickly is through a chatbot. We explored the open source python library, Chatterbot, to apply Itchat API (a WeChat interface) with the aim of building a robot chatting application, I&C Chat, on the topic of cryptocurrency. First, we collected question and answer pairs datasets from Quora websites. Furthermore, we also created API calls to query the real time quote for the top 25 cryptocurrencies. Then we used the collected data to train our chatbot and implemented a logic adapter to receive the price quote of cryptocurrencies based on the incoming question. The Itchat API method will return the best matched answer to the asking party automatically. The response time of different questions has been investigated. The results imply that this application is quite useful, feasible and beneficial to the digital currency world.

Deep neural networks are vulnerable to adversarial examples which are inputs modified with unnoticeable but malicious perturbations.Most defending methods only focus on tuning the DNN itself, but we propose a novel defending method which modifies the input data to detect the adversarial examples.We establish a detection framework based on normalizing filters that can partially erase those perturbations by smoothing the input image or depth reduction work.The framework gives the decision by comparing the classification results of original input and multiple normalized inputs.Using several combinations of gaussian blur filter, median blur filter and depth reduction filter, the evaluation results reaches a high detection rate and achieves partial restoration work of adversarial examples in MNIST dataset.The whole detection framework is a low-cost highly extensible strategy in DNN defending works.

Much recent effort has been devoted to employing Unmanned Aerial Vehicles (UAVs) to implement airport-related tasks. However, a critical issue, collision avoidance, must be fully considered in this scenario. Herein, we study the efficient UAV navigation problem considering the safety issue near an airport. In detail, we first define the safe separation between the UAV and airplanes according to related aviation regulations. Thereafter, an effective tree-based scheme for navigating the UAV has been proposed to cope with the extra uncertainties induced by keeping the safe separation. An analytical derivation of the UAV's flying time is conducted to determine the optimal battery life. Extensive simulation is conducted to verify our proposed navigation scheme and the analytical derivation.

Visual-based Unmanned Aerial Vehicles (UAVs) (e.g. equipped with an optical camera) have become more popular in daily life, because of their flexibility and convenience in capturing images/videos. Existing works mainly focus on the image/video capturing efficiency, but overlook the privacy violations that may be caused by the misuse of UAVs. In this paper, we study the Privacy Preserving Navigation (PPN) problem of the path planning of a UAV in 3D space to cover a 2D Target Area (TA), so that TA is covered while the privacy of sub-areas within TA is violated. We prove the PPN is NP-hard and propose a heuristic solution to PPN. The real word data trace driven emulation results show our solution is effective.

Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical field. Many of the existing brain tumor segmentation methods are semiautomatic which need interventions of raters or specialists. In this paper an automatic method, named wide residual & pyramid pool network (WRN-PPNet), which can automatically segment glioma end to end is put forward. The main idea is described below. Firstly, substantial two-dimensional (2D) slices are obtained from three-dimensional (3D) MRI brain tumor images. Secondly, the 2D slices are normalized and put into the WRN-PPNet model, and the model will output the tumor segmentation results. Finally, dice coefficient (Dice), sensitivity coefficient (Sensitivity) and predictive positivity value (PPV) coefficient are used to evaluate the performance of WRN-PPNet quantitatively. The experimental results show that the proposed method is simple and robust compared with the other state-of- the-art methods, and the average Dice, Sensitivity and PPV on the randomly selected test data can reach 0.94, 0.92 and 0.97 respectively.

With the exponentially increasing number of Internet of Things (IoT) devices and the huge volume of data generated by these devices, there is a pressing need to investigate a more efficient communication method in both frequency and time domains at the edge of the IoT networks. In this paper, we present Amphista, a novel cross-layer design for IoT communication and data forwarding that can more efficiently utilize the ever increasingly crowded 2.4 GHz spectrum near the gateway. Specifically, by using a single ZigBee data stream, Amphista enables a ZigBee device to send out two different pieces of information to both the WiFi gateway and another ZigBee device. We further leverage this unique feature and design a novel forwarding protocol that can simultaneously forward uplink (e.g., collecting sensing data) and downlink (e.g., disseminating software updates) data by using a single ZigBee data stream. Our extensive experimental results show that Amphista significantly improves throughput (by up to 400×) and reduces the latency.

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi networks by 1.2 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee networks (i.e., 31 times faster than the state-of-the-art flooding protocol).

WiFi and Bluetooth Low Energy (BLE) are widely used in Internet of Things (IoT) devices. Since WiFi and BLE work within the overlapped ISM 2.4 GHz band, they will interfere with each other. Existing approaches have demonstrated their effectiveness in mitigating the interference. However, further performance improvement has been hampered by the design goal of exclusive communication of WiFi or BLE, which only allows one WiFi or BLE device to transmit packets at any specific time slot on the overlapped channel within the communication range. In this paper, we explore a new communication method, called Parallel Inclusive Communication (PIC), which leverages the unique modulation schemes of WiFi and BLE for parallel inclusive bi-directional transmission of both WiFi and BLE data at the same time within the overlapped channel. In this communication system, the PIC gateway is designed upon the IEEE 802.11g and 802.15.1 frameworks while the WiFi and BLE clients are commercial off-the-shelf devices. PIC achieves similar data rates for these parallel WiFi and BLE communications as if WiFi and BLE are communicating separately. PIC's system architecture naturally fits at the edge of the Internet, which is an optimal site for concurrently collecting (or disseminating) data from (or to) an exponentially increasing number of IoT devices that are using WiFi or BLE. We conducted extensive evaluations under four real-world scenarios. Results show that compared with existing approaches, PIC can significantly i) increase the packet reception ratios by 183%; ii) reduce the round-trip delay time by 590 times and energy consumption by 50.5 times; and iii) improve the throughput under WiFi and BLE coexistence scenarios.

The exponentially increasing number of Internet of Things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4-GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among Wi-Fi and Bluetooth low energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to Wi-Fi cross-technology communication while supporting the concurrent BLE to BLE and Wi-Fi to Wi-Fi communications. We conducted extensive experiments under different real-world settings, and results show that its throughput is more than 85X times higher than that of the most recently reported cross-technology communication system, which only supports one-way communication (i.e., broadcasting) at any specific time.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same Industrial, Scientific, and Medical band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT , opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes’ priorities and reduce the delivery delay from high-priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi access point. We extensively evaluate ECT under different network settings, and results show that our ECT’s packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Tracking moving objects is always a critical challenge in cyber-physical systems. Researchers have proposed many tracking algorithms. However, most of the proposed algorithms cannot be used for on-demand deployment because of the unavailable preset fingerprints (prior landmark or context information) in their assumption. Another issue is that those algorithms with models built in an interference-free environment cannot work in interference-rich environments. To address those issues, we propose a localizing and tracking algorithm called Enhanced Field Division (EFD), which dynamically divides the field into areas with unique signatures and tracks the target without any fingerprints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference-rich environments.

This study presents a new method for detecting Shrew DDoS (Distributed Denial of Service) attacks and analyzes the characteristics of the Shrew DDoS attack. Shrew DDoS is periodic to be suitable for the server’s TCP (Transmission Control Protocol) timer. It has lower maximum to bypass peak detection. This periodicity makes it distinguishable from normal data packets. By proposing the CCID (Cross-Correlation Identity Distinction) method to distinguish the flow properties, it quantifies the difference between a normal flow and an attack flow. Simultaneously, we calculated the cross-correlation between the attack flow and the normal flow in three different situations. The server can use its own TCP flow timer to construct a periodic attack flow. The cross-correlation between Gaussian white noise and simulated attack flow is less than 0.3. The cross-correlation between single-door function and simulated attack flow is 0.28. The cross-correlation between actual attack flow and simulated attack flow is more than 0.8. This shows that we can quantitatively distinguish the attack effects of different signals. By testing 4 million data, we can prove that it has a certain effect in practice.

Password has become today's dominant method of authentication in social network. While the brute-force attack methods, such as HashCat and John the Ripper, are unpractical, the research then switches to the password guess. The state-of-the-art approaches, such as Markov Model and probabilistic context-free grammars(PCFG), are all based on statistical probability. These approaches have a low matching rate. The methods on neural network have been proved more accurate and practical for password guessing than traditional methods. However, a raw neural network model is not qualified for cross-sites attack since each data set has its own features. This paper proposes a general deep learning model for password guessing, called GENPass. GENPass can learn features from several data sets and ensure the output wordlist high accuracy in different data sets by using adversarial generation. The password generator of GENPass is PCFG+LSTM(PL), where LSTM is a kind of Recurrent Neural Network. We combine neural network with PCFG because we found people were used to set their passwords with meaningful strings. Compared with LSTM, PL increased the matching rate by 16%-30% in the cross-sites tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The result shows that the matching rate of GENPass is 20% higher than that of simply mixing those datasets in the cross-sites test.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same ISM band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT, opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes' priorities and reduce the delivery delay from high priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi AP. We extensively evaluate ECT under different network settings and results show that our ECT's packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Researchers have demonstrated the feasibility of detecting human motion behind the wall with radio frequency (RF) sensing techniques. With these techniques, an eavesdropper can monitor people's behavior from outside of the room without the need to access the room. This introduces a severe privacy-leakage issue. To address this issue, we propose Aegis, an interference-negligible RF sensing shield that i) incapacitates the RF sensing of eavesdroppers that work at any unknown locations outside of the protected area; ii) has minimum interference to the ongoing WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, it i) has a negligible impact on the legitimate sensing system; ii) effectively prevents the illegitimate sensing system from sensing human motions. Moreover, the ongoing data communication throughput is even increased.

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices motivate us to explore more efficient and higher throughput communication, especially at the bottleneck (i.e., edge) of the IoT networks. Our work, named Chiron, opens a promising direction for Physical (PHY) layer concurrent high throughput communication to heterogeneous IoT devices (e.g., wider-band WiFi and narrower-band ZigBee). Specifically, at the PHY layer, Chiron enables concurrently transmitting (or receiving) 1 stream of WiFi data and up to 4 streams of ZigBee data to (or from) commodity WiFi and ZigBee devices as if there is no interference between these simultaneous connections. We extensively evaluate our system under different real-world settings. Results show that Chiron's concurrent WiFi and ZigBee communication can achieve similar throughput as the sole WiFi or ZigBee communication. Chiron's spectrum utilization is more than 16 times better than the traditional gateway.

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

Within heterogenous IoT sensor networks, users of ZigBee devices expect long-lasting battery usage due to its ultra-low power and duty cycle. In IoT networks, to demonstrate even further ultra-low power consumption, we introduce Passive-ZigBee that demonstrates we can transform an existing productive WiFi signal into a ZigBee packet for a CoTS low-power consumption receiver while consuming 1,440 times lower power compared to traditional ZigBee. Moreover, this low power backscatter radio can bridge between the ZigBee and WiFi devices by relaying data allowing heterogenous radios to communicate with each other. We built a hardware prototype and implement these devices on a commodity ZigBee, WiFi, and an FPGA platform. Our experimental evaluation demonstrates the backscattered WiFi packets can be decoded by CoTS ZigBee receivers over a distance of 55 meters in none-line-of-sight and with human movements. Our Passive-ZigBee can consume only 25μW when transferring sensor data and relay ZigBee and WiFi data compared to traditional ZigBee (36mW). Our FPGA synthesis tool demonstrated the extremely low power consumption.

WiFi-fingerprint localization is recognized as a promising indoor localization technique. However, it suffers from high implementation overhead such as heavy initial training and fingerprint map maintenance overtime. In this paper, we present the design, implementation, and evaluation of AP-Sequence. It is a fingerprint-based localization system that achieves extremely low overhead in fingerprint map construction and maintenance. AP-Sequence achieves this by treating a scan from any reference locations as an input to adjust a large portion of the fingerprint map. The power of AP-Sequence comes from dynamic region partitioning mechanism generating a fingerprint based on relative RSS values. AP-Sequence offers several advantages over existing methods with respect to robustness against environment noises, ability to handle dynamic power control, and mobile device heterogeneity. We have implemented AP-Sequence on an Android platform. Experiment results with over one month of evaluation demonstrate that our design achieves an average localization accuracy of 4-7.6 m over an extended time period with low-overhead in fingerprint map construction and maintenance.

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.

Unmanned Aerial Vehicles (UAVs) have wide applications in many fields, e.g. multiple Unmanned Aerial Vehicles (UAVs) cooperatively tracking multiple targets. This paper studies the multiple UAVs cooperatively tracking multiple targets by vision surveillance system, where the images/videos of targets have direction requirements. One target is covered by a UAV if and only if its position is within the Field Of View (UAV) as well as the UAV is within a requested angle of the target's face direction. The objective is to maximize the total covered targets number by the UAVs, which can not be solved by existing models. A simple effective distributed, online cooperation algorithm for this problem is designed in this paper. The theoretical analysis shows our algorithm achieves constant factor to the optimal.

Energy internet is now an industry hot spot which enables the interconnection and sharing of energy just like the Internet. Inspired by the concept of energy internet, this paper will focus on a designed energy distribution network, using city bus lines running Electric Vehicles (EV) to achieve electric power storage and transmission. This network is made of renewable energy sources providing power, charge stations for power exchange and bus lines as delivery, electric buses serving as the carriers of flowing power in network. This paper will mainly discuss and solve the problem of placing charge stations on city bus map to compose the network framework. Our work includes two optimization algorithms using some ideas of graph theory, simulating with real-world transporting data of different city maps and analyzing the results to evaluate efficiency as well as advantages and disadvantages on algorithms and data sets.

The exponentially increasing number of Internet of things (IoT) devices introduces spectrum crisis to the widely used industrial, scientific, and medical (ISM) frequency band. Since IoT devices use heterogeneous radios with different bandwidths (e.g., 20 MHz for WiFi and 2 MHz for ZigBee), traditional interference avoidance methods, such as time-division multiple access (TDMA) and carrier-sense multiple access (CSMA), have very low spectrum utilization. This is because TDMA and CSMA allocate the packets at time domain, without considering the bandwidth difference of different IoT radios. To address this issue, we propose PMC, a novel communication system that enables parallel multi-protocol communication to heterogeneous IoT radios (i.e., WiFi and ZigBee) within a single WiFi channel. Our extensive evaluations show that PMC achieves the throughput of up to 121.02 kbit/s and 319.76 Mbit/s for parallel communication to ZigBee and WiFi, respectively. Compared with TDMA and CSMA, the spectrum utilization of PMC is increased by 2.3 and 1.8 times, respectively.

The exponentially increasing number of IoT devices makes the unlicensed industrial, scientific, and medical (ISM) radio bands (e.g., 2.4 GHz) extremely crowded. Currently, there is no efficient solution to coordinate the large amount heterogeneous IoT devices that have different communication technologies (e.g., WiFi and ZigBee). To fill this gap, in this paper, we introduce embedded multiple flows (EMF) communication method, which (i) embeds different pieces of information in existing traffic and (ii)concurrently sends out these information from one IoT sender to multiple IoT receivers that have a different communication technology from the sender. By doing this, our EMF method (i) enables cross-technology communication among heterogeneous IoT devices, (ii) does not introduce any extra control traffic, and (iii) is transparent to the higher layer applications. Our approach is implemented on USRPs and commercial off-the-shelf (COTS) ZigBee devices. We also conducted extensive experiments to evaluate our approach in real-world settings. The evaluation results show that EMF's throughput is more than 14 times higher than the latest cross-technology communication technique (i.e. FreeBee[1]).

UAVs can support different applications, such as forest fire surveillance and precise agriculture. UAVs' service-on-demand preference drives the need for multiple UAVs to enhance surveillance coverage and data stability. Due to the limited capacity of the UAV, UAVs desire network coordination based on the importances of collected data. For example, UAVs at different locations may have different priorities of data forwarding tasks while the required data sizes of different tasks are varying. In this paper, we propose a system framework for UAV array to structure and prioritize the data forwarding based on i) forward-looking channel quality; ii) priorities of tasks of multimodal data on demand. Our proposed distributed real-time framework aims to optimize effective data throughput given a channel quality and effective scheduling of the channel usage among multiple UAVs. We conducted extensive evaluations using multiple UAVs and results show that our modeling of forward-looking channel quality prediction achieves 90% accuracy. Moreover, our scheduling algorithms can effectively optimize the overall data quality of forwarding tasks between UAVs and the base station.

Renewable energy (e.g., solar energy) is an attractive option to provide green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either (i) requiring batteries to store surplus energy, which typically incurs ∼ 20% energy conversion losses, or (ii) using net metering to transmit surplus energy via the electric grid’s AC lines, which severely limits the maximum percentage of renewable penetration possible. In this article, we propose an alternative structure where nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when to minimize system-wide energy transmission losses in the microgrid. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system (i) reduces the energy loss on the AC line by 64% without requiring large batteries, (ii) performance scales up with larger battery capacities, and (iii) is robust to different energy consumption patterns and energy prediction accuracy in the microgrid.

Significant research has been devoted to reduce the energy consumption of mobile devices, but how to increase their energy supply has received far less attention. Moreover, reducing the energy consumption alone does not always extend the device operation time due to a unique battery property - the capacity it delivers hinges critically upon how it is discharged. In this paper, we propose B-MODS, a novel design of battery-aware mobile data service on mobile devices. B-MODS constructs battery-friendly discharge patterns utilizing the recovery effect so as to increase the capacity delivered from batteries while meeting data service requirements. We implement B-MODS as an application layer library on the Android platform. Our experiments with diverse mobile devices under various application scenarios have shown that B-MODS increases the capacity delivery from the battery by up to 49.5 percent, with which an increase in the user-perceived data service utilities of up to 28.6 percent is observed.

As sensors spread across almost every industry, the Internet of Things (IoT) is going to trigger an era of big data. However, the abundance of available sensing data causes new challenges when building IoT applications. One main challenge is how to select proper data from large amount of sensing data for learning useful information efficiently. Existing approaches require developers to manage data for each specific application, which is very time consuming since the developers may not have enough knowledge about the dynamic changing data quality of different sensors. In this paper, we propose a data management middleware to learn the correlations between time series sensor data without prior knowledge. The learned correlation is then applied to select the useful sensor and reconstruct the incorrect data. To generalize the correlation models for each application, we utilize the dynamic feedback from the application to update the data selection and reconstruction. We evaluate our data management middleware in smart grids. The evaluation results show that our middleware can achieve better application performance with the help of dynamic feedback, data reconstruction and data selection.

Accurate energy demand prediction is very important for smart grids to conduct demand response and stabilize the grids. In previous work, many prediction algorithms are proposed to improve the energy consumption prediction accuracy based on the aggregated energy consumption in the whole grid. Recently, with the increasing installations of smart meters in individual homes, high granularity (e.g., per minute) energy consumption data in individual homes becomes available and provides us a great opportunity for better energy consumption prediction. In this paper, we propose M-Pred to utilize the high granularity energy consumption data collected by smart meters in individual homes for better energy consumption prediction in smart grids. In M-Pred, we propose a learning algorithm to learn energy consumption patterns of individual homes from the high granularity energy consumption data. The consumption patterns we learn from homes are then applied for energy consumption prediction in smart grids. Furthermore, since not every home in a smart grid is equipped with a smart meter, we propose a matching and prediction algorithm to leverage the multi-granularity energy data for accurate consumption prediction. We conducted extensive system evaluations with 726 homes' minute-level power consumption data for more than 12 months. The simulation results show that our design can provide accurate energy consumption prediction for the next hour with negligible errors (e.g., Mean Absolute Percentage Error is 2.12%).

Human posture recognition has a wide range of applications including elderly care and video surveillance. This paper discusses how to recognize human postures using wearable devices. From real-world data, we analyze the challenges in terms of result performance, recognition efficiency and sensor selection. To deal with the challenges, we present our design with five techniques: i) oversampling and undersampling methods, ii) ensemble learning, iii) sensor selection, iv) stream data classification and v) post-processing techniques. We verify our design and show our findings through extensive experiments on real-world data, which shows our approach can achieve up to 91.5% overall weighted average accuracy for all three postures. We also discuss possible extensions of our work.

With the advent of the Internet of Things (IoT) and big data, high fidelity localization and tracking systems that employ cameras, RFIDs, and attached sensors intrude on personal privacy. However, the benefit of localization information sharing enables trend forecasting and automation. To address this challenge, we introduce Wobly, an attribute based signature (ABS) that measures gait. Wobly passively receives Wi-Fi beacons and produces human signatures based on the Doppler Effect and multipath signals without attached devices and out of direct line-of-sight. Because signatures are specific to antenna placement and room configuration and do not require sensor attachments, the identities of the individuals can remain anonymous. However, the gait based signatures are still unique, and thus Wobly is able to track individuals in a building or home. Wobly uses the physical layer channel and the unique human gait as a means of encoding a person's identity. We implemented Wobly on a National Instruments Radio Frequency (RF) test bed. Using a simple naive Bayes classifier, the correct identification rate was 87% with line-of-sight (LoS) and 77% with non-line-of-sight (NLoS).

Network intrusion detection based on SVM is the hot topic of network security research, and the existing researches have low detection rate, high false positive rate and other issues. Optimizing particle swarm optimization parameters of SVM is an effective solution, but the PSO algorithm is easy to fall into local optimum and results premature convergence.We propose an improved particle swarm optimization algorithm ICPSO, which use chaos operator ergodicity, randomness, sensitivity to initial conditions and other characteristics and the ICPSO is used to make the chaos into the inertia weight factor parameters and The chaos is applied to the optimization of the RBF kernel function parameter g and the penalty factor C, and to improve the convergence speed and precision of the particle swarm optimization. The experimental results show that: relative to the PSO-SVM algorithm and GA-SVM algorithm, ICPSO-SVM improves the efficiency of intrusion detection, and is an effective intrusion detection model.

Traffic engineering (TE) has long been used by network providers to reduce network congestion and improve resource utilization. Due to its significance, several traffic engineering algorithms have been proposed in literature. However, most of these algorithms optimize maximum link utilization (MLU) in network, and/or assume that network has the capability to route demands on arbitrary paths. Optimizing only for MLU can result in longer route computations to save bandwidth along shorter paths, thereby hurting application performance (as shown by recent research). Further, minimizing MLU can lead to solutions where several links have utilization close to MLU, while many others are under- utilized. Besides, as large fraction of today's Internet uses OSPF routing protocol, it cannot benefit from TE algorithms assuming arbitrary routing capabilities. To address these problems, we present Wise-OSPF (WOSPF), a traffic engineering solution for OSPF networks. WOSPF formulates TE as an optimization problem. The objective of WOSPF is to minimize the difference between the maximum and minimum link utilizations across the network, which leads to more uniform traffic distribution compared to optimizing MLU. As WOSPF uses OSPF for routing demands, it does not compute unnecessarily long routes and can be employed in legacy OSPF networks with minimal changes. Our results show that WOSPF reduces standard deviation of link utilizations in network by 31.35% compared to an optimal MLU based TE approach, while achieving an MLU within 1.9% of the optimal.

The Internet of Things (IoT) applications is envisioned to require higher throughput protocols because of the increasing data amount. To significantly enhance the network throughput between IoT devices, this paper proposes a new link-layer data forwarding technique that is aware of link correlation (LC) and supports receiver initiated acknowledgement (RI-ACK). We also propose a multicast communication protocol based on LC-aware forwarding and RI-ACKs to further enhance the throughput. In a simulation study, our protocol improves the throughput by 35%-55% comparing to a state-of-the-art baseline.

The emerging smart health and smart home applications require pervasive and non-intrusive human activity recognition and monitoring. Traditional technologies (e.g., using cameras or accelerometers and gyroscopes) may introduce privacy issues or require people to wear sensors. To address these issues, recent approaches exploit fine-grained wireless signals for activity recognition. However, these approaches require devices that are costly or need to provide unique wireless features (e.g., Doppler shifts or phase information). With the increasingly available Internet of Things (IoT) devices, in this paper, we propose Harmony, a human activity recognition and monitoring middleware which can utilize the coarse-grained (but pervasively available) received signal strength (RSS) measurements from the radios of IoT devices. We implement a complete evaluation platform (from data collection to data analysis) of the middleware on top of low cost ZigBee compliant MICAz nodes and a laptop. We also conducted extensive experiments. Our results show that our design can achieve similar accuracy as fine-grained WiFi channel state information (CSI) measurement-based approaches. Specifically, our overall human activities recognition accuracy is up to 74% and 90% for RSS readings from a single pair and 3 pairs of IoT devices, respectively.

Many-to-one data collection is a fundamental operation in wireless sensor networks (WSNs). To support long-term deployment of WSNs, sensor nodes normally operate at low-duty-cycles. However, the low-duty-cycle operation significantly reduces the communication chance between nodes. Consequently, the risk of data collisions significantly increases when multiple senders transmit packets to a receiver during its very short active period. Data collision not only results in wasted packet transmissions, but also incurs a large delivery latency. Under such conditions, collision-free medium access is more appealing than recovering after collision for low-duty-cycle WSNs. In this work, we propose an incast-collision-free data collection protocol, named iCore, to address the many-to-one collision problem in low-duty-cycle WSNs. iCore employs the dynamic forwarding technique and establishes a non-conflicting schedule for delay reduction. Specifically, we design efficient forwarder assignment and forwarding optimization algorithms that ensure low end-to-end latency under diverse data traffic types. Through comprehensive performance evaluations, we demonstrate that, compared with the state-of-the-art protocol, iCore effectively minimizes the end-to-end delay by 25% ∼ 57% and maintains high delivery ratio and energy efficiency for different many-to-one convergecast scenarios.

Traditional power grid is not resistant to severe weather conditions, especially in remote areas. For some areas with few people, such as islands, it is difficult and expensive to maintain their connectivity to the traditional power grid. Therefore, a self-sustainable microgrid is desired. However, given the limited local energy storage and energy generation, it is extremely challenging for a microgrid to balance the power demand and generation in real-time. To realize the real-time power quality monitoring, the power quality information of microgrid, such as voltage, frequency and phase angle in each home, needs to be collected in real- time. Furthermore, the unreliable sensing results and data collection in a microgrid make the real-time data collection more difficult. To address these challenges, we designed an accurate real-time power quality data sensing hardware to sense the voltage, frequency and phase angle in each home. A novel data management technique is also proposed to reconstruct the missing data caused by unreliable sensing. We implemented our system over off-the-shelf smartphones with a few peripheral hardware components, and realized an accuracy of 1.7 mHz and 0.01 rad for frequency and phase angle monitoring, respectively. We also show our data management technique can reconstruct the missing data with more than 99% accuracy.

Microgrids are desired in remote areas, such as islands and under developed countries. However, given the limited capacities of local energy generation and storage in such a community, it is extremely challenging for an isolated microgrid to balance the power demand and generation in real-time with dynamically changing energy demand. Meanwhile, more and more sensing devices (such as smart meters) are deployed in individual homes to monitor real-time energy data, which can be helpful for homes and microgrid to better schedule the workload and generation. However, it is still difficult to conduct real-time distributed control due to the unreliable sensing devices and communications between sensing devices and controllers. To address these issues in microgrids, we designed a novel approach for the system to i) process the collected sensing data, ii) reconstruct the missing data caused by sensing error or unreliable communication, and iii) predict the future demand for real-time distributed control with missing data in extreme situations. The control center then decides the operations of the local generator and each home decides the scheduling of the flexible workload of appliances based on the collected and predicted data. We conducted extensive experiments and simulations with real world energy consumption data from 100 homes for one year. The evaluation results show that our design can recover the missing data with more than 99% accuracy and our distributed control can balance power demand and generation in real-time and reduce the operational cost by 23%.

The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

Localization proves to be a promising application of wireless sensor networks. Although a considerable number of algorithms have been designed for low-overhead and high-accuracy localization, problems remain to be tackled such as the way to use anchor-deploying. In this paper, we present a mechanism for range-free localization called Enhanced Map Segmentation (EMS) to deploy and segment the map where precise indoor localization is required. Despite the limits of environmental noise, sensing irregularity, received signal strength (RSS) variation and other unavoidable factors, EMS can be reliable by improving the quality of map segmentation. This paper will present and analyze the enhancing method by a series of simulations. In addition, to deal with ambiguous context positions that confounds the localization, this paper ameliorates the segmentation with context conception mentioned in [1] by statistical methods. In fact, a well-organized deployment and a context-based decision mechanism can make such a layer of abstraction more reliable and compatible.

Wireless sensor networks are often deployed for tracking moving objects. Many tracking algorithms have been proposed with two general assumptions: the preset fingerprints(prior landmark or context information) and an interference-free environment. These algorithms, however, cannot be used for on-demand deployment where finger-prints are unavailable and would perform poorly in interference-rich environments. In this paper, we present a fingerprint-free localizing and tracking algorithm, called Enhanced Field Division (EFD). The EFD algorithm is used to dynamically divide the field into areas with unique signatures and tracks the target, without any finger-prints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference rich environment.

In wireless sensor networks, owing to the limited energy of the sensor node, it is very meaningful to propose a dynamic scheduling scheme with data management that reduces energy as soon as possible. However, traditional techniques treat data management as an isolated process on only selected individual nodes. In this article, we propose an aggressive data reduction architecture, which is based on error control within sensor segments and integrates three parallel dynamic control mechanisms. We demonstrate that this architecture not only achieves energy savings but also guarantees the data accuracy specified by the application. Furthermore, based on this architecture, we propose two implementations. The experimental results show that both implementations can raise the energy savings while keeping the error at an predefined and acceptable level. We observed that, compared with the basic implementation, the enhancement implementation achieves a relatively higher data accuracy. Moreover, the enhancement implementation is more suitable for the harsh environmental monitoring applications. Further, when both implementations achieve the same accuracy, the enhancement implementation saves more energy. Extensive experiments on realistic historical soil temperature data confirm the efficacy and efficiency of two implementations.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging (DMC) problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work, we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

To reduce peak demand, many utility companies are transitioning from fixed rate pricing plans to real-time pricing plans. To apply real-time pricing plans, it is crucial to collect accurate real-time power consumption readings from individual homes. Thus, utility companies are increasing the installation of smart meters in individual homes. Smart meters can record energy related data (e.g., power consumption) every second. However, power consumption data with high time granularity needs huge data storage space and generates significant communication overhead for utility companies to gather all the data for the pricing plans. In this paper, we present E-Sketch, a middleware for utility companies to gather data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power consumption changes in time domain. Then frequency compression is applied to further compress the sampled data. We conducted extensive system evaluations with 30 homes' second-level power consumption data for more than 2 months. Results indicate i) our design can reduce data storage space significantly by 90% with more than 99% accuracy of second-level power consumption on average for a single home, and ii) our design can achieve even more than 99.8% accuracy on average for aggregated power consumption of 30 homes.

The main duty of key management is to keep cryptographic keys in secret. However, it is difficulty to quantitatively assess that how well does a key management scheme protect the keys. In this paper, we propose to use reliability theory, which was mainly used to evaluate performance persistence for engineering systems, to estimate the performance of key management schemes. The reliability analysis leads to counter-intuitive results such as the widely deployed periodic key update scheme is ineffective when key thefts are possible. The analysis also shows that using password with an electronic security token for authentication is a strong security measure in the beginning but is unreliable in the long run. In general, the reliability analysis demonstrates that current key management schemes focus too much on postponing the first key theft from occurring but lack of considerations on quickly recovering stolen keys. In the later part of this paper, we discuss possible directions that may improve the reliability of key management schemes.

Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.

Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs, such as Facebook Places, Yelp, and Google+ Local. However, it is often prohibitive, and sometimes too costly, to obtain a detailed and complete snapshot of a LBSN due to its usually massive scale. In this work, taking Foursquare as an example, we focus on sampling and estimating restricted geographic regions in LBSNs, such as a city or a country. By exploiting the application programming interfaces (APIs) provided by Foursquare for geographic search, we first introduce how to obtain the “ground truth”, namely, a complete set of all venues (i.e., places) in a specified region. Then, we propose random region sampling algorithms that allow us to draw representative samples of venues, and design unbiased estimators of regional characteristics of venues. We validate the efficiency of our sampling algorithms on Foursquare using complete datasets obtained from 12 regions, such as Switzerland, New York City and Los Angeles. Our results are applicable to perform sampling and estimation in all GeoDatabases, such as Facebook Places, Yelp, and Google+ Local, which have similar venue search APIs as Foursquare. These location service providers can also benefit from our results to enable efficient online statistic estimation.

Link correlation in wireless sensor networks has recently attracted a considerable amount of attention in the research community. Various pioneer works have empirically demonstrated the existence of link correlations and designed novel network protocols to exploit such link correlations. While all existing works focus on the correlated receptions at multiple receivers from a single sender, in this work we empirically demonstrate another type of link correlation, called sender-based link correlation. For sender-based link correlation, we observe wireless links from multiple senders to a single receiver are also correlated. Based on this observation, we design a two-tiered data forwarding scheme for improving the energy efficiency of unicast in the network. At the micro-level, individual nodes reduce their transmission energy consumption by temporarily switching to a new forwarder or suppressing the current transmission with the knowledge of link correlations. At the macro-level, we schedule the ordering of transmission times among neigh boring nodes so that the gains from all link correlation information in the network is maximized. Through trace-driven emulations and large scale simulations, we demonstrate that our design reduces data retransmissions by an average of 12% when compared with already highly energy efficient ETX-based protocols.

Recently, much research effort has been devoted to employing mobile chargers for energy replenishment of the robots in robotic sensor networks. Observing the discrepancy between the charging latency of robots and charger travel distance, we propose a novel tree-based charging schedule for the charger, which minimizes its travel distance without causing the robot energy depletion. We analytically evaluate its performance and show its closeness to the optimal solutions. Furthermore, through a queue-based approach, we provide theoretical guidance on the setting of the remaining energy threshold at which the robots request energy replenishment. This guided setting guarantees the feasibility of the tree-based schedule to return a depletion-free charging schedule. The performance of the tree-based charging schedule is evaluated through extensive simulations. The results show that the charger travel distance can be reduced by around 20%, when compared with the schedule that only considers the robot charging latency.

Smartphones and mobile devices have greatly penetrated the daily lives of many people. While participatory/pervasive sensing has gained wide adoptions by leveraging various onboard sensors on mobile devices, another powerful resource, the computational power on these mobile devices has been less frequently harnessed by researchers and practitioners. To fill this gap, we propose in this work the modeling, analysis, and implementation of participatory computing. Specifically, we propose REPC, a generic randomized task assignment framework for the participatory computing paradigm, which guarantees the overall system performance with close to minimal workload at individual participating devices. To achieve these design objectives, we model the intrinsic relationship between the workload of individual devices and the probability they complete their assigned tasks. Based on our modeling results, we analyze the maximal system capacity for any given participatory computing system and derive the minimal workload for individual participating devices to achieve the overall system performance requirement. We have fully implemented our design on the Android platform and demonstrated its performance through a representative participatory computing application. Extensive experiments and simulation results demonstrate that our design is able to achieve more than 90% task completion ratios with only 10% system overhead in practice.

With advances in energy-harvesting techniques, it is now feasible to build sustainable sensor networks to support long-term applications. Unlike battery-powered sensor networks, the objective of sustainable sensor networks is to effectively utilize a continuous stream of ambient energy. Instead of pushing the limits of energy conservation, we aim to design energy-synchronized schemes that keep energy supplies and demands in balance. Specifically, this work presents Energy-Synchronized Communication (ESC) as a transparent middleware between the network layer and MAC layer that controls the amount and timing of RF activity at receiving nodes. In this work, we first derive a delay model for cross-traffic at individual nodes, which reveals an interesting stair effect . This effect allows us to design a localized energy synchronization control with ℴ( d 3 ) time complexity that shuffles or adjusts the working schedule of a node to optimize cross-traffic delays in the presence of changing duty cycle budgets, where d is the node degree in the network. Under different rates of energy fluctuations, shuffle-based and adjustment-based methods have different influences on logical connectivity and cross-traffic delay , due to the inconsistent views of working schedules among neighboring nodes before schedule updates. We study the trade-off between them and propose methods for updating working schedules efficiently. To evaluate our work, ESC is implemented on MicaZ nodes with two state-of-the-art routing protocols. Both testbed experiment and large-scale simulation results show significant performance improvements over randomized synchronization controls.

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than the systems that harvested dynamic energy from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the on-demand mobile charging problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present an example on determining the optimal remaining energy level for individual sensor nodes to send out their recharging requests. Through extensive simulation with real-world system settings, we verify our analysis matches the simulation results well and the system designs based on our analysis are effective.

Vehicle-to-Grid (V2G) is that the energy stored in the batteries of electric vehicles can be utilized to send back to the power grid. And then, the energy in the batteries of electric vehicles can move with electric vehicles (EVs). Based on above characteristics, this paper introduces the concept of a mobile electrical grid and discusses the energy routing problem. It focuses on the optimization problem of how to find routes from the energy sources to charge stations, especially, when some paths are clogged by traffic jam. A bipartite graph model is used to analyze the route problem and two algorithms are presented to compute minimal energy metric route. Both of algorithms are tested by real-world transporting data in Manhattan and the Pioneer Valley Transit Authority(PVTA). Simulations show that the method is efficient.

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (that is, duty cycling) sensors could reduce energy consumption, however, this would occur at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques focus mainly on scheduling a network with static anchors. Few methods provides a rigorous approach to confining sensing errors within desirable bounds while seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this work, we propose a sensing scheduling scheme, called MAS, to support mobile anchors in sensor networks. Within a node, we use a sensing probability bound to control tolerable sensing errors. While communicating with the mobile anchor, nodes trigger additional sensing activities to accommodate the QoS requirement in mobile communication. We validated the concept by constructing a lab-grade mobile anchor that fully supports 4G-LTE communications for monitoring applications. We further conducted simulations to investigate system performance. The simulation results demonstrated that the MAS achieved enhancement performance compared to several other sensing schemes.

Location-based services, such as targeted advertisement, geo-social networking and emergency services, are becoming increasingly popular for mobile applications. While GPS provides accurate outdoor locations, accurate indoor localization schemes still require either additional infrastructure support (e.g., ranging devices) or extensive training before system deployment (e.g., WiFi signal fingerprinting). In order to help existing localization systems to overcome their limitations or to further improve their accuracy, we propose Social-Loc, a middleware that takes the potential locations for individual users, which is estimated by any underlying indoor localization system as input and exploits both social encounter and non-encounter events to cooperatively calibrate the estimation errors. We have fully implemented Social-Loc on the Android platform and demonstrated its performance on two underlying indoor localization systems: Dead-reckoning and WiFi fingerprint. Experiment results show that Social-Loc improves user's localization accuracy of WiFi fingerprint and dead-reckoning by at least 22% and 37%, respectively. Large-scale simulation results indicate Social-Loc is scalable, provides good accuracy for a long duration of time, and is robust against measurement errors.

Distributed generation (DG) uses many small on-site energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a building's electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharge's savings for a typical home today are near 20%, which are greater than the savings from using only net metering.

Although existing flooding protocols can provide efficient and reliable communication in wireless sensor networks on some level, further performance improvement has been hampered by the assumption of link independence, which requires costly acknowledgments (ACKs) from every receiver. In this paper, we present collective flooding (CF), which exploits the link correlation to achieve flooding reliability using the concept of collective ACKs. CF requires only 1-hop information at each node, making the design highly distributed and scalable with low complexity. We evaluate CF extensively in real-world settings, using three different types of testbeds: a single-hop network with 20 MICAz nodes, a multihop network with 37 nodes, and a linear outdoor network with 48 nodes along a 326-m-long bridge. System evaluation and extensive simulation show that CF achieves the same reliability as state-of-the-art solutions while reducing the total number of packet transmission and the dissemination delay by 30%-50% and 35%-50%, respectively.

Wireless sensor networks (WSNs) are vulnerable to security attacks due to the broadcast nature of transmission and limited computation capability. After intrusion detection systems (IDSs) identifies an mobile intruder, IDS may broadcast the blacklist to all nodes in network. This method is energy inefficient because all nodes have to receive and forward the alarm packet so as to exhaust communication bandwidth and node energy, especially when there are a large number of sensor nodes in the network. This paper develops an energy efficient intrusion prevention mechanism in WSNs called green firewall. It can isolate an intruder with less overhead, and track the intruder to continually prevent the attack. The paper analyzes the overhead cost of the green firewall and compare it with the flooding broadcast method. Extensive analysis and simulations show that green firewall can prevent the attack and effectively reduce redundant alarm packet transmissions which results in less energy consumption.

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in long lifetime monitoring applications. But typical techniques treat data management as an isolated process on only selected individual nodes, e.g. the centroid node. In this paper, we propose and evaluate an aggressive data reduction algorithm based on error inference within sensor segments. The architecture integrates three parallel dynamic error control mechanisms to optimize the trade-off between energy saving and data validity. We demonstrate that not only substantial energy savings can be achieved but also that an error bound specified by the application can be guaranteed. Moreover, we have investigate the system performance by using the realistic historical soil temperature data as an experimental context. The experimental results demonstrate that the system error meets the specified error tolerance and produces up to a 50 percent of the energy savings compared to several sensing schemes.

Connectivity and trust within social networks have been exploited to build applications on top of these networks, including information dissemination, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-based information dissemination applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance on such applications. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.

Vehicle-to-grid provides a viable approach that feeds the battery energy stored in electric vehicles (EVs) back to the power grid. Meanwhile, since EVs are mobile, the energy in EVs can be easily transported from one place to another. Based on these two observations, we introduce a novel concept called EV energy network for energy transmission and distribution using EVs. We present a concrete example to illustrate the usage of an EV energy network, and then study the optimization problem of how to deploy energy routers in an EV energy network. We prove that the problem is NP-hard and develop a greedy heuristic solution. Simulations using real-world data shows that our method is efficient.

Energy is the most precious resource in wireless sensor networks. To ensure sustainable operations, wireless sensor systems need to harvest energy from environments. The time-varying environmental energy results in the dynamic change of the system's available energy. Therefore, how to dynamically schedule tasks to match the time-varying energy is a challenging problem. In contrast to traditional computing-oriented scheduling methods that focus on reducing computational energy consumption and meeting the tasks' deadlines, we present DEOS, a dynamic energy-oriented scheduling method, which treats energy as a first-class schedulable resource and dynamically schedules tasks based on the tasks' energy consumption and the system's real-time available energy. We extensively evaluate our system in indoor and outdoor settings. Results indicate that DEOS is extremely lightweight (e.g., energy consumption overhead in the worst case is only 0.039%) and effectively schedules tasks to utilize the dynamically available energy.

In this paper, we propose Social-Loc, which uniquely utilizes social interactions in addition to common on-board sensors such as accelerometer and gyroscope on modern smartphones, to localize indoor mobile users. Specifically, Social-Loc takes the potential locations for individual users estimated by a novel particle filter tailored for indoor localization as input, and exploits both social encounter and non-encounter events to further improve the localization accuracy. We have implemented Social-Loc on the Android platform and extensively evaluated its performance. The simulation results demonstrate that Social-Loc improves the accuracy of the particle-filter-only scheme by as much as 560% on average and is able to achieve accuracy of few meters without any external ranging device or system training.

Energy is the most precious resource in sensor networks. The ability to move energy around makes it feasible to build distributed energy storage systems that can robustly extend the lifetime of networked sensor systems. eShare supports the concept of energy sharing among multiple embedded sensor devices by providing designs for energy routers (i.e., energy storage and routing devices) and related energy access and network protocols. In a nutshell, energy routers exchange energy sharing control information using their data network while sharing energy freely among connected embedded sensor devices using their energy network. To improve sharing efficiency subject to energy leakage, we develop an effective energy charging and discharging mechanism using an array of ultra-capacitors as the main component of an energy router. We extensively evaluate our system under seven real-world settings. Results indicate our charging and discharging control can effectively minimize the energy leaked away. Moreover, the energy sharing protocol can quantitatively share 113J energy with 96.82% accuracy in less than 2 seconds.

Flooding in low-duty-cycle wireless sensor networks is very costly due to asynchronous schedules of sensor nodes. To adapt existing flooding-tree-based designs for low-duty-cycle networks, we shall schedule nodes of common parents wake up simultaneously. Traditionally, energy optimality in a designated flooding-tree is achieved by selecting parents with the highest link quality. In this work, we demonstrate that surprisingly more energy can be saved by considering link correlation. Specifically, this work first experimentally verifies the existence of link correlation and mathematically proves that the energy consumption of broadcasting can be reduced by letting nodes with higher correlation receive packets simultaneously. A novel flooding scheme, named Correlated Flooding, is then designed so that nodes with high correlation are assigned to a common sender and their receptions of a broadcasting packet are only acknowledged by a single ACK. This unique feature effectively ameliorates the ACK implosion problem, saving energy on both data packets and ACKs. We evaluate Correlated Flooding with extensive simulations and a testbed implementation with 20 MICAz nodes. We show that Correlated Flooding saves more than 66% energy on ACKs and 15%-50% energy on data packets for most network settings, while having similar performance on flooding delay and reliability.

The ability to move energy around makes it feasible to build distributed energy storage systems that can robustly extend the lifetime of networked sensor systems. eShare supports the concept of energy sharing among multiple embedded sensor devices by providing designs for energy routers (i.e., energy storage and routing devices) and related energy access and network protocols. In a nutshell, energy routers exchange energy sharing control information using their data network while sharing energy freely among connected embedded sensor devices using their energy network. To improve sharing efficiency subject to energy leakage, we develop an effective energy charging and discharging mechanism using an array of ultra-capacitors as the main component of an energy router. We extensively evaluate our system under six real-world settings. Results indicate our charging and discharging control can effectively minimize the energy leaked away. Moreover, the energy sharing protocol can quantitatively share 113J energy with 96.82% accuracy in less than 2 seconds.

With advances in energy harvesting techniques, it is now feasible to build sustainable sensor networks (SSN) to support long-term applications. Unlike battery-powered sensor networks, the objective of sustainable sensor networks is to effectively utilize a continuous stream of ambient energy. Instead of pushing the limits of energy conservation, we are aiming at energy-synchronized designs1to keep energy supplies and demands in balance. Specifically, this work presents the Energy Synchronized Communication (ESC) as a transparent middle-ware between the network layer and data link layer that controls the amount and timing of RF activity at receiving nodes. In this work, we first derive a delay model for cross-traffic at individual nodes, which reveals an interesting stair effect in low-duty-cycle networks. This effect allows us to design a localized energy synchronization control with O(1) time complexity that shuffles or adjusts the working schedule of a node to optimize cross-traffic delays in the presence of changing duty-cycle budgets. Under different rates of energy fluctuations, shuffle-based and adjustment-based methods have different influences on logical connectivity and cross-traffic delay, due to the inconsistent views of working schedules among neighboring nodes before schedule updates. We study the trade-off between them and propose methods to update working schedules efficiently. To evaluate our work, ESC is implemented on MicaZ nodes with two state-of-the-art routing protocols. Both test-bed experiment and large scale simulation results show significant performance improvements over randomized synchronization controls.

Tracking mobile targets using sensor networks is a challenging task because of the impacts of in-the-flled 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.

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 leakage-prone. 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 settings - indoor, 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.

The ability to accurately profile energy consumption is of great importance for energy management in low-power devices. This work presents a novel energy profiling architecture by combining the high-speed CPLD bus signaling capability of mPlatform with the smart TwinStar power board. By running mPlatform under different modes, we are able to utilize an MMSE estimator to analyze per-component energy consumption rates with sufficient accuracy. In our experiment, we use the profiling results to determine the working condition of individual components, as well as the hardware configuration of the sensor node.

Starting from the features and impact of energy leakage in ultra-capacitor powered systems, this demonstration highlights the design of a capacitor-only Twin-Star node, and a leakage-aware energy synchronization methodology.

Due to the high flexibility, mobility and low cost features, wireless ad hoc networks are widely used. A particularly challenging problem is how to feasibly detect and defend the major attacks against routing protocols of such networks that have susceptible links and dynamic topology. Most of the existing secure routing protocols for ad hoc networks either avoid the most challenging internal attacks such as Byzantine behaviors, or have often produced inefficient security mechanisms. In this paper, we develop a new efficient distributed key management scheme and a novel algorithm that can detect the most difficult internal attacks, such as Byzantine attacks. We also present a novel secure quality of service routing (SQSR) protocol, which provides both secure and QoS routing. The simulation results have demonstrated the effectiveness of the proposed routing protocol.