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Modern distributed applications are moving toward a microservice architecture, in which each service is developed and managed independently, and new features and updates are delivered continuously. A guiding principle of microservice architecture is that it is vital to anticipate and mitigate a variety of hardware and software failures. In order to test the fault handling capabilities of microservices automatically, this paper presents IntelliFT, a guided resilience testing technique for microservice based applications, which aims to expose the defects in the fault-handling logic effectively within a fixed time limit. The characteristic of IntelliFT is that it leverages existing integration tests of the applications under test to explore the fault space, and decides whether injected faults can lead to severe failures by designing fitness-guided search technique. Our experimental results on a medium-size microservice benchmark system show that the proposed technique is effective, improves the state-of-the-art, and can quickly expose bugs in the recovery logic.

The demand side management of power consumption is one of the most important components in fulfilling the promise of smart grid. In such context, the smart metering, monitoring and control terminals close to the customers effectively serve as the devices and components for the implementation of smart functionalities. Currently, to effectively support the operation of energy Internet system and services, the ubiquitous electric power Internet of Things (IoT) that forms a flexible electricity energy allocation and management platform is proposed and developed for promoting the development of integrated diverse energy services and new businesses. This paper presents the demand-side ubiquitous power Internet of Things architecture and key technologies as well as the requirements. In conclusion, the IoT enabled infrastructure can significantly improve the system performance in the future for power utilities.

Concentrators play an important relay role in the power metering of the power internet of things. In order to realize the transformation of power metering automation to intelligence, this paper proposes a class of intelligent concentrators for the application of power IoT, which mainly adopts the idea of edge computing technology to fully tap the data potential of intelligent concentrators. This method considers the different scenarios in which the edge computing and cloud computing are applied in the power IoT, analyzes the feasibility of the edge computing technology applied to the power IoT intelligence of the concentrator. In addition, this paper discusses a class of concentrator intelligent solutions using edge computing technology, and considers the operation methods in combination with three data application scenarios. By introducing edge computing technology into the intelligent concentrator, it can realize the calculation offload of the cloud computing data center and reduce the system pressure. This solution does not require excessive manual intervention, and can automatically implement the meter reading and data fusion processing, which effectively improves Operation and maintenance efficiency of electric energy metering system in the power IoT.

At present, the power metering equipment used at the grass-level sends the collected data to the intelligent concentrator, and then it is stored and forwarded to the master station through the concentrator, and finally the data is processed through cloud computing of the main station. Because the concentrator currently used cannot process data, it brings great trouble to the system master station. Aiming at this situation, this paper proposes the design method of concentrator based on edge computing technology. Firstly, the edge computing technology is introduced briefly, and then the problems of current concentrator are improved, and a new generation of intelligent concentrator is proposed. Finally, the system function is designed as a whole, and the hardware architecture and communication scheme are given. The system can not only solve the problem of data processing, but also has real-time monitoring and secure and reliable communication.

Performance of a collaborative task is mostly dependent on the collective effort from participants. To accomplish a participatory task effectively and efficiently, the team formation problem (TFP) outweighs all other considerations. It is even more complicated when social connections among candidates is taken into account. As we can imagine, a large number of tasks require members of the team to be socially close. On the contrary, a portion of tasks, e.g., proposal review, pay more attention to a multidimensional view, and team members should be selected from a variety of cliques. Due to the nature of tasks, it is challenging to find a subset that meets the skill requirement of the task as well as socially diversity demand of team members from a pool of candidates. In this paper, we explore the TFP in a social network. Based on different task objectives, we first formulate the TFP as TFP with strong ties (TFP-ST) and TFP with weak ties (TFP-WT), respectively. Both TFP-ST and TFP-WT are proven to be NP-hard, and we then design corresponding heuristic algorithms to solve the two problems. Through extensive simulations, we show that the solution to TFP-ST can achieve significant improvement in terms of collaboration cost, team size, as well as running time, and the solution to TFP-WT can provide better performance than existing approaches at the same time.

There has been an increasing interest to monitor driver’s behavior, vehicles and driving data aim to identify risky driving maneuvers and prevent accidents. Current monitor methods, however, most data obtained from the vehicle sensors are sent to the control units through buses such as: CAN, MOST, LIN and FlexRay, but cannot recognize the dangerous actions of the drivers accurately. Past proposals for addressing this limitation rely on leveraging more sensors attached to drivers and hence significantly increase the cost and affect the driver’s comfort. This paper introduces the first fine-grained and device-free driver’s behavior monitoring system that is accurate to 15 dangerous driving actions. Unlike past work, which only depends on sensors, our system exploits fusion data combining smartphone and WiFi signals to accurately identify risky driving operations. We present the design and implementation of WisDriver and discuss the evaluation by experiment from the 20-drivers field trial. Our evaluation results show that WisDriver can achieve high accuracy (92%) in detecting 15 different dangerous driving behaviors. It will have a great significance for assisting the driver and improving traffic safety.

Target material identification is playing an important role in our everyday life. Traditional camera and video-based methods bring in severe privacy concerns. In the last few years, while RF signals have been exploited for indoor localization, gesture recognition and motion tracking, very little attention has been paid in material identification. This paper introduces WiMi, a device-free target material identification system, implemented on ubiquitous and cheap commercial off-the-shelf (COTS) Wi-Fi devices. The intuition is that different materials produce different amounts of phase and amplitude changes when a target appears on the line-of-sight (LoS) of a radio frequency (RF) link. However, due to multipath and hardware imperfection, the measured phase and amplitude of the channel state information (CSI) are very noisy. We thus present novel CSI pre-processing schemes to address the multipath and hardware noise issues before they can be used for accurate material sensing. We also design a new material feature which is only related to the material type and is independent of the target size. Comprehensive real-life experiments demonstrate that WiMi can achieve fine-grained material identification with cheap commodity Wi-Fi devices. WiMi can identify 10 commonly seen liquids at an overall accuracy higher than 95% with strong multipath indoors. Even for very similar items such as Pepsi and Coke, WiMi can still differentiate them at a high accuracy.

Google congestion control (GCC) is the de-facto standard for web real-time communications (WebRTC) applications and has been implemented in mainstream browsers including Chrome and Firefox. While GCC is designed to achieve high video bit-rate and low latency simultaneously, we find that GCC's performance is far from satisfactory particularly under good network conditions. In particular, we collect a GCC trace dataset with over 1.18 million sessions from a major crowd-sourced live video streaming service provider. We perform in-depth analytics using the dataset, which shows that the sending video bit-rate unnecessarily experiences frequent rollbacks caused by minor fluctuation of transmission delay. To address the issue, we propose a mechanism called GCC-β, which can distinguish normal network fluctuation from real network congestion, and then adaptively sends appropriate bitrates. We implement GCC-β in the WebRTC framework and evaluate its performance using test-bed experiments. The results show that GCC-β is able to avoid up to 90% unnecessary bitrate rollbacks.

RFID-based positioning is emerging as a promising solution for inventory management in places like warehouses and libraries. However, existing solutions either are too sensitive to the environmental noise, or require deploying a large number of reference tags which incur expensive deployment cost and increase the chance of data collisions. This paper presents CSRP, a novel RFID based positioning system, which is highly accurate and robust to environmental noise, but relies on much less reference tags compared with the state-of-the-art. CSRP achieves this by employing an noise-resilient RFID fingerprint scheme and a compressive sensing based algorithm that can recover the target tag's position using a small number of signal measurements. This work provides a set of new analysis, algorithms and heuristics to guide the deployment of reference tags and to optimize the computational overhead. We evaluate CSRP in a deployment site with 270 commercial RFID tags. Experimental results show that CSRP can correctly identify 84.7% of the test items, achieving an accuracy that is comparable to the state-of-the-art, using an order of magnitude less reference tags.

The augmented reality(AR) technology can enrich a person actual life, and is attracting more and more attention. However, the tradition methods have some problems such as high time delay, high deployment cost and low accuracy. These problems greatly hinder the AR technology to ubiquitous applications. In this paper, we design a AR system deployed on mobile devices, named SpeAR, which leveraging the feature matching algorithm to fast recognize the object with high accuracy. In SpeAR, we employ the depth camera embedded in the mobile device to obtain the distance between mobile device and object to use as a feature of this object image. For the accurately matching, the flutter-free algorithm will be designed to extract more accurate image feature. For the fast matching, the shrunken SURF(sSURF) is proposed to match images combining the distance feature. We have implemented a prototype system to evaluate the actual performance. The experiment results show that our solution achieves an 141ms delay in object recognition in this system.

Apps repackaged through reverse engineering pose a significant security threat to the Android smart phone ecosystem. Previous solutions have mostly focused on the detection and identification of repackaged apps. Nevertheless, current app anti-repackaging services can only protect applications at a coarse level and get a significant performance overhead. These approaches can neither meet the performance requirements of Android nor achieve fine-grained protection against cumulative attack 1 at the same time. Specifically, these solutions rely on a fix-structure detecting engine and then will execute the same path at different times, which lead to the entire protection performs poorly when faced with dynamic cumulative attack, which is typical in real-world attack. This paper introduces AppIS, a reinforced anti-repackaging immune system, that is robust to app-repackaging attack scenarios. Unlike prior work, which mostly focuses on simple protection only from just one respect, our design exploits an interlocking guarding net with time diversity for the tamper-proofing of Android applications. The intuition underlying our design is that a dynamic and static combining method can provide a multi-level protection for the codes, core algorithm and sensitive data. We analyze and classify the existing threats on Android platform and furthermore abstract then model the repackaging attack scenarios. We then adopt a random controller used by the dispatcher to randomly construct guarding net with different structure every time. We have built a prototype of our design using Java Native Interface cross-layer calling mechanism for performance requirement. Results from a deployment of AppIS on several kinds of popular apps demonstrate that the new design can prevent our apps from cumulative attack without extra performance cost.

Unmanned aerial vehicle (UAV) is favored by the industry to search and locate the lost personss in mountains and the trapped personss in earthquakes, fires and other disasters because it is not limited by the obstruction on the ground. Currently, however, a UAV always searches and locates the targets along a fixed flight path, which consumes more time and has lower accuracy. This kind of method can only provide a rough position estimation. Guideloc takes the UAVs GPS coordinates as the location information of the target and the genetic algorithm (GA) is used for path planning in order to shorten the flight path to improve the search efficiency and obtain a good result. But its performance still has room for improvement. In this paper, the path optimization algorithm used in Guideloc was further discussed and studied, and then a 2-OptACO method was proposed. The method is based on the 2-opt algorithm to improve the ant colony optimization algorithm (ACO) and is applied to optimize the UAVs path for search and rescue. The simulation results show that the 2-OptACO method has a faster convergence rate than the GA and ACO. It can obtain a better global optimal solution.

Increasingly sophisticated code obfuscation techniques are quickly adopted by malware developers to escape from malware detection and to thwart the reverse engineering effort of security analysts. State-of-the-art de-obfuscation approaches rely on dynamic analysis, but face the challenge of low code coverage as not all software execution paths and behavior will be exposed at specific profiling runs. As a result, these approaches often fail to discover hidden malicious patterns. This paper introduces SEEAD, a novel and generic semantic-based de-obfuscation system. When building SEEAD, we try to rely on as few assumptions about the structure of the obfuscation tool as possible, so that the system can keep pace with the fast evolving code obfuscation techniques. To increase the code coverage, SEEAD dynamically directs the target program to execute different paths across different runs. This dynamic profiling scheme is rife with taint and control dependence analysis to reduce the search overhead, and a carefully designed protection scheme to bring the program to an error free status should any error happens during dynamic profile runs. As a result, the increased code coverage enables us to uncover hidden malicious behaviors that are not detected by traditional dynamic analysis based de-obfuscation approaches. We evaluate SEEAD on a range of benign and malicious obfuscated programs. Our experimental results show that SEEAD is able to successfully recover the original logic from obfuscated binaries.

Unauthorized code modification based on reverse engineering is a serious threat for software industry. Virtual machine based code obfuscation is emerging as a powerful technique for software protection. However, the current Virtual machine code protection are vulnerable under semantic attacks which use dynamic profiling to transform an obfuscated program to construct a simpler program that is functionally equivalent to the obfuscated program but easier to analyze. This paper presents DSA-VMP, a novel VM-based code obfuscation technique, to address the issue of semantic attacks. Our design goal is to exploit dynamic data flows to increase the diversity of the program behaviour. Our approach uses multiple bytecode handlers to interpret a single bytecode and hides the logics that determine the program execution path (it is difficult for the attacker to anticipate the program execution flow). These two techniques greatly increase the diversity of the program execution where the protected code regions exhibit a complex data flow across multiple runs, making it harder and more time consuming to trace the program execution through profiling. Our approach is evaluated using a set of real-world applications. Experimental results show that DSA-VMP can well protect software against semantic attacks at the cost of little extra runtime overhead when compared to two commercial VM-based code obfuscation tools.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.

Wireless Sensor Networks (WSNs) based on RSSIbased localization techniques have been widely used in various applications, such as intrusion detection, battlefield surveillance, and animal monitoring. One fundamental performance measure in those applications is the sensing coverage of the target field. Most existing works on coverage assume that the sensing area of a sensor node is a disc. However, this disc model is too simplistic for many information processing systems, and there are sensing techniques whose sensing areas are non-disc. In this paper, we focus on coverage performance of WSNs based on RSSI-based localization techniques whose sensing area is an ellipse. We propose an algorithm inspired by Voronoi diagram and Delaunay triangulation to detect and recover coverage holes. Simulation results show that our algorithm can recover all holes, and can reach any set coverage rate, up to 100% coverage, while using the minimum number of sensors.

While most existing indoor localization techniques are device-based, many emerging applications such as intruder detection and elderly monitoring drive the needs of device-free localization, in which the target can be localized without any device attached. Among the diverse techniques, received signal strength (RSS) fingerprint-based methods are popular because of the wide availability of RSS readings in most commodity hardware. However, current fingerprint-based systems suffer from high human labor cost to update the fingerprint database and low accuracy due to the large degree of RSS variations. In this paper, we propose a fingerprint-based device-free localization system named iUpdater to significantly reduce the labor cost and increase the accuracy. We present a novel self-augmented regularized singular value decomposition (RSVD) method integrating the sparse attribute with unique properties of the fingerprint database. iUpdater is able to accurately update the whole database with RSS measurements at a small number of reference locations, thus reducing the human labor cost. Furthermore, iUpdater observes that although the RSS readings vary a lot, the RSS differences between both the neighboring locations and adjacent wireless links are relatively stable. This unique observation is applied to overcome the short-term RSS variations to improve the localization accuracy. Extensive experiments in three different environments over 3 months demonstrate the effectiveness and robustness of iUpdater.

Smartphone theft is a non-negligible problem that causes serious concerns on personal property, privacy, and public security. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the ‘take-out’ behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion, instead of the entire motion, to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov Chain based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios.

Vehicle-to-Vehicle (V2V) communication is a vital component of vehicular ad-hoc networks (VANET) under the situation that infrastructure for vehicle-to-infrastructure (V2I) has not been well deployed due to its cost and suffering. However, messages transmission path is so challenge to be found without infrastructures supporting that packets are inevitably spread in a sparsely or competitive area, in which they should avoid being trapped because of its poor communication links between vehicles, which eventually lowers down the performance of messages propagation. In this paper, we analyze the relationship between vehicular geographical distribution and packets propagation of VANET in a realistic large-scale urban scenario. It is demonstrated that, from a macro perspective, we could guide the path selection of data propagation between source and destination through V2V communication on the basis of the feature of vehicle density in different geographic locations. Furthermore, to be further close to the actual traffic environment, we model the vehicular geographical distribution by four typical real scenes to present the real environment and develop appropriate messages propagation strategies respectively.

Operating in unlicensed ISM bands, ZigBee devices often yield poor throughput and packet reception ratio due to the interference from ever increasing wireless devices in 2.4 GHz band. Although there have been many efforts made for interference avoidance, they come at the cost of miscellaneous overhead, which oppositely hurts channel utilization. Our empirical results show that, a specific interference is likely to have different influence on different outbound links of a ZigBee sender, which indicates the chance of concurrent transmissions. Based on this insight, we propose Smoggy-Link, a practical protocol to exploit the potential concurrency for adaptive ZigBee transmissions under harsh interference. Smoggy-Link maintains an accurate link model to describe and trace the relationship between interference and link quality of the sender's outbound links. With such a link model, Smoggy-Link can obtain fine-grained spatiotemporal link information through a low-cost interference identification method. The link information is further utilized for adaptive link selection and intelligent transmission schedule. We implement and evaluate a prototype of our approach with TinyOS and TelosB motes. The evaluation results show that Smoggy-Link has consistent improvements in both throughput and packet reception ratio under interference from various interferer.

Code virtualization built upon virtual machine (VM) technologies is emerging as a viable method for implementing code obfuscation to protect programs against unauthorized analysis. State-of-the-art VM-based protection approaches use a fixed scheduling structure where the program follows a single, static execution path for the same input. Such approaches, however, are vulnerable to certain scenarios where the attacker can reuse knowledge extracted from previously seen software to crack applications using similar protection schemes. This paper presents DSVMP, a novel VM-based code obfuscation approach for software protection. DSVMP brings together two techniques to provide stronger code protection than prior VM-based schemes. Firstly, it uses a dynamic instruction scheduler to randomly direct the program to execute different paths without violating the correctness across different runs. By randomly choosing the program execution paths, the application exposes diverse behavior, making it much more difficult for an attacker to reuse the knowledge collected from previous runs or similar applications to perform attacks. Secondly, it employs multiple VMs to further obfuscate the relationship between VM bytecode and their interpreters, making code analysis even harder. We have implemented DSVMP in a prototype system and evaluated it using a set of widely used applications. Experimental results show that DSVMP provides stronger protection with comparable runtime overhead and code size when compared to two commercial VM-based code obfuscation tools.

Motion trajectory tracing in indoor environment has become increasingly important, has the potential to support a broad array of applications including elder care, business analysis, pedestrian navigation. Traditional approaches involve wearable sensors, specialized hardware installations. This paper presents SmartMTra, an infrastructure-free, inertial sensor based motion trajectory tracing system in indoor environment through the use of smartphone. SmartMTra exploits various scenarios in daily activity. It figures out common feature states both in pedestrian's motion mode, smartphone carrying pattern, which can be uniquely identified by adapting domain-divided statistics model techniques. Pedestrian Dead Reckoning (PDR) is applied to trace the pedestrian's real-time motion trajectory with the detected step counts, step-length, heading information. Our experimental evaluation in a campus building demonstrates that errors during 150m travelled distance were 5.2m horizontally, 0.2m during up-down stairs with robustness to scenarios.

Applying mobile sink to collect data in wireless sensor networks can avoid the energy hole problem and lengthen the networks' life time. Current related researches have achieved significant improvements on reducing the data collection latency in the scenario where the mobile sink has enough or limited energy. However, it will lead to waste of network resource if not taking the spatial and temporal correlation of data into consideration during the collection because of the high similarity and low quality in the collected data. In this paper, we first propose a method named Dynamic Estimation of Data Value (DEDV) to estimate the value of data stored in the nodes dynamically. Furthermore, we define a heuristic method that drives the mobile sink to collect data from the nodes to maximize the value of the collected data in a power-limited mobile sink scenario. Both simulation and experiments results show that our method can achieve at most over 85% of the theoretical maximum data value determined by the OPT model.

In this paper, an energy-based interference signal detection framework is proposed for the analysis of spectrum utilization in the ISM band cognitive Radio Sensor Networks (CRSNs). A hypothesis testing is used to derive the exact solution and approximate solution (with less computational complexity) of the quality of detection (i.e., detection probability and false alarm probability), from which the metric of spectrum utilization is defined and analyzed. Numerical results are provided to validate the analysis. The proposed framework and analytic results are expected to be applied to the design of future CRSNs.

The low-cost crystal oscillators embedded in wireless sensor nodes are prone to be affected by their working condition, leading to undesired variation of clock skew. To preserve synchronized clocks, nodes have to undergo frequent re-synchronization to cope with the time-varying clock skew, which in turn means excessive energy consumption. In this paper, we propose DualSync, a synchronization approach for low-power wireless networks under dynamic working condition. By utilizing time-stamp exchanges and local measurement of temperature and voltage, DualSync maintains an accurate clock model to closely trace the relationship between clock skew and the influencing factors. We further incorporate an error-driven mechanism to facilitate interplay between Inter-Sync and Self-Sync, so as to preserve high synchronization accuracy while minimizing communication cost. We evaluate the performance of DualSync across various scenarios and compare it with state-of-art approaches. The experimental results illustrate the superior performance of DualSync in terms of both accuracy and energy efficiency.

Many emerging applications driven the fast development of the device-free localization (DfL) technique, which does not require the target to carry any wireless devices. Most current DfL approaches have two main drawbacks in practical applications. First, as the pre-calibrated received signal strength (RSS) in each location ( i.e. , radio-map) of a specific area cannot be directly applied to the new areas, the manual calibration for different areas will lead to a high human effort cost. Second, a large number of RSS are needed to accurately localize the targets, thus causes a high communication cost and the areas variety will further exacerbate this problem. This paper proposes FitLoc, a fine-grained and low cost DfL approach that can localize multiple targets over various areas, especially in the outdoor environment and similar furnitured indoor environment. FitLoc unifies the radio-map over various areas through a rigorously designed transfer scheme, thus greatly reduces the human effort cost. Furthermore, benefiting from the compressive sensing theory, FitLoc collects a few RSS and performs a fine-grained localization, thus reduces the communication cost. Theoretical analyses validate the effectivity of the problem formulation and the bound of localization error is provided. Extensive experimental results illustrate the effectiveness and robustness of FitLoc.

We analytically model an unreliable opportunistic spectrum sharing (OSS) system through a queueing theoretic framework. Both initiating secondary users and ongoing secondary users sense the channels and perform appropriate activities. The sensing errors from either initiating or ongoing secondary users cause false alarm and misdetection events, which may impose different types of call collision results (CCRs) on both types of users. We study the performance of the OSS system under multiple CCRs and provide numerical results for validation.

Motivated by the needs of WSNs with strip structure that is ubiquitous in real world deployment, such as pipeline monitoring, water quality monitoring and GreatWall monitoring, this paper presents a new geographic opportunistic routing to cope with the routing problem that is the transmission success rate will decrease in the complicated strip networks. In this paper, depending on investigating the transmission direction, we propose a new candidate selection algorithms S2 and an improved algorithm S3. These two algorithms bring layer slicing and virtual sinks to improve the transmission success rate in strip WSNs. Based on the above candidate selection algorithms, we propose the LSVS routing mechanism, theoretical analysis and extensive simulation illustrate the efficiency and higher transmission performance of routing in strip WSNs. Compared with the classic algorithm, LSVS with S3 improves the transmission successful rate up to 12.6%, reduces the communication overhead up to 6.0% and the energy consumption rate up to 14.3%.

Wireless localization technology has become an important application in wildlife conservation. However, such systems require carrying devices in wildlife unsuited to wildlife conservation, or costing larger energy consumption under the large-scale area, impeding their wide-scale adoption. This poster introduces Trepli, a multi-link animal localization system in large-scale area. Unlike the previous work which needs the target to participate in during the localization, Trepli presents a wireless localization approach using received signal strength indicator (RSSI) without any human or target efforts which called passive localization. Our key contribution is low cost that can turn the large-scale positioning area into the small one using selected random deployment of three links. Finally we obtain the target location using the target localization algorithm on this small area. We evaluate its performance through real-world deployment and simulation experiments. Experimental result shows that Trepli is feasible to achieve target localization with low cost in theory.

Wireless sensor network can provide long-term and real-time data support to earthen sites preventive conservation. The knowledge obtained from the monitoring data could provide decision supports for earthen sites preventive conservation. How to solve missing, faults and systematic errors has been an urgent problem for the improvement of data quality. And it is also imperative to obtain the scientific laws between existing environment and survival conditions of earthen sites. At first,the methods of data cleaning are studied with the comparison analysis among average method, Bayes method and regression model via defining the evaluation criterion of data quality in this paper. Then the common norms between environment conditions and survival conditions of earthen sites are analyzed and summarized. And the danger theory of immune systems is used as immune factors to build survival model. From the processing of monitoring data of Daming Palace soil sample, results show that regression model method has the highest completion accuracy rate among three methods. Cracks growth conditions and stress tolerance also verify the certain accuracy rate of survival condition model.

When we seek to protect and store a large number of collections of the treasures, its display in an exhibition cabinet needs to follow certain principles. Once their positions are determined, the big changes is not allowed. To determine whether a treasure is in the right place of a showcase or has been moved is a fundamental task for intelligent treasure protection and management system. Recently, location based on RSS (Receive Signal Strength) is emerging as a vital component of wireless communications. Relying on RSS, the location of the treasures with the wireless communication devices is easy. Contrast to previous works, our approach can tell whether the position of treasures are changed. However, the localization accuracy is not high based on RSS. In the monitoring and management of treasures, we are more concerned about whether they beyond the safe range, rather than their precise locations. Therefore, in this paper, we present a cultural position monitoring algorithm (CPMA) based on the RSS, whose main work is to determine whether a treasure has been moved using RSS, to calculate the exact position only when a movement is detected, to promptly give an alarm, and to guide the managers in the position correction. In CPMA, firstly, we try to determine whether the treasures positions change based on the LDPL model, the average moving window detection algorithms and the dynamic threshold setting method. When a change is detected, we will give out the exact position by Least Square for the managers to refer. CPMA can effectively monitor the treasures' position in noisy ambient. Extensive simulation and experiment results illustrate the superior performance of the proposed method in terms of validity and reliability.

This paper addresses the localization embedded in a wild environment with huge obstacles and limitations of energy and costs. This kind of wild large scale localization often selects the range-free approaches for the reason of hardware consumption. While these approaches always suffered from low accuracy compared with the range-based methods, especially in the scenario of wild environment with huge obstacles and hardware limitation. Here we introduced a regional partition and cooperation (RPC) localization method and applied it to sensor localization for the Great Wall site in Yulin, Shaanxi province. We proposed a novel regional partition method to divide the large scale scene into small sub-regions which have no obstacles, consequently the well known DV-Hop is used for sub-region location, in the end a new sub-region cooperation method is provided to increase the accuracy, it should be noted our regional partition method and sub-region cooperation method only use the count information and sensor connectivity information. Theory analysis shows the proposed RPC can locate all unknown sensor nodes only requires the information of hop count and connectivity, furthermore RPC works even the anchor nodes are deployed sparsely, meanwhile the sensor nodes do not need any extra distance-measuring equipment. In the simulations, the positioning error of per node, the performance versus different number of anchor node and different node density are analyzed, the results demonstrate the performance extensions compared with classic DV-Hop.

Wireless localization technology has become an important part of pervasive computing applications. This paper presents DE2, a wireless localization approach that is responsive to single beacon conditions. Unlike previous work that needs a priori knowledge of the scenario during the pre-deployment phase with significant human effort, or uses a certain number of beacons during the localization phase with high energy consumption, to accomplish localization, DE2 leverages a single beacon without much prior human effort to locate multiple targets. The key challenge is how to utilize the limited information caught by a single beacon and little human effort to obtain the positions of multiple unknowns. The intuition underlying DE2 is that, direction and distance constraints between an unknown position and the single beacon are adequate to determine the unknown position. When a user rotates around an RF receiver (the single beacon), the human body acts as a signal-blocking obstacle. It causes the signal from a transmitter (position-unknown) to the single receiver to attenuate in a certain scope. The blocking effect caused by a human body can be utilized to obtain the direction constraint between the unknown position and the single beacon. Moreover, a corresponding distance constraint is also concluded in the rotating RSS according to the RF propagation model. DE2 pushes the limit of minimum beacons needed for localization without much pre-configuration effort. To demonstrate the utility of DE2, we implement DE2 in realworld single beacon wireless networks. The results show that these applications can significantly benefit from DE2.

The passive target detection and localization in wireless sensor networks(WSNs) is a critical issue in many applications. However, the data type been used for detection and localization is limited, due to the target does not carry any devices connected to any transmit(or receive). Thus, the great challenge is that how to use the limited data type for DLF working. In this paper, we analyze the wireless signal characteristic of the local stability, then design the "Double Level Fusion"(DLF) as the key structure of the designed system. DLF includes two layers confusion. The data level fusion is used for detecting the target while the decision level fusion is used for localizing the target. Combined with the receive signal strength indication (RSSI) characteristic of local stability, we propose the sliding average detection algorithm in data level fusion. We remedy the imperfection of data by using the advantage of Bayesian network in decision level fusion. Our approach examined by real experiment with square, grass, and woods, and the result demonstrates effective and accuracy for this work.

How to utilize the resources effectively so that good throughput and delay performance can be achieved in the target-tracking wireless sensor networks (WSNs) is a hot topic. In this paper, we introduce a "Queue" as a key structure in the design of our medium access control protocol. By using several narrow out-of -band busy tone signals, the main function of the queue is to keep the right accessing to the data channel for nodes that have sent the control packets successfully. Therefore, it reduces idle time of the data channel. In this paper, we propose and analyze a multi-channel access protocol based on the Queue, i.e., "Queue-based Multi-channel Medium Access Control Protocol" (QMMAC). QMMAC could not only alleviates collision, but also improves the efficiency of system resources because it takes the advantage of parallel transmission of multiple channels adequately. Thus, it can reduce delay and enhance the throughput of the network. Our work is very significant in the target-tracking application for the data coming from the active targets. The data is very important for tracking and is assigned a higher priority, finally, data could be transmitted to the sink with less delay. By taking into account the most complex network scenarios, we analyze the throughput of QMMAC protocol and do simulations to discuss the performance of our protocol. Our analysis and simulation results show that QMMAC achieves significantly better throughput and delay over the protocol without the queue in the same condition.

Bandwidth reservation is a key resource management strategy for target tracking in Wireless Sensor Networks (WSNs). To keep the losing probability of targets coming in a specific region below an optimal value, we propose a QoS-guaranteed bandwidth reservation scheme in this paper. To detect targets with high probability, we reserve enough available bandwidth for potential incoming targets and take quality of link into account at the same time. Furthermore, we regulate the rate for every detected target in case we lose track of it because of lack of bandwidth. Performance analysis and simulation results show that our scheme can provide guarantee for keeping trace of both incoming targets and detected targets.

A novel immune theory based health monitoring and risk evaluation model with Internet of things was proposed for health monitoring and risk evaluation of earthen sites. This model uses artificial antibodies identify unusual environmental factors, and take advantage of the value of danger signal to obtain the quantitative value of health risk of earthen sites. Theoretical analysis and experimental results show that the model can accurately calculate the health risks facing single environmental factors and the total risk facing many environmental factors of earthen sites, and provides a new way for the preventive conservation of earthen sites.

Without relying on devices carried by the target, device-free localization (DFL) is attractive for many applications, such as wildlife monitoring. There still exist many challenges for DFL for multiple targets without dense deployment of sensor nodes. To fit the gap, in this paper, we propose a multi-target localization method based on compressive sensing, named LCS. The key observation is that given a pair of nodes, the received signal strength (RSS) will be different when a target locates at different locations. Taking advantage of compressive sensing in sparse recovery to handle the sparse property of the localization problem, (i.e., the vector which contains the number and location information of k targets is an ideal k-sparse signal), we presented a scalable compressive sensing based multiple target counting and localization method i.e., LCS, and rigorously justify the validity of the problem formulation. The results from our realistic deployment in a 12m×12m open space are promising. For 12 people with 24 nodes, the worst localization error ratio and counting error ratio of our LCS is no more than 8.3% and 33.3% respectively.

The Radio Signal Strength Indicator (RSSI)-based localization algorithm is an effective solution for passive object localization. However, the localization accuracy of the existing methods highly depends on the transceiver distance and deployment density. Generally speaking, to obtain higher accuracy, we need a denser sensor node deployment, which results in a higher deployment cost and more communication overheads. In this paper, we investigate this problem based on extensive measurements. According to the measurement results, we propose to localize objects using an RSSI distribution based localization (RDL) model to identify the object location by different RSSI distributions of the communicating links. Experimental results show that the RDL method can achieve higher localization accuracy with less sensor nodes.

Vibration is the main reason for earthen sites to deform and collapse. It is necessary to get vibration data for providing basis to further conserving earthen sites. Traditional monitoring methods are not suitable for the earthen sites because it is destructive and unsustainable. We use the technique of wireless sensor network (WSN) to get the vibration data, and delve into how to transmit large amounts of data reliably while ensuring the transmission quality. An algorithm CAPK with probability caching based on the Wisden system of the University of California was proposed in this paper. The analysis of simulation and simulation experiment results prove that the algorithm has better performance, low consumption and high reliability, which makes the foundation of future research work.

It comes out to be the first problem needs to be resolved for wireless sensor networks, that is, to make data acquisition at a lower energy consumption but with high reliability on transmission. So, a reliable transmission protocol with dynamic adjustment for caching position is presented in this paper, which makes a good compromise between transmission reliability and energy consumption. This protocol caches data packet based on the intermediate nodes, so the link nodes can be divided into near-source nodes and near-sink nodes according to the communications distance. The node section for cache packet is dynamically adjusted in accordance with the link quality. To make more a uniform distribution of cache, the packets are normally distributed to cache at their corresponding section. Simulation and energy consumption model based on Markov Analysis show that the protocol can improve the transmission reliability with an effectively reduced energy consumption compared to similar protocols.

The application-oriented IoT is an integration of various kinds of technologies, rapidly developing in the globe. To ensure the quality its practical use, it is necessary to research its QoS architecture. We summarize QoS requirements through analysis of characteristic of IoT application service, transmission network, perception in this paper. Then we propose a QoS architecture based on IoT layered structure. This architecture sets QoS agent in lower layers then transmits QoS requirements, trying to guarantee the consistency as well as effectively use the existing QoS mechanisms in every layer.

In wireless sensor networks (WSNs), due to the event-driven features, congestion may occur unpredictably. Packet losses or errors may lead to fatal failure in certain applications (e.g., network program updating). Therefore, reliability of data transmission plays an important role in WSNs. And also, energy efficiency is a critical issue in WSNs because of its restricted power supply. In this paper, taking reliability and energy efficiency into account, we propose a reliable point-to-point transport protocol (RP2PT) for WSNs. In RP2PT, a virtual circuit between the source nodes and the sink is established and reliability is guaranteed by local caching and local retransmission. Theoretical analysis and simulation experiments show that RP2PT can provide better performance than other protocols in terms of the number of retransmitted packets, the amount of transmitted packets, and the amount of ACKs.

The technology of software protection based on virtual machine is a hot research field of software protection. Considering the impact on the performance of target software, traditional virtual machine can only protect several key functions of target software, which makes it unable to prevent reverse engineering for the whole software. In order to balance the performance overheads against protection strength, we proposed a thin virtual machine based software protection framework using distorted encryption (TVMBDE) in this paper. Based on improving the algorithms of instruction transformation of traditional virtual machines, the framework makes use of thin virtual machine protection technology to enhance the efficiency and integration of distorted encryption so as to ensure the security. In order to implement overall protection, the common part of target software will be protected in low-power, while the key parts will be protected in high-power. Analysis and testing show the framework is effective, which achieves the purpose of protecting the whole software.