{"title":"A case for hierarchical routing in low-power wireless embedded networks","authors":"K. Iwanicki, M. Steen","doi":"10.1145/2240092.2240099","DOIUrl":"https://doi.org/10.1145/2240092.2240099","url":null,"abstract":"Hierarchical routing has often been mentioned as an appealing point-to-point routing technique for wireless sensor networks (sensornets). While there is a volume of analytical and high-level simulation results demonstrating its merits, there has been little work evaluating it in actual sensornet settings. This article bridges the gap between theory and practice.\u0000 Having analyzed a number of proposed hierarchical routing protocols, we have developed a framework that captures the common characteristics of the protocols and identifies design points at which the protocols differ. We use a sensornet implementation of the framework in TOSSIM and on a 60-node testbed to study various trade-offs that hierarchical routing introduces, as well as to compare the performance of hierarchical routing with the performance of other routing techniques, namely shortest-path routing, compact routing, and beacon vector routing. The results show that hierarchical routing is a compelling routing technique also in practice. In particular, despite only logarithmic routing state, it can offer small routing stretch: an average of ∼ 1.25 and a 99th percentile of 2. It can also be robust, minimizing the maintenance traffic or the latency of reacting to changes in the network. Moreover, the trade-offs offered by hierarchical routing are attractive for many sensornet applications when compared to the other routing techniques. For example, in terms of routing state, hierarchical routing can offer scalability at least an order of magnitude better than compact routing, and at the same time, in terms of routing stretch, its performance is within 10--15% of that of compact routing; in addition, this performance can further be tuned to a particular application. Finally, we also identify a number of practical issues and limitations of which we believe sensornet developers adopting hierarchical routing should be aware.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125246235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sensor network data fault detection with maximum a posteriori selection and bayesian modeling","authors":"Kevin Ni, G. Pottie","doi":"10.1145/2240092.2240097","DOIUrl":"https://doi.org/10.1145/2240092.2240097","url":null,"abstract":"Current sensor networks experience many faults that hamper the ability of scientists to draw significant inferences. We develop a method to systematically identify when these faults occur so that proper corrective action can be taken. We propose an adaptable modular framework that can utilize different modeling methods and approaches to identifying trustworthy sensors. We focus on using hierarchical Bayesian space-time (HBST) modeling to model the phenomenon of interest, and use maximum a posteriors selection to identify a set of trustworthy sensors. Compared to an analogous linear autoregressive system, we achieve excellent fault detection when the HBST model accurately represents the phenomenon.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126718667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Coverage estimation for crowded targets in visual sensor networks","authors":"M. Karakaya, H. Qi","doi":"10.1145/2240092.2240100","DOIUrl":"https://doi.org/10.1145/2240092.2240100","url":null,"abstract":"Coverage estimation is one of the fundamental problems in sensor networks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D (omnidirectional) scalar sensor networks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion in crowded environments. This article represents a first attempt toward a closed-form solution for the visual coverage estimation problem in the presence of occlusions. We investigate a new target detection model, referred to as the certainty-based target detection (as compared to the traditional uncertainty-based target detection) to facilitate the formulation of the visual coverage problem. We then derive the closed-form solution for the estimation of the visual coverage probability based on this new target detection model that takes visual occlusions into account. According to the coverage estimation model, we further propose an estimate of the minimum sensor density that suffices to ensure a visual K-coverage in a crowded sensing field. Simulation is conducted which shows extreme consistency with results from theoretical formulation, especially when the boundary effect is considered. Thus, the closed-form solution for visual coverage estimation is effective when applied to real scenarios, such as efficient sensor deployment and optimal sleep scheduling.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127024929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information-theoretic modeling of false data filtering schemes in wireless sensor networks","authors":"Z. Cao, Hui Deng, Zhi Guan, Zhong Chen","doi":"10.1145/2140522.2140527","DOIUrl":"https://doi.org/10.1145/2140522.2140527","url":null,"abstract":"False data filtering schemes are designed to filter out false data injected by malicious sensors; they keep the network immune to bogus event reports. Theoretic understanding of false data filtering schemes and guidelines to further improve their designs are still lacking. This article first presents an information-theoretic model of false data filtering schemes. From the information-theoretic view, we define the scheme's filtering capacity CFi as the uncertainty-reduction ratio of the target input variable, given the output. This metric not only performs better than existing metrics but also implies that only by optimizing the false negative rate and false positive rate simultaneously, can we promote a scheme's overall performance. Based on the investigation from the modeling efforts, we propose HiFi, a hybrid authentication-based false data filtering scheme. HiFi leverages the benefits of both symmetric and asymmetric cryptography and achieves a high filtering capacity, as well as low computation and communication overhead. Performance analysis demonstrates that our proposed metric is rational and useful, and that HiFi is effective and energy efficient.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":" 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120829653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A dynamic programming approach to maximizing a statistical measure of the lifetime of sensor networks","authors":"M. Ilyas, H. Radha","doi":"10.1145/2140522.2140531","DOIUrl":"https://doi.org/10.1145/2140522.2140531","url":null,"abstract":"The inherent many-to-one flow of traffic in wireless sensor networks (WSNs) produces a skewed distribution of energy consumption rates, leading to the early demise of those sensors that are critical to the ability of surviving nodes to communicate their measurements to the base station. Numerous previous approaches aimed at balancing the consumption of energy in wireless networks are either too complex or do not address problems unique to the flow of traffic in WSNs. In this article, we propose the use of a dynamic programming algorithm (DPA), an operational, low-complexity algorithm, used in conjunction with four different route discovery algorithms. We perform complexity analysis, statistical evaluation of changes in power consumption rates effected, and verify spatial redistribution of energy consumption of sensors in the network. Our results on multihop networks of 100 randomly placed nodes show that, on average, the two best performing variants of DPA yield a reduction of up to 28% and 36% in power consumption rate variance at the cost of raising average power consumption by 15% and 21%, respectively. Computational complexities of DPA variants range from O(N3) to O(N4), which is significantly lower than linear search of the solution space of O(N!Ni). Analysis by diffusion plots shows that DPA reduces power consumption of sensors that experience the highest power consumption under the shortest path routes.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124832877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data authenticity and availability in multihop wireless sensor networks","authors":"Erman Ayday, F. Delgosha, F. Fekri","doi":"10.1145/2140522.2140523","DOIUrl":"https://doi.org/10.1145/2140522.2140523","url":null,"abstract":"Security services such as data confidentiality, authenticity, and availability are critical in wireless sensor networks (WSNs) deployed in adversarial environments. Due to the resource constrain's of sensor nodes, the existing protocols currently in use in adhoc networks cannot be employed in WSNs. In this article, we propose a protocol called location-aware network-coding security (LNCS) that provides all the aforementioned security services. By dividing the terrain into nonoverlapping cells, the nodes take advantage of the location information to derive different location-binding keys. The key idea in LNCS is that all the nodes involved in the protocol collaborate in every phase. We employ random network coding in order to provide data availability significantly higher than that in other schemes. A hash tree-based authentication mechanism is utilized to filter the bogus packets enroute. We provide a comparison between our scheme and previously proposed schemes. The results reveal significant improvement in data availability while maintaining the same level of data confidentiality and authenticity.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126411618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Quantization, channel compensation, and optimal energy allocation for estimation in sensor networks","authors":"Xusheng Sun, E. Coyle","doi":"10.1145/2140522.2140528","DOIUrl":"https://doi.org/10.1145/2140522.2140528","url":null,"abstract":"In clustered networks of wireless sensors, each sensor collects noisy observations of the environment, quantizes these observations into a local estimate of finite length, and forwards them through one or more noisy wireless channels to the cluster head (CH). The measurement noise is assumed to be zero-mean and have finite variance, and each wireless hop is modeled as a binary symmetric channel (BSC) with a known crossover probability. A novel scheme is proposed that uses dithered quantization and channel compensation to ensure that each sensor's local estimate received by the CH is unbiased. The CH fuses these unbiased local estimates into a global one, using a best linear unbiased estimator (BLUE). Analytical and simulation results show that the proposed scheme can achieve much smaller mean square error (MSE) than two other common schemes, while using the same amount of energy. The sensitivity of the proposed scheme to errors in estimates of the crossover probability of the BSC channel is studied by both analysis and simulation. We then determine both the minimum energy required for the network to produce an estimate with a prescribed error variance and how this energy must be allocated amongst the sensors in the multihop network.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131449163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Practical RSA signature scheme based on periodical rekeying for wireless sensor networks","authors":"Shih-Ying Chang, Yue-Hsun Lin, Hung-Min Sun, Mu-En Wu","doi":"10.1145/2140522.2140526","DOIUrl":"https://doi.org/10.1145/2140522.2140526","url":null,"abstract":"Broadcast is an efficient communication channel on wireless sensor networks. Through authentic broadcast, deployed sensors can perform legitimate actions issued by a base station. According to previous literature, a complete solution for authentic broadcast is digital signature based on asymmetric cryptography. However, asymmetric cryptography utilizes expensive operations, which result in computational bottlenecks. Among these cryptosystems, Elliptic Curve Cryptography (ECC) seems to be the most efficient and the most popular choice. Unfortunately, signature verification in ECC is not efficient enough. In this article, we propose an authentic broadcast scheme based on RSA. Unlike conventional approaches, the proposed scheme adopts short moduli to enhance performance. Meanwhile, the weakness of short moduli can be fixed with rekeying strategies. To minimize the rekeying overhead, a Multi-Modulus RSA generation algorithm, which can reduce communication overhead by 50%, is proposed. We implemented the proposed scheme on MICAz. On 512-bit moduli, each verification spends at most 0.077 seconds, which is highly competitive with other public-key cryptosystems.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131068103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Self-localizing smart camera networks","authors":"Babak Shirmohammadi, C. J. Taylor","doi":"10.1145/2140522.2140524","DOIUrl":"https://doi.org/10.1145/2140522.2140524","url":null,"abstract":"This article describes a novel approach to localizing networks of embedded cameras and sensors. In this scheme, the cameras and the sensors are equipped with controllable light sources (either visible or infrared), which are used for signaling. Each camera node can then determine automatically the bearing to all of the nodes that are visible from its vantage point. By fusing these measurements with the measurements obtained from onboard accelerometers, the camera nodes are able to determine the relative positions and orientations of other nodes in the network.\u0000 The method uses angular measurements derived from images, rather than range measurements derived from time-of-flight or signal attenuation. The scheme can be implemented relatively easily with commonly available components, and it scales well since the localization calculations exploit the sparse structure of the system of measurements. Additionally, the method provides estimates of camera orientation which cannot be determined solely from range measurements.\u0000 The localization technology could serve as a basic capability on which higher-level applications could be built. The method could also be used to automatically survey the locations of sensors of interest, to implement distributed surveillance systems, or to analyze the structure of a scene, based on images obtained from multiple registered vantage points. It also provides a mechanism for integrating the imagery obtained from the cameras with the measurements obtained from distributed sensors.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126230420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Event prediction in a hybrid camera network","authors":"U. M. Erdem, S. Sclaroff","doi":"10.1145/2140522.2140529","DOIUrl":"https://doi.org/10.1145/2140522.2140529","url":null,"abstract":"Given a hybrid camera layout—one containing, for example, static and active cameras—and people moving around following established traffic patterns, our goal is to predict a subset of cameras, respective camera parameter settings, and future time windows that will most likely lead to success the vision tasks, such as, face recognition when a camera observes an event of interest. We propose an adaptive probabilistic model that accrues temporal camera correlations over time as the cameras report observed events. No extrinsic, intrinsic, or color calibration of cameras is required. We efficiently obtain the camera parameter predictions using a modified Sequential Monte Carlo method. We demonstrate the performance of the model in an example face detection scenario in both simulated and real environment experiments, using several active cameras.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. Networks","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133395336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}