Shigeng Zhang

Secure localization and location verification in wireless sensor networks: a survey

The locations of sensor nodes are very important to many wireless sensor networks (WSNs). When WSNs are deployed in hostile environments, two issues about sensors’ locations need to be considered. First, attackers may attack the localization process to make estimated locations incorrect. Second, since sensor nodes may be compromised, the base station (BS) may not trust the locations reported by sensor nodes. Researchers have proposed two techniques, secure localization and location verification, to solve these two issues, respectively. In this paper, we present a survey of current work on both secure localization and location verification. We first describe the attacks against localization and location verification, and then we classify and describe existing solutions. We also implement typical secure localization algorithms of one popular category and study their performance by simulations.

Unknown Tag Identification in Large RFID Systems: An Efficient and Complete Solution

Radio-Frequency Identification (RFID) technology brings revolutionary changes to many fields like retail industry. One important research issue in large RFID systems is the identification of unknown tags, i.e., tags that just entered the system but have not been interrogated by reader(s) covering them yet. Unknown tag identification plays a critical role in automatic inventory management and misplaced tag discovery, but it is far from thoroughly investigated. Existing solutions either trivially interrogate all the tags in the system and thus are highly time inefficient due to re-identification of already identified tags, or use probabilistic approaches that cannot guarantee complete identification of all the unknown tags. In this paper, we propose a series of protocols that can identify all of the unknown tags with high time efficiency. We develop several novel techniques to quickly deactivate already identified tags and prevent them from replying during the interrogation of unknown tags, which avoids re-identification of these tags and consequently improves time efficiency. To our knowledge, our protocols are the first non-trivial solutions that guarantee complete identification of all the unknown tags. We illustrate the effectiveness of our protocols through both rigorous theoretical analysis and extensive simulations. Simulation results show that our protocols can save up to 70 percent time when compared with the best existing solutions.

Accurate Range-Free Localization for Anisotropic Wireless Sensor Networks

Position information plays a pivotal role in wireless sensor network (WSN) applications and protocol/algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named <underline>S</underline>elective <underline>M</underline>ultilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.

Complete and fast unknown tag identification in large RFID systems

The RFID technology greatly improves efficiency of many applications including inventory control, object tracking, and supply chain management. In such applications, it is common that new objects are added into the system or existing objects are misplaced in wrong regions. When this happens, fast and complete identification of such tags is very important. We name this problem unknown tag identification, as these tags appear to be unknown by the reader(s) currently covering them. In this paper, we propose a series of protocols to identify unknown tags completely and fast. In these protocols, we develop several novel techniques to efficiently resolve collisions caused by known tags when identifying unknown tags, which greatly improve the time efficiency. To our knowledge, this is the first work that completely identify all the unknown tags with deterministic approaches. Simulation results show the superior performance of the proposed protocols: Compared with a baseline method which collects IDs of all the tags in the system, our best protocol reduces the execution time by 63% in average and by 85% at most.

Defending collaborative false data injection attacks in wireless sensor networks

False data filtering is an important issue in wireless sensor networks. In this paper, we consider a new type of false data injection attacks called collaborative false data injection, and propose two schemes to defend such attacks. In collaborative false data injection attacks, multiple compromised nodes collaboratively forge a fake report and inject the report into the network. This type of attacks is hard to defend with existing approaches, because they only verify a fixed number of message authentication codes (MACs) carried in the data report but the adversary can easily obtain enough compromised nodes from different geographical areas of the network to break their security. Our novel solution is to bind the keys of sensor nodes to their geographical locations, and verify the legitimacy of a data report by checking whether the locations of the sensors endorsing the report are logical (e.g., the sensors should be close enough to each other to sense the same event). We propose two filtering schemes: The geographical information based false data filtering scheme (GFFS) which utilizes the absolute positions of sensors in the verification, and the neighbor information based false data filtering scheme (NFFS) which utilizes relative positions of sensors when absolute positions cannot be obtained. We theoretically analyze the filtering probability of the two proposed schemes, and evaluate their performance through extensive simulations. Simulation results show that, when there are totally ten nodes compromised in a 400 nodes network, the detection probability of collaborative false data injection attacks is higher than 97% in GFFS and NFFS, but is less than 7% in traditional false data filtering approaches such as SEF.

Anchor supervised distance estimation in anisotropic wireless sensor networks

Distance estimation is a key issue in range-free localization algorithms for wireless sensor networks. Approaches that assume isotropy of networks, such as Dv-hop and Gradient, cannot obtain accurate distance estimations in anisotropic sensor networks thus are not applicable to such networks. The anisotropy of sensor networks comes from two aspects: uneven nodal distribution and irregularity of deployment region. Existing localization algorithms for anisotropic wireless sensor networks usually only deal with one of the two aspects. In this paper, we propose an anchor supervised distance estimation approach which can simultaneously cope with both of the two aspects. In this approach, an anchor node selects a “friendly” subset from all other anchor nodes to which its distance estimates are accurate and broadcasts the selection result to neighboring common nodes. The common nodes then use these friendly anchors to perform distance estimation. We analyze distance estimation accuracy of this approach through extensive simulations. The results show that, compared with Dv-hop, our proposed approach dramatically reduces distance estimation error in anisotropic wireless sensor networks with an average factor of 67%. Consequently, the localization error of Dv-hop is reduced by an average factor of 71% if enhanced with our distance estimation approach.

Flexible and Time-Efficient Tag Scanning with Handheld Readers

Tag scanning is an important issue to dynamically manage tag IDs in radio frequency identification (RFID) systems. Different from tag identification that collects IDs of all the tags, tag scanning first verifies whether or not a responding tag has already been identified and retrieves its ID when the answer is yes, and collects the tags ID only when it is unidentified. In this paper, we present the first study on spot scanning with a handheld reader, which aims to scan tags in the readers interrogation range at an arbitrarily specified position in the system. Existing studies mainly focus on continuous scanning, and they are highly time inefficient in performing spot scanning. The inefficiency stems from the small overlap between tag populations in different spot scanning operations, in which case existing solutions cannot efficiently recognize unidentified tags. We develop a novel technique called LOCK to efficiently recognize unidentified tags even when the overlapped tags are few. LOCK does not simply use a tags reply slot index but also compact short responses from tags to efficiently distinguish unidentified tags from identified ones. The valuable compact short responses are firstly investigated, which are the keys for efficient tag identification in the paper. Based on LOCK, three tag scanning protocols are proposed to solve the spot scanning problem. Simulation results show that, for spot scanning, our best protocol reduces per tag scanning time by up to 70 percent when compared with the state-of-the-art solution. Moreover, the proposed protocols can also be employed to perform continuous scanning with better time efficiency than the best existing solutions.

Range-free selective multilateration for anisotropic wireless sensor networks

In anisotropic wireless sensor networks, range-free multilateration-based localization (RFML) protocols severely suffer from large error in node-anchor distance estimations or the bad geometry of anchors involved in the localization process. In this paper, we propose selective multilateration (SM), a RFML protocol in which a node adaptively selects a subset of anchor nodes with accurate distance estimates and good geometric distribution to perform multilateration. We make two main contributions: (1) We exploit the locality property of per hop length (two nearby nodes share similar per hop length to a far anchor node) to estimate node-anchor distances. This induces smaller distance estimation error than previous approaches that use a unified average per hop length for all nodes. (2) We propose a method to heuristically select a subset of good anchor nodes that have small distance estimation errors and good geometry quality measured by geometric dilution of precision (GDOP) to perform multilateration. We also use a method which mines sub-hop resolution proximity between neighboring nodes to reduce error in distance estimates which further improves localization accuracy. Simulation results show that, compared with PDM, SM improves distance estimation accuracy by up to 45 percent. The localization accuracy in SM is improved by up to 45 percent and 27 percent in average. Furthermore, SM incurs much less communication and computational overhead than PDM does, making it more suitable for large scale WSNs.

Adaptive explicit congestion control based on bandwidth estimation for high bandwidth-delay product networks

Abstract Existing congestion control protocols for high bandwidth-delay product (HBDP) networks either suffer from efficiency degradation caused by insufficient feedback of the network state, or are difficult to deploy in real networks because they require modification in IP header to provide enough feedback information. Based on VCP, this paper proposes the VCP-BE protocol that can provide more accurate network state information by estimating available bandwidth for sender with an adaptive bandwidth estimator. The estimator can dynamically adjust its parameters (e.g., observing interval) to achieve different accuracy and responsiveness to adapt to the network state indicated by two ECN bits, without no more modification in IP header than VCP. Furthermore, when the network load is high, VCP-BE calculates the ratio of current throughput to the available bandwidth to judge if network is close to the overload state. With these fine-grained network state estimation, VCP-BE can adjust the congestion window more precisely than VCP, thus greatly improves its convergence speed of achieving high bandwidth utilization and fairness. Simulation results show that VCP-BE also outperforms MLCP, which uses seven bits for explicit feedback while VCP-BE only uses two ECN bits.

STEP: A Time-Efficient Tag Searching Protocol in Large RFID Systems

The radio frequency identification (RFID) technology is greatly revolutionizing applications such as warehouse management and inventory control in retail industry. In large RFID systems, an important and practical issue is tag searching: Given a particular set of tags called wanted tags, tag searching aims to determine which of them are currently present in the system and which are not. As an RFID system usually contains a large number of tags, the intuitive solution that collects IDs of all the tags in the system and compares them with the wanted tag IDs to obtain the result is highly time inefficient. In this paper, we design a novel technique called testing slot, with which a reader can quickly figure out which wanted tags are absent from its interrogation region without tag ID transmissions. The testing slot technique thus greatly reduces transmission overhead during the searching process. Based on this technique, we propose two protocols to perform time-efficient tag searching in practical large RFID systems containing multiple readers. In our protocols, each reader first employs the testing slot technique to obtain its local searching result by iteratively eliminating wanted tags that are absent from its interrogation region. The local searching results of readers are then combined to form the final searching result. The proposed protocols outperform existing solutions in both time efficiency and searching precision. Simulation results show that, compared with the state-of-the-art solution, our best protocol reduces execution time by up to 60 percent, meanwhile promotes the searching precision by nearly an order of magnitude.