Sharing the big data generated by IoT via cloud is slow and expensive. Besides, transmitting and sharing data among IoT devices via cloud may be insecure. To address these issues, a novel efficient and secure data sharing scheme termed EB-SDSS (Edge Blockchain Secure Data Sharing Scheme) is proposed in this paper for edge-enabled IoT applications. EB-SDSS constructs a blockchain on edge servers. It guarantees the confidentiality and unforgeability of data by combining the symmetric encryption scheme with an edge blockchain. To ensure the device authenticity and the reliability of shared data, EB-SDSS adopts a certificateless signature scheme. It also provides efficient large-scale data searches for IoT devices through a locality-sensitive hashing algorithm. EB-SDSS has been proven to be secure against the adaptive chosen message attacks under the random oracle model. The experimental results indicate that EB-SDSS is feasible for IoT inter-device data sharing.

Most data center services are finished by the cooperation among the connected servers. However, the malicious attackers always try to divide the network into disconnected components to start some attacks, such as the address resolution protocol (ARP) attack, the denial of service (DoS) attack, the botnet attack, and so on. The connectivity is an excellent indicator to measure the reliability and fault-tolerant ability of the network. Whereas, the traditional connectivity and current conditional connectivity cannot well reflect the fault-tolerant performance of the network when attackers are a block or have a certain structure and the components of the remaining network still have a certain structure. Based on this fact, we propose a new measure: the double-structure connectivity, which can accurately reflect the fault-tolerant ability of the network when attackers are structured and each component of the network has a certain structure after removing the attacked servers. Meanwhile, a hypercube is a high-performance interconnection network that can also be used to design some data center networks. Therefore, we study the double-structure fault-tolerance of the hypercube and determine the double-structure connectivity of distinct structures of the hypercube. Furthermore, we propose algorithms to construct structures of attackers directly to measure the fault-tolerant ability of the hypercube under this attack. Our results can be applied not only to interconnection networks but also to some data center networks.

Internet of Things (IoT) devices usually stores data on clouds for computational overhead offloading and easy data sharing. The data owners, as a result, usually have concerns about the security and privacy of their data stored in such cloud-assisted IoT (C-IoT) systems. Traditional encryption and search primitives, including attribute-based encryption (ABE) and public-key encryption with keyword search (PEKS), however, suffer from high overheads in decryption and revocation, and privacy leakage in search. To address these issues, we propose an efficient revocable and searchable multiauthority ABE (MA-ABE) scheme named ERS-ABE, which utilizes blockchain (BC) technology to implement keyword-based search and dynamic user management. ERS-ABE also adopts cloud-assisted decryption to improve the efficiency of IoT devices. It has been proven to be secure against the selective replayable chosen-ciphertext attacks and the chosen-keyword attacks under the random oracle model. The feasibility and efficiency of ERS-ABE have been evaluated through theoretical analysis and extensive simulation studies. The results indicate that EAR-ABE performs better over the state-of-the-art in both storage and computational overheads. Particularly, the operations that are usually done by a central server but taken by a BC in EAR-ABE cost only a few seconds.

Link scheduling plays a key role in the network capacity and the transmission delay. In this paper, we study the problem of maximum link scheduling (MLS), aiming to characterize the maximum number of links that can be successfully scheduled simultaneously under Rayleigh-fading and multiuser interference. After analyzing the minimum distance between successful links in the existing GHW scheduling algorithm, we propose a DLS (Distance-based Link Scheduling) algorithm. Then, the global interference is characterized and bounded by introducing a separation distance between selected links, building on which we propose a distributed version of DLS (denoted by DDLS) that converges to a constant factor of the non-fading optimum within time complexity$O(n\ln n)$, where$n$is the number of links. Furthermore, we study the Shortest Link Scheduling (SLS) problem, which minimizes the number of time slots to successfully schedule each link for at least once. An algorithm for SLS with approximation factor of$O(\ln n)$is obtained by executing DDLS. Extensive simulations show that DDLS greatly outperforms GHW and the other two popular algorithms.

In this poster, an accurate distance ranging system for off-the-shelf smartphones is introduced. Two ranging methods, namely improved Microsoft Beep-Beep and our Single-Beep, are developed and evaluated on 6 different Android phones.

Today, there is widespread use of mobile applications that take advantage of a user's location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user's behalf can significantly improve a smartphone's battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy.

Ultraviolet (UV) radiation has a great impact on human health. Nowadays, the public basically gets information about UV radiation through weather forecasts, which can only provide rough and average prediction for a certain large area. Since CMOS sensors in smartphone cameras are very sensitive to UV radiation, smartphones have potential to be the ideal equipment to measure it. At the same time, result optimization can be achieved in real time by taking advantage of fog computing because fog servers are able to aggregate UV radiation data and compute the results at local areas. This paper exhaustively discussed a novel procedure that could measure UV radiation through smartphone cameras, and also briefly covered how to leverage fog computing to improve UV measurement accuracy. To implement the procedure, an Android app called UV meter was developed. Experiments were conducted by utilizing the app to validate and evaluate the correctness and accuracy of the procedure on both smartphones and smart watches. Results showed that the proposed procedure could achieve an average of 95% accuracy of a typical professional digital UV meter, and could be easily implemented on smart devices.

This paper focuses on designing a distributed medium access control algorithm for fairly sharing network resources among contending stations in an 802.11 wireless network. Because the notion of fairness is not universal and there lacks a rigorous analysis on the relationships among the four types of most popular fairness criteria, we first mathematically prove that there exist certain connections between these types of fairness criteria. We then propose an efficient medium access algorithm that aims at achieving time fairness and throughput enhancement in a fully distributed manner. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is addressed rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

Time-critical Location Based Service (LBS) applications in mobile ad hoc networks require fast localization. The conventional localization techniques are, unfortunately, unsuitable for such applications, for they neglect the time needed for localization. As a result, time-critical information may become obsolete, and the mobile users such as vehicles may have moved to new locations before the localization procedure is completed. To address this issue, we formulate a notion of On-Demand Fast Localization (ODFL) and devise a framework to implement this concept over existing routing protocols in MANETs. We present analytical and simulation results to demonstrate that ODFL can significantly reduce the time solely needed for localization before starting time-critical applications. Moreover, we show that ODFL can also improve location privacy and reduce energy consumptions.

Mobile sensing is becoming a popular paradigm to collect information from and outsource tasks to mobile users. These applications deal with lot of personal information, e.g., identity and location. Therefore, we need to pay a deeper attention to privacy and anonymity. However, the knowledge of the data source is desired to evaluate the trustworthiness of the sensing data. Anonymity and trust become two conflicting objectives in mobile sensing. In this paper, we proposeARTSense, a framework to solve the problem of “trust without identity” in mobile sensing. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. In contrast to other recent solutions, our scheme does not require a trusted third party and both positive and negative reputation updates can be enforced. In the trust assessment, we consider contextual factors that dynamically affects the trustworthiness of the sensing data as well as the mutual support and conflict among data from difference sources. Security analysis shows that ARTSense achieves our desired anonymity and security goals. Our prototype implementation on Android demonstrates that ARTSense incurs minimal computation overhead on mobile devices, and simulation results justify that ARTSense captures the trust of information and reputation of participants accurately.

RSS (Received Signal Strength) has been widely utilized in wireless applications. It is, however, susceptible to environmental unknowns from both temporal and spatial domains. As a result, the fluctuation of RSS may degrade performance of RSS based applications. In this work, we propose a novel RSS processing method at the receiver for three antenna based systems. The output of our approach is `RSS-Ratio', which eliminates the environmental unknowns and thus is a more stable variable compared to RSS itself. To validate the efficacy of the proposed method, we conduct a series of experiments in a range of wireless scenarios, including indoor laptop based measurement, indoor software defined radio - WARP based measurement, and outdoor wireless measurement. In addition, we also give an analysis to the relationship between the location of transmitter and the value of RSS-Ratio, and examine the accuracy of the estimated RSS-Ratio value via both simulations and experiments. All the experimental, analytical, and simulated results demonstrate that RSS-Ratio will be a better replacement for RSS to improve the performance of RSS based applications.

Deployment of public wireless access points (also known as public hotspots) and the prevalence of portable computing devices has made it more convenient for people on travel to access the Internet. On the other hand, it also generates large privacy concerns due to the open environment. However, most users are neglecting the privacy threats because currently there is no way for them to know to what extent their privacy is revealed. In this paper, we examine the privacy leakage in public hotspots from activities such as domain name querying, web browsing, search engine querying and online advertising. We discover that, from these activities multiple categories of user privacy can be leaked, such as identity privacy, location privacy, financial privacy, social privacy and personal privacy. We have collected real data from 20 airport datasets in four countries and discover that the privacy leakage can be up to 68%, which means two thirds of users on travel leak their private information while accessing the Internet at airports. Our results indicate that users are not fully aware of the privacy leakage they can encounter in the wireless environment, especially in public WiFi networks. This fact can urge network service providers and website designers to improve their service by developing better privacy preserving mechanisms.

With the proliferation of sensor-embedded mobile computing devices, participatory sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in participatory sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately.

Collaborative spectrum sensing is a key technology in cognitive radio networks (CRNs). Although mobility is an inherent property of wireless networks, there has been no prior work studying the performance of collaborative spectrum sensing under attacks in mobile CRNs. Existing solutions based on user trust for secure collaborative spectrum sensing cannot be applied to mobile scenarios, since they do not consider the location diversity of the network, thus over penalize honest users who are at bad locations with severe path-loss. In this paper, we propose to use two trust parameters, location reliability and malicious intention (LRMI), to improve both malicious user detection and primary user detection in mobile CRNs under attack. Location reliability reflects path-loss characteristics of the wireless channel and malicious intention captures the true intention of secondary users, respectively. We propose a primary user detection method based on location reliability (LR) and a malicious user detection method based on LR and Dempster-Shafer (D-S) theory. Simulations show that mobility helps train location reliability and detect malicious users based on our methods. Our proposed detection mechanisms based on LRMI significantly outperforms existing solutions. In comparison to the existing solutions, we show an improvement of malicious user detection rate by 3 times and primary user detection rate by 20% at false alarm rate of 5%, respectively.

In many practical applications of wireless sensor networks, it is crucial to accomplish the localization of sensors within a given time bound. We find that the traditional definition of relative localization is inappropriate for evaluating its actual overhead in localization time. To address this issue, we define a novel problem called essential localization and present the first rigorous study on the essential localizability of a wireless sensor network within a given time bound. Additionally, we propose an efficient distributed algorithm for time-bounded essential localization over a sensor network and evaluate the performance of the algorithm with analysis and extensive simulation studies.

This paper focuses on designing a distributed medium access control algorithm that aims at achieving time fairness among contending stations and throughput maximization in an 802.11 wireless LAN. The core idea of our proposed algorithm lies in that each station needs to select an appropriate contention window size so as to fairly share the channel occupancy time and maximize the throughput under the time fairness constraint. The derivation of the proper contention window size is presented rigorously. We evaluate the performance of our proposed algorithm through an extensive simulation study, and the evaluation results demonstrate that our proposed algorithm leads to nearly perfect time fairness, high throughput, and low collision overhead.

In this paper, we propose a systematic approach to designing and deploying a RFID Assisted Navigation System (RFID-ANS) for VANETs. RFID-ANS consists of passive tags deployed on roads to provide navigation information while the RFID readers attached to the center of the vehicle bumper query the tag when passing by to obtain the data for navigation guidance. We analyze the design criteria of RFID-ANS and present the design of the RFID reader in detail to support vehicles at high speeds. We also jointly consider the scheduling of the read attempts and the deployment of RFID tags based on the navigation requirements to support seamless navigations. The estimation of the vehicle position and its accuracy are also investigated.

This paper addresses the problem of target counting based on the Monte Carlo simulation. We rely on an Accept-Reject process to guide the placement of virtual targets in a virtual sensor field, which has exactly the same sensor layout as the real one. The objective of this construction is to generate a virtual target energy landscape whose shape is close enough to an energy landscape estimated from the real sensor readings. Based on the number of virtual targets placed on the virtual field and the total virtual and real target energy volumes, the number of real targets can be estimated. We consider both single-epoch and multi-epoch sensor readings and our theoretical analysis indicates that by exploiting the information from multiple epochs, our approach yields a target count that approximately converges to the true target count when the number of epochs is large enough. Extensive comparison based simulation study has been performed and the results verify the effectiveness of our target counting algorithms.

Abstract In this paper, we study the problem of distributed virtual backbone construction in sensor networks, where the coverage area of nodes are disks with different radii. This problem is modeled by the construction of a minimum connected dominating set (MCDS) in geometric k‐disk graphs. We derive the size relationship of any maximal independent set (MIS) and MCDS in geometric k‐disk graphs, and apply it to analyze the performances of two distributed connected dominating set (CDS) algorithms we propose in this paper. These algorithms have bounded performance ratio and low communication overhead. To the best of our knowledge, the results reported in this paper represent the state‐of‐the‐art. Copyright © 2009 John Wiley & Sons, Ltd.

In many practical applications of wireless sensor networks, it is crucial to accomplish the localization of sensors within a given time bound. We find that the traditional definition of relative localization is inappropriate for evaluating its actual overhead. To address this problem, we define a novel problem called essential localization, and present the first rigorous study on the essential localizability of a wireless sensor network within a given time bound. We propose an efficient distributed algorithm for time-bounded essential localization over a sensor network, and evaluate the performance of our algorithm with extensive simulations.

The complexity of channel scheduling in Multi- Radio Multi-Channel (MR-MC) wireless networks is an open research topic. This problem asks for the set of edges that can support maximum amount of simultaneous traffic over orthogonal channels under a certain interference model. There exist two major interference models for channel scheduling, with one under the physical distance constraint, and one under the hop distance constraint. The complexity of channel scheduling under these two interference models serves as the foundation for many problems related to network throughput maximization. However, channel scheduling was proved to be NP-Hard only under the hop distance constraint for SR-SC wireless networks. In this paper, we fill the void by proving that channel scheduling is NP-Hard under both models in MR-MC wireless networks. In addition, we propose a polynomial-time approximation scheme (PTAS) framework that is applicable to channel scheduling under both interference models in MR-MC wireless networks. Furthermore, we conduct a comparison study on the two interference models and identify conditions under which these two models are equivalent for channel scheduling.

We transform the 3D underwater sensor network (USN) localization problem into its 2D counterpart by employing sensor depth information and a simple projection technique. We first prove that a nondegenerative projection preserves network localizability. We then prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed bilateration localization scheme for 3D USNs termed as underwater sensor positioning (USP). Through extensive simulations, we show that USP has the following nice features: (1) improved localization capabilities over existing 3D methods, (2) low storage and computation requirements, (3) predictable and balanced communication overhead, and (4) robustness to errors from the underwater environment.

In this paper, we study the problem of minimum connected dominating set in geometric k-disk graphs. This research is motivated by the problem of virtual backbone construction in wireless ad hoc and sensor networks, where the coverage area of nodes are disks with different radii. We derive the size relationship of any maximal independent set and the minimum connected dominating set in geometric k-disk graphs, and apply it to analyze the performances of two distributed connected dominating set algorithms we propose in this paper. These algorithms have a bounded performance ratio and low communication overhead, and therefore have the potential to be applied in real ad hoc and sensor networks.

We study the localization problem in sparse 3D underwater sensor networks. Considering the fact that depth information is typically available for underwater sensors, we transform the 3D underwater positioning problem into its two- dimensional counterpart via a projection technique and prove that a non-degenerative projection preserves network localizability. We further prove that given a network and a constantk, all of the geometrick-lateration localization methods are equivalent. Based on these results, we design a purely distributed localization framework termed USP. This framework can be applied with any ranging method proposed for 2D terrestrial sensor networks. Through theoretical analysis and extensive simulation, we show that USP preserves the localizability of the original 3D network via a simple projection and improves localization capabilities when bilateration is employed. USP has low storage and computation requirements, and predictable and balanced communication overhead.

Multipath routing is attractive for load-balancing, fault-tolerance, and security enhancement. However, constructing and maintaining a set of node-disjoint paths between the data source and sink is non-trivial in a dynamic environment. In this paper, we study the problem of route recovery in vertex-disjoint multipath routing for sensor networks with many-to-one traffic patterns. We identify the sufficient conditions for multipaths to be recovered when the existing node-disjoint paths are broken, and provide a simple framework for multipath maintenance. This framework is very efficient in time when multipath source routing is employed. Our findings can help to conserve network resource by not launching any route discovery when the data source realizes that a new route may not exist, to guide mobile data sources to relocate themselves in order to reconstruct the new multipaths, and to help newly-deployed data sources quickly determine whether the required number of multipaths exist for sure or not and then compute them. The technique proposed in this paper is a good complement to the classic max-flow algorithm when node-disjoint multipaths are needed.