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Location based Social Networks (LBSNs) have become an integral part of mobile social networks, and with increasing popularity, the use of these networks has become more frequent. With the increasing use of these platforms, a lot of information is leaked, posing serious privacy threats to the users. To handle this, most platforms currently have different privacy settings that are extreme and render the processed checkin data useless to the user as the changes made completely deviates from the user motivation behind the check-in. To this end, we propose a model called User Motivation based Privacy Preservation (UMPP), which provides different privacy policies for different user motivations to retain user motivation for a check-in, which is otherwise lost in most other privacy policies in applications today. To the best of our knowledge, this is the first work that proposes user motivation based privacy policies. We evaluate the performance of our proposed methods on real- world datasets in terms of privacy and information loss.

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are envisioned as two promising techniques to satisfy the increasing energy and computation requirements of latency-sensitive and computation-intensive applications installed on mobile devices. The integration of MEC and WPT introduces a novel paradigm named Wireless Powered Mobile Edge Computing (WP-MEC). In WP-MEC networks, edge devices located at the edge of radio access networks, such as access points and base stations, transmit radio frequency signals to power mobile devices and mobile devices can offload their intensive computation workloads to edge devices. In this paper, we study the Computation Completion Ratio Maximization Scheduling problem for WP-MEC networks with multiple edge devices, which is proved to be NP-hard. We jointly optimize the WPT time allocation and computation scheduling for mobile devices in a WP-MEC network to maximize the computation completion ratio of the WP-MEC network and propose approximation algorithms. The approximation ratio and computation complexity of the proposed algorithms are theoretically analyzed. Extensive simulations are conducted to verify the performance of the proposed algorithms.

With the rapid development in mobile devices, mobile crowdsourcing has become an important research focus. In large-scale mobile crowdsourcing, the effective evolution prediction and incentive mechanism are the key focuses to improve the efficiency of systems. The evolution model based on evolutionary game theory is researched to predict the evolution trends of mobile crowdsourcing systems effectively. Based on the evolution trends, the reputation updating mechanism (RUMG) is proposed to address free-riding and false-reporting problems. According to spatio-temporal privacy preserving, the incentive mechanism with spatio-temporal privacy preserving for mobile crowdsourcing is researched. In order to protect worker’s spatio-temporal privacy information effectively, a spatio-temporal privacy preserving based on $k$ -anonymity (LKAC) is proposed. In addition, the effectivenesses of the proposed RUMG and LKAC are verified through comparison experiments. This proposed mechanism also improves the security of system and resolves the free-riding and false-reporting problems of mobile crowdsourcing.

Blockchain has been treated as one of the most promising technologies to promote crowdsourcing by providing new nice features, such as decentralization and accountability. Unfortunately, some inherent limitations of blockchain have been rarely addressed by the most existing works when applying blockchain into crowdsourcing, which becomes the performance bottleneck of crowdsourcing systems. In this paper, we propose a novel hybrid blockchain crowdsourcing platform to achieve decentralization and privacy preservation. Our platform integrates with a hybrid blockchain structure, dual-ledgers, and dual consensus algorithms to ensure secure communication between the requesters and the workers. Moreover, the smart contract and zero-knowledge proof are employed to ensure automatic operation of the tasks and the protection users' privacy, respectively. Finally, we conduct experiments to confirm the efficiency of the adopted consensus algorithm on our platform by comparing it with the state-of-the-art.

This paper studies the deletion propagation problem in terms of minimizing view side-effect. It is a problem funda-mental to data lineage and quality management which could be a key step in analyzing view propagation and repairing data. The investigated problem is a variant of the standard deletion propagation problem, where given a source database D, a set of key preserving conjunctive queries Q, and the set of views V obtained by the queries in Q, we try to identify a set T of tuples from D whose elimination prevents all the tuples in a given set of deletions on views △V while preserving any other results. The complexity of this problem has been well studied for the case with only a single query. Dichotomies, even trichotomies, for different settings are developed. However, no results on multiple queries are given which is a more realistic case. We study the complexity and approximations of optimizing the side-effect on the views, i.e., find T to minimize the additional damage on V after removing all the tuples of △V. We focus on the class of key-preserving conjunctive queries which is a dichotomy for the single query case. It is surprising to find that except the single query case, this problem is NP-hard to approximate within any constant even for a non-trivial set of multiple project-free conjunctive queries in terms of view side-effect. The proposed algorithm shows that it can be approximated within a bound depending on the number of tuples of both V and △V. We identify a class of polynomial tractable inputs, and provide a dynamic programming algorithm to solve the problem. Besides data lineage, study on this problem could also provide important foundations for the computational issues in data repairing. Furthermore, we introduce some related applications of this problem, especially for query feedback based data cleaning.

Location-based services (LBS) leveraged by ubiquitous mobile devices have brought great convenience to mobile users in various aspects, including communication, information exchange, social activities, and so on. However, privacy concerns arise at the same time, since the users need to submit their locations and query contents to the LBS servers. To this end, location privacy and query privacy have been recognized. In particular, this paper focuses on query privacy for preventing the leakage of users’ query contents. Cloaking region-based techniques using untrusted servers and client-based $k$ -anonymity approaches have been devised to preserve query privacy in LBS. However, these works suffer from single point of failure or insufficiency of query privacy. To address this issue, we investigate effective $k$ -anonymity-based solutions for query privacy in LBS. We formulate a probabilistic framework $\textit{P}k\textit{A}$ , under which $k$ -anonymity-based mechanisms can be initiated, and analyze a recent proposed algorithm $DLS$ as an instance of $\textit{P}k\textit{A}$ . An algorithm circle segment is presented to provide effective query privacy when query interests have similar prior probability. To obtain more effective query privacy in general cases, we propose two algorithms MEE and MER , which optimize two individual privacy metrics, denoted expected entropy and expected max-min ratio, adopted in this paper. We recognize two practical properties— $No~More~Leakage$ and $k$ - $Effectiveness$ for effective query privacy, and our proposed algorithms satisfy both of $No~More~Leakage$ and $k$ - $Effectiveness$ . We conduct evaluation based on real-life data sets and synthetic distributions of query interests, and the evaluation results demonstrate that our proposed algorithms produce significantly improved query privacy.

Due to the increasing concerns of securing private information, context-aware Internet of Things (IoT) applications are in dire need of supporting data privacy preservation for users. In the past years, game theory has been widely applied to design secure and privacy-preserving protocols for users to counter various attacks, and most of the existing work is based on a two-player game model, i.e., a user/defender-attacker game. In this paper, we consider a more practical scenario which involves three players: a user, an attacker, and a service provider, and such a complicated system renders any two-player model inapplicable. To capture the complex interactions between the service provider, the user, and the attacker, we propose a hierarchical two-layer three-player game framework. Finally, we carry out a comprehensive numerical study to validate our proposed game framework and theoretical analysis.

Beaconing is a fundamental networking service where each node broadcasts a packet to all its neighbors locally. Unfortunately, the problem Minimum Latency Beaconing Schedule (MLBS) in duty-cycled scenarios is not well studied. Existing works always have rigid assumption that each node is only active once per working cycle. Aiming at making the work more practical and general, MLBS problem in duty-cycled network where each node is allowed to active multiple times in each working cycle (MLBSDCA for short) is investigated in this paper. Firstly, a modified first-fit coloring based algorithm is proposed for MLBSDCA under protocol interference model. After that, a (ρ + 1)2 ∗ |W|-approximation algorithm is proposed to further reduce the beaconing latency, where ρ denotes the interference radius, and |W| is the maximum number of active time slots per working cycle. When ρ and |W| is equal to 1, the approximation ratio is only 4, which is better than the one (i.e., 10) in existing works. Furthermore, two approximation algorithms for MLBSDCA under physical interference model are also investigated. The theoretical analysis and experimental results demonstrate the efficiency of the proposed algorithms in term of latency.

This paper studies a novel approach to processing massive uncertain graph data. In this approach, we propose a new framework to simultaneously process a query on a set of randomly sampled possible worlds of an uncertain graph. Based on this framework, we develop a series of algorithms to analyze massive uncertain graphs, including breadth-first search, shortest distance queries, triangle counting, and core decomposition. We implement this approach based on GraphLab, one of the stateof-the-art graph processing frameworks. By sharing fine-grained internal processing steps on common substructures of sampled possible worlds, the new approach achieves tens to hundreds of times speedup in execution time on a cluster of 20 servers.

This paper studies social influence from the perspective of users' characteristics. The importance of users' characteristics in word-of-mouth applications has been emphasized in economics and marketing fields. We model a category of users called mavens where their unique characteristics nominate them to be the preferable seeds in viral marketing applications. In addition, we developed and verified methods to learn their characteristics from a real dataset. Also, we illustrated ways to maximize information flow through mavens in social networks. Our experiments show that our model successfully detected mavens as well as fulfilled significant roles in maximizing the information flow in a social network comparing to the spread that was a result of traditional influencer users in influence maximization problem. These results showed the compatibility of our model with real marketing approaches.

Over years, many efforts are made for the problem of constructing fault-tolerant virtual backbones in wireless network. In case that the wireless network consists of physically equivalent nodes, e.g. with the same communication range, unit disk graph model is universally used to abstract the wireless network and the problem is defined as the $k$-connected $m$- dominating set problem on the unit disk graph. As this problem is NP-hard, the design and analysis of an approximation algorithm for the problem is of great theory interest. To the best of our knowledge, there has been no officially published work which introduces a constant factor approximation algorithm for the $k$-connected $m$-dominating set problem where $k$ and $m$ can be arbitrary pair of positive integers, and the existing literatures are presenting only partial solutions, e.g. works only under the condition that $m \geq k \geq 1$ and $k \leq 3$. In this paper, we introduce a new constant factor algorithm to compute the minimum $k$- connected $m$-dominating set problem in unit disk graphs, which works with arbitrary pair of positive integers $k$ and $m$ which are satisfying the condition $m \geq k \geq 1$. This algorithm is based on the decomposition of the graph and is completely different from any known strategies, official ones as well as unofficial ones, and we provide theoretical analysis to prove that the approximation ratio is constant.

Online social networks have gained significant popularity recently. The problem of influence maximization in online social networks has been extensively studied. However, in prior works, influence propagation in the physical world, which is also an indispensable factor, is not considered. The Location-Based Social Networks (LBSNs) are a special kind of online social networks in which people can share location-embedded information. In this paper, we make use of mobile crowdsourced data obtained from location-based social network services to study influence maximization in LBSNs. A novel network model and an influence propagation model taking influence propagation in both online social networks and the physical world into consideration are proposed. An event activation position selection problem is formalized and a corresponding solution is provided. The experimental results indicate that the proposed influence propagation model is meaningful and the activation position selection algorithm has high performance.

The appearance of social networks provides great opportunities for people to communicate, share and disseminate information. Meanwhile, it is quite challenge for utilizing a social networks efficiently in order to increase the commercial profit or alleviate social problems. One feasible solution is to select a subset of individuals that can positively influence the maximum other ones in the given social network, and some algorithms have been proposed to solve the optimal individual subset selection problem. However, most of the existing works ignored the constraint on time. They either assume that the time is infinite or only suitable to solve the snapshot selection problems. Obviously, both of them are impractical in the real system. Due to such reason, we study the problem of selecting the optimal individual subset to diffuse the positive influence when time is bounded. We proved that such a problem is NP-hard, and a heuristic algorithm based on greedy strategy is proposed. The experimental results on both simulation and real-world social networks based on the trace data in Shanghai show that our proposed algorithm outperforms the existing algorithms significantly, especially when the network structure is sparse.

Holistic aggregation results are important for users to obtain summary information from Wireless Sensor Networks (WSNs). Holistic aggregation requires all the sensory data to be sent to the sink, which costs a huge amount of energy. Fortunately, in most applications, approximate results are acceptable. We study the approximated holistic aggregation algorithms based on uniform sampling. In this paper, four holistic aggregation operations are investigated. The mathematical methods to construct their estimators and determine the optional sample size are proposed, and the correctness of these methods is proved. Four corresponding distributed holistic algorithms are presented. The theoretical analysis and simulation results show that the algorithms have high performance.

Testing of Multi Phase High Power Permanent Magnet Synchronous Motor (PMSM) having multiple units is a difficult task. In this paper, prediction problems to obtain the overall characteristics for such motor under limited experimental conditions have been analyzed. A simplified testing model of the motor is established for testing of the overall characteristics and calculation methods were studied. Finally, some valuable conclusions resulting from the analysis are provided.

Complicated collisions and spectrum uncertainty con- strain the usage of Cognitive Radio Networks (CRNs) on heavy transmission and time sensitive applications. On the other hand, data aggregation has been considered as an essential operation in wireless networks. A large amount of effort has been dedicated to the investigation of CRNs and data aggregation in wireless networks. However, the existing literatures rarely concentrate on how to use cognitive radio technique to promote the performance of data aggregation in conventional wireless networks. In this paper, we investigate the Minimum Latency Data Aggregation Scheduling in wireless networks with Cognitive Radio capabil- ity (MLDAS-CR) problem. As the first try, an approximation scheduling algorithm based on Integer Linear Programming (ILP) and Linear Programming (LP) is proposed. According to the simulation results, this method performances great, however, it is difficult to theoretically evaluate the solution. Therefore, a heuristic scheduling algorithm with guaranteed latency bound is presented in our further investigation. The performance of the proposed solutions are evaluated through extensive simulations.

COUNT is a typical aggregation operation in Wire- less Sensor Networks (WSNs). In such an operation, the total number of the items which are of the same kind is obtained and only one numerical value is returned as the result. This paper identifies the multiple count problem which counts items belonging to multiple categories. For each category, the total number of the items belonging to this category is calculated. Therefore, the returned result is a set of values instead of a single value. The multiple count problem is more challenging than the traditional count problem as the former incurs more communication overhead. This paper proposes a distributed approximate multiple count algorithm which can derive an error bounded result under a specified communication cost constraint for each node. The error of the derived result is hN L , where h is the depth of the routing tree, N is the total number of all the items belonging to all the categories, and L is a representation of the communication cost constraint for each node. Furthermore, the weighted multiple count problem is investigated where different kinds of items to be counted have different weights. The proposed algorithms are evaluated through TOSSIM, a widely used simulation tool for WSNs. The theoretical analysis and simulation results both demonstrate the correctness and effectiveness of the proposed algorithms.

A Connected Dominating Set (CDS) is used as a Virtual Backbone (VB) for efficient routing and broadcasting in Wireless Sensor Networks (WSNs). Currently, almost all existing works focus on constructing Minimum-sized CDS under the Deterministic Network Model (DNM). However, due to the existence of many probabilistic lossy links in WSNs, it is more practical to obtain a VB under the realistic Probabilistic Network Model (PNM). Moreover, load-balance factor cannot be neglected when constructing a VB to prolong network lifetime. Hence, in this paper, we propose a Multi-Objective Genetic Algorithm (MOGA) to construct a Load-Balanced Virtual Backbone under PNM (LBVBP). Through simulations, we demonstrate that our proposed methods extend network lifetime by 65% on average compared with the existing state-of-the-art approaches.

Routing in Disruption Tolerant Networks (DTNs) is a challenging problem due to the intermittent connectivity between the nodes. Researchers have proposed many routing protocols that adapt to the temporary connections of DTNs. One classification of routing protocols makes use of historical information to predict future contact patterns for any pair of nodes. However, most existing protocols focus on the probability of a path from the source to the destination without considering the information in a packet which includes the source, destination, size, TTL (Time-To-Live) and limited resources such as available buffer size and bandwidth. In this paper, we propose a new prediction-based routing algorithm that takes into account packet information under the conditions of limited transmission opportunities. The goal of this protocol is to increase the overall delivery ratio through scheduling packets at each node. Meanwhile, this protocol may sacrifice some messages' delivery delay time to some extent. Extensive simulation results with real traces show that our protocol with packet scheduling has better performance than the pure probabilistic routing algorithms in term of delivery ratio. Our protocol's performance advantage is more obvious for nodes with higher packet intensity and shorter TTL in packets.

Data sharing for data collection among multiple applications is an efficient way to reduce the communication cost of Wireless Sensor Networks (WSNs). This paper is the first work to introduce the interval data sharing problem which is to investigate how to transmit as less data as possible over the network, and meanwhile the transmitted data satisfies the requirements of all the applications. Different from current studies where each application requires a single data sampling during each task, we study the problem where each application requires a continuous interval of data sampling in each task instead. The proposed problem is a nonlinear nonconvex optimization problem. In order to lower the high complexity for solving a nonlinear nonconvex optimization problem in resource restricted sensor nodes, a 2-factor approximation algorithm whose time complexity is O(n2) and memory complexity is O(n) is provided. A special instance of this problem is also analyzed. This special instance can be solved with a dynamic programming algorithm in polynomial time, which gives an optimal result in O(n2) time complexity and O(n) memory complexity. We evaluate the proposed algorithms with TOSSIM, a widely used simulation tool in WSNs. Theoretical analysis and simulation results both demonstrate the effectiveness of the proposed algorithms.

Wireless Sensor Networks (WSNs) are deployed for monitoring in a range of critical domains (e.g., health care, military, critical infrastructure). Accordingly, these WSNs should be resilient to attacks. The current approach to defending against malicious threats is to develop and deploy a specific defense mechanism for a specific attack. However, the problem with this traditional approach to defending sensor networks is that the solution for the Jamming attack does not defend against other attacks (e.g., Sybil and Selective Forwarding). In reality, one cannot know a priori what type of attack an adversary will launch. This work addresses the challenges with the traditional approach to securing sensor networks and presents a comprehensive framework, Di-Sec, that can defend against all known and forthcoming attacks. At the heart of Di-Sec lies the monitoring core (M-Core), which is an extensible and lightweight layer that gathers statistics relevant for the defense mechanisms. The M-Core allows for the monitoring of both internal and external threats and supports the execution of multiple detection and defense mechanisms (DDMs) against different threats in parallel. Along with Di-Sec, a new user-friendly domain-specific language was developed, the M-Core Control Language (MCL). Using the MCL, a user can implement new defense mechanisms without the overhead of learning the details of the underlying software architecture (i.e., TinyOS, Di-Sec). Hence, the MCL expedites the development of sensor defense mechanisms by significantly simplifying the coding process for developers. The Di-Sec framework has been implemented and tested on real sensors to evaluate its feasibility and performance. Our evaluation of memory, communication, and sensing components shows that Di-Sec is feasible on today's resource-limited sensors and has a nominal overhead. Furthermore, we illustrate the basic functionality of Di-Sec by implementing and simultaneously executing DDMs for attacks at various layers of the communication stack (i.e., Jamming, Selective Forwarding, Sybil, and Internal attacks).

A Connected Dominating Set (CDS) is used as a virtual backbone for efficient routing and broadcasting in Wireless Sensor Networks (WSNs). Most existing works focus on constructing a Minimum CDS (MCDS), a k-connect m-dominating CDS, a minimum routing cost CDS or a bounded-diameter CDS. However, no work considers the load-balance factor of CDSs in WSNs. In this paper, we propose a new concept - the Load-Balanced CDS (LBCDS), and a new problem - the Load-Balanced Allocate Dominatee (LBAD) problem. Consequently, a greedy-based approximation algorithm is proposed to construct an LBCDS in a WSN in this paper. Moreover, we propose an optimal centralized algorithm and an efficient probability-based distributed algorithm to solve the LBAD problem. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by 80% compared with the latest CDS construction algorithm.

A Connected Dominating Set (CDS) is used as a virtual backbone for efficient routing and broadcasting in Wireless Sensor Networks (WSNs). Most existing works focus on constructing a Minimum CDS (MCDS), a k-connect m-dominating CDS, a minimum routing cost CDS or a bounded-diameter CDS. However, no work considers the load-balance factor of CDSs in WSNs. In this paper, we propose a novel problem — the Load-Balanced CDS (LBCDS) problem, in which constructing an LBCDS and load-balancedly allocating dominatees to dominators are investigated simultaneously. A Genetic Algorithm (GA) based strategy called LBCDS-GA is proposed to construct an LBCDS in a WSN. As a matter of fact, constructing a CDS as a virtual backbone in a WSN is an efficient way to extend network lifetime through reducing the number of the nodes involved in communication, while building an LBCDS and load-balancedly allocating dominatees to dominators can further prolong network lifetime through balancing the workloads of all the dominators. Through extensive simulations, we demonstrate that our proposed methods extend network lifetime by 65% on average compared with the best and latest MCDS construction algorithm.

Query scheduling as one of the most important technologies used in query processing has been widely studied recently. Unfortunately, to the best of our knowledge, no previous work focuses on the Minimum Latency Multi-Regional Query Scheduling (ML-MRQS) problem. In this paper, we investigate the ML-MRQS problem in Wireless Sensor Networks (WSNs), which aims to generate a scheduling plan with minimum latency for a more practical query model called Multi-Regional Query (MRQ). A MRQ targets at user interested data from multiple region-sofa WSN, where each region is a subarea of the WSN. We claim that the ML-MRQS problem is NP-hard. Therefore, we propose a heuristic scheduling algorithm Multi-Regional Query Scheduling Algorithm (MRQSA) to solve this problem. Theoretical analysis shows that the latency of MRQSA is upper bounded by 23A + B + C for a MRQ with m query regions R 1 , R 2 …, R m , where A = max i=1 m D i left, B = max i=1 m {(23D i +5Δ+21)k i }, C = Σ i=1 m H i +5Δ−m+17, m is the number of regions, Δ is the maximum node degree in the WSN, A is the diameter of R i , k i is the maximum overlapped degree of sensor nodes in TU, Hi represents the distance of R i with respect to the sink, and D i left is the diameter of the non-overlapped part of R i . Extensive simulations are conducted to verify the performance of our algorithm, which show that MRQSA significantly reduces the query latency when compared with the most recently published multi-query scheduling algorithm.

Data collection is a common operation of Wireless Sensor Networks (WSNs). The performance of data collection can be measured by its achievable \emph{network capacity}. However, most existing works focus on the network capacity of \emph{unicast}, \emph{multicast} or/and \emph{broadcast}, which are different communication modes from data collection, especially continuous data collection. In this paper, we study the \emph{Snapshot/Continuous Data Collection} (SDC/CDC) problem under the Physical Interference Model (PhIM) for randomly deployed dense WSNs. For SDC, we propose a \emph{Cell-Based Path Scheduling} (CBPS) algorithm based on network partitioning. Theoretical analysis shows that its achievable network capacity is $\Omega(W)$ ($W$ is the data transmitting rate, \emph{i.e.} bandwidth, over a channel), which is order-optimal. For CDC, we propose a novel \emph{Segment-Based Pipeline Scheduling} (SBPS) algorithm that significantly speeds up the CDC process, and achieves a surprising network capacity, which is at least $\sqrt{\frac{n}{\log n}}$ or $\frac{n}{\log n}$ times better than the current best result.

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

Wireless sensors are attached to all kinds of mobile devices/entities such as mobile phones, PDAs, vehicles, robots and animals. This generates Mobile Wireless Sensor Networks (MWSNs) with very dynamic topologies and loose connectivity that depend on mobility of the mobile devices. Data collection from these mobile sensors has become a great challenge considering volatile topologies, loose connectivity and limited buffer storage. This paper proposes a stochastic compressive data collection protocol for MWSNs named SMITE. SMITE consists of three parts: random collector election, stochastic direct transmission from common nodes to collectors when common nodes are in the collectors' transmission range, and angle transmission from collectors to the mobile sink when collectors gather enough data using a predictive method. The collectors use bloom filters to compress the received data. The protocol's performance is theoretically analyzed. The analytic results show that data from the common nodes can be gathered to the collectors with a high probability and gathered data on the collectors can also be forwarded to the mobile sink with a high probability. Simulations are carried out for performance evaluation. The simulation results show that SMITE significantly outperforms the state-of-the-art solutions such as DFT-MSN, SCAR and Sidewinder on the aspects of delivery ratio, transmission overhead, and time delay.

Data collection is an important operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most existing works focus on the capacity of unicast, multicast or snapshot data collection in single-radio single-channel wireless networks, and no dedicated works consider the continuous data collection capacity for WSNs in detail under the protocol interference model. In this paper, we first propose a multi-path scheduling algorithm for the snapshot data collection in single-radio multi-channel WSNs and prove that its achievable network capacity is at least equation, which is a tighter lower bound compared with the previously best result in [5] which is W over 8ρ2, where W is the bandwidth over a channel, H is the number of the available orthogonal channels, ρ is the ratio of the interference radius over the transmission radius of a sensor and o(ρ) is a linear equation of ρ. For the continuous data collection problem, although the authors in [5] claim that data collection can be pipelined with existing works, we find that such an idea cannot actually improve network capacity. We explain the reason for this and propose a novel continuous data collection method for dual-radio multi-channel WSNs. This method significantly speeds up the data collection process, and achieves a capacity of equation when Δ e ≤ 12, or equation when Δ e > 12, where n is the number of sensors, M is a constant value and usually M ≪ n, and Δ e is the maximum number of leaf nodes having a same parent node in the routing tree (i.e. data collection tree). The simulation results also indicate that the proposed algorithms significantly improve network capacity compared with the existing works.

In this paper, a duty cycle based multi-channel MAC protocol with multiple channel reservation, called M-cube, is proposed to tackle the triple hidden terminal problems. M-cube can make nodes to choose one actually idle channel from all the expected idle channels. Therefore, M-cube can avoid data packet collisions resulted by the triple hidden terminal problems. By minimizing the lower bound of the average number of times of channel switching in M-cube, the optimal duty cycle is obtained through theoretical analysis. To validate the effectiveness of multiple channel reservation and dynamic optimal duty cycling, extensive simulations and real test bed experiments were conducted. Both the simulation and experiment results show that when the number of channels is large or network loads are heavy, M-cube improves energy efficiency and throughput significantly compared with other works in the literature.

This paper proposes ARM, an receiver-initiated MAC protocol with duty cycling to tackle control channel saturation, triple hidden terminal and low broadcast reliability problems in asynchronous multi-channel WSNs. By adopting a receiver-initiated transmission scheme and probability-based random channel selection, ARM effectively solves control channel saturation and triple hidden terminal problems. Further, ARM employs a receiver-adjusted broadcast scheme to guarantee broadcast reliability for broadcast-intensive applications. Via the theoretical analysis, two factors that assist ARM to handle these problems are derived. The simulation and real testbed experimental results show that via solving these three problems ARM achieves significant improvement in energy efficiency and throughput. Moreover, ARM exhibits a prominent ability to enhance its broadcast reliability.

Wireless sensor networks (WSNs) have many applications that handle sensitive information such as surveillance, reconnaissance, and target tracking. Therefore, a WSN deployed in a hostile region should be resilient to attacks. The current approach to defending against malicious threats is to develop and deploy a specific defense mechanism for a specific attack. However, the problem with this traditional approach to defending sensor networks is that the solution for the jamming attack does not defend against other attacks (e.g., sybil, selective forwarding, and wormhole attacks). In reality, one cannot know a priori what type of attack an adversary will launch. Also, given the resource constraints of sensor nodes, the current defense mechanisms cannot be simply combined on the node to provide a complete solution. This work addresses the challenges with the traditional approach to securing sensor networks and presents a collaborative framework (the Distributed Security Framework -DSF) that can defend against all known attacks. The framework is extensible, therefore, as new attacks are discovered they can also be defended against. The DSF leverages existing defense mechanisms created by researchers. These defense mechanisms are distributed in such a way that they can, collectively, provide comprehensive defense to the network. The efficacy of the DSF is determined using simulations for scenarios consisting of multiple stationary and multiple mobile attackers. The simulation results show that though the DSF consumes more energy than single defense schemes, it can significantly enhance the network security even when the network is under multiple types of attacks.

Data aggregation is an essential yet time-consuming task in wireless sensor networks (WSNs). This paper studies the well-known Minimum-Latency Aggregation Schedule (MLAS) problem and proposes an energy-efficient distributed scheduling algorithm named Clu-DDAS based on a novel cluster-based aggregation tree. Our approach differs from all the previous schemes where Connected Dominating Sets or Maximal Independent Sets are employed. We prove that Clu-DDAS has a latency bound of 4R′ + 2Delta − 2, where Delta is the maximum degree and R′ is the inferior network radius which is smaller than the network radius R. Clu-DDAS has comparable latency as the previously best centralized algorithm E-PAS, while Clu-DDAS consumes 78% less energy as shown by the simulation results. Clu-DDAS outperforms the previously best distributed algorithm DAS whose latency bound is 16R′ + Delta − 14 on both latency and energy consumption. On average, Clu-DDAS transmits 67% fewer total messages than DAS does. We also propose an adaptive strategy for updating the schedule to accommodate dynamic network topology.

The complete area coverage problem in Wireless Sensor Networks (WSNs) where every point inside an area is covered by an active sensor has been extensively studied in the literature. However, there are many applications that do not always require complete coverage. For such applications, an effective method to save energy and prolong network lifetime is to partially cover the area. However, due to the hardness to verify the ratio of the covered area over the entire monitored area (coverage ratio), all the existing algorithms for partial coverage have very high time complexities (either centralized algorithms or distributed but non-parallel algorithms). Besides, all the existing algorithms are intentionally designed for partial coverage, thus they do not utilize the various exiting methods for the complete coverage problem. In this work, we propose a framework that can convert almost any existing algorithm for complete coverage to a one for partial coverage with any coverage ratio. Our framework can preserve the characteristics of the original algorithms and the conversion process has low time complexity. The framework also guarantees some degree of uniform partial coverage of the monitored area.

Data mining in wireless sensor networks (WSNs) is a new emerging research area. This paper investigates the problem of real time clustering of sensory data in WSNs. The objective is to cluster the data collected by sensor nodes in real time according to data similarity in a d-dimensional sensory data space. To perform in-network data clustering efficiently, a Hilbert Curves based mapping algorithm, HilbertMap, is proposed to convert a d-dimensional sensory data space into a two-dimensional area covered by a sensor network. Based on this mapping, a distributed algorithm for clustering sensory data, H-Cluster, is proposed. It guarantees that the communications for sensory data clustering mostly occur among geographically nearby sensor nodes and sensory data clustering is accomplished in in-network manner. Extensive simulation experiments were conducted using both real-world datasets and synthetic datasets to evaluate the algorithms. H-Cluster consistently achieves the lowest data loss rate, the highest energy efficiency, and the best clustering quality.

Many data dissemination techniques have been proposed for wireless sensor networks to facilitate data dissemination and query processing. However, these techniques may not work well in a large scale sensor network where a huge amount of sensing data are generated. In this paper, we propose an integrated distributed Connected dominating set Based Indexing (CBI) data dissemination scheme to support scalable handling of large amount of sensing data in large scale wireless sensor networks. Our CBI can minimize the use of limited network and computational resources while providing timely responses to queries. Moreover, our data dissemination framework ensures scalability and load balance. Analysis and simulations are conducted to evaluate the performance of our CBI scheme. The results show that the CBI scheme outperforms the external storage-based scheme, local storage-based scheme and the datacentric storage-based scheme in overall performance.

The more areas the multimedia used in, the more effective measures about quality of service are required. This paper presents a new method for multimedia quality assessment. This method uses Media Delivery Index algorithm to analyze the situation of current network and get a series of index as media attributes. Using an intelligent algorithm -- Support Vector Machine as a classifier which maps the network parameters with end-users’ feeling. This is a No-Reference method to evaluate the media quality. There is no need to access the original signal in the operation phase, and the obtained results correlate well with human perception. And compared with the Neural Network quality assessment, the new method is more stable and expansive. So this method can be widely used in real-time system to calculate the magnitude of quality of service automatically.

Data aggregation is an essential operation in wireless sensor network applications. This paper focuses on the data aggregation scheduling problem. Based on maximal independent sets, a distributed algorithm to generate a collision-free schedule for data aggregation in wireless sensor networks is proposed. The time latency of the aggregation schedule generated by the proposed algorithm is minimized using a greedy strategy. The latency bound of the schedule is 24D + 6 Delta + 16, where D is the network diameter and Delta is the maximum node degree. The previous data aggregation algorithm with least latency has the latency bound (Delta- Delta 1)R, where R is the network radius. Thus in our algorithm Delta contributes to an additive factor instead of a multiplicative factor, which is a significant improvement. To the best of our knowledge, the proposed algorithm is the first distributed algorithm for data aggregation scheduling. This paper also proposes an adaptive strategy for updating the schedule when nodes fail or new nodes join in a network. The analysis and simulation results show that the proposed algorithm outperforms other aggregation scheduling algorithms.

Digital fingerprinting is a technique for identifying users who use multimedia content for unintended purpose, such as redistribution. In order to characterize the fingerprints that can withstand collusion attack, Dittmann et. al proposed the definition of Anti-Collusion Codes (ACCs). In this paper, we present a new approach to construct anti-collusion codes based on cover-free families. All the previous constructions of ACCs are special cases of our construction. Especially, we can construct ACCs by using error-correcting codes. Collusion resistance comparison of different schemes show that our scheme is more efficient than previous ACCs. Some related problems on this subject are proposed.

Area query processing is significant for various applications of wireless sensor networks. No previous study has specifically addressed this issue. We can adopt a naive method, which is to send all data to Base Station for centralized processing. However, this method wastes a large amount of energy for reporting useless data. This motivates us to propose an energy-efficient in-network area query processing scheme. In our scheme, the whole monitored area is partitioned into grids, and a gray code is used to represent a Grid ID (GID), which is a smart way to describe an area. Furthermore, a reporting tree is constructed to process merging areas and aggregations. Based on the properties of GIDs, useless data can be dropped and areas can be merged as early as possible. Incremental update is used to continuously generate query results. In essence, all of these strategies are pivots to conserve energy consumption. With a thorough simulation study, it is shown that our scheme is energy-efficient.

Virtually all query optimization methods in data stream management system (DSMS) require a means of estimating the number of distinct values of an attribute in a data stream. Accurate assessment of the number of distinct values can be crucial for selecting a good query plan. Due to data streams’ continuous, real-time and unbounded characteristics, data streams may not be stored in limited memory an effective method. Therefore, estimating the number of distinct values over data streams is a more difficult problem. In this paper, combining with data streams’ properties and analyzing BloomFilter, we present a new estimation method based on circular BloomFilter using limited space. We store the distinct values in circular BloomFilter to solve effectively the problem that data streams could not be stored in limited memory. The theoretical analysis and the results of experiment indicate that the estimation method is more feasible and highly effective.

Data centric storage is an effective algorithm to organize data in the sensor networks. Even though such kind of storage can save more energy than other algorithms, such as flooding based algorithm, it also wastes energy in some cases. For example, the event frequency is much more than the query frequency. To overcome this problem, we propose an index based query processing algorithm. We analyze the energy consumption of the algorithm and present conditions for the index based algorithm to save more energy than the traditional data centric storage. Finally a time-based index management algorithm is proposed to adopt the proper algorithm in different cases. Extensive experiments showed that our time-based index management algorithm can save more energy than the traditional data centric algorithm.

The recently proposed active queue management (AQM) is an effective method used in Internet routers for congestion control, and to achieve a tradeoff between link utilization and delay. The de facto standard, the random early detection (RED) AQM scheme, and most of its variants use average queue length as a congestion indicator to trigger packet dropping. In this paper, we propose a novel proportional and differential RED algorithm, called NPDRED, as an extension of RED. NPD-RED is based on a self-tuning proportional and differential controller, which not only considers the instantaneous queue length at the current time point, but also takes into consideration the ratio of the current differential error signal to the buffer size. Furthermore, we give theoretical analysis of the system stability and give guidelines for the selection of feedback gains for the TCP/RED system to stabilize the instantaneous queue length at a desirable level. Extensive simulations have been conducted with ns2. The simulation results have demonstrated that the proposed NPD-RED algorithm outperforms the existing AQM schemes in terms of average queue length,average throughput, and stability.

With the ever-increasing number of multicast data applications recently, considerable efforts have been focused on the design of flow control schemes for multicast services. The main difficulties in designing a flow controller for multicast service are caused by heterogeneous multicast receivers, especially those with large propagation delays, since the feedback arriving at the source is somewhat outdated, and can be harmful to the control operations. To attack the above problem, the present paper describes a novel multicast flow control scheme, the so-called proportional, integrative, derivative plus neural network (PIDNN) predictive technique, which consists of two components: the proportional integrative plus derivative (PID) controller and the back propagation BP neural network (BPNN). This network-assisted property is different from the existing control schemes, in that the PIDNN controller can release the irresponsiveness of a multicast flow caused by those long propagation delays from the receivers. By using BPNN for the receivers with longer propagation delay, this active scheme makes the control more responsive to network status. Thus the rate adaptation can be performed in a timely manner, for the sender to respond to network congestion quickly. We analyze the theoretical aspects of the proposed algorithm, show how the control mechanism can be used to design a controller to support multi-rate multicast transmission based on feedback of explicit rates, and verify this matching using simulations. Simulation results demonstrate the efficiency of our scheme in terms of high link utilization, quick response, scalability, high unitary throughput, intra-session fairness and inter-session fairness.