Wireless Algorithms, Systems, and Applications

Designing better proposal distributions can greatly affect the performance of the particle filters, which has been extensively studied in the literature. In this paper, we propose to design better proposal distribution using a new version of unscented Kalman filter- the iterated unscented Kalman filter (IUKF). The IUKF makes use of both statistical and analytical linearization techniques in different steps of the filtering process, which makes it a better candidate for designing proposal distribution in particle filter framework. Each particle is updated using an iterative manner. Through this process, the algorithm can make better use of the current observation for state estimation. To evaluate the performance of the proposed particle filter, we use a synthetic model and a real-world model for the experiments. The experimental results have shown that the proposed algorithm outperforms the alternatives.

The coexistence performance with control channel when TD-LTE and LTE-FDD systems coexist at the adjacent frequency band within the same geographical area is investigated. A simple mathematical model mapping from control channel performance to data channel throughput is employed to estimate system performance when considering the impact of the adjacent channel interference (ACI) on the control channels. The simulation results indicate that the ACI generated by Base Station (BS) of collocated LTE system significantly degrades the performance of the uplink of the victim LTE system. The link between BSs needs an extra isolation of 83.6 dB in the typical coexistence scenarios.

In this paper, we investigate the buffer occupation in wireless social networks.

n

source nodes are randomly distributed in the networks, and each of them chooses several friend nodes, whose number follows power-law distribution. Two different strategies are proposed to achieve optimal buffer occupation or throughput, and the upper bound and lower bound for buffer size and throughput are also investigated. The results show that there exists trade-off between buffer occupation and throughput, and we also find that the buffer occupation for each node will not increase when the size of network becomes larger, which is opposite to our intuitive understanding.

We propose a new construction of identity-based encryption without key escrow over the tradition cryptosystems. The security of our scheme follows from the decisional Diffie-Hellman assumption and the difficulty of a new problem – modular inversion hidden number problem with error (MIHNPwE). The latter can be seen as a generalization of the modular inversion hidden number problem. We give an analysis on the hardness of MIHNPwE by lattice techniques. In our construction, we generate each user’s partial private key in the form of an MIHNPwE instance. The hardness of MIHNPwE provides our scheme with resistance against key-collusion attacks from any number of traitors.

The events of information leak and illegal content propagation often occur on the network. The existing techniques cannot collect sufficient evidences about users’ contents to support forensics for these events. A new approach and a system are proposed which apply Chinese word segment and bloom filter to store the digest of users’ contents. With this system, investigators can trace back the events that happened months or even years ago without extra cost of hardware storage.

This paper studies the problem of selecting a maximum independent set of links with a fixed monotone and sublinear power assignment under the physical interference model. The best-known approximation bound for this problem is a very large constant. In this paper, we present an approximation algorithm for this problem, which not only has a much smaller approximation bound but also produces an independent set of links with a stronger property, i.e., strong independence.

A mobile cloud computing system is composed of heterogeneous services and resources to be allocated by the cloud service provider to mobile cloud users. On one hand, some of these resources are substitutable (e.g., users can use storage from different places) that they have similar functions to the users. On the other hand, some resources are complementary that the user will need them as a bundle (e.g., users need both wireless connection and storage for online photo posting). In this paper, we first model the resource allocation process of a mobile cloud computing system as an auction mechanism with premium and discount factors. The premium and discount factors indicate complementary and substitutable relations among cloud resources provided by the service provider. Then, we analyze the individual rationality and incentive compatibility (truthfulness) properties of the users in the proposed auction mechanism. The optimal solutions of the resource allocation and cost charging schemes in the auction mechanism is discussed afterwards.

Smart measurement devices play an important role in smart grid and might always be connected through open network interfaces. In this scenario, the adversary could launch code injection attacks to compromise these measurement devices and gain benefits by these compromised devices. To deal with this issue, a number of attestation schemes have been designed to defense the malicious attacks in the past. However, because the detection methods of these schemes are based on extra CPU clock cycles, they could be ineffective when the network delivery delay is significant. To address this problem, in this paper we propose a novel Delay-resilient Remote Memory Attestation scheme (DRMA), which can eliminate the impact of network delivery delay in the multi-hop networks and achieve great accuracy on compromised measurement devices detection. Specially, without sending beacon packets periodically, the proposed scheme can not only get the real-time end-to-end delay via evaluating the time difference reported by the relay nodes in the challenge-response attestation process, but also reduce the network load and achieve great accuracy of network delay. Via extensive theoretical analysis and experiments, our scheme shows better performance and less computing overhead in comparison with existing schemes.

In this paper, the two-way opportunistic amplify-and-forward relaying system with multi-source multi-relay in underground tunnel is investigated; in which two sources intend to exchange information with help of one relay. The joint source-relay selection with power allocation is proposed to minimize the total transmit power subject to constraints on the received signal-to-noise-ratio (SNR) of each source node. We show that this problem has a closed-form solution and requires only a few parameters to be broadcasted to all relays during the source-relay selection period. Simulation results show that the outage probability can be substantially decreased using the proposed power allocation scheme when two source node have different quality of service (QoS) requirement, comparing to existing power allocation algorithms.

Broadcast is a fundamental operation for wireless mesh networks. It plays an important role in the communication protocol design. Many existing work have studied the NP-hard broadcast problem in multi-hop networks. However, most of them assume a single-channel and single-radio wireless network model. We investigate broadcast in multi-channel multi-radio wireless mesh networks. In multi-channel multi-radio wireless mesh networks, the wireless interference due to simultaneous transmissions from the same channel and intra-node interference render the broadcast problem nontrivial. In this work, we analyze the performance of a broadcast protocol with MAC-layer scheduling under different networking conditions. We also explore the performance improvement by incoporating the neighbor elimination scheme with the broadcast protocol. We analyze the performance improvement of the integrated protocol in environments of multi-channel multi-radio and multi-rate.

The rapid growth of wireless technology has led to increasing demand for spectrum. In the past, spectrum is statically allocated. As a result, many wireless applications cannot use idle spectrum even though it is left unused by the owner for a long period of time. The low utilization of already scarce spectrum resource requires us to dynamically reallocate the idle spectrum to achieve better spectrum usage. In this paper, we model the problem of spectrum reallocation as a sealed-bid reserve auction, and propose SAFE, which is a

S

trategy-proof

A

uction mechanism

F

or multi-radio, multi-channel sp

E

ctrum allocation. We prove the strategy-proofness of SAFE theoretically, and evaluate its performance extensively. Evaluation results show that SAFE achieve good performance, in terms of spectrum utilization and buyer satisfaction ratio.

Collision-free transmission is important for reliable and delay-bounded wireless communication, which is required by many safety-related applications in vehicular ad-hoc networks (VANETs). However, with the increase of the vehicle node density, the channel contention collision of IEEE 802.11p known as the media access control (MAC) protocol standard for VANETs increases as well. Aiming at this problem, we propose a context-aware MAC protocol for VANETs with the basic idea of ensuring only one vehicle node access the channel initiatively while others conceal their contend intentions. According to the context message, we use the hamming competing network to decide which node will access the channel. Simulation results show that the proposed protocol has a considerably low collision probability and high transmission reliability while keeping a low access delay even in high dense scenario.

A balance storage nodes assignment problem is proposed for wireless sensor networks in order to save history data as much as possible when the network cannot connect to the sink node(s). Its aim is to assign storage nodes as evenly as possible to each source node, while it can minimize the storage path cost of the whole network. This problem can be reduced into a classic assignment problem by adding “dummy” source nodes, which is not easy to solve through the existing centric algorithms in sensor networks. Then two algorithms (Random Greedy Algorithm and Voronoi Graph Based Algorithm) are proposed in order to solve this problem. These two algorithms can be implemented distributed for the wireless sensor networks. The simulation implements and compares the performance of these two algorithms. The result shows that the Voronoi Graph Based Algorithm can satisfy the load balance storage for all source nodes and achieve approximate minimal storage path cost of the whole network.

As many wireless sensor networks (WSNs) are deployed in complicated environment without good physical protection, the sensor nodes are more vulnerable to be affected by uncertain factors from inside or outside so that the sensed data always cannot reflect the real world situation well. Thus the trustworthiness of sensor nodes should be evaluated for revising the faulty ones in after-deployment maintenances. In this paper, we propose a trustworthiness evaluation method based on D-S evidence theory in data level for sensor nodes which can sense multi-dimensional data. Different dimensions of a sensor node are regarded as its different trustworthiness attributes in this method. For a single node, the trustworthiness of each attribute is evaluated firstly based on evidence theory, and then the lower and upper limits of trust degree for this node are calculated by fusing the evaluation results of different attributes. Moreover, in order to figure out whether regional uncertain factors exist or not, the trust degree of a local region is given by fusing the judgments of deployed sensor nodes according to the combination rules of evidence theory. Extensive experiments based on actual data samples are conducted to evaluate the performance of our method. The theoretical analysis and experimental results show that our method can give effective trustworthiness evaluation for one single sensor node or a local region. Also, robustness and stability of this method are verified in the experiments.

Recently, large amounts of researches have demonstrated that dynamic changes of topological structure as well as unbalance of data traffic/energy consumption in wireless sensor networks have made routing protocol expansibility challenging work. According to the phenomenon that large-scale wireless sensor network will shorten longevity due to funneling effect in energy consumption; we proposed Unequally Clustering-based Hierarchical Opportunistic Routing (UCOR) protocol to reduce redundant network traffic. The protocol can further increase packets delivery ratio, decrease network delay and achieve more balanced and lower energy consumption. Experiment results show that UCOR protocol prolongs network longevity by about 20% to support for larger scale network, compared with HEED, EEUC, etc.

In this paper, a novel distributed TDMA medium access control (MAC) protocol for multi-hop underwater acoustic sensor networks (UASNs), termed opportunistic network coding supported quadri-stage contention protocol (NC-QSCP), has been proposed. The QSCP employs a concentrated contention procedure to form a transmission schedule, according to which nodes can perform collision free channel access in the following dedicated transmission stage. A contention probability calculation algorithm is designed to improve the efficiency of the data transmission, so that a heavy loaded node could acquire more channel resource. In the contention stage, the data flow information in 2-hop neighborhood is gathered, and can be exploited for both probability calculation and network coding opportunity discovery. When network coding is available, an XOR operation is applied to the packets with opposite directions. Both the contention probability calculation algorithm and the network coding scheme can remarkably improve the throughput and energy efficiency of QSCP protocol, especially in a tandem network carrying bidirectional traffics. The simulation illustrates that the opportunistic network coding achieves a 15% improvement in end-to-end throughput and reduces 20% energy consumption per delivered packet against QSCP.

A selective encryption algorithm joint with compression coding is proposed to protect JPEG 2000 images in wireless multimedia sensor networks (WMSN). The algorithm selectively encrypts the lookup table of probability model in MQ coding. As the size of lookup table is fixed and only such one table is used for an image, the proposed algorithm is fairly efficient and thus can perform fast encryption on large volume of JPEG 2000 images in WMSN. Experimental results and their analysis show that the algorithm is secure and energy saving, meanwhile, it does not impair the compression performance of JPEG 2000 coding obviously.

Medium Access Control (MAC) is an essential component of protocol stacks in Underwater Acoustic Networks (UANs). Numerous dedicated UAN MAC protocols have been proposed and studied via analysis and simulations. However, limited work has been done on evaluating these protocols in real ocean environments. To achieve a better understanding on how MAC protocols perform in real world UANs, we implemented Selective ARQ and Slotted Handshake based Access (SASHA) on UAN nodes. SASHA embraces some most essential and representative techniques in UAN MAC design, including selective ARQ, time slotting, handshake and collision avoidance. Moreover, a sea test was conducted at Atlantic Ocean to evaluate the performance of SASHA. With the experimental data, we are able to study how the aforementioned techniques affect the performance of SASHA. we also analyze the hop-by-hop and end-to-end behavior of SASHA. Specifically, we investigate the transmission delay and queuing delay of a data packet on one hop. From the findings, some issues are discovered and the corresponding design guidelines are emerged.

The amplitude of a received RF-signal is affected by physical phenomena, such as reflection, refraction or scattering due to objects and individuals in the signal propagation path. Activities in the proximity of a receiver can thus induce a characteristic pattern on amplitude-based features. We investigate the use of the radio frequency channel to detect activities. ActiviTune, our passive device-free recognition system, implements a multi-stage classifier to recognise activities and situations in an indoor environment leveraging amplitude-based features of RF signals from an ambient FM radio source. Comparing with other RF-based approaches, ActiviTune has the advantage of neither installing a transmitter generating the signal nor equipping the monitored entities with any active component of the system. We experimentally demonstrate the distinction of two dynamic activities, ’walking’, ’crawling’, and three static activities, ’empty room’, ’standing’, ’lying’ with an average true positive rate of over 80%.

This paper considers the localization problem in a radar sensor network (RSN), where the estimation is made based on fusing the received signals from multiple radar sensors. For practical radar receivers, the moving target indication (MTI) technique is often adopted to suppress the clutter in the relatively small Doppler frequency shift regime, although it may filter out the desired target signal as well. As a result, when multiple radar sensors are deployed and the target is moving along one direction, it is likely that only a subset of the radar receivers can observe the target, which we call an

observation pattern

. In this paper, we explore how to utilize the information of all possible observation patterns to derive the Cramer-Rao lower bound (CRLB) for the localization problem, which is shown to hinge heavily on radars and the prior statistic information of the observation patterns. Next, we generalize the localization problem to the case for an area

$\mathcal{S}$

, and investigate the localization games between the RSN and the intrude target. We propose a two-stage Stakcelberg game framework to model the interactions between the RSN and the target, for cases that the target can adopt mixed and pure strategies, respectively. Finally, numerical results demonstrate that the proposed scheme can significantly improve the localization performance.

Partially overlapped channels were demonstrated to have the potential of improving the network performance. One example is an increased capacity in a well saturated network. We address the problem of Wi-Fi network planning incorporating partially overlapped channels by more efficiently exploring the spatial reuse to increase the network capacity. We exploit that the interference ranges for separated channels are different, which can be utilized to deploy access points non-uniformly. In this paper, we formulate the problem, show that it can not be solved in polynomial time. Therefore, we propose a greedy optimization algorithm and validate the theoretical results through computer-based simulations.

Patient’s motion recognition is quite popular in the area of healthcare and medical service nowadays. By analyzing the data from variant sensors within the network, we can estimate the activities a person does. The analyzing job is usually done by a classifier which can classify each motion into one category with similar movements. Self-Organizing Map (SOM) is a kind of algorithm that can be used to arrange data into different categories without any guidance. Decision tree is a mature tool for classification. In this paper, we propose a new kind of classification method with data from BAN called SOM-Decision Tree. Firstly, we use SOM on each of the sensor nodes to categorize motions into different classes, so that motions in different classes can be distinguished by this sensor. Secondly, a decision tree is constructed to discriminate each kind of movements from other motions. Finally, any action of the same patient can be recognized by query through the decision tree. According to our experiment, this algorithm is feasible and quite efficient.

In this paper we have proposed a deterministic, range-free, distributed localization algorithm for mobile sensor nodes with static anchors. Mobile node calculates its approximate line of movement and corresponding position based on received beacons from two different anchors. The positional error can be further reduced by updating the approximate line of movement on receiving beacons from more anchors. We also have incorporated irregular radio propagation in our model. We have compared performance of our algorithm with existing localization algorithms. Simulation results show 80% improvement in performance of our proposed algorithm over the existing algorithms in terms of positional accuracy.

In this paper, aiming at the problem that the node discovers its neighbor nodes, when it is moving in mobile sensor network, we propose an algorithm for dynamical regulating the duty cycle of the node which needs to discover it neighbor node. This algorithm uses the boundary nodes in the communication range of the node to predict potential neighbor nodes, applies passion point process to predict the number of nodes in communication range of the node, regulates the duty-cycle of the node based on the model of balls and boxes, and then schedules the waking time of the node periodically by using the duty-cycle which is regulated to finish the detection. Finally, theoretical analysis and simulation experiments show that the proposed algorithm can discover more neighbor nodes in the short period of time with less energy.

The explosive growth of mobile apps has given rise to the significant challenge of app discovery. To meet this challenge, the Google Play market utilizes the user behaviors data to provide app recommendations. By making use of experiences of the user crowd, such recommendations are of help to users for discovering apps. However, they are concurrently restricted to the local scope of the user experiences, as most users have only accessed a limited amount of apps. To conquer this constraint, we propose a novel recommending method by utilizing the global information of apps. To be specific, we leverage the Latent Semantic Indexing method to analyze the metadata of apps, which is globally held by the market. We thus obtain the similarity measurements among apps and based on them we generate app recommendations. To further understand both the human behavior based and the metadata analysis based methods, we then measure the diversity within them from multiple levels and scopes. Through such measurements, we eventually discover new knowledge of user preferences and gain better understanding of both recommending methods. These observations further indicate that there are necessities and potentials to evolve the existing mobile app recommender systems by integrating new recommending methods.

Traffic information query in Vehicular Ad Hoc Network has various significant applications. Real-time traffic information can provide support for users to choose an optimal route according to current traffic situation. In this paper, we propose an urban area-oriented traffic information query processing mechanism, which can acquire the realtime traffic information of multiple paths from source to destination in relatively fast and accurate manner, and help users to determine an optimal route. The proposed mechanism includes two key algorithms - query dissemination and processing, and routing results backward to query requester. The query processing algorithm determines the scope of each query, so that a vehicle can query and collect data within a certain efficient scope to avoid returning overwhelmed large amount results. For queried vehicles, returning results to the moving query requester is a dynamic routing problem. We proposed a position predicting method to estimate the current location of the requester according to the information stored in the query packet. Simulation results show that the proposed strategy can improve the efficiency of data transmission, and the returned query results is effective for choosing an optimal route.

A practical system is proposed to solve the navigation problem for indoor mobile robot based on wireless sensor network (WSN). The discrete data acquired by WSN is processed to form a three-dimen- sional global topographic map, which is then converted into a 0-1 grid map through binarization. The grids where obstacles locate are expanded according to specific criteria to construct the robot route network. Then, the route-network-grid map is converted to directional weighted graph, with which the D*Lite algorithm can be used to solve the problem of shortest path between two fixed nodes and acquire the optimal node set to construct the optimal path. Simulation result shows that the indoor environment can be well expressed by the proposed modeling method, from which we can accomplish the navigation to lead the mobile robot arrive to destination with the shortest distance in a dynamic environment.

Mobile crowdsourcing

with smartphones advocates the cooperative effort of mobile smartphones to perform a joint distributed sensing task, which has gained growing importance for its potential to support a wide spectrum of large-scale sensing applications. Smartphone users in the real world are

strategic and rational

. Thus, one crucial problem in mobile crowdsourcing with smartphones is to stimulate cooperation from smartphone users. Several major challenges should be addressed.

First

, the actual cost incurred for a sensing task is

private

information and unknown to other users and the mobile crowdsourcing platform.

Second

, smartphone users are strategic, which suggest a user may deliberately misreport its cost (different from the real cost) in order to maximize its own utility. In this paper, we propose a

strategy-proof

incentive mechanism called

iMac

based on the Vickrey-Clarke-Groves (VCG) mechanism. The main idea of

iMac

is to stimulate smartphone users to truthfully disclose their real costs in spite of strategic behavior of the users.

iMac

introduces two main components. The first component determines the allocation of a sensing task to smartphone users given the user costs. And the second component decides the payment to each user. We prove that

iMac

can successfully produce a unique Nash equilibrium at which each user truthfully discloses the cost. Meanwhile, the minimization of the social cost is achieved. Simulation results demonstrate

iMac

achieves the desired design objectives and the overpayment is modest.

Participatory smartphone sensing

has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application. Most existing studies assume that smartphone users are strictly strategic and completely rational, which can achieve only sub-optimal system performance. Few existing studies can maximize a system-wide objective which takes both the platform and smartphone users into account. This paper focuses on the crucial problem of maximizing the system-wide performance or social welfare for a participatory smartphone sensing system. There are two great challenges.

First

, the social welfare maximization can not be realized on the platform side because the cost of each user is

private

and

unknown

to the platform in reality.

Second

, the participatory sensing system is a large-scale real-time system due to the huge number of smartphone users who are geo-distributed in the whole world. We propose

a novel price-based decomposition framework

, in which the platform provides a unit price for the sensing time spent by each user and the users return the sensing time via maximizing the monetary reward. This pricing framework is an effective incentive mechanism as users are motivated to participate for monetary rewards from the platform. The original problem is equivalently converted into an optimal pricing problem, and a distributed solution via a

step-size-free

price-updating algorithm is proposed. More importantly, the distributed algorithm ensures that the cost privacy of each user is not compromised. Experimental results show that our novel distributed algorithm can achieve the maximum social welfare of the participatory smartphone system.

There have been many protocols proposed for wireless sensor networks (WSN) where the energy awareness is an essential issue. Low Energy Adaptive Clustering Hierarchy (LEACH) is a widely adopted cluster-based structure for the energy-aware WSN, which utilized a Time Division Multiple Access(TDMA)-based MAC protocol to maintain balanced energy consumption and has shown effectiveness in prolonging the lifetime of sensors. However the related parameters setting in LEACH is the tricky and essential part for achieving good performance e.g., the number of clusters, the rotation time for each round. In literature, researchers used the empirical value as the round rotation time to obtain good performance. In this paper, we use Voronoi region to describe the distribution form of the cluster head and its members to conduct the theoretically optimal solution of the duration for each round. The experimental results show that using our suggested setting for the round rotation time is much more effective and efficient than the conventional LEACH with the empirical settings in terms of energy saving and the network surviving, and the amount of data delivered to the base station.

After comparing the spatial reuse efficiency between RF based networks and acoustic based networks in terms of our newly defined metric, spatial reuse index, we found that the spatial reuse efficiency in acoustic based networks is significantly lower due to the relatively low spreading loss of acoustic signals, which further degrades the network throughput. To improve the system performance, we propose a slotted based Underwater Power Control MAC protocol (UPC-MAC), which leverages transmission power and long propagation delays to enhance the spatial reuse efficiency. UPC-MAC is a reservation based channel access scheme and makes use of long propagation delays to collect neighboring nodes’ sending requests and channel information between these senders and receivers. With such information, UPC-MAC allows for concurrent transmissions by applying Nash Equilibrium to transmission power adjustment , which can be done on every sending node in a distributed way. Simulation results show that UPC-MAC outperforms Slotted FAMA in terms of network goodput by 15-20% and 35-60% respectively in two representative network scenarios.

Media access control plays a critical role in cognitive radio networks (CRNs). In our previous work, we have proposed a

fairness-oriented media access control

(FMAC) protocol to achieve fair and efficient coexistence of infrastructure-based CRNs. In this paper, we enhance FMAC to be used for coexisting ad hoc CRNs, where no centralized base stations exist, and

secondary users

(SUs) access channel independently. In FMAC, the contention window size is essential to network performance such as throughput. We first derive the optimal contention window size, which can then be used by SUs to achieve optimal throughput. However, the optimal contention window size is closely related to the total number of users of all CRNs, which is typically unknown to each individual SU of coexisting ad hoc CRNs. We attack this problem by building a bridge between the

average number of consecutive idle time slots

and

optimal contention window size

, since the average number of consecutive idle time slots can be easily observed by each individual SU. Hence, SUs can independently adjust their contention window size by observing their current average number of consecutive idle time slots and eventually approach the optimal contention window without the information of the total number of SUs. Extensive simulations are conducted and the results verify that the enhanced FMAC is able to significantly improve the fairness among coexisting ad hoc CRNs while maintaining good throughput.

In many applications such as wireless crime scene investigation, we want to use a single device moving along a route for accurate and efficient localization without the help of any positioning infrastructure or trained signal strength map. Our experiments show that in a complicated environment, such as building corridors and downtown areas, triangulation or trilateration cannot be used for accurate localization via single device. A simple approach, which is better and robust, is to use where the maximum RSS (received signal strength) is sensed as the target’s location. The question is how to make sure the maximum RSS is received while moving. Our novel RSS sampling theory presented in this paper answers this question: if RSS samples can reconstruct a target transmitter’s power distribution over space, the location corresponding to the peak of such power distribution is the target’s location. We apply the Nyquist sampling theory to the RSS sampling process, and derive a mathematical model to determine the RSS sampling rate given the target’s distance and its packet transmission rate. To validate our RSS sampling theory, we developed BotLoc, which is a programmable and self-coordinated robot armed with a wireless sniffer. We conducted extensive simulations and real-world experiments and the experimental results match the theory very well. A video of BotLoc is at

http://youtu.be/FsWLrH8Nj50

.

It is difficult to detect small leakages in underground pipelines with high accuracy and low-energy cost due to the inaccessible underground environments. To this end, the Magnetic Induction (MI)-based wireless sensor network for underground pipeline monitoring (MISE-PIPE) is introduced in [13]. The MISE-PIPE deploys high-density underground MI sensors along the pipelines, which provide necessary measurements for leakage detection with very high resolution. However, in order to provide accurate and low-cost leakage detection based on MISE-PIPE, an optimal deployment and activation strategy for those sensors is needed. In this paper, we provide an optimal strategy to detect leakages in underground pipelines based on the MISE-PIPE framework. Based on the proposed detection strategy, the error bound is derived to characterize the accuracy, while the energy consumption is analyzed to model the system energy cost. By trading off the accuracy and the energy consumption, an optimization function is developed to achieve the optimal performance.

On-demand data retrieval is a crucial routine operation in a vehicular sensor network. However, on-demand data retrieval in a vehicular environment is particularly challenging because of frequent network disruption, large number of data readings and limited transmission opportunities. Real world vehicular datasets usually contain a lot of

data redundancy

. Motivated by this important observation, we propose an approach called

CDR

with compressive sensing for on-demand data retrieval in the highly dynamic vehicular environment. The distinctive feature of CDR is that it supports

tunable accuracy

of data collection. There are two major challenges for the design of

CDR

.

First

, the sparsity level of the vehicular dataset is typically unknown beforehand.

Second

, it is even worse that the sparsity level of the dataset is changing over time. To combat the challenge posed by time-varying data sparsity,

CDR

can terminate from further collection of measurements, based on an adaptive condition on which only localized measurements and computation are needed. Extensive simulations with real datasets and real vehicular GPS traces show that our approach achieves good performance of data retrieval with user-customized accuracy.

In Cognitive Radio Networks (CRNs) with dynamic spectrum access, it is of paramount importance to ensure the spectrum access rules are honestly followed by each secondary user. Existing approaches either require significant modifications to the system hardware, or can only deter the spectrum abuse from happening by punishing abusers after-the-fact, which is ineffective in reality as there lacks universal identification for each device. In this paper, we propose a novel spectrum access rule enforcing scheme by introducing a “spectrum guardian”, who punishes abusers immediately on-the-scene through optimally jamming their signals using multiple antennas while without affecting the communication between primary users, thus removing abusers’ incentive to exploit the spectrum for their own benefit. Our scheme requires no modifications to Commercial-Off-The-Shelf (COTS) CR devices, nor the need of device identification.

There is an increasing number of crime cases involving mobile phones. In particular, due to the low cost of China shanzhai mobile phone (Chinese pirated mobile phone), a significant portion of these crime cases (all over the world) is related to these shanzhai phones. Quite a number of the cases also involve pictures. The difficulty of conducting forensic investigation on shanzhai phones is the lack of specifications. In this paper, we try to provide some important information of how a photo is stored inside a MTK-based shanzhai phone (one of the most popular platforms for shanzhai phones), and provide a method to recover deleted photos from the physical segments of flash memory of a shanzhai phone.

abstract

environment.

Local information dissemination is a potential application of VANETs. By cooperative communication among vehicles, the information could be stored in a certain size of region for some time. However, it is difficult to determine the size for time requirement. Moreover, it is not suitable to use too large size for the consideration of communication overhead on the whole network. This paper focuses on a common traffic flow in urban road network, and proposes a distributed protocol for persistent local information storage (LISP). We firstly give an approximated size of the region under the condition of time requirement. We propose a novel mechanism of header responsibility to suppress the impact of broadcast storm and reduce storage consumption on vehicles. The simulation results show good performance in various scenarios. Our protocol could satisfy the time requirement with much lighter overhead of storage consumption.

This paper addresses the joint selection and power assignment of a largest number of given links which can communicate successfully at the same time under the physical interference model. For this optimization problem, we present a constant-approximation algorithm with improved performance over existing approximation algorithms. In addition, both the algorithm design and analysis are applicable to arbitrary path-loss exponent and arbitrary dimension of the deployment space.

Most of the existing results in sweep-coverage focused on minimizing the number of the mobile sensor nodes by carefully planning their corresponding trajectories such that each target of interest can be periodically monitored (within every

t

time unit). However, the starting locations of the mobile sensors, at which the service depots (or equivalently base stations) of the nodes are usually located, are never considered in the trajectory planning. In order to provide sweep-coverage for a long period of time, each node also needs to periodically visit a base station to replace a battery or refueled (within every

T

time unit). Motivated by this observation, this paper introduces two new sweep-coverage problems, in which each mobile sensor node is required to visit a base station periodically, namely (

t

,

T

)-SCOPe-1 and (

t

,

T

)-SCOPe-

M

, each of which considers one single base station and

M

base stations for all of the nodes, respectively. We prove those problems are NP-hard and propose heuristic algorithms for them. In addition, we conduct simulations to evaluate the average performance of the proposed algorithms and study their average behavior characteristics.

This paper considers the problem that the virtual mobile operator (VMO) buys services from the wireless service provider (WSP) to serve its users. When faced with poor indoor coverage or at the edge of marcocell, the WSP would ask nearby femto base stations (FBSs) to help serve the VMO. We focus on the interesting questions when the WSP prefers to ask help from FBSs and how WSP, VMO and FBSs adjust their strategies to maximize their profits. A two-stage Stackelberg game is built for the situation when the WSP serve the VMO by itself and a four-stage Stackelberg game is considered when the WSP rents FBSs by offering spectrum bands to FBSs. We firstly define the utility functions of VMO, WSP and FBSs to reflect their satisfaction and cost in participating into the game and then give the analysis based on the backward induction method. Meanwhile, the competition among FBSs when the WSP allocates spectrum bands for FBSs’ help is formulated as a non-cooperative spectrum competition game (NSCG) and the existence and uniqueness of the Nash equilibrium in NSCG is proved. Simulation results of the two service modes show the WSP can obtain more profits through renting FBSs in poor coverage areas.

In this paper, we study the problem of trading access permissions (ACPs) between a wireless service provider (WSP) and femtocell owners by truthful auctions. We propose a

T

ruthful

O

nline

R

everse

A

uction (TORA) mechanism that allows the WSP to purchase ACPs at a lower cost in an online manner while preventing femtocell owners from falsely reporting their bids and/or available time. To be specific, we develop an efficient allocation method with flexible preemption so as to enable a multiple-round online allocation and provide bid-truthfulness. In addition, we devise an effective pricing strategy so as to realize time-truthfulness. To the best of our knowledge, we are the first to study the truthful online reverse auction in a hybrid macro-femtocell network. We analytically prove the truthfulness of TORA. Our proof also shows that the truthfulness of TORA does not depend on the knowledge of the bidder behavior. An extensive evaluation study is performed to examine the performance of TORA. Our evaluation results indicate that TORA is able to achieve better bidder satisfaction with a lower cost.

This work explores the use of social communications for epidemic disease control. Since the most infectious diseases spread through human contacts, we focus on modeling the diffusion of diseases by analyzing the social relationship among individuals. In other words, we try to capture the interaction pattern among human beings using the social contact information, and investigate its impact on the spread of diseases. Particularly, we investigate the problem of minimizing the expected number of infected persons by treating a small fraction of the population with vaccines. We prove that this problem is NP-hard, and propose an approximate algorithm representing a preventive disease control strategy based on the social patterns. Simulation results confirm the superiority of our strategy over existing ones.