This letter considers the estimation of modulation index for binary continuous phase modulated (CPM) partial response signals. The actual value of modulation index in low-cost implementations turns out to be different than the nominal value used at the transmitter, leading to severe performance degradation. One possible solution is the deployment of an estimation algorithm at the receiver. The proposed data-aided best linear unbiased estimator (BLUE) is based upon a linear model approximation of the differential phase of pre-averaged CPM signal. Theoretical analysis of the variance of the estimator is presented to establish its improved performance. Simulation results also corroborate significant improvement of the proposed estimator at lower signal-to-noise ratio (SNR) values as compared to existing estimators, while retaining the linear bound performance at high SNRs. We show that the bit error rate (BER) performance at low SNRs is also improved when the modified BLUE estimator is deployed at the receiver.

In this paper, we propose a spatio-temporal coordination-based media access control (STMAC) protocol for efficiently sharing driving safety information in urban vehicular networks. STMAC exploits a unique spatio-temporal feature characterized from a geometric relation among vehicles to form a line-of-collision graph, which shows the relationship among vehicles that may collide with each other. Based on this graph, we propose a contention-free channel access scheme to exchange safety messages simultaneously by employing directional antenna and transmission power control. Based on an urban road layout, we propose an optimized contention period schedule by considering the arrival rate of vehicles at an intersection in the communication range of a road-side unit to reduce vehicle registration time. Using theoretical analysis and extensive simulations, it is shown that STMAC outperforms legacy MAC protocols especially in a traffic congestion scenario. In the congestion case, STMAC can reduce the average superframe duration by 66.7%, packet end-to-end delay by 68.3%, and packet loss ratio by 88% in comparison with the existing MAC protocol for vehicle-to-infrastructure communication, based on the IEEE 802.11p.

This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them into one of three classes: positive, negative or neutral. As part of this work, we contributed a dataset consisting of approximately 10, 000 tweets, each labeled on a five point sentiment scale by three different people. Experiments demonstrate a detection rate between approximately 70% and an average false alarm rate of approximately 18% across all three classes. The developed classifier has been made available for online use.

The popular uprisings in a number of countries in the Middle East and North Africa in the Spring of 2011 were broadcasted live and enabled by local populations' access to social networking services such as Twitter and Facebook. The goal of this paper is to study the flow characteristics of the information flow of these broadcasts on Twitter. We have used language independent features of Twitter traffic to identify differences in information flows on Twitter mentioning countries experiencing some form of unrest, compared to traffic mentioning countries with peaceful political situations. We used these features to identify countries with political unstable situation. For empirical analysis, we collected several data sets of countries that were experiencing political unrest, as well as a set of countries in a control group that were not subject to such socio-political condition. Several different methods are used to model the flow of information between Twitter users in data sets as graphs, called information cascades. By using the dynamic properties of information cascades, naïve Bayes and SVM classifiers both achieve true positives rates of 100%, with false positives rates of 3% and 0%, respectively.

Smart meters are being deployed worldwide on a trial basis and are expected to enable remote reading and demand response among other advanced functions, by establishing a two-way communication network. However, it remains to be determined as to how these meters would transmit their data to an aggregation point. Our paper employs a medium access control (MAC) protocol for a cooperative network to address this problem. The said protocol has been developed on the GNU Radio platform, while the test-bed consists of universal software radio peripherals (USRPs) that serve as network nodes. The performance of the system has then been evaluated and compared with that of fully cooperative and single-input-single-output (SISO) networks. It has been shown that the proposed system outperforms the SISO network in terms packet error rates and throughput.

The breach of privacy in encrypted instant messenger (IM) service is a serious threat to user anonymity. Performance of previous de-anonymization strategies was limited to 65%. We perform network de-anonymization by taking advantage of the cause-effect relationship between sent and received packet streams and demonstrate this approach on a data set of Yahoo! IM service traffic traces. An investigation of various measures of causality shows that IM networks can be breached with a hit rate of 99%. A KCI Causality based approach alone can provide a true positive rate of about 97%. Individual performances of Granger, Zhang and IGCI causality are limited owing to the very low SNR of packet traces and variable network delays.

Motion sensor embedded smartphones have provided a new platform for activity inference. These sensors, initially used for cell phone feature enhancement, are now being used for a variety of applications. Providing cell phone users information about their own physical activity in an understandable format can enable users to make more informed and healthier lifestyle choices. In this work, we built a smartphone application which tracks users' physical activities and provide feedback requiring no user input during routine operation. The application reports estimates of the calories burned, broken up by physical activities. Detectable physical activities include walking, running, climbing stairs, descending stairs, driving, cycling and being inactive. We evaluated a number of classification algorithms from the area of Machine Learning, including Naïve Bayes, Decision Tree, K-Nearest Neighbor and Support Vector Machine classifiers. For training and verification of classifiers, we collected a dataset of 510 activity traces using cell phone sensors. We developed a smartphone app that performs activity recognition that does not require any user intervention. The classifier implemented in the Android app performs at an average true positives rate of greater than 95%, false positives rate of less than 1.5% and an ROC area of greater than 98%.

We present a novel attack on relayed instant messaging (IM) traffic that allows an attacker to infer who's talking to whom with high accuracy. This attack only requires collection of packet header traces between users and IM servers for a short time period, where each packet in the trace goes from a user to an IM server or vice-versa. The specific goal of the attack is to accurately identify a candidate set of top-k users with whom a given user possibly talked to, while using only the information available in packet header traces (packet payloads cannot be used because they are mostly encrypted). Towards this end, we propose a wavelet-based scheme, called COmmunication Link De-anonymization (COLD), and evaluate its effectiveness using a real-world Yahoo! Messenger data set. The results of our experiments show that COLD achieves a hit rate of more than 90% for a candidate set size of 10. For slightly larger candidate set size of 20, COLD achieves almost 100% hit rate. In contrast, a baseline method using time series correlation could only achieve less than 5% hit rate for similar candidate set sizes.

This paper describes the experience of deploying and operating the LOng-range Ultra-wIdeband Spectrum Sensing (LOUISS) testbed at the School of Electrical Engineering amp; Computer Science (SEECS) of the National University of Sciences amp; Technology (NUST), Islamabad. The LOUISS testbed uses the Agilent N6841A RF sensors and was used to conduct a survey of spectrum occupancy in Islamabad. This paper provides a detailed description of the sensors' architecture as well as the architecture and configuration of the testbed. This is followed by results from an RF survey conducted on NUST's campus using LOUISS as a tool for dynamic spectrum sensing. Sample data sets from this RF survey have been made available online to the research community. Finally, two applications developed on top of this testbed are presented; 1) A desktop application that flags illegal RF spectrum transmitters, tracks spectrum use, and allows interested parties to search for contiguous bands that are available for local use. 2) An Android app that makes geolocation information of transmitters available on mobile devices.

This paper addresses the problem of identifying the top-k information hubs in a social network. Identifying top-k information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. Existing solutions are centralized and require time stamped information about pair-wise user interactions and can only be used by social network owners as only they have access to such data. Existing distributed algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. Our method can identify hubs without requiring a central entity to access the complete friendship graph. We achieve this by fully distributing the computation using the Kempe-McSherry algorithm, while addressing user privacy concerns. We evaluate the effectiveness of our proposed technique using three real-world data set; The first two are Facebook data sets containing about 6 million users and more than 40 million friendship links. The third data set is from Twitter and comprises of a little over 2 million users. The results of our analysis show that our algorithm is up to 50% more accurate than existing algorithms. Results also show that the proposed algorithm can estimate the rank of the top-k information hubs users more accurately than existing approaches.

Identifying leaders and followers in online social networks is important for various applications in many domains such as advertisement, community health campaigns, administrative science, and even politics. In this paper, we study the problem of identifying leaders and followers in online social networks using user interaction information. We propose a new model, called the Longitudinal User Centered Influence (LUCI) model, that takes as input user interaction information and clusters users into four categories: introvert leaders, extrovert leaders, followers, and neutrals. To validate our model, we first apply it to a data set collected from an online social network called Everything2. Our experimental results show that our LUCI model achieves an average classification accuracy of up to 90.3% in classifying users as leaders and followers, where the ground truth is based on the labeled roles of users. Second, we apply our LUCI model on a data set collected from Facebook consisting of interactions among more than 3 million users over the duration of one year. However, we do not have ground truth data for Facebook users. Therefore, we analyze several important topological properties of the friendship graph for different user categories. Our experimental results show that different user categories exhibit different topological characteristics in the friendship graph and these observed characteristics are in accordance with the expected ones based on the general definition of the four roles.

The inherent many-to-one flow of traffic in wireless sensor networks (WSNs) produces a skewed distribution of energy consumption rates, leading to the early demise of those sensors that are critical to the ability of surviving nodes to communicate their measurements to the base station. Numerous previous approaches aimed at balancing the consumption of energy in wireless networks are either too complex or do not address problems unique to the flow of traffic in WSNs. In this article, we propose the use of a dynamic programming algorithm (DPA), an operational, low-complexity algorithm, used in conjunction with four different route discovery algorithms. We perform complexity analysis, statistical evaluation of changes in power consumption rates effected, and verify spatial redistribution of energy consumption of sensors in the network. Our results on multihop networks of 100 randomly placed nodes show that, on average, the two best performing variants of DPA yield a reduction of up to 28% and 36% in power consumption rate variance at the cost of raising average power consumption by 15% and 21%, respectively. Computational complexities of DPA variants range from O ( N 3 ) to O ( N 4 ), which is significantly lower than linear search of the solution space of O ( N ! N i ). Analysis by diffusion plots shows that DPA reduces power consumption of sensors that experience the highest power consumption under the shortest path routes.

Identifying the most influential nodes in social networks is a key problem in social network analysis. However, without a strict definition of centrality the notion of what constitutes a central node in a network changes with application and the type of commodity flowing through a network. In this paper we identify social hubs, nodes at the center of influential neighborhoods, in massive online social networks using principal component centrality (PCC). We compare PCC with eigenvector centrality's (EVC), the de facto measure of node influence by virtue of their position in a network. We demonstrate PCC's performance by processing a friendship graph of 70, 000 users of Google's Orkut social networking service and a gaming graph of 143, 020 users obtained from users of Facebook's 'Fighters Club' application.

In this paper, we develop a Reliable Energy Adept Link-layer (REAL) protocol for power-constrained networks to provide reliable data transmissions among battery-operated wireless nodes. REAL dynamically performs error recovery with respect to the overall distortions in the system and the available energy at wireless nodes. REAL employs rate-adaptive low density parity check (LDPC) codes for error recovery. We develop a theoretical model to estimate the distortion imposed by wireless channels and to compute the likelihood of successful decoding at the link-layer. Next, we present a recovery model using Markovian decision process to formulate the optimal decoding policy as a linear optimization problem. We design and implement the REAL protocol which provides system reliability with efficient energy utilization. We demonstrate experimentally that REAL achieves 5%-40% throughput and 3%-18% energy consumption improvements over channel traces with varying bit error rates (BERs) collected on 802.15.4 environment. Further, REAL shows 5-15db PSNR better video quality over various realtime video scenarios.

This paper addresses the problem of identifying the top-k information hubs in a social network. Identifying top-k information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. Existing solutions are centralized and require time stamped information about pair-wise user interactions and can only be used by social network owners as only they have access to such data. Existing distributed and privacy preserving algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. The intuition is that highly connected users tend to have more interactions with their neighbors than less connected users. Our method can identify hubs without requiring a central entity to access the complete friendship graph. We achieve this by fully distributing the computation using the Kempe-McSherry algorithm to address user privacy concerns. To the best of our knowledge, the proposed algorithm represents an arguably first attempt that (1) uses friendship graphs (instead of interaction graphs), (2) employs a truly distributed method over friendship graphs, and (3) maintains user privacy by not requiring them to disclose their friend associations and interactions, for identifying information hubs in social networks. We evaluate the effectiveness of our proposed technique using a real-world Facebook data set containing about 3.1 million users and more than 23 million friendship links. The results of our experiments show that our algorithm is 50% more accurate than existing distributed algorithms. Results also show that the proposed algorithm can estimate the rank of the top-k information hubs users more accurately than existing approaches.

This paper presents the 'Poor Man's SIMO System' (PMSS) which combines two ideas, cooperative communication and diversity combination, to reduce packet losses over links in Wireless Sensor Networks (WSN). The work is based on the IEEE 802.15.4 standard and is distinct from previous works that apply the same concepts because it foregoes the need for any changes to mote hardware. We describe a Poor Man's SIMO System protocol that governs the cooperation between receivers. Three diversity combination methods are evaluated including selection diversity, equal gain and maximal ratio combining. The latter relies on a model of the instantaneous Bit Error Rate (BER) driven by Channel State Information (CSI), i.e. Received Signal Strength Indication (RSSI) and Link Quality Indication (LQI). First, we demonstrate the PMSS on residual bit error traces in a fully reproducible manner. This is followed by an implementation of PMSS in C# on the .NET Micro Framework edition of the recently released Imote2 WSN mote platform. Both, trace based analysis and implementation demonstrate significant improvements over the single receiver baseline configuration. We deliberately verified PMSS by residual bit error traces and implementation to avoid the use of simulators that depend on abstract models of wireless channels.

In this work we analyze the nature of the error process in IEEE 802.15.4 low rate-wireless personal area network (LR-WPAN) channels experiencing varying levels of interference from nearby 802.11b/g networks co-habiting the unlicensed 2.4 GHz industrial-scientific-medical (ISM) band. The entire analysis is performed on a set of residual bit error traces, the data collection methodology for which is described in detail. These traces allow us to identify individual bit positions in a received packet that were in error. We use lagged phase space analysis, a technique commonly used in the analysis of chaotic systems. The analysis uncovers the stable nature of the BER process during short bursts. This simple behavior of the bit error rate (BER) can be captured by three parameters; the burst length of consecutive good packets, bad packets, and the distribution with which different BERs occur. Using collected residual bit error traces, we model the BER process by estimating the probability distributions of its parameters using maximum likelihood estimates (MLE).

Measurement of a wireless channel's memory length is of great, fundamental interest to designers of communication systems. The principal tool used for measuring the memory length of wireless channels has been the correlation function which has been shown to work well for the measurement of channel memory at the level of individual bits and symbols. However, as we demonstrate, applied to the packet-level bit-error process of 802.15.4 wireless channels, the correlation function fails to provide us with a clear answer even in a wide variety of channels. To overcome this limitation we propose the use of a new information theoretic measure. We introduce the use of relative mutual information and demonstrate its application for measuring the memory length of the BER process in sensor 802.15.4 wireless channels.

Several works have attempted to determine whether the process that introduces errors into transmissions over a wireless channels is long range dependent (LRD). Most of these efforts were focused on IEEE 802.11b/g channels. Knowledge of LRD is of significant interest to designers of wireless network systems. This experiment-based study attempts to gain a better understanding of the standardized IEEE 802.15.4 low rate-wireless personal area network (LR-WPAN) specification. We determine whether the bit, symbol and packet level error processes are LRD or not. Is the memory length of the channel measurable and finite, or is it sufficiently long to qualify it as an LRD process? We record channel behavior by capturing all transmissions at the receiver including the ones that would under normal operation be dropped and use these to generate residual error traces. We perform statistical data analysis of these traces using various tools for scenarios with and without interference from 802.11b/g sources. For the bit and symbol level error process we conclude that channel memory remains fixed at 2 bits and 2 symbols respectively regardless of channel and environment. For the packet level error process we conclude that the perception of on and off LRD is actually due to interference from co-located 802.11b/g networks.

Knowledge of the error process and related channel parameters in wireless networks is invaluable and highly instrumental in a broad range of applications. Under the IEEE 802.15.4 Low Rate-Wireless Personal Area Networks (LR- WPAN) standard, compliant devices are capable of providing two pieces of information about the channel conditions along with each received packet, the Link Quality Indication (LQI) and Received Signal Strength Indication (RSSI). Together they constitute a form of Channel State Information (CSI). This work is based on statistical and information theoretic analysis of a very extensive data set of wireless channel traffic between a transmitter and receiver, called packet traces. Data is collected in a variety of documented environments. To our knowledge, this is the first detailed trace collection effort for this type of network. The traces distinguish themselves from data sets of other studies in that they record individual bit errors as well as packets that are never detected by receivers. First, we provide a detailed analysis of the IEEE 802.15.4 wireless channel. More specifically, we provide a detailed analysis of the Bit Error Rate (BER) process at individual bit and on a packet-by-packet basis. We explore the relationship between the packet-level BER process and the LQI and RSSI processes (also observable on a packet-by- packet). The analysis shows that measurements of both LQI and RSSI provide information that allows us to reduce uncertainty about the BER. Secondly, we develop a model of the BER process that is driven by observable CSI parameters. Thirdly, we continue our analysis with measurements of channel memory at the packet and bit level. We determine that the wireless channel 2 bits memory. At the packet level we observe that the amount of channel memory is more varied.

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack

In a multi-hop 802.15.4 Wireless Sensor Network the volume of traffic that is processed by intermediate nodes increases considerably as data approaches the PAN coordinator. This problem is referred to as the reachback problem. In this work we focus on a more fundamental implication of reachback on operational network lifetime of 802.15.4-based wireless sensor networks. This paper proposes the joint minimization of two network lifetime metrics, previously proposed for independent use, to more accurately characterize the degree of longevity of wireless sensor networks. The method uses the k-shortest simple path algorithm and a dynamic programming method rooted in operational rate-distortion (RD) theory to increase the operational lifetime of wireless sensor networks.

Recent wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lesser delay than retransmissions. To facilitate FEC decoding at a wireless receiver, it is desirable to relay maximum number of (error-free and corrupted) packets to the receiver's application layer. To that end, most cross-layer multimedia schemes perform partial checksum only on packet headers. However, even with a partial checksum, bursty wireless errors introduce frequent header corruptions, thereby causing considerable packet drops. In this paper, we extend our work in [1], which proposed receiver-based schemes to correct a packet's critical (and corrupted) header fields. This paper: (a) poses header detection as the well-known decision theoretic problem of detecting known parameters in noise, (b) evaluates two detectors using bit-error traces collected over an 802.11b network, (c) provides throughput results for the detectors, and (d) uses video in conjunction with FEC as an example to highlight the efficacy of the proposed schemes. We show that header detection provides significant improvements in throughput and video quality over the conventional UDP/IP protocol stack.

Delay-sensitivity of real-time applications stipulates resilience against errors and losses in multimedia content. Such resilience is particularly important for bandwidth-constrained and error-prone wireless networks. Recent wireless studies have highlighted that significant improvements in bandwidth utilization and multimedia quality can be achieved if the decision to retain or drop "corrupted" packets is made at the application layer instead of medium access, network or transport layers. Such a strategy, however, necessitates that the maximum number of (good and bad) packets are relayed to the application layer while minimizing any modifications (if any) to the widely-deployed UDP/IP protocol stack. Previous studies have proposed that, while ignoring corruptions in packet payload, only packets with errors in the headers should be dropped. We introduce a receiver-based scheme which uses the history of a multimedia session to correct packet header errors, thereby improving the throughput of real-time applications over wireless local area networks (LANs). The proposed scheme is truly receiver-based and, therefore, does not require any modifications to the source or any intermediate network node. Only minor modifications are required to the protocol stack of the multimedia receiver. Specifically, the proposed scheme generates statistics based on the history of "critical" protocol header fields for active multimedia sessions. These statistics are in turn employed to (a) correct errors in the critical fields and (b) determine if the relevant packets belong to the receiver's session of interest.

Practical mobile ad hoc systems include heterogeneous classes of mobile nodes, such as people and vehicles. The simultaneous presence and movement of these classes influence the mobility pattern of their members in a variety of random and deterministic ways. In addition, there is an inherent hierarchical and multi-resolution structure to the mobility patterns. We present a novel hierarchical mobility framework which incorporates the fact that the movement of certain classes of mobile nodes (e.g., people) is affected by their surroundings and the movement of other forms of mobile nodes. The proposed model takes into account that a mobile node's movement is neither completely random, nor a mere function of its routing decision and/or the trip's source and destination points. The proposed model is neither classified as microscopic nor macroscopic. Instead, it is categorized as a multi-scale mobility framework capable of modeling mobility scenarios of different scales, i.e. it is equally capable of modeling the movement of mobile nodes in a street intersection as it is to model the movement of nodes between a number of population centers. The proposed mobility framework integrates and extends an influence model and hierarchical graph-based representations of the mobility area to achieve this goal. Our simulation results show that this framework accurately captures the influences among different groups of mobile nodes and the constraints imposed by their surroundings.