Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, we consider the scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. To obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target’s location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by using a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.Note to Practitioners—This article is motivated by the need for an efficient path-planning algorithm for drones during specific SAR operations. In particular, situations where someone is lost in a snow-covered hike and a fire spot that is in its initial levels are of interest. In fact, since the target location is not known, it is required that the UAV be able to efficiently search the entire area until it finds the target in the shortest possible time. The proposed Bayesian framework along with the ResNet learning algorithm shows an efficient performance in terms of average time duration and accuracy, respectively. The framework developed in this paper can be extended to a multi-UAV scenario where UAVs coordinate to optimize the overall performance.

Coverage holes directly affect the quality of service (QoS) and reliability of wireless networks and should be avoided as much as possible. In this paper we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations and we propose proper UAV path planning. While most of the works in the literature define holes based on statistical sense, i.e., when the coverage probability for a point on cell is below a certain threshold, e.g., 95%, in this paper, we target applications that allow only for short time disconnections, and define a location that is not covered for a certain amount of time to be in a coverage hole. To minimize such holes, we use optimal UAV path planning based on the two families of trajectories, namely, spiral and oval curves. We show that the proposed oval curves will result in a better performance in addressing the coverage holes and can guarantee a minimum signal to noise ratio over the whole coverage area, and over a guaranteed amount of time.

This paper introduces a novel adversarial attack targeting Graph Neural Network (GNN)-based radio resource management in point-to-point networks. Our proposed attack, executed during the test phase, manipulates the system's input by exploiting specific constraints. Formulated as an optimization problem, the attack aims to maximize resource stealing, thereby degrading the quality of communication. We assess the attack's efficacy with respect to the number of users, signal-to-noise ratio, and the adversary's power budget. The results demonstrate that our proposed attack approaches the performance of an established upper-bound adversarial benchmark while maintaining lower complexity, highlighting its effectiveness and potential for real-world applicability.

In this paper, we propose various path-planning scenarios for unmanned aerial vehicles (UAV) surveillance applications, aiming to provide uniform coverage over the region of interest while minimizing mechanical energy consumption. We demonstrate that depending on the specific nature of the application, the optimal path, as well as the preferred UAV type (fixed-wing versus rotarywing), can vary. We subsequently provide recommendations about the choice of UAV type and optimal paths for surveillance applications such as fire outbreak detection or intrusion detection. Generally, it is commonly perceived that, for a given application and path, rotarywing UAVs consume significantly more energy than their fixed-wing counterparts. However, to our surprise, we identify scenarios where the rotarywing UAV outperforms its fixed-wing counterpart in terms of energy consumption.

Unmanned aerial vehicles (UAVs) offer intriguing possibilities for Internet of Things (IoT) data collection. However, it can also jeopardize the privacy of IoT devices. In particular, an adversary can deduce the location of IoTs by monitoring the UAV’s mobility patterns, which necessitates the analysis of privacy-preserving mechanisms that protect IoT location privacy. Nonetheless, integrating privacy measures into operations incurs additional expenses. One of these costs is the added distance that UAVs may need to travel to accomplish their task, in turn increasing energy consumption. This paper investigates the trade-off between privacy and energy in the UAV-assisted IoT data collection application. First, we consider a preliminary privacy mechanism and analytically obtain the upper bounds of the extra flight distance and energy consumption for the UAV. Then, we consider a location-based differential privacy mechanism to achieve geo-indistinguishability. As expected, our study shows that imposing privacy constraints on UAV-assisted IoT data collection leads to increased UAV energy consumption. Specifically, as privacy guarantees become more restrictive, the energy consumption of UAVs increases exponentially. Nevertheless, given an energy constraint, one can assure a certain level of privacy guarantee.

In this paper, we design and implement a fire detection algorithm using an unmanned aerial vehicle (UAV). In particular, we consider a scenario where a UAV is employed to find a fire spot where the fire is in its early stages. To this end, we first propose a path planning algorithm where the goal is to make sure that the UAV finds the fire in the shortest amount of time. During this stage, the UAV visits different parts of the area, takes an image from each part, and sends the image to the control center for further image processing. Then, we deploy a machine learning (ML) model, which is a residual neural network (ResNet), to process the image and determine whether a fire is detected or not. The ML algorithm has been trained using images taken by the drone and images from the Internet. Through experimental results, we show that the proposed path-planning algorithm along with the ML model can detect fire efficiently. The problem and the proposed solution in this paper can be also applied to a search and rescue scenario, where for example, a hiker is missing in a remote area.

Utilization of moving unmanned aerial vehicles (UAV) has attracted a lot of attention in recent years. Accordingly, path planning to optimize a given utility function, such as mechanical energy, has been the subject of many works. In a prior work, we have proposed path panning schemes to uniformly cover an area for communication coverage and surveillance applications with minimum mechanical energy. As far as energy and power minimization is concerned, an important issue that is sometimes being overlooked is the peak-power that the UAV has to afford to provide the path planning of interest. In this paper, we address this issue and find paths that provide a uniform coverage with minimum peak power. We then compare the results with the case where mechanical energy was minimized. This is done for fixed-wing as well as rotary-wing UAVs. It is observed that depending on the UAV specs, we can expect a mild increase (7% in our case) in peak power in some cases when going from peak power optimized scenario to energy optimized scenario. There are also cases where there is no major difference between the 2 scenarios.

Unmanned aerial vehicles (UAVs) are well known for violating citizen’s privacy either inadvertently or deliberately. However, UAVs could be victims of privacy violations themselves in the sense that an adversary observing a UAV can infer its destination. This article proposes several privacy-preserving mechanisms (PPMs) for protecting a UAV’s location privacy. In particular, we address the privacy protection problem in two major UAV applications that require significantly different measures: 1) package delivery and 2) Internet of Things (IoT) data collection. In the package delivery application, we propose two different PPMs to randomize the UAV’s trajectory such that the observing adversary is confused about the UAV’s destination; we provide privacy guarantees and analyze the tradeoff with energy consumption. In the IoT data collection scenario, the UAV is not necessarily required to hover exactly above the IoT device; hence, we propose a different PPM according to which the UAV chooses a random spot around the IoT device for data collection. Then, considering a minimum mean squared error (MMSE) criterion, we obtain the privacy leakage to the adversary. We also analyze the mean Peak Age of Information (PAoI) of the network and show that the proposed method does not degrade the mean PAoI significantly. Finally, considering the limitations of the MMSE approach for some applications, we also develop a differential privacy (DP)-based counterpart for this PPM. We observe that the mean PAoI degrades significantly in Laplacian DP but is acceptable in Gaussian DP.

Recently, we have proposed a model-free privacy-preserving mechanism (PPM) against attacks that compromise user privacy by matching patterns in data sequences to those that are unique to a given user [1]. Because the PPM is model-free, there are no requirements on the statistical model for the data, which is desirable when the model is not perfectly known. However, the proposed PPM did not enforce any constraints on the value to which a data point might be obfuscated, hence allowing an unlikely pattern that would make it easy for the adversary to detect which values have been obfuscated. In this paper, we consider a constrained PPM that enforces a continuity constraint so as to avoid abrupt jumps in the obfuscated data. To design such, we employ a graph-based analytical framework and the concept of consecutive patterns. At each point, the obfuscated data should be chosen strictly from that point’s neighbors. Unfortunately, this might undesirably increase the noise level employed in data obfuscation and hence unacceptably reduce utility. We propose a new obfuscation algorithm, namely the obfuscation-return algorithm, and characterize its privacy guarantees under continuity and noise level constraints.

Because of their potential ubiquity, unmanned aerial vehicles (UAVs) are often viewed as a threat to people’s privacy. However, the users of UAVs for applications such as package delivery can also have their own privacy compromised by observations of UAV behavior by an adversary. Hence, this paper looks at privacy-preserving path-planning for a UAV. In particular, we consider a UAV which is delivering a package or operating a service, e.g. a health-emergency service, for users while an adversary tries to infer the UAV’s destination by observing its trajectory. We consider two models for the UAV motion for which we provide privacy-preserving path-planning mechanisms (PPPMs) while taking into account the UAV’s energy consumption as well. We obtain the tradeoff between privacy and energy consumption guarantees and show that the proposed PPPMs not only satisfy the privacy guarantees but also meet the energy efficiency criteria.

In this paper, we consider a communication system where an agent is receiving update information from a source/controller through a wireless channel. In particular, we investigate the problem of optimal quantization considering a unified distortion measure caused by the quantization and the age of information (AoI). To this end, we propose an upperbound for the quantization distortion given an arbitrarily distributed stochastic process. We then provide a specific example of the above problem where the question is whether to send the commands (actions) or the command generator (action distribution) to the agent. We show that a command generator which is a probability distribution function (PDF) can be a more efficient policy when the cost of communication is of interest. We analyze the existing tradeoff between the rate of change of the update and the channel use. We show that since the faster the process varies, the larger the distortion becomes, it requires a higher quantization rate to meet the same level of distortion.

User privacy can be compromised by matching user data traces to records of their previous behavior. The matching of the statistical characteristics of traces to prior user behavior has been widely studied. However, an adversary can also identify a user deterministically by searching data traces for a pattern that is unique to that user. Our goal is to thwart such an adversary by applying small artificial distortions to data traces such that each potentially identifying pattern is shared by a large number of users. Importantly, in contrast to statistical approaches, we develop data-independent algorithms that require no assumptions on the model by which the traces are generated. By relating the problem to a set of combinatorial questions on sequence construction, we are able to provide provable guarantees for our proposed constructions. We also introduce data-dependent approaches for the same problem. The proposed obfuscation methods are evaluated on synthetic data traces and on the Reality Mining Data set to demonstrate the performance of the proposed algorithms relative to alternatives.

In this paper, we introduce two general categories of stochastic trajectory processes that provide uniform Binomial distribution for unmanned aerial vehicles (UAVs) in finite convex areas with arbitrary geometry. First, we introduce radial trajectory process as a special case of the first category along with its properties. Then, we delve into the general trajectory processes, namely spiral and oval, and show that independent of the areas geometry, these trajectories not only provide uniform scattering of UAVs but also provide ergodic coverage across the area.

Unmanned aerial vehicles (UAVs) have become important in many applications including last-mile deliveries, surveillance and monitoring, and wireless networks. This paper aims to design UAV trajectories that simultaneously perform multiple tasks. We aim to design UAV trajectories that efficiently perform some transportation operation (e.g., package delivery), and at the same time provide uniform coverage over a neighborhood area which is needed for applications such as network coverage, Internet of Things (IoT) devices data collection, wireless power transfer, and surveillance. We first consider multi-task UAVs for a simplified scenario where the neighborhood area is a circular region where UAV missions start from the center and the destinations are assumed to be uniformly distributed on the circle boundary. We propose a trajectory process such that if according to which the UAV's move, a uniform coverage can be achieved while the transport (delivery) efficiency is still preserved. We then consider a more practical scenario in which the transport destinations are arbitrarily distributed in an arbitrarily-shaped region. We show that simultaneous uniform coverage and efficient transport trajectory (e.g. package delivery) is possible for such realistic scenarios. This is shown using both rigorous analysis as well as simulations.

Suppose we are given a large number of sequences on a given alphabet, and an adversary is interested in identifying (de-anonymizing) a specific target sequence based on its patterns. Our goal is to thwart such an adversary by obfuscating the target sequences by applying artificial (but small) distortions to its values. A key point here is that we would like to make no assumptions about the statistical model of such sequences. This is in contrast to existing literature where assumptions (e.g., Markov chains) are made regarding such sequences to obtain privacy guarantees. We relate this problem to a set of combinatorial questions on sequence construction based on which we are able to obtain provable guarantees. This problem is relevant to important privacy applications: from fingerprinting webpages visited by users through anonymous communication systems to linking communicating parties on messaging applications to inferring activities of users of IoT devices.

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifthgeneration (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each endto-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

Unmanned aerial vehicles(UAVs) have become important in many application fields including last-mile deliveries, surveillance and monitoring, and wireless networks. This paper aims to design UAVs that simultaneously perform multiple tasks. We aim to design UAV trajectories that minimize package delivery time, and at the same time provide uniform coverage over a neighborhood area which is needed for applications such as network coverage or surveillance. First, we study multi task UAVs for a simplified scenario where the neighborhood area is a circular region with the post office located at its center and the houses are assumed to be uniformly distributed on the circle boundary. We propose a trajectory process such that if according to which the drones move, a uniform coverage can be achieved while the delivery efficiency (delivery time with respect to unconstrained case) tends to 1. Second, we consider a more practical scenario in which the delivery destinations are arbitrarily distributed in an arbitrarily-shaped region. We also do not assume any restrictions on the package arrivals. We show that simultaneous uniform coverage and efficient package delivery is possible for such realistic scenarios.

Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve a user's experience or be essential for the application to work, the exposure of user data to the application presents a significant privacy threat to the users-even when the traces are anonymized-since the statistical matching of an anonymized trace to prior user behavior can identify a user and their habits. Because of the current and growing algorithmic and computational capabilities of adversaries, provable privacy guarantees as a function of the degree of anonymization and obfuscation of the traces are necessary. Our previous work has established the requirements on anonymization and obfuscation in the case that data traces are independent between users. However, the data traces of different users will be dependent in many applications, and an adversary can potentially exploit such. In this paper, we consider the negative impact of dependency between user traces on their privacy. First, we demonstrate that the adversary can readily identify the association graph of the obfuscated and anonymized version of the data, revealing which user data traces are dependent. Next, we demonstrate that the adversary can use this association graph to break user privacy with significantly shorter traces than in the case of independent users, and that obfuscating data traces independently across users is often insufficient to remedy such leakage. In other words, we have shown that inter-user dependency is disastrous to privacy, and any non-negligible dependency between users significantly reduces the effectiveness of anonymization and obfuscation schemes. Finally, we discuss how users can improve privacy by employing joint obfuscation that removes or reduces the data dependency.

The literature has extensively studied various location privacy-preserving mechanisms (LPPMs) in order to improve the location privacy of the users of location-based services (LBSes). Such privacy, however, comes at the cost of degrading the utility of the underlying LBSes. The main body of previous work has used a generic distance-only based metric to quantify the quality loss incurred while employing LPPMs. In this paper, we argue that using such generic utility metrics misleads the design and evaluation of LPPMs, since generic utility metrics do not capture the actual utility perceived by the users. We demonstrate this for ride-hailing services, a popular class of LBS with complex utility behavior. Specifically, we design a privacy-preserving ride-hailing service, called PRide, and demonstrate the significant distinction between its generic and tailored metrics. Through various experiments we show the significant implications of using generic utility metrics in the design and evaluation of LPPMs. Our work concludes that LPPM design and evaluation should use utility metrics that are tailored to the individual LBSes.

The rapid growth of the Internet of Things (IoT) necessitates employing privacy-preserving techniques to protect users' sensitive information. Even when user traces are anonymized, statistical matching can be employed to infer sensitive information. In our previous work, we have established the privacy requirements for the case that the user traces are instantiations of discrete random variables and the adversary knows only the structure of the dependency graph, i.e., whether each pair of users is connected. In this paper, we consider the case where data traces are instantiations of Gaussian random variables and the adversary knows not only the structure of the graph but also the pairwise correlation coefficients. We establish the requirements on anonymization to thwart such statistical matching, which demonstrate the significant degree to which knowledge of the pairwise correlation coefficients further significantly aids the adversary in breaking user anonymity.

Many popular applications use traces of user data to offer various services to their users. However, even if user data are anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that match a user trace to prior user behavior. In this paper, we derive the theoretical bounds on the privacy of users in such a scenario. We build on our recent study in the area of location privacy, in which we introduced formal notions of location privacy for anonymization-based location privacy-protection mechanisms. Here, we derive the fundamental limits of user privacy when both anonymization and obfuscation-based protection mechanisms are applied to users' time series of data. We investigate the impact of such mechanisms on the tradeoff between privacy protection and user utility. We first study achievability results for the case where the time-series of users are governed by an independent and identically distributed (i.i.d.) process. The converse results are proved both for the i.i.d. case as well as the more general Markov chain model. We demonstrate that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.

Recently, the utilization of aerial base stations (ABSs) has attracted a lot of attention. For the static implementation of ABSs, it has been shown that if the ABSs are statistically distributed in a given height over a cell, according to a binomial point process (BPP), a fairly uniform coverage across the cell is achievable. However, such a static deployment exhibits poor performance in terms of average fade duration (AFD) for the static or low speed moving users and power consumption. Therefore, considering a network of moving ABSs is of practical importance. On the other hand, once such a moving ABS network is considered, the coverage probability may not necessarily remain at an acceptable level. This paper is concerned with the design of stochastic trajectory processes such that if according to which the ABSs move, in addition to improving the AFD, an acceptable coverage profile can be obtained. We propose two families of such processes, namely, spiral and oval processes, and analytically demonstrate that the same coverage as the static case is achievable. We then focus on two special cases of such processes, namely, radial and ring processes, and show that the AFD is reduced about two orders of magnitude with respect to the static case. To obtain a more practical scenario, we also consider deterministic counterparts of the proposed radial and ring processes and show that similar coverage and AFD as the stochastic case can be obtained.

Stochastic geometry has been successfully applied for performance analysis of wireless networks. Performance of some emerging applications such as unmanned aircraft systems (UAS) relies heavily on unique mobility characteristics that are sometimes not fully captured by the current stochastic geometry results. This paper focuses on this issue by introducing families of trajectory processes that preserve uniformity within a cell in the framework of aerial base stations (ABS) networks. This means if the ABS move according to such trajectory processes, they will be distributed according to a binary point process (BPP) at any time snapshot. We propose two families of such processes, namely spiral and oval processes, and analytically prove our claim. We then focus on 2 special cases of such processes, namely, radial and ring processes and demonstrate their attractive properties as far as implementation is concerned.

Modern applications significantly enhance user experience by adapting to each user's individual condition and/or preferences. While this adaptation can greatly improve utility or be essential for the application to work (e.g., for ride-sharing applications), the exposure of user data to the application presents a significant privacy threat to the users, even when the traces are anonymized, since the statistical matching of an anonymized trace to prior user behavior can identify a user and their habits. Because of the current and growing algorithmic and computational capabilities of adversaries, provable privacy guarantees as a function of the degree of anonymization and obfuscation of the traces are necessary. Our previous work has established the requirements on anonymization and obfuscation in the case that data traces are independent between users. However, the data traces of different users will be dependent in many applications, and an adversary can potentially exploit such. In this paper, we consider the impact of correlation between user traces on their privacy. First, we demonstrate that the adversary can readily identify the association graph, revealing which user data traces are correlated. Next, we demonstrate that the adversary can use this association graph to break user privacy with significantly shorter traces than in the case when traces are independent between users, and that independent obfuscation of the data traces is often insufficient to remedy such. Finally, we discuss how the users can employ dependency in their obfuscation to improve their privacy.

In this paper, we propose a stochastic geometry based pricing for infrastructure sharing in Internet of Things (IoT) networks. We consider a game consisting of a Network Operator (NO) as the seller and an IoT Device Owner (DO) as the buyer in which the seller owns an infrastructure that can address the communication needs of the DO. Using the proposed scheme, we show that DO and NO can reach a win-win deal in which a reasonable cost is imposed to DO in exchange of providing an acceptable coverage by the NO. In particular, we show that the DO can achieve a coverage probability of interest at a lower cost compared to the case in which the proposed pricing model is absent. The proposed idea provides a transparent pricing model between NOs and DOs and paves the road for IoT applications to become more widespread.

The prevalence of mobile devices and location-based services (LBS) has generated great concerns regarding the LBS users' privacy, which can be compromised by statistical analysis of their movement patterns. A number of algorithms have been proposed to protect the privacy of users in such systems, but the fundamental underpinnings of such remain unexplored. Recently, the concept of perfect location privacy was introduced and its achievability was studied for anonymization-based LBS systems, where user identifiers are permuted at regular intervals to prevent identification based on statistical analysis of long time sequences. In this paper, we significantly extend that investigation by incorporating the other major tool commonly employed to obtain location privacy: obfuscation, where user locations are purposely obscured to protect their privacy. Since anonymization and obfuscation reduce user utility in LBS systems, we investigate how location privacy varies with the degree to which each of these two methods is employed. We provide: (1) achievability results for the case where the location of each user is governed by an i.i.d. process; (2) converse results for the i.i.d. case as well as the more general Markov Chain model. We show that, as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect location privacy; and, in the second region, no user has location privacy.

In this paper, a new approach for multiple access (MA) in fifth generation (5G) of cellular networks called power domain sparse code multiple access (PSMA) is proposed. In PSMA, we adopt both the power domain and the code domain to transmit multiple users' signals over a subcarrier simultaneously. In such a model, the same sparse code multiple access (SCMA) codebook can be used by multiple users where, for these users, power domain non-orthogonal multiple access (PD-NOMA) technique is used to send signals non-orthogonally. Although different SCMA codebooks are orthogonal and produce no interference over each other, the same codebook used by multiple users produces interference over these users. We investigate the signal model as well as the receiver and transmitter of the PSMA method. To evaluate the performance of PSMA, we consider a single cell with multiple users. In this case, our design objective is to maximize the system sum rate of the network subject to some system level and QoS constraints such as transmit power constraints. We formulate the proposed resource allocation problem as an optimization problem and solve it by successive convex approximation (SCA) techniques. Finally, the effectiveness of the proposed approach is investigated using numerical results.

There are numerous applications of Unmanned Aerial Systems (UAS) introduced to the technology world. However, there is an unclear relation between the number of Unmanned Aerial Vehicles (UAVs) in a specific area and the performance of the system. This paper studies the suitable spatial density of UAVs in an ad hoc network by looking at safety, communication interference, connectivity, and the required coverage for various applications of the network.

This paper considers secure energy-efficient routing in the presence of multiple passive eavesdroppers. Previous work in this area has considered secure routing assuming probabilistic or exact knowledge of the location and channel-state-information (CSI) of each eavesdropper. In wireless networks, however, the locations and CSIs of passive eavesdroppers are not known, making it challenging to guarantee secrecy for any routing algorithm. We develop an efficient (in terms of energy consumption and computational complexity) routing algorithm that does not rely on any information about the locations and CSIs of the eavesdroppers. Our algorithm guarantees secrecy even in disadvantaged wireless environments, where multiple eavesdroppers try to eavesdrop each message, are equipped with directional antennas, or can get arbitrarily close to the transmitter. The key is to employ additive random jamming to exploit inherent non-idealities of the eavesdropper's receiver, which makes the eavesdroppers incapable of recording the messages. We have simulated our proposed algorithm and compared it with the existing secrecy routing algorithms in both single-hop and multi-hop networks. Our results indicate that when the uncertainty in the locations of eavesdroppers is high and/or in disadvantaged wireless environments, our algorithm outperforms existing algorithms in terms of energy consumption and secrecy.

The popularity of mobile devices and location-based services (LBSs) has raised significant concerns regarding the location privacy of their users. A popular approach to protect location privacy is anonymizing the users of LBS systems. In this paper, we introduce an information-theoretic notion for location privacy, which we call perfect location privacy. We then demonstrate how anonymization should be used by LBS systems to achieve the defined perfect location privacy. We study perfect location privacy under two models for user movements. First, we assume that a user's current location is independent from her past locations. Using this independent identically distributed (i.i.d.) model, we show that if the pseudonym of the user is changed before O(n2/r-1 ) observations are made by the adversary for that user, then the user has perfect location privacy. Here, n is the number of the users in the network and r is the number of all possible locations. Next, we model users' movements using Markov chains to better model real-world movement patterns. We show that perfect location privacy is achievable for a user if the user's pseudonym is changed before O(n 2/IEI-r observations are collected by the adversary for that user, where IEI is the number of edges in the user's Markov chain model.

This paper studies the need for individualizing vehicular communications in order to improve safety for a highway scenario. Adapting a vehicular ad hoc network to both its individual driver's characteristic and traffic conditions enables it to transmit in a smart manner to other vehicles. This radical improvement is now possible due to the progress that is being made in vehicular ad hoc networks (VANET). In this paper, we first derive the packet success probability for a chain of vehicles by taking multi-user interference, path loss, and fading into account. Then, by considering the delay constraints and types of potential collisions, we approximate the optimal channel access probabilities. Lastly, we propose an algorithm for customizing channel access probabilities in VANET. Our Monte Carlo simulation results show that this approach achieves more than 25% reduction in traffic collision probability compared with the case with equal channel access probabilities in its optimal range. Therefore, it has a huge advantage over other non-optimal systems.

The effectiveness and the simple implementation of physical layer jammers make them an essential threat for wireless networks. In a multihop wireless network, where jammers can interfere with the transmission of user messages at intermediate nodes along the path, one can employ jamming oblivious routing and then employ physical-layer techniques (e.g., spread spectrum) to suppress jamming. However, whereas these approaches can provide significant gains, the residual jamming can still severely limit system performance. This motivates the consideration of routing approaches that account for the differences in the jamming environment between different paths. First, we take a straightforward approach where an equal outage probability is allocated to each link along a path and develop a minimum energy routing solution. Next, we demonstrate the shortcomings of this approach and then consider the joint problem of outage allocation and routing by employing an approximation to the link outage probability. This yields an efficient and effective routing algorithm that only requires knowledge of the measured jamming at each node. Numerical results demonstrate that the amount of energy saved by the proposed methods with respect to a standard minimum energy routing algorithm, especially for parameters appropriate for terrestrial wireless networks, is substantial.

Secure communication over a wiretap channel is considered in the disadvantaged wireless environment, where the eavesdropper channel is (possibly much) better than the main channel. We present a method to exploit inherent vulnerabilities of the eavesdroppers receiver to obtain everlasting secrecy. Based on an ephemeral cryptographic key pre-shared between the transmitter Alice and the intended recipient Bob, a random jamming signal is added to each symbol. Bob can subtract the jamming signal before recording the signal, while the eavesdropper Eve is forced to perform these non-commutative operations in the opposite order. Thus, information-theoretic secrecy can be obtained, hence achieving the goal of converting the vulnerable “cheap” cryptographic secret key bits into “valuable” information-theoretic (i.e., everlasting) secure bits. We evaluate the achievable secrecy rates for different settings, and show that, even when the eavesdropper has perfect access to the output of the transmitter (albeit through an imperfect analog-to-digital converter), the method can still achieve a positive secrecy rate. Next we consider a wideband system, where Alice and Bob perform frequency hopping in addition to adding the random jamming to the signal, and we show the utility of such an approach even in the face of substantial eavesdropper hardware capabilities.

The effectiveness and straightforward implementation of physical layer jammers make them an essential security threat for wireless networks. In this paper, reliable communication in a wireless multi-hop network in the presence of multiple malicious jammers is considered. Since energy consumption is an important issue in wireless ad hoc networks, minimum energy routing with and without security constraints has received significant attention in the literature; however, energy-aware routing in the presence of active adversary (jammers) has not been considered. We propose an efficient algorithm for minimum energy routing between a source and a destination in the presence of both static and dynamic malicious jammers such that an end-to-end probability of outage is guaranteed. The percentage of energy saved by the proposed method with respect to a shortest path routing benchmark is evaluated. It is shown that the amount of energy saved, especially in terrestrial wireless networks with path-loss exponents greater than two, is substantial.

Adapting the functioning of the collision warning systems to the specific drivers' characteristics is of great benefit to drivers. For example, by customizing collision warning algorithms we can minimize false alarms, thereby reducing injuries and deaths in highway traffic accidents. In order to take the behaviors of individual drivers into account, the system needs to have a Real-Time estimation of the distribution of brake response times for an individual driver. In this paper, we propose a method for doing this estimation which is not computationally intensive and can take advantage of the information contained in all data points.

Secure communication over a wireless channel in the presence of a passive eavesdropper is considered. We present a method to exploit the eavesdropper's inherent receiver vulnerabilities to obtain everlasting secrecy. An ephemeral cryptographic key is pre-shared between the transmitter and the legitimate receiver and is utilized to induce intentional intersymbol interference (ISI). The legitimate receiver uses the key to cancel the ISI while the eavesdropper, since it does not have the key, cannot do such. It is shown that although ISI reduces the capacity of the main channel, it can lead to a net gain in secrecy rate. The achievable secrecy rates for different ISI filter settings are evaluated and the proposed method is compared with other information-theoretic security schemes.

We analytically study parameter design for safety applications in Vehicular Ad hoc Networks. Our goal is to fill in the current gap between purely traffic-based studies that fail to account for the non-idealities of communications, and communications-based ones which neglect the application needs of the system. Initially and by studying the dynamic behavior of vehicles we address the delay requirement of the safety application. We then derive the delay-bounded packet success probability of three Media Access Control (MAC) schemes proposed for the dissemination of periodic safety messages. By simultaneously accounting for multi-user interference and propagation effects such as path loss and fading, we determine the optimal transmission rate, channel access probability, and when pertinent, carrier sensing range of those schemes that satisfy the delay requirement at a target success probability. Of our findings is that the optimal transmission rate varies only with the path loss exponent, irrespective of the MAC scheme used. Also, the optimal communications parameters that minimize the maximum expected collision probability in a chain of vehicles is only dependent on the human factors (perception-reaction time and deceleration behavior) and highway characteristics (number of lanes and path loss exponent) and hence do not have to vary with traffic conditions.

Secure communication over a memoryless wiretap channel in the presence of a passive eavesdropper is considered. Traditional information-theoretic security methods require an advantage for the main channel over the eavesdropper channel to achieve a positive secrecy rate, which in general cannot be guaranteed in wireless systems. Here, we exploit the non-linear conversion operation in the eavesdropper's receiver to obtain the desired advantage - even when the eavesdropper has perfect access to the transmitted signal at the input to their receiver. The basic idea is to employ an ephemeral cryptographic key to force the eavesdropper to conduct two operations, at least one of which is non-linear, in a different order than the desired recipient. Since non-linear operations are not necessarily commutative, the desired advantage can be obtained and information-theoretic secrecy achieved even if the eavesdropper is given the cryptographic key immediately upon transmission completion. In essence, the lack of knowledge of the key during the short transmission time inhibits the recording of the signal in such a way that the secret information can never be extracted from it. The achievable secrecy rates for different countermeasures that the eavesdropper might employ are evaluated. It is shown that even in the case of an eavesdropper with uniformly better conditions (channel and receiver quality) than the intended recipient, a positive secrecy rate can be achieved.

This paper investigates properties of polar codes that can be potentially useful in real-world applications. We start with analyzing the performance of finite-length polar codes over the binary erasure channel (BEC), while assuming belief propagation as the decoding method. We provide a stopping set analysis for the factor graph of polar codes, where we find the size of the minimum stopping set. We also find the girth of the graph for polar codes. Our analysis along with bit error rate (BER) simulations demonstrate that finite-length polar codes show superior error floor performance compared to the conventional capacity-approaching coding techniques. In order to take advantage from this property while avoiding the shortcomings of polar codes, we consider the idea of combining polar codes with other coding schemes. We propose a polar code-based concatenated scheme to be used in Optical Transport Networks (OTNs) as a potential real-world application. Comparing against conventional concatenation techniques for OTNs, we show that the proposed scheme outperforms the existing methods by closing the gap to the capacity while avoiding error floor, and maintaining a low complexity at the same time.

Many analytic results for the connectivity, coverage, and capacity of wireless networks have been reported for the case where the number of nodes, n , tends to infinity (large-scale networks). The majority of these results have not been extended for small or moderate values of n ; whereas in many practical networks, n is not very large. In this article, we consider finite (small-scale) wireless sensor networks. We first show that previous asymptotic results provide poor approximations for such networks. We provide a set of differences between small-scale and large-scale analysis and propose a methodology for analysis of finite sensor networks. Furthermore, we consider two models for such networks: unreliable sensor grids and sensor networks with random node deployment. We provide easily computable expressions for bounds on the coverage and connectivity of these networks. With validation from simulations, we show that the derived analytic expressions give very good estimates of such quantities for finite sensor networks. Our investigation confirms the fact that small-scale networks possess unique characteristics different from their large-scale counterparts, necessitating the development of a new framework for their analysis and design.

Physical layer secrecy in wireless networks in the presence of eavesdroppers of unknown location is considered. In contrast to prior schemes, which have expended energy in the form of cooperative jamming to enable secrecy, we develop schemes where multiple transmitters send their signals in a cooperative fashion to confuse the eavesdroppers. Hence, power is not expended on “artificial noise”; rather, the signal of a given transmitter is protected by the aggregate interference produced by the other transmitters. We introduce a two-hop strategy for the case of equal path-loss between all pairs of nodes, and then consider its embedding within a multi-hop approach for the general case of an extended network. In each case, we derive an achievable number of eavesdroppers that can be present in the region while secure communication between all sources and intended destinations is ensured.

Generalized Extrinsic Information Transfer (GEXIT) charts were introduced as an extension of EXIT charts which have an extensive use in analysis and design of many iterative schemes including Low-Density Parity-Check (LDPC) codes. While a powerful as well as an insightful concept, their full potential as a designing tool for LDPC code ensembles has not been realized due to some missing steps. This papers aims at filling these gaps by proving some important properties of GEXIT charts and using them to design capacity-approaching LDPC code ensembles. The primary results on GEXIT charts are limited to regular variable and check node degrees. Moreover, variable node GEXIT curves have only been derived for the case where no physical channel is present. In a recent paper, GEXIT curves for irregular variable node and check node degree distributions have been derived. In this paper, we derive GEXIT charts of LDPC code ensembles over binary-input output-symmetric memoryless channels with any channel parameter. For the case of binary symmetric channel, we derive closed form expression for the GEXIT curve of variable nodes. We also propose to use an alternative representation of GEXIT charts in which we plot the inverse of variable node GEXIT curve together with dual GEXIT curve of the check node. We prove that the area theorem still holds in this case. Using these results, we analyze and design capacity-approaching LDPC codes using GEXIT charts.

This paper investigates throughput scaling laws for Vehicular Ad Hoc Networks (VANETs). We show that the road geometry greatly affects the throughput of a VANET. To this end we introduce the notion of sparseness to capture the geometrical properties of roads. We then start by addressing scaling laws for single roads. We shall see that even a single road can have very different scaling behaviors based on its path trajectory. Scaling laws for more complex systems such as downtown grids and general road systems are studied next. Here, the concept of road-connectivity plays a major role in determining the scaling behavior. In our analysis we account for a spectrum of node distributions that represent different vehicular traffic conditions. We also introduce the distance-limited throughput, a notion of throughput especially introduced for VANET-specific applications, and see how it scales in a single road system and in the presence of infrastructure. Our results are obtained by combining geometrical analysis, network flow arguments, and the probabilistic study of VANETs.

In this paper, we study polar codes from a practical point of view. In particular, we study concatenated polar codes and rate-compatible polar codes. First, we propose a concatenation scheme including polar codes and Low-Density Parity-Check (LDPC) codes. We will show that our proposed scheme outperforms conventional concatenation schemes formed by LDPC and Reed-Solomon (RS) codes. We then study two rate-compatible coding schemes using polar codes. We will see that polar codes can be designed as universally capacity achieving rate-compatible codes over a set of physically degraded channels. We also study the effect of puncturing on polar codes to design rate-compatible codes.

Recently, a number of communications schemes have been proposed for Vehicular Ad hoc Networks (VANETs). A promising approach, the Universal Geocast Scheme (UGS), provides for a diverse variety of VANET-specific characteristics such as time-varying topology, protocol variation based on road congestion, and support for non line-of-sight communication. In this paper, the UGS protocol is extended to consider inter-vehicle multi-hop connections in intersections with surrounding obstructions. Since UGS is a probabilistic, repetition-based scheme, it supports the capacity-delay tradeoffs crucial for periodic safety message exchange. The approach is shown to support both vehicle-to-vehicle and vehicle-to-infrastructure communication. This research accurately evaluates this scheme using network (NS-2) and mobility (SUMO) simulators, verifying two crucial elements of successful VANETs, received packet ratio and message delay. A contemporary wireless radio propagation model is used to augment accuracy. Our results show a 6% improvement in received packet ratio combined with a decrease in average packet delay versus a previous, well-known inter vehicle communication protocol.

We address the analytical design of a communications system that seeks to provide timely safety information for drivers who are unaware of an imminent collision. We develop a model to characterize the delay requirements needed to prevent such collisions. By simultaneously addressing the multi-user interference and propagation effects such as path loss and fading, we then analytically derive the optimal channel access probability and transmission range and rate of nodes that satisfy the delay requirements at a target success probability.

A systematic construction of capacity achieving low-density parity-check (LDPC) code ensemble sequences over the Binary Erasure Channel (BEC) has been proposed by Saeedi et al. based on a method, here referred to as Successive Maximization (SM). In SM, the fraction of degree-i nodes are successively maximized starting from i = 2 with the constraint that the ensemble remains convergent over the channel. In this paper, we propose SM to design universally capacity approaching rate-compatible LDPC code ensemble sequences over the general class of Binary-Input Output-Symmetric Memoryless (BIOSM) channels. This is achieved by first generalizing the SM method to other BIOSM channels to design a sequence of capacity approaching ensembles called the parent sequence. The SM principle is then applied to each ensemble within the parent sequence, this time to design rate-compatible puncturing schemes. As part of our results, we extend the stability condition which was previously derived for degree-2 variable nodes to other variable node degrees as well as to the case of rate-compatible codes. Consequently, we prove that using the SM principle, one is able to design universally capacity achieving rate-compatible LDPC code ensemble sequences over the BEC. Unlike the previous results in the literature, the proposed SM approach is naturally extendable to other BIOSM channels. The performance of the rate-compatible schemes designed based on our method is comparable to those designed by optimization.

A universal communications scheme for vehicular ad hoc networks (VANETs) is proposed. This scheme accounts for a diverse variety of VANET-speciflc characteristics such as the gradual introduction of technology, highly dynamic topology, road-constrained vehicle movement and the presence of obstacles. The scheme incorporates a geometrical framework previously proposed by the authors which makes it appropriate for urban as well as rural area deployments. Moreover, by making the scheme probabilistic, capacity-delay tradeoffs crucial for safety message exchange are addressed. Although the presence of infrastructure is a privilege to our scheme, the network can still operate in a pure ad hoc manner. Simulation results confirm that our heuristic method dramatically improves the probability of reception of nodes in different scenarios.

Analysis of finite wireless networks is a fundamental problem in the area of wireless networking. Today, due to the vast amount of literature on large-scale wireless networks, we have a fair understanding of the asymptotic behavior of such networks. However, in real world we have to face finite networks for which the asymptotic results cease to be valid. We refer to networks as being finite when the number of nodes is less than a few hundred. Here we study a model of wireless networks, represented by random geometric graphs. In order to address a wide class of the network's properties, we study the threshold phenomena. Being extensively studied in the asymptotic case, the threshold phenomena occurs when a graph theoretic property (such as connectivity) of the network experiences rapid changes over a specific interval of the underlying parameter. Here, we find an upper bound for the threshold width of finite line networks represented by random geometric graphs. These bounds hold for all monotone properties of such networks. We then turn our attention to an important non-monotone characteristic of line networks which is the Medium Access (MAC) layer capacity, i.e. the maximum number of possible concurrent transmissions. Towards this goal, we provide a linear time algorithm which finds a maximal set of concurrent non-interfering transmissions and further derive lower and upper bounds for the cardinality of the set. Using simulations, we show that these bounds serve as reasonable estimates for the actual value of the MAC-layer capacity.

Conventional hybrid RF and optical wireless communication systems make use of parallel Free Space Optical (FSO) and Radio Frequency (RF) channels to achieve higher reliability than individual channels. True hybridization can be accomplished when both channels collaboratively compensate the shortcomings of each other and thereby improve the performance of the system as a whole. In this paper, we propose a novel coding paradigm called "Hybrid Channel Coding" that not only optimally achieves the capacity of the combined FSO and RF channels but also can potentially provide carrier grade reliability (99.999%) for hybrid FSO/RF systems. The proposed mechanism uses non-uniform and rate-compatible LDPC codes to achieve the desired reliability and capacity limits. We propose a design methodology for constructing these Hybrid Channel Codes. Using analysis and simulation, we show that by using Hybrid Channel Codes, we can obtain significantly better availability results in terms of the required link margin while the average throughput obtained is more than 33% better than the currently existing systems. Also by avoiding data duplication, we preserve to a great extent the crucial security benefits of FSO communications. Simulations also show that Hybrid Channel Codes can achieve more than two orders of magnitude improvement in bit error rate compared to present systems.

In the past decade, there has been tremendous amount of research on the analysis and design of the Low-Density Parity-Check (LDPC) codes with belief propagation decoding over different types of Binary-Input Output-Symmetric Memoryless (BIOSM) channels. However, with the exception of the Binary Erasure Channel (BEC), analytical results on LDPC codes over such channels are limited and most results are based on numerical methods and optimization. In particular, systematic design of provably capacity achieving sequences of LDPC code ensembles over general class of BIOSM channels has remained a fundamental open problem. Such sequences have been designed only for the binary erasure channel (BEC). In this paper, we first prove some novel analytical properties of the LDPC code ensembles over BIOSM channels. In particular, we prove a result that generalizes the previously known stability condition over symmetric channels. This suggests that a modified version of the flatness condition, a property which has been shown to be critical for capacity achieving sequences over the BEC, can be used to devise capacity achieving sequences for general class of BIOSM channels. Based on this assumption, we propose a method which could result in the systematic design of such sequences over BIOSM channels. Numerical evidence is promising and provides a consistent convergence behavior to capacity over the considered BIOSM channels as the average check node degree increases.

In this paper we propose a framework to study the asymptotical capacity of vehicular ad hoc networks (VANET)s when nodes are expected to communicate only when they reside in a certain distance of each other. This is quite a favorable scenario for VANETs when they are utilized for accident avoidance and safety applications. Moreover we develop formulations to predict the behavior of VANETs with specific geometrical shapes like the single road and grid topologies. Also, the capacity scaling behavior of VANETs when a node needs to transmit to all its neighbors within a certain range, is studied. Results are obtained by combining geometrical analysis, network flow arguments, and probabilistic study of VANETs.

In the past, many analytic results for wireless networks have been reported for the case where the number of nodes n in the network tends to infinity (large-scale networks). These include connectivity, coverage, and capacity. These results have not been extended for small or moderate values of n, although in many practical networks n is not very large. In this paper, we first show that previous asymptotic results provide poor approximations for the finite networks (small-scale networks). We then aim to develop a framework to analytically study network properties without assuming that n is large. We provide a set of differences between small-scale and large-scale analysis. We consider wireless networks in which the location of the nodes is random. We study routing algorithms, coverage, connectivity and capacity of finite wireless networks. We provide easily computable expressions for different network properties. With validation from simulations, we show that these analytic expressions give very good estimates of these quantities for finite wireless networks. Our investigation suggests that the small- scale networks posses unique characteristics that require a new framework for analysis and design.

With the objective of minimizing the total cost, which includes both sensor and congestion costs, the authors adopted a novel sampling theorem approach to address the problem of sensor spacing optimization. This paper presents the analysis and modeling of the power spectral density of traffic information as a 2-D stochastic signal using highly detailed field data. The field data were captured by the next-generation simulation (NGSIM) program in 2005. To the best knowledge of the authors, field data with such a level of detail were previously unavailable. The resulting model enables the derivation of a characterization curve that relates sensor error to sensor spacing. The characterization curve, concurring in general with observations of a previous work, provides much more detail to facilitate sensor deployment. Based on the characterization curve and a formulation relating sensor error to congestion cost, the optimal sensor spacing that minimizes the total cost can be determined.

This paper discusses a first attempt to investigate, using analytic means, the transmission delay and energy characteristics of a multi-state wireless sensor network. For such a network with in nodes, where each node can be active, resting or sleeping, a model that describes the transition of a node from one state to another and the probability associated with each state is proposed. Asymptotic analyses of transmission delay and energy are presented. We report the presence of a threshold for the arrival rate of data packets that decides which energy component dominates. The transmission delay-energy tradeoff is presented for the case where transmission energy varies directly with distance raised to a power and when the reported threshold is exceeded.

Optical wireless systems can solve many of the long standing problems of providing low-cost time-constrained high- bandwidth communication in a variety of network scenarios. But their unreliability due to variations in atmospheric channel make them the least deployed solutions so far. Achieving enterprise level reliabilities in such scenarios is difficult. Earlier solutions make use of a backup RF channel which transmits data whenever the FSO link is down. However, only one channel is operational at a time wasting the bandwidth of the other channel. Also this system faces the problem of alternate switching between both the channels in case the channel conditions vary from good to bad. In this paper, we propose a novel coding paradigm called "Hybrid Channel Coding" that not only optimally achieves the capacity of the combined FSO and RF channels but still provides carrier grade (99.999%) reliabilities for the FSO link. We provide the design methodology to be used in constructing Hybrid Channel Codes. Using analysis and simulation, we show that we can obtain many-fold decrease in the channel availability and maximum throughput with Hybrid Channel Codes.

It is generally believed harmful to have transmission errors in the wireless communications. The high decoding complexity of dense parity check codes is unfavorable. This paper proposes to apply these two "negative" facts to enable the secrecy sharing with the information theoretical security. We claim that the secrecy sharing is always possible if the wiretap channel is not error-free, regardless of the main channel performance. Particularly, the proposed secrecy sharing protocol can provide provable and testable security using the existing wireless technologies.

Practical or economic considerations may impose a finite limit on the number of nodes deployed in a wireless network. Results derived from asymptotic analysis are reportedly ill-suited for such cases. This motivates the analysis of finite wireless sensor networks. In this paper, we discuss the upper and lower bounds of the coverage probability of a randomly-deployed finite wireless network. Comparisons of the bounds with simulation and asymptotic results are presented. A discussion on the optimal deployment of sensors to achieve maximum coverage probability for a line network is also presented.

Voice and video traffic can tolerate some errors in end-to-end transfer but require very low latency. Data traffic typically requires an almost error free transfer but can tolerate higher latency. Thus, coding and forward error corrections are the main mechanisms for error controls for voice and video traffic while ARQ is the main mechanism for error controls for data traffic. When the error rates are high (for example, in wireless links), it is beneficial to have both forward error corrections and ARQ for data traffic. In addition, the effectiveness of the coding and FEC can be improved by using interleaving. The combined effects of coding, interleaving, and ARQ on data traffic effective throughput is not well understood. In this paper, we take the first steps. In particular, we show the effective throughput is very sensitive to channel, coding, and interleaving parameters, thus identifying a need to study parameter optimization further. We also show that appropriately sized LDPC codes provide better throughput than traditional codes do. Early results also suggest that, for mixed traffic environment, it may pay to have some coding and interleaving be done at higher layer to allow application specific parameters.

In this paper, we propose a scheme to construct low-density parity-check (LDPC) codes that are suitable for unequal error protection (UEP). We derive density evolution (DE) formulas for the proposed unequal error protecting LDPC ensembles over the binary erasure channel (BEC). Using the DE formulas, we optimize the codes. For the finite-length cases, we compare our codes with some other LDPC codes, the time-sharing method, and a previous work on UEP using LDPC codes. Simulation results indicate the superiority of the proposed design methodology for UEP

This paper first introduces an improved decoding algorithm for low-density parity-check (LDPC) codes over binary-input-output-symmetric memoryless channels. Then some fundamental properties of punctured LDPC codes are presented. It is proved that for any ensemble of LDPC codes, there exists a puncturing threshold. It is then proved that for any rates R1and R2satisfying 0121to R2resulting in asymptotically good codes for all rates R1lesRlesR2. Specifically, this implies that rates arbitrarily close to one are achievable via puncturing. Bounds on the performance of punctured LDPC codes are also presented. It is also shown that punctured LDPC codes are as good as ordinary LDPC codes. For BEC and arbitrary positive numbers R121lesRlesR2is shown. Based on the above observations, a method is proposed to design good punctured LDPC codes over a broad range of rates. Finally, it is shown that the results of this paper may be used for the proof of the existence of the capacity-achieving LDPC codes over binary-input-output-symmetric memoryless channels

We introduce a construction of raptor codes to be used over symmetric channels. An important property of the proposed scheme is that unlike the previous construction of raptor codes, it allows to design codes that are approaching the capacity of the underlying channel in a rate-compatible way. We also show that for finite-length codes, the proposed construction outperforms the previous constructions of raptor codes.

Asymptotic analysis of unreliable sensor grides has been studied previously. Some analytic results for sensor grids have been reported for the case where the number of nodes n in the network tends to infinity (large-scale grids). This includes connectivity, coverage, and diameter of the networks. These results have not been extended for small or moderate values of n, although in many practical sensor grids, n might not be very large. In this paper, we first show that previous asymptotic results may provide poor approximations for the finite grids (small-scale grids). We then aim to develop a methodology to analytically study unreliable sensor grids properties without assuming that n is large. We prove some properties of finite sensor grids. We show that a large class of network parameters can be expressed as piecewise constant functions of communication and sensing radii. We obtain simple analytic expressions for connectivity and coverage probabilities of finite sensor grids. Using simulations, we show that the expressions give good estimates of these probabilities.

This correspondence studies the performance of the iterative decoding of low-density parity-check (LDPC) code ensembles that have linear typical minimum distance and stopping set size. We first obtain a lower bound on the achievable rates of these ensembles over memoryless binary-input output-symmetric channels. We improve this bound for the binary erasure channel. We also introduce a method to construct the codes meeting the lower bound for the binary erasure channel. Then, we give upper bounds on the rate of LDPC codes with linear minimum distance when their right degree distribution is fixed. We compare these bounds to the previously derived upper bounds on the rate when there is no restriction on the code ensemble.

Temporal and spatial correlation in the sensed data in wireless distributed sensor networks gives room for better energy efficiency in the network. Several data aggregation schemes have been suggested in the literature. However a clear-cut solution which quantitatively describes most energy-efficient routing scheme is still lacking. In this paper, we propose a novel, generalized clustering-based aggregation scheme, called "annular slicing-based clustering (ASC)" and show that by varying the cluster size and the distribution of clusters in the deployment area, one can approach the most energy-efficient aggregation scheme. Analytical expressions for the optimal cluster size and distribution have been arrived at, for a specific correlation model and a cost function based on the Euclidean distance traversed by the transmitted data. With the help of numerical simulation, it has been found that the proposed aggregation technique can achieve optimality over a wide range of correlation

Hierarchical routing protocols are studied in terms of energy usage, packet latency, and security in the presence of node compromise attacks. We analyze clustering and tree-based structures of hierarchical algorithms to establish a method by which to design wireless sensor networks with particular energy, latency and security demands. Networks of homogeneous nodes and random deployment over a field are considered. We present analysis of the distribution of the distances between nodes of the sensor network and also provide simulations to validate and expound on these ideas.

This correspondence introduces a framework to design and analyze low-density parity-check (LDPC) codes over nonuniform channels. We study LDPC codes for channels with nonuniform noise distributions, rate-adaptive coding, and unequal error protection. First, we propose a technique to design LDPC codes for volume holographic memory (VHM) systems for which the noise distribution is nonuniform. We show that the proposed coding scheme has an easy design procedure and results in efficient codes for holographic memories. An important property of the proposed technique is the design of the codes that have a low error floor and low variable node degrees, while maintaining performance close to the Shannon limit. We then show that punctured LDPC codes can be studied as a special case of our design methodology for nonuniform channels. Finally, we propose a method to generate LDPC codes that can provide unequal error protection in addition to having a good overall performance. Moreover, the highly protected bits can be decoded without requiring the entire word to be decoded.

This paper studies the performance of the iterative decoding of LDPC code ensembles that have linear typical minimum distance and stopping set size. We first obtain a lower bound on the achievable rates of these ensembles over MBIOS channels. Then, we give upper bounds on the rate of LDPC codes with linear minimum distance, given their right degree distribution over the BEC.

In this paper we study some fundamental properties of punctured LDPC codes. We first prove that for any ensemble of LDPC codes, there exists a puncturing threshold p*. We then find lower bounds on the achievable rates of punctured codes over general MBIOS channels. These bounds are satisfied by using only one encoder and decoder for all rates. We then prove that for any rates R/sub 1/ and R/sub 2/ satisfying 0 < R/sub 1/ < R/sub 2/ < 1, there exists an ensemble of LDPC codes with the following property. The ensemble can be punctured from rate R/sub 1/ to R/sub 2/ resulting in asymptotically good codes for all rates R/sub 1/ /spl les/ R /spl les/ R/sub 2/. Specifically, this implies that rates arbitrarily close to one are achievable via puncturing. We also show that punctured LDPC codes are as good as ordinary LDPC codes. For binary erasure channel (BEC) and arbitrary positive numbers R/sub 1/ < R/sub 2/ < 1, we prove the existence of the sequences of punctured LDPC codes that are capacity achieving for all rates R/sub 1/ /spl les/ R /spl les/ R/sub 2/. Based on the above observations, we then propose a method to design good punctured LDPC codes over a broad range of rates. The method is very simple and does not suffer from the performance degradation at high rates. Finally, we show that punctured codes might be useful for proof of the existence of capacity-achieving LDPC codes over memoryless binary-input output-symmetric channels.

In this paper, we study connectivity properties of large-scale wireless sensor networks and discuss their effect on routing algorithms. In our model, n sensors are distributed randomly over a field based on a given distribution function. Two sensor nodes are connected with probability p/sub e/(n) if they are within the communication range of each other. The sensor nodes may also be unreliable. We find the necessary and sufficient conditions for the network to be k-connected, where k is a positive integer. We also find the distribution of isolated vertices. While connectivity (i.e, k = 1) insures that all nodes can communicate with each other, k-connectivity for k > 1 is required for multi-path routing. Additionally, it was found that the lengths of these multiple paths in a k-connected network are all close to the shortest path.

This paper investigates decoding of low-density parity-check (LDPC) codes over the binary erasure channel (BEC). We study the iterative and maximum-likelihood (ML) decoding of LDPC codes on this channel. We derive bounds on the ML decoding of LDPC codes on the BEC. We then present an improved decoding algorithm. The proposed algorithm has almost the same complexity as the standard iterative decoding. However, it has better performance. Simulations show that we can decrease the error rate by several orders of magnitude using the proposed algorithm. We also provide some graph-theoretic properties of different decoding algorithms of LDPC codes over the BEC which we think are useful to better understand the LDPC decoding methods, in particular, for finite-length codes.

We propose a technique for designing low-density parity-check (LDPC) codes over non-uniform channels. In particular, we investigate LDPC codes for volume holographic memory (VHM) systems. We show that the proposed coding scheme for holographic memories has an easy design procedure and results in efficient codes. An important property of the proposed technique is that we can design simple codes whose performance is close to the Shannon limit, while they are very good in terms of the error floor effect. We also derive a capacity bound and the stability condition for the proposed codes over the binary erasure channel. We briefly discuss other applications like punctured codes, OFDM systems and multilevel coding.

Recently we introduced a new framework to study error control coding using finite-field wavelets. In this paper we show that any double-circulant code over an arbitrary finite-field can be constructed by a simple two-band filter bank structure. Additionally for all double-circulant wavelet codes we introduce efficient tail-biting trellises on which we can perform soft-decision decoding. These tail-biting trellises are called π-minimal in which the product of all state space sizes is minimized.