This article presents a novel practical and theoretical foundation for detecting and characterizing events in continuously recorded, time-synchronized voltage waveform measurements, a.k.a., synchro-waveforms. The proposed methods leverage an in-depth analysis of two waveform concepts through formalized formulations: per-cycle waveform distortions and differential waveforms based on varying cycle-shift parameters. Their mathematical and empirical relationships are established, leading to algorithms that effectively identify and characterize subtle disturbances in waveform data. The methods detect and classify diverse events, including local and nonlocal, subcycle and multicycle, single-phase and three-phase, oscillatory and nonoscillatory, and rare as well as recurring events, and periodic disturbances. Extensive case studies, based on nearly a terabyte of real-world synchro-waveform data, provide insightful observations for each scenario.

Convergence bidding, a.k.a., virtual bidding, is a market mechanism that is used by several independent system operators (ISOs) to increase market efficiency in electricity markets by closing the gap between the day-ahead market (DAM) prices and the real-time market (RTM) prices. However, some recent reports by ISOs have questioned whether convergence bids (CBs) act as intended. Motivated by such reports, this article provides a methodology to identify under what conditions a CB results in price divergence, instead of price convergence. The analysis is done in nodal electricity markets and factors such as transmission line congestion are investigated. It is proven that price convergence is guaranteed under some transmission lines congestion configurations. In contrast, there are certain transmission lines congestion configurations that can result in price divergence when CBs are submitted at certain nodes. It also explains how the aggregate impact of multiple CBs may lead to decrease (or increase) in the price gap at each bus. Importantly, the analysis in this article also covers the stochastic case, where we obtain the probability of price convergence (or divergence) when we are uncertain about some system parameters.

Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under burstable billing where it is charged based on the 95th percentile bandwidth usage. To do this, we first develop a tractable mathematical expression to calculate the 95th percentile usage of a user. Then, we develop an optimization problem to maximize the user's surplus based on both deterministic and stochastic predictions of the user's demand. We show that the resulted optimization problem, while non-convex by nature, can be efficiently solved or approximated using a convex program. We also show that the proposed approach can also be applied in a more general scenario where the user gets services from multiple service providers. Using real-world workload traces, we show that the proposed approach can reduce a colocation data center user's IP transit cost by 26 percent and increase its total surplus by 23 percent, compared to the current practice of allocating bandwidth on-demand.

The Smart Grid represents an unprecedented opportunity to move the electric grid into a new era of reliability, availability, and efficiency. It uses two-way communications, digital technologies, advanced sensing and computing infrastructure, and software abilities to provide improved monitoring, protection and optimization of all the grids’ components including generation, transmission, distribution and consumers.

The current optimization-based algorithms to operate grid-tied battery energy storage systems (BESS) typically do not look much under the hood of the BESS, i.e., the device-level characteristics of the batteries. This is often due to modeling as well as optimization complexities. However, simplified models may significantly degrade the performance of BESS operation in practice. Therefore, in this article, we propose a new BESS scheduling optimization framework that accounts for features such as cell-to-cell variations in maximum capacity, charge level balance, and internal resistance. The proposed framework is in the form of tractable mixed integer linear programs. Our approach is to estimate and update the device-level battery model parameters continuously, without the need to interrupt BESS normal operation. We validate the performance compared to an offline approach, which is based on dedicated model testing and calibration. To assure accurate performance evaluation, this article also includes developing a power hardware-in-the-loop testbed that allows for flexible operation and detailed monitoring of BESS under different design scenarios.

Data centers spend millions of dollars on their electricity bills annually. Therefore, there is an interest among data center operators to control the electricity usage so as to minimize energy expenditure. However, when it comes to cloud data centers, the electricity usage is mainly controlled by the tenants. Yet, since most cloud data centers charge their tenants with flat rates, the tenants do not have incentives to change their electricity usage to contribute in cutting electricity bills by participation in demand response. Accordingly, in this paper, we propose a game-theoretic framework together with a time varying pricing (TVP) mechanism for cloud data centers to charge their tenants. In this approach, the TVP propagates the actual energy bill, which comprises both demand and energy charges, onto tenants' service costs. As an extension to this core idea, the time- varying amount of renewable energy generated by data center's on-site renewable generators is also taken into account to affect the payments. Our proposed pricing method is evaluated under various experimental data and simulations. We show that TVP can boost data center's profit by 8.2% and reduce the energy bill by 33.0% and improve tenants' aggregate surplus by 12.3%, when comparing it with a flat rates model that uses a widely employed billing method in today's cloud data centers.

A common assumption in the existing literature on energy consumption scheduling in smart grid is that users are aware in advance of their daily energy consumption needs. Therefore, most existing studies along this line of research have been inherently deterministic, e.g., see [1]-[3]. However, this assumption may not hold in practice. In particular, the energy consumption scheduling (ECS) devices may face load uncertainty. If a user is equipped with a behind-the-meter renewable generator, then the optimal operation of ECS devices becomes even more challenging due to combined load and supply uncertainties. Therefore, in this paper, we formulate a stochastic optimization problem to operate an ECS device in a residential unit that is equipped with a behind-the-meter renewable generator and a local battery bank. In our problem formulation, we consider different sets of must-run and controllable appliances. Our design only requires knowledge of some estimates of the users' future demand. To reduce computational complexity, we approximate the expected load in the upcoming time slots by adopting the certainty equivalent approximation technique. Simulation results show that the proposed energy consumption scheduling algorithm can tackle the uncertainties in load and supply and it can benefit both users and the utility companies.

We analyze a detailed set of driving traces for 536 GPS-equipped taxi vehicles and combine them with the features of four different plug-in hybrid electric vehicle (PHEV) brands that currently dominate the North American market in order to develop a test data set for PHEV-related research in the field of smart grid. Our developed data set is made available to public in [1]. It consists of various information, including but not limited to per-PHEV traces of state-of-charges (SoCs), per-PHEV traces of charging loads at different carefully identified charging stations, per-PHEV information on SoC and charging deadline when the PHEV is parked at a charging station, and some information about the potential of PHEVs for vehicle-to-grid applications.

Recent results have shown that selfish users do not have an incentive to participate in intersession network coding in a static noncooperative game setting. Because of this, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 20%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be improved to 36%. We design a grim-trigger strategy that encourages users to cooperate and participate in the intersession network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We resolve the conflict of interest among the users through a bargaining process and obtain tight upper bounds for the price-of-anarchy that are valid for any possible bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve these upper bounds, as confirmed through simulation studies. The coexistence of multiple selfish network coding sessions as well as the coexistence of selfish network coding and routing sessions are also investigated. Our results represent a first step toward designing practical intersession network coding schemes that achieve reasonable performance for selfish users.

In this paper, we propose a novel real-time energy consumption scheduling algorithm that takes into account load uncertainty to minimize the energy payment for each user. We formulate the problem of load scheduling as an optimization problem. To reduce the computational complexity, we devise an approximate dynamic programming approach to schedule the operation of appliances. In our problem formulation, we consider different sets of appliances including must-run and controllable. Unlike most of the existing demand side management algorithms that assume perfect knowledge of users' energy needs, our design only requires knowledge of some estimates of the future demand. Simulation results confirm that the proposed energy scheduling algorithm can benefit both the users by reducing their energy expenses and the utility companies by improving the peak-to-average ratio in load demand.

We analyze inter-session network coding in a wired network using game theory. We assume that users are selfish and act as strategic players to maximize their own utility, which leads to a resource allocation game among users. In particular, we study a butterfly network, where a bottleneck link is shared by network coding and routing flows. We assume that network coding is performed using pairwise XOR operations. We prove the existence of Nash equilibrium for a wide range of utility functions. We also show that the number of Nash equilibria can be large (even infinite) for certain choices of parameters. This is in sharp contrast to a similar game setting with traditional packet forwarding, where the Nash equilibrium is always unique. We characterize the worst-case efficiency bound, i.e., the Price-of-Anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a discriminatory pricing scheme which charges encoded and forwarded packets differently, we can improve the PoA in comparison with the case where a single pricing scheme is used. However, even when a discriminatory pricing scheme is used, the PoA is still worse than for the case when network coding is not applied. This implies that, although inter-session network coding can improve performance compared to routing, it is much more sensitive to users' strategic behavior.

Consider a smart grid system in which every user may or may not choose to participate in Demand Side Management (DSM). This will lead to a general co-existence problem between participant and non-participant users. To gain insights, first, we show that some existing electricity billing mechanisms suffer from severe fairness and co-existence defects. Next, we propose an alternative billing mechanism that can tackle the coexistence and fairness problems by taking into account not only the users' total load, but also the exact shape of their load profiles. Our analytical results provide mild sufficient conditions on the choice of system parameters to assure fairness. Furthermore, our simulation results confirm that the proposed billing mechanism significantly improves the fairness index of the DSM system.

False data injection attacks have recently been introduced as an important class of cyber attacks against smart grid's wide area measurement and monitoring systems. These attacks aim to compromise the readings of multiple power grid sensors and phasor measurement units in order to mislead the operation and control centers. Recent studies have shown that if an adversary has complete knowledge on the power grid topology and transmission-line admittance values, he can adjust the false data injection attack vector such that the attack remains undetected and successfully passes the residue-based bad data detection tests that are commonly used in power system state estimation. However, in this paper, we explain that a realistic false data injection attack is essentially an attack with incomplete information due to the attackers lack of real-time knowledge with respect to various grid parameters and attributes such as the position of circuit breaker switches and transformer tap changers and also because of the attacker's limited physical access to most grid facilities. We mathematically characterize false data injection attacks with incomplete information from both the attacker's and grid operator's viewpoints. Furthermore, we introduce a novel vulnerability measure that can compare and rank different power grid topologies against such attacks. To the best of our knowledge, this paper is the first study to investigate false data injection attacks with line admittance uncertainty.

Because of their intermittency and inter-temporal variations, the integration of renewable energy resources is usually very challenging. Some of the previously proposed solutions, such as the use of energy storage units and fast-responding generators, are costly and may not be desirable in practice. Instead, in this paper, we argue that due to the workload flexibility in the Internet and Cloud Computing data centers they can offer a unique opportunity to tackle the challenges in integrating renewable energy resources. Moreover, by running data centers with behind-the-meter renewable generators, we can significantly reduce not only data centers' energy cost but also their Carbon Footprint. To achieve these objectives, we propose to optimally distribute the workload among geographically dispersed data centers such that we can benefit from the location diversity of different types of available renewable energy resources. The quality-of-service is guaranteed by real-time monitoring of the queue lengths and effectively stabilizing short queue lengths in each data center. Simulation results, based on various experimental data, show that the proposed design can significantly reduce data centers' cost of electricity while maintaining quality-of-service.

Wireless links are often unreliable and prone to transmission error due to varying channel conditions. These can degrade the performance in wireless networks, particularly for applications with tight quality-of-service requirements. A common remedy is to use channel coding where the transmitter node adds redundant bits to the transmitted packets in order to reduce the error probability at the receiver. However, this per-link solution can compromise the link data rate, leading to undesired end-to-end performance. In this paper, we show that this latter shortcoming can be mitigated if the end-to-end transmission rates and channel code rates are selected properly over multiple routing paths. We formulate the joint channel coding and end-to-end data rate allocation problem in multipath wireless networks as a network throughput maximization problem, which is non-convex. We tackle the non-convexity by using function approximation and iterative techniques from signomial programming. Simulation results confirm that by using channel coding jointly with multi-path routing, the end-to-end network performance can be improved significantly.

In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design framework for direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account the spectral mask constraints that are usually imposed in practice by the telecommunications regulation and standardization bodies. This avoids the need for an inefficient power back-off, which is necessary for the existing pre-Rake-based transmitter structures designed solely based on average transmit power constraints. We consider two PEF design structures. In the first structure, the PEF is placed before the up-sampling unit at the transmitter. In the second structure, the PEF is placed after the up-sampling unit. We show that these two structures are significantly different in terms of their capabilities in reducing the residual inter-symbol interference at the receiver and also in following the spectral mask at the transmitter. This introduces a trade-off such that one design structure outperforms the other one, depending on the system parameters. In this regard, we show that if the spreading factor is large enough, the second design structure has a superior performance. Simulation results confirm that both of the proposed PEF structures lead to significant performance gains over PEF designs without explicit spectral mask considerations. In addition, our PEF designs have the capability of adhering to spectral masks with arbitrary shapes.

The aggregate capacity of wireless ad hoc networks can be increased substantially if each node is equipped with multiple network interface cards (NICs) and each NIC operates on a distinct frequency channel. Most of the recently proposed channel assignment algorithms are based on combinatorial techniques. Combinatorial channel assignment schemes may sometimes result in computationally complicated algorithms as well as inefficient utilization of the available frequency spectrum. In this paper, we analytically model channel and interface assignment problems as tractable continuous optimization problems within the framework of network utility maximization (NUM). In particular, the link data rate models for both single-channel reception and multichannel reception scenarios are derived. The assignment of both nonoverlapped and partially overlapped channels is also considered. We then propose two distributed multi-interface multichannel random access (DMMRA) algorithms for single-channel reception and multichannel reception scenarios. The DMMRA algorithms are fast, distributed, and easy to implement. Each algorithm solves the formulated NUM problem for each scenario. DMMRA requires each node to only iteratively solve a local, myopic, and convex optimization problem. Convergence and optimality properties of our algorithms are studied analytically. Simulation results show that our proposed algorithms significantly outperform utility-optimal combinatorial channel assignment algorithms in terms of both achieved network utility and throughput.

In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design for direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account the spectral mask constraints that are usually imposed by the telecommunication regulation bodies. This avoids the need for an inefficient power back-off, which is necessary for transmit structures designed solely based on average transmit power constraints. Simulation results confirm that the proposed PEF design leads to significant performance gains over UWB PEF structures without explicit spectral mask considerations.

Most of the previous work on network coding has assumed that the users are not selfish and always follow the designed coding schemes. However, recent results have shown that selfish users do not have the incentive to participate in inter-session network coding in a static non-cooperative game setting. As a result, the worst-case network efficiency (i.e., the price-of-anarchy) can be as low as 22%. In this paper, we show that if the same game is played repeatedly, then the price-of-anarchy can be significantly improved to 48%. We propose a grim-trigger strategy that encourages users to cooperate and participate in the inter-session network coding. A key challenge is to determine a common cooperative coding rate that the users should mutually agree on. We propose to resolve the conflict of interest among the users through a bargaining process. We derive a tight upper bound for the price-of-anarchy which is valid for any bargaining scheme. Moreover, we propose a simple and efficient min-max bargaining solution that can achieve this upper bound. Our results represent one of the first steps towards designing practical inter-session network coding schemes that can achieve reasonable performance for selfish users.

Random access protocols, such as Aloha, are commonly modeled in wireless ad-hoc networks by using the protocol model. However, it is well-known that the protocol model is not accurate and particularly it cannot account for aggregate interference from multiple interference sources. In this paper, we use the more accurate physical model, which is based on the signal-to-interference-plus-noise-ratio (SINR), to study optimization-based design in wireless random access systems, where the optimization variables are the transmission probabilities of the users. We focus on throughput maximization, fair resource allocation, and network utility maximization, and show that they entail non-convex optimization problems if the physical model is adopted. We propose two schemes to solve these problems. The first design is centralized and leads to the global optimal solution using a sum-of-squares technique. However, due to its complexity, this approach is only applicable to small-scale networks. The second design is distributed and leads to a close-to-optimal solution using the coordinate ascent method. This approach is applicable to medium-size and large-scale networks. Based on various simulations, we show that it is highly preferable to use the physical model for optimization-based random access design. In this regard, even a sub-optimal design based on the physical model can achieve a significantly better performance than an optimal design based on the inaccurate protocol model.

In this paper, we consider the problem of random access in wireless local area networks (WLANs) with each station generating either elastic or inelastic traffic. Elastic traffic is usually non-real-time, while inelastic traffic is usually coming from real-time applications. We formulate a network utility maximization (NUM) problem, where the optimization variables are the persistent probabilities of the stations and the utilities are either concave or sigmoidal functions. Sigmoidal utility functions can better represent inelastic traffic sources compared to concave utility functions commonly used in the existing random access literature. However, they lead to non-convex NUM problems which are not easy to solve in general. By applying the dual decomposition method, we propose a subgradient algorithm to solve the formulated NUM problem. We also develop closed-form solutions for the dual subproblems involving sigmoidal functions that have to be solved in each iteration of the proposed algorithm. Furthermore, we obtain a sufficient condition on the link capacities which guarantees achieving the global optimal solution when our proposed algorithm is being used. If this condition is not satisfied, then we can still guarantee that the optimal value of the objective function is within some lower and upper bounds. We perform various simulations to validate our analytical models when the available link capacities meet or do not meet the sufficient optimality condition.

Radio frequency identification (RFID) is an emerging wireless communication technology which allows objects to be identified automatically. An RFID system consists of a set of readers and several objects, equipped with small and inexpensive computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. High probability of collision impairs the benefit of multiple reader deployment and results in misreading. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not achieve optimal system performance. In this paper, we systematically design two optimization-based distributed channel selection and randomized interrogation algorithms for dense RFID systems. For this purpose, we develop elaborate models for the reader-to-tag and reader-to-reader collision problems. The first algorithm is fully distributed and is guaranteed to find a local optimum of a max-min fair resource allocation problem for RFID systems. The second algorithm is semi-distributed and achieves the global optimal system performance. Max-min fair optimality balances the performance and the processing load among readers. Simulation results show that our algorithms have significantly better performance than the previous heuristic algorithms.

A radio frequency identification (RFID) system consists of a set of readers and several objects, equipped with small computer chips, called tags. In a dense RFID system, where several readers are placed together to improve the read rate and correctness, readers and tags can frequently experience packet collision. A common approach to avoid collision is to use a distinct frequency channel for interrogation for each reader. Various multi-channel anti-collision protocols have been proposed for RFID readers. However, due to their heuristic nature, most algorithms may not fully utilize the achievable system performance. In this paper, we develop an optimization-based distributed randomized multi-channel interrogation algorithm, called FDFA, for large-scale RFID systems. For this purpose, we develop elaborate models for reader-to-tag and reader-to-reader collision problems. FDFA algorithm is guaranteed to find a local optimum of a max-min fair resource allocation problem to balance the processing load among readers. Simulation results show that FDFA has a significantly better performance than the existing heuristic algorithms in terms of the number of successful interrogations. It also better utilizes the frequency spectrum.

Network coding has recently received increasing attention to improve performance and increase capacity in both wired and wireless communication networks. In this paper, we focus on inter-session network coding, where multiple unicast sessions jointly participate in network coding. Wireless links are often unreliable because of varying channel conditions. We consider multi-hop unicast sessions over unreliable links and propose a distributed end-to-end transmission rate adjustment mechanism to maximize the aggregate network utility by taking into account the wireless link reliability information. This includes an elaborate modeling of end-to-end reliability. Simulation results show that by taking into account the reliability information, we can increase the network throughput by up to 100% for some network topologies. We can also increase the aggregate network utility significantly for various choices of utility functions.

In wireless local area networks (WLANs), quality of service (QoS) can be provided by mapping applications with different requirements (e.g., delay and throughput) into one of the available access categories (ACs), as is done in the IEEE 802.11e standard. With the increasing programmability of network adapters, a malicious user can strategically declare a higher AC for its application to gain an unfair share of resources. This can drastically degrade the network performance and avoid adequate service distinction among different ACs. In this paper, we use the technique of mechanism design in game theory to tackle this problem in WLANs with random access. We propose to use the Vickrey-Clarke-Groves (VCG) mechanism in order to motivate each station to inform the access point (AP) truthfully, about the required AC of its application. The AP will then inform each station about its persistent probability and the price it needs to pay for the offered service. The result of the allocation of the persistent probabilities can be used for admission control. Simulation results show that the use of mechanism design can lead to a higher aggregate utility and prevents malicious users from gaining an unfair share of the network bandwidth.

A common assumption in the network coding literature is that the users are cooperative and will not pursue their own interests. However, this assumption can be violated in practice. In this paper, we analyze inter-session network coding in a wired network, assuming that the users are selfish and act as strategic players to maximize their own utility. We prove the existence of Nash equilibria for a wide range of utility functions. The number of Nash equilibria can be large (even infinite) under certain conditions, which is in sharp contrast to a similar game setting with traditional packet forwarding. We then characterize the worst-case efficiency bounds, i.e., the price-of-anarchy (PoA), compared to an optimal and cooperative network design. We show that by using a novel discriminatory pricing scheme that charges encoded and forwarded packets differently, we can improve PoA in comparison with the case where a single pricing scheme is being used. However, PoA is still worse than the case when network coding is not applied. This implies that inter-session network coding is more sensitive to strategic behavior. For example, for the case where only two network coding flows share a single bottleneck link, the efficiency at certain Nash equilibria can be as low as 48%. These results generalize the well-known result of guaranteed 67% efficiency bounds shown by Johari and Tsitsiklis for traditional packet forwarding networks.

In this paper, we propose two distributed contention-based medium access control (MAC) algorithms for solving a network utility maximization (NUM) problem in wireless ad hoc networks. Most of the previous NUM-based random access algorithms have one or more of the following performance bottlenecks: (1) extensive signaling among the nodes to achieve semi-distributed implementations, (2) synchronous updates of contention probabilities, (3) small update stepsizes to ensure convergence but with typically slow speed, and (4) supporting a limited range of utility functions under which the NUM is shown to be convex. Our proposed algorithms overcome the bottlenecks in all four aspects. First, only limited amount of message passing among nodes is required. Second, fully asynchronous updates of contention probabilities are allowed. Furthermore, our algorithms are robust to arbitrary large message passing delay and message loss. Third, we do not utilize any stepsize during updates, thus our algorithms can achieve faster convergence. Finally, our proposed algorithms have provable convergence, optimality, and robustness properties under a wider range of utility functions, even if the NUM problem is non-convex. Simulation results show the optimality and fast convergence of our algorithms, performance improvements compared with the subgradient-based MAC, and better efficiency-fairness tradeoff compared with the IEEE 802.11 distributed coordination function.

Random access has been studied for decades as a simple and practical wireless medium access control (MAC). Some of the recently developed distributed scheduling algorithms for throughput or utility maximization also take the form of random access, although extensive message passing among the nodes is required. In this paper, we would like to answer this question: is it possible to design a MAC algorithm that can achieve the optimal network utility without message passing? We provide the first positive answer to this question through a simple Aloha-type random access protocol. We prove the convergence of our algorithm for certain sufficient conditions on the system parameters, e.g., with a large enough user population. If each wireless node is capable of decoding the source MAC address of the transmitter from the interferring signal, then our algorithm indeed converges to the global optimal solution of the NUM problem. If such decoding is inaccurate, then the algorithm still converges, although optimality may not be always guaranteed. Proof of these surprisingly strong performance properties of our simple random access algorithm leverages the idea from distributed learning: each node can learn as much about the contention environment through the history of collision as through instantaneous but explicit message passing.

In a multi-hop wireless access network, where each node is an independent self-interested commercial entity, pricing is helpful not only to encourage collaboration but also to utilize the network resources efficiently. In this paper, we propose a market-based model with two-fold pricing (TFP) for wireless access networks. In our model, the relay-pricing is used to encourage nodes to forward packets for other nodes. Each node receives a payment for the relay service that it provides. We also consider interference-pricing to leverage optimal resource allocation. Together, the relay and interference prices incorporate both cooperative and competitive interactions among the nodes. We prove that TFP guarantees positive profit for each individual wireless node for a wide range of pricing functions. The profit increases as the node forwards more packets. Thus, the cooperative nodes are well rewarded. We then determine the relay and interference pricing functions such that the network social welfare and the aggregate network utility are maximized. Simulation results show that, compared to two recently proposed single-fold pricing models, where only the relay or only the interference prices are considered, our proposed TFP scheme significantly increases the total network profit as well as the aggregate network throughput. TFP also leads to more fair revenue sharing among the wireless relay nodes.

The aggregate capacity of wireless ad-hoc networks can be substantially increased if each wireless node is equipped with multiple network interface cards (NICs) and each NIC operates over a distinct orthogonal frequency channel. Most of the recently proposed channel assignment algorithms are based on formulating combinatorial channel assignment problems. The key is to assign exactly one frequency channel to each NIC. However, combinatorial channel assignment models may result in computationally complicated algorithms as well as inefficient utilization of the available frequency spectrum. In this paper, we revisit channel assignment problem by formulating a novel continuous multi-interface multi-channel random access model. This includes elaborate modeling of the link data rates for various multi-interface multi-channel networking scenarios. We then propose a fast, fully distributed and easy to implement multi- interface multi-channel random access algorithm. Simulation results show that our proposed algorithm significantly outperforms combinatorial channel assignment algorithms in terms of achieved network utility and aggregate network throughput.

In a multi-hop wireless access network where each node is an independent self-interested commercial entity, pricing is helpful not only to encourage collaboration but also to utilize the network resources efficiently. In this paper, we propose a market-based model with two-fold pricing (TFP) for wireless access networks. In our model, the relay-pricing is used to encourage nodes to forward each other's packets. That is, each node is paid off for the relay service it provides. We also consider interference- pricing to leverage optimal resource allocation. We prove that TFP guarantees positive profit for each individual wireless node for a wide range of pricing functions. The profit increases as the node forwards more packets. Thus, the cooperative nodes are well rewarded. We then determine the relay and the interference pricing functions such that the network social welfare and the aggregate network utility are maximized. Simulation results show that, compared to a recently proposed single-fold pricing (SFP) model where only the relay prices are considered, our proposed TFP scheme significantly increases the total network profit and the network throughput. TFP also leads to more fair revenue sharing and profit distribution among the wireless relay nodes.

Wireless sensor networks (WSNs) have recently received increasing attention from research and development communities. In a WSN, the field information (e.g., temperature, humidity, airflow) is acquired via several battery-equipped wireless devices and is relayed towards a sink node. As the size of the WSNs increases, it becomes inefficient to gather all information in one sink. To tackle this problem, the number of sinks can be increased. The data information flow towards each of the sinks is called a commodity. In this paper, we formulate a lexicographically optimal commodity lifetime (LOCL) routing problem. A stepwise algorithm is proposed to obtain the optimal routing solution which can lead to lexicographical fairness among commodity lifetimes. Simulation results show that our proposed algorithm increases the normalized commodity lifetime compared to MLMS [1] and LMM [2] routing algorithms.

In a wireless mesh network (WMN) with a number of stationary wireless routers, the aggregate capacity can be increased when each router is equipped with multiple network interface cards (NICs) and each NIC is assigned to a distinct orthogonal frequency channel. In this paper, given the logical topology of the network, we mathematically formulate a crosslayer fair bandwidth sharing problem as a non-linear mixedinteger network utility maximization problem. An optimal joint design, based on exact binary linearization techniques, is proposed which leads to a global maximum. A near-optimal joint design, based on approximate dual decomposition techniques, is also proposed which is practical for deployment. Performance is assessed through several numerical examples in terms of network utility, aggregate network throughput, and fairness index. Results show that our proposed designs can lead to multi-channelWMNs which are more efficient and fair compared to their singlechannel counterparts. The performance gain on both efficiency and fairness increase as the number of available NICs per router or the number of available frequency channels increases.

The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, MPEG-4 video streams are deemed to be a widespread traffic type. However, in the current IEEE 802.15.3 standard for media access control (MAC) of high-rate WPANs, the implementation details of some key issues such as scheduling and quality of service (QoS) provisioning have not been addressed. In this paper, we first propose a mathematical model for the optimal scheduling scheme for MPEG-4 flows in high-rate WPANs. We also propose an RL scheduler based on the reinforcement learning (RL) technique. Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT (Mangharam et al., 2004), EDD+SRPT (Torok et al., 2005), and PAP (Kim and Cho, 2005) scheduling algorithms in terms of a lower decoding failure rate.

The complementary characteristics of cellular systems and wireless local area networks (WLANs) make them attractive candidates to jointly offer a seamless wireless solution. In an integrated cellular/WLAN system, the quality of service (QoS) requirements for different services (e.g., voice and real-time video) require adequate admission control policies to limit the number of connections in each network. In this paper, we analytically develop a comprehensive model to facilitate the optimal evaluation of different admission control policies in a multi-service integrated cellular/WLAN system. Given the model, we formulate two optimization problems to adjust admission control parameters. The first problem is to maximize the network revenue while the second problem is to minimize the required network resources subject to QoS constraints. We evaluate each problem when different combinations of cutoff priority and fractional guard channel admission control policies are being used. We show that a combination of two cutoff priority policies achieves the best solution for both problems.

The aggregate capacity of wireless mesh networks can be increased by the use of multiple frequency channels and multiple network interface cards in each router. Recent results have shown that the performance can further be increased when both non-overlapped and partially overlapped channels are being used. In this paper, we propose a linear model for a joint channel assignment, interface assignment, and scheduling design. We propose the channel overlapping matrix and mutual interference matrices to model the non-overlapped and partially overlapped channels. Since the model is formulated as a linear mixed-integer program with a few integer variables, the computation complexity is low and it is feasible for implementation. Simulation results show that the aggregate network capacity increases by 90% when all partially overlapped channels within the 802.11b frequency band are being used.

In a wireless mesh network (WMN) with a number of stationary wireless routers, the aggregate capacity can be increased when each router is equipped with multiple network interface cards (NICs) and each NIC within a router is assigned to a distinct orthogonal frequency channel. In this paper, given the logical topology of the network, we formulate the joint channel allocation, interface assignment, and media access control (MAC) problem as a cross-layer non-linear mixed-integer network utility maximization problem. An optimal joint design, based on exact binary linearization techniques, is proposed which leads to a global maximum. A near-optimal joint design, based on approximate dual decomposition techniques, is also proposed which is of more interest in terms of practical deployment. Performance evaluation is given through a number of numerical examples in terms of network utility maximization and aggregate network throughput.

A multi-channel wireless mesh network (MC-WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each NIC operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an NIC operating on a common channel. Given the physical topology of the routers and other constraints, four important issues should be addressed in MC-WMNs: logical topology formation, interface assignment, channel allocation, and routing. Logical topology determines the set of logical links. Interface assignment decides how the logical links should be assigned to the NICs in each wireless router. Channel allocation selects the operating channel for each logical link. Finally, routing determines through which logical links the packets should be forwarded. In this paper, we mathematically formulate the logical topology design, interface assignment, channel allocation, and routing as a joint linear optimization problem. Our proposed MC-WMN architecture is calledTiMesh. Extensive ns-2 simulation experiments are conducted to evaluate the performance ofTiMeshand compare it with two other MC-WMN architecturesHyacinth[1] andCLICA[2]. Simulation results show thatTiMeshachieves higher aggregated network throughput and lower end-to-end delay thanHyacinthandCLICAfor both TCP and UDP traffic. It also provides better fairness among different flows.

A multi-channel wireless mesh network (MC- WMN) consists of a number of stationary wireless routers, where each router is equipped with multiple network interface cards (NICs). Each interface operates on a distinct frequency channel. Two neighboring routers establish a logical link if each one has an interface operating on a common channel. Given the physical topology of the routers and other constraints, the logical topology formation algorithm determines the set of logical links. In general, since the number of NICs is limited, some logical links need to share an NIC in a router. The interface assignment algorithm determines the interface that a logical link should be attached to. In this paper, we formulate the logical topology design and interface assignment as a joint optimization problem to obtain an MC-WMN architecture, called TiMesh. We conducted extensive ns-2 simulation experiments to evaluate our algorithm and compared it with another MC-WMN architecture called Hyacinth. Simulation results show that our proposed scheme achieves a higher aggregated network goodput and lower end-to-end delay for both TCP and UDP traffic.

The aggregate capacity of wireless mesh networks can be increased by the use of multiple channels. Stationary wireless routers are equipped with multiple network interface cards (NICs). Each NIC is assigned with a distinct frequency channel. In this paper, we formulate the Joint Optimal Channel Assignment and Congestion Control (JOCAC) as a decentralized utility maximization problem with constraints that arise from the interference of the neighboring transmissions. Unlike other previous work, the JOCAC algorithm is able to assign not only the non-overlapping (orthogonal) channels, but also the partially-overlapping channels within the IEEE 802.11 frequency bands. Using 802.11b with 3 non-overlapping channels, simulation results show that our algorithm provides a higher aggregated goodput than the recently proposed load-aware algorithm by 20%. The goodput is further increased by 40% when all the 11 partially-overlapping channels are being used.