Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

A fundamental problem in survivable routing in wavelength division multiplexing (WDM) optical networks is the computation of a pair of link-disjoint (or node-disjoint) lightpaths connecting a source with a destination, subject to the wavelength continuity constraint. However, this problem is NP-hard when the underlying network topology is a general mesh network. As a result, heuristic algorithms and integer linear programming (ILP) formulations for solving this problem have been proposed. In this paper, we advocate the use of 2-edge connected (or 2-node connected) subgraphs of minimum isolated failure immune networks as the underlying topology for WDM optical networks. We present a polynomial-time algorithm for computing a pair of link-disjoint lightpaths with shortest total length in such networks. The running time of our algorithm is O(nW2), where n is the number of nodes, and W is the number of wavelengths per link. Numerical results are presented to demonstrate the effectiveness and scalability of our algorithm. Extension of our algorithm to the node-disjoint case is straightforward.

Tremendous efforts have been made to protect the location privacy of mobile users. Some of them, e.g., k-anonymity, require the participation of multiple mobile users to impede the adversary from tracing. These participating mobile users constitute an anonymity set. However, not all mobile users are seriously concerned about their location privacy. Therefore, to achieve k-anonymity, we need to provide incentives for mobile users to participate in the anonymity set. In this paper, we study the problem of incentive mechanism design for k-anonymity location privacy. We first consider the case where all mobile users have the same privacy degree requirement. We then study the case where the requirements are different. Finally, we consider a more challenging case where mobile users can cheat about not only their valuations but also their requirements. We design an auction-based incentive mechanism for each of these cases and prove that all the auctions are computational efficient, individually rational, budget-balanced, and truthful. We evaluate the performance of different auctions through extensive simulations.

Anypath routing has been proposed to improve the performance of unreliable wireless networks by exploiting the spatial diversity and broadcast nature of the wireless medium. Previous studies on anypath routing have concentrated on finding an anypath, which optimizes a single quality of service (QoS) parameter. In this paper, we study anypath routing subject to multiple constraints. We first prove that the problem is NP-hard when the number of constraints is larger than one. We then present a polynomial time K--approximation algorithm MAP, where K is the number of constraints. Our algorithm is as simple as Dijkstra's shortest path algorithm. Therefore, it is suitable for implementation in wireless routing protocols.

In this paper, we present a game-theoretic study of the problem of routing in networks with max-min fair congestion control at the link level. The problem is formulated as a noncooperative game, in which each user aims to maximize its own bandwidth by selecting its routing path. We first prove the existence of Nash equilibria. This is important, because at a Nash equilibrium (NE), no user has any incentive to change its routing strategy-leading to a stable state. In addition, we investigate how the selfish behavior of users may affect the performance of the network as a whole. We next introduce a novel concept of observed available bandwidth on each link. It allows a user to find a path with maximum bandwidth under max-min fair congestion control in polynomial time, when paths of other users are fixed. We then present a game-based algorithm to compute an NE and prove that by following the natural game course, the network converges to an NE. Extensive simulations show that the algorithm converges to an NE within 10 iterations and also achieves better fairness compared to other algorithms .

Jamming defense is an important yet challenging problem. In this paper, we study the jamming defense problem in the presence of a smart jammer, who can quickly learn the transmission power of the user and adaptively adjust its transmission power to maximize the damaging effect. We consider both the single-channel model and the multi-channel model. By modeling the problem as a Stackelberg game, we compute the optimal transmission power for the user to maximize its utility, in the presence of a smart jammer. For the single-channel model, we prove the existence and uniqueness of the Stackelberg Equilibrium (SE) by giving closed-form expressions for the SE strategies of both the user and the player. For the multi-channel model, we prove the existence of the SE. We design algorithms for computing the jammer's best response strategy and approximating the user's optimal strategy. Finally, we validate our theoretical analysis through extensive simulations.

Jamming defense is an important yet challenging problem. In this paper, we study the jamming defense problem in the presence of a smart jammer, who can quickly learn the transmission power of the user and adaptively adjust its transmission power to maximize the damaging effect. By modeling the problem as a Stackelberg game, we compute the optimal transmission power for the user to maximize its utility, in spite of the existence of the smart jammer. We prove that the smart jammer is not more damaging than a jammer without the intelligence, provided that the user plays its strategy corresponding to a Stackelberg equilibrium. This nice property is due to the user's ability to predict the jammer's behavior.

Auction theory has been applied to cooperative communications to either efficiently allocate resources or incentivize wireless devices to participate in cooperative communications. However, a common shortcoming of the existing studies is that the revenue generation is neglected. Revenue generation is the ultimate goal of commercial networks, e.g., WiMAX networks. In this paper, we study the problem of how to use auction mechanisms to allocate the relay nodes and charge the source nodes, such that the revenue of the seller, e.g., the base station, is maximized. We first propose a VCG-based auction mechanism, which can maximize the revenue while enforcing the truthfulness. To overcome the high time complexity of the VCG-based auction mechanism, we further design another truthful auction mechanism with low time complexity. Experiment results show that the suboptimal auction mechanism significantly reduces the time complexity without severely sacrificing the revenue.

Surveillance systems, such as sensor networks and surveillance camera networks, have been widely deployed to monitor events in many different scenarios. One common way to conserve resource (such as energy) usage is to have only a subset of devices activated at any give time. In this paper, we look at this classic problem from a new perspective: we do not try to cover all the event areas as usually studied, but aim to find the most valuable event areas among all the event areas (i.e., the ones leading to the most utility) to monitor, subject to resource constraints. This problem poses two major challenges. First, the utility brought by monitoring an event area is not known beforehand. Second, even if this information is known in advance, solving the problem of which event areas should be monitored to maximize the total utility, subject to resource constraints, is NP-hard. We formulate this problem as a novel programming system, called online integer linear programming, and present a polynomial time algorithm to solve this problem. For any given σ∈(0, 1), we prove a bound on the gap between the expected utility obtained by constantly using the global optimal strategy multiplied by σ and the expected utility obtained by following our algorithm.

In this paper, we study the influence of network entity load constraints on network resource allocation. We focus on the problem of allocating network resources to optimize the total utility of multiple users in a wireless network, taking into account four resource and social requirements: 1) user QoS rate constraints, 2) node max load constraints, 3) node load balance constraints, and 4) node-user load constraints. We formulate this problem as a programming system. In order to solve this programming system, we first propose an optimization framework, called α-approximation dual subgradient algorithm, which may be applicable for many networking optimization problems. Given an approximation/optimal algorithm for solving the subproblem at each iteration, the framework leads to a result that can provide the following bounds at each iteration: 1) the bounds on the Lagrangian multipliers; 2) the bound on the amount of feasibility violation of the generated primal solutions; and 3) the upper and lower bounds on the gap between the optimal solution and the generated primal solutions. Based on this framework, we then present a distributed iterative algorithm to solve the network resource allocation problem. At each iteration, we provide bounds on the amount of feasibility violation, the gap between our solution and the optimal solution, node queue lengths, user utility deficits, and node load violation ratios.

While tremendous efforts have been made on channel allocation problems in wireless networks, most of them are on cooperative networks with few exceptions [6, 8, 31, 32]. Among those works on non-cooperative networks, none of them considers the network with multiple collision domains. Instead, they all assume the single collision domain, where all transmissions interfere with each other if they are on the same channel. In this paper, we fill this void and generalize the channel allocation problem to non-cooperative multi-radio multi-channel wireless networks with multiple collision domains. We formulate the problem as a strategic game, called ChAlloc. We show that the ChAlloc game may result in an oscillation when there are no exogenous factors to influence players' strategies. To avoid this possible oscillation, we design a charging scheme to induce players to converge to a Nash Equilibrium (NE). We bound the convergence speed and prove that the system performance in an NE is at least (1 - r̅/h) of the system performance in an optimal solution, where r̅ is the maximum number of radios equipped on wireless devices and h is the number of available channels. In addition, we develop a localized algorithm for players to find an NE strategy. Finally, we evaluate our design through extensive experiments. The results validate our analysis of the possible oscillation in the ChAlloc game lacking the charging scheme, confirm the convergence of the ChAlloc game with the charging scheme, and verify our proof on the system performance compared to the upper bounds returned by an LP-based algorithm.

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Exploiting the nature of broadcast and the relaying capability of wireless devices, cooperative communication is becoming a promising technology to increase the channel capacity in wireless networks. In cooperative communication, the scheme for assigning relay nodes to users plays a critical role in the resulting channel capacity. A significant challenge is how to make the scheme robust to selfish and cheating behavior of users while guaranteeing the social optimal system capacity. In this paper, we design an integrated optimal marriage scheme called HERA for cooperative networks. To avoid system performance degradation due to the selfish relay selections by the source nodes, we propose a payment mechanism for charging the source nodes to induce them to converge to the optimal assignment. To prevent relay nodes from manipulating the marriage by reporting transmission power untruthfully, we propose a payment mechanism to pay them for providing relaying service. We also show that HERA is budget-balanced, meaning that the payment collected from source nodes is no smaller than the payment paid to relay nodes.

Delay constrained path selection is concerned with finding a source-to-destination path so that the delay of the path is within a given delay bound. When the network is modeled by a directed graph where the delay of a link is a random variable with a known mean and a known variance, the problem becomes that of computing a most probable delay constrained path. In this paper, we present a comprehensive theoretical study of this problem. First, we prove that the problem is NP-hard. Next, for the case where there exists a source-to-destination path with a delay mean no more than the given delay bound, we present a fully polynomial time approximation scheme. In other words, for any given constant ε such that 0 <; ε <; 1, our algorithm computes a path whose probability of satisfying the delay constraint is at least (1-ε) times the probability that the optimal path satisfies the delay constraint, with a time complexity bounded by a polynomial in the number of network nodes and 1/ε. Finally, for the case where any source-to-destination path has a delay mean larger than the given delay bound, we present a simple approximation algorithm with an approximation ratio bounded by the square root of the hop count of the optimal path.

In wireless sensor networks, relay node placement has been proposed to improve energy efficiency. In this paper, we study two-tiered constrained relay node placement problems, where the relay nodes can be placed only at some prespecified candidate locations. To meet the connectivity requirement, we study the connected single-cover problem where each sensor node is covered by a base station or a relay node (to which the sensor node can transmit data), and the relay nodes form a connected network with the base stations. To meet the survivability requirement, we study the 2-connected double-cover problem where each sensor node is covered by two base stations or relay nodes, and the relay nodes form a 2-connected network with the base stations. We study these problems under the assumption that R \ge 2r > 0, where R and r are the communication ranges of the relay nodes and the sensor nodes, respectively. We investigate the corresponding computational complexities, and propose novel polynomial time approximation algorithms for these problems. Specifically, for the connected single-cover problem, our algorithms have {\cal O}(1)-approximation ratios. For the 2-connected double-cover problem, our algorithms have {\cal O}(1)-approximation ratios for practical settings and {\cal O}(\ln n)-approximation ratios for arbitrary settings. Experimental results show that the number of relay nodes used by our algorithms is no more than twice of that used in an optimal solution.

The smart grid, perceived as the next generation power grid, uses two-way flow of electricity and information to create a widely distributed automated energy delivery network. By grouping distributed renewable energy generations and loads, a microgrid, which is seen as one of the cornerstones of the future smart grids, can disconnect from the macrogrid and function autonomously. This intentional islanding of generations and loads has the potential to provide a higher local reliability than that provided by the power system as a whole. One of the fundamental issues for a user in an islanded microgrid is how to find the one among the distributed renewable energy resources (DRERs) in a microgrid, which can supply the power most efficiently, effectively and reliably, as its power supply source. This problem is difficult since the power pattern of renewable resources, such as wind and solar, is variable and generally speaking is not easy to accurately predict. In order to solve this problem, we first propose a distributed DRER discovery approach to discover all the available DRERs within a microgrid. Furthermore, based on the online machine learning theory, we propose two distributed algorithms according to the information the user can obtain, in order to compute a good DRER access strategy, with no assumption on what distribution the power patterns of the DRERs follow. We prove that when the time horizon is sufficiently large, on average the upper bound on the gap between the expected profit obtained at each time slot by using the global optimal strategy and that by using our algorithms is arbitrarily small.

In the IEEE 802.16j standard, the relay station has been introduced to increase the coverage and the throughput of WiMAX networks. The placement of the relay station plays a critical role in the system performance and therefore draws tremendous attention from the research community. In this paper, we study the relay station placement problem in the WiMAX network, with the cooperative communication as the relaying strategy. In particular, given a base station and a set of subscriber stations, we determine the location of a relay station, and the set of subscriber stations using the relay station. The objective is to minimize the maximum transmission power among all the subscriber stations while satisfying the data rate requirements of the subscriber stations. We develop a near-optimal algorithm to solve this problem and prove that the maximum transmission power computed by our algorithm is at most Popt+ ε, where Poptis the maximum transmission power in the optimal solution and ε >; 0 is an arbitrary constant. The experiments show that we can dramatically reduce the maximum transmission power by deploying the relay station according to our algorithm.

Anypath routing, a new routing paradigm, has been proposed to improve the performance of wireless networks by exploiting the spatial diversity and broadcast nature of the wireless medium. In this paper, we study the problem of finding an anypath subject to multiple (K) constraints, which has been proved to be NP-hard. We present a polynomial distributed K-approximation routing algorithm. Our algorithm is as simple as Bellman-Ford's shortest path algorithm. Extensive experiments show that our algorithm is very efficient and its result is as good as that obtained by the best centralized algorithm which requires the global information.

Cooperative communication has been proposed to increase the capacity of wireless networks. By exploiting a relay node, it achieves spatial diversity to cope with fading channel without requiring wireless nodes to be equipped with multiple antennas. However, the selection of relay nodes has a significant impact on the final capacity. In this paper, we study the problem of relay assignment in cooperative networks, where multiple source-destination transmission pairs share the same set of relay nodes. Specifically, we propose a system model where a relay node can be shared by multiple source-destination pairs and present a corresponding formulation for the capacity calculation. Our objective is to find a relay assignment to maximize the total capacity of the network. As the main contribution, we develop an optimal relay assignment algorithm to solve this problem in polynomial time. We also show that our algorithm has several attractive properties.

Opportunistic routing is proposed to improve the performance of wireless networks by exploiting the broadcast nature and spatial diversity of the wireless medium. In this paper, we study the problems of how to choose opportunistic route for each user to optimize the total utility or profit of multiple simultaneous users in a wireless mesh network (WMN) subject to node constraints. We formulate these two problems as two convex programming systems. By combining primal-dual and subgradient methods, we present a distributed iterative algorithm Consort (node-Constrained Opportunistic Routing). In each iteration, Consort updates Lagrange multipliers in a distributed manner according to the user and node behaviors obtained in the previous iteration, and then each user and each node individually adjusts its own behavior based on the updated Lagrange multipliers. We prove the convergence of this iterative algorithm, and provide bounds on the amount of feasibility violation and the gap between our solution and the optimal solution in each iteration.

The notion of probabilistic network has been used to characterize the unpredictable environment in wireless communication networks or other unstable networks. In this paper, we are interested in the problem of placing servers in probabilistic networks subject to budget constraint, so as to maximize the expected number of servable clients that can successfully connect to a server. We study this problem in both the single-hop model and the multi-hop model. We discuss the computational complexity of this problem and show that it is NP-hard under both models.We then develop efficient approximation algorithms, which produce solutions provably close to optimal. If the costs of candidate locations are uniform, when extra budget is available in the future, the progressive feature of our algorithms allows for placing additional servers instead of relocating all the servers, while retaining the guaranteed performance. Results of extensive experiments on different topologies confirm the performance of our algorithms compared to the optimal algorithm and other heuristic algorithms.

Recently Choi et al. designed the first practical wireless full-duplex system, which challenges the basic assumption in wireless communications that a radio cannot transmit and receive on the same frequency at the same time. Along this line, in this paper we study the cross-layer optimization for routing in full-duplex wireless networks, comprehensively considering various resource competitions and constraints. We first propose a collision-free full-duplex broadcast MAC and prove its necessary and sufficient conditions. We then focus on 1) the problem of how to choose routes to maximize the total profit of multiple users subject to node constraints, and 2) the problem of how to choose routes to minimize the network power consumption subject to the minimum user rate demands and node constraints. We formulate these two problems as convex programming systems. By combining Lagrangian decomposition and subgradient methods, we present distributed iterative algorithms to solve these two problems, which compute the optimized user information flow (i.e. user behavior) on the network layer and the optimized node broadcast rate (i.e. node behavior) on the MAC layer. Our algorithms allow each user and each node to adjust its own behavior individually in each iteration. We prove the convergence, and provide bounds on the amount of constraint violation, and the gap between the optimal solution and our solution in each iteration. Our work comprehensively considers various resource competitions and constraints, and provides a theoretical foundation for the future study on full-duplex wireless networks. To the best of our knowledge, this is the first work to study cross-layer optimization for full-duplex wireless networks.

Anypath routing is proposed to improve the performance of wireless networks by exploiting the spatial diversity and broadcast nature of the wireless medium. In this paper, we study anypath routing in wireless networks with directional antennas, and propose DART (Directional Anypath RouTing), a crosslayer design of MAC and routing layers. For the routing layer, we propose two shortest directional anypath routing algorithms based on two antenna models. Our routing algorithms are simple and fast, thus are suitable for implementation in practical protocols. For the MAC layer, we present a directional anycast MAC, which is an enhancement to the IEEE 802.11 MAC, making DART suitable for integration into current systems. Simulation results show that DART can significantly reduce the packet transmission delay.

On one hand, cooperative communication has been gaining more and more popularity since it has great potential to increase the capacity of wireless networks. On the other hand, the applications of cooperative communication technology are rarely seen in reality, even in some scenarios where the demands for bandwidth-hungry applications have pushed the system designers to develop innovative network solutions. A main obstacle lying between the potential capability of channel capacity improvement and the wide adoption of cooperative communication is the lack of incentives for the participating wireless nodes to serve as relay nodes. Hence, in this paper, we design TASC, an auction scheme for the cooperative communications, where wireless node can trade relay services. TASC makes an important contribution of maintaining truthfulness while fulfilling other design objectives. We show analytically that TASC is truthful and has polynomial time complexity. Extensive experiments show that TASC can achieve multiple economic properties without significant performance degradation compared with pure relay assignment algorithms.

In this paper, we study the problem of maximizing the number of concurrent requests and the problem of minimizing the number of time-slots needed to schedule all requests in wireless networks under the physical interference model. It has been proved that both problems are NP-complete. Thus either approximation algorithms with guaranteed approximation factors or effective heuristics with practically good performances are desirable. We focus on the latter and present simple and effective heuristic algorithms for these two problems. Extensive experiments show that our algorithm for the first problem outperforms the best approximation algorithm by 62%-72% on average, and our two algorithms for the second problem give the best results among existing algorithms.

The recently emerging WiMAX (IEEE 802.16) is a promising telecommunication technology to provide low-cost, high-speed and long-range wireless communications. To meet the growing demand for throughput, Relay Station is introduced by IEEE 802.16j to relay traffic for Subscriber Stations. By incorporating Cooperative Communications scheme in WiMAX, we can further improve the network capacity. In this paper, we study the Relay Station placement problem, which seeks to deploy a minimum number of Relay Stations to satisfy all data rate requests from Subscriber Stations via Cooperative Communications. We analyze the computational complexity of the problem and prove it to be NP- Complete. Then we present efficient algorithms with guaranteed approximation ratios. Extensive experiments show that the number of Relay Stations returned by our algorithms is close to those returned by optimal solution.

In this paper, we study the problem of routing in networks with max-min fair congestion control at the link level. The goal of each user is to maximize its own bandwidth by selecting its path. The problem is formulated as a non-cooperative game. We first prove the existence of Nash Equilibria. This is important, because at a Nash Equilibrium (NE), no user has the incentive to change its routing strategy. In addition, we investigate how the selfish behavior of the users may affect the performance of the network as a whole. We next introduce a novel concept of observed available bandwidth on each link. It allows a user to find a path with maximum bandwidth under max-min fair congestion control in polynomial time. We then present a game based algorithm to compute an NE and prove that by following the natural game course the network converges to an NE. Extensive experiments show that the network can converge to an NE in less than 10 iterations and also significantly improves the fairness compared with other algorithms. Our results have the implication for the future routing protocol design.

Anypath routing has been proposed to improve the performance of unreliable wireless networks by exploiting the spatial diversity and broadcast nature of the wireless medium. In this paper, we focus on anypath routing subject to K constraints, and present a polynomial time K-approximation algorithm. When K = 1, our algorithm is the optimal polynomial time algorithm for the corresponding problem. When K ≥ 2, the corresponding problem is NP-hard. We are the first to present an O(1)-approximation algorithm. Furthermore, our algorithm is as simple as Dijkstra's shortest path algorithm, and is therefore suitable for implementation in actual wireless routing protocols.

In a wireless sensor network, short range multihop transmissions are preferred to prolong the network lifetime due to super-linear nature of energy consumption with communication distance. It has been proposed to deploy some relay nodes such that the sensors can transmit the sensed data to a nearby relay node, which in turn delivers the data to the base stations. In general, the relay node placement problems aim to meet certain connectivity and/or survivability requirements of the network by deploying a minimum number of relay nodes. In this paper, we study two-tiered constrained relay node placement problems, where the relay nodes can only be placed at some pre-specified candidate locations. To meet the connectivity requirement, we study the connected single-cover problem where each sensor node is covered by a relay node (to whom the sensor node can transmit data), and the relay nodes form a connected network with the base stations. To meet the survivability requirement, we study the 2-connected double-cover problem where each sensor node is covered by at least two relay nodes, and the relay nodes form a 2-connected network with the base stations. We focus on the computational complexities of the problems, and propose novel polynomial time approximation algorithms for these problems. For the connected single-cover problem, our algorithms have O(1) approximation ratios. For the 2-connected double-cover problem, our algorithms have O(1) approximation ratios for practical settings and O(lnn) approximation ratios for arbitrary settings. Experimental results show that the number of relay nodes used by our algorithms is no more than twice of the number of relay nodes used in an optimal solution.

In wireless networks, mutual interference prevents wireless devices from correctly receiving packages from others and becomes one of the challenges in the design of protocols for wireless networks. Spatial-reuse time division multiple access (STDMA) has been used to cope with this problem. In this scheme, links are assigned to several time slots and in each slot all the links can transmit simultaneously. In this paper, we propose a greedy link scheduling algorithm to find a short schedule for a problem instance in the physical interference model. Our scheduling algorithm is inspired by the k-MAX-CUT algorithm. Experimental results show that our greedy algorithm can give a better schedule compared with the greedy algorithm, with an improvement about 20%-30% when the density of links is high.