Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced expanding use in urban environments in recent years. However, the growing density of drones raises significant challenges, such as avoiding collisions and managing air traffic efficiently, especially in congested areas. To address these issues, a structured drone road system (DRS) and an effective guidance algorithm are essential. In this paper, we focus on the guidance aspect and propose a novel short-term decentralized greedy (STDG) algorithm designed to operate within a DRS framework. The algorithm uses only the position and speed information of nearby drones —- communicated via periodically transmitted beacons —- to make real-time decisions such as stopping, changing lanes, or adjusting speed for the next few seconds. Unlike existing methods that rely on centralized coordination, our algorithm enables drones to operate independently while ensuring safety and efficiency. We present simulation results showing the impact of key wireless and algorithm parameters on performance metrics like the drone collision rate, average speed and throughput of the drone road system.

One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area does not exceed a threshold$R_t$. Nevertheless, they overlook the EMR jitter that leads to exceeding of$R_t$even if the expected EMR is no more than$R_t$. This paper studies the fundamental problem ofRObustlySafEcharging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all rechargeable devices is maximized while the probability that EMR anywhere does not exceed$R_t$is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program. After that, we propose the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a$(1-\epsilon)$-approximation centralized algorithm. Further, we propose a$(1-\epsilon)$-approximation fully distributed algorithm scalable with network size for ROSE. We conduct both simulation and field experiments, and the results show that our algorithms can outperform comparison algorithms by 480.19 percent.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors' activation schedules according to their received energy. We formulate our problem as the maximum QoM CHArging and SchEduling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve 1/6- and 1/(4 + ε)-approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

This paper considers the problem of practical Heterogeneous wireless charger Placement with Obstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies, on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of 1/2 - ε. We conduct experiments to evaluate the performance. Results show that our algorithm outperforms the comparison algorithms by at least 33.49 percent on average.

Shopping malls, restaurants, and book shops usually offer free WiFi to customers, but they cannot afford non-customers (i.e., WiFi free riders) consuming their limited bandwidth. To provide WiFi information (i.e., SSID and password) secretly to customers, they usually display it in places where only customers can see, and update it regularly. The drawbacks are that customers need to enter the password manually and sometimes they do not know the location of the information. In this paper, we propose an alternative solution. In our system, the WiFi providers set up a speaker to continuously broadcast acoustic signals encoding WiFi information, and the customers use their smartphones to receive and decode the signals. Because acoustic signals attenuate rapidly through walls, only customers in the venue can acquire WiFi information. We implement a prototype system and show the feasibility of our approach. Experiments indicate that in our system, a user can connect successfully to the WiFi within several seconds.

Recently, wireless power transfer technology (WPT) has attracted considerable attention and become a promising technology to prolong the lifetime of wireless sensor networks (WSNs) by providing perpetual energy to sensors. However, electromagnetic radiation (EMR) incurred by WPT is largely overlooked in most of the existing literature. In this article, we first propose and study the radiation constrained fair charging problem for WPT, i.e., maximizing the minimum utility of sensors by adjusting the power of wireless chargers with no EMR intensity at any location in the field exceeding a given threshold R t . To address this problem, we first adopt an area discretization method to transform it from nonlinear to linear. Then, we propose four algorithms to deal with the reformulated problem, i.e., 1/3 and 1/4 Approximation Algorithms, Primal-Dual algorithm, and area division algorithm. In particular, the area division algorithm is not only fully distributed but also provably achieves an approximation ratio of (1 − ϵ). Further, we conduct extensive simulations and build a field testbed to verify our theoretical findings. Our simulation results show that the approximation ratios of the proposed algorithms hold; the Primal-Dual and area division algorithms have comparable performance of the optimal results and outperform baseline algorithms obviously.

This paper considers the problem of Heterogeneous wIreless charger Placement with Obstacles (HIPO), i.e., given a number of heterogeneous rechargeable devices distributed on a 2D plane where obstacles of arbitrary shapes exist, deploying heterogeneous chargers with a given cardinality of each type, i.e., determining their positions and orientations, the combination of which we name as strategies, on the plane such that the rechargeable devices achieve maximized charging utility. After presenting our practical directional charging model, we first propose to use a piecewise constant function to approximate the nonlinear charging power, and divide the whole area into multi-feasible geometric areas in which a certain type of chargers have constant approximated charging power. Next, we propose the Practical Dominating Coverage Set extraction algorithm to reduce the unlimited solution space to a limited one by exacting a finite set of candidate strategies for all multi-feasible geometric areas. Finally, we prove the problem falls in the realm of maximizing a monotone submodular function subject to a partition matroid constraint, which allows a greedy algorithm to solve with approximation ratio of 1/2 -- ϵ. We conduct both simulations and field experiments to evaluate the performance of our algorithm and other five comparison algorithms. The results show that our algorithm outperforms the comparison algorithms by at least 33.49% on average.

One critical issue for wireless power transfer is to avoid human health impairments caused by electromagnetic radiation (EMR) exposure. The existing studies mainly focus on scheduling wireless chargers so that (expected) EMR at any point in the area doesn't exceed a threshold Rt. Nevertheless, they overlook the EMR jitter that leads to exceeding of Rteven if the expected EMR is no more than Rt. This paper studies the fundamental problem of RObustly SafE charging for wireless power transfer (ROSE), that is, scheduling the power of chargers so that the charging utility for all rechargeable devices is maximized while the probability that EMR anywhere doesn't exceed Rt is no less than a given confidence. We first build our empirical probabilistic charging model and EMR model. Then, we present EMR approximation and area discretization techniques to formulate ROSE into a Second-Order Cone Program, and the first redundant second-order cone constraints reduction algorithm to reduce the computational cost, and therefore obtain a (1-ε)-approximation centralized algorithm. Further, we propose a (1-ε)-approximation fully distributed algorithm scalable with network size for ROSE. Simulations and field experiments show that our algorithms can outperform comparison algorithms by 480.19%.

On-street parking is an essential component of parking infrastructure for smart cities, which allows users to park near their destinations for short term. However, due to limited capacity, saturated on-street parking becomes a serious and widespread problem for urban transportation systems. Greedily searching for an on-street parking spot in a saturated area is often a frustrating task for drivers, and cruising for vacant parking spots results in additional delays and impaired local circulation. With the recent development of networked smart parking meter, real-time city-wide on- street parking information becomes available for more efficient parking management. In this paper, we design an online parking guidance system that recommends parking spots in real-time based on the parking availability prediction. With a receding horizon optimization framework, our solution minimizes the user's driving and walking cost by adapting the spatiotemporally dynamic supply and demand in the local area, significantly reducing parking competitions in a timely manner. We implement and evaluate our solution with a dataset of 13,503,655 parking records collected from 5228 in-ground sensors distributed in the Australian city Melbourne. The evaluation results show that our approach achieves up to 63.8% delay reduction compared with existing solutions.

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold Rt. We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a series of distributed algorithms, including a fully distributed algorithm that provably achieves (1- ϵ) approximation ratio and requires only communications with neighbors within a constant distance for each charger. Through extensive simulation and testbed experiments, we demonstrate that our proposed algorithms can outperform the set-cover algorithm by up to 17.05%, and has an average performance gain of 41.1% over the existing algorithm in terms of the overall charging utility.

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology, which allows the power to be transferred from a charger to ambient devices wirelessly, receives intensive interests. Existing schemes mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the safe charging problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold Rt. We show that SCP is NP-hard and propose a solution, which provably outperforms the optimal solution to SCP with a relaxed EMR threshold (1-ε)Rt. Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when ε = 0.1, while a naive greedy algorithm is 34.6% below our solution.

One ultimate goal of wireless sensor networks is to collect the sensed data from a set of sensors and transmit them to some sink node via a data gathering tree. In this work, we are interested in data aggregation, where the sink node wants to know the value for a certain function of all sensed data, such as minimum, maximum, average, and summation. Given a data aggregation tree, sensors receive messages from children periodically, merge them with its own packet, and send the new packet to its parent. The problem of finding an aggregation tree with the maximum lifetime has been proved to be NP-hard and can be generalized to finding a spanning tree with the minimum maximum vertex load, where the load of a vertex is a nondecreasing function of its degree in the tree. Although there is a rich body of research in those problems, they either fail to meet a theoretical bound or need high running time. In this paper, we develop a novel algorithm with provable performance bounds for the generalized problem. We show that the running time of our algorithm is in the order of O(mnα(m, n)), where m is the number of edges, n is the number of sensors, and α is the inverse Ackerman function. Though our work is motivated by applications in sensor networks, the proposed algorithm is general enough to handle a wide range of degree-oriented spanning tree problems, including bounded degree spanning tree problem and minimum degree spanning tree problem. When applied to these problems, it incurs a lower computational cost in comparison to existing methods. Simulation results validate our theoretical analysis.

Existing work on localization in wireless rechargeable sensor networks (WRSNs) assumes that the charger is equipped with an omnidirectional antenna. We notice that the current available COTS (Commercial off-the-shelf) energy harvesting kit such as the one manufactured by Powercast includes wireless charger with directional antenna. We consider the localization problem in a wireless rechargeable sensor network with a mobile wireless charger. We propose two efficient region dividing methods, Angle Division and Grid Division. Both exploit the field angle of the directional charger to localize individual sensor nodes. The Angle Division is more suitable for general convex polygons. Grid Division algorithm is efficient when the nodes are distributed uniformly and the area is approximate to a rectangle. Further, we extend the two methods by combining the charging time to improve the localization precision. To verify the design, we have extensively evaluated our design by both field experiments and large- scale simulations. The experiment and simulation results show that our algorithm can achieve a localization distance error of less than a quarter meter by as less as four stops.

Tree-based routing structures are widely used to gather data in wireless sensor networks. Along with tree structures, in-network aggregation is adopted to reduce transmissions, to save energy and to prolong the network lifetime. Most existing works that focus on the lifetime optimization for data aggregation do not take the link quality into consideration. In this paper, we study the problem of Maximizing Reliability of Lifetime Constrained data aggregation trees (MRLC) in WSNs. Considering the NP-completeness of the MRLC problem, we propose an algorithm, namely Iterative Relaxation Algorithm (IRA), to iteratively relax the optimization program and to find the aggregation tree subject to the lifetime bound with a sub-optimal cost. To adapt to the distributed nature of the WSNs in practice, we further propose a Prufer code based distributed updating protocol. Through extensive simulations, we demonstrate that IRA outperforms the best known related work in term of reliability.

Heterogeneous cellular networks use small base stations, such as femtocells and WiFi APs, to offload traffic from macrocells. While network operators wish to globally balance the traffic, users may selfishly select the nearest base stations and make some base stations overcrowded. In this paper, we propose to use an auction-based algorithm - Femto-Matching, to achieve both load balancing among base stations and fairness among users. Femto-Matching optimally solves the global proportional fairness problem in polynomial time by transforming it into an equivalent matching problem. Furthermore, it can efficiently utilize the capacity of randomly deployed small cells. Our trace-driven simulations show Femto-Matching can reduce the load of macrocells by more than 30% compared to non-cooperative game based strategies.

Many applications in wireless sensor networks require communication performance that is both consistent and of high quality. Unfortunately, performance of current network protocols can vary significantly because of various interferences and environmental changes. Current protocols estimate link quality based on the reception of probe packets over a short time period. This method is neither efficient nor accurate enough to capture the dramatic variations of link quality. Therefore, we propose a link metric called competence that characterizes links over a longer period of time. We combine competence with current short-term estimations in routing algorithm designs. To further improve network performance, we have designed a distributed route maintenance framework based on feedback control solutions. This framework allows every link along an end-to-end (E2E) path to adjust its link protocol parameters, such as transmission power and number of retransmissions, to ensure specified E2E reliability and latency under dynamic link qualities. Our solutions are evaluated in both extensive simulations and real system experiments. In real system evaluations with 48 T-Motes, our overall solution improves E2E packet delivery ratio over existing solutions by up to 40% while reducing transmission energy consumption by up to 22%. Importantly, our solution also achieves more stable and better transient performance than current approaches.

Multihop broadcasting in low-duty-cycle Wireless Sensor Networks (WSNs) is a very challenging problem, since every node has its own working schedule. Existing solutions usually use unicast instead of broadcast to forward packets from a node to its neighbors according to their working schedules, which is, however, not energy efficient. In this article, we propose to exploit the broadcast nature of wireless media to further save energy for low-duty-cycle networks, by adopting a novel broadcasting communication model. The key idea is to let some early wake-up nodes postpone their wake-up slots to overhear broadcasting messages from its neighbors. This model utilizes the spatiotemporal locality of broadcast to reduce the total energy consumption, which can be essentially characterized by the total number of broadcasting message transmissions. Based on such model, we aim at minimizing the total number of broadcasting message transmissions of a broadcast for low-duty-cycle WSNs, subject to the constraint that the broadcasting latency is optimal. We prove that it is NP-hard to find the optimal solution, and design an approximation algorithm that can achieve a polylogarithmic approximation ratio. Extensive simulation results show that our algorithm outperforms the traditional solutions in terms of energy efficiency.

One fundamental question for wireless power transfer technology is the energy provisioning problem, i.e., how to provide sufficient energy to mobile rechargeable nodes for their continuous operation. Most existing works overlooked the impacts of node speed and battery capacity. However, we find that if the constraints of node speed and battery capacity are considered, the continuous operation of nodes may never be guaranteed, which invalidates the traditional energy provisioning concept. In this paper, we propose a novel metric-Quality of Energy Provisioning (QoEP)-to characterize the expected portion of time that a node sustains normal operation by taking into account node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. As there exist more than one mobility models corresponding to the same spatial distribution, and different mobility models typically lead to different QoEPs, we investigate upper and lower bounds of QoEP in 1D and 2D cases. We derive tight upper and lower bounds of QoEP for 1D case with a single source, and tight lower bounds and loose upper bounds for general 1D and 2D cases with multiple sources. Finally, we perform extensive simulations to verify our theoretical findings.

Wireless power transfer technology is considered as one of the promising solutions to address the energy limitation problems for end-devices, but its incurred potential risk of electromagnetic radiation (EMR) exposure is largely overlooked by most existing works. In this paper, we consider the Safe Charging with Adjustable PowEr (SCAPE) problem, namely, how to adjust the power of chargers to maximize the charging utility of devices, while assuring that EMR intensity at any location in the field does not exceed a given threshold Rt. We present novel techniques to reformulate SCAPE into a traditional linear programming problem, and then remove its redundant constraints as much as possible to reduce computational effort. Next, we propose a distributed algorithm with provable approximation ratio (1 - ε). Through extensive simulation and testbed experiments, we demonstrate that our (1 - ε)-approximation algorithm outperforms the Set-Cover algorithm by up to 23%, and has an average performance gain of 41.1% over the SCP algorithm in terms of the overall charging utility.

As battery-powered mobile devices become more popular and energy hungry, wireless power transfer technology receives intensive interests, as it allows the power to be transferred from a charger to ambient devices wirelessly. The existing studies mainly focus on the power transfer efficiency but overlook the health impairments caused by RF exposure. In this paper, we study the Safe Charging Problem (SCP) of scheduling power chargers so that more energy can be received while no location in the field has electromagnetic radiation (EMR) exceeding a given threshold Rt. We prove that SCP is NP-hard and propose a solution which provably outperforms the optimal solution to SCP with a relaxed EMR threshold (1 - ε)Rt. Testbed results based on 8 Powercast TX91501 chargers validate our results. Extensive simulation results show that the gap between our solution and the optimal one is only 6.7% when ε = 0.1, while a naive greedy algorithm is 34.6% below our solution.

Low-power access points, such as pico base stations (BSs), femto BSs, and relays are introduced to the next generation cellular systems to enhance coverage and improve system capacity. Deploying low-power access points to offload the conventional macro BSs is deemed as a spectrum- and cost-efficient way to meet the sharp increase of traffic requirements of cellular networks. However, it also leads to heterogeneous network framework and raises new challenges for cell planning. In this paper, we study the minimum cost cell planning problem in such a heterogeneous network. Our optimization task is to select a subset of candidate sites to lay BSs, including macro BSs, pico BSs and relays, to minimize the total deployment cost while satisfying the rate requirements of the demand nodes (DNs) served by the cellular network. We prove that the general case of the formulated problem is APX-hard, where a DN is constrained to be associated with only one BS. However, if each DN can be served by multiple BSs, which is a reasonable case for practical cellular systems, we show it is not APX-hard and develop an approximation algorithm to work out promising solutions. Our proposed algorithm guarantees an approximation ratio of O(logR) to the global optimum, where R is the maximum achievable capacity of the BSs. Numerical results indicate that our proposal can significantly reduce the deployment cost of the cellular network with given rate requirements of DNs compared to other cell planning schemes.

Flooding is one of the most fundamental functions in wireless sensor networks (WSNs) and has been investigated extensively. Existing flooding schemes in duty-cycle WSNs can be categorized into two categories: synchronous ones and asynchronous ones. In practice asynchronous schemes are preferable since synchronous ones introduce more complexity and overhead for necessary clock synchronization. Existing asynchronous approaches are however imperfect for reliable flooding in duty-cycle WSNs. For example, opportunistic flooding is less flexible and Asynchronous Duty-cycle Broadcasting (ADB) suffers unsatisfactory flooding latency. We propose Constructive Interference-based Reliable Flooding (CIRF) in this paper, a novel design for reliable flooding in asynchronous duty-cycle WSNs. CIRF is integrated with the MAC protocol Receiver-Initiated MAC (RI-MAC) to improve the utilization of wireless medium and guarantee one-hop reliable transmission. The key idea of CIRF is to exploit the constructive interference feature when concurrent transmission occurs, which can be common in RI-MAC based WSNs. Simulation results indicate that CIRF achieves reliable flooding with reduced flooding latency, higher energy efficiency and delivery ratio compared to existing schemes.

In wireless sensor networks, the spanning tree is usually used as a routing structure to collect data. In some situations, nodes do in-network aggregation to reduce transmissions, save energy, and maximize network lifetime. Because of the restricted energy of sensor nodes, how to build an aggregation tree of maximum lifetime is an important issue. It has been proved to be NP-complete in previous works. As shortest path spanning trees intuitively have short delay, it is imperative to find an energy-efficient shortest path tree for time-critical applications. In this article, we first study the problem of building maximum lifetime shortest path aggregation trees in wireless sensor networks. We show that when restricted to shortest path trees, building maximum lifetime aggregation trees can be solved in polynomial time. We present a centralized algorithm and design a distributed protocol for building such trees. Simulation results show that our approaches greatly improve the lifetime of the network and are very effective compared to other solutions. We extend our discussion to networks without aggregation and present interesting results.

Wireless Rechargeable Sensor Networks (WRSNs) can be recharged after deployment for sustainable operations. Recent works propose to use a single mobile charger (MC) traveling through the network fields to recharge every sensor node. These algorithms work well in small scale networks. However, in large scale networks these algorithms do not work efficiently, especially when the amount of energy the MC can provide is limited. To address these challenges, multiple MCs can be used. In this paper, we investigate the minimum MCs problem (MinMCP) for rechargeable sensor networks: how to find the minimum number of energy-constrained MCs and design their recharging routes given a sensor network such that each sensor node in the WRSN maintains continuous work. Our results are three folds. We first prove that for any ϵ > 0, there is no (2-ϵ)-approximation algorithm for Distance Constrained Vehicle Routing Problem (DVRP) on a general metric space, which is the best as far as we know. By reducing from DVRP, we prove that MinMCP is NP-hard, and the inapproximability bound for MinMCP is the same as that of DVRP. Then we propose approximation algorithms for this problem. Finally, we conduct simulations to validate the effectiveness of our algorithms.

Though much existing work exploits wireless power charging to enhance sensor network performance such as routing and data aggregation, few efforts focus on issues of stochastic event capture. In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly, to maximize the Quality of Monitoring (QoM) for stochastic events. Towards this goal, two closely related research issues need to be addressed. One is how to choose the sensors for charging and decide the charging time for each of them, the other is how to best schedule the sensors' activation schedules according to their received energy. In this paper, we jointly design the charging scheme and sensor schedules to maximize the QoM. We formulate our problem formally as the maximum QoM charging and scheduling problem (MQCSP). Obtaining an exact solution of MQCSP is challenging. Thus we first ignore the MC's travel time and study the resulting relaxed version of MQCSP, R-MQCSP. We show both MQCSP and R-MQCSP are NP-hard. For R-MQCSP, however, under a special condition, we prove that it can be formulated as a sub modular function maximization problem. This formulation allows a 1/6-approximation algorithm for the general case, and a unified algorithm with a series of approximation factors (up to 1-1/e) for a special case. Then, for MQCSP, we propose approximation algorithms by extending our R-MQCSP results. Finally, we conduct extensive trace-driven simulations to validate our algorithm design. The empirical results corroborate our theoretical analysis.

For low-duty-cycle wireless sensor networks, multihop broadcasting is a challenging problem, since every node has its own working schedules. In this paper, we design a novel broadcasting algorithm, of which key idea is to let some early wake-up nodes postpone their wake-up slots to overhear broadcasting message from its neighbors. This design utilizes the spatiotemporal locality of broadcasting to reduce the number of transmissions. We show that to find the broadcasting schedule with minimal latency and optimized total energy consumption is NP-hard, and then design an approximation algorithm that can guarantee the optimality of broadcasting latency and achieve a polylogarithmic approximation ratio for total energy consumption. Compared with the traditional solution, extensive experimental results show that our algorithm achieves the minimal broadcasting latency while reducing energy consumption significantly.

Existing scheduling schemes for stochastic event capture with rechargeable sensors either adopt simplified assumptions on event staying time or provide no performance guarantee. Considering the stochasticity of event staying time, we investigate the sensor scheduling problem aiming to maximize the overall Quality of Monitoring (QoM) in events capture application of wireless rechargeable sensor networks. We first provide a paradigm to calculate the QoM of a point of interests (PoI) and formulate the scheduling problem into an optimization problem. Although we find that that this problem is NP-complete, we prove that the objective function of the optimization problem is monotone submodular. Therefore we can express the problem as a maximization of a submodular function subject to a matroid constraint. Accordingly we can design an approximation algorithm which achieves a factor of 1/2 of the optimum. We evaluate the performance of our solution through simulations, and simulation results show that our scheme outperforms former works.

One fundamental question in Wireless Rechargeable Sensor Networks (WRSNs) is the energy provisioning problem, i.e., how to deploy energy sources in a network to ensure that the nodes can harvest sufficient energy for continuous operation. Though the potential mobility of nodes has been exploited to reduce the number of sources necessary in energy provisioning problem in existing literature, the non-negligible impacts of the constraints of node speed and battery capacity on energy provisioning are completely overlooked, in order to simplify the analysis. In this paper, we propose a new metric - Quality of Energy Provisioning (QoEP) - to characterize the expected portion of time that a mobile node can sustain normal operation in WRSNs, which factors in the constraints of node speed and battery capacity. To avoid confining the analysis to a specific mobility model, we study spatial distribution instead. We investigate the upper and lower bounds of QoEP in one-dimensional case with one single source and multiple sources respectively. For single source case, we prove the tight lower bound and upper bound of QoEP. Extending the results to multiple sources, we obtain tight lower bound and relaxed upper bound in normal cases, together with tight upper bound for one special case. Moreover, we give the tight lower bounds in both 2D and 3D cases. Finally, we perform extensive simulations to verify our findings. Simulation results show that our bounds perfectly hold, and outperform the former works.

In many sensor surveillance systems such as environment and habitat monitoring, sensors are deployed in an area to monitor several important locations where targets appear. As the sensor nodes are often powered by non-rechargeable batteries, it is crucial to extend the lifetime of sensor surveillance systems. How to cover multiple random targets and extend the system lifetime at the same time is a challenge. Previous studies show that data fusion improve coverage by exploiting the collaboration among sensors. However, they assume that targets deterministically appear at some specific locations and do not consider the randomness of the appearance of the targets. Considering both the high cost of the sensor surveillance system and various reliability requirements in practice, we explore the tradeoff between the system lifetime and the system reliability in this paper. We formulate the stochastic target coverage problem as probabilistic set covering. After showing its NP-Completeness, we design a PSC-based heuristic algorithm in the data fusion model. Extensive simulations show that the system lifetime can be extended dramatically with a small loss to the system reliability.

Point of Interest (PoI) coverage problem has been discussed lately. Both practical strategies and theoretical analysis have been proposed. We however argue that coverage solution alone is not enough. In real applications, data from the covered area need to be collected and sent back to the sink. We notice that existing protocols are for general purpose use and not specially designed for PoI monitoring. Accordingly, we propose Smaller tree Collection Protocol (SCP) for data collection in this kind of applications. SCP is a distributed approximation for building a minimum Steiner tree connecting all PoI nodes and the sink. SCP aims to activate minimum relay nodes and supports data aggregation better than other collection protocol, e.g., Collection Tree Protocol (CTP), Backpressure Collection Protocol (BCP) and opportunistic routing protocols. We also design low cost distributed mechanisms to handle PoI dynamics. Extensive simulations have been performed to evaluate the proposed protocol.

In wireless sensor networks (WSNs), asynchronous duty-cycle technique can significantly reduce energy consumption. However, packets in low-duty-cycle networks suffer high end-to-end (E2E) delay. Besides, recent experimental studies have also shown that links in WSNs are highly unreliable and radio irregularity has adverse impact on routing protocols. In this work, we introduce a dynamic data forwarding (DDF) scheme which combines a realistic link model with asynchronous duty cycle. Different from most of other routing protocols, each node in our solution first finds out a set of candidate nodes and then forwards packet to the first waking up node in this set. Our solution can reduce E2E delay, guarantee delivery ratio and improve network lifetime. We evaluate this dynamic data forwarding scheme with extensive simulations and the simulation results demonstrate the efficiency of our solution.

In many applications of wireless sensor networks, a sensor node senses the environment to get data and delivers them to the sink via a single hop or multi-hop path. Many systems use a tree rooted at the sink as the underlying routing structure. Since the sensor node is energy constrained, how to construct a good tree to prolong the lifetime of the network is an important problem. We consider this problem under the scenario where nodes have different initial energy, and they can do in-network aggregation. In previous works, it has been proved that finding a maximum lifetime tree from all feasible spanning trees is NP-complete. Since delay is also an important element in time-critical applications, and shortest path trees intuitively have short delay, it is imperative to find a shortest path tree with long lifetime. This paper studies the problem of maximizing the lifetime of data aggregation trees, which are limited to shortest path trees. We find that when it is restricted to shortest path trees, the original problem is in P. We transform the problem into a general version of semi-matching problem, and show that the problem can be solved by min-cost max-flow approach in polynomial time. Also we design a distributed solution. Simulation results show that our approach greatly improves the lifetime of the network and is more competitive when it is applied in a dense network.

Underwater sensor networks were recently proposed to support quite a few aquatic applications such as ocean exploration and harbor monitoring. Different from terrestrial WSNs, the underwater sensor networks have many unique characteristics, such as greater propagation delay, higher dynamic topology and sparse node density. These features bring many new challenges to the protocol design of underwater sensor networks, especially the routing protocols. In this paper, we propose SEANAR, an energy-efficient and topology-aware routing protocol for underwater sensor networks. SEANAR relies on topology information, in specific, degree information of neighboring nodes for making routing decisions. Although this is a simple greedy approach, simulation results show that, compared with Greedy Forwarding and VBF-based routing protocol, SEANAR achieves much higher packet delivery ratio, while keeps the energy consumption in a very low level.

Given a set P of products, a set O of customers, and a product p ε P, a bichromatic reverse skyline query retrieves all the customers in O that do not find any other product in P to be absolutely better than p. More specifically, a customer o ε O is in the reverse skyline of p ε P if and only no other product in P better matches the preference of o on all dimensions.

Navigation with wireless sensor networks (WSNs) can help people escape safely from an emergency. Previous navigation algorithms attempt to find safe and efficient escape paths for individuals under various environmental dynamics but ignore possible congestion caused by the individuals rushing for the exits. Moreover, all the previous works have overlooked the fact that the emergency rescue force can take actions strategically in order to save people out of danger. We propose ERN, Emergence Rescue Navigation algorithm by treating WSNs as navigation infrastructure. ERN takes both pedestrian congestion and rescue force flexibility into account. A directed graph is used to model the emergency regions. Human's movements are regarded as network flows on the graph. By calculating the maximum flow and minimum cut on the graph, the system can provide firemen rescue commands to eliminate key dangerous areas, which may significantly reduce congestion and save trapped people. We have performed extensive simulations under dynamic environments to evaluate the effectiveness and response time of ERN. Simulation results show that with ERN people in emergency are evacuated much faster and less congestion is observed.

Most previous localization and tracking systems in wireless sensor networks are based on RF signals, ultrasounds, and UWB. However, systems using RF signals suffer accuracy fluctuations and systems using ultrasound or UWB need extra hardware. In our paper, we propose to utilize light, an easily accessible and pervasive resource in our daily life, and off-the-shelf TelosB Motes to achieve the goal of stability and high accuracy in localization and tracking. The iTracking system is a mobile light source location-tracking system based on light intensity. Our main contribution is the first demonstration to track mobile light sources based on light intensity with high accuracy, which may provide an alternative method for localization and tracking or inspire game designers. We examine point light based tracking and flashlight based tracking in our demonstrations, which proves these two main sources of light in our daily life can achieve centimeter-level location-tracking requirements. Our future work will focus on enabling the iTracking system to writing Chinese characters in flashlight.

This paper presents novel distributed algorithms for scheduling transmissions in multi-hop wireless networks. Our algorithms generate new schedules in a distributed manner via simple local changes to existing schedules. Two classes of algorithms are designed: one assumes that the location information of all wireless nodes are known, and the other does not. Both classes of algorithms are parameterized by an integer k (called algorithm-k). We show that algorithm-k that uses geometry location achieves (1 - 2/k)2of the capacity region, for every k ges 3; algorithm-k which does not use geometry location achieves 1/rho of the capacity region, for every k ges 3 and a constant rho depending on k. Our algorithms have small worst-case overheads. Both classes of algorithms can generate a new schedule by requiring communications within Theta(k) hops for every node, which can be implemented by letting each node transmit at most O(k) messages. The parameter k explicitly captures the tradeoff between control overhead and the throughput performance of any scheduler. Additionally, the class of algorithms with known geometry location of nodes can And a new schedule in time Theta(k2Delta), where Delta is the minimum mini-time-slots such that each of the n nodes can communicate with its neighbors once, which is the minimum time-slots required by any scheduling algorithm.

In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in wireless sensor networks (WSNs). We conclude that in a circular multihop sensor network (modeled as concentric coronas) with nonuniform node distribution and constant data reporting, the unbalanced energy depletion among all the nodes in the network is unavoidable. Even if the nodes in the inner coronas of the network have used up their energy simultaneously, the ones in the outermost corona may still have unused energy. This is due to the intrinsic many-to-one traffic pattern of WSNs. Nevertheless, nearly balanced energy depletion in the network is possible if the number of nodes increases in geometric progression from the outer coronas to the inner ones except the outermost one. Based on the analysis, we propose a novel nonuniform node distribution strategy to achieve nearly balanced energy depletion in the network. We regulate the number of nodes in each corona and derive the ratio between the node densities in the adjacent (i + 1)th and ith coronas by the strategy. Finally, we propose (q-switch routing, a distributed shortest path routing algorithm tailored for the proposed nonuniform node distribution strategy. Extensive simulations have been performed to validate the analysis.

Mobile sink has been adopted by many schemes for lifetime improvement in wireless sensor networks. It is necessary to propagate the topological or location changes caused by the sink movement across the network for successful data delivery. However, previous studies on using mobile sink either assume that some global information of the network is already available or let the mobile sink convey the global information through repeated network-wide broadcasting. As a result, the gains in network lifetime from sink mobility can be offset by the broadcasting which incurs extra high energy loss. To address this problem, we propose dual-sink, an energy-efficient and distributed protocol for data collection in wireless sensor networks. Both a mobile sink and a static sink are used by Dual-Sink. The mobile sink only needs to broadcast its location to a subset of nodes in the network each time when it stops. Simulation results show that when it scales up, the network using Dual-Sink enjoys steady lifetime improvement from sink mobility, whereas the network with only one mobile sink performs no better than the network with a single static sink.

This paper deals with the concept that effective robotic function emerges from intelligence and the balance between morphology and intelligence, the morphology and intelligence of the robot are represented respectively. Both them are automatically generated and evolved by genetic programming for a task of maintaining a certain distance between the robot and an object. And then evolutionary simulation and two experiments are performed. Furthermore, the symmetry properties which have two phases and emerge are discussed

In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy in wireless sensor networks, which aims to avoid the energy hole around the sink. We find that in a circular sensor network with a nonuniform node distribution and constant data reporting, the unbalanced energy depletion among the nodes in the whole network is unavoidable. This is because although all the inner nodes have used up their energy simultaneously, the outmost part of the network may still have energy left. In spite of this fact, a suboptimal energy efficiency among the inner parts of the network is possible if the number of nodes increases with geometric proportion from the outer parts to the inner ones. In our proposed nonuniform node distribution strategy, the ratio between the node densities of the adjacent (i+1)th corona and the ith corona is equal to (2i-1)/q(2i+1), where q is the geometric proportion mentioned above. We also present a routing algorithm with this node distribution strategy. Simulation experiments demonstrate that when the network lifetime has ended, the nodes in the inner parts of the network achieve nearly balanced energy depletion, and only less than 10% of the total energy is wasted

Wireless mesh networking has emerged as a promising technology in providing economical and scalable broadband Internet accesses. A wireless mesh network consists of mesh routers and mesh clients, connected in an ad hoc manner via wireless links. A subset of the mesh routers, referred to as gateway nodes, are capable of external Internet connections. Since other mesh routers and clients have to access the Internet through the gateway nodes, these nodes can easily become bottlenecks in the network. In this paper, we present a novel queuing model based analysis of the delay and throughput of the gateway nodes. Our simulation results suggest that the analytical results are quite accurate, which provides an effective guideline for gateway-related design and optimization in wireless mesh networks

This paper presents a novel knowledge representation method and learning system for XML documents. The traditional machine learning methods which use attribute-value languages are not suitable for representing XML documents due to their complex structures. In this paper, we propose a decision-tree algorithm for XML learning, which is based on a rich representation language for structured data and driven by precision/recall heuristic.