Shibo He

Energy Provisioning in Wireless Rechargeable Sensor Networks

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks

In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

Utility-based asynchronous flow control algorithm for wireless sensor networks

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results.

Mobility and Intruder Prior Information Improving the Barrier Coverage of Sparse Sensor Networks

The barrier coverage problem in emerging mobile sensor networks has been an interesting research issue due to many related real-life applications. Existing solutions are mainly concerned with deciding one-time movement for individual sensors to construct as many barriers as possible, which may not be suitable when there are no sufficient sensors to form a single barrier. In this paper, we aim to achieve barrier coverage in the sensor scarcity scenario by dynamic sensor patrolling. Specifically, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensors current movement strategy is derived from the information of intruder arrivals in the past. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. We study the scenario where sensors are moving on two barriers and propose two heuristic algorithms to guide the movement of sensors. Finally, we generalize our results to work for different intruder arrival models. Through extensive simulations, we demonstrate that the proposed algorithms have desired barrier coverage performances.

Toward optimal allocation of location dependent tasks in crowdsensing

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme.

Energy-Efficient Probabilistic Area Coverage in Wireless Sensor Networks

As the binary sensing model is a coarse approximation of reality, the probabilistic sensing model has been proposed as a more realistic model for characterizing the sensing region. A point is covered by sensor networks under the probabilistic sensing model if the joint sensing probability from multiple sensors is larger than a predefined threshold ε. Existing work has focused on probabilistic point coverage since it is extremely difficult to verify the coverage of a full continuous area (i.e., probabilistic area coverage). In this paper, we tackle such a challenging problem. We first study the sensing probabilities of two points with a distance of d and obtain the fundamental mathematical relationship between them. If the sensing probability of one point is larger than a certain value, the other is covered. Based on such a finding, we transform probabilistic area coverage into probabilistic point coverage, which greatly reduces the problem dimension. Then, we design the ε-full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so that the network lifetime can be prolonged as much as possible. We also theoretically derive the approximation ratio obtained by FCO to that by the optimal one. Finally, through extensive simulations, we demonstrate that FCO outperforms the state-of-the-art solutions significantly.

Energy provisioning in wireless rechargeable sensor networks

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition, and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions

We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the “contribution” of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.

On Optimal Information Capture by Energy-Constrained Mobile Sensors

A mobile sensor is used to cover a number of points of interest (PoIs), where dynamic events appear and disappear according to the given random processes. The sensor, which is of sensing range r, visits the PoIs in a cyclic schedule and gains information about any event that falls within its range. We consider the temporal dimension of the sensing as given by a utility function, which specifies how much information is gained about an event as a function of the cumulative sensing or observation time. The quality of monitoring (QoM), i.e., the fraction of information captured about all events, depends on the speed of the sensor and has been analyzed in an earlier paper for different utility functions. The prior work, however, does not consider the energy of motion, which is an important constraint for mobile sensor coverage. In this paper, we analyze the expected Information captured Per unit of Energy consumption (IPE) as a function of the event type (in terms of the utility function), the event dynamics, and the speed of the mobile sensor. Our analysis uses a realistic energy model of motion, and it allows the sensor speed to be optimized for information capture. The case of multiple sensors will also be discussed. Extensive simulation results verify and illustrate the analytical results.

Cost-effective barrier coverage by mobile sensor networks

Barrier coverage problem in emerging mobile sensor networks has been an interesting research issue. Existing solutions to this problem aim to decide one-time movement for individual sensors to construct as many barriers as possible, which may not work well when there are no sufficient sensors to form a single barrier. In this paper, we try to achieve barrier coverage in sensor scarcity case by dynamic sensor patrolling. In specific, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensors current movement strategy is decided based on the past intruder arrival information. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that the sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. Through extensive simulations, we demonstrate that CSP has a desired barrier coverage performance and S-DCSP and G-DCSP have similar performance as that of CSP.