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Wireless charging is a promising way to solve the energy constraint problem in sensor networks. While extensive efforts have been made to improve the performance of charging and communication in wireless rechargeable sensor networks (WRSNs), little has been done to address the operation scheduling problem. To fill this void, we propose a joint energy replenishment and scheduling mechanism so as to maximize the network lifetime while making strict sensing guarantees in the WRSN. We first formulate the problem in a general 2-D space and prove its NP-completeness. We then devise an f-approximate scheduling mechanism by transforming the classical minimum set cover problem and develop an optimal energy-replenish strategy based on the energy consumption of nodes returned by the scheduling mechanism. Large-scale simulation results validate our design and show a $39.2\%$ improvement of network lifetime over a baseline method. [ABSTRACT FROM PUBLISHER]

Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel transmission and to reduce interference. However, the extra overhead posed by the multi-channel usage coordination dramatically challenges the energy-constrained WSNs. In this paper, we propose a Regret Matching based Channel Assignment algorithm (RMCA) to address this challenge, in which each sensor node updates its choice of channels according to the historical record of these channels’ performance to reduce interference. The advantage of RMCA is that it is highly distributed and requires very limited information exchange among sensor nodes. It is proved that RMCA converges almost surely to the set of correlated equilibrium. Moreover, RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and network topology. Simulations show that RMCA achieves better network performance in terms of both delivery ratio and packet latency than CONTROL refid="ref1" , MMSN refid="ref2"/ and randomized CSMA. In addition, real hardware experiments are conducted to demonstrate that RMCA is easy to be implemented and performs better. [ABSTRACT FROM PUBLISHER]

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 $\varepsilon$. 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 $\varepsilon$-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. [ABSTRACT FROM PUBLISHER]

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 sensor's 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. [ABSTRACT FROM PUBLISHER]

This paper studies deterministic sensor deployment for barrier coverage in wireless sensor networks. Most of existing works focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under a general setting. We first present a condition under which the line-based deployment is suboptimal, revealing the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. Based on the optimal deployment curve, we design sensor deployment algorithms by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by \min(π, \frac||\widetildeAB||||\widetildeAGB||\frac2n+\sqrt2-12n ), where ||\widetildeAB|| and ||\widetildeAGB|| are some constants, and n is the number of sensors. We generalize the study to the heterogeneous sensing model, and show that the proposed algorithm can provide close-to-optimal performance. Extensive numerical results corroborate our analysis. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of peer-to-peer networking for data dissemination among actors in wireless sensor and actor networks (WSANs), which consist of static sensors, responsible for environment monitoring, and mobile actors, in charge of data collection and task performing. This problem has not been received much attention although peer-to-peer networking has achieved great successes in other networks such as the Internet and mobile ad hoc networks (MANETs). Unlike the Internet and MANETs, WSANs contain static sensors that are energy-constrained and actors that cannot communicate with each other directly. These unique characteristics make the data dissemination problem in WSANs extremely challenging. We present an Energy-Efficient Message Dissemination protocol (EMD) to solve this problem in delay-tolerant WSANs. EMD is grounded on a novel principle of "Carry-Disseminate-Store-and-Forward" proposed for the first time here. While traveling, a source actor disseminates messages (data) to sensors upon contact, which will store the messages and forward them to other actors when they come into communication range. The actors receiving the messages from sensors work as source actors and help to distribute the messages. We theoretically analyze the data dissemination strategy under which the original source actor can distribute its messages to all other actors at minimum communication cost within a given delay bound. Through extensive simulations we demonstrate the performance of EMD. [ABSTRACT FROM AUTHOR]

We consider the problem of gathering correlated sensor data by a single sink node in a wireless sensor network. We assume that the sensor nodes are energy constrained and design efficient distributed protocols to maximize the network lifetime. Many existing approaches focus on optimizing the routing layer only, but in fact the routing strategy is often coupled with power control in the physical layer and link access in the MAC layer. This paper represents a first effort on network lifetime maximization that jointly considers the three layers. We first assume that link access probabilities are known and consider the joint optimal design of power control and routing. We show that the formulated optimization problem is convex and propose a distributed algorithm, JRPA, for the solution. We also discuss the convergence of JRPA. When the optimal link access probabilities are unknown, as in many practical networks, we generalize the problem formulation to encompass all the three layers of routing, power control, and link-layer random access. In this case, the problem cannot be converted into a convex optimization problem, but there exists a duality gap when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem, and show through numerical experiments that it can significantly narrow the gap between the computed and optimal solutions. Moreover, even without a priori knowledge of the best link access probabilities predetermined for JRPA, JRPRA achieves extremely competitive performance with JRPA. Beyond the metric of network lifetime, we also discuss how to solve the problem of correlated data gathering under general utility functions. Numerical results are provided to show the convergence of the algorithms and their advantages over existing solutions. [ABSTRACT FROM AUTHOR]

A promising technology that tackles the conflict between spectrum scarcity and underutilization is cognitive radio (CR), of which spectrum sensing is one of the most important functionalities. The use of dedicated sensors is an emerging service for spectrum sensing, where multiple sensors perform cooperative spectrum sensing. However, due to the energy constraint of battery-powered sensors, energy efficiency arises as a critical issue in sensor-aided CR networks. An optimal scheduling of each sensor active time can effectively extend the network lifetime. In this paper, we divide the sensors into a number of nondisjoint feasible subsets such that only one subset of sensors is turned on at a period of time while guaranteeing that the necessary detection and false alarm thresholds are satisfied. Each subset is activated successively, and nonactivated sensors are put in a low-energy sleep mode to extend the network lifetime. We formulate such problem of energy-efficient cooperative spectrum sensing in sensor-aided CR networks as a scheduling problem, which is proved to be \cal N\cal P-complete. We employ Greedy Degradation to degrade it into a linear integer programming problem and propose three approaches, namely, Implicit Enumeration (IE), General Greedy (GG), and \lambda-Greedy (\lambdaG), to solve the subproblem. Among them, IE can achieve an optimal solution with the highest computational complexity, whereas GG can provide a solution with the lowest complexity but much poorer performance. To achieve a better tradeoff in terms of network lifetime and computational complexity, a brand new \lambdaG is proposed to approach IE with the complexity comparable with GG. Simulation results are presented to verify the performance of our approaches, as well as to study the effect of adjustable parameters on the performance. [ABSTRACT FROM PUBLISHER]

Wireless sensor and actuator networks (WSANs) bring many benefits to industrial automation systems. When a control system is integrated by a WSAN, and particularly if the network scale is large, distributed communication and control methods are quite necessary. However, unreliable wireless and multihop communications among sensors and actuators cause challenges in designing such systems. This paper proposes and evaluates a new distributed estimation and collaborative control scheme for industrial control systems with WSANs. Extensive results show that the proposed method effectively achieves control objectives and maintains robust against inaccurate system parameters. We also discuss how to dynamically extend the scale of a WSAN with only local adjustments of sensors and actuators. [ABSTRACT FROM PUBLISHER]

This paper considers joint problems of control and communication in wireless sensor and actuator networks (WSANs) for building-environment control systems. In traditional control systems, centralized control (CC) and distributed control (DC) are two major approaches. However, little work has been done in comparing the two approaches in joint problems of control and communication, particularly in WSANs serving as components of control loops. In this paper, we develop a CC scheme in which control decisions are made based on global information and a DC scheme which enables distributed actuators to make control decisions locally. We also develop methods that enable wireless communications among system devices compatible with the control strategies, and propose a method for reducing packet-loss rate. We compare the two schemes using simulations in many aspects. Simulation results show that the DC can achieve a comparable control performance of the CC, while the DC is more robust against packet loss and has lower computational complexity than the CC. Furthermore, the DC has shorter actuation latency than the CC under certain conditions. [ABSTRACT FROM PUBLISHER]

Abstract: Wireless Sensor Network (WSN) is increasingly popular in the field of micro-environmental monitoring due to its promising capability. However, most systems using WSN for environmental monitoring reported in the literature are developed for specific applications without functions for exploiting user’s data processing methods. In this paper, a new system is designed in detail to perform micro-environmental monitoring taking the advantages of the WSN. The application-oriented hardware working style is designed, and the system platform for data acquisition, validation, processing and visualization is systematically presented. Several strategies are proposed to guarantee the system capability in terms of extracting useful information, visualizing events to their authentic time are also described. Moreover, a web-based surveillance subsystem is presented for remote control and monitoring. In addition, the system is extensible for engineers to carry their own data analysis algorithms. Experimental results are to show the path reliability and real-time characteristics, and to display the feasibility and applicability of the developed system into practical deployment. [Copyright &y& Elsevier]

In this paper, we consider using actors to maintain the quality of sensing in the wireless sensor networks. Due to factors such as battery drainage or physical malfunctions, the number of available sensors normally decreases over time after initial deployment, resulting in performance degradation. To maintain the quality of sensing in the network, actors can be used to allocate spare sensors to sensor-deficient regions (sensor allocation) or to relocate sensors from sensor-abundant regions to sensor-deficient regions (sensor relocation). We first focus on the sensor allocation problem. We introduce a baseline centralized greedy algorithm (GA) for sensor allocation, where global sensor information is communicated to obtain the optimal solution. As GA is only efficient for small networks, we proceed to design a distributed patrolling algorithm for achieving global optimization (DPAG) by using only local information. We then extend our work to the application scenario of sensor relocation by proposing a modified GA and DPAG (M-GA and M-DPAG), respectively. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

As sensors are energy constrained devices, one challenge in wireless sensor networks (WSNs) is to guarantee coverage and meanwhile maximize network lifetime. In this paper, we leverage prediction to solve this challenging problem, by exploiting temporal-spatial correlations among sensory data. The basic idea lies in that a sensor node can be turned off safely when its sensory information can be inferred through some prediction methods, like Bayesian inference. We adopt the concept of entropy in information theory to evaluate the information uncertainty about the region of interest (RoI). We formulate the problem as a minimum weight submodular set cover problem, which is known to be NP hard. To address this problem, an efficient centralized truncated greedy algorithm (TGA) is proposed. We prove the performance guarantee of TGA in terms of the ratio of aggregate weight obtained by TGA to that by the optimal algorithm. Considering the decentralization nature of WSNs, we further present a distributed version of TGA, denoted as DTGA, which can obtain the same solution as TGA. The implementation issues such as network connectivity and communication cost are extensively discussed. We perform real data experiments as well as simulations to demonstrate the advantage of DTGA over the only existing competing algorithm [1] and the impacts of different parameters associated with data correlations on the network lifetime. [ABSTRACT FROM AUTHOR]

In duty-cycled wireless sensor networks (WSNs) for stochastic event monitoring, existing efforts are mainly concentrated on energy-efficient scheduling of sensor nodes to guarantee the coverage performance, ignoring another crucial issue of connectivity. The connectivity problem is extremely challenging in the duty-cycled WSNs due to the fact that the link connections between nodes are transient thus unstable. In this paper, we propose a new kind of network, partitioned synchronous network, to jointly address the coverage and connectivity problem. We analyze the coverage and connectivity performances of partitioned synchronous network and compare them with those of existing asynchronous network. We perform extensive simulations to demonstrate that the proposed partitioned synchronous network has a better connectivity performance than that of asynchronous network, while coverage performances of two types of networks are close. [ABSTRACT FROM AUTHOR]

Network coverage is one of the most critical issues to implement Wireless Sensor Networks (WSNs). It is important to find out a sensor set with maximal residual energy to cover all points of interest (PoIs). This issue was named as minimum weight sensor coverage problem (MWSCP) based on a boolean disc model and a probabilistic sensing model in the literature. In this paper, we introduce intelligent algorithms to solve this problem, yielding a better solution to MWSCP to extend network lifetime. Simulation results are conducted to demonstrate the effectiveness of our proposed algorithm in terms of network lifetime over existing algorithms. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a new interface is developed in detail that provides terminal users with ways for direct and convenient interactions with the wireless sensor networks (WSNs). It also embraces an Internet based subsystem for remote monitoring and controlling of the WSNs. Different from existing platforms and interfaces, our design is general, extensible and not constrained for specific applications. More importantly, it paves the way for improving the data processing efficiency and avoiding data execution congestion. It proposes the idea utilizing the advantages of existing professional mathematical software tools, such as Matlab, Mathematica and Maple, to process the WSN data. [Copyright &y& Elsevier]

Abstract: Recently, Wireless Sensor Network (WSN) has become a significant research field. Imote2 is an advanced WSN platform, which supports Linux operating system. However, building Linux on Imote2 is not trivial. In this paper, we introduce a general approach in detail, including installing bootloader, communication between Imote2 and host PC, as well as loading kernel and file system. After it, some fundamental configurations on Imote2 and host PC are presented. We further provide test method by sending and receiving radio on Imote2. More development on Imote2 with Linux can benefit from our approach. [Copyright &y& Elsevier]

Abstract: The wireless sensor/actuator networks (WSANs) can be used for spatially distributed control systems. With smart sensors and actuators, the WSANs are able to not only sense the control system states and report measurements, but also perform control and actuation. This paper investigates WSANs on their ability of control. A centralized controller is introduced into WSANs to make up closed-loop control systems, in which control decisions are made based on global network-wide information. A model of the control and communication over WSANs is made theoretically, based on which we achieved an optimal control method. It is demonstrated by simulations that the control method proposed could stabilize the control system quickly. [Copyright &y& Elsevier]