Top PDF The coverage problem in a wireless sensor network

Abstract. One of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. The sensing ranges of sensors can be unit disks or non-unit disks. We present polynomial-time algorithms, in terms of the number of sensors, that can be easily translated to distributed protocols. The result is a generalization of some earlier results where only k = 1 is assumed. Applications of the result include determining insufficiently covered areas in a sensor network, enhancing fault-tolerant capability in hostile regions, and conserving energies of redundant sensors in a randomly deployed network. Our solutions can be easily translated to distributed protocols to solve the coverage problem.

In this paper, we define a new k-angle object coverage problem in a wireless sensor network. Each sensor can only cover a limited angle and range, but can freely rotate to any direction to cover a particular angle. Given a set of sensors and a set of objects at known locations, the goal is to use the least number of sensors to k-angle-cover the largest number of objects such that each object is monitored by at least k sensors satisfying some angle constraint. We propose centralized and distributed polynomial- time algorithms to solve this problem. Simulation results show that our algorithms can be effective in maximizing coverage of objects. A prototype system is developed to demonstrate the usefulness of angle coverage.

We propose our model to analyze the event detection latency. Specifically, we are given a sensing field, on which there are n homogeneous sensors. Each sensor has a sensing distance of r. Without loss of generality, we assume that these n sensors form a connected network. To simplify the analysis, we assume that the time axis is divided into fixed-length slots and the working schedule of each sensor is modeled by a sequence of working cycles, each of length T slots. Each working cycle is led by an active phase fol- lowed by an idle phase. The former consists of the first D slots, and the latter the rest of the T  D slots. Sensors only conduct detection jobs in their active phases, and go to sleep in idle phases. However, sensors do not synchronize their clocks, so their working cycles are not necessarily aligned. Fig. 1 shows an example. Note that this model can be applied to most of the MAC/network protocols that are proposed for WSN recently. For example, for energy conservation, the Zigbee/IEEE 802.15.4 standard [14]

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Author’s addresses: Department of Computer Science, National Chiao-Tung University, 1001 Ta- Hsueh Road, Hsin-Chu, Taiwan 30050, R.O.C.; email: {cfhuang, yctseng, hlwu}@csie.nctu.edu.tw. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or direct commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or permissions@acm.org.

1. Introduction Wireless sensor networks are composed of a large number of tiny sensor nodes, which consist of sensing, data processing, storage capacity, limited memory, and communicating components. The sensor nodes are densely deployed to monitor the phenomenon inside an area. For example, surveillance sensor networks in a museum can keep the safety of priceless art crafts from burglaries; similar networks in forest alert when temperature arises abnormally. Different networks in these applications have different requirements on network coverage and the quality of sensed data. Some of them, e.g., hazard monitoring, may require higher coverage as delayed information or incomplete data can cause tragedies. How to evaluate the coverage of a sensor network, thus, becomes an essential issue in practice. In this paper, we study this problem and find a way to find the optimal line paths for both worst-case and best-case coverage problems.

Index Terms―modification energy model, ideal energy model, wireless sensor network, optimum number of clusters. 一、前言 一 個 無 線 感 測 器 網 路 (wireless sensor network, WSN) 是由多個具有通訊能力的無線 感測器 (sensor node, SN) 所組成的。一個SN是 一種具備偵測、感應與傳輸能力的電子裝置。隨 著嵌入式微處理技術的進步，增加了SN一些功 能，包括增強計算能力、具有資料處理的能力、

An optimization approach has been applied to maximize cov- erage and capacity for the considered WMN, subject to the user throughput requirement. In the ring-based WMN, a simple ring-based frequency planning scheme has been employed to reduce collisions, and to make the network more scalable in terms of coverage. We have also suggested sectorizing the congested inner rings to resolve the throughput bottleneck issue of the WMN. From the system design perspective, this paper has three impor- tant components. First, an analytical throughput model has been developed, which considers the effects of ring-based cell structure and frame contentions in the CSMA MAC proto- col. Second, we have developed a bulk-arrival semi-Markov queueing model to describe user behavior in the non-saturation condition. Third, to investigate the optimal tradeoff between user throughput and cell coverage, we have applied an opti- mization approach to determine the optimal number of rings and the associated ring widths in a mesh cell. Numerical re- sults have demonstrated that the optimal system parameters (that is, the number of rings and ring widths) can be deter- mined analytically. In addition, both the capacity enhancement and coverage extension can be achieved with a guaranteed throughput for each user.

Hsinchu 300, Taiwan R.O.C. sstseng@cis.nctu.edu.tw Abstract In this paper, we investigate the network expanded prob- lem which optimally assigns new adding and splitting cells in PCS (Personal Communication Service) network to switches in an ATM (Asynchronous Transfer Mode) network. Moreover, the locations of all cells in PCS net- work are xed and known, but new switches should be installed to ATM network and the topology of the back- bone network may be changed. Given some potential sites of new switches, the problem is to determine how many switches will be added to the backbone network, the locations of new switches, the topology of the new backbone network, and the assignments of new adding and splitting cells in the PCS to switches on the new ATM network in an optimum manner. We would like to do the expansion in as attempt to minimize the to- tal communication cost under budget and capacity con- straints. This problem is modeled as a complex integer programming problem, and nding an optimal solution to this problem is NP-hard. A genetic algorithm is pro- posed to solve this problem. The genetic algorithm con- sists of three phases, Switch Location Selection Phase, Switch Connection Decision Phase, and Cell Assignment Decision Phase. First, in the Switch Location Selec- tion Phase, the number of new switches and the loca- tions of the new switches are determined. Then, Switch Connection Phase is used to construct the topology of the expanded backbone network. Final, Cell Assignment Phase is used to assign cells to switches on the expanded network. Experimental results indicate that the three- phase genetic algorithm has good performances.

the network routing. The data is formed and aggregated by many sensor nodes and transmitted to sink by way of wireless transmission or to base station by way of route. These sensor nodes could not only react and detect the change of the object in this environment, but also deal with the collected data. It is very difficult to manage the network because the number of the node in the sensor network could be from hundreds to thousands of thousands. In addition, the energy control is almost the main struggle faced by sensor design and network manager under the impossibility of replacement and the limited electric power. Furthermore, since the monitor scope is wider, the demand quantity of the sensor facility is larger. That is to say, the sensor facility has to be cheaper and the malfunction opportunity is relatively raised. Therefore, the fault tolerant function is essential to wireless sensor network design and management.

protection. e.g., MBC can keep outlining the contaminated region of the diffusing poisonous gas (due to BIS feature), then sense and warn any entrant to leave away from this region. There are three major challenges in maintaining MBC for dynamic objects. First, the object is dynamic and its time- varying shape is unpredictable. Second, it is desirable to maximize the detection capability of MBC, which is usually characterized by the number of barriers K [14]. Third, the energy of a sensor node is always limited. In short, the problem we are interested in is to provide MBC while maximizing its monitoring performance and reducing the cost of sensor movement. Moreover, since a WSN may extend to a very large scale, it is practical that the solution is distributed.

The noise-prone signal strengths are another challenge to the BMD problem. In real environments, signal strengths may be influenced by many factors, such as hardware difference, remaining battery, multipath propagation, and dynamic signal fading. When combining these factors, it is even harder to correctly determine a beacon movement event. To relieve this influence, we import the concept of tolerable regions in the proposed schemes. To evaluate the proposed BMD schemes, we adopt a close-to-reality radio irregularity model (RIM) [28] to simulate the decay of signal strengths. This model has been shown to be able to reflect the propagation of radio signals, especially in indoor environments. In our simulation study, we have tuned the parameters of RIM to evaluate the performance of LB, NB, SSB, and SSR under different conditions. The results show that the SSB and SSR schemes perform well under most situations. The NB scheme is easy to implement but has limited movement detection capability. Compared to SSB, SSR, and NB, the LB scheme has higher computation complexity and is quite sensitive to the density of beacons.

Extensive research efforts have been devoted to the design of power-saving mechanisms such that the total power consumption in a WSNET is minimized and the network lifetime is maximized. In this paper, the lifetime of a WSNET is defined to be the time period from the beginning of the network operation to when one of the targets can not be monitored. A possible power-saving mechanism is to schedule each sensor to alternate its states between the active and sleep mode. This is because while some requirements are met, compared with another case of each sensor being active continuously, the case of each sensor altering its states between the active and sleep mode will generate a longer network lifetime [1-3].

From these simulation results, we can conclude that d 2 - Cluster is an effective clustering algorithm for WSNs partition. V. C ONCLUSION In this paper, we studied the multi-hop clustering problem for a given wireless sensor network, which is defined as d- MCHS problem. We presented a distributed, approximation algorithm, named d 2 -Cluster, to address d-MCHS problem. d 2 - Cluster is a degree-based algorithm. Each node uses the local information of its neighbors within 2d hops to decide whether it can be selected as a cluster head (CH). We discussed its time complexity and message complexity. For time complexity, we compared d 2 -Cluster with the previous fastest algorithm under UDG model and we showed that d 2 -Cluster is almost as fast as the previous fastest algorithm. Moreover, we also proved the performance ratio of d 2 -Cluster under UDG model is λ, where λ is a constant parameter related to d. As far as we know, this is the first constant result for d-MDS problem in UDG.

First, the centralized heuristic clusters event locations, from a global view, when there are fewer mobile sensors, while the distributed heuristic always clusters event locations into fixed grids. Second, the centralized heuristic requires a central node (e.g., the sink) to calculate the dispatch schedules, which incurs network flooding to gather/dis- seminate global information. In contrast, the distributed heuristic adopts grid-quorum to reduce the message com- plexity but lets event locations compete for mobile sensors using partial information. Our simulation results reflect that when there are more mobile sensors, the grid structure helps extend the system lifetime. On the other hand, when there are fewer mobile sensors, the centralized heuristic has a longer system lifetime due to its efficient clustering and global knowledge. Nevertheless, both heuristics result in a longer system lifetime compared to the greedy scheme. Also, simulation results show that the distributed heuristic is more message efficient.

The problem if formulated as follows. Initially, all S and M are randomly deployed in F. Deployment of S is assumed to be dense enough so that the low-tier network is always connected. Deployment of M is sparse and thus the high-tier network could be partitioned. A data-pump that is connected to m 0 in the high-tier network is called attached, and is unattached otherwise. We assume no knowledge about the location of any node in S and M. Data collection is conducted by the cooperation of S and M. A s i ∈ S can first send its sensory data, along the low-tier network, to the nearest attached data-pump, called the master data- pump χ(s i ). Then χ(s i ) can relay the data, via the high-tier network, to m 0 . Let C(m i ) = {s j |χ(s j ) = m i }, the set of sensors served by m i , called m i ’s virtual Voronoi cell (‘cell’ for short). Our goal is to design a distributed range- free relocation protocol for data-pumps to achieve both connectivity and load balance. The connectivity requirement is to enforce all data-pumps to remain attached to m 0 after relocation, while the load balance requirement is to keep the sizes of virtual Voronoi cells as similar as possible under all possible combinations of |M| and |S|. Note that the solution should be ‘range-free’ in the sense that navigating theses data-pumps can not rely on any geographic locations.

The WSN may fail due to a variety of causes, including the following: the routing path might experience a break; the WSN sensing area might experience a leak; the batteries of some sensor nodes might be depleted, requiring more relay nodes; or the nodes wear out after the WSN has been in use a long period of time. In Fig. 2, the situation in which the outside nodes transfer event data to the sink node via the inside nodes (the sensor nodes near the sink node) in a WSN illustrate the accommodation measures for non-working nodes. The inside nodes thus have the largest data transmission loading, consuming energy at a faster rate. If all the inside nodes deplete their energy or otherwise cease to function, the event data can no longer be sent to the sink node, and the WSN will no longer function.

2.1. Wireless-Sensor-Network-Based Sensor The self-developed WSN-based ISN operates according to the ZigBee protocol and contains a tri- axial accelerometer as well as bi-axial and uni-axial gyroscope chips with low dropout (LDO) voltage. The MEMS-enabled chips were embedded in a wearable device with dimensions of 40 × 37 × 2 mm as shown in Figure 1a. The sensor is initialized by coordinating both acceleration and angular velocity on a negative x-axis. The modulated antenna is directed upward for reducing the reflection loss of signals to 10 dB. The WSN device supports 11 channels for wireless communication bands ranging between 2.4 and 2.48 GHz; the sample rate is one packet per 1/1024 s. Essential properties were detailed in our previous study [36]. The sensor can be worn on the body to enable mobile measurement and ubiquitous management. The prototype of device is powered by a 4-mm- thick rechargeable battery, which can be padded on skin to isolate the antenna and prevent wireless transmission loss from WSN packets.

other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in- network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages.

Abstract—The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in- network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest- weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions.