Trade-off Between Coverage and Data Reporting Latency for Energy

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Trade-off Between Coverage and Data Reporting Latency for Energy-Conserving Data Gathering in Wireless Sensor Networks Wook Choi and Sajal K. Das Center for Research in Wireless Mobility and Networking (CReWMaN) Department of Computer Science and Engineering The University of Texas at Arlington choi,das @cse.uta.edu 

Abstract In this paper, we propose a novel energy-conserving data gathering strategy for wireless sensor networks. The proposed strategy is based on a trade-off between coverage and data reporting latency with an ultimate goal of maximizing the network’s lifetime. The basic idea is to select in each round only a minimum of sensors as data reporters which are sufficient for a desired sensing coverage given by the users or applications. Such a selection of the minimum data reporters also reduces the amount of traffic flow to the data gathering point in each round, and thus avoids network congestion as well as channel interference/contention. The proposed strategy includes three schemes for the minimum -sensor selection. Using these schemes we evaluate such fundamental issues as event detection integrity and data reporting latency, which can be critical in deploying the proposed data gathering strategy. Simulation results demonstrate that the average data reporting latency is hardly affected and the real-time event detection ratio is greater than 80% when the desired sensing coverage is at least 80%. It is also shown that the sensors can conserve a significant amount of energy with a small trade-off, and that the higher the network density, the higher is the energy conservation rate without any additional computation cost. 



1 Introduction Wireless sensor networks are task-specific information gathering platforms. They can be deployed both indoors and outdoors, substituting for our sensory organs in inaccessible or inhospitable areas. Depending on the deployment platform of sensor networks, there is a variety of applications such as environment or equipment monitoring [15], smart home/smart space [5], intrusion detection, and surveillance, etc. Such sensor networks can be characterized by high node density and highly limited resources such as bandwidth, energy, computational capability, and storage space. This distinguishes the sensor networks from the traditional ad hoc networks [1].

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The sensors sense their vicinity (sensing coverage) and deliver sensed data to a data gathering point, consuming the limited energy resource which may not often be possible to replenish. Therefore, an important challenge in designing data gathering protocols for sensor networks is to make them highly energyefficient so as to maximize their lifetime. The frequency of data delivery depends on the models which can be classified into continuous, event-driven, on-demand, or hybrid, based on the application’s or the user’s interest [19]. The continuous model requests all sensors to transmit their sensed data periodically while they are alive. A cluster-based continuous data gathering scheme, called LEACH, is proposed in [9]. Further improvement on energy-conservation achieved by the LEACH is shown in [14] which connects all the sensors as a linear chain. Forming a chain requires sensors to have a global knowledge which makes the data gathering scheme unscalable. The clustering scheme requires sensors to consume a certain amount of energy while forming and maintaining clusters. Moreover, the role of cluster head leads to a relatively large amount of energy consumption as compared to an ordinary cluster member (i.e., non-cluster head). Thus rotating the role of cluster head is necessary to reduce the time variance of sensor failures caused by energy depletion. We proposed a two-phase clustering (TPC) in [4] which reduces the cluster head’s workload and thus the cluster head rotation by requiring cluster members to maintain two types of paths to the cluster head: direct link (one-hop) and data relay link (multi-hop). Unlike the continuous model, in the event-driven data gathering model [11], sensors start reporting their sensed data only when a specific event occurs. Whereas, in the ondemand model [16] they report sensed data only at the users’ request. Due to high density of the network, it is common for multiple sensors to generate and transmit redundant sensed data which results in unnecessary power consumption and hence significantly decreases the network’s lifetime. Among the sensors’ actions, such as data transmission and target sens-

ing, the energy consumption for wireless data transmission is the most critical. Therefore, minimizing the number of data transmissions between sensors by eliminating redundant data without losing data accuracy, or aggregating multiple sensed data saves a significant amount of energy. For this purpose, many protocols have been designed for routing and managing topological connectivity, as summarized below. Data-centric routing [12] attempts to reduce duplicate data transmissions by aggregating multiple packets cached for a certain amount of time (i.e., in-network data processing with some data transmission delay), thereby increasing the energy conservation. In [18], a sensing coverage preserving scheme is proposed which turns off the sensors having their coverage area overlapped with other sensors. More recently, two elegant algorithms, called connected sensor cover [8] and coverage configuration protocol [20] have been proposed considering coverage and connectivity problems simultaneously. The former selects a minimum number of sensors to cover a specified area for query execution, thus reducing unnecessary energy consumption from redundant sensing. The latter selects a minimum number of sensors to guarantee that any senpoint within a monitored area is covered by at least sors. These protocols find a relatively small set of (connected) sensors by running an algorithm with relatively high computational complexity, exchanging control information with local neighbors to cover the entire monitored area by 100%. The execution and implementation of such algorithms, however, are challenging because the sensors are basically under the highly limited resource environment. In fact, finding the smallest set of connected sensors that completely cover a given monitored area is an NP-hard problem [8].

1.1 Our Contributions To this end, we propose a novel energy-conserving data gathering strategy for mainly the continuous data gathering model, based on a trade-off between coverage and data reporting latency with an ultimate goal of maximizing the network lifetime. The proposed strategy attempts to select at every data reporting round only a minimum of sensors as data reporters which are sufficient to cover as much of the monitored area as the user/application requests. Only these sensors transmit data to the gathering point while the others cache their sensed data waiting for the next reporting round, thus saving energy. All the sensors take turns in being selected as a data reporter. Thus, the parts of the area not covered by the first set of selected sensors will be covered by the next set of selected sensors with some delay. The lower the desired sensing coverage, the longer is the data reporting latency in each sensor; whereas the energy conservation rate is inversely proportional to the coverage. Besides the enhanced energy conservation, there is a subsequential benefit such as congestion avoidance and low channel interference/contention, which can 

be achieved by our proposed strategy using only sensors in each reporting round. This also contributes to energy savings, improving the overall network performance. The proposed strategy adopts three schemes for -sensor selection: nonfixed randomized selection (NRS), non-fixed, and fixed disjoint randomized selections (N-DRS and F-DRS). They differ from one another in terms of data reporting latency and implementation simplicity. The computational complexity of these three sensor selection schemes is constant (i.e., independent of network density and size), thereby providing a high scalability. In addition, they do not exchange (periodic) control information with local neighbors in selecting sensors. Thus, the proposed strategy is well suited for sensor networks which are required to run for a long time under highly limited resource constraints. Through intensive simulation studies, we evaluate fundamental issues such as event detection integrity and data reporting latency which are critical in deploying the proposed strategy. Simulation results demonstrate that ) the proposed schemes can meet the desired sensing coverage by making approximately sensors to report their sensed data in each reporting round and ) sensors can conserve a significant amount of energy with a small trade-off. More specifically, in network field in which sensors have 30 cira cular sensing range, the real-time event detection ratio is more sensors, which are selected based than 90% using only on an 80% desired sensing coverage of the entire monitored area. It is also shown that the average sensed data reporting latency is hardly affected when the desired coverage is greater . Furthermore, since the selection of sensors is than not affected by the network density, the energy conservation rate increases without any additional computation cost as the network size grows. The remainder of this paper is organized as follows. Section 2 presents the motivation and problem under consideration. Section 3 introduces basic definitions and assumptions. Section 4 describes how to find the minimum sensors that meet the users’ desired sensing coverage. Section 5 presents three -sensor selection schemes for the deployment of the proposed data gathering strategy. Section 6 discusses simulation results and Section 7 concludes the paper. 





























































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2 Motivation and Problem Description In this paper, we focus on enhancing energy conservation while meeting the user/application’s requirements such as data delivery latency and desired sensing coverage. We consider (uniformly distributed) randomly deployed sensor networks but the application of the proposed strategy is not limited to such networks only. Our motivation lies in the fact that depending on the type of applications used, the network lifetime can be much more critical than covering the entire monitored area at every data reporting round. The user may

desire that only a certain portion of the area be covered at every data reporting round for the extended network lifetime if the sensed result for the entire monitored area can be acquired with a fixed delay. One example is: for a sensor network deployed for a statistical study of scientific measurement in a certain area, it may be accurate enough to monitor the status of the specific area’s certain condition if the network covers approximately 80% of the field on an average in each round. Another example is: for a sensor network monitoring slowlymoving objects, it may be acceptable if the network covers only 50% of the area at every round on the condition that the sensed result covering the entire monitored area can be collected with a fixed delay.

area will be overlapped when a circle is randomly placed over a geometrical figure.

3 Basic Definitions and Assumptions A large number of sensors is densely-deployed in a twodimensional geographic space, forming a network. Although there is a feasible means to make the sensors aware of their location, such as global positioning system (GPS) or directional beaconing [2, 17], we do not assume that sensors are located by any specific coordination system because such localization mechanisms may not be available or practical in building lowcost and low-power sensors with small form factor. Formally, we shall define a sensor network as an undirected connected , where and are the sets of nodes (sengraph = sors and data gathering points) and edges (bidirectional wireforms less links), respectively. A sequence of edges in the path, for , where is a sensor and is a data gathering point. Thus, is considered as a multihop routing path and each node on acts as an individual generates a fixed-size data packet router. A sensor node for a time unit as a sensed result. We call this time unit as a data reporting round and the interval between two consecutive data reporting rounds is denoted by . All the sensor nodes are supposed to forward the generated data packet to the data gathering point using a routing path , making the communication pattern many-to-one. Since our proposed scheme is considered as a data gathering protocol running on top of the routing layer, in this work we assume a non-geographical sensor routing protocol which connects all sensors at the deployment time [6, 10]. A control message from is delivered to the sensor nodes through flooding [11]. Each node has its specific radio and sensing ranges with radius . Both . A sensor can directly of the ranges are denoted by communicate with any nodes in its radio range . 

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Figure 1. Illustration of Coverage-Data Reporting Latency Trade-off Based Data Gathering

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The problem is associated with the selection of a minimum number of sensors, based on a desired sensing coverage specified by the user. Figures 1 (a) and (b) illustrate the problem. The black solid dots within the small circles (i.e., ) in both of the figures and the hollow dots in the figure (b) represent the currently selected sensors and the previously selected sensors, respectively. The large solid-line circle represents the sensing coverage of each sensor. Suppose that the first selected sensors cover a desired portion of the area but not the entire sensing area, as shown in Figure 1 (a) (i.e., shaded area is not covered). The shaded area is being covered in the second set of selected sensors as shown in Figure 1 (b). Therefore, the user receives the sensed result for the entire monitored area with a fixed delay (i.e., two consecutive reporting rounds). We thus define the problem as follows: 























sensors which are Problem Definition: Given a set of such that each has sensing placed over a region region . A minimum of sensors has to be chosen from such that 























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In this paper, the term “desired sensing coverage (DSC)” represents a probabilistic percentage for covering any point within the entire monitored area. The user specifies the DSC as the desired “quality of service” to be achieved by sensor data gathering. Thus, we define the desired sensing coverage as a trade-off factor for energy conservation. The DSC is proportional to the amount of sensed data traffic over the network and inversely proportional to both the energy conservation rate and data reporting latency. The question is: in order to meet the DSC specified by the user, how many sensors do we need to select at each data reporting round? To answer this question, let us first introduce the following basic definitions: Definition 4.1: A monitored area, denoted by , is the actual P

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area which has to be monitored by the sensors. We consider this area as an square. Definition 4.2: A sensor-deployed area, denoted by , is a square area including all sensors which have an effect on covering such that the square will have rounded corners with distance less than or equal to (radius of sensing range) from the boundary of (refer to Figure 2). Thus, the circular sensing range of a sensor residing in is not fully overlapped with the area . Definition 4.3: A probabilistic sensing coverage, denoted by , is the probability of any point in being covered by at least one of the selected sensors’ (residing in ) circular sensing range with radius . This is given by either the user or the application as the desired sensing coverage. 







is the probability that is located on a where point . Eq. (1) represents the fraction of not covered by a randomly-selected sensor’s circular sensing range. Thus, the probability that a point is not covered by randomlyselected sensors is obtained as /





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